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va-project/indexer/Employees.ipynb

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employees and eps functions
2023-05-24 08:02:09 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 71,
"id": "d9083f1e",
"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",
" </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",
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" <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": {
"text/html": [
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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",
" </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",
" </tr>\n",
" <tr>\n",
" <th>1078</th>\n",
" <td>XOM</td>\n",
" <td>64366.11240308755</td>\n",
" </tr>\n",
" <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": {
"text/html": [
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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>...</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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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>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
}
Reference in a new issue Copy permalink