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+ {
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+ "metadata": {
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+ "language_info": {
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+ "codemirror_mode": {
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+ "name": "python",
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+ "version": 3
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+ },
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+ "file_extension": ".py",
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+ "mimetype": "text/x-python",
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+ "name": "python",
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+ "nbconvert_exporter": "python",
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+ "pygments_lexer": "ipython3",
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+ "version": "3.8"
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+ },
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+ "kernelspec": {
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+ "name": "python",
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+ "display_name": "Python (Pyodide)",
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+ "language": "python"
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+ }
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+ },
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+ "nbformat_minor": 4,
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+ "nbformat": 4,
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+ "cells": [
24
+ {
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+ "cell_type": "code",
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+ "source": "import pandas as pd",
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+ "metadata": {
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+ "trusted": true
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+ },
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+ "execution_count": 1,
31
+ "outputs": []
32
+ },
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+ {
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+ "cell_type": "code",
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+ "source": "df=[1,2,3,4]\nprint(pd.DataFrame(df))",
36
+ "metadata": {
37
+ "trusted": true
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+ },
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+ "execution_count": 2,
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "text": " 0\n0 1\n1 2\n2 3\n3 4\n",
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+ "output_type": "stream"
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": "print(pd.Series(df))",
51
+ "metadata": {
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+ "trusted": true
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+ },
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+ "execution_count": 3,
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+ "outputs": [
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+ {
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+ "name": "stdout",
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+ "text": "0 1\n1 2\n2 3\n3 4\ndtype: int64\n",
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+ "output_type": "stream"
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": "Employ=pd.read_csv(\"employees.csv\")",
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+ "metadata": {
67
+ "trusted": true
68
+ },
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+ "execution_count": 115,
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+ "outputs": []
71
+ },
72
+ {
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+ "cell_type": "code",
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+ "source": "Employ_dub=Employ.head(20)",
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+ "metadata": {
76
+ "trusted": true
77
+ },
78
+ "execution_count": 116,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": "Employ_dub",
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+ "metadata": {
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+ "trusted": true
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+ },
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+ "execution_count": 117,
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+ "outputs": [
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+ {
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+ "execution_count": 117,
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": " First Name Gender Start Date Last Login Time Salary Bonus % \\\n0 Douglas Male 8/6/1993 12:42 PM 97308 6.945 \n1 Thomas Male 3/31/1996 6:53 AM 61933 4.170 \n2 Maria Female 4/23/1993 11:17 AM 130590 11.858 \n3 Jerry Male 3/4/2005 1:00 PM 138705 9.340 \n4 Larry Male 1/24/1998 4:47 PM 101004 1.389 \n5 Dennis Male 4/18/1987 1:35 AM 115163 10.125 \n6 Ruby Female 8/17/1987 4:20 PM 65476 10.012 \n7 NaN Female 7/20/2015 10:43 AM 45906 11.598 \n8 Angela Female 11/22/2005 6:29 AM 95570 18.523 \n9 Frances Female 8/8/2002 6:51 AM 139852 7.524 \n10 Louise Female 8/12/1980 9:01 AM 63241 15.132 \n11 Julie Female 10/26/1997 3:19 PM 102508 12.637 \n12 Brandon Male 12/1/1980 1:08 AM 112807 17.492 \n13 Gary Male 1/27/2008 11:40 PM 109831 5.831 \n14 Kimberly Female 1/14/1999 7:13 AM 41426 14.543 \n15 Lillian Female 6/5/2016 6:09 AM 59414 1.256 \n16 Jeremy Male 9/21/2010 5:56 AM 90370 7.369 \n17 Shawn Male 12/7/1986 7:45 PM 111737 6.414 \n18 Diana Female 10/23/1981 10:27 AM 132940 19.082 \n19 Donna Female 7/22/2010 3:48 AM 81014 1.894 \n\n Senior Management Team \n0 True Marketing \n1 True NaN \n2 False Finance \n3 True Finance \n4 True Client Services \n5 False Legal \n6 True Product \n7 NaN Finance \n8 True Engineering \n9 True Business Development \n10 True NaN \n11 True Legal \n12 True Human Resources \n13 False Sales \n14 True Finance \n15 False Product \n16 False Human Resources \n17 False Product \n18 False Client Services \n19 False Product ",
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+ "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>First Name</th>\n <th>Gender</th>\n <th>Start Date</th>\n <th>Last Login Time</th>\n <th>Salary</th>\n <th>Bonus %</th>\n <th>Senior Management</th>\n <th>Team</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Douglas</td>\n <td>Male</td>\n <td>8/6/1993</td>\n <td>12:42 PM</td>\n <td>97308</td>\n <td>6.945</td>\n <td>True</td>\n <td>Marketing</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Thomas</td>\n <td>Male</td>\n <td>3/31/1996</td>\n <td>6:53 AM</td>\n <td>61933</td>\n <td>4.170</td>\n <td>True</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Maria</td>\n <td>Female</td>\n <td>4/23/1993</td>\n <td>11:17 AM</td>\n <td>130590</td>\n <td>11.858</td>\n <td>False</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Jerry</td>\n <td>Male</td>\n <td>3/4/2005</td>\n <td>1:00 PM</td>\n <td>138705</td>\n <td>9.340</td>\n <td>True</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Larry</td>\n <td>Male</td>\n <td>1/24/1998</td>\n <td>4:47 PM</td>\n <td>101004</td>\n <td>1.389</td>\n <td>True</td>\n <td>Client Services</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Dennis</td>\n <td>Male</td>\n <td>4/18/1987</td>\n <td>1:35 AM</td>\n <td>115163</td>\n <td>10.125</td>\n <td>False</td>\n <td>Legal</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Ruby</td>\n <td>Female</td>\n <td>8/17/1987</td>\n <td>4:20 PM</td>\n <td>65476</td>\n <td>10.012</td>\n <td>True</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>7</th>\n <td>NaN</td>\n <td>Female</td>\n <td>7/20/2015</td>\n <td>10:43 AM</td>\n <td>45906</td>\n <td>11.598</td>\n <td>NaN</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Angela</td>\n <td>Female</td>\n <td>11/22/2005</td>\n <td>6:29 AM</td>\n <td>95570</td>\n <td>18.523</td>\n <td>True</td>\n <td>Engineering</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Frances</td>\n <td>Female</td>\n <td>8/8/2002</td>\n <td>6:51 AM</td>\n <td>139852</td>\n <td>7.524</td>\n <td>True</td>\n <td>Business Development</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Louise</td>\n <td>Female</td>\n <td>8/12/1980</td>\n <td>9:01 AM</td>\n <td>63241</td>\n <td>15.132</td>\n <td>True</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Julie</td>\n <td>Female</td>\n <td>10/26/1997</td>\n <td>3:19 PM</td>\n <td>102508</td>\n <td>12.637</td>\n <td>True</td>\n <td>Legal</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Brandon</td>\n <td>Male</td>\n <td>12/1/1980</td>\n <td>1:08 AM</td>\n <td>112807</td>\n <td>17.492</td>\n <td>True</td>\n <td>Human Resources</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Gary</td>\n <td>Male</td>\n <td>1/27/2008</td>\n <td>11:40 PM</td>\n <td>109831</td>\n <td>5.831</td>\n <td>False</td>\n <td>Sales</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Kimberly</td>\n <td>Female</td>\n <td>1/14/1999</td>\n <td>7:13 AM</td>\n <td>41426</td>\n <td>14.543</td>\n <td>True</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Lillian</td>\n <td>Female</td>\n <td>6/5/2016</td>\n <td>6:09 AM</td>\n <td>59414</td>\n <td>1.256</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Jeremy</td>\n <td>Male</td>\n <td>9/21/2010</td>\n <td>5:56 AM</td>\n <td>90370</td>\n <td>7.369</td>\n <td>False</td>\n <td>Human Resources</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Shawn</td>\n <td>Male</td>\n <td>12/7/1986</td>\n <td>7:45 PM</td>\n <td>111737</td>\n <td>6.414</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Diana</td>\n <td>Female</td>\n <td>10/23/1981</td>\n <td>10:27 AM</td>\n <td>132940</td>\n <td>19.082</td>\n <td>False</td>\n <td>Client Services</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Donna</td>\n <td>Female</td>\n <td>7/22/2010</td>\n <td>3:48 AM</td>\n <td>81014</td>\n <td>1.894</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n </tbody>\n</table>\n</div>"
95
+ },
96
+ "metadata": {}
97
+ }
98
+ ]
99
+ },
100
+ {
101
+ "cell_type": "code",
102
+ "source": "",
103
+ "metadata": {
104
+ "trusted": true
105
+ },
106
+ "execution_count": null,
107
+ "outputs": []
108
+ },
109
+ {
110
+ "cell_type": "code",
111
+ "source": "#total info about the employee\nEmploy_dub.info()",
112
+ "metadata": {
113
+ "trusted": true
114
+ },
115
+ "execution_count": 26,
116
+ "outputs": [
117
+ {
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+ "name": "stdout",
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+ "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20 entries, 0 to 19\nData columns (total 8 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 First Name 19 non-null object \n 1 Gender 20 non-null object \n 2 Start Date 20 non-null object \n 3 Last Login Time 20 non-null object \n 4 Salary 20 non-null int64 \n 5 Bonus % 20 non-null float64\n 6 Senior Management 19 non-null object \n 7 Team 18 non-null object \ndtypes: float64(1), int64(1), object(6)\nmemory usage: 868.0+ bytes\n",
120
+ "output_type": "stream"
121
+ }
122
+ ]
123
+ },
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+ {
125
+ "cell_type": "code",
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+ "source": "Employ_dub.isnull()",
127
+ "metadata": {
128
+ "trusted": true
129
+ },
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+ "execution_count": 27,
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+ "outputs": [
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+ {
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+ "execution_count": 27,
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": " First Name Gender Start Date Last Login Time Salary Bonus % \\\n0 False False False False False False \n1 False False False False False False \n2 False False False False False False \n3 False False False False False False \n4 False False False False False False \n5 False False False False False False \n6 False False False False False False \n7 True False False False False False \n8 False False False False False False \n9 False False False False False False \n10 False False False False False False \n11 False False False False False False \n12 False False False False False False \n13 False False False False False False \n14 False False False False False False \n15 False False False False False False \n16 False False False False False False \n17 False False False False False False \n18 False False False False False False \n19 False False False False False False \n\n Senior Management Team \n0 False False \n1 False True \n2 False False \n3 False False \n4 False False \n5 False False \n6 False False \n7 True False \n8 False False \n9 False False \n10 False True \n11 False False \n12 False False \n13 False False \n14 False False \n15 False False \n16 False False \n17 False False \n18 False False \n19 False False ",
137
+ "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>First Name</th>\n <th>Gender</th>\n <th>Start Date</th>\n <th>Last Login Time</th>\n <th>Salary</th>\n <th>Bonus %</th>\n <th>Senior Management</th>\n <th>Team</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>2</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>4</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>5</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>6</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>7</th>\n <td>True</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n <td>False</td>\n </tr>\n <tr>\n <th>8</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>9</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>10</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>True</td>\n </tr>\n <tr>\n <th>11</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>12</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>13</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>14</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>15</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>16</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>17</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>18</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>19</th>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>"
138
+ },
139
+ "metadata": {}
140
+ }
141
+ ]
142
+ },
143
+ {
144
+ "cell_type": "code",
145
+ "source": "#checking for the null values in the dataset of employee\nEmploy_dub.isnull().sum()",
146
+ "metadata": {
147
+ "trusted": true
148
+ },
149
+ "execution_count": 28,
150
+ "outputs": [
151
+ {
152
+ "execution_count": 28,
153
+ "output_type": "execute_result",
154
+ "data": {
155
+ "text/plain": "First Name 1\nGender 0\nStart Date 0\nLast Login Time 0\nSalary 0\nBonus % 0\nSenior Management 1\nTeam 2\ndtype: int64"
156
+ },
157
+ "metadata": {}
158
+ }
159
+ ]
160
+ },
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+ {
162
+ "cell_type": "code",
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+ "source": "#changing the name of the dataset\ned=Employ_dub",
164
+ "metadata": {
165
+ "trusted": true
166
+ },
167
+ "execution_count": 29,
168
+ "outputs": []
169
+ },
170
+ {
171
+ "cell_type": "code",
172
+ "source": "#dimension of the dataset\ned.shape",
173
+ "metadata": {
174
+ "trusted": true
175
+ },
176
+ "execution_count": 30,
177
+ "outputs": [
178
+ {
179
+ "execution_count": 30,
180
+ "output_type": "execute_result",
181
+ "data": {
182
+ "text/plain": "(20, 8)"
183
+ },
184
+ "metadata": {}
185
+ }
186
+ ]
187
+ },
188
+ {
189
+ "cell_type": "code",
190
+ "source": "ed.columns",
191
+ "metadata": {
192
+ "trusted": true
193
+ },
194
+ "execution_count": 31,
195
+ "outputs": [
196
+ {
197
+ "execution_count": 31,
198
+ "output_type": "execute_result",
199
+ "data": {
200
+ "text/plain": "Index(['First Name', 'Gender', 'Start Date', 'Last Login Time', 'Salary',\n 'Bonus %', 'Senior Management', 'Team'],\n dtype='object')"
201
+ },
202
+ "metadata": {}
203
+ }
204
+ ]
205
+ },
206
+ {
207
+ "cell_type": "code",
208
+ "source": "#working on the dictionary for a while:",
209
+ "metadata": {
210
+ "trusted": true
211
+ },
212
+ "execution_count": 32,
213
+ "outputs": []
214
+ },
215
+ {
216
+ "cell_type": "code",
217
+ "source": "#creating test objects:\nimport numpy as np\nff=pd.DataFrame(np.random.rand(20,5))",
218
+ "metadata": {
219
+ "trusted": true
220
+ },
221
+ "execution_count": 39,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
226
+ "source": "ff",
227
+ "metadata": {
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+ "trusted": true
229
+ },
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+ "execution_count": 40,
231
+ "outputs": [
232
+ {
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+ "execution_count": 40,
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+ "output_type": "execute_result",
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+ "data": {
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237
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238
+ },
239
+ "metadata": {}
240
+ }
241
+ ]
242
+ },
243
+ {
244
+ "cell_type": "code",
245
+ "source": "ff.info()",
246
+ "metadata": {
247
+ "trusted": true
248
+ },
249
+ "execution_count": 41,
250
+ "outputs": [
251
+ {
252
+ "name": "stdout",
253
+ "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 20 entries, 0 to 19\nData columns (total 5 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 0 20 non-null float64\n 1 1 20 non-null float64\n 2 2 20 non-null float64\n 3 3 20 non-null float64\n 4 4 20 non-null float64\ndtypes: float64(5)\nmemory usage: 868.0 bytes\n",
254
+ "output_type": "stream"
255
+ }
256
+ ]
257
+ },
258
+ {
259
+ "cell_type": "code",
260
+ "source": "#data functon:\ndate=pd.DataFrame(\n{\n\"date\":['10/9/2020','11/09/2020','12/09/2020'],\n\"students\":[10,20,30]})\n",
261
+ "metadata": {
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+ "trusted": true
263
+ },
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+ "execution_count": 43,
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+ "outputs": [
266
+ {
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+ "execution_count": 43,
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+ "output_type": "execute_result",
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+ "data": {
270
+ "text/plain": " date students\n0 10/9/2020 10\n1 11/09/2020 20\n2 12/09/2020 30",
271
+ "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>date</th>\n <th>students</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>10/9/2020</td>\n <td>10</td>\n </tr>\n <tr>\n <th>1</th>\n <td>11/09/2020</td>\n <td>20</td>\n </tr>\n <tr>\n <th>2</th>\n <td>12/09/2020</td>\n <td>30</td>\n </tr>\n </tbody>\n</table>\n</div>"
272
+ },
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+ "metadata": {}
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+ }
275
+ ]
276
+ },
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+ {
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+ "cell_type": "code",
279
+ "source": "ed.value_counts()",
280
+ "metadata": {
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+ "trusted": true
282
+ },
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+ "execution_count": 52,
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+ "outputs": [
285
+ {
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+ "execution_count": 52,
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": "First Name Gender Start Date Last Login Time Salary Bonus % Senior Management Team \nAngela Female 11/22/2005 6:29 AM 95570 18.523 True Engineering 1\nJerry Male 3/4/2005 1:00 PM 138705 9.340 True Finance 1\nRuby Female 8/17/1987 4:20 PM 65476 10.012 True Product 1\nMaria Female 4/23/1993 11:17 AM 130590 11.858 False Finance 1\nLillian Female 6/5/2016 6:09 AM 59414 1.256 False Product 1\nLarry Male 1/24/1998 4:47 PM 101004 1.389 True Client Services 1\nKimberly Female 1/14/1999 7:13 AM 41426 14.543 True Finance 1\nJulie Female 10/26/1997 3:19 PM 102508 12.637 True Legal 1\nJeremy Male 9/21/2010 5:56 AM 90370 7.369 False Human Resources 1\nBrandon Male 12/1/1980 1:08 AM 112807 17.492 True Human Resources 1\nGary Male 1/27/2008 11:40 PM 109831 5.831 False Sales 1\nFrances Female 8/8/2002 6:51 AM 139852 7.524 True Business Development 1\nDouglas Male 8/6/1993 12:42 PM 97308 6.945 True Marketing 1\nDonna Female 7/22/2010 3:48 AM 81014 1.894 False Product 1\nDiana Female 10/23/1981 10:27 AM 132940 19.082 False Client Services 1\nDennis Male 4/18/1987 1:35 AM 115163 10.125 False Legal 1\nShawn Male 12/7/1986 7:45 PM 111737 6.414 False Product 1\ndtype: int64"
290
+ },
291
+ "metadata": {}
292
+ }
293
+ ]
294
+ },
295
+ {
296
+ "cell_type": "code",
297
+ "source": "ed[['Gender','Salary']]",
298
+ "metadata": {
299
+ "trusted": true
300
+ },
301
+ "execution_count": 62,
302
+ "outputs": [
303
+ {
304
+ "execution_count": 62,
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+ "output_type": "execute_result",
306
+ "data": {
307
+ "text/plain": " Gender Salary\n0 Male 97308\n1 Male 61933\n2 Female 130590\n3 Male 138705\n4 Male 101004\n5 Male 115163\n6 Female 65476\n7 Female 45906\n8 Female 95570\n9 Female 139852\n10 Female 63241\n11 Female 102508\n12 Male 112807\n13 Male 109831\n14 Female 41426\n15 Female 59414\n16 Male 90370\n17 Male 111737\n18 Female 132940\n19 Female 81014",
308
+ "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>Gender</th>\n <th>Salary</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Male</td>\n <td>97308</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Male</td>\n <td>61933</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Female</td>\n <td>130590</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Male</td>\n <td>138705</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Male</td>\n <td>101004</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Male</td>\n <td>115163</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Female</td>\n <td>65476</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Female</td>\n <td>45906</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Female</td>\n <td>95570</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Female</td>\n <td>139852</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Female</td>\n <td>63241</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Female</td>\n <td>102508</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Male</td>\n <td>112807</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Male</td>\n <td>109831</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Female</td>\n <td>41426</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Female</td>\n <td>59414</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Male</td>\n <td>90370</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Male</td>\n <td>111737</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Female</td>\n <td>132940</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Female</td>\n <td>81014</td>\n </tr>\n </tbody>\n</table>\n</div>"
309
+ },
310
+ "metadata": {}
311
+ }
312
+ ]
313
+ },
314
+ {
315
+ "cell_type": "code",
316
+ "source": "#selection by position:rows data:\ned.iloc[8:12]",
317
+ "metadata": {
318
+ "trusted": true
319
+ },
320
+ "execution_count": 69,
321
+ "outputs": [
322
+ {
323
+ "execution_count": 69,
324
+ "output_type": "execute_result",
325
+ "data": {
326
+ "text/plain": " First Name Gender Start Date Last Login Time Salary Bonus % \\\n8 Angela Female 11/22/2005 6:29 AM 95570 18.523 \n9 Frances Female 8/8/2002 6:51 AM 139852 7.524 \n10 Louise Female 8/12/1980 9:01 AM 63241 15.132 \n11 Julie Female 10/26/1997 3:19 PM 102508 12.637 \n\n Senior Management Team \n8 True Engineering \n9 True Business Development \n10 True NaN \n11 True Legal ",
327
+ "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>First Name</th>\n <th>Gender</th>\n <th>Start Date</th>\n <th>Last Login Time</th>\n <th>Salary</th>\n <th>Bonus %</th>\n <th>Senior Management</th>\n <th>Team</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8</th>\n <td>Angela</td>\n <td>Female</td>\n <td>11/22/2005</td>\n <td>6:29 AM</td>\n <td>95570</td>\n <td>18.523</td>\n <td>True</td>\n <td>Engineering</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Frances</td>\n <td>Female</td>\n <td>8/8/2002</td>\n <td>6:51 AM</td>\n <td>139852</td>\n <td>7.524</td>\n <td>True</td>\n <td>Business Development</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Louise</td>\n <td>Female</td>\n <td>8/12/1980</td>\n <td>9:01 AM</td>\n <td>63241</td>\n <td>15.132</td>\n <td>True</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Julie</td>\n <td>Female</td>\n <td>10/26/1997</td>\n <td>3:19 PM</td>\n <td>102508</td>\n <td>12.637</td>\n <td>True</td>\n <td>Legal</td>\n </tr>\n </tbody>\n</table>\n</div>"
328
+ },
329
+ "metadata": {}
330
+ }
331
+ ]
332
+ },
333
+ {
334
+ "cell_type": "code",
335
+ "source": "ed.loc[8]",
336
+ "metadata": {
337
+ "trusted": true
338
+ },
339
+ "execution_count": 64,
340
+ "outputs": [
341
+ {
342
+ "execution_count": 64,
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+ "output_type": "execute_result",
344
+ "data": {
345
+ "text/plain": "First Name Angela\nGender Female\nStart Date 11/22/2005\nLast Login Time 6:29 AM\nSalary 95570\nBonus % 18.523\nSenior Management True\nTeam Engineering\nName: 8, dtype: object"
346
+ },
347
+ "metadata": {}
348
+ }
349
+ ]
350
+ },
351
+ {
352
+ "cell_type": "code",
353
+ "source": "#data cleaning:\ned.isnull().sum()",
354
+ "metadata": {
355
+ "trusted": true
356
+ },
357
+ "execution_count": 73,
358
+ "outputs": [
359
+ {
360
+ "execution_count": 73,
361
+ "output_type": "execute_result",
362
+ "data": {
363
+ "text/plain": "First Name 1\nGender 0\nStart Date 0\nLast Login Time 0\nSalary 0\nBonus % 0\nSenior Management 1\nTeam 2\ndtype: int64"
364
+ },
365
+ "metadata": {}
366
+ }
367
+ ]
368
+ },
369
+ {
370
+ "cell_type": "code",
371
+ "source": "#total null values:\ned.isnull().sum().sum()",
372
+ "metadata": {
373
+ "trusted": true
374
+ },
375
+ "execution_count": 74,
376
+ "outputs": [
377
+ {
378
+ "execution_count": 74,
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+ "output_type": "execute_result",
380
+ "data": {
381
+ "text/plain": "4"
382
+ },
383
+ "metadata": {}
384
+ }
385
+ ]
386
+ },
387
+ {
388
+ "cell_type": "code",
389
+ "source": "ed.notnull().sum().sum()",
390
+ "metadata": {
391
+ "trusted": true
392
+ },
393
+ "execution_count": 77,
394
+ "outputs": [
395
+ {
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+ "execution_count": 77,
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": "156"
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+ },
401
+ "metadata": {}
402
+ }
403
+ ]
404
+ },
405
+ {
406
+ "cell_type": "code",
407
+ "source": "#fpr practice on drop we will take the copy of the original data:\ned2=ed",
408
+ "metadata": {
409
+ "trusted": true
410
+ },
411
+ "execution_count": 78,
412
+ "outputs": []
413
+ },
414
+ {
415
+ "cell_type": "code",
416
+ "source": "ed2",
417
+ "metadata": {
418
+ "trusted": true
419
+ },
420
+ "execution_count": 79,
421
+ "outputs": [
422
+ {
423
+ "execution_count": 79,
424
+ "output_type": "execute_result",
425
+ "data": {
426
+ "text/plain": " First Name Gender Start Date Last Login Time Salary Bonus % \\\n0 Douglas Male 8/6/1993 12:42 PM 97308 6.945 \n1 Thomas Male 3/31/1996 6:53 AM 61933 4.170 \n2 Maria Female 4/23/1993 11:17 AM 130590 11.858 \n3 Jerry Male 3/4/2005 1:00 PM 138705 9.340 \n4 Larry Male 1/24/1998 4:47 PM 101004 1.389 \n5 Dennis Male 4/18/1987 1:35 AM 115163 10.125 \n6 Ruby Female 8/17/1987 4:20 PM 65476 10.012 \n7 NaN Female 7/20/2015 10:43 AM 45906 11.598 \n8 Angela Female 11/22/2005 6:29 AM 95570 18.523 \n9 Frances Female 8/8/2002 6:51 AM 139852 7.524 \n10 Louise Female 8/12/1980 9:01 AM 63241 15.132 \n11 Julie Female 10/26/1997 3:19 PM 102508 12.637 \n12 Brandon Male 12/1/1980 1:08 AM 112807 17.492 \n13 Gary Male 1/27/2008 11:40 PM 109831 5.831 \n14 Kimberly Female 1/14/1999 7:13 AM 41426 14.543 \n15 Lillian Female 6/5/2016 6:09 AM 59414 1.256 \n16 Jeremy Male 9/21/2010 5:56 AM 90370 7.369 \n17 Shawn Male 12/7/1986 7:45 PM 111737 6.414 \n18 Diana Female 10/23/1981 10:27 AM 132940 19.082 \n19 Donna Female 7/22/2010 3:48 AM 81014 1.894 \n\n Senior Management Team \n0 True Marketing \n1 True NaN \n2 False Finance \n3 True Finance \n4 True Client Services \n5 False Legal \n6 True Product \n7 NaN Finance \n8 True Engineering \n9 True Business Development \n10 True NaN \n11 True Legal \n12 True Human Resources \n13 False Sales \n14 True Finance \n15 False Product \n16 False Human Resources \n17 False Product \n18 False Client Services \n19 False Product ",
427
+ "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>First Name</th>\n <th>Gender</th>\n <th>Start Date</th>\n <th>Last Login Time</th>\n <th>Salary</th>\n <th>Bonus %</th>\n <th>Senior Management</th>\n <th>Team</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Douglas</td>\n <td>Male</td>\n <td>8/6/1993</td>\n <td>12:42 PM</td>\n <td>97308</td>\n <td>6.945</td>\n <td>True</td>\n <td>Marketing</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Thomas</td>\n <td>Male</td>\n <td>3/31/1996</td>\n <td>6:53 AM</td>\n <td>61933</td>\n <td>4.170</td>\n <td>True</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Maria</td>\n <td>Female</td>\n <td>4/23/1993</td>\n <td>11:17 AM</td>\n <td>130590</td>\n <td>11.858</td>\n <td>False</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Jerry</td>\n <td>Male</td>\n <td>3/4/2005</td>\n <td>1:00 PM</td>\n <td>138705</td>\n <td>9.340</td>\n <td>True</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Larry</td>\n <td>Male</td>\n <td>1/24/1998</td>\n <td>4:47 PM</td>\n <td>101004</td>\n <td>1.389</td>\n <td>True</td>\n <td>Client Services</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Dennis</td>\n <td>Male</td>\n <td>4/18/1987</td>\n <td>1:35 AM</td>\n <td>115163</td>\n <td>10.125</td>\n <td>False</td>\n <td>Legal</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Ruby</td>\n <td>Female</td>\n <td>8/17/1987</td>\n <td>4:20 PM</td>\n <td>65476</td>\n <td>10.012</td>\n <td>True</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>7</th>\n <td>NaN</td>\n <td>Female</td>\n <td>7/20/2015</td>\n <td>10:43 AM</td>\n <td>45906</td>\n <td>11.598</td>\n <td>NaN</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Angela</td>\n <td>Female</td>\n <td>11/22/2005</td>\n <td>6:29 AM</td>\n <td>95570</td>\n <td>18.523</td>\n <td>True</td>\n <td>Engineering</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Frances</td>\n <td>Female</td>\n <td>8/8/2002</td>\n <td>6:51 AM</td>\n <td>139852</td>\n <td>7.524</td>\n <td>True</td>\n <td>Business Development</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Louise</td>\n <td>Female</td>\n <td>8/12/1980</td>\n <td>9:01 AM</td>\n <td>63241</td>\n <td>15.132</td>\n <td>True</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Julie</td>\n <td>Female</td>\n <td>10/26/1997</td>\n <td>3:19 PM</td>\n <td>102508</td>\n <td>12.637</td>\n <td>True</td>\n <td>Legal</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Brandon</td>\n <td>Male</td>\n <td>12/1/1980</td>\n <td>1:08 AM</td>\n <td>112807</td>\n <td>17.492</td>\n <td>True</td>\n <td>Human Resources</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Gary</td>\n <td>Male</td>\n <td>1/27/2008</td>\n <td>11:40 PM</td>\n <td>109831</td>\n <td>5.831</td>\n <td>False</td>\n <td>Sales</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Kimberly</td>\n <td>Female</td>\n <td>1/14/1999</td>\n <td>7:13 AM</td>\n <td>41426</td>\n <td>14.543</td>\n <td>True</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Lillian</td>\n <td>Female</td>\n <td>6/5/2016</td>\n <td>6:09 AM</td>\n <td>59414</td>\n <td>1.256</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Jeremy</td>\n <td>Male</td>\n <td>9/21/2010</td>\n <td>5:56 AM</td>\n <td>90370</td>\n <td>7.369</td>\n <td>False</td>\n <td>Human Resources</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Shawn</td>\n <td>Male</td>\n <td>12/7/1986</td>\n <td>7:45 PM</td>\n <td>111737</td>\n <td>6.414</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Diana</td>\n <td>Female</td>\n <td>10/23/1981</td>\n <td>10:27 AM</td>\n <td>132940</td>\n <td>19.082</td>\n <td>False</td>\n <td>Client Services</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Donna</td>\n <td>Female</td>\n <td>7/22/2010</td>\n <td>3:48 AM</td>\n <td>81014</td>\n <td>1.894</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n </tbody>\n</table>\n</div>"
428
+ },
429
+ "metadata": {}
430
+ }
431
+ ]
432
+ },
433
+ {
434
+ "cell_type": "code",
435
+ "source": "#removing the totyal columns if they are with the null values:\ned3=ed2.dropna(axis=1)\nprint(\"prasent null values:\",ed3.isnull().sum().sum())",
436
+ "metadata": {
437
+ "trusted": true
438
+ },
439
+ "execution_count": 90,
440
+ "outputs": [
441
+ {
442
+ "name": "stdout",
443
+ "text": "prasent null values: 0\n",
444
+ "output_type": "stream"
445
+ }
446
+ ]
447
+ },
448
+ {
449
+ "cell_type": "code",
450
+ "source": "ed2.fillna(10,inplace=True)",
451
+ "metadata": {
452
+ "trusted": true
453
+ },
454
+ "execution_count": 96,
455
+ "outputs": [
456
+ {
457
+ "name": "stderr",
458
+ "text": "<ipython-input-96-9a2616dc4607>:1: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n ed2.fillna(10,inplace=True)\n",
459
+ "output_type": "stream"
460
+ }
461
+ ]
462
+ },
463
+ {
464
+ "cell_type": "code",
465
+ "source": "ed2.isnull().sum()",
466
+ "metadata": {
467
+ "trusted": true
468
+ },
469
+ "execution_count": 97,
470
+ "outputs": [
471
+ {
472
+ "execution_count": 97,
473
+ "output_type": "execute_result",
474
+ "data": {
475
+ "text/plain": "First Name 0\nGender 0\nStart Date 0\nLast Login Time 0\nSalary 0\nBonus % 0\nSenior Management 0\nTeam 0\ndtype: int64"
476
+ },
477
+ "metadata": {}
478
+ }
479
+ ]
480
+ },
481
+ {
482
+ "cell_type": "code",
483
+ "source": "ed2",
484
+ "metadata": {
485
+ "trusted": true
486
+ },
487
+ "execution_count": 98,
488
+ "outputs": [
489
+ {
490
+ "execution_count": 98,
491
+ "output_type": "execute_result",
492
+ "data": {
493
+ "text/plain": " First Name Gender Start Date Last Login Time Salary Bonus % \\\n0 Douglas Male 8/6/1993 12:42 PM 97308 6.945 \n1 Thomas Male 3/31/1996 6:53 AM 61933 4.170 \n2 Maria Female 4/23/1993 11:17 AM 130590 11.858 \n3 Jerry Male 3/4/2005 1:00 PM 138705 9.340 \n4 Larry Male 1/24/1998 4:47 PM 101004 1.389 \n5 Dennis Male 4/18/1987 1:35 AM 115163 10.125 \n6 Ruby Female 8/17/1987 4:20 PM 65476 10.012 \n7 10 Female 7/20/2015 10:43 AM 45906 11.598 \n8 Angela Female 11/22/2005 6:29 AM 95570 18.523 \n9 Frances Female 8/8/2002 6:51 AM 139852 7.524 \n10 Louise Female 8/12/1980 9:01 AM 63241 15.132 \n11 Julie Female 10/26/1997 3:19 PM 102508 12.637 \n12 Brandon Male 12/1/1980 1:08 AM 112807 17.492 \n13 Gary Male 1/27/2008 11:40 PM 109831 5.831 \n14 Kimberly Female 1/14/1999 7:13 AM 41426 14.543 \n15 Lillian Female 6/5/2016 6:09 AM 59414 1.256 \n16 Jeremy Male 9/21/2010 5:56 AM 90370 7.369 \n17 Shawn Male 12/7/1986 7:45 PM 111737 6.414 \n18 Diana Female 10/23/1981 10:27 AM 132940 19.082 \n19 Donna Female 7/22/2010 3:48 AM 81014 1.894 \n\n Senior Management Team \n0 True Marketing \n1 True 10 \n2 False Finance \n3 True Finance \n4 True Client Services \n5 False Legal \n6 True Product \n7 10 Finance \n8 True Engineering \n9 True Business Development \n10 True 10 \n11 True Legal \n12 True Human Resources \n13 False Sales \n14 True Finance \n15 False Product \n16 False Human Resources \n17 False Product \n18 False Client Services \n19 False Product ",
494
+ "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>First Name</th>\n <th>Gender</th>\n <th>Start Date</th>\n <th>Last Login Time</th>\n <th>Salary</th>\n <th>Bonus %</th>\n <th>Senior Management</th>\n <th>Team</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Douglas</td>\n <td>Male</td>\n <td>8/6/1993</td>\n <td>12:42 PM</td>\n <td>97308</td>\n <td>6.945</td>\n <td>True</td>\n <td>Marketing</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Thomas</td>\n <td>Male</td>\n <td>3/31/1996</td>\n <td>6:53 AM</td>\n <td>61933</td>\n <td>4.170</td>\n <td>True</td>\n <td>10</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Maria</td>\n <td>Female</td>\n <td>4/23/1993</td>\n <td>11:17 AM</td>\n <td>130590</td>\n <td>11.858</td>\n <td>False</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Jerry</td>\n <td>Male</td>\n <td>3/4/2005</td>\n <td>1:00 PM</td>\n <td>138705</td>\n <td>9.340</td>\n <td>True</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Larry</td>\n <td>Male</td>\n <td>1/24/1998</td>\n <td>4:47 PM</td>\n <td>101004</td>\n <td>1.389</td>\n <td>True</td>\n <td>Client Services</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Dennis</td>\n <td>Male</td>\n <td>4/18/1987</td>\n <td>1:35 AM</td>\n <td>115163</td>\n <td>10.125</td>\n <td>False</td>\n <td>Legal</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Ruby</td>\n <td>Female</td>\n <td>8/17/1987</td>\n <td>4:20 PM</td>\n <td>65476</td>\n <td>10.012</td>\n <td>True</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>7</th>\n <td>10</td>\n <td>Female</td>\n <td>7/20/2015</td>\n <td>10:43 AM</td>\n <td>45906</td>\n <td>11.598</td>\n <td>10</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Angela</td>\n <td>Female</td>\n <td>11/22/2005</td>\n <td>6:29 AM</td>\n <td>95570</td>\n <td>18.523</td>\n <td>True</td>\n <td>Engineering</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Frances</td>\n <td>Female</td>\n <td>8/8/2002</td>\n <td>6:51 AM</td>\n <td>139852</td>\n <td>7.524</td>\n <td>True</td>\n <td>Business Development</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Louise</td>\n <td>Female</td>\n <td>8/12/1980</td>\n <td>9:01 AM</td>\n <td>63241</td>\n <td>15.132</td>\n <td>True</td>\n <td>10</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Julie</td>\n <td>Female</td>\n <td>10/26/1997</td>\n <td>3:19 PM</td>\n <td>102508</td>\n <td>12.637</td>\n <td>True</td>\n <td>Legal</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Brandon</td>\n <td>Male</td>\n <td>12/1/1980</td>\n <td>1:08 AM</td>\n <td>112807</td>\n <td>17.492</td>\n <td>True</td>\n <td>Human Resources</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Gary</td>\n <td>Male</td>\n <td>1/27/2008</td>\n <td>11:40 PM</td>\n <td>109831</td>\n <td>5.831</td>\n <td>False</td>\n <td>Sales</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Kimberly</td>\n <td>Female</td>\n <td>1/14/1999</td>\n <td>7:13 AM</td>\n <td>41426</td>\n <td>14.543</td>\n <td>True</td>\n <td>Finance</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Lillian</td>\n <td>Female</td>\n <td>6/5/2016</td>\n <td>6:09 AM</td>\n <td>59414</td>\n <td>1.256</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Jeremy</td>\n <td>Male</td>\n <td>9/21/2010</td>\n <td>5:56 AM</td>\n <td>90370</td>\n <td>7.369</td>\n <td>False</td>\n <td>Human Resources</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Shawn</td>\n <td>Male</td>\n <td>12/7/1986</td>\n <td>7:45 PM</td>\n <td>111737</td>\n <td>6.414</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Diana</td>\n <td>Female</td>\n <td>10/23/1981</td>\n <td>10:27 AM</td>\n <td>132940</td>\n <td>19.082</td>\n <td>False</td>\n <td>Client Services</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Donna</td>\n <td>Female</td>\n <td>7/22/2010</td>\n <td>3:48 AM</td>\n <td>81014</td>\n <td>1.894</td>\n <td>False</td>\n <td>Product</td>\n </tr>\n </tbody>\n</table>\n</div>"
495
+ },
496
+ "metadata": {}
497
+ }
498
+ ]
499
+ },
500
+ {
501
+ "cell_type": "code",
502
+ "source": "ed5=Employ.head(20)\ned5",
503
+ "metadata": {
504
+ "trusted": true
505
+ },
506
+ "execution_count": 118,
507
+ "outputs": [
508
+ {
509
+ "execution_count": 118,
510
+ "output_type": "execute_result",
511
+ "data": {
512
+ "text/plain": " First Name Gender Start Date Last Login Time Salary Bonus % \\\n0 Douglas Male 8/6/1993 12:42 PM 97308 6.945 \n1 Thomas Male 3/31/1996 6:53 AM 61933 4.170 \n2 Maria Female 4/23/1993 11:17 AM 130590 11.858 \n3 Jerry Male 3/4/2005 1:00 PM 138705 9.340 \n4 Larry Male 1/24/1998 4:47 PM 101004 1.389 \n5 Dennis Male 4/18/1987 1:35 AM 115163 10.125 \n6 Ruby Female 8/17/1987 4:20 PM 65476 10.012 \n7 NaN Female 7/20/2015 10:43 AM 45906 11.598 \n8 Angela Female 11/22/2005 6:29 AM 95570 18.523 \n9 Frances Female 8/8/2002 6:51 AM 139852 7.524 \n10 Louise Female 8/12/1980 9:01 AM 63241 15.132 \n11 Julie Female 10/26/1997 3:19 PM 102508 12.637 \n12 Brandon Male 12/1/1980 1:08 AM 112807 17.492 \n13 Gary Male 1/27/2008 11:40 PM 109831 5.831 \n14 Kimberly Female 1/14/1999 7:13 AM 41426 14.543 \n15 Lillian Female 6/5/2016 6:09 AM 59414 1.256 \n16 Jeremy Male 9/21/2010 5:56 AM 90370 7.369 \n17 Shawn Male 12/7/1986 7:45 PM 111737 6.414 \n18 Diana Female 10/23/1981 10:27 AM 132940 19.082 \n19 Donna Female 7/22/2010 3:48 AM 81014 1.894 \n\n Senior Management Team \n0 True Marketing \n1 True NaN \n2 False Finance \n3 True Finance \n4 True Client Services \n5 False Legal \n6 True Product \n7 NaN Finance \n8 True Engineering \n9 True Business Development \n10 True NaN \n11 True Legal \n12 True Human Resources \n13 False Sales \n14 True Finance \n15 False Product \n16 False Human Resources \n17 False Product \n18 False Client Services \n19 False Product ",
513
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514
+ },
515
+ "metadata": {}
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+ }
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+ ]
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+ },
519
+ {
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+ "cell_type": "code",
521
+ "source": "ed2.isnull().sum()",
522
+ "metadata": {
523
+ "trusted": true
524
+ },
525
+ "execution_count": 111,
526
+ "outputs": [
527
+ {
528
+ "execution_count": 111,
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+ "output_type": "execute_result",
530
+ "data": {
531
+ "text/plain": "First Name 0\nGender 0\nStart Date 0\nLast Login Time 0\nSalary 0\nBonus % 0\nSenior Management 0\nTeam 0\ndtype: int64"
532
+ },
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+ "metadata": {}
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
539
+ "source": "date",
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+ "metadata": {
541
+ "trusted": true
542
+ },
543
+ "execution_count": 120,
544
+ "outputs": [
545
+ {
546
+ "execution_count": 120,
547
+ "output_type": "execute_result",
548
+ "data": {
549
+ "text/plain": " date students\n0 10/9/2020 10\n1 11/09/2020 20\n2 12/09/2020 30",
550
+ "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>date</th>\n <th>students</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>10/9/2020</td>\n <td>10</td>\n </tr>\n <tr>\n <th>1</th>\n <td>11/09/2020</td>\n <td>20</td>\n </tr>\n <tr>\n <th>2</th>\n <td>12/09/2020</td>\n <td>30</td>\n </tr>\n </tbody>\n</table>\n</div>"
551
+ },
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+ "metadata": {}
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+ }
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+ ]
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+ },
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+ {
557
+ "cell_type": "code",
558
+ "source": "date.dtypes",
559
+ "metadata": {
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+ "trusted": true
561
+ },
562
+ "execution_count": 124,
563
+ "outputs": [
564
+ {
565
+ "execution_count": 124,
566
+ "output_type": "execute_result",
567
+ "data": {
568
+ "text/plain": "date object\nstudents int64\ndtype: object"
569
+ },
570
+ "metadata": {}
571
+ }
572
+ ]
573
+ },
574
+ {
575
+ "cell_type": "code",
576
+ "source": "#rename for the date with the check:\ndate.rename(columns={'date':'check'})",
577
+ "metadata": {
578
+ "trusted": true
579
+ },
580
+ "execution_count": 131,
581
+ "outputs": [
582
+ {
583
+ "execution_count": 131,
584
+ "output_type": "execute_result",
585
+ "data": {
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+ "text/plain": " check students\n0 10/9/2020 10\n1 11/09/2020 20\n2 12/09/2020 30",
587
+ "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>check</th>\n <th>students</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>10/9/2020</td>\n <td>10</td>\n </tr>\n <tr>\n <th>1</th>\n <td>11/09/2020</td>\n <td>20</td>\n </tr>\n <tr>\n <th>2</th>\n <td>12/09/2020</td>\n <td>30</td>\n </tr>\n </tbody>\n</table>\n</div>"
588
+ },
589
+ "metadata": {}
590
+ }
591
+ ]
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+ },
593
+ {
594
+ "cell_type": "code",
595
+ "source": "date.sort_values('students',ascending=False)",
596
+ "metadata": {
597
+ "trusted": true
598
+ },
599
+ "execution_count": 134,
600
+ "outputs": [
601
+ {
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+ "execution_count": 134,
603
+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": " date students\n2 12/09/2020 30\n1 11/09/2020 20\n0 10/9/2020 10",
606
+ "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>date</th>\n <th>students</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2</th>\n <td>12/09/2020</td>\n <td>30</td>\n </tr>\n <tr>\n <th>1</th>\n <td>11/09/2020</td>\n <td>20</td>\n </tr>\n <tr>\n <th>0</th>\n <td>10/9/2020</td>\n <td>10</td>\n </tr>\n </tbody>\n</table>\n</div>"
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+ },
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+ "metadata": {}
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+ }
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+ ]
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+ },
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+ {
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+ "source": "",
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+ "metadata": {
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+ "trusted": true
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+ },
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+ "execution_count": null,
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+ "metadata": {},
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+ "execution_count": null,
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+ "outputs": []
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+ }
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+ ]
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+ }