diff --git a/.github/FUNDING.yml b/.github/FUNDING.yml
new file mode 100644
index 0000000..4b16f59
--- /dev/null
+++ b/.github/FUNDING.yml
@@ -0,0 +1,12 @@
+# These are supported funding model platforms
+
+github: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]
+patreon: # Replace with a single Patreon username
+open_collective: # Replace with a single Open Collective username
+ko_fi: # Replace with a single Ko-fi username
+tidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel
+community_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry
+liberapay: # Replace with a single Liberapay username
+issuehunt: # Replace with a single IssueHunt username
+otechie: # Replace with a single Otechie username
+custom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']
diff --git a/Assignment/Assignment1 Numpy Solution.ipynb b/Assignment/Assignment1 Numpy Solution.ipynb
new file mode 100644
index 0000000..77853a7
--- /dev/null
+++ b/Assignment/Assignment1 Numpy Solution.ipynb	
@@ -0,0 +1,394 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Assignment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a python list => \\[1,2,3,4,5\\]\n",
+    "\n",
+    "Convert it into numpy array and print it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a=[1,2,3,4,5]\n",
+    "np.array(a)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a python matrix (3 x 3) => \\[[1,2,3],[4,5,6],[7,8,9]\\]\n",
+    "\n",
+    "Convert it into numpy array and print it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array( [[1,2,3],[4,5,6],[7,8,9]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make a matrix (3 x 3) using built-in methods (like arange(), reshape() etc.):\n",
+    "\n",
+    "\\[ [1,3,5],\n",
+    "\n",
+    " [7,9,11],\n",
+    " \n",
+    " [13,15,17] \\]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  3,  5],\n",
+       "       [ 7,  9, 11],\n",
+       "       [13, 15, 17]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.arange(1,18,2).reshape(3,3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a numpy array with 10 random numbers from 0 to 10 (there should be few numbers greater than 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5.21123512, 3.42271411, 8.41863607, 9.05091877, 0.87481072,\n",
+       "       9.42826073, 4.98176773, 9.20505637, 5.70994345, 1.70182866])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.rand(10)*10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create numpy array => \\[1,2,3,4,5\\] and convert it to 2D array with 5 rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1],\n",
+       "       [2],\n",
+       "       [3],\n",
+       "       [4],\n",
+       "       [5]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b=np.array([1,2,3,4,5]).reshape(5,1)\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print the shape of the above created array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 1)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a numpy array with 10 elements in it. Access and print its 3rd, 4th and 9th element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c=np.arange(10)\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 3 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(c[2], c[3], c[8])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Print alternate elements of that array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 2, 4, 6, 8])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Change last 3 elements into 100 using broadcasting and print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  0,   1,   2,   3,   4,   5,   6, 100, 100, 100])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c[-3:]=100\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a 5 x 5 matrix (fill it with any element you like), print it.\n",
+    "\n",
+    "Then print the middle (3 x 3) matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0,  1,  2,  3,  4],\n",
+       "       [ 5,  6,  7,  8,  9],\n",
+       "       [10, 11, 12, 13, 14],\n",
+       "       [15, 16, 17, 18, 19],\n",
+       "       [20, 21, 22, 23, 24]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d=np.arange(25).reshape(5,5)\n",
+    "d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6,  7,  8],\n",
+       "       [11, 12, 13],\n",
+       "       [16, 17, 18]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d[1:4,1:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Assignment/Assignment2 pandas.ipynb b/Assignment/Assignment2 pandas.ipynb
new file mode 100644
index 0000000..f382b27
--- /dev/null
+++ b/Assignment/Assignment2 pandas.ipynb	
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "#%matplotlib notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "import the dataset into a dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "display the column names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "display the number of rows and cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "display the dataframe info (types of data in columns and not null values etc.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "display stats of the dataframe like count, mean, std, max, 25% etc....."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "display null values per column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "remove columns will all values as NaN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "display number of unique values in each column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mean of total pay of all people based on year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "how many people have 0 overtime pay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "max, min, mean, median and other stats of TotalPay of people having 0 OvertimePay"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "find Id of that person with max TotalPay you got in previous question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "name of employee with total pay benefits = 87619.78"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "how many people have BasePay > 150000 and OvertimePay > 100000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "which job title generally has highest average TotalPayBenefits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many employees are POLICE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# .str.contains()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Day 5 pandas.ipynb b/Day 5 pandas.ipynb
new file mode 100644
index 0000000..85c85c1
--- /dev/null
+++ b/Day 5 pandas.ipynb	
@@ -0,0 +1,1083 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2=pd.DataFrame(np.random.rand(15).reshape(5,3), columns=['col1','col2','col3'], index=['a','b','c','d','e'])\n",
+    "df2['col4']= np.arange(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.994700</td>\n",
+       "      <td>0.374323</td>\n",
+       "      <td>0.830171</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.650780</td>\n",
+       "      <td>0.924054</td>\n",
+       "      <td>0.834534</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0.825124</td>\n",
+       "      <td>0.198440</td>\n",
+       "      <td>0.024221</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.931559</td>\n",
+       "      <td>0.076659</td>\n",
+       "      <td>0.382428</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.652708</td>\n",
+       "      <td>0.995672</td>\n",
+       "      <td>0.572294</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2      col3  col4\n",
+       "a  0.994700  0.374323  0.830171     0\n",
+       "b  0.650780  0.924054  0.834534     1\n",
+       "c  0.825124  0.198440  0.024221     2\n",
+       "d  0.931559  0.076659  0.382428     3\n",
+       "e  0.652708  0.995672  0.572294     4"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "      <th>col5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.994700</td>\n",
+       "      <td>0.374323</td>\n",
+       "      <td>0.830171</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.824871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.650780</td>\n",
+       "      <td>0.924054</td>\n",
+       "      <td>0.834534</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.485314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0.825124</td>\n",
+       "      <td>0.198440</td>\n",
+       "      <td>0.024221</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.849345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.931559</td>\n",
+       "      <td>0.076659</td>\n",
+       "      <td>0.382428</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.313987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.652708</td>\n",
+       "      <td>0.995672</td>\n",
+       "      <td>0.572294</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.225002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2      col3  col4      col5\n",
+       "a  0.994700  0.374323  0.830171     0  1.824871\n",
+       "b  0.650780  0.924054  0.834534     1  1.485314\n",
+       "c  0.825124  0.198440  0.024221     2  0.849345\n",
+       "d  0.931559  0.076659  0.382428     3  1.313987\n",
+       "e  0.652708  0.995672  0.572294     4  1.225002"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2['col5']= df2['col1'] + df2['col3']\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.994700</td>\n",
+       "      <td>0.374323</td>\n",
+       "      <td>0.830171</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.650780</td>\n",
+       "      <td>0.924054</td>\n",
+       "      <td>0.834534</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0.825124</td>\n",
+       "      <td>0.198440</td>\n",
+       "      <td>0.024221</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.931559</td>\n",
+       "      <td>0.076659</td>\n",
+       "      <td>0.382428</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.652708</td>\n",
+       "      <td>0.995672</td>\n",
+       "      <td>0.572294</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2      col3  col4\n",
+       "a  0.994700  0.374323  0.830171     0\n",
+       "b  0.650780  0.924054  0.834534     1\n",
+       "c  0.825124  0.198440  0.024221     2\n",
+       "d  0.931559  0.076659  0.382428     3\n",
+       "e  0.652708  0.995672  0.572294     4"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop\n",
+    "df2.drop('col5', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "      <th>col5</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.994700</td>\n",
+       "      <td>0.374323</td>\n",
+       "      <td>0.830171</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.824871</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.650780</td>\n",
+       "      <td>0.924054</td>\n",
+       "      <td>0.834534</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.485314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0.825124</td>\n",
+       "      <td>0.198440</td>\n",
+       "      <td>0.024221</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.849345</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.931559</td>\n",
+       "      <td>0.076659</td>\n",
+       "      <td>0.382428</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1.313987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.652708</td>\n",
+       "      <td>0.995672</td>\n",
+       "      <td>0.572294</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1.225002</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2      col3  col4      col5\n",
+       "a  0.994700  0.374323  0.830171     0  1.824871\n",
+       "b  0.650780  0.924054  0.834534     1  1.485314\n",
+       "c  0.825124  0.198440  0.024221     2  0.849345\n",
+       "d  0.931559  0.076659  0.382428     3  1.313987\n",
+       "e  0.652708  0.995672  0.572294     4  1.225002"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2.drop('col5', axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.994700</td>\n",
+       "      <td>0.374323</td>\n",
+       "      <td>0.830171</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.650780</td>\n",
+       "      <td>0.924054</td>\n",
+       "      <td>0.834534</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0.825124</td>\n",
+       "      <td>0.198440</td>\n",
+       "      <td>0.024221</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.931559</td>\n",
+       "      <td>0.076659</td>\n",
+       "      <td>0.382428</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>e</th>\n",
+       "      <td>0.652708</td>\n",
+       "      <td>0.995672</td>\n",
+       "      <td>0.572294</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2      col3  col4\n",
+       "a  0.994700  0.374323  0.830171     0\n",
+       "b  0.650780  0.924054  0.834534     1\n",
+       "c  0.825124  0.198440  0.024221     2\n",
+       "d  0.931559  0.076659  0.382428     3\n",
+       "e  0.652708  0.995672  0.572294     4"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.994700</td>\n",
+       "      <td>0.374323</td>\n",
+       "      <td>0.830171</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.650780</td>\n",
+       "      <td>0.924054</td>\n",
+       "      <td>0.834534</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0.825124</td>\n",
+       "      <td>0.198440</td>\n",
+       "      <td>0.024221</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.931559</td>\n",
+       "      <td>0.076659</td>\n",
+       "      <td>0.382428</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2      col3  col4\n",
+       "a  0.994700  0.374323  0.830171     0\n",
+       "b  0.650780  0.924054  0.834534     1\n",
+       "c  0.825124  0.198440  0.024221     2\n",
+       "d  0.931559  0.076659  0.382428     3"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drooping a row\n",
+    "df2.drop('e')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.994700</td>\n",
+       "      <td>0.374323</td>\n",
+       "      <td>0.830171</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>b</th>\n",
+       "      <td>0.650780</td>\n",
+       "      <td>0.924054</td>\n",
+       "      <td>0.834534</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>c</th>\n",
+       "      <td>0.825124</td>\n",
+       "      <td>0.198440</td>\n",
+       "      <td>0.024221</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.931559</td>\n",
+       "      <td>0.076659</td>\n",
+       "      <td>0.382428</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2      col3  col4\n",
+       "a  0.994700  0.374323  0.830171     0\n",
+       "b  0.650780  0.924054  0.834534     1\n",
+       "c  0.825124  0.198440  0.024221     2\n",
+       "d  0.931559  0.076659  0.382428     3"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# drop rows based on conditions\n",
+    "df2.drop(df2[df2['col4'] > 3].index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['e'], dtype='object')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[df2['col4']>3].index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a</th>\n",
+       "      <td>0.994700</td>\n",
+       "      <td>0.374323</td>\n",
+       "      <td>0.830171</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>d</th>\n",
+       "      <td>0.931559</td>\n",
+       "      <td>0.076659</td>\n",
+       "      <td>0.382428</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       col1      col2      col3  col4\n",
+       "a  0.994700  0.374323  0.830171     0\n",
+       "d  0.931559  0.076659  0.382428     3"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.drop(df2[(df2['col4']>3) | (df2['col1']<0.9)].index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multi-Index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex(levels=[['Task1', 'Task2'], ['Lucky', 'Ram']],\n",
+       "           labels=[[0, 0, 1, 1], [1, 0, 1, 0]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "outer=['Task1','Task1','Task2','Task2']\n",
+    "inner=['Ram','Lucky','Ram','Lucky']\n",
+    "multiIndex= pd.MultiIndex.from_tuples(list(zip(outer, inner)))\n",
+    "multiIndex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>level1</th>\n",
+       "      <th>level2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Task1</th>\n",
+       "      <th>Ram</th>\n",
+       "      <td>0.176130</td>\n",
+       "      <td>0.080082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lucky</th>\n",
+       "      <td>0.102994</td>\n",
+       "      <td>0.235086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Task2</th>\n",
+       "      <th>Ram</th>\n",
+       "      <td>0.846569</td>\n",
+       "      <td>0.390396</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lucky</th>\n",
+       "      <td>0.464271</td>\n",
+       "      <td>0.384739</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               level1    level2\n",
+       "Task1 Ram    0.176130  0.080082\n",
+       "      Lucky  0.102994  0.235086\n",
+       "Task2 Ram    0.846569  0.390396\n",
+       "      Lucky  0.464271  0.384739"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3= pd.DataFrame(np.random.rand(4,2), index=multiIndex, columns=['level1','level2'])\n",
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>level1</th>\n",
+       "      <th>level2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Ram</th>\n",
+       "      <td>0.176130</td>\n",
+       "      <td>0.080082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Lucky</th>\n",
+       "      <td>0.102994</td>\n",
+       "      <td>0.235086</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         level1    level2\n",
+       "Ram    0.176130  0.080082\n",
+       "Lucky  0.102994  0.235086"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.loc['Task1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "level1    0.102994\n",
+       "level2    0.235086\n",
+       "Name: Lucky, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.loc['Task1'].loc['Lucky']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Ram      0.080082\n",
+       "Lucky    0.235086\n",
+       "Name: level2, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.loc['Task1','level2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Task1  Ram      0.080082\n",
+       "       Lucky    0.235086\n",
+       "Task2  Ram      0.390396\n",
+       "       Lucky    0.384739\n",
+       "Name: level2, dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.loc[['Task1','Task2'], 'level2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "level1    0.176130\n",
+       "level2    0.080082\n",
+       "Name: (Task1, Ram), dtype: float64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.loc[('Task1','Ram')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1761304116915441"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.loc[('Task1','Ram'),'level1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Task1  Ram      0.080082\n",
+       "Task2  Lucky    0.384739\n",
+       "Name: level2, dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.loc[[('Task1','Ram'), ('Task2','Lucky')],'level2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Day 6 Pandas.ipynb b/Day 6 Pandas.ipynb
new file mode 100644
index 0000000..4f3cfea
--- /dev/null
+++ b/Day 6 Pandas.ipynb	
@@ -0,0 +1,1474 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>c</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>d</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col1  col2  col3\n",
+       "0    a    10     1\n",
+       "1    b    20     2\n",
+       "2    c    30     1\n",
+       "3    d    40     1"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.DataFrame({'col1':['a','b','c','d'],'col2':[10,20,30,40],'col3':[1,2,1,1]})\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Handy DataFrame Operations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 3)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>c</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>d</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col1  col2  col3\n",
+       "0    a    10     1\n",
+       "1    b    20     2\n",
+       "2    c    30     1\n",
+       "3    d    40     1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 4 entries, 0 to 3\n",
+      "Data columns (total 3 columns):\n",
+      "col1    4 non-null object\n",
+      "col2    4 non-null int64\n",
+      "col3    4 non-null int64\n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 176.0+ bytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>12.909944</td>\n",
+       "      <td>0.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>17.500000</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>1.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>32.500000</td>\n",
+       "      <td>1.25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>40.000000</td>\n",
+       "      <td>2.00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            col2  col3\n",
+       "count   4.000000  4.00\n",
+       "mean   25.000000  1.25\n",
+       "std    12.909944  0.50\n",
+       "min    10.000000  1.00\n",
+       "25%    17.500000  1.00\n",
+       "50%    25.000000  1.00\n",
+       "75%    32.500000  1.25\n",
+       "max    40.000000  2.00"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2], dtype=int64)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col3'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col3'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    3\n",
+       "2    1\n",
+       "Name: col3, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col3'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['col1', 'col2', 'col3'], dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RangeIndex(start=0, stop=4, step=1)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sorting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>c</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>d</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col1  col2  col3\n",
+       "0    a    10     1\n",
+       "2    c    30     1\n",
+       "3    d    40     1\n",
+       "1    b    20     2"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_values(by='col3')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "sort_values() missing 1 required positional argument: 'by'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-12-9baf0daa5fc6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m: sort_values() missing 1 required positional argument: 'by'"
+     ]
+    }
+   ],
+   "source": [
+    "df.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>b</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>a</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>c</td>\n",
+       "      <td>30</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>d</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  col1  col2  col3\n",
+       "1    b    20     2\n",
+       "0    a    10     1\n",
+       "2    c    30     1\n",
+       "3    d    40     1"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.sort_values(by='col3', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "2    1\n",
+       "3    1\n",
+       "1    2\n",
+       "Name: col3, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col3'].sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## apply()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add(x):\n",
+    "    return x+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    12\n",
+       "1    22\n",
+       "2    32\n",
+       "3    42\n",
+       "Name: col2, dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col2'].apply(lambda x:x+2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    10\n",
+       "1    20\n",
+       "2    30\n",
+       "3    40\n",
+       "Name: col2, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col2'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col2'].apply('sum')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "25.0"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['col2'].apply('mean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    a.?\n",
+       "1    b.?\n",
+       "2    c.?\n",
+       "3    d.?\n",
+       "Name: col1, dtype: object"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def rename(x):\n",
+    "    return x+'.?'\n",
+    "df['col1'].apply(rename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>long</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22°35'</td>\n",
+       "      <td>82°35'</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>25°55'</td>\n",
+       "      <td>85°55'</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      lat    long\n",
+       "0  22°35'  82°35'\n",
+       "1  25°55'  85°55'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2=pd.DataFrame({'lat':[\"22°35'\",\"25°55'\"],'long':[\"82°35'\",\"85°55'\"]})\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    22.583333\n",
+       "1    25.916667\n",
+       "Name: lat, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def deg(x):\n",
+    "    degree= float(x.split(\"°\")[0])\n",
+    "    minute= float(x.split(\"°\")[1][:-1])\n",
+    "    total= degree+ (minute/60)\n",
+    "    return total\n",
+    "\n",
+    "df2['lat'].apply(deg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    82.583333\n",
+       "1    85.916667\n",
+       "Name: long, dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2['long'].apply(deg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c\n",
+       "0   1.0   NaN   7.0\n",
+       "1   NaN   NaN   8.0\n",
+       "2   3.0   6.0   NaN\n",
+       "3  10.0  11.0  12.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df=pd.DataFrame({'a':[1, np.nan, 3,10], 'b':[np.nan,np.nan,6,11], 'c':[7,8,np.nan,12]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>True</td>\n",
+       "      <td>True</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       a      b      c\n",
+       "0  False   True  False\n",
+       "1   True   True  False\n",
+       "2  False  False   True\n",
+       "3  False  False  False"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "a    1\n",
+       "b    2\n",
+       "c    1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    1\n",
+       "1    2\n",
+       "2    1\n",
+       "3    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dropna"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c\n",
+       "3  10.0  11.0  12.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th></th>\n",
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+       "      <th>1</th>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: []\n",
+       "Index: [0, 1, 2, 3]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
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+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c\n",
+       "0   1.0   NaN   7.0\n",
+       "2   3.0   6.0   NaN\n",
+       "3  10.0  11.0  12.0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(thresh=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
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+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c\n",
+       "0   1.0   NaN   7.0\n",
+       "1   NaN   NaN   8.0\n",
+       "2   3.0   6.0   NaN\n",
+       "3  10.0  11.0  12.0"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c\n",
+       "3  10.0  11.0  12.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dropna(thresh=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Day 7 Pandas.ipynb b/Day 7 Pandas.ipynb
new file mode 100644
index 0000000..7bf8c4e
--- /dev/null
+++ b/Day 7 Pandas.ipynb	
@@ -0,0 +1,1715 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Groupby"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ram</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sam</td>\n",
+       "      <td>2</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hari</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Lucky</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sita</td>\n",
+       "      <td>2</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Name  Group  Score  Gender\n",
+       "0    Ram      1     20       1\n",
+       "1    Sam      2     40       1\n",
+       "2   Hari      1     35       1\n",
+       "3  Lucky      3     11       1\n",
+       "4   Sita      2     54       0"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.DataFrame({'Name':['Ram','Sam','Hari','Lucky','Sita'],\n",
+    "                 'Group':[1,2,1,3,2],'Score': [20,40,35,11,54], 'Gender':[1,1,1,1,0]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.groupby.DataFrameGroupBy object at 0x0000018AE73E76A0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Group')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.5</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Score  Gender\n",
+       "Group               \n",
+       "1       27.5     1.0\n",
+       "2       47.0     0.5\n",
+       "3       11.0     1.0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Group').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2.00</td>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.75</td>\n",
+       "      <td>26.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Group  Score\n",
+       "Gender              \n",
+       "0        2.00   54.0\n",
+       "1        1.75   26.5"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Gender').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a=df.groupby('Group').mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.5</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Score  Gender\n",
+       "Group               \n",
+       "1       27.5     1.0\n",
+       "2       47.0     0.5\n",
+       "3       11.0     1.0"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Group\n",
+       "1    27.5\n",
+       "2    47.0\n",
+       "3    11.0\n",
+       "Name: Score, dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a['Score']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Score\n",
+       "Group       \n",
+       "1       27.5\n",
+       "2       47.0\n",
+       "3       11.0"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a[['Score']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.bar([1,2,3], df.groupby('Group').mean()['Score'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.5</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>0.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Score  Gender\n",
+       "Group               \n",
+       "1       27.5     1.0\n",
+       "2       47.0     0.5\n",
+       "3       11.0     1.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Group').agg('mean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.606602</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.899495</td>\n",
+       "      <td>0.707107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Score    Gender\n",
+       "Group                     \n",
+       "1      10.606602  0.000000\n",
+       "2       9.899495  0.707107\n",
+       "3            NaN       NaN"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Group').std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Group\n",
+       "1    2\n",
+       "2    2\n",
+       "3    1\n",
+       "Name: Score, dtype: int64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby('Group').count()['Score']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    4\n",
+       "0    1\n",
+       "Name: Gender, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Gender.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Gender</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ram</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sam</td>\n",
+       "      <td>2</td>\n",
+       "      <td>40</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hari</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Lucky</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sita</td>\n",
+       "      <td>2</td>\n",
+       "      <td>54</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Name  Group  Score  Gender\n",
+       "0    Ram      1     20       1\n",
+       "1    Sam      2     40       1\n",
+       "2   Hari      1     35       1\n",
+       "3  Lucky      3     11       1\n",
+       "4   Sita      2     54       0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 Ram\n",
+      "1 Sam\n",
+      "2 Hari\n",
+      "3 Lucky\n",
+      "4 Sita\n"
+     ]
+    }
+   ],
+   "source": [
+    "for index, row in df.iterrows():\n",
+    "    print(index, row['Name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Group</th>\n",
+       "      <th>Score</th>\n",
+       "      <th>Class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Ram</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sam</td>\n",
+       "      <td>2</td>\n",
+       "      <td>40</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Hari</td>\n",
+       "      <td>1</td>\n",
+       "      <td>35</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Lucky</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Sita</td>\n",
+       "      <td>2</td>\n",
+       "      <td>54</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Name  Group  Score Class\n",
+       "0    Ram      1     20     A\n",
+       "1    Sam      2     40     A\n",
+       "2   Hari      1     35     A\n",
+       "3  Lucky      3     11     B\n",
+       "4   Sita      2     54     B"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.DataFrame({'Name':['Ram','Sam','Hari','Lucky','Sita'],\n",
+    "                 'Group':[1,2,1,3,2],'Score': [20,40,35,11,54], 'Class':['A','A','A','B','B']})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Class</th>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Name  Score\n",
+       "Class Group             \n",
+       "A     1         2      2\n",
+       "      2         1      1\n",
+       "B     2         1      1\n",
+       "      3         1      1"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g1= df.groupby(['Class','Group']).count()\n",
+    "g1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Class</th>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
+       "      <th>1</th>\n",
+       "      <td>27.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
+       "      <th>2</th>\n",
+       "      <td>54.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Score\n",
+       "Class Group       \n",
+       "A     1       27.5\n",
+       "      2       40.0\n",
+       "B     2       54.0\n",
+       "      3       11.0"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(['Class','Group']).agg('mean')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Class</th>\n",
+       "      <th>Group</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">A</th>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">B</th>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Name  Score\n",
+       "Class Group             \n",
+       "A     1         2      2\n",
+       "      2         1      1\n",
+       "B     2         1      1\n",
+       "      3         1      1"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex(levels=[['A', 'B'], [1, 2, 3]],\n",
+       "           labels=[[0, 0, 1, 1], [0, 1, 1, 2]],\n",
+       "           names=['Class', 'Group'])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g1.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g1.loc[('B',2),'Name']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## fillna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c\n",
+       "0   1.0   NaN   7.0\n",
+       "1   NaN   NaN   8.0\n",
+       "2   3.0   6.0   NaN\n",
+       "3  10.0  11.0  12.0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.DataFrame({'a':[1,np.nan,3,10],'b':[np.nan,np.nan,6,11],'c':[7,8,np.nan,12]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      a     b     c\n",
+       "0   1.0   0.0   7.0\n",
+       "1   0.0   0.0   8.0\n",
+       "2   3.0   6.0   0.0\n",
+       "3  10.0  11.0  12.0"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>.?</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>.?</td>\n",
+       "      <td>.?</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>.?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a   b   c\n",
+       "0   1  .?   7\n",
+       "1  .?  .?   8\n",
+       "2   3   6  .?\n",
+       "3  10  11  12"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna('.?')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>7.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.666667</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>8.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           a     b     c\n",
+       "0   1.000000   8.5   7.0\n",
+       "1   4.666667   8.5   8.0\n",
+       "2   3.000000   6.0   9.0\n",
+       "3  10.000000  11.0  12.0"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.fillna(df.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     1.000000\n",
+       "1     4.666667\n",
+       "2     3.000000\n",
+       "3    10.000000\n",
+       "Name: a, dtype: float64"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['a'].fillna(df['a'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     1.0\n",
+       "1     1.0\n",
+       "2     3.0\n",
+       "3    10.0\n",
+       "Name: a, dtype: float64"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['a'].fillna(method='ffill')\n",
+    "#bfill/ backfill, ffill/pad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     1.0\n",
+       "1     3.0\n",
+       "2     3.0\n",
+       "3    10.0\n",
+       "Name: a, dtype: float64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['a'].fillna(method='bfill')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 10,  1 , 1  , 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>temp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>low</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>medium</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>high</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>high</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     temp\n",
+       "0     low\n",
+       "1     low\n",
+       "2  medium\n",
+       "3  medium\n",
+       "4    high\n",
+       "5    high\n",
+       "6     NaN"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# example\n",
+    "df2= pd.DataFrame({'temp':['low','low','medium','medium','high','high',np.nan]})\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       low\n",
+       "1       low\n",
+       "2    medium\n",
+       "3    medium\n",
+       "4      high\n",
+       "5      high\n",
+       "6      high\n",
+       "Name: temp, dtype: object"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2['temp'].fillna(method='ffill')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       low\n",
+       "1       low\n",
+       "2    medium\n",
+       "3    medium\n",
+       "4      high\n",
+       "5      high\n",
+       "6       NaN\n",
+       "Name: temp, dtype: object"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2['temp']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Day 8 Pandas.ipynb b/Day 8 Pandas.ipynb
new file mode 100644
index 0000000..b313aa8
--- /dev/null
+++ b/Day 8 Pandas.ipynb	
@@ -0,0 +1,2190 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Concatenation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1     2     3\n",
+       "1     4     5     6\n",
+       "2     7     8     9"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1= pd.DataFrame({'col1':[1,4,7],'col2':[2,5,8],'col3':[3,6,9]})\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0    11    12    13\n",
+       "1    14    15    16\n",
+       "2    17    18    19"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2= pd.DataFrame({'col1':[11,14,17],'col2':[12,15,18],'col3':[13,16,19]})\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27</td>\n",
+       "      <td>28</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0    21    22    23\n",
+       "1    24    25    26\n",
+       "2    27    28    29"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3= pd.DataFrame({'col1':[21,24,27],'col2':[22,25,28],'col3':[23,26,29]})\n",
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27</td>\n",
+       "      <td>28</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1     2     3\n",
+       "1     4     5     6\n",
+       "2     7     8     9\n",
+       "0    11    12    13\n",
+       "1    14    15    16\n",
+       "2    17    18    19\n",
+       "0    21    22    23\n",
+       "1    24    25    26\n",
+       "2    27    28    29"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1,df2,df3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
+       "      <th>0</th>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
+       "      <th>0</th>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27</td>\n",
+       "      <td>28</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     col1  col2  col3\n",
+       "1 0     1     2     3\n",
+       "  1     4     5     6\n",
+       "  2     7     8     9\n",
+       "2 0    11    12    13\n",
+       "  1    14    15    16\n",
+       "  2    17    18    19\n",
+       "3 0    21    22    23\n",
+       "  1    24    25    26\n",
+       "  2    27    28    29"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a=pd.concat([df1,df2,df3], keys=[1,2,3])\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex(levels=[[1, 2, 3], [0, 1, 2]],\n",
+       "           labels=[[0, 0, 0, 1, 1, 1, 2, 2, 2], [0, 1, 2, 0, 1, 2, 0, 1, 2]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
+       "      <th>0</th>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">3</th>\n",
+       "      <th>0</th>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27</td>\n",
+       "      <td>28</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     col1  col2  col3\n",
+       "1 0     1     2     3\n",
+       "  1     4     5     6\n",
+       "  2     7     8     9\n",
+       "2 0    11    12    13\n",
+       "  1    14    15    16\n",
+       "  2    17    18    19\n",
+       "3 0    21    22    23\n",
+       "  1    24    25    26\n",
+       "  2    27    28    29"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1,df2,df3], keys=[1,2,3,4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>27</td>\n",
+       "      <td>28</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1     2     3\n",
+       "1     4     5     6\n",
+       "2     7     8     9\n",
+       "3    11    12    13\n",
+       "4    14    15    16\n",
+       "5    17    18    19\n",
+       "6    21    22    23\n",
+       "7    24    25    26\n",
+       "8    27    28    29"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1,df2,df3]).reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>27</td>\n",
+       "      <td>28</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1     2     3\n",
+       "1     4     5     6\n",
+       "2     7     8     9\n",
+       "3    11    12    13\n",
+       "4    14    15    16\n",
+       "5    17    18    19\n",
+       "6    21    22    23\n",
+       "7    24    25    26\n",
+       "8    27    28    29"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1,df2,df3], ignore_index=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100</td>\n",
+       "      <td>200</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>400</td>\n",
+       "      <td>500</td>\n",
+       "      <td>600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>700</td>\n",
+       "      <td>800</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0   100   200   300\n",
+       "4   400   500   600\n",
+       "5   700   800   900"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# prevent the result from including duplicate index\n",
+    "df10= pd.DataFrame({'col1':[100,400,700],'col2':[200,500,800],'col3':[300,600,900]}, index=[0,4,5])\n",
+    "df10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1     2     3\n",
+       "1     4     5     6\n",
+       "2     7     8     9"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>100</td>\n",
+       "      <td>200</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>400</td>\n",
+       "      <td>500</td>\n",
+       "      <td>600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>700</td>\n",
+       "      <td>800</td>\n",
+       "      <td>900</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1     2     3\n",
+       "1     4     5     6\n",
+       "2     7     8     9\n",
+       "0   100   200   300\n",
+       "4   400   500   600\n",
+       "5   700   800   900"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.concat([df1,df10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Indexes have overlapping values: Int64Index([0], dtype='int64')",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-20-6e19a97f7a27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdf10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36mconcat\u001b[1;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[0;32m    223\u001b[0m                        \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    224\u001b[0m                        \u001b[0mverify_integrity\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 225\u001b[1;33m                        copy=copy, sort=sort)\n\u001b[0m\u001b[0;32m    226\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[0;32m    376\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 378\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnew_axes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_new_axes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    380\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m_get_new_axes\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    456\u001b[0m                 \u001b[0mnew_axes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    457\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 458\u001b[1;33m         \u001b[0mnew_axes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_concat_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    459\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnew_axes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m_get_concat_axis\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    514\u001b[0m                                                   self.levels, self.names)\n\u001b[0;32m    515\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 516\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_check_integrity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconcat_axis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    518\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mconcat_axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\reshape\\concat.py\u001b[0m in \u001b[0;36m_maybe_check_integrity\u001b[1;34m(self, concat_index)\u001b[0m\n\u001b[0;32m    523\u001b[0m                 \u001b[0moverlap\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconcat_index\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mconcat_index\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mduplicated\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    524\u001b[0m                 raise ValueError('Indexes have overlapping values: '\n\u001b[1;32m--> 525\u001b[1;33m                                  '{overlap!s}'.format(overlap=overlap))\n\u001b[0m\u001b[0;32m    526\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    527\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: Indexes have overlapping values: Int64Index([0], dtype='int64')"
+     ]
+    }
+   ],
+   "source": [
+    "pd.concat([df1,df10], verify_integrity= True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>11</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>21</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "      <td>24</td>\n",
+       "      <td>25</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>17</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "      <td>27</td>\n",
+       "      <td>28</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3  col1  col2  col3  col1  col2  col3\n",
+       "0     1     2     3    11    12    13    21    22    23\n",
+       "1     4     5     6    14    15    16    24    25    26\n",
+       "2     7     8     9    17    18    19    27    28    29"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xyz=pd.concat([df1,df2,df3], axis=1)\n",
+    "xyz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['col1', 'col2', 'col3', 'col1', 'col2', 'col3', 'col1', 'col2', 'col3'], dtype='object')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xyz.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Merging\n",
+    "like in SQL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1    12    13\n",
+       "1     4    15    16\n",
+       "2     7    18    19\n",
+       "3    10    20    25"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4= pd.DataFrame({'col1':[1,4,7,10],'col2':[12,15,18,20], 'col3':[13,16,19,25]})\n",
+    "df4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1     2     3\n",
+       "1     4     5     6\n",
+       "2     7     8     9\n",
+       "3    11    21    26"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df5= pd.DataFrame({'col1':[1,4,7,11],'col2':[2,5,8,21], 'col3':[3,6,9,26]})\n",
+    "df5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2_x</th>\n",
+       "      <th>col3_x</th>\n",
+       "      <th>col2_y</th>\n",
+       "      <th>col3_y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2_x  col3_x  col2_y  col3_y\n",
+       "0     1       2       3      12      13\n",
+       "1     4       5       6      15      16\n",
+       "2     7       8       9      18      19"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df5, df4, on='col1', how='inner')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "      <th>col3_x</th>\n",
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+       "    </tr>\n",
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+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2_x  col3_x  col2_y  col3_y\n",
+       "0     1       2       3      12      13\n",
+       "1     4       5       6      15      16\n",
+       "2     7       8       9      18      19"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df5.merge(df4, on='col1')"
+   ]
+  },
+  {
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+   "execution_count": 31,
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+       "      <th>col2_df4</th>\n",
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+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2_df5  col3_df5  col2_df4  col3_df4\n",
+       "0     1         2         3        12        13\n",
+       "1     4         5         6        15        16\n",
+       "2     7         8         9        18        19"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df5,df4, on='col1', suffixes=['_df5','_df4'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th>col3_x</th>\n",
+       "      <th>col3_y</th>\n",
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+       "  <tbody>\n",
+       "  </tbody>\n",
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+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [col1, col2, col3_x, col3_y]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df5,df4, on=['col1','col2'])"
+   ]
+  },
+  {
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+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
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+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>16.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
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+       "      <td>9.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>19.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>11</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>25.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2_x  col3_x  col2_y  col3_y\n",
+       "0     1     2.0     3.0    12.0    13.0\n",
+       "1     4     5.0     6.0    15.0    16.0\n",
+       "2     7     8.0     9.0    18.0    19.0\n",
+       "3    11    21.0    26.0     NaN     NaN\n",
+       "4    10     NaN     NaN    20.0    25.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df5, df4, on='col1', how='outer')"
+   ]
+  },
+  {
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+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
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+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2_x  col3_x  col2_y  col3_y\n",
+       "0     1       2       3    12.0    13.0\n",
+       "1     4       5       6    15.0    16.0\n",
+       "2     7       8       9    18.0    19.0\n",
+       "3    11      21      26     NaN     NaN"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df5, df4, how='left', on='col1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2_x  col3_x  col2_y  col3_y\n",
+       "0     1     2.0     3.0      12      13\n",
+       "1     4     5.0     6.0      15      16\n",
+       "2     7     8.0     9.0      18      19\n",
+       "3    10     NaN     NaN      20      25"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df5, df4, how='right', on='col1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "</style>\n",
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+       "      <th>col3</th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10</td>\n",
+       "      <td>20</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3\n",
+       "0     1    12    13\n",
+       "1     4    15    16\n",
+       "2     7    18    19\n",
+       "3    10    20    25"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11</td>\n",
+       "      <td>21</td>\n",
+       "      <td>26</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    A   B   C\n",
+       "0   1   2   3\n",
+       "1   4   5   6\n",
+       "2   7   8   9\n",
+       "3  11  21  26"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df6= pd.DataFrame({'A':[1,4,7,11],'B':[2,5,8,21],'C':[3,6,9,26]})\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>15</td>\n",
+       "      <td>16</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>18</td>\n",
+       "      <td>19</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3  A  B  C\n",
+       "0     1    12    13  1  2  3\n",
+       "1     4    15    16  4  5  6\n",
+       "2     7    18    19  7  8  9"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.merge(df4, df6, left_on='col1', right_on='A')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Day 9 Pandas.ipynb b/Day 9 Pandas.ipynb
new file mode 100644
index 0000000..fbd5e4c
--- /dev/null
+++ b/Day 9 Pandas.ipynb	
@@ -0,0 +1,2015 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Joining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2\n",
+       "0     1     2\n",
+       "1     4     5\n",
+       "2     7     8"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1= pd.DataFrame({'col1':[1,4,7],'col2':[2,5,8]})\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col3  col4\n",
+       "0     1     2\n",
+       "1     4     5\n",
+       "3     7     9"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2= pd.DataFrame({'col3':[1,4,7],'col4':[2,5,9]}, index=[0,1,3])\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3  col4\n",
+       "0     1     2   1.0   2.0\n",
+       "1     4     5   4.0   5.0\n",
+       "2     7     8   NaN   NaN"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.join(df2, how='left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3  col4\n",
+       "0   1.0   2.0     1     2\n",
+       "1   4.0   5.0     4     5\n",
+       "3   NaN   NaN     7     9"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3=df1.join(df2, how='right')\n",
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.float64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df3.col1.values[2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3  col4\n",
+       "0   1.0   2.0   1.0   2.0\n",
+       "1   4.0   5.0   4.0   5.0\n",
+       "2   7.0   8.0   NaN   NaN\n",
+       "3   NaN   NaN   7.0   9.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.join(df2, how='outer')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>col1</th>\n",
+       "      <th>col2</th>\n",
+       "      <th>col3</th>\n",
+       "      <th>col4</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   col1  col2  col3  col4\n",
+       "0     1     2     1     2\n",
+       "1     4     5     4     5"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.join(df2, how='inner')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Input and Output using Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# csv files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a   b   c   d\n",
+       "0   0   1   2   3\n",
+       "1   4   5   6   7\n",
+       "2   8   9  10  11\n",
+       "3  12  13  14  15"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.read_csv('example')\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_csv('H:\\\\example.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>roll</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Subham</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Sarthak</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Khsitiz</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Name  roll  class\n",
+       "0   Subham     1     10\n",
+       "1  Sarthak     2      9\n",
+       "2  Khsitiz     3     10"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_csv('example2.tsv', sep='\\t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Excel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>a</th>\n",
+       "      <th>b</th>\n",
+       "      <th>c</th>\n",
+       "      <th>d</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8</td>\n",
+       "      <td>9</td>\n",
+       "      <td>10</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12</td>\n",
+       "      <td>13</td>\n",
+       "      <td>14</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    a   b   c   d\n",
+       "0   0   1   2   3\n",
+       "1   4   5   6   7\n",
+       "2   8   9  10  11\n",
+       "3  12  13  14  15"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.read_excel('Excel_Sample.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.to_excel('......',sheet_name='')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HTML"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2= pd.read_html('https://en.wikipedia.org/wiki/Coronavirus_disease_2019', header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Coronavirus disease 2019(COVID-19)</th>\n",
+       "      <th>Unnamed: 1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Other names</td>\n",
+       "      <td>Coronavirus Corona COVID 2019-nCoV acute respi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>False-color transmission electron microscope i...</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Pronunciation</td>\n",
+       "      <td>/kəˈroʊnəˌvaɪrəs dɪˈziːz//ˌkoʊvɪdnaɪnˈtiːn, ˌk...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Specialty</td>\n",
+       "      <td>Infectious disease</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Symptoms</td>\n",
+       "      <td>Fever, cough, fatigue, shortness of breath, lo...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Complications</td>\n",
+       "      <td>Pneumonia, viral sepsis, acute respiratory dis...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Usual onset</td>\n",
+       "      <td>2–14 days (typically 5) from infection</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Causes</td>\n",
+       "      <td>Severe acute respiratory syndrome coronavirus ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Diagnostic method</td>\n",
+       "      <td>rRT-PCR testing, CT scan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Prevention</td>\n",
+       "      <td>Hand washing, face coverings, quarantine, soci...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Treatment</td>\n",
+       "      <td>Symptomatic and supportive</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Frequency</td>\n",
+       "      <td>26,065,382[8] confirmed cases</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Deaths</td>\n",
+       "      <td>863,826 ([8]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Coronavirus disease 2019(COVID-19)  \\\n",
+       "0                                         Other names   \n",
+       "1                                                 NaN   \n",
+       "2   False-color transmission electron microscope i...   \n",
+       "3                                       Pronunciation   \n",
+       "4                                           Specialty   \n",
+       "5                                            Symptoms   \n",
+       "6                                       Complications   \n",
+       "7                                         Usual onset   \n",
+       "8                                              Causes   \n",
+       "9                                   Diagnostic method   \n",
+       "10                                         Prevention   \n",
+       "11                                          Treatment   \n",
+       "12                                          Frequency   \n",
+       "13                                             Deaths   \n",
+       "\n",
+       "                                           Unnamed: 1  \n",
+       "0   Coronavirus Corona COVID 2019-nCoV acute respi...  \n",
+       "1                                                 NaN  \n",
+       "2                                                 NaN  \n",
+       "3   /kəˈroʊnəˌvaɪrəs dɪˈziːz//ˌkoʊvɪdnaɪnˈtiːn, ˌk...  \n",
+       "4                                  Infectious disease  \n",
+       "5   Fever, cough, fatigue, shortness of breath, lo...  \n",
+       "6   Pneumonia, viral sepsis, acute respiratory dis...  \n",
+       "7              2–14 days (typically 5) from infection  \n",
+       "8   Severe acute respiratory syndrome coronavirus ...  \n",
+       "9                            rRT-PCR testing, CT scan  \n",
+       "10  Hand washing, face coverings, quarantine, soci...  \n",
+       "11                         Symptomatic and supportive  \n",
+       "12                      26,065,382[8] confirmed cases  \n",
+       "13                                       863,826 ([8]  "
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Part of a series on the</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>COVID-19 pandemic</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SARS-CoV-2 (virus) COVID-19 (disease)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Timeline 2019 December 2020 January February r...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Locations By continent Africa Antarctica Asia ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>International response United Nations National...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Medical response Disease testing Vaccine resea...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Impact Socio-economic Crime Death rates by cou...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>COVID-19 Portal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>vte</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             Part of a series on the\n",
+       "0                                  COVID-19 pandemic\n",
+       "1              SARS-CoV-2 (virus) COVID-19 (disease)\n",
+       "2  Timeline 2019 December 2020 January February r...\n",
+       "3  Locations By continent Africa Antarctica Asia ...\n",
+       "4  International response United Nations National...\n",
+       "5  Medical response Disease testing Vaccine resea...\n",
+       "6  Impact Socio-economic Crime Death rates by cou...\n",
+       "7                                    COVID-19 Portal\n",
+       "8                                                vte"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Symptom</th>\n",
+       "      <th>Range</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Fever</td>\n",
+       "      <td>83–99%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Cough</td>\n",
+       "      <td>59–82%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Loss of appetite</td>\n",
+       "      <td>40–84%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Fatigue</td>\n",
+       "      <td>44–70%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Shortness of breath</td>\n",
+       "      <td>31–40%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Coughing up sputum</td>\n",
+       "      <td>28–33%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Muscle aches and pains</td>\n",
+       "      <td>11–35%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Symptom   Range\n",
+       "0                   Fever  83–99%\n",
+       "1                   Cough  59–82%\n",
+       "2        Loss of appetite  40–84%\n",
+       "3                 Fatigue  44–70%\n",
+       "4     Shortness of breath  31–40%\n",
+       "5      Coughing up sputum  28–33%\n",
+       "6  Muscle aches and pains  11–35%"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Unnamed: 2</th>\n",
+       "      <th>Unnamed: 3</th>\n",
+       "      <th>Unnamed: 4</th>\n",
+       "      <th>Unnamed: 5</th>\n",
+       "      <th>Unnamed: 6</th>\n",
+       "      <th>Unnamed: 7</th>\n",
+       "      <th>Unnamed: 8</th>\n",
+       "      <th>Unnamed: 9</th>\n",
+       "      <th>Unnamed: 10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Country</td>\n",
+       "      <td>0–9</td>\n",
+       "      <td>10–19</td>\n",
+       "      <td>20–29</td>\n",
+       "      <td>30–39</td>\n",
+       "      <td>40–49</td>\n",
+       "      <td>50–59</td>\n",
+       "      <td>60–69</td>\n",
+       "      <td>70–79</td>\n",
+       "      <td>80–89</td>\n",
+       "      <td>90+</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Argentina as of 7 May[164]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>18.8</td>\n",
+       "      <td>28.4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Australia as of 4 June[165]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>40.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Canada as of 3 June[166]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>30.7</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alberta as of 3 June[167]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>30.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Br. Columbia as of 2 June[168]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>4.6</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>33.8</td>\n",
+       "      <td>33.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Ontario as of 3 June[169]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>17.7</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>33.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Quebec as of 2 June[170]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>21.4</td>\n",
+       "      <td>30.4</td>\n",
+       "      <td>36.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Chile as of 31 May[171][172]</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>15.6</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>China as of 11 February[173]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>14.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Colombia as of 3 June[174]</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>25.6</td>\n",
+       "      <td>35.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Denmark as of 4 June[175]</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>4.1</td>\n",
+       "      <td>16.5</td>\n",
+       "      <td>28.1</td>\n",
+       "      <td>48.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Finland as of 4 June[176]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>&lt;0.4</td>\n",
+       "      <td>&lt;0.4</td>\n",
+       "      <td>&lt;0.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>18.1</td>\n",
+       "      <td>42.3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Germany as of 5 June[177]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Bavaria as of 5 June[178]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>35.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Israel as of 3 May[179]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>22.9</td>\n",
+       "      <td>30.8</td>\n",
+       "      <td>31.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Italy as of 3 June[180]</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>2.7</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>25.9</td>\n",
+       "      <td>32.4</td>\n",
+       "      <td>29.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Japan as of 7 May[181]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>6.8</td>\n",
+       "      <td>14.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Mexico as of 3 June[182]</td>\n",
+       "      <td>3.3</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>2.9</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>25.3</td>\n",
+       "      <td>33.7</td>\n",
+       "      <td>40.3</td>\n",
+       "      <td>40.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Netherlands as of 3 June[183]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>25.6</td>\n",
+       "      <td>33.3</td>\n",
+       "      <td>34.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>Norway as of 4 June[184]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>57.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Philippines as of 4 June[185]</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>2.4</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>20.9</td>\n",
+       "      <td>31.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Portugal as of 3 June[186]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>South Africa as of 28 May[187]</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>South Korea as of 17 July[188]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>25.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>Spain as of 29 May[189]</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>20.8</td>\n",
+       "      <td>21.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>Sweden as of 5 June[190]</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>23.4</td>\n",
+       "      <td>35.6</td>\n",
+       "      <td>40.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>Switzerland as of 4 June[191]</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>3.4</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>28.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>United States</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>Colorado as of 3 June[192]</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>Connecticut as of 3 June[193]</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>Georgia as of 3 June[194]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>Idaho as of 3 June[195]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>3.1</td>\n",
+       "      <td>8.9</td>\n",
+       "      <td>31.4</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>Indiana as of 3 June[196]</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>7.3</td>\n",
+       "      <td>17.1</td>\n",
+       "      <td>30.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>Kentucky as of 20 May[197]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>5.9</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>29.1</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>Maryland as of 20 May[198]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>14.6</td>\n",
+       "      <td>28.8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>Massachusetts as of 20 May[199]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>5.2</td>\n",
+       "      <td>16.8</td>\n",
+       "      <td>28.9</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>Minnesota as of 13 May[200]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>26.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>Mississippi as of 19 May[201]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>16.1</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>27.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>Missouri as of 19 May[202]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>14.3</td>\n",
+       "      <td>22.5</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>Nevada as of 20 May[203]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>22.3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>N. Hampshire as of 12 May[204]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>21.2</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>Oregon as of 12 May[205]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>12.1</td>\n",
+       "      <td>28.9</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Texas as of 20 May[206]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>30.6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Virginia as of 19 May[207]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>4.4</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Washington as of 10 May[208]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>1.3</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>Wisconsin as of 20 May[209]</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>19.9</td>\n",
+       "      <td>30.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Unnamed: 0  Age Unnamed: 2 Unnamed: 3 Unnamed: 4  \\\n",
+       "0                           Country  0–9      10–19      20–29      30–39   \n",
+       "1        Argentina as of 7 May[164]  0.0        0.0        0.1        0.4   \n",
+       "2       Australia as of 4 June[165]  0.0        0.0        0.0        0.0   \n",
+       "3          Canada as of 3 June[166]  0.0        0.1        0.7       11.2   \n",
+       "4         Alberta as of 3 June[167]  0.0        0.0        0.1        0.1   \n",
+       "5    Br. Columbia as of 2 June[168]  0.0        0.0        0.0        0.0   \n",
+       "6         Ontario as of 3 June[169]  0.0        0.0        0.1        0.2   \n",
+       "7          Quebec as of 2 June[170]  0.0        0.1        0.1        0.2   \n",
+       "8      Chile as of 31 May[171][172]  0.1        0.3        0.7        2.3   \n",
+       "9      China as of 11 February[173]  0.0        0.2        0.2        0.2   \n",
+       "10       Colombia as of 3 June[174]  0.3        0.0        0.2        0.5   \n",
+       "11        Denmark as of 4 June[175]  0.2        4.1       16.5       28.1   \n",
+       "12        Finland as of 4 June[176]  0.0        0.0       <0.4       <0.4   \n",
+       "13        Germany as of 5 June[177]  0.0        0.0        0.1        1.9   \n",
+       "14        Bavaria as of 5 June[178]  0.0        0.0        0.1        0.1   \n",
+       "15          Israel as of 3 May[179]  0.0        0.0        0.0        0.9   \n",
+       "16          Italy as of 3 June[180]  0.3        0.0        0.1        0.3   \n",
+       "17           Japan as of 7 May[181]  0.0        0.0        0.0        0.1   \n",
+       "18         Mexico as of 3 June[182]  3.3        0.6        1.2        2.9   \n",
+       "19    Netherlands as of 3 June[183]  0.0        0.2        0.1        0.3   \n",
+       "20         Norway as of 4 June[184]  0.0        0.0        0.0        0.0   \n",
+       "21    Philippines as of 4 June[185]  1.6        0.9        0.5        0.8   \n",
+       "22       Portugal as of 3 June[186]  0.0        0.0        0.0        0.0   \n",
+       "23   South Africa as of 28 May[187]  0.3        0.1        0.1        0.4   \n",
+       "24   South Korea as of 17 July[188]  0.0        0.0        0.0        0.1   \n",
+       "25          Spain as of 29 May[189]  0.3        0.2        0.2        0.3   \n",
+       "26         Sweden as of 5 June[190]  0.5        0.0        0.2        0.2   \n",
+       "27    Switzerland as of 4 June[191]  0.6        0.0        0.0        0.1   \n",
+       "28                    United States  NaN        NaN        NaN        NaN   \n",
+       "29       Colorado as of 3 June[192]  0.2        0.2        0.2        0.2   \n",
+       "30    Connecticut as of 3 June[193]  0.2        0.1        0.1        0.3   \n",
+       "31        Georgia as of 3 June[194]  0.0        0.1        0.5        0.9   \n",
+       "32          Idaho as of 3 June[195]  0.0        0.0        0.0        0.0   \n",
+       "33        Indiana as of 3 June[196]  0.1        0.1        0.2        0.6   \n",
+       "34       Kentucky as of 20 May[197]  0.0        0.0        0.0        0.2   \n",
+       "35       Maryland as of 20 May[198]  0.0        0.1        0.2        0.3   \n",
+       "36  Massachusetts as of 20 May[199]  0.0        0.0        0.1        0.1   \n",
+       "37      Minnesota as of 13 May[200]  0.0        0.0        0.0        0.1   \n",
+       "38    Mississippi as of 19 May[201]  0.0        0.1        0.5        0.9   \n",
+       "39       Missouri as of 19 May[202]  0.0        0.0        0.1        0.2   \n",
+       "40         Nevada as of 20 May[203]  0.0        0.3        0.3        0.4   \n",
+       "41   N. Hampshire as of 12 May[204]  0.0        0.0        0.4        0.0   \n",
+       "42         Oregon as of 12 May[205]  0.0        0.0        0.0        0.0   \n",
+       "43          Texas as of 20 May[206]  0.0        0.5        0.4        0.3   \n",
+       "44       Virginia as of 19 May[207]  0.0        0.0        0.0        0.1   \n",
+       "45     Washington as of 10 May[208]  0.0        0.2        1.3        9.8   \n",
+       "46      Wisconsin as of 20 May[209]  0.0        0.0        0.2        0.2   \n",
+       "\n",
+       "   Unnamed: 5 Unnamed: 6 Unnamed: 7 Unnamed: 8 Unnamed: 9 Unnamed: 10  \n",
+       "0       40–49      50–59      60–69      70–79      80–89         90+  \n",
+       "1         1.3        3.6       12.9       18.8       28.4         NaN  \n",
+       "2         0.1        0.2        1.1        4.1       18.1        40.8  \n",
+       "3        30.7        NaN        NaN        NaN        NaN         NaN  \n",
+       "4         0.1        0.2        1.9       11.9       30.8         NaN  \n",
+       "5         0.5        0.8        4.6       12.3       33.8        33.6  \n",
+       "6         0.5        1.5        5.6       17.7       26.0        33.3  \n",
+       "7         1.1        6.1       21.4       30.4       36.1         NaN  \n",
+       "8         7.7       15.6        NaN        NaN        NaN         NaN  \n",
+       "9         0.4        1.3        3.6        8.0       14.8         NaN  \n",
+       "10        1.6        3.4        9.4       18.1       25.6        35.1  \n",
+       "11       48.2        NaN        NaN        NaN        NaN         NaN  \n",
+       "12       <0.5        0.8        3.8       18.1       42.3         NaN  \n",
+       "13       19.7       31.0        NaN        NaN        NaN         NaN  \n",
+       "14        0.2        0.9        5.4       15.8       28.0        35.8  \n",
+       "15        0.9        3.1        9.7       22.9       30.8        31.3  \n",
+       "16        0.9        2.7       10.6       25.9       32.4        29.9  \n",
+       "17        0.3        0.6        2.5        6.8       14.8         NaN  \n",
+       "18        7.5       15.0       25.3       33.7       40.3        40.6  \n",
+       "19        0.5        1.7        8.1       25.6       33.3        34.5  \n",
+       "20        0.3        0.4        2.2        9.0       22.7        57.0  \n",
+       "21        2.4        5.5       13.2       20.9       31.5         NaN  \n",
+       "22        0.3        1.3        3.6       10.5       21.2         NaN  \n",
+       "23        1.1        3.8        9.2       15.0       12.3         NaN  \n",
+       "24        0.2        0.6        2.3        9.5       25.2         NaN  \n",
+       "25        0.6        1.4        5.0       14.3       20.8        21.7  \n",
+       "26        0.6        1.7        6.6       23.4       35.6        40.3  \n",
+       "27        0.1        0.6        3.4       11.6       28.2         NaN  \n",
+       "28        NaN        NaN        NaN        NaN        NaN         NaN  \n",
+       "29        0.8        1.9        6.2       18.5       39.0         NaN  \n",
+       "30        0.7        1.8        7.0       18.0       31.2         NaN  \n",
+       "31        2.0        6.1       13.2       22.0        NaN         NaN  \n",
+       "32        0.0        0.4        3.1        8.9       31.4         NaN  \n",
+       "33        1.8        7.3       17.1       30.2        NaN         NaN  \n",
+       "34        0.5        1.9        5.9       14.2       29.1         NaN  \n",
+       "35        0.7        1.9        6.1       14.6       28.8         NaN  \n",
+       "36        0.4        1.5        5.2       16.8       28.9         NaN  \n",
+       "37        0.3        1.6        5.4       26.9        NaN         NaN  \n",
+       "38        2.1        8.1       16.1       19.4       27.2         NaN  \n",
+       "39        0.8        2.2        6.3       14.3       22.5         NaN  \n",
+       "40        1.7        2.6        7.7       22.3        NaN         NaN  \n",
+       "41        1.2        0.0        2.2       12.0       21.2         NaN  \n",
+       "42        0.5        0.8        5.6       12.1       28.9         NaN  \n",
+       "43        0.8        2.1        5.5       10.1       30.6         NaN  \n",
+       "44        0.4        1.0        4.4       12.9       24.9         NaN  \n",
+       "45       31.2        NaN        NaN        NaN        NaN         NaN  \n",
+       "46        0.6        2.0        5.0       14.7       19.9        30.4  "
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Coronavirus Corona COVID 2019-nCoV acute respi...\n",
+       "1                                                   NaN\n",
+       "2                                                   NaN\n",
+       "3     /kəˈroʊnəˌvaɪrəs dɪˈziːz//ˌkoʊvɪdnaɪnˈtiːn, ˌk...\n",
+       "4                                    Infectious disease\n",
+       "5     Fever, cough, fatigue, shortness of breath, lo...\n",
+       "6     Pneumonia, viral sepsis, acute respiratory dis...\n",
+       "7                2–14 days (typically 5) from infection\n",
+       "8     Severe acute respiratory syndrome coronavirus ...\n",
+       "9                              rRT-PCR testing, CT scan\n",
+       "10    Hand washing, face coverings, quarantine, soci...\n",
+       "11                           Symptomatic and supportive\n",
+       "12                        26,065,382[8] confirmed cases\n",
+       "13                                         863,826 ([8]\n",
+       "Name: Unnamed: 1, dtype: object"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[0]['Unnamed: 1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     Coronavirus Corona COVID 2019-nCoV acute respi...\n",
+       "1                                                   nan\n",
+       "2                                                   nan\n",
+       "3     /kəˈroʊnəˌvaɪrəs dɪˈziːz//ˌkoʊvɪdnaɪnˈtiːn, ˌk...\n",
+       "4                                    Infectious disease\n",
+       "5     Fever, cough, fatigue, shortness of breath, lo...\n",
+       "6     Pneumonia, viral sepsis, acute respiratory dis...\n",
+       "7                2–14 days (typically 5) from infection\n",
+       "8     Severe acute respiratory syndrome coronavirus ...\n",
+       "9                              rRT-PCR testing, CT scan\n",
+       "10    Hand washing, face coverings, quarantine, soci...\n",
+       "11                           Symptomatic and supportive\n",
+       "12                           26,065,382 confirmed cases\n",
+       "13                                            863,826 (\n",
+       "Name: Unnamed: 1, dtype: object"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2[0]['Unnamed: 1'].apply(lambda x: re.sub('\\[\\d\\]', '', str(x)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Day10 Matplot.ipynb b/Day10 Matplot.ipynb
new file mode 100644
index 0000000..9b29531
--- /dev/null
+++ b/Day10 Matplot.ipynb	
@@ -0,0 +1,821 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip install matplotlib\n",
+    "# conda install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "# notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x= np.arange(0,11)\n",
+    "y= x*2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "_=plt.plot(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y)\n",
+    "plt.xlabel('X Axis')\n",
+    "plt.ylabel('Y axis')\n",
+    "plt.title('Plot Title')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26eabe94128>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,y,'r*-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Subplots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26eabcc4390>]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.subplot(2,2,1)\n",
+    "plt.plot(x,y,'r*-')\n",
+    "plt.ylabel('Y axis')\n",
+    "\n",
+    "plt.subplot(2,2,2)\n",
+    "plt.plot(y,x,'g--')\n",
+    "\n",
+    "plt.subplot(2,2,3)\n",
+    "plt.plot(x, y**2, 'bo-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## object oriented method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26eabffd438>]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig= plt.figure() # empty canvas\n",
+    "\n",
+    "axes= fig.add_axes([0,0,0.2,0.2]) # left, bottom, width, height\n",
+    "\n",
+    "#plotting on the axes\n",
+    "axes.plot(x,y,'b')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'title of axes3')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create plots inside plot\n",
+    "fig= plt.figure()\n",
+    "\n",
+    "axes1= fig.add_axes([0.0, 0.0, 0.8, 0.8])\n",
+    "axes2= fig.add_axes([0.1,0.4,0.3,0.3])\n",
+    "axes3= fig.add_axes([0.4,0.1,0.3,0.3])\n",
+    "\n",
+    "axes1.plot(x,y, 'b')\n",
+    "axes1.set_xlabel('X_label of axis1')\n",
+    "\n",
+    "axes2.plot(y,x, 'r')\n",
+    "axes2.set_ylabel('Y label of axes2')\n",
+    "\n",
+    "axes3.hist(y)\n",
+    "axes3.set_title('title of axes3')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x26ead624f98>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig= plt.figure()\n",
+    "ax= fig.add_axes([0,0,1,1])\n",
+    "\n",
+    "ax.plot(x,y, label='x vs y')\n",
+    "ax.plot(x,y**2, label='x vs y**2')\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x26ead706a90>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig= plt.figure()\n",
+    "ax= fig.add_axes([0,0,1,1])\n",
+    "\n",
+    "ax.plot(x,y)\n",
+    "ax.plot(x,y**2)\n",
+    "ax.legend(labels=['x vs y','x vs y**2'], loc='best')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## set x axis ticks and tick labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 11 artists>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig= plt.figure()\n",
+    "ax= fig.add_axes([0,0,1,1])\n",
+    "ax.bar(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig= plt.figure()\n",
+    "ax= fig.add_axes([0,0,1,1])\n",
+    "ax.bar(x,y)\n",
+    "ax.set_xticks([1,2,3,4,5,6,7,8,9,10,12])\n",
+    "ax.set_xticklabels(['B','C','D','E','F','G','H','I','J','K','Z'])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## subplot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26ead943588>]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax= plt.subplots()\n",
+    "# similar to plt.figure()\n",
+    "ax.plot(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26eaeca30b8>]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x864 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax= plt.subplots(2,2, figsize=(10,12))\n",
+    "ax[0][0].bar(x,y)\n",
+    "ax[1,1].plot(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x26eaed70b00>]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
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RPbNVOjpj94SP/v0Bkhh6BwCARdwgY+Nsb5tdZILRTt6IAAAAYCc7dSAzMhIAAAAAq/bsJBfd4M9+M8lDV9gFAAAAdpd7DmROS/I3s4sAAADANnLKYO7AqS3mOGgwd9LUFgAAALA1jeyLnZTkQ7OLLKO7z0rynoHoTapq/9l9gJ3D0DsAAGzcLQdzW3Ho/UMZ+1KBoXcAAAB2snMGMqM3NgAAAMBKVNVdktx1gchDu/ubq+oDAAAAu0NVXTHJtQeib+xun/sCAACwk315MHeRqS3mGO10wtQWAAAAsMVU1YFJbjgQfWd3nzu7zwQju28HZex3AJDE0DsAACziRgOZbyf52Owiy1p/Y+TdA9HrVdU+s/sAAADANnHIQObU6S0AAABgUFVdJMmzF4i8rbv/blV9AAAAYDe682DuFVNbAAAAwDaz/gC0bw1ErzC5ygyHDeY+P7UFAAAAbD2HJ9l/IPeO2UUmeftgztA7MMzQOwAAbNzPDWQ+0t09vckcHx7IHJDkKrOLAAAAwFZXVftn7UnsizpxdhcAAABYwjOSXHKDP3t6kt9aYRcAAADYnW4+kDktyT/NLgIAAADb0McGMlef3mJ5I52+m+TY2UUAAABgi7nWYO5DU1vM84kkZw7krj27CLBzGHoHAIANqKorJLnoQPRf5zaZ6iODudE3ZAAAAGA7u+pg7nNTWwAAAMCgqjoyyX0XiDy+u92sDgAAwLZXVXsnudFA9L3dfdrsPgAAALANHTOQuVZVHTi9yXJ+YSDzge7u6U0AAABgaxkZOD8vYw+HW7nuPjtrY++Lsq8GDDP0DgAAGzP6lLXRMfXd4cODOU+cAwAAYCcavR429A4AAMCmq6qDkzxvgchHkzx9RXUAAABgdzs8yQUHcm+fXQQAAAC2qTcNZPZNctvZRUZV1aWTXHcg+s+zuwAAAMAWNHIf9ae7+3vTm8wzsrF2WFVdZHoTYEcw9A4AABsz+pS1j09tMVF3fz3JiQNRT5wDAABgJ7rRYG5LPokeAACAHefPklxugz97bpL7d/c5K+wDAAAAu9PNBnPvmFkCAAAAtrH3Z+ye5PvMLrKEX83iW0vnJnnNCroAAADAllFV+ye5ykB0y+6rrRvt93NTWwA7hqF3AADYmKsOZM5L8oXZRSb7j4HMyO8CAAAAtq2q2jvJHQeiJyX56OQ6AAAAsJCq+oUkD1og8pfd/eFV9QEAAIBNcMOBzGlJPjK7CAAAAGxH6w8Kf+lA9A5VNTIUN1VVHZDkIQPRN3X3l2f3AQAAgC3mp5PsM5Ab2S/bnUb72VgDhoz8ixQAAHaiwwYyx3f3WdObzPUfWfzGhUOr6uDuPnUVhQAAAGALulmSiw3k3tPd503uAgAAABu2frP6C5PUBiNfTPLY1TUCAACATXG1gcwnuvvckRerqp9Ocvj6614tyRWSXDjJhZIcnOScrA3Jn5a1B4gfn7Vr8uOSfDzJMd397ZHXBgAAgBV6dpLfy2J7RZXkGUlus5JGG/fYJJccyD1ldhEAAADYgkb21ZI9d+h99PcB7HCG3gEAYGNGLrw/N73FfMu8EfGJmUUAAABgC3vQYO4NU1sAAADA4h6f5KcX+PkHdff3VtQFAAAAdruqOihrQ+uL+uiCr3PjJHdOcvskV9rFj++T5IAkF01y2SQ/ez7nfS7JO5O8Osnbu/vsRfoAAADAbN19bFW9NMn9FoweWVX37+7nr6DWLlXV9ZM8YiB6dHe/a3YfAAAA2IJGh823+sba8UnOSrLfgjlD78CQvTa7AAAAbHVVdUiSQwaiX5jdZQVGO3ojAgAAgB2hqn4yyZ0GomcleeXkOgAAALBhVXXtJA9bIPKy7n7jqvoAAADAJrlKxu6j3OXQe1UdUFW/XlUfS/LuJH+QXY+8b9SVktw/yZuSfL2qnltVV5l0NgAAAIx6TJJTBnJ/uf6QtN2qqg5L8posPuh2VpIHz28EAAAAW9LontiW3ljr7nOTnDAQta8GDDH0DgAAuzZ60f21qS1WY7SjNyIAAADYKf4wY5+p/XN3f3t2GQAAANiIqtonyYuS7LPByHeSPGR1jQAAAGDT/Oxg7pM/6v+oNb+atZvWX5jkGoOvsVEXSfKAJP9WVf9UVTdY8esBAADA+eruryR51EB0/yRvrKojJlf6karqZ5K8M8mlBuKP7+5/n9sIAAAAtqyRPbHzkpw4u8gKjGys2VcDhhh6BwCAXbvcYG47DL1/dTB3+aktAAAAYAuqqmsk+Y3B+LNmdgEAAIAFPSrJNRf4+Yd399dXVQYAAAA20ZUHc8ee3/+4/jnye5L8bZJLjpYaVElum+R9VfV3VTUyVAcAAABL6e7/leS1A9GDk7ylqv6gqmpyrR9SVXdP8sGM3SP+xiR/NrcRAAAAbGkj18/f7O5zpzeZb2Rj7ZCqutD0JsAez9A7AADs2sUHc9th6H2046FTWwAAAMDW9BdJ9h7I/Ut3v312GQAAANiIqrpqkscuEHlnd79oVX0AAABgk112IHNmzudm76q6f9ZG4m60bKklVZJfSfLZqrrfJncBAABgZ/rVJO8byO2T5OlZe4jZDeZWSqrqKlX1uiSvSDIyyPbuJHfv7p7bDAAAALa0kY217bCvlthYA3YjQ+8AALBrFxvMbfk3Irr7W0nOHoiO/k4AAABgW6iq+yY5YjD+xJldAAAAYKOqaq8kL0yy/wYjZyb5rdU1AgAAgE13yYHMcT846FZV+1fVi5I8Lxu/5t4dDk7y4qp6SVUdtNllAAAA2Dm6+3tJfjHJMYNH3CDJ+6vq7VV176q6wGiXqtqvqu5YVa9N8skkdxw86n1Jbtfdp412AQAAgG1qZE9sy++rrRvtaWMNWNg+m10AAAC2gYsO5r47tcXqnJTFnx43+jsBAACALa+qLpfkWYPxd3X3G2b2AQAAgAX8XpLrL/Dzf9Ldn11VGQAAANgCLjWQ+eJ//kNV7Z/kdUmOnNZovvsmuWZVHdndJ252GQAAAHaG7j6lqo5M8qYs9jn1Dzpi/c9ZVfX+JB9I8vEkxyb5Stbu1T4jSSc5IMkFk1w6yeWTXCPJ9ZLcNMnwUPy6Nye5e3efuuQ5AAAAsK1U1YFZu+Ze1HbZVxvtaWMNWJihdwAA2LXRJ6udMrXF6pySxYfePW0OAACAPVJVVZKXJLnQQPzcJL87tRAAAABsUFUdluQJC0Q+meTPV1QHAAAAtopLDmS+kXx/5P0fsrVH3v/TzyV5d1XdqrtP2OwyAAAA7AzdfVJV3STJE5M8PEkNHrVfkput/9mdzkryR0me0d29m18bAAAAtoKdsK82wsYasLC9NrsAAABsA6NPVjt5aovVGenpaXMAAADsqR6d5IjB7LO7+5MzywAAAMACnp/kAhv82fOSPKC7z15hHwAAANhUVbVvxm6+/ub6Q8L/Pslt5rZaqZ9J8p6qGhm3BwAAgCHdfXZ3PzLJrZJ8ZbP7LODjSX6+u59u5B0AAIAdzL7a+bOxBixsn80uAAAA28CFB3OnTm2xOiNPnDtkegsAAADYZFV1qyT/32D8i0keN7EOAAAAbFhV/WaSWywQ+evu/sCq+gAAAMAWcUiSGsh9K8kfJrnLkq9/XpLjk3w+yXez9v3yfbL2oLaLJ7likkss+Rr/1eWT/GNV3bS7T5t8NgAAAPxI3X10VV0lyYOT/EHGHr62O3wsyROSvNrAOwAAAAzvq43slm2G0Z421oCFGXoHAIBd238gc1p3nze9yWqMDNLvXVV7baO/IwAAAPxYVXW5JH+fZK+BeCe5X3dvl6fPAwAAsAepqksneeoCkS8nefSK6gAAAMBWcuBg7hpJ7jyYPS7JUUmOTvL+7j79x/1wVR2a5GZJjkxy94zfRP+DDk/yd0nuOuEsAAAA2LD171M/saqemeR3kvxekktvbqskyVlJ3p7kOUleb+AdAAAAvm9kXy0Z2y3bDKM995vaAtgRRoYqAABgpxm54D57eovVGe3qjQgAAAD2CFV1UJLXJDl08Ii/6O53zmsEAAAAC/nrLDYC9zseVgYAAMAOMXpD+t2S7L1g5gNZG2v/ye5+dHcfvauR9yTp7m9296u6+/5JLpnkAUmOX7jx/+suVfWACecAAADAwrr71O5+cpLLJvmFJE9P8oXdXSPJPyS5d5KLd/cvdvfrjLwDAADADxndEdsuG2v21YDdxtA7AADs2r4DmXOmt1gdb0QAAACwY1XVXkn+d5LrDB7xiSSPntcIAAAANq6q7pnkjgtEXt3dr1tVHwAAANhiDtgNr/HNJPfs7ht291uWGYvr7jO6+/lJrpTkCVn+O+lPq6qfXPIMAAAAGLZ+nfzJJP+S5K1JTt2NL19JjsjaA91+uaouuhtfGwAAALaLkX21ZPtsrNlXA3YbQ+8AALBrIxfc2+VNiGS8qzciAAAA2BP8eZI7DWZPTnK37j5jYh8AAADYkKo6NMmzFoiclOTBK6oDAAAAW9Gqh97fn+Rq3f3ymYd291nd/bgkN0rytSWOOjjJ8+a0AgAAgI2rqr2r6s5V9dokX0/yiiQPyNq16u504SR3TvL8JF+rqtdX1a2rqnZzDwAAANiqRnfEtsvGmn01YLcx9A4AALtm6P38jT6JDwAAALaEqvqtJA9b4ohf7+7PzuoDAAAAC3pWkosv8POP6u6vrqoMAAAAbEH7r/DsVye5eXd/fVUv0N0fTHK9JJ9Z4phbVNWRkyoBAADAj1VVB1TVQ5J8Pslrktwpq70+X8S+Se6Q5M1JPllVdzf4DgAAAIbefwT7asDCDL0DAMCu7TOQ2S5vQiTeiAAAAGAHqqo7JHn2Ekc8rbtfPasPAAAALKKqbp/kXgtE3pfkuSuqAwAAAFvVqsba/jnJvbr7zBWd/33dfUKSWyb54hLH/KnhOgAAAFatqu6b5HNJnpHk8ptcZ1eumuQVSY6pqmttdhkAAADYRCP7asn22VizrwbsNobeAQBg10Yu1Pee3mJ19vQ3WgAAAOCHVNVNkrw849f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+      "text/plain": [
+       "<Figure size 6000x4000 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# dpi= dots/pixel per inch\n",
+    "fig= plt.figure(dpi=1000)\n",
+    "ax= fig.add_axes([0,0,0.9,0.9])\n",
+    "ax.plot(x,y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## saving"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.savefig('abc.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from google.colab import files\n",
+    "# plt.savefig('abc.png')\n",
+    "# files.download('abc.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## annotation() and text()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(2, 18, 'my_text')"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax= plt.subplots()\n",
+    "ax.plot(x,y,'*-')\n",
+    "ax.annotate('(6,12)', xy=(6,12), xytext=(2,15), arrowprops={'facecolor':'green'})\n",
+    "ax.text(2, 18,'my_text', style='italic', bbox={'facecolor':'yellow'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x26ead839be0>"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img1= plt.imread('male.jpg')\n",
+    "plt.imshow(img1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[151, 167, 182],\n",
+       "        [151, 167, 182],\n",
+       "        [151, 167, 182],\n",
+       "        ...,\n",
+       "        [131, 145, 154],\n",
+       "        [131, 145, 154],\n",
+       "        [131, 145, 154]],\n",
+       "\n",
+       "       [[151, 167, 182],\n",
+       "        [151, 167, 182],\n",
+       "        [151, 167, 182],\n",
+       "        ...,\n",
+       "        [129, 143, 152],\n",
+       "        [129, 143, 152],\n",
+       "        [129, 143, 152]],\n",
+       "\n",
+       "       [[151, 167, 182],\n",
+       "        [151, 167, 182],\n",
+       "        [151, 167, 182],\n",
+       "        ...,\n",
+       "        [128, 142, 151],\n",
+       "        [128, 142, 151],\n",
+       "        [128, 142, 151]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[185, 186, 170],\n",
+       "        [185, 186, 170],\n",
+       "        [185, 186, 170],\n",
+       "        ...,\n",
+       "        [195, 196, 180],\n",
+       "        [195, 196, 180],\n",
+       "        [195, 196, 180]],\n",
+       "\n",
+       "       [[183, 184, 168],\n",
+       "        [183, 184, 168],\n",
+       "        [183, 184, 168],\n",
+       "        ...,\n",
+       "        [198, 199, 183],\n",
+       "        [198, 199, 183],\n",
+       "        [198, 199, 183]],\n",
+       "\n",
+       "       [[182, 183, 167],\n",
+       "        [182, 183, 167],\n",
+       "        [182, 183, 167],\n",
+       "        ...,\n",
+       "        [192, 193, 177],\n",
+       "        [192, 193, 177],\n",
+       "        [192, 193, 177]]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12,6))\n",
+    "\n",
+    "ax.plot(x, y+1, color=\"red\", linewidth=0.25)\n",
+    "ax.plot(x, y+2, color=\"red\", linewidth=0.50)\n",
+    "ax.plot(x, y+3, color=\"red\", linewidth=1.00)\n",
+    "ax.plot(x, y+4, color=\"red\", linewidth=2.00)\n",
+    "\n",
+    "# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’\n",
+    "ax.plot(x, y+5, color=\"green\", lw=3, linestyle='-')\n",
+    "ax.plot(x, y+6, color=\"green\", lw=3, ls='-.')\n",
+    "ax.plot(x, y+7, color=\"green\", lw=3, ls=':')\n",
+    "\n",
+    "# custom dash\n",
+    "line, = ax.plot(x, y+8, color=\"black\", lw=1.50)\n",
+    "line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...\n",
+    "\n",
+    "# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...\n",
+    "ax.plot(x, y+ 9, color=\"blue\", lw=3, ls='-', marker='+')\n",
+    "ax.plot(x, y+10, color=\"blue\", lw=3, ls='--', marker='o')\n",
+    "ax.plot(x, y+11, color=\"blue\", lw=3, ls='-', marker='s')\n",
+    "ax.plot(x, y+12, color=\"blue\", lw=3, ls='--', marker='1')\n",
+    "\n",
+    "# marker size and color\n",
+    "ax.plot(x, y+13, color=\"purple\", lw=1, ls='-', marker='o', markersize=2)\n",
+    "ax.plot(x, y+14, color=\"purple\", lw=1, ls='-', marker='o', markersize=4)\n",
+    "ax.plot(x, y+15, color=\"purple\", lw=1, ls='-', marker='o', markersize=8, markerfacecolor=\"red\")\n",
+    "ax.plot(x, y+16, color=\"purple\", lw=1, ls='-', marker='s', markersize=8, \n",
+    "        markerfacecolor=\"yellow\", markeredgewidth=3, markeredgecolor=\"green\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Day11 plotting.ipynb b/Day11 plotting.ipynb
new file mode 100644
index 0000000..cbe2cb6
--- /dev/null
+++ b/Day11 plotting.ipynb	
@@ -0,0 +1,1070 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x= np.arange(0,10)\n",
+    "y= np.array(random.sample(range(1, 50), 10))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([20, 38, 11, 26, 17, 47, 39, 30, 36, 23])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bar plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig= plt.figure()\n",
+    "ax= fig.add_axes([0,0,1,1])\n",
+    "ax.bar(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 10 artists>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(x,y, width=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Histogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[73 78 70 99 42 87 18 20  7 33 19 46  6 69 97 31 52 40 71 49 22 55 26 91\n",
+      " 81  2 96 17 50 41 28 39 59 83 34 85 79 23  5 80 53 58  1 43 47 68 16 60\n",
+      " 56 36]\n"
+     ]
+    }
+   ],
+   "source": [
+    "data1= np.array(random.sample(range(1,100), 50))\n",
+    "print(data1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax= plt.subplots()\n",
+    "ax.hist(data1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([18,  7,  6,  2, 17,  5,  1, 16])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data1[data1<19]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([10., 10., 13.,  8.,  9.]),\n",
+       " array([ 1. , 20.6, 40.2, 59.8, 79.4, 99. ]),\n",
+       " <a list of 5 Patch objects>)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data1, bins=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pie chart"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "group= ['A','B','C']\n",
+    "students=[20,30,25]\n",
+    "fig, ax= plt.subplots()\n",
+    "_=ax.pie(students, labels=group, autopct='%1.2f%%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## scatter plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x2289b5a8048>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x= np.arange(0,50)\n",
+    "fig, ax= plt.subplots()\n",
+    "ax.scatter(x,x, color='b')\n",
+    "ax.scatter(x,data1, color='g')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## boxplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'whiskers': [<matplotlib.lines.Line2D at 0x2289b6064e0>,\n",
+       "  <matplotlib.lines.Line2D at 0x2289b6067b8>],\n",
+       " 'caps': [<matplotlib.lines.Line2D at 0x2289b606b00>,\n",
+       "  <matplotlib.lines.Line2D at 0x2289b606e48>],\n",
+       " 'boxes': [<matplotlib.lines.Line2D at 0x2289b606128>],\n",
+       " 'medians': [<matplotlib.lines.Line2D at 0x2289b606f28>],\n",
+       " 'fliers': [<matplotlib.lines.Line2D at 0x2289b611518>],\n",
+       " 'means': []}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAD8CAYAAACVZ8iyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAD01JREFUeJzt3W9sXXd9x/H3BzfAtImRqm4JSbN0KExuLK2Dqw5pHsKA1pYnaZE61Q9GhSwFprbaJJ6U+UE7JEtogk0iY52CXNFK6+0qbawRsDFaResswcDZKkjwKkIp1CRqA4mgiLVLyncPfMJui5O4vvf6xM37JR3dc77nd879Vqr1yfl7U1VIki5ur2m7AUlS+wwDSZJhIEkyDCRJGAaSJAwDSRKGgSQJw0CShGEgSQIuabuB1brssstqx44dbbchSRvGwYMHf1hVo6sZu2HCYMeOHSwsLLTdhiRtGEm+t9qxniaSJBkGkiTDQJKEYSBJwjCQJDGAMEhyZZIDSRaTHE7yJ0390iRfTvLt5nNzU0+STyU5kuQbSd7Wbw9SG7rdLuPj44yMjDA+Pk632227JWnNBnFkcBr4SFWNAe8AbktyNXAn8GhV7QQebZYBbgB2NtMe4J4B9CCtq263y8zMDHv37uX5559n7969zMzMGAjasPoOg6o6VlX/2cw/BywCW4HdwH3NsPuAG5v53cD9teyrwBuTbOm3D2k9zc7OMjc3x+TkJJs2bWJycpK5uTlmZ2fbbk1ak4FeM0iyA/gd4D+AK6rqGCwHBnB5M2wr8HTPZktNbaX97UmykGTh+PHjg2xV6svi4iITExMvqU1MTLC4uNhSR1J/BhYGSX4N+AfgT6vqJ+caukKtVhpYVfuqqlNVndHRVT1RLa2LsbEx5ufnX1Kbn59nbGyspY6k/gwkDJJsYjkI/q6q/rEpP3Pm9E/z+WxTXwKu7Nl8G3B0EH1I62VmZobp6WkOHDjAqVOnOHDgANPT08zMzLTdmrQmfb+bKEmAOWCxqv6yZ9V+4Fbg483nwz3125M8CPwu8OMzp5OkjWJqagqAO+64g8XFRcbGxpidnf1FXdpoUrXiGZrV7yCZAP4d+Cbw86b8ZyxfN3gI2A58H7i5qk404fHXwPXAz4APVtV530DX6XTKF9VJ0uolOVhVndWM7fvIoKrmWfk6AMB7VhhfwG39fq8kaXB8AlmSZBhIkgwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBtKadbtdxsfHGRkZYXx8nG6323ZL0pr1/bOX0sWo2+0yMzPD3NwcExMTzM/PMz09DcDU1FTL3Umv3ECODJLcm+TZJId6ancn+UGSx5vpfT3rPprkSJInklw3iB6k9TQ7O8vc3ByTk5Ns2rSJyclJ5ubmmJ2dbbs1aU2y/Pv0fe4keSfwU+D+qhpvancDP62qT7xs7NVAF7gWeDPwCPDWqnrxXN/R6XRqYWGh716lQRgZGeH5559n06ZNv6idOnWK17/+9bz44jn/V5bWTZKDVdVZzdiBHBlU1WPAiVUO3w08WFUvVNV3gSMsB4O0YYyNjTE/P/+S2vz8PGNjYy11JPVn2NcMbk/yAWAB+EhVnQS2Al/tGbPU1H5Jkj3AHoDt27cPuVUJkqx67Lvf/e6+9jGIo3JpUIZ5N9E9wFuAa4BjwCeb+kp/KSv+VVTVvqrqVFVndHR0OF1KPapq1dMDDzzArl27ANi1axcPPPDAK9peupAM7cigqp45M5/kM8Dnm8Ul4MqeoduAo8PqQxqWqakppqamSMKhQ4fOv4F0ARvakUGSLT2LNwFn/lr2A7ckeV2Sq4CdwNeG1Yck6fwGcmSQpAu8C7gsyRJwF/CuJNewfAroKeBDAFV1OMlDwLeA08Bt57uTSJI0XAO5tXQ9eGupLlRJvAagC9K631oqSdrYDANJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCRhGEiSGFAYJLk3ybNJDvXULk3y5STfbj43N/Uk+VSSI0m+keRtg+hBkrR2gzoy+Cxw/ctqdwKPVtVO4NFmGeAGYGcz7QHuGVAPkqQ1GkgYVNVjwImXlXcD9zXz9wE39tTvr2VfBd6YZMsg+pAkrc0wrxlcUVXHAJrPy5v6VuDpnnFLTU2S1JI2LiBnhVqtODDZk2QhycLx48eH3JYkXbyGGQbPnDn903w+29SXgCt7xm0Djq60g6raV1WdquqMjo4OsVVJurgNMwz2A7c287cCD/fUP9DcVfQO4MdnTidJktpxySB2kqQLvAu4LMkScBfwceChJNPA94Gbm+FfBN4HHAF+BnxwED1IktZuIGFQVVNnWfWeFcYWcNsgvleSNBg+gSxJMgwkSYaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAb0BLJ0obr00ks5efLk0L8nWellvIOzefNmTpx4+U+GSINjGOhV7eTJkyy/AWVjG3bYSJ4mkiQZBpIkw0CShGEgScIwkCRhGEiSMAwkSRgGkiTW4aGzJE8BzwEvAqerqpPkUuDvgR3AU8AfVtXwHxOVJK1ovY4MJqvqmqrqNMt3Ao9W1U7g0WZZktSStk4T7Qbua+bvA25sqQ9JEusTBgX8a5KDSfY0tSuq6hhA83n5OvQhSTqL9XhR3e9V1dEklwNfTvLfq92wCY89ANu3bx9Wf5J00Rt6GFTV0ebz2SSfA64FnkmypaqOJdkCPHuWbfcB+wA6nc7Gf/Wk1l3d9Qa4+9fbbqNvddcb2m5Br3JDDYMkvwq8pqqea+b/APgYsB+4Ffh48/nwMPvQxSt//pNXzSus6+62u9Cr2bCPDK4APte8i/0S4IGq+pckXwceSjINfB+4ech9SJLOYahhUFVPAr+9Qv1HwHuG+d2SpNXzCWRJkmEgSTIMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJJYn1dYS61q3o21oW3evLntFvQqZxjoVW093lia5FXxZlRd3DxNJEkyDCRJhoEkCcNAkoRhIEnCMJAkYRhIkjAMJEm0GAZJrk/yRJIjSe5sqw9JUkthkGQE+DRwA3A1MJXk6jZ6kSS1d2RwLXCkqp6sqv8FHgR2t9SLJF302gqDrcDTPctLTe0lkuxJspBk4fjx4+vWnCRdbNoKg5VeI/lLb/qqqn1V1amqzujo6Dq0JUkXp7bCYAm4smd5G3C0pV4k6aLXVhh8HdiZ5KokrwVuAfa31IskXfRa+T2Dqjqd5HbgS8AIcG9VHW6jF0lSiz9uU1VfBL7Y1vdLkv6fTyBLkgwDSZJhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSWKIYZDk7iQ/SPJ4M72vZ91HkxxJ8kSS64bVgyRpdS4Z8v7/qqo+0VtIcjVwC7ALeDPwSJK3VtWLQ+5FknQWbZwm2g08WFUvVNV3gSPAtS30IUlqDDsMbk/yjST3Jtnc1LYCT/eMWWpqvyTJniQLSRaOHz8+5FYlSPKKp362ky4UfYVBkkeSHFph2g3cA7wFuAY4BnzyzGYr7KpW2n9V7auqTlV1RkdH+2lVWpWqWrdJupD0dc2gqt67mnFJPgN8vllcAq7sWb0NONpPH5Kk/gzzbqItPYs3AYea+f3ALUlel+QqYCfwtWH1IUk6v2HeTfQXSa5h+RTQU8CHAKrqcJKHgG8Bp4HbvJNIkto1tDCoqj86x7pZYHZY3y1JemV8AlmSZBhIkgwDSRKGgSQJw0CShGEgScIwkCRhGEiSMAwkSRgGkiQMA0kShoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEn2GQZKbkxxO8vMknZet+2iSI0meSHJdT/36pnYkyZ39fL8kaTD6PTI4BLwfeKy3mORq4BZgF3A98DdJRpKMAJ8GbgCuBqaasZKkFl3Sz8ZVtQiQ5OWrdgMPVtULwHeTHAGubdYdqaonm+0ebMZ+q58+JEn9GdY1g63A0z3LS03tbHVJUovOe2SQ5BHgTSusmqmqh8+22Qq1YuXwqXN89x5gD8D27dvP06kkaa3OGwZV9d417HcJuLJneRtwtJk/W32l794H7APodDpnDQ1JUn+GdZpoP3BLktcluQrYCXwN+DqwM8lVSV7L8kXm/UPqQZK0Sn1dQE5yE7AXGAW+kOTxqrquqg4neYjlC8Ongduq6sVmm9uBLwEjwL1Vdbiv/wJJUt9StTHOvnQ6nVpYWGi7DUnaMJIcrKrO+Uf6BLIkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSaLPMEhyc5LDSX6epNNT35Hkf5I83kx/27Pu7Um+meRIkk8lST89SJL61++RwSHg/cBjK6z7TlVd00wf7qnfA+wBdjbT9X32ILWi2+0yPj7OyMgI4+PjdLvdtluS1uySfjauqkWA1f7jPskW4A1V9ZVm+X7gRuCf++lDWm/dbpeZmRnm5uaYmJhgfn6e6elpAKamplruTnrlhnnN4Kok/5Xk35L8flPbCiz1jFlqatKGMjs7y9zcHJOTk2zatInJyUnm5uaYnZ1tuzVpTc57ZJDkEeBNK6yaqaqHz7LZMWB7Vf0oyduBf0qyC1jpEKLO8d17WD6lxPbt28/XqrRuFhcXmZiYeEltYmKCxcXFljqS+nPeMKiq977SnVbVC8ALzfzBJN8B3srykcC2nqHbgKPn2M8+YB9Ap9M5a2hI621sbIz5+XkmJyd/UZufn2dsbKzFrqS1G8ppoiSjSUaa+d9k+ULxk1V1DHguyTuau4g+AJzt6EK6YM3MzDA9Pc2BAwc4deoUBw4cYHp6mpmZmbZbk9akrwvISW4C9gKjwBeSPF5V1wHvBD6W5DTwIvDhqjrRbPbHwGeBX2H5wrEXj7XhnLlIfMcdd7C4uMjY2Bizs7NePNaGlaqNcfal0+nUwsJC221I0oaR5GBVdc4/0ieQJUkYBpIkDANJEoaBJAnDQJLEBrqbKMlx4Htt9yGt4DLgh203Ia3gN6pqdDUDN0wYSBeqJAurvX1PulB5mkiSZBhIkgwDaRD2td2A1C+vGUiSPDKQJBkG0poluTfJs0kOtd2L1C/DQFq7zwLXt92ENAiGgbRGVfUYcOK8A6UNwDCQJBkGkiTDQJKEYSBJwjCQ1ixJF/gK8FtJlpJMt92TtFY+gSxJ8shAkmQYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgSQL+D0n9eG41rJ+BAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data2=[]\n",
+    "data2.extend(data1)\n",
+    "data2.extend([-10,-49,-150,150,200,155])\n",
+    "plt.boxplot(data2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Id</th>\n",
+       "      <th>EmployeeName</th>\n",
+       "      <th>JobTitle</th>\n",
+       "      <th>BasePay</th>\n",
+       "      <th>OvertimePay</th>\n",
+       "      <th>OtherPay</th>\n",
+       "      <th>Benefits</th>\n",
+       "      <th>TotalPay</th>\n",
+       "      <th>TotalPayBenefits</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Notes</th>\n",
+       "      <th>Agency</th>\n",
+       "      <th>Status</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>NATHANIEL FORD</td>\n",
+       "      <td>GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY</td>\n",
+       "      <td>167411.18</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>400184.25</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>567595.43</td>\n",
+       "      <td>567595.43</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>GARY JIMENEZ</td>\n",
+       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
+       "      <td>155966.02</td>\n",
+       "      <td>245131.88</td>\n",
+       "      <td>137811.38</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>538909.28</td>\n",
+       "      <td>538909.28</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>ALBERT PARDINI</td>\n",
+       "      <td>CAPTAIN III (POLICE DEPARTMENT)</td>\n",
+       "      <td>212739.13</td>\n",
+       "      <td>106088.18</td>\n",
+       "      <td>16452.60</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>335279.91</td>\n",
+       "      <td>335279.91</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>CHRISTOPHER CHONG</td>\n",
+       "      <td>WIRE ROPE CABLE MAINTENANCE MECHANIC</td>\n",
+       "      <td>77916.00</td>\n",
+       "      <td>56120.71</td>\n",
+       "      <td>198306.90</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>332343.61</td>\n",
+       "      <td>332343.61</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>PATRICK GARDNER</td>\n",
+       "      <td>DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)</td>\n",
+       "      <td>134401.60</td>\n",
+       "      <td>9737.00</td>\n",
+       "      <td>182234.59</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>326373.19</td>\n",
+       "      <td>326373.19</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>San Francisco</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Id       EmployeeName                                        JobTitle  \\\n",
+       "0   1     NATHANIEL FORD  GENERAL MANAGER-METROPOLITAN TRANSIT AUTHORITY   \n",
+       "1   2       GARY JIMENEZ                 CAPTAIN III (POLICE DEPARTMENT)   \n",
+       "2   3     ALBERT PARDINI                 CAPTAIN III (POLICE DEPARTMENT)   \n",
+       "3   4  CHRISTOPHER CHONG            WIRE ROPE CABLE MAINTENANCE MECHANIC   \n",
+       "4   5    PATRICK GARDNER    DEPUTY CHIEF OF DEPARTMENT,(FIRE DEPARTMENT)   \n",
+       "\n",
+       "     BasePay  OvertimePay   OtherPay  Benefits   TotalPay  TotalPayBenefits  \\\n",
+       "0  167411.18         0.00  400184.25       NaN  567595.43         567595.43   \n",
+       "1  155966.02    245131.88  137811.38       NaN  538909.28         538909.28   \n",
+       "2  212739.13    106088.18   16452.60       NaN  335279.91         335279.91   \n",
+       "3   77916.00     56120.71  198306.90       NaN  332343.61         332343.61   \n",
+       "4  134401.60      9737.00  182234.59       NaN  326373.19         326373.19   \n",
+       "\n",
+       "   Year  Notes         Agency  Status  \n",
+       "0  2011    NaN  San Francisco     NaN  \n",
+       "1  2011    NaN  San Francisco     NaN  \n",
+       "2  2011    NaN  San Francisco     NaN  \n",
+       "3  2011    NaN  San Francisco     NaN  \n",
+       "4  2011    NaN  San Francisco     NaN  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.read_csv('Salaries.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Id                       0\n",
+       "EmployeeName             0\n",
+       "JobTitle                 0\n",
+       "BasePay                609\n",
+       "OvertimePay              4\n",
+       "OtherPay                 4\n",
+       "Benefits             36163\n",
+       "TotalPay                 0\n",
+       "TotalPayBenefits         0\n",
+       "Year                     0\n",
+       "Notes               148654\n",
+       "Agency                   0\n",
+       "Status              148654\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2= df.drop(['Notes','Status'], axis=1).dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Id                  0\n",
+       "EmployeeName        0\n",
+       "JobTitle            0\n",
+       "BasePay             0\n",
+       "OvertimePay         0\n",
+       "OtherPay            0\n",
+       "Benefits            0\n",
+       "TotalPay            0\n",
+       "TotalPayBenefits    0\n",
+       "Year                0\n",
+       "Agency              0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## distplot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2289d07fb70>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df2['BasePay'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2289cc10b70>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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yd6wXww2BlcXD3WOC1Nr+4ni3k7Y+O87JDwGrF1fQM+TjzcP6sK1SKrRJA5OIeIEHgeuARuBmEWkMOuxWoNMYUw88ANxvz20E1gFLgDXAQyLinaTO+4EHjDENQKetO2wbwMeATGPMUuBC4H+OD4zJ1hXBiCmVUsZbbWByY8QEcHnDLDK8Hl7Q6TylVBiRjJguApqMMQeMMSPAemBt0DFrgcfs6yeBq8RZf2ctsN4YM2yMOQg02fpC1mnPWW3rwNZ54yRtGCBXRNKAbGAE6In4Crjs3ZXFQyc/wLvbX6RCYHp3xOROYMrLTOPi+SX8dtcJV+pXSk1/kQSmauDouO+bbVnIY4wxPqAbKJ3g3HDlpUCXrSO4rXBtPAn0A63AEeDbxpiUeYoz3Lbq46XS9het3UPkZnhDLjgbL1ctLmd/ez+HOvpda0MpNX1FEpjeu/KoM0qJ5Jh4lU/UxkXAGFAFzAP+XETmBx8oIreJyGYR2dzenrh05UjuMaXS9hfHewapKMwKueBsvKxeXAGg03lKqZAiCUzNQO2472uAlnDH2Cm1QuDUBOeGK+8AimwdwW2Fa+OTwLPGmFFjTBvwCrAi+EMYYx42xqwwxqwoKyuL4GPHR9fgKLkZ3km3KJ+TIttfuPUM03hzSnNoKM/TwKSUCimSwLQJaLDZchk4yQwbgo7ZANxiX98EvGCMMbZ8nc2omwc0AG+Eq9Oe86KtA1vnU5O0cQRYLY5c4BJgd+SXwF1dA6MUhXm4drw5JTkcPpX8qa3j3UPMLnAnI2+81eeUs/HgSXqHkp+JqJRKLZMGJns/53bgOWAX8IQxZoeI3CMiN9jDHgFKRaQJuAO4y567A3gC2Ak8C3zRGDMWrk5b153AHbauUlt32DZwsvvygHdwAt73jDHbYroaLgjsxTSZOSU5tHQNJfX5Ht+Yn7beYddHTABXLa5gdMzw8r4O19tSSk0vEd3hNsY8AzwTVPbVca+HcNK2Q517H3BfJHXa8gM4942Cy0O2YYzpC9d2KugeHIk4MI35Da1dQ6cXdk20jr4RxvzGtYy88S6YU0Rhdjq/3d3GdUsrXW9PKTV96MoPLnOm8iYPTKnwLFNrt7MPUyJGTGleD1csKuPF3W34/cG5NEqpmUwDk8u6JtiLabxU2P7C7WeYgq1eXM7J/hHeOtqVkPaUUtODBiYXGWPsPabJkx9mF2SR4fUkNzD1BFZ9cD/5AeCKReVkeD38antrQtpTSk0PGphcNDTqZ8Tnj+gek7P9RTZHkpiZd7x7iIw0D8URjPDioTA7nfcvnMXT21t1Ok8pdZoGJhcFVhaPJDCBc58pufeYnGeY3Hy4NtgfLauitXuILUd0UVellEMDk4v6hp2VlXIzvREdP6ckhyMnk3uPaXZBYu4vBVzdWEFmmodfbtPpPKWUQwOTiwZGnMCUF+G6c3NKcugZ8iVt+4vWnsGEZOSNl5eZxpWLynl6eytjOp2nlEIDk6sCI6acjMgCUzJTxv1+w4nuYSoSHJgA/ui8Stp7h3njYMqsvauUSiINTC4aGB4DIp/Km2tTxpOxNFFH3zAjY35qihKTkTfe6sXlZKd7+flbxxLetlIq9WhgclH/SOAeU2QjprrSXETgQHviA1Nzl/NwbVUSAlNORho3XVjDk1ua2XeiN+HtK6VSiwYmF/UHRkwRTuVlZ3ipKc5mbxJ+ObfYwFRdnPjABPClqxvIzfDyN0/vwlmbVyk1U2lgctHASHRZeQAN5fk0tfW51aWwjnUmb8QEUJqXyZ9dvZDf723nxT26HYZSM5kGJhdFm/wA0FCRx4H2fnwJXmW8pWuQ/Mw0CrIS83BtKJ++dC4LynK55xc7ae8dTlo/lFLJpYHJRQMjY2Sle/B6In9gtaE8n5ExP4cTnJl3rGsoadN4AeleD/feuJTW7iH+6F/+wKZDmqWn1EykgclF/cO+iJ9hCmgozwNg34nETucd6xpM2jTeeJcuKOVnf7qK7HQv6x5+nZ9sPprsLimlEkwDk4v6h31RTeMB1NvA1NSW2ASIlq5BqlMgMAE0VhWw4X9fzmULSrnzp9t4Rhd5VWpG0cDkov6RsYhTxQNyM9OoLspmbwJHTH3DProHR1NixBRQkJXOd//kQs6fU8yfrX+L3+9tT3aXlFIJooHJRf3DPnIzIs/IC2ioyGNfAjPzWk4/w5T4VR8mkpORxqO3rGRBWR5fenwrw76xZHdJKZUAGphc1D8yRk6UIyaAhRX57G/vS9jaccdsYKpJcvJDKIU56fzf68/hVP8Iv95xItndUUolgAYmFznJD9GPmOrL8xjx+RO2Zl6yn2GazOX1s6guymb9piPJ7opSKgE0MLloIIbkBxifmZeYBIiWrkHSPEJ5fmpN5QV4PMInVtbyStNJDp9M3kaKSqnE0MDkov6RsajTxQEaKvIBEnafqaVrkNmFWVE9b5VoH1tRg0fg8U2aPq7U2U4Dk0uMMTZdPPqpvLzMNKoKsxI2YkqVZ5gmUlmYzZWLyvnJm82MJnhVDKVUYmlgcsnImB+f30SdLh7QUJHP7uOJmsobSsp2F9H6xMpa2nuHeaWpI9ldUUq5SAOTS95dWTz6ERPA0upC9rX1MTTqboq0b8zP8Z6hlB8xAbx/YRkZaR5e3qeBSamzWUSBSUTWiMgeEWkSkbtCvJ8pIo/b9zeKSN249+625XtE5NrJ6hSRebaOfbbOjAjaWCYir4nIDhHZLiJJv4vfH1jANcYR09KaQsb8hh0tPfHs1nuc6B1mzG+mRWDKSveyYm4xL8cwYmrtHuSHrx/mzie38edPvJ3wRXKVUpGbNDCJiBd4ELgOaARuFpHGoMNuBTqNMfXAA8D99txGYB2wBFgDPCQi3knqvB94wBjTAHTauidqIw34EfC/jDFLgCuA0SivQ9wFNgmMJfkBYFlNIQDbm7vi1qdQkr0PU7RW1c9i9/FeOvoiX318dMzPRx96lb/++Tv8YlsLP93SzGsHTrrYS6XUVEQyYroIaDLGHDDGjADrgbVBx6wFHrOvnwSuEhGx5euNMcPGmINAk60vZJ32nNW2DmydN07SxjXANmPM2wDGmJPGmKQvERCYyosl+QFgdkEWZfmZbDvWHc9uvUdzp/OsVHWKrfoQzqr6WQC8tj/ywPKbnSdo6R7iX24+ny1//UHyMtP4xdstbnVRKTVFkQSmamB8jm6zLQt5jDHGB3QDpROcG668FOiydQS3Fa6NhYARkedEZIuI/FUEn8l1A1Fuqx5MRFhWXcj2ZncD08H2fjwCtSU5rrYTL0urC8nPSosqAeKHrx+muiib65dWkpXu5ZrGCp5957gucaRUiookMIV6uCV4rZxwx8SrfKI20oDLgU/ZPz8iIlcFHygit4nIZhHZ3N7u/oKggXtMkW6rHsrSmkKa2vtO1+WG/R391BTnkJkW28gu0bwe4dL5pbyyP7LAtL+9j1f3n+STF885/ZzWh8+romfIxx/2ahKFUqkoksDUDNSO+74GCJ4HOX2MvedTCJya4Nxw5R1Aka0juK2J2njJGNNhjBkAngEuCP4QxpiHjTErjDErysrKIvjYU3M6Ky+GJYkCltUUYgy84+J03oH2fuaX5bpWvxtW1c/i6KlBjpycfMmmH79+hHSv8PEV7/64raqfRVFOOr/YptN5SqWiSALTJqDBZstl4CQzbAg6ZgNwi319E/CCMcbY8nU2o24e0AC8Ea5Oe86Ltg5snU9N0sZzwDIRybEB6wPAzsgvgTv6pziVB7C0ugiA7S4FJr/fcLCjj/mz8lyp3y2r6ksBJh01DY6M8eSbR1lzbiVl+ZmnyzPSPFx37mye33mCwRGdzlMq1UwamOz9nNtxAsAu4AljzA4RuUdEbrCHPQKUikgTcAdwlz13B/AETqB4FviiMWYsXJ22rjuBO2xdpbbuidroBP4BJ9htBbYYY56O9YLEy7vPMcUemMryM6kqzGKbS/eZWnuGGBr1s6B8eo2YFpTlUVGQOenzTL/eeZyeIR+fvGjOe9778LIqBkbGeGF3m1vdVErFKKLfmsaYZ3CmyMaXfXXc6yHgY2HOvQ+4L5I6bfkBnKy94PKJ2vgRTsp4yhgY8eERyEqf2jPMS2sKXRsxHWh31uKbbiMmEeHKReU8va2VEZ+fjLTQ1/gXb7dQWZjFxfNK3vPexfNLycnwsunQKT60rNLtLiuloqArP7ikb9hHbkYaTkZ77JbVFHGwo5/uwfg/mnWg3Vmpe8E0u8cEcPU5FfQO+3jj4KmQ73cPjPLS3nb+aFklnhCL03o9wuLZ+exsdfcBZqVU9DQwuWRgeIycKSQ+BJxX49xn2no0/g/aHmjvIy8z7Yz7L9PFqvpZZKZ5+M2u0JsHPrfjOKNjhg+fVxW2jsaqAna19OBP0IaMSqnIaGBySd+Ib0qJDwHnzynC6xE2hRkZTMV+m5E31VFdMmRneHlfwyye33kCJwfmTL/Y1sLc0hyWVheGrWNJVSG9wz6OdiZmQ0alVGQ0MLlkwE7lTVVuZhrnVhXwxqH4B6YD7X3MnzX9pvECrj6ngmNdg+wJ2h6ko89ZgfzDy6omDLpLqgoA2OnyeoRKqehoYHJJ//BYzMsRBVtZV8LWo11xXalgYMRHS/cQ88umV+LDeKsXlwPOkkPj/Wp7K37DhNN4AAsr8vF6xPWFcpVS0dHA5JL+EV/MC7gGWzmvhBGfP67LEx3scBIfptvDteOVF2RxXm0Rz+96N+W7b9jHv/1uP0uqClg0O3/C87PSvdSX5WkChFIpRgOTSwZGxmLe8iLYyjon3XljHO8zBTLypluqeLBrGit4+2gXv7SrOHzr2d209gxxz9pzIzp/SVUBO1rcXY9QKRUdDUwu6Rv2kReHrDyAktwM6svz2BTH+0yBwDRvGt9jAvjsqjouqivhz9Zv5VvP7eYHrx/mlkvruHBucUTnN1YVcKJnOKptNJRS7tLA5JKBYR85cUh+CFhZV8KbhzoZi1Nq84GOPqqLssmO032wZMnJSOPRz67k/NoiHnxxP1WF2fzltYsiPr9REyCUSjkamFzg9xv6R8Zi3lY9lIvmFdM77GP38fj8At1zvJf68uk9jReQl5nG9z93EZ+6eA7/fPP5UaXpL6l00sk1AUKp1KGByQWDo4GVxeM7YgLi8jxT37CPvSd6WV5bNOW6UkVeZhr3fWRpxFN4AYU56dQUZ2sChFIpRAOTCwL7J8Ur+QGgpjiH6qLsuGwJvu1oF37jPLyroLFSEyCUSiUamFzQb7dSiFfyQ8Dl9bN4df9JfGP+KdXzll3e6Pza6EYXZ6tzKgs41NHP0KhugaFUKtDA5ILTI6Y4Jj8AXN4wi94hH9umuNr4lsOdLCjLpTAnPU49m94Wz87Hb2Dfib5kd0UphQYmV8RjW/VQVtXPQoRJ9yGaiDGGt452cf4cHS0FLK50MvPilViilJoaDUwuGBiZ+rbqoZTkZrCkqmBKgenIqQFO9Y9wgQam0+aU5JCV7mH38d7JD1ZKuU4Dkwv6hqe+rXo4l9eXseVI5+k2orXlSCegiQ/jeT3Cwop89mhgUiolaGBywcCIe4HpfQ2z8PkNG2PMznvrSBe5GV4WVky8jtxMs3h2vk7lKZUiNDC5oH/YTuW5sKrChXOLyUzz8IcYp/PeOtLFebXOHk/qXYtmF9DRN0J7ry5NpFSyaWBygVtZeeCsiH3RvBJeboo+MA2OjLGrtUen8UI4x65ErtN5SiWfBiYX9A37yEjzkJHmzuW9YlE5TW19p7euiNSr+zvw+c3pVSTUuwJbZOh0nlLJp4HJBb3DPgqy4j9aCrju3NkAPLO9NarzfrmtlcLsdC5bMMuNbk1rpXmZlOVnamaeUilAA5MLeofit0lgKFVF2Zw/p4int0UemIZGx3h+5wnWLJnt2khuuls8WzPzlEoF+hvKBX1Do+RnubuqwoeWVrKztYdDEU7nvbS3nb5hHx9aVulqv6azxbPz2XuiN25biyilYqOByQVuj5gArlvqBJinI5zOe3pbK8U56Vy2oNTNbk1ri2YXMOzzR33vTikVXxqYXNA37CPfxXtMANV2Oi+S+0yDI2P8ZtcJ1pxbSZpX/8rDWWI3Ddx+rCvJPVFqZovot5SIrBGAYx3jAAAY+UlEQVSRPSLSJCJ3hXg/U0Qet+9vFJG6ce/dbcv3iMi1k9UpIvNsHftsnRmTtWHfnyMifSLyF9FehHjrHfKR53JgAmc6b0fL5NN5L+5pY2BkjA/rNN6EFlbkk5eZxpbDGpiUSqZJA5OIeIEHgeuARuBmEWkMOuxWoNMYUw88ANxvz20E1gFLgDXAQyLinaTO+4EHjDENQKetO2wb4zwA/CrSD+6m3qFRCly+xwRw/dJKvB7h+68eCnuMMYaHf3+A2QVZXDRP08Qn4vUIy2uLePNwZ7K7otSMFsmI6SKgyRhzwBgzAqwH1gYdsxZ4zL5+ErhKRMSWrzfGDBtjDgJNtr6QddpzVts6sHXeOEkbiMiNwAFgR+Qf3R3GGPqG3b/HBE523sdX1PDjjYc5emog5DEb3m5h69Eu/vyahTqNF4EL5haz+3hPzGsRKqWmLpLfVNXA0XHfN9uykMcYY3xAN1A6wbnhykuBLltHcFsh2xCRXOBO4BsTfQgRuU1ENovI5vb29kk+cuwGRsbwG1y/xxTwf65qwCPCA7/Z+573hkbH+Oaze1hSVcBHL6hJSH+muwvnFuM3zi6/SqnkiCQwhVpULTifNtwx8SqfqI1v4Ez9TbjLmzHmYWPMCmPMirKysokOnZLeISemJuIeE0BlYTafuayOn7117D3P4Dzy8kGOdQ3y5Q+dg0fXxovI8lpnuSadzlMqeSIJTM1A7bjva4CWcMeISBpQCJya4Nxw5R1Aka0juK1wbVwMfFNEDgFfAv6viNwewedyRd/wKEBCpvICvnDFAvIy0/jS41vZerSL0TE///zbfTzw/F4+2FihKz1EoTA7nYUVebx5RAOTUskSSWDaBDTYbLkMnGSGDUHHbABusa9vAl4wxhhbvs5m1M0DGoA3wtVpz3nR1oGt86mJ2jDGvM8YU2eMqQP+EfhbY8y/RnEN4iowYkpE8kNAUU4G3/nYebT3DnPjg69wxbd+xz88v5frl1by7ZvOS1g/zhYXzCnmrSNd+PVBW6WSYtL/1htjfHYE8hzgBR41xuwQkXuAzcaYDcAjwA9FpAlnFLPOnrtDRJ4AdgI+4IvGmDGAUHXaJu8E1ovIvcBbtm7CtZFqEj2VF3DNktlcVj+L7760n2ffOc5Dn7qA65dqengsLphbzPpNRznQ0Ud9ue5bpVSiiTNImVlWrFhhNm/e7Erdz2xv5U9/vIVnv/Q+Fs8ucKUN5a797X1c9Z2XuP+jS/nEyjnJ7o5SKUNE3jTGrHC7Hc0fjrPeocTfY1LxNX9WLkU56Ww8eCrZXVFqRtLAFGeBqTy3F3FV7hERVi8q5/mdJxj2jSW7O0rNOBqY4uz0PSYdMU1rNyyvonfIx0t73HvmTSkVmgamOOsb9pGb4cWrzw1Na6vqZ1Gam8FTbwc/GaGUcpsGpjjrHRpNeEaeir90r4cPLavkNztP6PJESiWYBqY4c7a80PtLZ4Mbzqti2Ofn1zuOJ7srSs0oGpjiLBGbBKrEuGBOMdVF2WzQ6TylEkoDU5z1Drm/SaBKDI9HuGF5FX/Y10FT24RLMSql4kgDU5z1Do1qYDqLfG7VPHIyvHz1qXeYiQ+jK5UMGpjirG/YR36m3mM6W5TlZ/JX1y7i1f0n+cW2ybexV0pNnQamOEvUtuoqcT558VyWVhdy7y93nl7ZQynlHg1McTTmNwyMjOlU3lnG6xH+5sZzae8b5ss/0yk9pdymgSmO+nTVh7PW8toi/uKaRWx4u4WHfrc/2d1R6qymv0HjqNduEpjIvZhU4vzpFQvYc7yXb/96Dwsr8vlgY0Wyu6TUWUlHTHGUrL2YVGKICN+8aRlLqwu544mtnOgZSnaXlDoraWCKo8DSNXqP6eyVle7lHz+xnBGfn6/8XO83KeUGDUxxpHsxzQzzy/L482sW8vzOE/xSU8iVijsNTHGkezHNHJ9bNY/zagr52oYddPaPJLs7Sp1VNDDFkU7lzRxpXg9//9FlnOof4XuvHkp2d5Q6q2hgiqN3R0wamGaCcyoLuKaxgsdePUS/bo2hVNxoYIqjviEfXo+Qne5NdldUgvyvKxbQPTjKf71xJNldUeqsoYEpjnqHRsnLTENEd6+dKS6YU8wl80v4jz8cZMTnT3Z3lDoraGCKo95h3YtpJvrCFfUc7xni528dS3ZXlDoraGCKI92LaWZ6f8MsFs/O57HXDiW7K0qdFTQwxVGfBqYZSUT45MVz2NHSw/bm7mR3R6lpL6LAJCJrRGSPiDSJyF0h3s8Ukcft+xtFpG7ce3fb8j0icu1kdYrIPFvHPltnxkRtiMgHReRNEdlu/1wd68WYqt7hUX2GaYZau7yarHQP6zdpEoRSUzVpYBIRL/AgcB3QCNwsIo1Bh90KdBpj6oEHgPvtuY3AOmAJsAZ4SES8k9R5P/CAMaYB6LR1h20D6AA+bIxZCtwC/DC6SxA/fUN6j2mmKsxO5/qllTy1tYWBEU0dV2oqIhkxXQQ0GWMOGGNGgPXA2qBj1gKP2ddPAleJk5q2FlhvjBk2xhwEmmx9Ieu056y2dWDrvHGiNowxbxljWmz5DiBLRDIjvQDx1DkwSmG2jphmqnUr59A37NNlipSaokgCUzVwdNz3zbYs5DHGGB/QDZROcG648lKgy9YR3Fa4Nsb7KPCWMWY4gs8VV0OjY3QPjlJRkJSYqFLAyrpiFpTlsl6faVJqSiIJTKEeygleUjncMfEqn7QfIrIEZ3rvf4Y4DhG5TUQ2i8jm9vb2UIdMSXuvEwvLC7LiXreaHkSEdSvnsOVIF3tP9Ca7O0pNW5EEpmagdtz3NUBLuGNEJA0oBE5NcG648g6gyNYR3Fa4NhCRGuBnwKeNMSG3FzXGPGyMWWGMWVFWVhbBx45OYG+eCg1MM9ofX1BNuldY/8bRyQ9WSoUUSWDaBDTYbLkMnGSGDUHHbMBJPAC4CXjBOBvVbADW2Yy6eUAD8Ea4Ou05L9o6sHU+NVEbIlIEPA3cbYx5JZoPH08nepwRk07lzWyleZlcs2Q2//1WM0OjY8nujlLT0qSByd7PuR14DtgFPGGM2SEi94jIDfawR4BSEWkC7gDusufuAJ4AdgLPAl80xoyFq9PWdSdwh62r1NYdtg1bTz3w1yKy1X6Vx3g9YtbW64yYyvN1xDTTrVtZS9fAKM/tOJ7srig1LclM3IFzxYoVZvPmzXGt8+9/tZtHXj7A3nuv07XyZji/3/CBb79IbXEO//n5S5LdHaXiRkTeNMascLsdXfkhTtp6hijPz9KgpPB4hE+sqOXV/Sc5fLI/2d1RatrRwBQnJ3qH9P6SOu1jK2rxCKzfpEkQSkVLA1OcnOgZ1ow8dVpFQRarF5fzk83NjI7pdhhKRUMDU5w4U3k6YlLvWrdyDh19w7ywuy3ZXVFqWtHAFAeDI2P0DPn04Vp1hisWlVFRkKkrQSgVJQ1McRBIFdepPDVemtfDx1fU8tLedlq6BpPdHaWmDQ1McaAP16pwPr6iFgM8sVmTIJSKlAamONARkwqntiSH9zWU8cPXDtM9MJrs7ig1LWhgioPAiEmTH1Qof3XtIjoHRvjWr3cnuytKTQsamOKgrWeIjDSP7sWkQjq3upBbLqvjxxuPsPVoV7K7o1TK08AUByd6nIdrddUHFc4dH1xIeX4mX/n5dnz6XJNSE9LAFAdtvcNU6OKtagL5Wel87cNLeOdYD5//wWb6h3X7daXC0cAUByd6hijXjDw1ieuXVnLvjefy0t521j38Om12Dy+l1Jk0MMVBW8+wbnehIvI/LpnLw3+ygqa2Pj74wO/50euHGfPPvBX+lZqIBqYp6h/20Tvs01RxFbGrGyv4xf9exTmV+Xzl5+/wkYdeYVuzJkUoFaCBaYraevXhWhW9+vJ8/uvzl/BP65bT2j3E2gdf4cs/2376mTilZrK0ZHdgujvRow/XqtiICGuXV3Pl4nIeeH4vj716iJ9sbmbt8ir++IIaFs3OpyQ3I+L6TvWP8HZzF+X5mcwtzSUvU/95q+lJf3KnaHtzNwALyvKS3BM1XRXYjL1PX1rHoy8f5CdvHuUnbzYDUJyTTkVBFmX5mcyblcu51YUsnp3P7IIsCrLT2XO8l02HTvHbXW1sPHiS8ber1iyZzd/+8dKogptSqUC3Vp+iWx59g2Ndg/zmjg/EpT6lugdG2XK0k/1tfRzo6Ke9d5i2niGa2vroHxkLec6CslyuO7eSy+pL6ewf5Z2Wbh75w0EKc9L59sfO4wMLyxL8KdTZKFFbq+uIaQqGfWNsPHiSdSvnJLsr6ixSmJPOlYvKuXJR+Rnlfr/h4Ml+mtr6aOsd5lTfCAvKc1lZV/KeqeQPLavkhvOq+NL6rXzme29wx9ULuX11vT4ErqYFDUxTsOVwF0OjflbVz0p2V9QM4PEIC8ryIp42PqeygKduX8Xd/72d7zy/l52tPfz9R5fp0lkq5WlgmoJXmjrweoSL55ckuytKhZSV7uUfPn4ejZUF/N2vdvFKUwdfuKKez1xWR3aGN9ndUyokDUxT8HJTB8triyjI0v+BqtQlInz+/fNZVT+Lbz23m/uf3c0//XYvl8wv5fL6WSyrKaKxqkCz+FTK0J/EGHUPjrKtuYvbVzckuytKRaSxqoDvffYiNh86xS+3tfL7ve3cu2cXACJQV5pLY1UBjZUF1Jc7U4ZzS3NI9+rjjiqxNDDF6PUDTmru5Xp/SU0zK+pKWFHnTD+f6BliR0s3O471sKOlh7ePdvH0ttbTx6Z5hLpZuSwoy6W+PI+FFfmsqCuhuig7Wd1XM0BEgUlE1gD/BHiB/zDG/H3Q+5nAD4ALgZPAJ4wxh+x7dwO3AmPA/zHGPDdRnSIyD1gPlABbgD8xxozE0oZbjDH8ansrORleltcWudmUUq6qKMiioiCL1YsrTpf1DfvY39ZHU1sf+9udP/e19fGbXW2n1/WrKc7m4nmlXDK/hEvml1JTnK0ZfypuJg1MIuIFHgQ+CDQDm0RkgzFm57jDbgU6jTH1IrIOuB/4hIg0AuuAJUAV8BsRWWjPCVfn/cADxpj1IvLvtu5/i7YNY0zoBz6myBjDN5/bw8+3tvC5VfPISNNpDnV2yctM47zaIs4L+k/XiM/PvrZe3jh4itcPnOSF3Sf46RbnQeCqwiwunl/K4tn51JfnUVWUTVFOOkXZGWSlezRoqahEMmK6CGgyxhwAEJH1wFpgfGBaC3zdvn4S+FdxfhLXAuuNMcPAQRFpsvURqk4R2QWsBj5pj3nM1vtvMbTxWoTXIGLGGO59ehePvHyQT108h6986Jx4N6FUyspI87CkqpAlVYV8dtU8/H7DvrY+Xj9wko0HT/JyUwc/e+vYe8/zeijMSScnw0tmmoeMNA+Zac7rTPs6I/A6ffx7ocszxp3nlDtfHpeDn0cEr0cQAa9H8Ih9Lc5rj0fwhHhPREi1sOzxpFqPzhRJYKoGjo77vhm4ONwxxhifiHQDpbb89aBzq+3rUHWWAl3GGF+I42NpI64OdPTzo9cP85nL6vjahxv1f4FqRvN4hEWz81k0O59bLqsDoGtghP3t/bT1DNE1OErXwChdgyN0D4wyODrGiM/PsM/PsG+M4VE/fcM+hkft9z7/Ge+Pjs28VWkSYXltET//4qpkd2NCkQSmUL99g39iwh0TrjzU/NdEx8fSxpkdFLkNuM1+2ycie0KcF5Fv2K8ozAI6Ym0vCbS/7plOfQXtr9sS3t/DgNwe8+mL4teT8CIJTM1A7bjva4CWMMc0i0gaUAicmuTcUOUdQJGIpNlR0/jjY2njNGPMw8DDEXzeuBORzYlYXypetL/umU59Be2v26ZjfxPRTiR37jcBDSIyT0QycBINNgQdswG4xb6+CXjBOKvDbgDWiUimzbZrAN4IV6c950VbB7bOp2JsQyml1DQ06YjJ3s+5HXgOJ7X7UWPMDhG5B9hsjNkAPAL80CYenMIJNNjjnsBJlPABXwxky4Wq0zZ5J7BeRO4F3rJ1E0sbSimlpp8Zue1FoonIbXYqcVrQ/rpnOvUVtL9u0/6GaUcDk1JKqVSiT4cqpZRKKRqYXCQia0Rkj4g0ichdSWj/kIhsF5GtgWwaESkRkedFZJ/9s9iWi4j8s+3rNhG5YFw9t9jj94nILePKL7T1N9lzo3qwS0QeFZE2EXlnXJnr/QvXRoz9/bqIHLPXeKuIXD/uvbtt23tE5Npx5SF/Lmwy0Ebbr8dtYhA2sedxe/xGEamLoK+1IvKiiOwSkR0i8mepfH0n6G+qXt8sEXlDRN62/f1GrG3E63PE2N/vi8jBcdd3uS1P6s8Dxhj9cuELJ6ljPzAfyADeBhoT3IdDwKygsm8Cd9nXdwH329fXA7/CeS7sEmCjLS8BDtg/i+3rYvveG8Cl9pxfAddF2b/3AxcA7ySyf+HaiLG/Xwf+IsSxjfbvPBOYZ38WvBP9XABPAOvs638HvmBf/ynw7/b1OuDxCPpaCVxgX+cDe22fUvL6TtDfVL2+AuTZ1+nARnvdomojnp8jxv5+H7gpxPHJ/XmI5heJfkX+Zf+Cnhv3/d3A3QnuwyHeG5j2AJX2dSWwx77+LnBz8HHAzcB3x5V/15ZVArvHlZ9xXBR9rOPMX/Su9y9cGzH29+uE/sV5xt83TgbqpeF+Luw/5g4gLfjnJ3CufZ1mj5Mor/NTOGtTpvT1DdHflL++QA7OgtMXR9tGPD9HjP39PqEDU1J/HnQqzz2hlnJyZamkCRjg1yLypjgrXwBUGGNaAeyf5bY8XH8nKm8OUT5ViehfuDZidbud7nh03DRFtP2NeDkuILAcV0TstNH5OP9LTvnrG9RfSNHrKyJeEdkKtAHP44xwom0jnp8jqv4aYwLX9z57fR8QZxeHM/obYb/i+vOggck9ES2V5LJVxpgLgOuAL4rI+yc4NtolnxL9+VK1f/8GLACWA63Ad2x5PPsb82cRkTzgp8CXjDE9Ex0aZb9cub4h+puy19cYM2aMWY6z2sxFQKhVnSdrI2HXPbi/InIuzihsMbASZ3ruzjj3NyYamNwT0VJJbjLGtNg/24Cf4fzjOSEilQD2zzZ7eLj+TlReE6J8qhLRv3BtRM0Yc8L+g/cD/493V8+Ptr+nl+MK0d/T58iZy3FNSETScX7J/9gY89+2OGWvb6j+pvL1DTDGdAG/w7kXE20b8fwc0fZ3jTGm1TiGge8R+/WN68+DBib3RLKUk2tEJFdE8gOvgWuAdzhzaadbOHPJp0/bbJxLgG477H4OuEZEiu00yjU4c9qtQK+IXGKzbz49rq6pSET/wrURtcA/OOsjONc40Ibby3FN1C/BWS1llzHmH8a9lZLXN1x/U/j6lolIkX2dDVwN7IqhjXh+jmj7u3tcwBDgRs68vsn79xbNTTP9ivoG7vU42UX7gS8nuO35OJk8bwM7Au3jzFH/Fthn/yyx5YKzeeN+YDuwYlxdnwOa7Ndnx5WvsD/I+4F/Jfob8v+FMz0zivM/rlsT0b9wbcTY3x/a/myz/wArxx3/Zdv2HsZlLIb7ubB/Z2/Yz/ETINOWZ9nvm+z78yPo6+U4UynbgK326/pUvb4T9DdVr+8ynCXTttlr8NVY24jX54ixvy/Y6/sO8CPezdxL6s+DrvyglFIqpehUnlJKqZSigUkppVRK0cCklFIqpWhgUkoplVI0MCmllEopGpiUmiIRGRNnZea3RWSLiFwW5/rHrwC9RUQujWf9SqUaDUxKTd2gMWa5MeY8nCVe/s6FNv7SOMvJ3IWzcKZSZy0NTErFVwHQCc66byLyWzvK2S4ia215rog8bUdY74jIJ2z5hSLykjiL7j4XtOpBwO+Benv850Vkk63npyKSIyL5dnSVbo8pEGdfrvSEfHql4kADk1JTl22n2XYD/wH8jS0fAj5inIV0rwS+Y5drWQO0GGPOM8acCzxrA8e/4GxBcCHwKHBfiLY+jPMkPsB/G2NW2pHaLuBWY0wvzjpoH7LHrAN+aowZjfNnVso1aZMfopSaxKCdZsPe//mBOCs3C/C34qzq7sfZBqACJ7B8W0TuB35pjPmDPf5c4HknduHFWf4o4Fsi8hWgHWcpJIBzReReoAjIw1nHDJzg+FfAz4HPAp9352Mr5Q4NTErFkTHmNRGZBZThrHVWBlxojBkVkUNAljFmr4hcaN//OxH5Nc7q7zuMMeESG/7SGPNkUNn3gRuNMW+LyGeAK2wfXhGROhH5AOA1xryDUtOITuUpFUcishhntHMSZ2uDNhuUrgTm2mOqgAFjzI+Ab+Ns174HKAtk3IlIuogsmaS5fKDVTgN+Kui9H+AsOvu9+HwypRJHR0xKTV22ODuDgjN9d4sxZkxEfgz8QkQ246yWvdsesxRnas6Ps1L5F4wxIyJyE/DPIlKI82/zH3FWhg/nr3F2eT2MMz2YP+69HwP34gQnpaYVXV1cqbOQDXJrjTF/kuy+KBUtHTEpdZYRkX8BrsO5h6XUtKMjJqWUUilFkx+UUkqlFA1MSimlUooGJqWUUilFA5NSSqmUooFJKaVUStHApJRSKqX8f19MU5KKM5K4AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(df2['BasePay'], hist=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## countplot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2012, 2013, 2014], dtype=int64)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2['Year'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2289ccae780>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(df2['Year'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>smoker</th>\n",
+       "      <th>day</th>\n",
+       "      <th>time</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>16.99</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.34</td>\n",
+       "      <td>1.66</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.01</td>\n",
+       "      <td>3.50</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>23.68</td>\n",
+       "      <td>3.31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>24.59</td>\n",
+       "      <td>3.61</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "      <td>Sun</td>\n",
+       "      <td>Dinner</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   total_bill   tip     sex smoker  day    time  size\n",
+       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
+       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
+       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
+       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
+       "4       24.59  3.61  Female     No  Sun  Dinner     4"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3= sns.load_dataset('tips')\n",
+    "df3.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(244, 7)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2289cd1ccf8>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot('sex', data=df3, hue='smoker')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## heatmap()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>total_bill</th>\n",
+       "      <th>tip</th>\n",
+       "      <th>size</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>total_bill</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.675734</td>\n",
+       "      <td>0.598315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>tip</th>\n",
+       "      <td>0.675734</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.489299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>size</th>\n",
+       "      <td>0.598315</td>\n",
+       "      <td>0.489299</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            total_bill       tip      size\n",
+       "total_bill    1.000000  0.675734  0.598315\n",
+       "tip           0.675734  1.000000  0.489299\n",
+       "size          0.598315  0.489299  1.000000"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x2289cd8e908>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(df3.corr())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/README.md b/README.md
index f6ebd4a..599a97a 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,3 @@
-# Python4DS
\ No newline at end of file
+# Python4DS
+
+[Project Database](https://drive.google.com/file/d/1RmOpB9-rk6APzxmzVZXyi6FlwWRGIWEA/view)
diff --git a/project.ipynb b/project.ipynb
new file mode 100644
index 0000000..1d4e60e
--- /dev/null
+++ b/project.ipynb
@@ -0,0 +1,3241 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>desc</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>title</th>\n",
+       "      <th>timeStamp</th>\n",
+       "      <th>twp</th>\n",
+       "      <th>addr</th>\n",
+       "      <th>e</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.297876</td>\n",
+       "      <td>-75.581294</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
+       "      <td>19525.0</td>\n",
+       "      <td>EMS: BACK PAINS/INJURY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NEW HANOVER</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>40.258061</td>\n",
+       "      <td>-75.264680</td>\n",
+       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>HATFIELD TOWNSHIP</td>\n",
+       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40.121182</td>\n",
+       "      <td>-75.351975</td>\n",
+       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>HAWS AVE</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40.116153</td>\n",
+       "      <td>-75.343513</td>\n",
+       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.251492</td>\n",
+       "      <td>-75.603350</td>\n",
+       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>EMS: DIZZINESS</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>LOWER POTTSGROVE</td>\n",
+       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         lat        lng                                               desc  \\\n",
+       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
+       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
+       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
+       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
+       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
+       "\n",
+       "       zip                    title            timeStamp                twp  \\\n",
+       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
+       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
+       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
+       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
+       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
+       "\n",
+       "                         addr  e  \n",
+       "0      REINDEER CT & DEAD END  1  \n",
+       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
+       "2                    HAWS AVE  1  \n",
+       "3          AIRY ST & SWEDE ST  1  \n",
+       "4    CHERRYWOOD CT & DEAD END  1  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df= pd.read_csv('911.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>ram</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>sam</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>sita</td>\n",
+       "      <td>3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>hari</td>\n",
+       "      <td>2000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>robin</td>\n",
+       "      <td>1000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>sita</td>\n",
+       "      <td>5000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>ram</td>\n",
+       "      <td>4000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>sam</td>\n",
+       "      <td>3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>robin</td>\n",
+       "      <td>2000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>lucky</td>\n",
+       "      <td>1000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    name  score\n",
+       "0    ram   5000\n",
+       "1    sam   4000\n",
+       "2   sita   3000\n",
+       "3   hari   2000\n",
+       "4  robin   1000\n",
+       "5   sita   5000\n",
+       "6    ram   4000\n",
+       "7    sam   3000\n",
+       "8  robin   2000\n",
+       "9  lucky   1000"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2=pd.DataFrame({'name': ['ram','sam','sita','hari','robin','sita','ram','sam','robin','lucky'],\n",
+    "                     'score':[5000,4000,3000,2000,1000,5000,4000,3000,2000,1000]})\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ram      2\n",
+       "sam      2\n",
+       "robin    2\n",
+       "sita     2\n",
+       "hari     1\n",
+       "lucky    1\n",
+       "Name: name, dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2['name'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>ram</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>robin</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sam</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sita</th>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>hari</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lucky</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       score\n",
+       "name        \n",
+       "ram        2\n",
+       "robin      2\n",
+       "sam        2\n",
+       "sita       2\n",
+       "hari       1\n",
+       "lucky      1"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.groupby('name').agg('count').sort_values(by='score', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>name</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>hari</th>\n",
+       "      <td>2000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lucky</th>\n",
+       "      <td>1000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ram</th>\n",
+       "      <td>9000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>robin</th>\n",
+       "      <td>3000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sam</th>\n",
+       "      <td>7000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>sita</th>\n",
+       "      <td>8000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       score\n",
+       "name        \n",
+       "hari    2000\n",
+       "lucky   1000\n",
+       "ram     9000\n",
+       "robin   3000\n",
+       "sam     7000\n",
+       "sita    8000"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.groupby('name').agg('sum')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>desc</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>title</th>\n",
+       "      <th>timeStamp</th>\n",
+       "      <th>twp</th>\n",
+       "      <th>addr</th>\n",
+       "      <th>e</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.297876</td>\n",
+       "      <td>-75.581294</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
+       "      <td>19525.0</td>\n",
+       "      <td>EMS: BACK PAINS/INJURY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NEW HANOVER</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>40.258061</td>\n",
+       "      <td>-75.264680</td>\n",
+       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>HATFIELD TOWNSHIP</td>\n",
+       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40.121182</td>\n",
+       "      <td>-75.351975</td>\n",
+       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>HAWS AVE</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40.116153</td>\n",
+       "      <td>-75.343513</td>\n",
+       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.251492</td>\n",
+       "      <td>-75.603350</td>\n",
+       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>EMS: DIZZINESS</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>LOWER POTTSGROVE</td>\n",
+       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>40.253473</td>\n",
+       "      <td>-75.283245</td>\n",
+       "      <td>CANNON AVE &amp; W 9TH ST;  LANSDALE; Station 345;...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>EMS: HEAD INJURY</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>LANSDALE</td>\n",
+       "      <td>CANNON AVE &amp; W 9TH ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>40.182111</td>\n",
+       "      <td>-75.127795</td>\n",
+       "      <td>LAUREL AVE &amp; OAKDALE AVE;  HORSHAM; Station 35...</td>\n",
+       "      <td>19044.0</td>\n",
+       "      <td>EMS: NAUSEA/VOMITING</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>HORSHAM</td>\n",
+       "      <td>LAUREL AVE &amp; OAKDALE AVE</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>40.217286</td>\n",
+       "      <td>-75.405182</td>\n",
+       "      <td>COLLEGEVILLE RD &amp; LYWISKI RD;  SKIPPACK; Stati...</td>\n",
+       "      <td>19426.0</td>\n",
+       "      <td>EMS: RESPIRATORY EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>SKIPPACK</td>\n",
+       "      <td>COLLEGEVILLE RD &amp; LYWISKI RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>40.289027</td>\n",
+       "      <td>-75.399590</td>\n",
+       "      <td>MAIN ST &amp; OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...</td>\n",
+       "      <td>19438.0</td>\n",
+       "      <td>EMS: SYNCOPAL EPISODE</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>LOWER SALFORD</td>\n",
+       "      <td>MAIN ST &amp; OLD SUMNEYTOWN PIKE</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>40.102398</td>\n",
+       "      <td>-75.291458</td>\n",
+       "      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD; PLYM...</td>\n",
+       "      <td>19462.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>PLYMOUTH</td>\n",
+       "      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>40.231990</td>\n",
+       "      <td>-75.251891</td>\n",
+       "      <td>RT202 PKWY &amp; KNAPP RD; MONTGOMERY; 2015-12-10 ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>MONTGOMERY</td>\n",
+       "      <td>RT202 PKWY &amp; KNAPP RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>40.084161</td>\n",
+       "      <td>-75.308386</td>\n",
+       "      <td>BROOK RD &amp; COLWELL LN; PLYMOUTH; 2015-12-10 @ ...</td>\n",
+       "      <td>19428.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 17:40:02</td>\n",
+       "      <td>PLYMOUTH</td>\n",
+       "      <td>BROOK RD &amp; COLWELL LN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>40.174131</td>\n",
+       "      <td>-75.098491</td>\n",
+       "      <td>BYBERRY AVE &amp; S WARMINSTER RD; UPPER MORELAND;...</td>\n",
+       "      <td>19040.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 17:40:02</td>\n",
+       "      <td>UPPER MORELAND</td>\n",
+       "      <td>BYBERRY AVE &amp; S WARMINSTER RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>40.062974</td>\n",
+       "      <td>-75.135914</td>\n",
+       "      <td>OLD YORK RD &amp; VALLEY RD; CHELTENHAM; 2015-12-1...</td>\n",
+       "      <td>19027.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 17:40:02</td>\n",
+       "      <td>CHELTENHAM</td>\n",
+       "      <td>OLD YORK RD &amp; VALLEY RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>40.097222</td>\n",
+       "      <td>-75.376195</td>\n",
+       "      <td>SCHUYLKILL EXPY &amp; CROTON RD UNDERPASS; UPPER M...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 17:40:02</td>\n",
+       "      <td>UPPER MERION</td>\n",
+       "      <td>SCHUYLKILL EXPY &amp; CROTON RD UNDERPASS</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>40.223778</td>\n",
+       "      <td>-75.235399</td>\n",
+       "      <td>STUMP RD &amp; WITCHWOOD DR; MONTGOMERY; 2015-12-1...</td>\n",
+       "      <td>18936.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 17:40:02</td>\n",
+       "      <td>MONTGOMERY</td>\n",
+       "      <td>STUMP RD &amp; WITCHWOOD DR</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>40.243258</td>\n",
+       "      <td>-75.286552</td>\n",
+       "      <td>SUSQUEHANNA AVE &amp; W MAIN ST;  LANSDALE; Statio...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>EMS: RESPIRATORY EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:46:01</td>\n",
+       "      <td>LANSDALE</td>\n",
+       "      <td>SUSQUEHANNA AVE &amp; W MAIN ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>40.312181</td>\n",
+       "      <td>-75.574260</td>\n",
+       "      <td>CHARLOTTE ST &amp; MILES RD;  NEW HANOVER; Station...</td>\n",
+       "      <td>19525.0</td>\n",
+       "      <td>EMS: DIZZINESS</td>\n",
+       "      <td>2015-12-10 17:47:01</td>\n",
+       "      <td>NEW HANOVER</td>\n",
+       "      <td>CHARLOTTE ST &amp; MILES RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>40.114239</td>\n",
+       "      <td>-75.338508</td>\n",
+       "      <td>PENN ST &amp; ARCH ST;  NORRISTOWN; Station 308A; ...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>EMS: VEHICLE ACCIDENT</td>\n",
+       "      <td>2015-12-10 17:47:01</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>PENN ST &amp; ARCH ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>40.209337</td>\n",
+       "      <td>-75.135266</td>\n",
+       "      <td>COUNTY LINE RD &amp; WILLOW DR; HORSHAM; 2015-12-1...</td>\n",
+       "      <td>18974.0</td>\n",
+       "      <td>Traffic: DISABLED VEHICLE -</td>\n",
+       "      <td>2015-12-10 17:47:02</td>\n",
+       "      <td>HORSHAM</td>\n",
+       "      <td>COUNTY LINE RD &amp; WILLOW DR</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>40.114239</td>\n",
+       "      <td>-75.338508</td>\n",
+       "      <td>PENN ST &amp; ARCH ST; NORRISTOWN; 2015-12-10 @ 17...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 17:47:02</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>PENN ST &amp; ARCH ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>40.117948</td>\n",
+       "      <td>-75.209848</td>\n",
+       "      <td>CHURCH RD &amp; REDCOAT DR; WHITEMARSH; 2015-12-10...</td>\n",
+       "      <td>19031.0</td>\n",
+       "      <td>Traffic: DISABLED VEHICLE -</td>\n",
+       "      <td>2015-12-10 17:57:02</td>\n",
+       "      <td>WHITEMARSH</td>\n",
+       "      <td>CHURCH RD &amp; REDCOAT DR</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>40.199006</td>\n",
+       "      <td>-75.300058</td>\n",
+       "      <td>LILAC CT &amp; PRIMROSE DR; UPPER GWYNEDD; 2015-12...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>Fire: APPLIANCE FIRE</td>\n",
+       "      <td>2015-12-10 18:02:01</td>\n",
+       "      <td>UPPER GWYNEDD</td>\n",
+       "      <td>LILAC CT &amp; PRIMROSE DR</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>40.143326</td>\n",
+       "      <td>-75.422819</td>\n",
+       "      <td>RT422  &amp; PAWLINGS RD OVERPASS; LOWER PROVIDENC...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Traffic: DISABLED VEHICLE -</td>\n",
+       "      <td>2015-12-10 18:02:02</td>\n",
+       "      <td>LOWER PROVIDENCE</td>\n",
+       "      <td>RT422  &amp; PAWLINGS RD OVERPASS</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>40.153268</td>\n",
+       "      <td>-75.189558</td>\n",
+       "      <td>SUMMIT AVE &amp; RT309 UNDERPASS; UPPER DUBLIN; 20...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 18:02:02</td>\n",
+       "      <td>UPPER DUBLIN</td>\n",
+       "      <td>SUMMIT AVE &amp; RT309 UNDERPASS</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>40.133037</td>\n",
+       "      <td>-75.408463</td>\n",
+       "      <td>SHANNONDELL DR &amp; SHANNONDELL BLVD;  LOWER PROV...</td>\n",
+       "      <td>19403.0</td>\n",
+       "      <td>EMS: GENERAL WEAKNESS</td>\n",
+       "      <td>2015-12-10 18:06:25</td>\n",
+       "      <td>LOWER PROVIDENCE</td>\n",
+       "      <td>SHANNONDELL DR &amp; SHANNONDELL BLVD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>40.155283</td>\n",
+       "      <td>-75.264230</td>\n",
+       "      <td>PENLLYN BLUE BELL PIKE &amp; VILLAGE CIR;  WHITPAI...</td>\n",
+       "      <td>19422.0</td>\n",
+       "      <td>EMS: HEAD INJURY</td>\n",
+       "      <td>2015-12-10 18:06:25</td>\n",
+       "      <td>WHITPAIN</td>\n",
+       "      <td>PENLLYN BLUE BELL PIKE &amp; VILLAGE CIR</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>40.028903</td>\n",
+       "      <td>-75.351822</td>\n",
+       "      <td>EDENTON PL &amp; DURHAM DR; DELAWARE COUNTY; 2015-...</td>\n",
+       "      <td>19085.0</td>\n",
+       "      <td>Fire: CARBON MONOXIDE DETECTOR</td>\n",
+       "      <td>2015-12-10 18:06:25</td>\n",
+       "      <td>DELAWARE COUNTY</td>\n",
+       "      <td>EDENTON PL &amp; DURHAM DR</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>40.097222</td>\n",
+       "      <td>-75.376195</td>\n",
+       "      <td>SCHUYLKILL EXPY &amp; WEADLEY RD OVERPASS; UPPER M...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2015-12-10 18:06:26</td>\n",
+       "      <td>UPPER MERION</td>\n",
+       "      <td>SCHUYLKILL EXPY &amp; WEADLEY RD OVERPASS</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>40.209337</td>\n",
+       "      <td>-75.135266</td>\n",
+       "      <td>COUNTY LINE RD &amp; WILLOW DR; HORSHAM; 2015-12-1...</td>\n",
+       "      <td>18974.0</td>\n",
+       "      <td>Traffic: DISABLED VEHICLE -</td>\n",
+       "      <td>2015-12-10 18:11:01</td>\n",
+       "      <td>HORSHAM</td>\n",
+       "      <td>COUNTY LINE RD &amp; WILLOW DR</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99462</th>\n",
+       "      <td>40.274137</td>\n",
+       "      <td>-75.660469</td>\n",
+       "      <td>UPLAND SQUARE DR &amp; SELL RD;  WEST POTTSGROVE; ...</td>\n",
+       "      <td>19464.0</td>\n",
+       "      <td>EMS: UNKNOWN MEDICAL EMERGENCY</td>\n",
+       "      <td>2016-08-24 09:41:00</td>\n",
+       "      <td>WEST POTTSGROVE</td>\n",
+       "      <td>UPLAND SQUARE DR &amp; SELL RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99463</th>\n",
+       "      <td>40.254768</td>\n",
+       "      <td>-75.660459</td>\n",
+       "      <td>SHOEMAKER RD &amp; ROBINSON ST;  POTTSTOWN; Statio...</td>\n",
+       "      <td>19464.0</td>\n",
+       "      <td>EMS: UNKNOWN MEDICAL EMERGENCY</td>\n",
+       "      <td>2016-08-24 09:42:00</td>\n",
+       "      <td>POTTSTOWN</td>\n",
+       "      <td>SHOEMAKER RD &amp; ROBINSON ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99464</th>\n",
+       "      <td>40.163730</td>\n",
+       "      <td>-75.082753</td>\n",
+       "      <td>KAREN LN &amp; BYBERRY RD;  UPPER MORELAND; Statio...</td>\n",
+       "      <td>19040.0</td>\n",
+       "      <td>EMS: FALL VICTIM</td>\n",
+       "      <td>2016-08-24 09:51:06</td>\n",
+       "      <td>UPPER MORELAND</td>\n",
+       "      <td>KAREN LN &amp; BYBERRY RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99465</th>\n",
+       "      <td>40.114928</td>\n",
+       "      <td>-75.340307</td>\n",
+       "      <td>AIRY ST &amp; GREEN ST;  NORRISTOWN; Station 308A;...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>EMS: RESPIRATORY EMERGENCY</td>\n",
+       "      <td>2016-08-24 09:56:13</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>AIRY ST &amp; GREEN ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99466</th>\n",
+       "      <td>40.159820</td>\n",
+       "      <td>-75.288436</td>\n",
+       "      <td>WENTZ RD &amp; SILO CIR;  WHITPAIN; Station 385; 2...</td>\n",
+       "      <td>19422.0</td>\n",
+       "      <td>EMS: NAUSEA/VOMITING</td>\n",
+       "      <td>2016-08-24 10:01:00</td>\n",
+       "      <td>WHITPAIN</td>\n",
+       "      <td>WENTZ RD &amp; SILO CIR</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99467</th>\n",
+       "      <td>40.255271</td>\n",
+       "      <td>-75.340722</td>\n",
+       "      <td>WOODS DR &amp; DETWILER RD;  TOWAMENCIN; Station 3...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>EMS: ALTERED MENTAL STATUS</td>\n",
+       "      <td>2016-08-24 10:12:01</td>\n",
+       "      <td>TOWAMENCIN</td>\n",
+       "      <td>WOODS DR &amp; DETWILER RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99468</th>\n",
+       "      <td>40.088355</td>\n",
+       "      <td>-75.382100</td>\n",
+       "      <td>DEKALB PIKE &amp; ALLENDALE RD; UPPER MERION; 2016...</td>\n",
+       "      <td>19406.0</td>\n",
+       "      <td>Fire: FIRE ALARM</td>\n",
+       "      <td>2016-08-24 10:12:01</td>\n",
+       "      <td>UPPER MERION</td>\n",
+       "      <td>DEKALB PIKE &amp; ALLENDALE RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99469</th>\n",
+       "      <td>40.123868</td>\n",
+       "      <td>-75.341678</td>\n",
+       "      <td>MARKLEY ST &amp; JAMES ST; NORRISTOWN; 2016-08-24 ...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>Fire: FIRE ALARM</td>\n",
+       "      <td>2016-08-24 10:12:01</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>MARKLEY ST &amp; JAMES ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99470</th>\n",
+       "      <td>40.185798</td>\n",
+       "      <td>-75.536484</td>\n",
+       "      <td>WALNUT ST &amp; S 5TH AVE;  ROYERSFORD; Station 32...</td>\n",
+       "      <td>19468.0</td>\n",
+       "      <td>EMS: ALTERED MENTAL STATUS</td>\n",
+       "      <td>2016-08-24 10:17:01</td>\n",
+       "      <td>ROYERSFORD</td>\n",
+       "      <td>WALNUT ST &amp; S 5TH AVE</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99471</th>\n",
+       "      <td>40.000763</td>\n",
+       "      <td>-75.279769</td>\n",
+       "      <td>WYNNEWOOD RD &amp; W OLD WYNNEWOOD RD;  LOWER MERI...</td>\n",
+       "      <td>19096.0</td>\n",
+       "      <td>EMS: RESPIRATORY EMERGENCY</td>\n",
+       "      <td>2016-08-24 10:17:01</td>\n",
+       "      <td>LOWER MERION</td>\n",
+       "      <td>WYNNEWOOD RD &amp; W OLD WYNNEWOOD RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99472</th>\n",
+       "      <td>40.129398</td>\n",
+       "      <td>-75.332213</td>\n",
+       "      <td>PINE ST &amp; W ROBERTS ST;  NORRISTOWN; Station 3...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
+       "      <td>2016-08-24 10:22:00</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>PINE ST &amp; W ROBERTS ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99473</th>\n",
+       "      <td>40.133533</td>\n",
+       "      <td>-75.056460</td>\n",
+       "      <td>BUCK RD &amp; WAVERLY LN;  BRYN ATHYN; Station 355...</td>\n",
+       "      <td>19009.0</td>\n",
+       "      <td>EMS: GENERAL WEAKNESS</td>\n",
+       "      <td>2016-08-24 10:27:01</td>\n",
+       "      <td>BRYN ATHYN</td>\n",
+       "      <td>BUCK RD &amp; WAVERLY LN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99474</th>\n",
+       "      <td>40.078678</td>\n",
+       "      <td>-75.086943</td>\n",
+       "      <td>HUNTINGDON PIKE &amp; FILLMORE ST;  ROCKLEDGE; Sta...</td>\n",
+       "      <td>19046.0</td>\n",
+       "      <td>EMS: SEIZURES</td>\n",
+       "      <td>2016-08-24 10:27:01</td>\n",
+       "      <td>ROCKLEDGE</td>\n",
+       "      <td>HUNTINGDON PIKE &amp; FILLMORE ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99475</th>\n",
+       "      <td>40.133715</td>\n",
+       "      <td>-75.229630</td>\n",
+       "      <td>SHEAFF LN &amp; WHITEMARSH VALLEY RD; WHITEMARSH; ...</td>\n",
+       "      <td>19034.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2016-08-24 10:32:01</td>\n",
+       "      <td>WHITEMARSH</td>\n",
+       "      <td>SHEAFF LN &amp; WHITEMARSH VALLEY RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99476</th>\n",
+       "      <td>40.257820</td>\n",
+       "      <td>-75.624294</td>\n",
+       "      <td>PARK DR &amp; N ADAMS ST;  POTTSTOWN; Station 329;...</td>\n",
+       "      <td>19464.0</td>\n",
+       "      <td>EMS: RESPIRATORY EMERGENCY</td>\n",
+       "      <td>2016-08-24 10:47:00</td>\n",
+       "      <td>POTTSTOWN</td>\n",
+       "      <td>PARK DR &amp; N ADAMS ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99477</th>\n",
+       "      <td>40.095652</td>\n",
+       "      <td>-75.244877</td>\n",
+       "      <td>FOX HOUND DR &amp; MARBLE HL;  WHITEMARSH; Station...</td>\n",
+       "      <td>19444.0</td>\n",
+       "      <td>EMS: SUBJECT IN PAIN</td>\n",
+       "      <td>2016-08-24 10:47:00</td>\n",
+       "      <td>WHITEMARSH</td>\n",
+       "      <td>FOX HOUND DR &amp; MARBLE HL</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99478</th>\n",
+       "      <td>40.100344</td>\n",
+       "      <td>-75.293955</td>\n",
+       "      <td>CHEMICAL RD &amp; GALLAGHER RD; PLYMOUTH; 2016-08-...</td>\n",
+       "      <td>19462.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2016-08-24 10:47:02</td>\n",
+       "      <td>PLYMOUTH</td>\n",
+       "      <td>CHEMICAL RD &amp; GALLAGHER RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99479</th>\n",
+       "      <td>40.221227</td>\n",
+       "      <td>-75.288737</td>\n",
+       "      <td>SUMNEYTOWN PIKE &amp; RR OVERPASS;  UPPER GWYNEDD;...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
+       "      <td>2016-08-24 10:52:01</td>\n",
+       "      <td>UPPER GWYNEDD</td>\n",
+       "      <td>SUMNEYTOWN PIKE &amp; RR OVERPASS</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99480</th>\n",
+       "      <td>40.221227</td>\n",
+       "      <td>-75.288737</td>\n",
+       "      <td>SUMNEYTOWN PIKE &amp; RR OVERPASS;  UPPER GWYNEDD;...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
+       "      <td>2016-08-24 10:52:01</td>\n",
+       "      <td>UPPER GWYNEDD</td>\n",
+       "      <td>SUMNEYTOWN PIKE &amp; RR OVERPASS</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99481</th>\n",
+       "      <td>40.221227</td>\n",
+       "      <td>-75.288737</td>\n",
+       "      <td>SUMNEYTOWN PIKE &amp; RR OVERPASS;  UPPER GWYNEDD;...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>EMS: DIZZINESS</td>\n",
+       "      <td>2016-08-24 10:52:01</td>\n",
+       "      <td>UPPER GWYNEDD</td>\n",
+       "      <td>SUMNEYTOWN PIKE &amp; RR OVERPASS</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99482</th>\n",
+       "      <td>40.340072</td>\n",
+       "      <td>-75.591709</td>\n",
+       "      <td>RT100 SB &amp; E PHILADELPHIA AVE OVERPASS; DOUGLA...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Traffic: DISABLED VEHICLE -</td>\n",
+       "      <td>2016-08-24 10:52:03</td>\n",
+       "      <td>DOUGLASS</td>\n",
+       "      <td>RT100 SB &amp; E PHILADELPHIA AVE OVERPASS</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99483</th>\n",
+       "      <td>40.084465</td>\n",
+       "      <td>-75.390173</td>\n",
+       "      <td>DEKALB PIKE &amp; KING OF PRUSSIA RD;  UPPER MERIO...</td>\n",
+       "      <td>19406.0</td>\n",
+       "      <td>EMS: BACK PAINS/INJURY</td>\n",
+       "      <td>2016-08-24 10:57:00</td>\n",
+       "      <td>UPPER MERION</td>\n",
+       "      <td>DEKALB PIKE &amp; KING OF PRUSSIA RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99484</th>\n",
+       "      <td>40.133037</td>\n",
+       "      <td>-75.408463</td>\n",
+       "      <td>SHANNONDELL DR &amp; SHANNONDELL BLVD; LOWER PROVI...</td>\n",
+       "      <td>19403.0</td>\n",
+       "      <td>Fire: FIRE ALARM</td>\n",
+       "      <td>2016-08-24 10:57:00</td>\n",
+       "      <td>LOWER PROVIDENCE</td>\n",
+       "      <td>SHANNONDELL DR &amp; SHANNONDELL BLVD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99485</th>\n",
+       "      <td>40.143601</td>\n",
+       "      <td>-75.427877</td>\n",
+       "      <td>EAGLEVILLE RD &amp; REDTAIL RD; LOWER PROVIDENCE; ...</td>\n",
+       "      <td>19403.0</td>\n",
+       "      <td>Traffic: DISABLED VEHICLE -</td>\n",
+       "      <td>2016-08-24 10:57:01</td>\n",
+       "      <td>LOWER PROVIDENCE</td>\n",
+       "      <td>EAGLEVILLE RD &amp; REDTAIL RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99486</th>\n",
+       "      <td>40.179225</td>\n",
+       "      <td>-75.180572</td>\n",
+       "      <td>WELSH RD &amp; NORRISTOWN RD; HORSHAM; 2016-08-24 ...</td>\n",
+       "      <td>19044.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2016-08-24 11:02:02</td>\n",
+       "      <td>HORSHAM</td>\n",
+       "      <td>WELSH RD &amp; NORRISTOWN RD</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99487</th>\n",
+       "      <td>40.132869</td>\n",
+       "      <td>-75.333515</td>\n",
+       "      <td>MARKLEY ST &amp; W LOGAN ST; NORRISTOWN; 2016-08-2...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2016-08-24 11:06:00</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>MARKLEY ST &amp; W LOGAN ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99488</th>\n",
+       "      <td>40.006974</td>\n",
+       "      <td>-75.289080</td>\n",
+       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL; LOWER MERION; ...</td>\n",
+       "      <td>19003.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2016-08-24 11:07:02</td>\n",
+       "      <td>LOWER MERION</td>\n",
+       "      <td>LANCASTER AVE &amp; RITTENHOUSE PL</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99489</th>\n",
+       "      <td>40.115429</td>\n",
+       "      <td>-75.334679</td>\n",
+       "      <td>CHESTNUT ST &amp; WALNUT ST;  NORRISTOWN; Station ...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>EMS: FALL VICTIM</td>\n",
+       "      <td>2016-08-24 11:12:00</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>CHESTNUT ST &amp; WALNUT ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99490</th>\n",
+       "      <td>40.186431</td>\n",
+       "      <td>-75.192555</td>\n",
+       "      <td>WELSH RD &amp; WEBSTER LN;  HORSHAM; Station 352; ...</td>\n",
+       "      <td>19002.0</td>\n",
+       "      <td>EMS: NAUSEA/VOMITING</td>\n",
+       "      <td>2016-08-24 11:17:01</td>\n",
+       "      <td>HORSHAM</td>\n",
+       "      <td>WELSH RD &amp; WEBSTER LN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99491</th>\n",
+       "      <td>40.207055</td>\n",
+       "      <td>-75.317952</td>\n",
+       "      <td>MORRIS RD &amp; S BROAD ST; UPPER GWYNEDD; 2016-08...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
+       "      <td>2016-08-24 11:17:02</td>\n",
+       "      <td>UPPER GWYNEDD</td>\n",
+       "      <td>MORRIS RD &amp; S BROAD ST</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>99492 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             lat        lng  \\\n",
+       "0      40.297876 -75.581294   \n",
+       "1      40.258061 -75.264680   \n",
+       "2      40.121182 -75.351975   \n",
+       "3      40.116153 -75.343513   \n",
+       "4      40.251492 -75.603350   \n",
+       "5      40.253473 -75.283245   \n",
+       "6      40.182111 -75.127795   \n",
+       "7      40.217286 -75.405182   \n",
+       "8      40.289027 -75.399590   \n",
+       "9      40.102398 -75.291458   \n",
+       "10     40.231990 -75.251891   \n",
+       "11     40.084161 -75.308386   \n",
+       "12     40.174131 -75.098491   \n",
+       "13     40.062974 -75.135914   \n",
+       "14     40.097222 -75.376195   \n",
+       "15     40.223778 -75.235399   \n",
+       "16     40.243258 -75.286552   \n",
+       "17     40.312181 -75.574260   \n",
+       "18     40.114239 -75.338508   \n",
+       "19     40.209337 -75.135266   \n",
+       "20     40.114239 -75.338508   \n",
+       "21     40.117948 -75.209848   \n",
+       "22     40.199006 -75.300058   \n",
+       "23     40.143326 -75.422819   \n",
+       "24     40.153268 -75.189558   \n",
+       "25     40.133037 -75.408463   \n",
+       "26     40.155283 -75.264230   \n",
+       "27     40.028903 -75.351822   \n",
+       "28     40.097222 -75.376195   \n",
+       "29     40.209337 -75.135266   \n",
+       "...          ...        ...   \n",
+       "99462  40.274137 -75.660469   \n",
+       "99463  40.254768 -75.660459   \n",
+       "99464  40.163730 -75.082753   \n",
+       "99465  40.114928 -75.340307   \n",
+       "99466  40.159820 -75.288436   \n",
+       "99467  40.255271 -75.340722   \n",
+       "99468  40.088355 -75.382100   \n",
+       "99469  40.123868 -75.341678   \n",
+       "99470  40.185798 -75.536484   \n",
+       "99471  40.000763 -75.279769   \n",
+       "99472  40.129398 -75.332213   \n",
+       "99473  40.133533 -75.056460   \n",
+       "99474  40.078678 -75.086943   \n",
+       "99475  40.133715 -75.229630   \n",
+       "99476  40.257820 -75.624294   \n",
+       "99477  40.095652 -75.244877   \n",
+       "99478  40.100344 -75.293955   \n",
+       "99479  40.221227 -75.288737   \n",
+       "99480  40.221227 -75.288737   \n",
+       "99481  40.221227 -75.288737   \n",
+       "99482  40.340072 -75.591709   \n",
+       "99483  40.084465 -75.390173   \n",
+       "99484  40.133037 -75.408463   \n",
+       "99485  40.143601 -75.427877   \n",
+       "99486  40.179225 -75.180572   \n",
+       "99487  40.132869 -75.333515   \n",
+       "99488  40.006974 -75.289080   \n",
+       "99489  40.115429 -75.334679   \n",
+       "99490  40.186431 -75.192555   \n",
+       "99491  40.207055 -75.317952   \n",
+       "\n",
+       "                                                    desc      zip  \\\n",
+       "0      REINDEER CT & DEAD END;  NEW HANOVER; Station ...  19525.0   \n",
+       "1      BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...  19446.0   \n",
+       "2      HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...  19401.0   \n",
+       "3      AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...  19401.0   \n",
+       "4      CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...      NaN   \n",
+       "5      CANNON AVE & W 9TH ST;  LANSDALE; Station 345;...  19446.0   \n",
+       "6      LAUREL AVE & OAKDALE AVE;  HORSHAM; Station 35...  19044.0   \n",
+       "7      COLLEGEVILLE RD & LYWISKI RD;  SKIPPACK; Stati...  19426.0   \n",
+       "8      MAIN ST & OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...  19438.0   \n",
+       "9      BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD; PLYM...  19462.0   \n",
+       "10     RT202 PKWY & KNAPP RD; MONTGOMERY; 2015-12-10 ...      NaN   \n",
+       "11     BROOK RD & COLWELL LN; PLYMOUTH; 2015-12-10 @ ...  19428.0   \n",
+       "12     BYBERRY AVE & S WARMINSTER RD; UPPER MORELAND;...  19040.0   \n",
+       "13     OLD YORK RD & VALLEY RD; CHELTENHAM; 2015-12-1...  19027.0   \n",
+       "14     SCHUYLKILL EXPY & CROTON RD UNDERPASS; UPPER M...      NaN   \n",
+       "15     STUMP RD & WITCHWOOD DR; MONTGOMERY; 2015-12-1...  18936.0   \n",
+       "16     SUSQUEHANNA AVE & W MAIN ST;  LANSDALE; Statio...  19446.0   \n",
+       "17     CHARLOTTE ST & MILES RD;  NEW HANOVER; Station...  19525.0   \n",
+       "18     PENN ST & ARCH ST;  NORRISTOWN; Station 308A; ...  19401.0   \n",
+       "19     COUNTY LINE RD & WILLOW DR; HORSHAM; 2015-12-1...  18974.0   \n",
+       "20     PENN ST & ARCH ST; NORRISTOWN; 2015-12-10 @ 17...  19401.0   \n",
+       "21     CHURCH RD & REDCOAT DR; WHITEMARSH; 2015-12-10...  19031.0   \n",
+       "22     LILAC CT & PRIMROSE DR; UPPER GWYNEDD; 2015-12...  19446.0   \n",
+       "23     RT422  & PAWLINGS RD OVERPASS; LOWER PROVIDENC...      NaN   \n",
+       "24     SUMMIT AVE & RT309 UNDERPASS; UPPER DUBLIN; 20...      NaN   \n",
+       "25     SHANNONDELL DR & SHANNONDELL BLVD;  LOWER PROV...  19403.0   \n",
+       "26     PENLLYN BLUE BELL PIKE & VILLAGE CIR;  WHITPAI...  19422.0   \n",
+       "27     EDENTON PL & DURHAM DR; DELAWARE COUNTY; 2015-...  19085.0   \n",
+       "28     SCHUYLKILL EXPY & WEADLEY RD OVERPASS; UPPER M...      NaN   \n",
+       "29     COUNTY LINE RD & WILLOW DR; HORSHAM; 2015-12-1...  18974.0   \n",
+       "...                                                  ...      ...   \n",
+       "99462  UPLAND SQUARE DR & SELL RD;  WEST POTTSGROVE; ...  19464.0   \n",
+       "99463  SHOEMAKER RD & ROBINSON ST;  POTTSTOWN; Statio...  19464.0   \n",
+       "99464  KAREN LN & BYBERRY RD;  UPPER MORELAND; Statio...  19040.0   \n",
+       "99465  AIRY ST & GREEN ST;  NORRISTOWN; Station 308A;...  19401.0   \n",
+       "99466  WENTZ RD & SILO CIR;  WHITPAIN; Station 385; 2...  19422.0   \n",
+       "99467  WOODS DR & DETWILER RD;  TOWAMENCIN; Station 3...  19446.0   \n",
+       "99468  DEKALB PIKE & ALLENDALE RD; UPPER MERION; 2016...  19406.0   \n",
+       "99469  MARKLEY ST & JAMES ST; NORRISTOWN; 2016-08-24 ...  19401.0   \n",
+       "99470  WALNUT ST & S 5TH AVE;  ROYERSFORD; Station 32...  19468.0   \n",
+       "99471  WYNNEWOOD RD & W OLD WYNNEWOOD RD;  LOWER MERI...  19096.0   \n",
+       "99472  PINE ST & W ROBERTS ST;  NORRISTOWN; Station 3...  19401.0   \n",
+       "99473  BUCK RD & WAVERLY LN;  BRYN ATHYN; Station 355...  19009.0   \n",
+       "99474  HUNTINGDON PIKE & FILLMORE ST;  ROCKLEDGE; Sta...  19046.0   \n",
+       "99475  SHEAFF LN & WHITEMARSH VALLEY RD; WHITEMARSH; ...  19034.0   \n",
+       "99476  PARK DR & N ADAMS ST;  POTTSTOWN; Station 329;...  19464.0   \n",
+       "99477  FOX HOUND DR & MARBLE HL;  WHITEMARSH; Station...  19444.0   \n",
+       "99478  CHEMICAL RD & GALLAGHER RD; PLYMOUTH; 2016-08-...  19462.0   \n",
+       "99479  SUMNEYTOWN PIKE & RR OVERPASS;  UPPER GWYNEDD;...      NaN   \n",
+       "99480  SUMNEYTOWN PIKE & RR OVERPASS;  UPPER GWYNEDD;...      NaN   \n",
+       "99481  SUMNEYTOWN PIKE & RR OVERPASS;  UPPER GWYNEDD;...      NaN   \n",
+       "99482  RT100 SB & E PHILADELPHIA AVE OVERPASS; DOUGLA...      NaN   \n",
+       "99483  DEKALB PIKE & KING OF PRUSSIA RD;  UPPER MERIO...  19406.0   \n",
+       "99484  SHANNONDELL DR & SHANNONDELL BLVD; LOWER PROVI...  19403.0   \n",
+       "99485  EAGLEVILLE RD & REDTAIL RD; LOWER PROVIDENCE; ...  19403.0   \n",
+       "99486  WELSH RD & NORRISTOWN RD; HORSHAM; 2016-08-24 ...  19044.0   \n",
+       "99487  MARKLEY ST & W LOGAN ST; NORRISTOWN; 2016-08-2...  19401.0   \n",
+       "99488  LANCASTER AVE & RITTENHOUSE PL; LOWER MERION; ...  19003.0   \n",
+       "99489  CHESTNUT ST & WALNUT ST;  NORRISTOWN; Station ...  19401.0   \n",
+       "99490  WELSH RD & WEBSTER LN;  HORSHAM; Station 352; ...  19002.0   \n",
+       "99491  MORRIS RD & S BROAD ST; UPPER GWYNEDD; 2016-08...  19446.0   \n",
+       "\n",
+       "                                title            timeStamp                twp  \\\n",
+       "0              EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
+       "1             EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
+       "2                 Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
+       "3              EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
+       "4                      EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
+       "5                    EMS: HEAD INJURY  2015-12-10 17:40:01           LANSDALE   \n",
+       "6                EMS: NAUSEA/VOMITING  2015-12-10 17:40:01            HORSHAM   \n",
+       "7          EMS: RESPIRATORY EMERGENCY  2015-12-10 17:40:01           SKIPPACK   \n",
+       "8               EMS: SYNCOPAL EPISODE  2015-12-10 17:40:01      LOWER SALFORD   \n",
+       "9         Traffic: VEHICLE ACCIDENT -  2015-12-10 17:40:01           PLYMOUTH   \n",
+       "10        Traffic: VEHICLE ACCIDENT -  2015-12-10 17:40:01         MONTGOMERY   \n",
+       "11        Traffic: VEHICLE ACCIDENT -  2015-12-10 17:40:02           PLYMOUTH   \n",
+       "12        Traffic: VEHICLE ACCIDENT -  2015-12-10 17:40:02     UPPER MORELAND   \n",
+       "13        Traffic: VEHICLE ACCIDENT -  2015-12-10 17:40:02         CHELTENHAM   \n",
+       "14        Traffic: VEHICLE ACCIDENT -  2015-12-10 17:40:02       UPPER MERION   \n",
+       "15        Traffic: VEHICLE ACCIDENT -  2015-12-10 17:40:02         MONTGOMERY   \n",
+       "16         EMS: RESPIRATORY EMERGENCY  2015-12-10 17:46:01           LANSDALE   \n",
+       "17                     EMS: DIZZINESS  2015-12-10 17:47:01        NEW HANOVER   \n",
+       "18              EMS: VEHICLE ACCIDENT  2015-12-10 17:47:01         NORRISTOWN   \n",
+       "19        Traffic: DISABLED VEHICLE -  2015-12-10 17:47:02            HORSHAM   \n",
+       "20        Traffic: VEHICLE ACCIDENT -  2015-12-10 17:47:02         NORRISTOWN   \n",
+       "21        Traffic: DISABLED VEHICLE -  2015-12-10 17:57:02         WHITEMARSH   \n",
+       "22               Fire: APPLIANCE FIRE  2015-12-10 18:02:01      UPPER GWYNEDD   \n",
+       "23        Traffic: DISABLED VEHICLE -  2015-12-10 18:02:02   LOWER PROVIDENCE   \n",
+       "24        Traffic: VEHICLE ACCIDENT -  2015-12-10 18:02:02       UPPER DUBLIN   \n",
+       "25              EMS: GENERAL WEAKNESS  2015-12-10 18:06:25   LOWER PROVIDENCE   \n",
+       "26                   EMS: HEAD INJURY  2015-12-10 18:06:25           WHITPAIN   \n",
+       "27     Fire: CARBON MONOXIDE DETECTOR  2015-12-10 18:06:25    DELAWARE COUNTY   \n",
+       "28        Traffic: VEHICLE ACCIDENT -  2015-12-10 18:06:26       UPPER MERION   \n",
+       "29        Traffic: DISABLED VEHICLE -  2015-12-10 18:11:01            HORSHAM   \n",
+       "...                               ...                  ...                ...   \n",
+       "99462  EMS: UNKNOWN MEDICAL EMERGENCY  2016-08-24 09:41:00    WEST POTTSGROVE   \n",
+       "99463  EMS: UNKNOWN MEDICAL EMERGENCY  2016-08-24 09:42:00          POTTSTOWN   \n",
+       "99464                EMS: FALL VICTIM  2016-08-24 09:51:06     UPPER MORELAND   \n",
+       "99465      EMS: RESPIRATORY EMERGENCY  2016-08-24 09:56:13         NORRISTOWN   \n",
+       "99466            EMS: NAUSEA/VOMITING  2016-08-24 10:01:00           WHITPAIN   \n",
+       "99467      EMS: ALTERED MENTAL STATUS  2016-08-24 10:12:01         TOWAMENCIN   \n",
+       "99468                Fire: FIRE ALARM  2016-08-24 10:12:01       UPPER MERION   \n",
+       "99469                Fire: FIRE ALARM  2016-08-24 10:12:01         NORRISTOWN   \n",
+       "99470      EMS: ALTERED MENTAL STATUS  2016-08-24 10:17:01         ROYERSFORD   \n",
+       "99471      EMS: RESPIRATORY EMERGENCY  2016-08-24 10:17:01       LOWER MERION   \n",
+       "99472          EMS: CARDIAC EMERGENCY  2016-08-24 10:22:00         NORRISTOWN   \n",
+       "99473           EMS: GENERAL WEAKNESS  2016-08-24 10:27:01         BRYN ATHYN   \n",
+       "99474                   EMS: SEIZURES  2016-08-24 10:27:01          ROCKLEDGE   \n",
+       "99475     Traffic: VEHICLE ACCIDENT -  2016-08-24 10:32:01         WHITEMARSH   \n",
+       "99476      EMS: RESPIRATORY EMERGENCY  2016-08-24 10:47:00          POTTSTOWN   \n",
+       "99477            EMS: SUBJECT IN PAIN  2016-08-24 10:47:00         WHITEMARSH   \n",
+       "99478     Traffic: VEHICLE ACCIDENT -  2016-08-24 10:47:02           PLYMOUTH   \n",
+       "99479          EMS: CARDIAC EMERGENCY  2016-08-24 10:52:01      UPPER GWYNEDD   \n",
+       "99480         EMS: DIABETIC EMERGENCY  2016-08-24 10:52:01      UPPER GWYNEDD   \n",
+       "99481                  EMS: DIZZINESS  2016-08-24 10:52:01      UPPER GWYNEDD   \n",
+       "99482     Traffic: DISABLED VEHICLE -  2016-08-24 10:52:03           DOUGLASS   \n",
+       "99483          EMS: BACK PAINS/INJURY  2016-08-24 10:57:00       UPPER MERION   \n",
+       "99484                Fire: FIRE ALARM  2016-08-24 10:57:00   LOWER PROVIDENCE   \n",
+       "99485     Traffic: DISABLED VEHICLE -  2016-08-24 10:57:01   LOWER PROVIDENCE   \n",
+       "99486     Traffic: VEHICLE ACCIDENT -  2016-08-24 11:02:02            HORSHAM   \n",
+       "99487     Traffic: VEHICLE ACCIDENT -  2016-08-24 11:06:00         NORRISTOWN   \n",
+       "99488     Traffic: VEHICLE ACCIDENT -  2016-08-24 11:07:02       LOWER MERION   \n",
+       "99489                EMS: FALL VICTIM  2016-08-24 11:12:00         NORRISTOWN   \n",
+       "99490            EMS: NAUSEA/VOMITING  2016-08-24 11:17:01            HORSHAM   \n",
+       "99491     Traffic: VEHICLE ACCIDENT -  2016-08-24 11:17:02      UPPER GWYNEDD   \n",
+       "\n",
+       "                                           addr  e  \n",
+       "0                        REINDEER CT & DEAD END  1  \n",
+       "1                    BRIAR PATH & WHITEMARSH LN  1  \n",
+       "2                                      HAWS AVE  1  \n",
+       "3                            AIRY ST & SWEDE ST  1  \n",
+       "4                      CHERRYWOOD CT & DEAD END  1  \n",
+       "5                         CANNON AVE & W 9TH ST  1  \n",
+       "6                      LAUREL AVE & OAKDALE AVE  1  \n",
+       "7                  COLLEGEVILLE RD & LYWISKI RD  1  \n",
+       "8                 MAIN ST & OLD SUMNEYTOWN PIKE  1  \n",
+       "9      BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD  1  \n",
+       "10                        RT202 PKWY & KNAPP RD  1  \n",
+       "11                        BROOK RD & COLWELL LN  1  \n",
+       "12                BYBERRY AVE & S WARMINSTER RD  1  \n",
+       "13                      OLD YORK RD & VALLEY RD  1  \n",
+       "14        SCHUYLKILL EXPY & CROTON RD UNDERPASS  1  \n",
+       "15                      STUMP RD & WITCHWOOD DR  1  \n",
+       "16                  SUSQUEHANNA AVE & W MAIN ST  1  \n",
+       "17                      CHARLOTTE ST & MILES RD  1  \n",
+       "18                            PENN ST & ARCH ST  1  \n",
+       "19                   COUNTY LINE RD & WILLOW DR  1  \n",
+       "20                            PENN ST & ARCH ST  1  \n",
+       "21                       CHURCH RD & REDCOAT DR  1  \n",
+       "22                       LILAC CT & PRIMROSE DR  1  \n",
+       "23                RT422  & PAWLINGS RD OVERPASS  1  \n",
+       "24                 SUMMIT AVE & RT309 UNDERPASS  1  \n",
+       "25            SHANNONDELL DR & SHANNONDELL BLVD  1  \n",
+       "26         PENLLYN BLUE BELL PIKE & VILLAGE CIR  1  \n",
+       "27                       EDENTON PL & DURHAM DR  1  \n",
+       "28        SCHUYLKILL EXPY & WEADLEY RD OVERPASS  1  \n",
+       "29                   COUNTY LINE RD & WILLOW DR  1  \n",
+       "...                                         ... ..  \n",
+       "99462                UPLAND SQUARE DR & SELL RD  1  \n",
+       "99463                SHOEMAKER RD & ROBINSON ST  1  \n",
+       "99464                     KAREN LN & BYBERRY RD  1  \n",
+       "99465                        AIRY ST & GREEN ST  1  \n",
+       "99466                       WENTZ RD & SILO CIR  1  \n",
+       "99467                    WOODS DR & DETWILER RD  1  \n",
+       "99468                DEKALB PIKE & ALLENDALE RD  1  \n",
+       "99469                     MARKLEY ST & JAMES ST  1  \n",
+       "99470                     WALNUT ST & S 5TH AVE  1  \n",
+       "99471         WYNNEWOOD RD & W OLD WYNNEWOOD RD  1  \n",
+       "99472                    PINE ST & W ROBERTS ST  1  \n",
+       "99473                      BUCK RD & WAVERLY LN  1  \n",
+       "99474             HUNTINGDON PIKE & FILLMORE ST  1  \n",
+       "99475          SHEAFF LN & WHITEMARSH VALLEY RD  1  \n",
+       "99476                      PARK DR & N ADAMS ST  1  \n",
+       "99477                  FOX HOUND DR & MARBLE HL  1  \n",
+       "99478                CHEMICAL RD & GALLAGHER RD  1  \n",
+       "99479             SUMNEYTOWN PIKE & RR OVERPASS  1  \n",
+       "99480             SUMNEYTOWN PIKE & RR OVERPASS  1  \n",
+       "99481             SUMNEYTOWN PIKE & RR OVERPASS  1  \n",
+       "99482    RT100 SB & E PHILADELPHIA AVE OVERPASS  1  \n",
+       "99483          DEKALB PIKE & KING OF PRUSSIA RD  1  \n",
+       "99484         SHANNONDELL DR & SHANNONDELL BLVD  1  \n",
+       "99485                EAGLEVILLE RD & REDTAIL RD  1  \n",
+       "99486                  WELSH RD & NORRISTOWN RD  1  \n",
+       "99487                   MARKLEY ST & W LOGAN ST  1  \n",
+       "99488            LANCASTER AVE & RITTENHOUSE PL  1  \n",
+       "99489                   CHESTNUT ST & WALNUT ST  1  \n",
+       "99490                     WELSH RD & WEBSTER LN  1  \n",
+       "99491                    MORRIS RD & S BROAD ST  1  \n",
+       "\n",
+       "[99492 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99492, 9)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 99492 entries, 0 to 99491\n",
+      "Data columns (total 9 columns):\n",
+      "lat          99492 non-null float64\n",
+      "lng          99492 non-null float64\n",
+      "desc         99492 non-null object\n",
+      "zip          86637 non-null float64\n",
+      "title        99492 non-null object\n",
+      "timeStamp    99492 non-null object\n",
+      "twp          99449 non-null object\n",
+      "addr         98973 non-null object\n",
+      "e            99492 non-null int64\n",
+      "dtypes: float64(3), int64(1), object(5)\n",
+      "memory usage: 6.8+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "lat              0\n",
+       "lng              0\n",
+       "desc             0\n",
+       "zip          12855\n",
+       "title            0\n",
+       "timeStamp        0\n",
+       "twp             43\n",
+       "addr           519\n",
+       "e                0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Questions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19401.0    6979\n",
+       "19464.0    6643\n",
+       "19403.0    4854\n",
+       "19446.0    4748\n",
+       "19406.0    3174\n",
+       "Name: zip, dtype: int64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# what are top 5 zip codes\n",
+    "df['zip'].value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LOWER MERION    8443\n",
+       "ABINGTON        5977\n",
+       "NORRISTOWN      5890\n",
+       "UPPER MERION    5227\n",
+       "CHELTENHAM      4575\n",
+       "Name: twp, dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['twp'].value_counts().head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Traffic: VEHICLE ACCIDENT -    2324\n",
+       "Name: title, dtype: int64"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Top reason for 911 calls from the top township\n",
+    "df[df['twp']=='LOWER MERION']['title'].value_counts().head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "110"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# how many unique titles we have in our dataset\n",
+    "df['title'].nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c909cc7198>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot distribution plot for longitude and latitude column\n",
+    "df[(df['lat']>39) & (df['lat']<41)]['lat'].hist(bins=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c909f8a5f8>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[(df['lng']>-76) & (df['lng']<-74)]['lng'].hist(bins=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating new feature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         EMS: BACK PAINS/INJURY\n",
+       "1        EMS: DIABETIC EMERGENCY\n",
+       "2            Fire: GAS-ODOR/LEAK\n",
+       "3         EMS: CARDIAC EMERGENCY\n",
+       "4                 EMS: DIZZINESS\n",
+       "5               EMS: HEAD INJURY\n",
+       "6           EMS: NAUSEA/VOMITING\n",
+       "7     EMS: RESPIRATORY EMERGENCY\n",
+       "8          EMS: SYNCOPAL EPISODE\n",
+       "9    Traffic: VEHICLE ACCIDENT -\n",
+       "Name: title, dtype: object"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['title'][:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>desc</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>title</th>\n",
+       "      <th>timeStamp</th>\n",
+       "      <th>twp</th>\n",
+       "      <th>addr</th>\n",
+       "      <th>e</th>\n",
+       "      <th>Reason</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.297876</td>\n",
+       "      <td>-75.581294</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
+       "      <td>19525.0</td>\n",
+       "      <td>EMS: BACK PAINS/INJURY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NEW HANOVER</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>40.258061</td>\n",
+       "      <td>-75.264680</td>\n",
+       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>HATFIELD TOWNSHIP</td>\n",
+       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40.121182</td>\n",
+       "      <td>-75.351975</td>\n",
+       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>HAWS AVE</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fire</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40.116153</td>\n",
+       "      <td>-75.343513</td>\n",
+       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.251492</td>\n",
+       "      <td>-75.603350</td>\n",
+       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>EMS: DIZZINESS</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>LOWER POTTSGROVE</td>\n",
+       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         lat        lng                                               desc  \\\n",
+       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
+       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
+       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
+       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
+       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
+       "\n",
+       "       zip                    title            timeStamp                twp  \\\n",
+       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:40:00        NEW HANOVER   \n",
+       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
+       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 17:40:00         NORRISTOWN   \n",
+       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 17:40:01         NORRISTOWN   \n",
+       "4      NaN           EMS: DIZZINESS  2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
+       "\n",
+       "                         addr  e Reason  \n",
+       "0      REINDEER CT & DEAD END  1    EMS  \n",
+       "1  BRIAR PATH & WHITEMARSH LN  1    EMS  \n",
+       "2                    HAWS AVE  1   Fire  \n",
+       "3          AIRY ST & SWEDE ST  1    EMS  \n",
+       "4    CHERRYWOOD CT & DEAD END  1    EMS  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def make_reason(x):\n",
+    "    x=x.split(':')[0]\n",
+    "    return x\n",
+    "df['Reason']= df['title'].apply(make_reason)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'EMS'"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# most common reason for 911 calls based on that new column\n",
+    "df['Reason'].value_counts().index[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['EMS: BACK PAINS/INJURY', 'EMS: DIABETIC EMERGENCY',\n",
+       "       'EMS: CARDIAC EMERGENCY', 'EMS: DIZZINESS', 'EMS: HEAD INJURY',\n",
+       "       'EMS: NAUSEA/VOMITING', 'EMS: RESPIRATORY EMERGENCY',\n",
+       "       'EMS: SYNCOPAL EPISODE', 'EMS: VEHICLE ACCIDENT',\n",
+       "       'EMS: GENERAL WEAKNESS', 'EMS: UNKNOWN MEDICAL EMERGENCY',\n",
+       "       'EMS: UNRESPONSIVE SUBJECT', 'EMS: ALTERED MENTAL STATUS',\n",
+       "       'EMS: CVA/STROKE', 'EMS: SUBJECT IN PAIN', 'EMS: HEMORRHAGING',\n",
+       "       'EMS: FALL VICTIM', 'EMS: ASSAULT VICTIM', 'EMS: SEIZURES',\n",
+       "       'EMS: MEDICAL ALERT ALARM', 'EMS: ABDOMINAL PAINS',\n",
+       "       'EMS: OVERDOSE', 'EMS: MATERNITY', 'EMS: UNCONSCIOUS SUBJECT',\n",
+       "       'EMS: CHOKING', 'EMS: LACERATIONS', 'EMS: FEVER',\n",
+       "       'EMS: ALLERGIC REACTION', 'EMS: FRACTURE', 'EMS: BURN VICTIM',\n",
+       "       'EMS: RESCUE - GENERAL', 'EMS: WARRANT SERVICE',\n",
+       "       'EMS: EMS SPECIAL SERVICE', 'EMS: FIRE SPECIAL SERVICE',\n",
+       "       'EMS: DEHYDRATION', 'EMS: CARBON MONOXIDE DETECTOR',\n",
+       "       'EMS: BUILDING FIRE', 'EMS: APPLIANCE FIRE', 'EMS: SHOOTING',\n",
+       "       'EMS: POISONING', 'EMS: RESCUE - TECHNICAL', 'EMS: EYE INJURY',\n",
+       "       'EMS: ELECTROCUTION', 'EMS: STABBING', 'EMS: AMPUTATION',\n",
+       "       'EMS: ANIMAL BITE', 'EMS: FIRE ALARM', 'EMS: VEHICLE FIRE',\n",
+       "       'EMS: HAZARDOUS MATERIALS INCIDENT', 'EMS: RESCUE - ELEVATOR',\n",
+       "       'EMS: FIRE INVESTIGATION', 'EMS: UNKNOWN TYPE FIRE',\n",
+       "       'EMS: GAS-ODOR/LEAK', 'EMS: TRANSFERRED CALL', 'EMS: TRAIN CRASH',\n",
+       "       'EMS: RESCUE - WATER', 'EMS: S/B AT HELICOPTER LANDING',\n",
+       "       'EMS: CARDIAC ARREST', 'EMS: PLANE CRASH', 'EMS: WOODS/FIELD FIRE',\n",
+       "       'EMS: HEAT EXHAUSTION', 'EMS: DEBRIS/FLUIDS ON HIGHWAY',\n",
+       "       'EMS: ACTIVE SHOOTER', 'EMS: DISABLED VEHICLE',\n",
+       "       'EMS: BOMB DEVICE FOUND', 'EMS: INDUSTRIAL ACCIDENT',\n",
+       "       'EMS: DROWNING', 'EMS: SUSPICIOUS'], dtype=object)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# display unique titles for Reason='EMS'\n",
+    "df[df['Reason']=='EMS']['title'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c90a3e5390>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plotting the reason column\n",
+    "sns.countplot(df['Reason'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## DateTime column\n",
+    "timeStamp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['timeStamp']=pd.to_datetime(df['timeStamp'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas._libs.tslibs.timestamps.Timestamp"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(df['timeStamp'][0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Hour']= df['timeStamp'].apply(lambda t: t.hour)\n",
+    "df['Month']= df['timeStamp'].apply(lambda t: t.month)\n",
+    "df['Day of Week']= df['timeStamp'].apply(lambda t: t.dayofweek)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>desc</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>title</th>\n",
+       "      <th>timeStamp</th>\n",
+       "      <th>twp</th>\n",
+       "      <th>addr</th>\n",
+       "      <th>e</th>\n",
+       "      <th>Reason</th>\n",
+       "      <th>Hour</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Day of Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.297876</td>\n",
+       "      <td>-75.581294</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
+       "      <td>19525.0</td>\n",
+       "      <td>EMS: BACK PAINS/INJURY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NEW HANOVER</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>40.258061</td>\n",
+       "      <td>-75.264680</td>\n",
+       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
+       "      <td>19446.0</td>\n",
+       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>HATFIELD TOWNSHIP</td>\n",
+       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>40.121182</td>\n",
+       "      <td>-75.351975</td>\n",
+       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>HAWS AVE</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>40.116153</td>\n",
+       "      <td>-75.343513</td>\n",
+       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
+       "      <td>19401.0</td>\n",
+       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>NORRISTOWN</td>\n",
+       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>40.251492</td>\n",
+       "      <td>-75.603350</td>\n",
+       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>EMS: DIZZINESS</td>\n",
+       "      <td>2015-12-10 17:40:01</td>\n",
+       "      <td>LOWER POTTSGROVE</td>\n",
+       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         lat        lng                                               desc  \\\n",
+       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
+       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
+       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
+       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
+       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
+       "\n",
+       "       zip                    title           timeStamp                twp  \\\n",
+       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:40:00        NEW HANOVER   \n",
+       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:40:00  HATFIELD TOWNSHIP   \n",
+       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 17:40:00         NORRISTOWN   \n",
+       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 17:40:01         NORRISTOWN   \n",
+       "4      NaN           EMS: DIZZINESS 2015-12-10 17:40:01   LOWER POTTSGROVE   \n",
+       "\n",
+       "                         addr  e Reason  Hour  Month  Day of Week  \n",
+       "0      REINDEER CT & DEAD END  1    EMS    17     12            3  \n",
+       "1  BRIAR PATH & WHITEMARSH LN  1    EMS    17     12            3  \n",
+       "2                    HAWS AVE  1   Fire    17     12            3  \n",
+       "3          AIRY ST & SWEDE ST  1    EMS    17     12            3  \n",
+       "4    CHERRYWOOD CT & DEAD END  1    EMS    17     12            3  "
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "d= {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}\n",
+    "df['Day of Week']= df['Day of Week'].map(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>desc</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>title</th>\n",
+       "      <th>timeStamp</th>\n",
+       "      <th>twp</th>\n",
+       "      <th>addr</th>\n",
+       "      <th>e</th>\n",
+       "      <th>Reason</th>\n",
+       "      <th>Hour</th>\n",
+       "      <th>Month</th>\n",
+       "      <th>Day of Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>40.297876</td>\n",
+       "      <td>-75.581294</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
+       "      <td>19525.0</td>\n",
+       "      <td>EMS: BACK PAINS/INJURY</td>\n",
+       "      <td>2015-12-10 17:40:00</td>\n",
+       "      <td>NEW HANOVER</td>\n",
+       "      <td>REINDEER CT &amp; DEAD END</td>\n",
+       "      <td>1</td>\n",
+       "      <td>EMS</td>\n",
+       "      <td>17</td>\n",
+       "      <td>12</td>\n",
+       "      <td>Thu</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         lat        lng                                               desc  \\\n",
+       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
+       "\n",
+       "       zip                   title           timeStamp          twp  \\\n",
+       "0  19525.0  EMS: BACK PAINS/INJURY 2015-12-10 17:40:00  NEW HANOVER   \n",
+       "\n",
+       "                     addr  e Reason  Hour  Month Day of Week  \n",
+       "0  REINDEER CT & DEAD END  1    EMS    17     12         Thu  "
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c90aca2f28>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# number of calls based on day of week\n",
+    "sns.countplot(df['Day of Week'], hue=df['Reason'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c90adab940>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(df['Month'], hue=df['Reason'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>desc</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>title</th>\n",
+       "      <th>timeStamp</th>\n",
+       "      <th>twp</th>\n",
+       "      <th>addr</th>\n",
+       "      <th>e</th>\n",
+       "      <th>Reason</th>\n",
+       "      <th>Hour</th>\n",
+       "      <th>Day of Week</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>11527</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13203</td>\n",
+       "      <td>13096</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>9930</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11465</td>\n",
+       "      <td>11396</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>9755</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11092</td>\n",
+       "      <td>11059</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>9895</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11323</td>\n",
+       "      <td>11283</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>9946</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11420</td>\n",
+       "      <td>11378</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>10212</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11777</td>\n",
+       "      <td>11732</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>10633</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12133</td>\n",
+       "      <td>12088</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>7832</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9073</td>\n",
+       "      <td>9025</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>6907</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7963</td>\n",
+       "      <td>7916</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
+       "Month                                                                      \n",
+       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
+       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
+       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
+       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
+       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
+       "6      11786  11786  11786  10212  11786      11786  11777  11732  11786   \n",
+       "7      12137  12137  12137  10633  12137      12137  12133  12088  12137   \n",
+       "8       9078   9078   9078   7832   9078       9078   9073   9025   9078   \n",
+       "12      7969   7969   7969   6907   7969       7969   7963   7916   7969   \n",
+       "\n",
+       "       Reason   Hour  Day of Week  \n",
+       "Month                              \n",
+       "1       13205  13205        13205  \n",
+       "2       11467  11467        11467  \n",
+       "3       11101  11101        11101  \n",
+       "4       11326  11326        11326  \n",
+       "5       11423  11423        11423  \n",
+       "6       11786  11786        11786  \n",
+       "7       12137  12137        12137  \n",
+       "8        9078   9078         9078  \n",
+       "12       7969   7969         7969  "
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "byMonth=df.groupby('Month').agg('count')\n",
+    "byMonth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1c90ae21780>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "byMonth['lat'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## lmplot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>desc</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>title</th>\n",
+       "      <th>timeStamp</th>\n",
+       "      <th>twp</th>\n",
+       "      <th>addr</th>\n",
+       "      <th>e</th>\n",
+       "      <th>Reason</th>\n",
+       "      <th>Hour</th>\n",
+       "      <th>Day of Week</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Month</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>11527</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13203</td>\n",
+       "      <td>13096</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>9930</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11465</td>\n",
+       "      <td>11396</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>9755</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11092</td>\n",
+       "      <td>11059</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>9895</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11323</td>\n",
+       "      <td>11283</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>9946</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11420</td>\n",
+       "      <td>11378</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>10212</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11777</td>\n",
+       "      <td>11732</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>10633</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12133</td>\n",
+       "      <td>12088</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>7832</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9073</td>\n",
+       "      <td>9025</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>6907</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7963</td>\n",
+       "      <td>7916</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
+       "Month                                                                      \n",
+       "1      13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
+       "2      11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
+       "3      11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
+       "4      11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
+       "5      11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
+       "6      11786  11786  11786  10212  11786      11786  11777  11732  11786   \n",
+       "7      12137  12137  12137  10633  12137      12137  12133  12088  12137   \n",
+       "8       9078   9078   9078   7832   9078       9078   9073   9025   9078   \n",
+       "12      7969   7969   7969   6907   7969       7969   7963   7916   7969   \n",
+       "\n",
+       "       Reason   Hour  Day of Week  \n",
+       "Month                              \n",
+       "1       13205  13205        13205  \n",
+       "2       11467  11467        11467  \n",
+       "3       11101  11101        11101  \n",
+       "4       11326  11326        11326  \n",
+       "5       11423  11423        11423  \n",
+       "6       11786  11786        11786  \n",
+       "7       12137  12137        12137  \n",
+       "8        9078   9078         9078  \n",
+       "12       7969   7969         7969  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "byMonth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Month</th>\n",
+       "      <th>lat</th>\n",
+       "      <th>lng</th>\n",
+       "      <th>desc</th>\n",
+       "      <th>zip</th>\n",
+       "      <th>title</th>\n",
+       "      <th>timeStamp</th>\n",
+       "      <th>twp</th>\n",
+       "      <th>addr</th>\n",
+       "      <th>e</th>\n",
+       "      <th>Reason</th>\n",
+       "      <th>Hour</th>\n",
+       "      <th>Day of Week</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>11527</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13203</td>\n",
+       "      <td>13096</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "      <td>13205</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>9930</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11465</td>\n",
+       "      <td>11396</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "      <td>11467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>9755</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11092</td>\n",
+       "      <td>11059</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "      <td>11101</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>9895</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11323</td>\n",
+       "      <td>11283</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "      <td>11326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>9946</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11420</td>\n",
+       "      <td>11378</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "      <td>11423</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>10212</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11777</td>\n",
+       "      <td>11732</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "      <td>11786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>10633</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12133</td>\n",
+       "      <td>12088</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "      <td>12137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>7832</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9073</td>\n",
+       "      <td>9025</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "      <td>9078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>12</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>6907</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7963</td>\n",
+       "      <td>7916</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "      <td>7969</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Month    lat    lng   desc    zip  title  timeStamp    twp   addr      e  \\\n",
+       "0      1  13205  13205  13205  11527  13205      13205  13203  13096  13205   \n",
+       "1      2  11467  11467  11467   9930  11467      11467  11465  11396  11467   \n",
+       "2      3  11101  11101  11101   9755  11101      11101  11092  11059  11101   \n",
+       "3      4  11326  11326  11326   9895  11326      11326  11323  11283  11326   \n",
+       "4      5  11423  11423  11423   9946  11423      11423  11420  11378  11423   \n",
+       "5      6  11786  11786  11786  10212  11786      11786  11777  11732  11786   \n",
+       "6      7  12137  12137  12137  10633  12137      12137  12133  12088  12137   \n",
+       "7      8   9078   9078   9078   7832   9078       9078   9073   9025   9078   \n",
+       "8     12   7969   7969   7969   6907   7969       7969   7963   7916   7969   \n",
+       "\n",
+       "   Reason   Hour  Day of Week  \n",
+       "0   13205  13205        13205  \n",
+       "1   11467  11467        11467  \n",
+       "2   11101  11101        11101  \n",
+       "3   11326  11326        11326  \n",
+       "4   11423  11423        11423  \n",
+       "5   11786  11786        11786  \n",
+       "6   12137  12137        12137  \n",
+       "7    9078   9078         9078  \n",
+       "8    7969   7969         7969  "
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "byMonth.reset_index(inplace=True)\n",
+    "byMonth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\lenovo\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
+      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x1c90ad8c470>"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 360x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(data=byMonth, x='Month', y='lat')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Date']= df['timeStamp'].apply(lambda x: x.date())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2015-12-10\n",
+       "1    2015-12-10\n",
+       "Name: Date, dtype: object"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Date'].head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1c90afef710>"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df[df['Reason']=='Fire'].groupby('Date').agg('count')['lat'].plot(label='Fire')\n",
+    "df[df['Reason']=='EMS'].groupby('Date').agg('count')['lat'].plot(label='EMS')\n",
+    "df[df['Reason']=='Traffic'].groupby('Date').agg('count')['lat'].plot(label='Traffic')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}