{
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "python",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8"
    },
    "kernelspec": {
      "name": "python",
      "display_name": "Python (Pyodide)",
      "language": "python"
    }
  },
  "nbformat_minor": 4,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "code",
      "source": "import pandas as pd",
      "metadata": {
        "trusted": true
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "df=[1,2,3,4]\nprint(pd.DataFrame(df))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 2,
      "outputs": [
        {
          "name": "stdout",
          "text": "   0\n0  1\n1  2\n2  3\n3  4\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "print(pd.Series(df))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 3,
      "outputs": [
        {
          "name": "stdout",
          "text": "0    1\n1    2\n2    3\n3    4\ndtype: int64\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "Employ=pd.read_csv(\"employees.csv\")",
      "metadata": {
        "trusted": true
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "Employ_dub=Employ.head(20)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "Employ_dub",
      "metadata": {
        "trusted": true
      },
      "execution_count": 117,
      "outputs": [
        {
          "execution_count": 117,
          "output_type": "execute_result",
          "data": {
            "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  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "#total info about the employee\nEmploy_dub.info()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 26,
      "outputs": [
        {
          "name": "stdout",
          "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",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "Employ_dub.isnull()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 27,
      "outputs": [
        {
          "execution_count": 27,
          "output_type": "execute_result",
          "data": {
            "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  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#checking for the null values in the dataset of employee\nEmploy_dub.isnull().sum()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 28,
      "outputs": [
        {
          "execution_count": 28,
          "output_type": "execute_result",
          "data": {
            "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#changing the name of the dataset\ned=Employ_dub",
      "metadata": {
        "trusted": true
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "#dimension of the dataset\ned.shape",
      "metadata": {
        "trusted": true
      },
      "execution_count": 30,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "(20, 8)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed.columns",
      "metadata": {
        "trusted": true
      },
      "execution_count": 31,
      "outputs": [
        {
          "execution_count": 31,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Index(['First Name', 'Gender', 'Start Date', 'Last Login Time', 'Salary',\n       'Bonus %', 'Senior Management', 'Team'],\n      dtype='object')"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#working on the dictionary for a while:",
      "metadata": {
        "trusted": true
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "#creating test objects:\nimport numpy as np\nff=pd.DataFrame(np.random.rand(20,5))",
      "metadata": {
        "trusted": true
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "ff",
      "metadata": {
        "trusted": true
      },
      "execution_count": 40,
      "outputs": [
        {
          "execution_count": 40,
          "output_type": "execute_result",
          "data": {
            "text/plain": "           0         1         2         3         4\n0   0.020780  0.365190  0.673825  0.800112  0.188644\n1   0.660845  0.265913  0.445028  0.889438  0.601047\n2   0.646987  0.926823  0.722838  0.475271  0.827945\n3   0.871724  0.290353  0.099578  0.109949  0.229182\n4   0.704794  0.884062  0.751327  0.595746  0.612269\n5   0.371269  0.560512  0.510264  0.247923  0.618853\n6   0.150398  0.116999  0.934865  0.315723  0.221538\n7   0.556336  0.875514  0.471526  0.539511  0.271221\n8   0.428221  0.546766  0.921274  0.500520  0.400341\n9   0.150170  0.802378  0.608124  0.342871  0.076631\n10  0.099049  0.280748  0.865939  0.214541  0.083318\n11  0.042867  0.701639  0.051457  0.691385  0.051529\n12  0.530845  0.248395  0.433733  0.049458  0.314959\n13  0.142230  0.746634  0.536247  0.096499  0.123294\n14  0.139630  0.056464  0.595644  0.764071  0.193826\n15  0.709624  0.590262  0.816268  0.187931  0.366224\n16  0.982939  0.260358  0.918897  0.531278  0.304655\n17  0.381823  0.003594  0.052597  0.921529  0.022103\n18  0.227944  0.706832  0.137266  0.129158  0.882734\n19  0.226257  0.818213  0.326071  0.230419  0.668891",
            "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>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.020780</td>\n      <td>0.365190</td>\n      <td>0.673825</td>\n      <td>0.800112</td>\n      <td>0.188644</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.660845</td>\n      <td>0.265913</td>\n      <td>0.445028</td>\n      <td>0.889438</td>\n      <td>0.601047</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.646987</td>\n      <td>0.926823</td>\n      <td>0.722838</td>\n      <td>0.475271</td>\n      <td>0.827945</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.871724</td>\n      <td>0.290353</td>\n      <td>0.099578</td>\n      <td>0.109949</td>\n      <td>0.229182</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.704794</td>\n      <td>0.884062</td>\n      <td>0.751327</td>\n      <td>0.595746</td>\n      <td>0.612269</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.371269</td>\n      <td>0.560512</td>\n      <td>0.510264</td>\n      <td>0.247923</td>\n      <td>0.618853</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.150398</td>\n      <td>0.116999</td>\n      <td>0.934865</td>\n      <td>0.315723</td>\n      <td>0.221538</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>0.556336</td>\n      <td>0.875514</td>\n      <td>0.471526</td>\n      <td>0.539511</td>\n      <td>0.271221</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.428221</td>\n      <td>0.546766</td>\n      <td>0.921274</td>\n      <td>0.500520</td>\n      <td>0.400341</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.150170</td>\n      <td>0.802378</td>\n      <td>0.608124</td>\n      <td>0.342871</td>\n      <td>0.076631</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>0.099049</td>\n      <td>0.280748</td>\n      <td>0.865939</td>\n      <td>0.214541</td>\n      <td>0.083318</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>0.042867</td>\n      <td>0.701639</td>\n      <td>0.051457</td>\n      <td>0.691385</td>\n      <td>0.051529</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.530845</td>\n      <td>0.248395</td>\n      <td>0.433733</td>\n      <td>0.049458</td>\n      <td>0.314959</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>0.142230</td>\n      <td>0.746634</td>\n      <td>0.536247</td>\n      <td>0.096499</td>\n      <td>0.123294</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0.139630</td>\n      <td>0.056464</td>\n      <td>0.595644</td>\n      <td>0.764071</td>\n      <td>0.193826</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0.709624</td>\n      <td>0.590262</td>\n      <td>0.816268</td>\n      <td>0.187931</td>\n      <td>0.366224</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>0.982939</td>\n      <td>0.260358</td>\n      <td>0.918897</td>\n      <td>0.531278</td>\n      <td>0.304655</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>0.381823</td>\n      <td>0.003594</td>\n      <td>0.052597</td>\n      <td>0.921529</td>\n      <td>0.022103</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>0.227944</td>\n      <td>0.706832</td>\n      <td>0.137266</td>\n      <td>0.129158</td>\n      <td>0.882734</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0.226257</td>\n      <td>0.818213</td>\n      <td>0.326071</td>\n      <td>0.230419</td>\n      <td>0.668891</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ff.info()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 41,
      "outputs": [
        {
          "name": "stdout",
          "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",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#data functon:\ndate=pd.DataFrame(\n{\n\"date\":['10/9/2020','11/09/2020','12/09/2020'],\n\"students\":[10,20,30]})\n",
      "metadata": {
        "trusted": true
      },
      "execution_count": 43,
      "outputs": [
        {
          "execution_count": 43,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         date  students\n0   10/9/2020        10\n1  11/09/2020        20\n2  12/09/2020        30",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed.value_counts()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 52,
      "outputs": [
        {
          "execution_count": 52,
          "output_type": "execute_result",
          "data": {
            "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed[['Gender','Salary']]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 62,
      "outputs": [
        {
          "execution_count": 62,
          "output_type": "execute_result",
          "data": {
            "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",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#selection by position:rows data:\ned.iloc[8:12]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 69,
      "outputs": [
        {
          "execution_count": 69,
          "output_type": "execute_result",
          "data": {
            "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  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed.loc[8]",
      "metadata": {
        "trusted": true
      },
      "execution_count": 64,
      "outputs": [
        {
          "execution_count": 64,
          "output_type": "execute_result",
          "data": {
            "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#data cleaning:\ned.isnull().sum()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 73,
      "outputs": [
        {
          "execution_count": 73,
          "output_type": "execute_result",
          "data": {
            "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#total null values:\ned.isnull().sum().sum()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 74,
      "outputs": [
        {
          "execution_count": 74,
          "output_type": "execute_result",
          "data": {
            "text/plain": "4"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed.notnull().sum().sum()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 77,
      "outputs": [
        {
          "execution_count": 77,
          "output_type": "execute_result",
          "data": {
            "text/plain": "156"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#fpr practice on drop we will take the copy of the original data:\ned2=ed",
      "metadata": {
        "trusted": true
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "ed2",
      "metadata": {
        "trusted": true
      },
      "execution_count": 79,
      "outputs": [
        {
          "execution_count": 79,
          "output_type": "execute_result",
          "data": {
            "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  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "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())",
      "metadata": {
        "trusted": true
      },
      "execution_count": 90,
      "outputs": [
        {
          "name": "stdout",
          "text": "prasent null values: 0\n",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed2.fillna(10,inplace=True)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 96,
      "outputs": [
        {
          "name": "stderr",
          "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",
          "output_type": "stream"
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed2.isnull().sum()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 97,
      "outputs": [
        {
          "execution_count": 97,
          "output_type": "execute_result",
          "data": {
            "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed2",
      "metadata": {
        "trusted": true
      },
      "execution_count": 98,
      "outputs": [
        {
          "execution_count": 98,
          "output_type": "execute_result",
          "data": {
            "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  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed5=Employ.head(20)\ned5",
      "metadata": {
        "trusted": true
      },
      "execution_count": 118,
      "outputs": [
        {
          "execution_count": 118,
          "output_type": "execute_result",
          "data": {
            "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  ",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "ed2.isnull().sum()",
      "metadata": {
        "trusted": true
      },
      "execution_count": 111,
      "outputs": [
        {
          "execution_count": 111,
          "output_type": "execute_result",
          "data": {
            "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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "date",
      "metadata": {
        "trusted": true
      },
      "execution_count": 120,
      "outputs": [
        {
          "execution_count": 120,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         date  students\n0   10/9/2020        10\n1  11/09/2020        20\n2  12/09/2020        30",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "date.dtypes",
      "metadata": {
        "trusted": true
      },
      "execution_count": 124,
      "outputs": [
        {
          "execution_count": 124,
          "output_type": "execute_result",
          "data": {
            "text/plain": "date        object\nstudents     int64\ndtype: object"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "#rename for the date with the check:\ndate.rename(columns={'date':'check'})",
      "metadata": {
        "trusted": true
      },
      "execution_count": 131,
      "outputs": [
        {
          "execution_count": 131,
          "output_type": "execute_result",
          "data": {
            "text/plain": "        check  students\n0   10/9/2020        10\n1  11/09/2020        20\n2  12/09/2020        30",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "date.sort_values('students',ascending=False)",
      "metadata": {
        "trusted": true
      },
      "execution_count": 134,
      "outputs": [
        {
          "execution_count": 134,
          "output_type": "execute_result",
          "data": {
            "text/plain": "         date  students\n2  12/09/2020        30\n1  11/09/2020        20\n0   10/9/2020        10",
            "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>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {
        "trusted": true
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": "",
      "metadata": {},
      "execution_count": null,
      "outputs": []
    }
  ]
}