280 lines
7.7 KiB
Plaintext
280 lines
7.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pandas Datareader\n",
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"\n",
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"**EMARQUE : Tous les emplacements géographiques ne fonctionnent pas bien avec le lecteur de données pandas, votre pare-feu peut aussi le bloquer !**\n",
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"\n",
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"Les fonctions de pandas_datareader.data et pandas_datareader.wb extraient les données de diverses sources Internet dans un DataFrame pandas. Actuellement, les sources suivantes sont supportées :\n",
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"\n",
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"* Yahoo! Finance\n",
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"* Enigma\n",
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"* St.Louis FED (FRED)\n",
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"* Kenneth French’s data library\n",
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"* World Bank\n",
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"* OECD\n",
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"* Eurostat\n",
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"* Thrift Savings Plan\n",
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"* Oanda currency historical rate\n",
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"* Nasdaq Trader symbol definitions (remote_data.nasdaq_symbols)\n",
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"\n",
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"Il convient de noter que diverses sources soutiennent différents types de données, de sorte que toutes les sources n'appliquent pas les mêmes méthodes et que les éléments de données retournés peuvent également différer."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas_datareader.data as web\n",
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"\n",
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"import datetime\n",
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"\n",
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"start = datetime.datetime(2015, 1, 1)\n",
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"\n",
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"end = datetime.datetime(2017, 1, 1)\n",
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"\n",
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"facebook = web.DataReader(\"FB\", 'yahoo', start, end)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>High</th>\n",
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" <th>Low</th>\n",
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" <th>Open</th>\n",
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" <th>Close</th>\n",
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" <th>Volume</th>\n",
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" <th>Adj Close</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Date</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2015-01-02</th>\n",
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" <td>78.930000</td>\n",
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" <td>77.699997</td>\n",
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" <td>78.580002</td>\n",
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" <td>78.449997</td>\n",
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" <td>18177500</td>\n",
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" <td>78.449997</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015-01-05</th>\n",
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" <td>79.250000</td>\n",
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" <td>76.860001</td>\n",
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" <td>77.980003</td>\n",
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" <td>77.190002</td>\n",
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" <td>26452200</td>\n",
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" <td>77.190002</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015-01-06</th>\n",
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" <td>77.589996</td>\n",
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" <td>75.360001</td>\n",
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" <td>77.230003</td>\n",
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" <td>76.150002</td>\n",
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" <td>27399300</td>\n",
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" <td>76.150002</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015-01-07</th>\n",
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" <td>77.360001</td>\n",
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" <td>75.820000</td>\n",
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" <td>76.760002</td>\n",
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" <td>76.150002</td>\n",
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" <td>22045300</td>\n",
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" <td>76.150002</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2015-01-08</th>\n",
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" <td>78.230003</td>\n",
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" <td>76.080002</td>\n",
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" <td>76.739998</td>\n",
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" <td>78.180000</td>\n",
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" <td>23961000</td>\n",
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" <td>78.180000</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" High Low Open Close Volume Adj Close\n",
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"Date \n",
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"2015-01-02 78.930000 77.699997 78.580002 78.449997 18177500 78.449997\n",
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"2015-01-05 79.250000 76.860001 77.980003 77.190002 26452200 77.190002\n",
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"2015-01-06 77.589996 75.360001 77.230003 76.150002 27399300 76.150002\n",
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"2015-01-07 77.360001 75.820000 76.760002 76.150002 22045300 76.150002\n",
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"2015-01-08 78.230003 76.080002 76.739998 78.180000 23961000 78.180000"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"facebook.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# FRED"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas_datareader.data as web\n",
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"\n",
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"import datetime\n",
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"\n",
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"start = datetime.datetime(2010, 1, 1)\n",
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"\n",
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"end = datetime.datetime(2017, 1, 1)\n",
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"\n",
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"gdp = web.DataReader(\"GDP\", \"fred\", start, end)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>GDP</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>DATE</th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>2010-01-01</th>\n",
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" <td>14721.350</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2010-04-01</th>\n",
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" <td>14926.098</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2010-07-01</th>\n",
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" <td>15079.917</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2010-10-01</th>\n",
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" <td>15240.843</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2011-01-01</th>\n",
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" <td>15285.828</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" GDP\n",
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"DATE \n",
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"2010-01-01 14721.350\n",
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"2010-04-01 14926.098\n",
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"2010-07-01 15079.917\n",
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"2010-10-01 15240.843\n",
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"2011-01-01 15285.828"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"gdp.head()"
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]
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}
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],
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"display_name": "Python 3",
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