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2023-08-21 15:12:19 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Ré-échantillonnage du temps\n",
"\n",
"Apprenons à échantillonner des données de séries temporelles ! Cela sera utile plus tard dans le cours !"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Saisir les données\n",
"# Une alternative plus rapide\n",
"# df = pd.read_csv('time_data/walmart_stock.csv',index_col='Date')\n",
"df = pd.read_csv('time_data/walmart_stock.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Date Open High Low Close Volume Adj Close\n",
"0 2012-01-03 59.970001 61.060001 59.869999 60.330002 12668800 52.619235\n",
"1 2012-01-04 60.209999 60.349998 59.470001 59.709999 9593300 52.078475\n",
"2 2012-01-05 59.349998 59.619999 58.369999 59.419998 12768200 51.825539\n",
"3 2012-01-06 59.419998 59.450001 58.869999 59.000000 8069400 51.459220\n",
"4 2012-01-09 59.029999 59.549999 58.919998 59.180000 6679300 51.616215"
],
"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>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2012-01-03</td>\n <td>59.970001</td>\n <td>61.060001</td>\n <td>59.869999</td>\n <td>60.330002</td>\n <td>12668800</td>\n <td>52.619235</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2012-01-04</td>\n <td>60.209999</td>\n <td>60.349998</td>\n <td>59.470001</td>\n <td>59.709999</td>\n <td>9593300</td>\n <td>52.078475</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2012-01-05</td>\n <td>59.349998</td>\n <td>59.619999</td>\n <td>58.369999</td>\n <td>59.419998</td>\n <td>12768200</td>\n <td>51.825539</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2012-01-06</td>\n <td>59.419998</td>\n <td>59.450001</td>\n <td>58.869999</td>\n <td>59.000000</td>\n <td>8069400</td>\n <td>51.459220</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2012-01-09</td>\n <td>59.029999</td>\n <td>59.549999</td>\n <td>58.919998</td>\n <td>59.180000</td>\n <td>6679300</td>\n <td>51.616215</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 4
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Créer un index de date à partir de la colonne Date"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df['Date'] = df['Date'].apply(pd.to_datetime)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Date Open High Low Close Volume Adj Close\n",
"0 2012-01-03 59.970001 61.060001 59.869999 60.330002 12668800 52.619235\n",
"1 2012-01-04 60.209999 60.349998 59.470001 59.709999 9593300 52.078475\n",
"2 2012-01-05 59.349998 59.619999 58.369999 59.419998 12768200 51.825539\n",
"3 2012-01-06 59.419998 59.450001 58.869999 59.000000 8069400 51.459220\n",
"4 2012-01-09 59.029999 59.549999 58.919998 59.180000 6679300 51.616215"
],
"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>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>2012-01-03</td>\n <td>59.970001</td>\n <td>61.060001</td>\n <td>59.869999</td>\n <td>60.330002</td>\n <td>12668800</td>\n <td>52.619235</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2012-01-04</td>\n <td>60.209999</td>\n <td>60.349998</td>\n <td>59.470001</td>\n <td>59.709999</td>\n <td>9593300</td>\n <td>52.078475</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2012-01-05</td>\n <td>59.349998</td>\n <td>59.619999</td>\n <td>58.369999</td>\n <td>59.419998</td>\n <td>12768200</td>\n <td>51.825539</td>\n </tr>\n <tr>\n <th>3</th>\n <td>2012-01-06</td>\n <td>59.419998</td>\n <td>59.450001</td>\n <td>58.869999</td>\n <td>59.000000</td>\n <td>8069400</td>\n <td>51.459220</td>\n </tr>\n <tr>\n <th>4</th>\n <td>2012-01-09</td>\n <td>59.029999</td>\n <td>59.549999</td>\n <td>58.919998</td>\n <td>59.180000</td>\n <td>6679300</td>\n <td>51.616215</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df.set_index('Date',inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Open High Low Close Volume Adj Close\n",
"Date \n",
"2012-01-03 59.970001 61.060001 59.869999 60.330002 12668800 52.619235\n",
"2012-01-04 60.209999 60.349998 59.470001 59.709999 9593300 52.078475\n",
"2012-01-05 59.349998 59.619999 58.369999 59.419998 12768200 51.825539\n",
"2012-01-06 59.419998 59.450001 58.869999 59.000000 8069400 51.459220\n",
"2012-01-09 59.029999 59.549999 58.919998 59.180000 6679300 51.616215"
],
"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>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>59.970001</td>\n <td>61.060001</td>\n <td>59.869999</td>\n <td>60.330002</td>\n <td>12668800</td>\n <td>52.619235</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>60.209999</td>\n <td>60.349998</td>\n <td>59.470001</td>\n <td>59.709999</td>\n <td>9593300</td>\n <td>52.078475</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>59.349998</td>\n <td>59.619999</td>\n <td>58.369999</td>\n <td>59.419998</td>\n <td>12768200</td>\n <td>51.825539</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>59.419998</td>\n <td>59.450001</td>\n <td>58.869999</td>\n <td>59.000000</td>\n <td>8069400</td>\n <td>51.459220</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>59.029999</td>\n <td>59.549999</td>\n <td>58.919998</td>\n <td>59.180000</td>\n <td>6679300</td>\n <td>51.616215</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## resample()\n",
"\n",
"Une opération courante avec les données de séries temporelles est la restructuration basée sur l'indice de série temporelle. Voyons comment utiliser la méthode resample().\n",
"\n",
"#### Toutes les possibilités"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table border=\"1\" class=\"docutils\">\n",
"<colgroup>\n",
"<col width=\"13%\" />\n",
"<col width=\"87%\" />\n",
"</colgroup>\n",
"<thead valign=\"bottom\">\n",
"<tr class=\"row-odd\"><th class=\"head\">Alias</th>\n",
"<th class=\"head\">Description</th>\n",
"</tr>\n",
"</thead>\n",
"<tbody valign=\"top\">\n",
"<tr class=\"row-even\"><td>B</td>\n",
"<td>business day frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>C</td>\n",
"<td>custom business day frequency (experimental)</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>D</td>\n",
"<td>calendar day frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>W</td>\n",
"<td>weekly frequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>M</td>\n",
"<td>month end frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>SM</td>\n",
"<td>semi-month end frequency (15th and end of month)</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>BM</td>\n",
"<td>business month end frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>CBM</td>\n",
"<td>custom business month end frequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>MS</td>\n",
"<td>month start frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>SMS</td>\n",
"<td>semi-month start frequency (1st and 15th)</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>BMS</td>\n",
"<td>business month start frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>CBMS</td>\n",
"<td>custom business month start frequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>Q</td>\n",
"<td>quarter end frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>BQ</td>\n",
"<td>business quarter endfrequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>QS</td>\n",
"<td>quarter start frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>BQS</td>\n",
"<td>business quarter start frequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>A</td>\n",
"<td>year end frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>BA</td>\n",
"<td>business year end frequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>AS</td>\n",
"<td>year start frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>BAS</td>\n",
"<td>business year start frequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>BH</td>\n",
"<td>business hour frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>H</td>\n",
"<td>hourly frequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>T, min</td>\n",
"<td>minutely frequency</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>S</td>\n",
"<td>secondly frequency</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>L, ms</td>\n",
"<td>milliseconds</td>\n",
"</tr>\n",
"<tr class=\"row-odd\"><td>U, us</td>\n",
"<td>microseconds</td>\n",
"</tr>\n",
"<tr class=\"row-even\"><td>N</td>\n",
"<td>nanoseconds</td>\n",
"</tr>\n",
"</tbody>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"DatetimeIndex(['2012-01-03', '2012-01-04', '2012-01-05', '2012-01-06',\n",
" '2012-01-09', '2012-01-10', '2012-01-11', '2012-01-12',\n",
" '2012-01-13', '2012-01-17',\n",
" ...\n",
" '2016-12-16', '2016-12-19', '2016-12-20', '2016-12-21',\n",
" '2016-12-22', '2016-12-23', '2016-12-27', '2016-12-28',\n",
" '2016-12-29', '2016-12-30'],\n",
" dtype='datetime64[ns]', name='Date', length=1258, freq=None)"
]
},
"metadata": {},
"execution_count": 9
}
],
"source": [
"# Notre index\n",
"df.index"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vous devez appeler **resample** avec le paramètre **rule**, puis vous devez appeler une fonction d'agrégation. En effet, à cause du rééchantillonnage, nous avons besoin d'une règle mathématique pour joindre les lignes entre elles (moyenne, somme, décompte, etc...)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Open High Low Close Volume \\\n",
"Date \n",
"2012-12-31 67.158680 67.602120 66.786520 67.215120 9.239015e+06 \n",
"2013-12-31 75.264048 75.729405 74.843055 75.320516 6.951496e+06 \n",
"2014-12-31 77.274524 77.740040 76.864405 77.327381 6.515612e+06 \n",
"2015-12-31 72.569405 73.064167 72.034802 72.491111 9.040769e+06 \n",
"2016-12-31 69.481349 70.019643 69.023492 69.547063 9.371645e+06 \n",
"\n",
" Adj Close \n",
"Date \n",
"2012-12-31 59.389349 \n",
"2013-12-31 68.147179 \n",
"2014-12-31 71.709712 \n",
"2015-12-31 68.831426 \n",
"2016-12-31 68.054229 "
],
"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>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-12-31</th>\n <td>67.158680</td>\n <td>67.602120</td>\n <td>66.786520</td>\n <td>67.215120</td>\n <td>9.239015e+06</td>\n <td>59.389349</td>\n </tr>\n <tr>\n <th>2013-12-31</th>\n <td>75.264048</td>\n <td>75.729405</td>\n <td>74.843055</td>\n <td>75.320516</td>\n <td>6.951496e+06</td>\n <td>68.147179</td>\n </tr>\n <tr>\n <th>2014-12-31</th>\n <td>77.274524</td>\n <td>77.740040</td>\n <td>76.864405</td>\n <td>77.327381</td>\n <td>6.515612e+06</td>\n <td>71.709712</td>\n </tr>\n <tr>\n <th>2015-12-31</th>\n <td>72.569405</td>\n <td>73.064167</td>\n <td>72.034802</td>\n <td>72.491111</td>\n <td>9.040769e+06</td>\n <td>68.831426</td>\n </tr>\n <tr>\n <th>2016-12-31</th>\n <td>69.481349</td>\n <td>70.019643</td>\n <td>69.023492</td>\n <td>69.547063</td>\n <td>9.371645e+06</td>\n <td>68.054229</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 10
}
],
"source": [
"# Moyennes annuelles\n",
"df.resample(rule='A').mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ré-échantillonnage personnalisé\n",
"\n",
"Vous pouvez également créer techniquement votre propre fonction de rééchantillonnage personnalisé:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def first_day(entry):\n",
" \"\"\"\n",
" Retourne la première instance de la période, \n",
" quel que soit le taux d'échantillonnage.\n",
" \"\"\"\n",
" return entry[0]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" Open High Low Close Volume Adj Close\n",
"Date \n",
"2012-12-31 59.970001 61.060001 59.869999 60.330002 12668800 52.619235\n",
"2013-12-31 68.930000 69.239998 68.449997 69.239998 10390800 61.879708\n",
"2014-12-31 78.720001 79.470001 78.500000 78.910004 6878000 72.254228\n",
"2015-12-31 86.269997 86.720001 85.550003 85.900002 4501800 80.624861\n",
"2016-12-31 60.500000 61.490002 60.360001 61.459999 11989200 59.289713"
],
"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>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-12-31</th>\n <td>59.970001</td>\n <td>61.060001</td>\n <td>59.869999</td>\n <td>60.330002</td>\n <td>12668800</td>\n <td>52.619235</td>\n </tr>\n <tr>\n <th>2013-12-31</th>\n <td>68.930000</td>\n <td>69.239998</td>\n <td>68.449997</td>\n <td>69.239998</td>\n <td>10390800</td>\n <td>61.879708</td>\n </tr>\n <tr>\n <th>2014-12-31</th>\n <td>78.720001</td>\n <td>79.470001</td>\n <td>78.500000</td>\n <td>78.910004</td>\n <td>6878000</td>\n <td>72.254228</td>\n </tr>\n <tr>\n <th>2015-12-31</th>\n <td>86.269997</td>\n <td>86.720001</td>\n <td>85.550003</td>\n <td>85.900002</td>\n <td>4501800</td>\n <td>80.624861</td>\n </tr>\n <tr>\n <th>2016-12-31</th>\n <td>60.500000</td>\n <td>61.490002</td>\n <td>60.360001</td>\n <td>61.459999</td>\n <td>11989200</td>\n <td>59.289713</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"df.resample(rule='A').apply(first_day)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Yearly Mean Close Price for Walmart')"
]
},
"metadata": {},
"execution_count": 13
},
{
"output_type": "display_data",
"data": {
"text/plain": "<Figure size 432x288 with 1 Axes>",
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}
}
],
"source": [
"df['Close'].resample('A').mean().plot(kind='bar')\n",
"plt.title('Yearly Mean Close Price for Walmart')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Monthly Max Opening Price for Walmart')"
]
},
"metadata": {},
"execution_count": 14
},
{
"output_type": "display_data",
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},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"df['Open'].resample('M').max().plot(kind='bar',figsize=(16,6))\n",
"plt.title('Monthly Max Opening Price for Walmart')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Voilà! Prochain notebook, nous apprendrons les décalages de temps !"
]
}
],
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"anaconda-cloud": {},
"kernelspec": {
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"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
"metadata": {
"interpreter": {
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
}
}
},
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"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"version": "3.7.9-final"
}
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