Task_4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd    \n",
    "import datetime as dt   \n",
    "import plotly.express as px     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('us_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>city</th>\n",
       "      <th>date</th>\n",
       "      <th>Employee</th>\n",
       "      <th>Employee_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>10/2/2020</td>\n",
       "      <td>10026</td>\n",
       "      <td>Adinolfi, Wilson  K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>11/2/2020</td>\n",
       "      <td>10084</td>\n",
       "      <td>Ait Sidi, Karthikeyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>11/3/2020</td>\n",
       "      <td>10196</td>\n",
       "      <td>Akinkuolie, Sarah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami</td>\n",
       "      <td>11/4/2020</td>\n",
       "      <td>10088</td>\n",
       "      <td>Alagbe,Trina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>11/5/2020</td>\n",
       "      <td>10069</td>\n",
       "      <td>Anderson, Carol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>2/13/2021</td>\n",
       "      <td>10142</td>\n",
       "      <td>Gill, Whitney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Winston-Salem</td>\n",
       "      <td>2/14/2021</td>\n",
       "      <td>10283</td>\n",
       "      <td>Gilles, Alex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Syracuse</td>\n",
       "      <td>2/15/2021</td>\n",
       "      <td>10018</td>\n",
       "      <td>Girifalco, Evelyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Boise</td>\n",
       "      <td>2/16/2021</td>\n",
       "      <td>10255</td>\n",
       "      <td>Givens, Myriam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Chattanooga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10246</td>\n",
       "      <td>Goble, Taisha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              city       date  Employee             Employee_Name\n",
       "0         New York  10/2/2020     10026       Adinolfi, Wilson  K\n",
       "1      Los Angeles  11/2/2020     10084  Ait Sidi, Karthikeyan   \n",
       "2          Chicago  11/3/2020     10196         Akinkuolie, Sarah\n",
       "3            Miami  11/4/2020     10088              Alagbe,Trina\n",
       "4           Dallas  11/5/2020     10069          Anderson, Carol \n",
       "..             ...        ...       ...                       ...\n",
       "104        Oakland  2/13/2021     10142           Gill, Whitney  \n",
       "105  Winston-Salem  2/14/2021     10283              Gilles, Alex\n",
       "106       Syracuse  2/15/2021     10018         Girifalco, Evelyn\n",
       "107          Boise  2/16/2021     10255            Givens, Myriam\n",
       "108    Chattanooga        NaN     10246             Goble, Taisha\n",
       "\n",
       "[109 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 109 entries, 0 to 108\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   city           109 non-null    object\n",
      " 1   date           108 non-null    object\n",
      " 2   Employee       109 non-null    int64 \n",
      " 3   Employee_Name  109 non-null    object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 3.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date'] = pd.to_datetime(df['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 109 entries, 0 to 108\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   city           109 non-null    object        \n",
      " 1   date           108 non-null    datetime64[ns]\n",
      " 2   Employee       109 non-null    int64         \n",
      " 3   Employee_Name  109 non-null    object        \n",
      "dtypes: datetime64[ns](1), int64(1), object(2)\n",
      "memory usage: 3.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>Employee</th>\n",
       "      <th>Employee_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>2020-10-02</td>\n",
       "      <td>10026</td>\n",
       "      <td>Adinolfi, Wilson  K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2020-11-02</td>\n",
       "      <td>10084</td>\n",
       "      <td>Ait Sidi, Karthikeyan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chicago</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>10196</td>\n",
       "      <td>Akinkuolie, Sarah</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Miami</td>\n",
       "      <td>2020-11-04</td>\n",
       "      <td>10088</td>\n",
       "      <td>Alagbe,Trina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dallas</td>\n",
       "      <td>2020-11-05</td>\n",
       "      <td>10069</td>\n",
       "      <td>Anderson, Carol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>Oakland</td>\n",
       "      <td>2021-02-13</td>\n",
       "      <td>10142</td>\n",
       "      <td>Gill, Whitney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>Winston-Salem</td>\n",
       "      <td>2021-02-14</td>\n",
       "      <td>10283</td>\n",
       "      <td>Gilles, Alex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>Syracuse</td>\n",
       "      <td>2021-02-15</td>\n",
       "      <td>10018</td>\n",
       "      <td>Girifalco, Evelyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>Boise</td>\n",
       "      <td>2021-02-16</td>\n",
       "      <td>10255</td>\n",
       "      <td>Givens, Myriam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>Chattanooga</td>\n",
       "      <td>NaT</td>\n",
       "      <td>10246</td>\n",
       "      <td>Goble, Taisha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              city       date  Employee             Employee_Name\n",
       "0         New York 2020-10-02     10026       Adinolfi, Wilson  K\n",
       "1      Los Angeles 2020-11-02     10084  Ait Sidi, Karthikeyan   \n",
       "2          Chicago 2020-11-03     10196         Akinkuolie, Sarah\n",
       "3            Miami 2020-11-04     10088              Alagbe,Trina\n",
       "4           Dallas 2020-11-05     10069          Anderson, Carol \n",
       "..             ...        ...       ...                       ...\n",
       "104        Oakland 2021-02-13     10142           Gill, Whitney  \n",
       "105  Winston-Salem 2021-02-14     10283              Gilles, Alex\n",
       "106       Syracuse 2021-02-15     10018         Girifalco, Evelyn\n",
       "107          Boise 2021-02-16     10255            Givens, Myriam\n",
       "108    Chattanooga        NaT     10246             Goble, Taisha\n",
       "\n",
       "[109 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>city</th>\n",
       "      <th>date</th>\n",
       "      <th>Employee</th>\n",
       "      <th>Employee_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Dayton</td>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>10311</td>\n",
       "      <td>Dee, Randy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      city       date  Employee Employee_Name\n",
       "64  Dayton 2021-01-04     10311    Dee, Randy"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Highest employment rate in the US\n",
    "pl =df[df['Employee'] == df['Employee'].max()]\n",
    "pl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Highest employment rate in the US\n",
    "group1 = df.groupby('Employee')['city'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Employee\n",
       "10001      Kansas City\n",
       "10002          Houston\n",
       "10003        St. Louis\n",
       "10006      Baton Rouge\n",
       "10012    San Francisco\n",
       "             ...      \n",
       "10306            Tulsa\n",
       "10308         Palm Bay\n",
       "10309    Staten Island\n",
       "10310          Concord\n",
       "10311           Dayton\n",
       "Name: city, Length: 109, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "alignmentgroup": "True",
         "hovertemplate": "city=New York<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "New York",
         "marker": {
          "color": "#636efa",
          "pattern": {
           "shape": ""
          }
         },
         "name": "New York",
         "offsetgroup": "New York",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "2020-10-02T00:00:00"
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         "xaxis": "x",
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         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "city=Los Angeles<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Los Angeles",
         "marker": {
          "color": "#EF553B",
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         "name": "Los Angeles",
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          "2020-11-02T00:00:00"
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         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "city=Chicago<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Chicago",
         "marker": {
          "color": "#00cc96",
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         "offsetgroup": "Chicago",
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         "showlegend": true,
         "textposition": "auto",
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          "2020-11-03T00:00:00"
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        {
         "alignmentgroup": "True",
         "hovertemplate": "city=Miami<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Miami",
         "marker": {
          "color": "#ab63fa",
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         },
         "name": "Miami",
         "offsetgroup": "Miami",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "2020-11-04T00:00:00"
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         "xaxis": "x",
         "y": [
          10088
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "city=Dallas<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Dallas",
         "marker": {
          "color": "#FFA15A",
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         "name": "Dallas",
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         "showlegend": true,
         "textposition": "auto",
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         "x": [
          "2020-11-05T00:00:00"
         ],
         "xaxis": "x",
         "y": [
          10069
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "city=Houston<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Houston",
         "marker": {
          "color": "#19d3f3",
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         "name": "Houston",
         "offsetgroup": "Houston",
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         "x": [
          "2020-11-06T00:00:00"
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         "y": [
          10002
         ],
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        },
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         "alignmentgroup": "True",
         "hovertemplate": "city=Philadelphia<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Philadelphia",
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          "color": "#FF6692",
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         "name": "Philadelphia",
         "offsetgroup": "Philadelphia",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
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         "x": [
          "2020-11-07T00:00:00"
         ],
         "xaxis": "x",
         "y": [
          10194
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "city=Atlanta<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Atlanta",
         "marker": {
          "color": "#B6E880",
          "pattern": {
           "shape": ""
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         },
         "name": "Atlanta",
         "offsetgroup": "Atlanta",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "2020-11-08T00:00:00"
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         "xaxis": "x",
         "y": [
          10062
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "city=Washington<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Washington",
         "marker": {
          "color": "#FF97FF",
          "pattern": {
           "shape": ""
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         },
         "name": "Washington",
         "offsetgroup": "Washington",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "2020-11-09T00:00:00"
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         "xaxis": "x",
         "y": [
          10114
         ],
         "yaxis": "y"
        },
        {
         "alignmentgroup": "True",
         "hovertemplate": "city=Boston<br>date=%{x}<br>Employee=%{y}<extra></extra>",
         "legendgroup": "Boston",
         "marker": {
          "color": "#FECB52",
          "pattern": {
           "shape": ""
          }
         },
         "name": "Boston",
         "offsetgroup": "Boston",
         "orientation": "v",
         "showlegend": true,
         "textposition": "auto",
         "type": "bar",
         "x": [
          "2020-11-10T00:00:00"
         ],
         "xaxis": "x",
         "y": [
          10250
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "barmode": "relative",
        "legend": {
         "title": {
          "text": "city"
         },
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
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   "source": [
    "#### Highest employment rate in the US"
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  },
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          "title": {
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     "metadata": {},
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    "px.bar(group1, x = group1.index, y = 'city', title='Highest employment rate in the US')"
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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