{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "d9083f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Employees_over_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>[91428.92277123446, 102233.67037565738, 115926...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABBV</td>\n",
       "      <td>[25197.96677105695, 25004.40890529943, 23750.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABT</td>\n",
       "      <td>[39483.100038636265, 44757.71382044369, 51525....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACN</td>\n",
       "      <td>[336961.3689040502, 374390.13833588944, 360097...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ADBE</td>\n",
       "      <td>[23394.52554785587, 21940.978728008624, 26402....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>V</td>\n",
       "      <td>[9980.877628021311, 10405.029487238351, 10411....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>VZ</td>\n",
       "      <td>[88949.17354024998, 97860.29179349614, 121300....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>WFC</td>\n",
       "      <td>[119806.58574102176, 123482.12611072823, 15253...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>WMT</td>\n",
       "      <td>[1109806.346506345, 1309312.9509547795, 145320...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>XOM</td>\n",
       "      <td>[48648.808836794415, 44519.68145644413, 55037....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticker                                Employees_over_time\n",
       "0    AAPL  [91428.92277123446, 102233.67037565738, 115926...\n",
       "1    ABBV  [25197.96677105695, 25004.40890529943, 23750.1...\n",
       "2     ABT  [39483.100038636265, 44757.71382044369, 51525....\n",
       "3     ACN  [336961.3689040502, 374390.13833588944, 360097...\n",
       "4    ADBE  [23394.52554785587, 21940.978728008624, 26402....\n",
       "..    ...                                                ...\n",
       "85      V  [9980.877628021311, 10405.029487238351, 10411....\n",
       "86     VZ  [88949.17354024998, 97860.29179349614, 121300....\n",
       "87    WFC  [119806.58574102176, 123482.12611072823, 15253...\n",
       "88    WMT  [1109806.346506345, 1309312.9509547795, 145320...\n",
       "89    XOM  [48648.808836794415, 44519.68145644413, 55037....\n",
       "\n",
       "[90 rows x 2 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "employees_df = pd.read_csv('../Elaborated_Data/employees_over_time.csv', index_col=[0])\n",
    "employees_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9401e797",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Employees_over_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>91428.92277123446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>102233.67037565738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>115926.34267742703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>137239.8786903178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>139194.57829987502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>XOM</td>\n",
       "      <td>47325.423122434426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>XOM</td>\n",
       "      <td>57436.23902499073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>XOM</td>\n",
       "      <td>53483.04798407412</td>\n",
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       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>XOM</td>\n",
       "      <td>64366.11240308755</td>\n",
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       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>XOM</td>\n",
       "      <td>62000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1080 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ticker  Employees_over_time\n",
       "0      AAPL    91428.92277123446\n",
       "1      AAPL   102233.67037565738\n",
       "2      AAPL   115926.34267742703\n",
       "3      AAPL    137239.8786903178\n",
       "4      AAPL   139194.57829987502\n",
       "...     ...                  ...\n",
       "1075    XOM   47325.423122434426\n",
       "1076    XOM    57436.23902499073\n",
       "1077    XOM    53483.04798407412\n",
       "1078    XOM    64366.11240308755\n",
       "1079    XOM                62000\n",
       "\n",
       "[1080 rows x 2 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees_df_exp = employees_df.set_index('Ticker').apply(lambda x: x.str.split(',').explode()).reset_index()\n",
    "employees_df_exp['Employees_over_time'] = employees_df_exp['Employees_over_time'].str.replace('[', \"\", regex=True).str.replace(']', '', regex=True)\n",
    "employees_df_exp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a273b5a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1080\n"
     ]
    }
   ],
   "source": [
    "first_date = '2012-01-01'\n",
    "second_date = '2013-01-01'\n",
    "third_date = '2014-01-01'\n",
    "fourth_date = '2015-01-01'\n",
    "fifth_date = '2016-01-01'\n",
    "sixth_date = '2017-01-01'\n",
    "seventh_date = '2018-01-01'\n",
    "eight_date = '2019-01-01'\n",
    "nineth_date = '2020-01-01'\n",
    "tenth_date ='2021-01-01'\n",
    "eleventh_date = '2022-01-01'\n",
    "twelveth_date = '2023-01-01'\n",
    "\n",
    "date_list = []\n",
    "\n",
    "for i in range(0, 90):\n",
    "    date_list.append(first_date)\n",
    "    date_list.append(second_date)\n",
    "    date_list.append(third_date)\n",
    "    date_list.append(fourth_date)\n",
    "    date_list.append(fifth_date)\n",
    "    date_list.append(sixth_date)\n",
    "    date_list.append(seventh_date)\n",
    "    date_list.append(eight_date)\n",
    "    date_list.append(nineth_date)\n",
    "    date_list.append(tenth_date)\n",
    "    date_list.append(eleventh_date)\n",
    "    date_list.append(twelveth_date)\n",
    "    \n",
    "print(len(date_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "64055950",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Employees_over_time</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>91429.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>102234.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>115926.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>137240.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>139195.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075</th>\n",
       "      <td>XOM</td>\n",
       "      <td>47325.0</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1076</th>\n",
       "      <td>XOM</td>\n",
       "      <td>57436.0</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>XOM</td>\n",
       "      <td>53483.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>XOM</td>\n",
       "      <td>64366.0</td>\n",
       "      <td>2022-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1079</th>\n",
       "      <td>XOM</td>\n",
       "      <td>62000.0</td>\n",
       "      <td>2023-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1080 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ticker  Employees_over_time        date\n",
       "0      AAPL              91429.0  2012-01-01\n",
       "1      AAPL             102234.0  2013-01-01\n",
       "2      AAPL             115926.0  2014-01-01\n",
       "3      AAPL             137240.0  2015-01-01\n",
       "4      AAPL             139195.0  2016-01-01\n",
       "...     ...                  ...         ...\n",
       "1075    XOM              47325.0  2019-01-01\n",
       "1076    XOM              57436.0  2020-01-01\n",
       "1077    XOM              53483.0  2021-01-01\n",
       "1078    XOM              64366.0  2022-01-01\n",
       "1079    XOM              62000.0  2023-01-01\n",
       "\n",
       "[1080 rows x 3 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees_df_exp['date'] = date_list\n",
    "employees_final['Employees_over_time'] = employees_final['Employees_over_time'].astype('float64')\n",
    "employees_final = employees_final.round({'Employees_over_time': 0})\n",
    "employees_final = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "dc7c1efc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ticker</th>\n",
       "      <th>Employees_over_time</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>91429.0</td>\n",
       "      <td>2012-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>102234.0</td>\n",
       "      <td>2013-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>115926.0</td>\n",
       "      <td>2014-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>137240.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>139195.0</td>\n",
       "      <td>2016-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>130029.0</td>\n",
       "      <td>2017-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>129131.0</td>\n",
       "      <td>2018-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>143143.0</td>\n",
       "      <td>2019-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>146079.0</td>\n",
       "      <td>2020-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>141409.0</td>\n",
       "      <td>2021-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>170843.0</td>\n",
       "      <td>2022-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>164000.0</td>\n",
       "      <td>2023-01-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticker  Employees_over_time        date\n",
       "0    AAPL              91429.0  2012-01-01\n",
       "1    AAPL             102234.0  2013-01-01\n",
       "2    AAPL             115926.0  2014-01-01\n",
       "3    AAPL             137240.0  2015-01-01\n",
       "4    AAPL             139195.0  2016-01-01\n",
       "5    AAPL             130029.0  2017-01-01\n",
       "6    AAPL             129131.0  2018-01-01\n",
       "7    AAPL             143143.0  2019-01-01\n",
       "8    AAPL             146079.0  2020-01-01\n",
       "9    AAPL             141409.0  2021-01-01\n",
       "10   AAPL             170843.0  2022-01-01\n",
       "11   AAPL             164000.0  2023-01-01"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "employees_final.loc[employees_final['Ticker'] == 'AAPL'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "63eb80de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def employees_time_line(ticker: str, employees_dataframe: pd.DataFrame()):\n",
    "    ticker_df = employees_dataframe.loc[employees_dataframe['Ticker'] == ticker]\n",
    "    line_plot = sns.lineplot(data=ticker_df, x='date', y='Employees_over_time')\n",
    "    plt.xticks(rotation=30)\n",
    "    plt.show()\n",
    "employees_time_line('XOM', employees_final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f3d3c22",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Visual-Analytics",
   "language": "python",
   "name": "visual-analytics"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}