495 lines
278 KiB
Plaintext
495 lines
278 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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"# Quandl\n",
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"Plus d'infos:\n",
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"https://www.quandl.com/tools/python"
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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 numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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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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"source": [
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"import quandl"
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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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"### Les bases de QUANDL\n",
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"Cette requête obtient le prix du pétrole brut WTI du ministère américain de l'énergie:"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"mydata = quandl.get(\"EIA/PET_RWTC_D\")"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" Value\n",
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"Date \n",
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"1986-01-02 25.56\n",
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"1986-01-03 26.00\n",
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"1986-01-06 26.53\n",
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"1986-01-07 25.85\n",
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"1986-01-08 25.87"
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],
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"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>Value</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1986-01-02</th>\n <td>25.56</td>\n </tr>\n <tr>\n <th>1986-01-03</th>\n <td>26.00</td>\n </tr>\n <tr>\n <th>1986-01-06</th>\n <td>26.53</td>\n </tr>\n <tr>\n <th>1986-01-07</th>\n <td>25.85</td>\n </tr>\n <tr>\n <th>1986-01-08</th>\n <td>25.87</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"metadata": {},
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"execution_count": 4
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}
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],
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"source": [
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"mydata.head()"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"<AxesSubplot:xlabel='Date'>"
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]
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},
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"metadata": {},
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"execution_count": 5
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},
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{
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"output_type": "display_data",
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"data": {
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"text/plain": "<Figure size 864x432 with 1 Axes>",
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205.113217 \r\nL 145.318863 203.648218 \r\nL 145.366629 204.029118 \r\nL 145.509926 203.780068 \r\nL 145.605458 201.802321 \r\nL 145.892053 190.067684 \r\nL 145.987584 195.795827 \r\nL 146.03535 194.784979 \r\nL 146.178647 193.77413 \r\nL 146.274179 194.550579 \r\nL 146.369711 191.459432 \r\nL 146.513008 191.488732 \r\nL 146.560774 191.737782 \r\nL 146.656305 187.035137 \r\nL 146.704071 187.870186 \r\nL 146.9429 195.121928 \r\nL 147.038432 193.21743 \r\nL 147.181729 193.21743 \r\nL 147.325026 187.269537 \r\nL 147.372792 189.349835 \r\nL 147.51609 188.265736 \r\nL 147.563855 189.056835 \r\nL 147.611621 188.236436 \r\nL 147.707153 186.859338 \r\nL 147.85045 184.017241 \r\nL 147.898216 184.38349 \r\nL 147.945982 184.82299 \r\nL 148.280342 176.1209 \r\nL 148.328108 175.168651 \r\nL 148.375874 175.520251 \r\nL 148.566937 182.991742 \r\nL 148.614703 179.168096 \r\nL 148.662469 179.578296 \r\nL 148.901298 173.762253 \r\nL 148.949063 175.8572 \r\nL 148.996829 173.264153 \r\nL 149.044595 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185.155535 203.750768 \r\nL 185.298832 203.208719 \r\nL 185.346598 203.340569 \r\nL 185.44213 203.853318 \r\nL 185.489896 204.073068 \r\nL 185.633193 203.589619 \r\nL 185.680959 203.662868 \r\nL 185.728724 203.824018 \r\nL 185.77649 203.399169 \r\nL 185.824256 203.589619 \r\nL 185.967553 204.117018 \r\nL 186.015319 204.043768 \r\nL 186.158617 203.706818 \r\nL 186.397446 205.039967 \r\nL 186.492977 205.669916 \r\nL 186.636275 205.347617 \r\nL 186.68404 206.050816 \r\nL 186.731806 205.801766 \r\nL 186.779572 205.215767 \r\nL 186.827338 205.464816 \r\nL 186.970635 205.508766 \r\nL 187.018401 205.684566 \r\nL 187.161698 204.410018 \r\nL 187.304996 204.248868 \r\nL 187.352761 204.439318 \r\nL 187.400527 204.175618 \r\nL 187.448293 204.175618 \r\nL 187.782654 204.878817 \r\nL 188.117014 205.713866 \r\nL 188.308077 205.918966 \r\nL 188.355843 206.754015 \r\nL 188.403609 206.314515 \r\nL 188.499141 205.757816 \r\nL 188.642438 205.684566 \r\nL 188.737969 206.548915 \r\nL 189.024564 204.615117 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205.347617 \r\nL 226.950604 205.332967 \r\nL 227.093902 205.523416 \r\nL 227.141667 205.435516 \r\nL 227.237199 204.512567 \r\nL 227.284965 204.615117 \r\nL 227.428262 204.703017 \r\nL 227.476028 204.366068 \r\nL 227.57156 205.362267 \r\nL 227.90592 203.369869 \r\nL 227.953686 203.457769 \r\nL 228.096983 203.941218 \r\nL 228.240281 203.428469 \r\nL 228.288047 203.604269 \r\nL 228.431344 203.355219 \r\nL 228.526875 204.307468 \r\nL 228.622407 203.648218 \r\nL 228.765704 203.662868 \r\nL 228.909002 205.010667 \r\nL 229.147831 204.117018 \r\nL 229.195597 204.219568 \r\nL 229.243362 204.146318 \r\nL 229.291128 204.043768 \r\nL 229.482191 204.410018 \r\nL 229.625489 206.036166 \r\nL 229.912083 205.757816 \r\nL 229.959849 205.391566 \r\nL 230.103147 205.230417 \r\nL 230.29421 205.845716 \r\nL 230.485273 205.699216 \r\nL 230.533039 205.523416 \r\nL 230.580805 205.743166 \r\nL 230.62857 205.889666 \r\nL 230.867399 207.662314 \r\nL 230.962931 207.340014 \r\nL 231.106228 207.545114 \r\nL 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204.556517 \r\nL 239.60854 205.845716 \r\nL 239.656305 205.420866 \r\nL 239.847369 205.230417 \r\nL 239.990666 204.805567 \r\nL 240.277261 204.190268 \r\nL 240.468324 203.399169 \r\nL 240.659387 206.651465 \r\nL 240.802684 206.446365 \r\nL 240.85045 206.885865 \r\nL 240.945982 205.420866 \r\nL 240.993748 205.684566 \r\nL 241.137045 206.343815 \r\nL 241.184811 206.211966 \r\nL 241.232577 205.669916 \r\nL 241.328108 207.530464 \r\nL 241.471406 208.043213 \r\nL 241.566937 207.413264 \r\nL 241.614703 207.603714 \r\nL 241.662469 207.310714 \r\nL 241.805766 207.750214 \r\nL 241.949063 207.369314 \r\nL 242.140127 207.047015 \r\nL 242.283424 205.567366 \r\nL 242.33119 205.362267 \r\nL 242.617785 200.996571 \r\nL 242.808848 204.937417 \r\nL 242.856614 204.644417 \r\nL 242.904379 205.157167 \r\nL 242.952145 204.732317 \r\nL 243.143208 205.245067 \r\nL 243.23874 203.897268 \r\nL 243.286506 204.322118 \r\nL 243.334271 204.629767 \r\nL 243.477569 204.248868 \r\nL 243.859695 197.451276 \r\nL 243.955227 200.557072 \r\nL 244.002993 199.956423 \r\nL 244.14629 199.399723 \r\nL 244.337353 202.03672 \r\nL 244.623948 200.102923 \r\nL 244.862777 199.399723 \r\nL 244.958308 196.586927 \r\nL 245.006074 197.846825 \r\nL 245.149372 196.616226 \r\nL 245.292669 199.048124 \r\nL 245.340435 198.330275 \r\nL 245.483732 198.359575 \r\nL 245.531498 197.700325 \r\nL 245.62703 197.758925 \r\nL 245.674795 200.718222 \r\nL 245.818093 201.099121 \r\nL 245.96139 202.988969 \r\nL 246.009156 202.38832 \r\nL 246.200219 202.56412 \r\nL 246.247985 202.47622 \r\nL 246.295751 203.179419 \r\nL 246.343516 202.65202 \r\nL 246.53458 202.15392 \r\nL 246.582345 202.25647 \r\nL 246.677877 203.194069 \r\nL 246.916706 199.707373 \r\nL 247.012238 201.816971 \r\nL 247.203301 202.52017 \r\nL 247.346598 204.468618 \r\nL 247.489896 204.336768 \r\nL 247.537661 203.721468 \r\nL 247.585427 204.527217 \r\nL 247.680959 203.721468 \r\nL 247.967553 204.087718 \r\nL 248.206382 201.89022 \r\nL 248.34968 203.574969 \r\nL 248.492977 203.985168 \r\nL 248.540743 203.941218 \r\nL 248.636275 202.69597 \r\nL 248.68404 202.783869 \r\nL 248.827338 201.96347 \r\nL 248.875104 202.54947 \r\nL 248.922869 202.10997 \r\nL 249.018401 201.714421 \r\nL 249.161698 202.18322 \r\nL 249.496059 200.571722 \r\nL 249.543825 200.791472 \r\nL 249.687122 202.73992 \r\nL 249.830419 202.59342 \r\nL 249.878185 201.816971 \r\nL 249.925951 202.71062 \r\nL 249.973717 202.59342 \r\nL 250.021483 203.941218 \r\nL 250.260312 203.457769 \r\nL 250.355843 202.15392 \r\nL 250.499141 202.30042 \r\nL 250.546906 202.54947 \r\nL 250.881267 200.688922 \r\nL 250.976798 201.274921 \r\nL 251.167862 199.590173 \r\nL 251.263393 200.981922 \r\nL 251.311159 200.498472 \r\nL 251.549988 202.15392 \r\nL 251.884349 199.223924 \r\nL 251.932114 199.370423 \r\nL 252.027646 198.491424 \r\nL 252.170943 198.637924 \r\nL 252.314241 196.879926 \r\nL 252.362006 197.539175 \r\nL 252.505304 199.297173 \r\nL 252.55307 199.150674 \r\nL 252.600835 198.491424 \r\nL 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203.765418 \r\nL 260.72102 202.15392 \r\nL 260.912083 203.911918 \r\nL 261.055381 202.22717 \r\nL 261.198678 202.813169 \r\nL 261.341976 201.201671 \r\nL 261.62857 203.179419 \r\nL 261.676336 203.106169 \r\nL 261.915165 203.692168 \r\nL 262.058463 205.376917 \r\nL 262.249526 205.083917 \r\nL 262.297292 205.230417 \r\nL 262.345057 204.790917 \r\nL 262.392823 204.864167 \r\nL 262.53612 204.278168 \r\nL 262.631652 205.083917 \r\nL 262.679418 204.864167 \r\nL 262.870481 203.618919 \r\nL 262.918247 204.717667 \r\nL 262.966013 204.644417 \r\nL 263.013778 204.351418 \r\nL 263.061544 204.424668 \r\nL 263.204842 204.351418 \r\nL 263.252607 203.545669 \r\nL 263.300373 203.838668 \r\nL 263.395905 204.717667 \r\nL 263.539202 204.717667 \r\nL 263.586968 204.571167 \r\nL 263.634734 204.717667 \r\nL 263.730265 203.472419 \r\nL 263.873563 202.08067 \r\nL 263.969094 202.15392 \r\nL 264.01686 202.22717 \r\nL 264.064626 201.055171 \r\nL 264.255689 202.37367 \r\nL 264.351221 201.787671 \r\nL 264.398986 202.30042 \r\nL 264.637815 203.106169 \r\nL 264.685581 202.44692 \r\nL 264.733347 202.66667 \r\nL 264.876644 202.44692 \r\nL 265.067708 205.596666 \r\nL 265.306536 205.962916 \r\nL 265.354302 205.596666 \r\nL 265.402068 205.669916 \r\nL 265.545365 205.450166 \r\nL 265.593131 205.157167 \r\nL 265.736429 206.182666 \r\nL 265.879726 205.743166 \r\nL 265.927492 206.021516 \r\nL 266.070789 204.981367 \r\nL 266.309618 203.560319 \r\nL 266.357384 204.893467 \r\nL 266.548447 204.820217 \r\nL 266.596213 204.615117 \r\nL 266.691745 205.274367 \r\nL 266.73951 205.025317 \r\nL 266.882808 205.713866 \r\nL 267.026105 204.248868 \r\nL 267.073871 205.274367 \r\nL 267.264934 205.230417 \r\nL 267.408231 204.556517 \r\nL 267.551529 204.615117 \r\nL 267.64706 203.501719 \r\nL 267.694826 203.853318 \r\nL 267.742592 203.736118 \r\nL 267.885889 202.945019 \r\nL 267.933655 202.974319 \r\nL 268.076953 204.746967 \r\nL 268.268016 204.263518 \r\nL 268.315781 203.853318 \r\nL 268.363547 204.102368 \r\nL 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280.639356 210.138161 \r\nL 280.687122 210.82671 \r\nL 280.734888 210.636261 \r\nL 280.782654 209.625412 \r\nL 280.925951 210.020961 \r\nL 281.069248 211.07576 \r\nL 281.117014 211.14901 \r\nL 281.308077 211.14901 \r\nL 281.594672 212.701908 \r\nL 281.642438 214.372006 \r\nL 281.690204 213.859257 \r\nL 281.785735 211.735009 \r\nL 282.024564 211.48596 \r\nL 282.07233 211.632459 \r\nL 282.120096 211.14901 \r\nL 282.311159 211.735009 \r\nL 282.358925 211.544559 \r\nL 282.406691 211.10506 \r\nL 282.454456 211.26621 \r\nL 282.597754 212.203809 \r\nL 282.788817 214.884756 \r\nL 282.932114 216.305804 \r\nL 283.027646 215.045905 \r\nL 283.123177 216.144654 \r\nL 283.314241 211.969409 \r\nL 283.362006 212.130559 \r\nL 283.409772 213.068158 \r\nL 283.457538 212.614008 \r\nL 283.648601 212.482158 \r\nL 283.744133 212.071959 \r\nL 283.982962 213.170708 \r\nL 284.078493 212.907008 \r\nL 284.126259 212.980258 \r\nL 284.269557 212.833758 \r\nL 284.365088 211.603159 \r\nL 284.603917 213.742057 \r\nL 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201.201671 \r\nL 304.426721 200.264072 \r\nL 304.522253 198.930924 \r\nL 304.66555 198.007975 \r\nL 304.713316 198.476774 \r\nL 304.761082 198.125175 \r\nL 304.856614 197.216876 \r\nL 305.095443 197.890775 \r\nL 305.190974 197.085026 \r\nL 305.382037 197.509875 \r\nL 305.429803 197.260826 \r\nL 305.477569 197.480576 \r\nL 305.525335 197.524525 \r\nL 305.811929 200.527772 \r\nL 305.859695 202.945019 \r\nL 306.002993 201.816971 \r\nL 306.098524 199.839223 \r\nL 306.14629 200.527772 \r\nL 306.194056 200.015023 \r\nL 306.432885 200.894022 \r\nL 306.528416 199.033474 \r\nL 306.719479 199.487623 \r\nL 306.815011 201.802321 \r\nL 306.862777 201.509321 \r\nL 307.149372 199.487623 \r\nL 307.197137 199.839223 \r\nL 307.340435 199.326473 \r\nL 307.531498 196.938526 \r\nL 307.674795 196.352527 \r\nL 307.770327 194.506629 \r\nL 307.818093 195.766527 \r\nL 307.865859 194.448029 \r\nL 308.009156 192.367731 \r\nL 308.056922 194.008529 \r\nL 308.104688 193.55438 \r\nL 308.200219 193.55438 \r\nL 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184.99879 \r\nL 323.915165 183.167542 \r\nL 324.058463 182.303193 \r\nL 324.440589 185.526189 \r\nL 324.488355 184.88159 \r\nL 324.53612 185.687339 \r\nL 324.583886 185.643389 \r\nL 324.774949 184.44209 \r\nL 324.822715 185.20389 \r\nL 324.918247 183.548441 \r\nL 325.061544 183.182192 \r\nL 325.252607 181.248394 \r\nL 325.395905 180.720995 \r\nL 325.443671 181.365594 \r\nL 325.491436 180.603795 \r\nL 325.586968 180.603795 \r\nL 325.730265 180.340095 \r\nL 325.921328 186.463788 \r\nL 326.064626 187.606487 \r\nL 326.112392 190.580433 \r\nL 326.207923 190.419284 \r\nL 326.255689 191.957532 \r\nL 326.446752 189.760034 \r\nL 326.542284 192.323781 \r\nL 326.59005 191.151783 \r\nL 326.733347 190.184884 \r\nL 326.781113 190.448584 \r\nL 326.828879 195.590728 \r\nL 326.92441 195.107278 \r\nL 327.115473 193.87668 \r\nL 327.211005 195.605378 \r\nL 327.258771 194.286879 \r\nL 327.449834 193.45183 \r\nL 327.593131 192.382381 \r\nL 327.736429 193.23208 \r\nL 327.784194 192.821881 \r\nL 327.927492 189.379135 \r\nL 328.118555 189.203335 \r\nL 328.166321 189.818634 \r\nL 328.40515 186.244038 \r\nL 328.73951 190.844133 \r\nL 328.787276 190.770883 \r\nL 328.835042 191.503382 \r\nL 328.930573 187.621137 \r\nL 329.121637 189.086135 \r\nL 329.217168 187.181637 \r\nL 329.264934 188.119236 \r\nL 329.455997 189.305885 \r\nL 329.551529 191.005283 \r\nL 329.599295 190.624383 \r\nL 329.885889 191.649882 \r\nL 329.933655 191.195733 \r\nL 330.124718 192.030782 \r\nL 330.172484 193.36393 \r\nL 330.22025 192.733981 \r\nL 330.268016 192.572831 \r\nL 330.506845 191.019933 \r\nL 330.602376 192.455631 \r\nL 330.745674 192.543531 \r\nL 330.841205 194.623829 \r\nL 330.936737 194.345479 \r\nL 331.1278 195.356328 \r\nL 331.271097 193.42253 \r\nL 331.414395 193.29068 \r\nL 331.462161 192.968381 \r\nL 331.605458 194.799629 \r\nL 331.748755 195.781177 \r\nL 331.892053 193.52508 \r\nL 331.939818 193.77413 \r\nL 332.083116 193.21743 \r\nL 332.226413 191.312933 \r\nL 332.417476 191.225033 \r\nL 332.60854 193.87668 \r\nL 332.751837 193.92063 \r\nL 332.799603 196.455077 \r\nL 332.847369 195.444228 \r\nL 332.895134 191.723132 \r\nL 332.9429 191.898932 \r\nL 333.086198 191.708482 \r\nL 333.133963 191.869632 \r\nL 333.181729 192.572831 \r\nL 333.229495 191.444782 \r\nL 333.277261 191.811032 \r\nL 333.468324 193.36393 \r\nL 333.611621 191.547332 \r\nL 333.802684 191.093183 \r\nL 333.85045 191.283633 \r\nL 333.898216 190.917383 \r\nL 333.945982 189.628184 \r\nL 334.089279 189.540285 \r\nL 334.137045 189.847934 \r\nL 334.232577 192.455631 \r\nL 334.280342 192.294481 \r\nL 334.471406 191.298283 \r\nL 334.519171 191.371532 \r\nL 334.614703 192.587481 \r\nL 334.758 192.206582 \r\nL 334.853532 193.056281 \r\nL 334.901298 192.543531 \r\nL 335.092361 191.034583 \r\nL 335.140127 190.756233 \r\nL 335.522253 194.579879 \r\nL 335.570019 194.096429 \r\nL 335.617785 193.84738 \r\nL 335.761082 193.71553 \r\nL 335.808848 193.92063 \r\nL 335.856614 195.825127 \r\nL 335.904379 195.707928 \r\nL 335.952145 194.799629 \r\nL 336.095443 195.312378 \r\nL 336.23874 193.77413 \r\nL 336.286506 192.265181 \r\nL 336.5731 194.096429 \r\nL 336.907461 197.319426 \r\nL 336.955227 195.825127 \r\nL 337.098524 195.517478 \r\nL 337.289587 193.90598 \r\nL 337.432885 194.462679 \r\nL 337.480651 194.316179 \r\nL 337.528416 194.125729 \r\nL 337.576182 192.616781 \r\nL 337.623948 193.12953 \r\nL 337.767245 192.851181 \r\nL 337.815011 192.162632 \r\nL 337.862777 192.968381 \r\nL 337.910543 192.821881 \r\nL 337.958308 192.235881 \r\nL 338.101606 192.631431 \r\nL 338.149372 192.265181 \r\nL 338.292669 194.389429 \r\nL 338.435966 193.58368 \r\nL 338.483732 192.514231 \r\nL 338.531498 193.62763 \r\nL 338.579264 195.883727 \r\nL 338.62703 191.913582 \r\nL 338.770327 194.330829 \r\nL 338.818093 193.64228 \r\nL 338.865859 193.77413 \r\nL 338.96139 194.389429 \r\nL 339.200219 193.83273 \r\nL 339.295751 192.426331 \r\nL 339.53458 192.939081 \r\nL 339.630111 190.082334 \r\nL 339.773409 191.181083 \r\nL 340.107769 201.99277 \r\nL 340.155535 201.743721 \r\nL 340.298832 199.092074 \r\nL 340.44213 199.560873 \r\nL 340.537661 200.952622 \r\nL 340.633193 200.732872 \r\nL 340.77649 200.835422 \r\nL 340.919788 199.018824 \r\nL 340.967553 200.483822 \r\nL 341.110851 200.659622 \r\nL 341.254148 202.18322 \r\nL 341.301914 201.216321 \r\nL 341.445211 201.523971 \r\nL 341.492977 202.25647 \r\nL 341.636275 201.099121 \r\nL 341.779572 201.055171 \r\nL 341.875104 202.37367 \r\nL 341.970635 203.780068 \r\nL 342.161698 204.234218 \r\nL 342.209464 203.970518 \r\nL 342.304996 200.864722 \r\nL 342.448293 202.28577 \r\nL 342.496059 201.846271 \r\nL 342.59159 207.794164 \r\nL 342.639356 206.929815 \r\nL 342.782654 207.442564 \r\nL 342.830419 206.021516 \r\nL 342.878185 206.504965 \r\nL 343.117014 206.050816 \r\nL 343.16478 204.761617 \r\nL 343.212546 205.054617 \r\nL 343.260312 206.255915 \r\nL 343.308077 204.922767 \r\nL 343.451375 203.736118 \r\nL 343.594672 206.050816 \r\nL 343.642438 205.479466 \r\nL 343.833501 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197.583125 \r\nL 348.466783 196.762726 \r\nL 348.514549 196.777376 \r\nL 348.562315 196.923876 \r\nL 348.61008 195.722578 \r\nL 348.657846 195.986277 \r\nL 348.896675 195.649328 \r\nL 348.944441 195.034028 \r\nL 349.135504 194.140379 \r\nL 349.18327 192.777931 \r\nL 349.231036 193.070931 \r\nL 349.326567 195.034028 \r\nL 349.469865 195.107278 \r\nL 349.660928 198.989524 \r\nL 349.995288 194.711729 \r\nL 350.138586 194.931478 \r\nL 350.234117 194.931478 \r\nL 350.281883 194.814279 \r\nL 350.329649 193.70088 \r\nL 350.472946 193.21743 \r\nL 350.520712 193.40788 \r\nL 350.616244 194.887528 \r\nL 350.66401 194.242929 \r\nL 350.807307 195.180528 \r\nL 350.99837 192.528881 \r\nL 351.189433 190.697633 \r\nL 351.284965 192.411681 \r\nL 351.332731 192.133332 \r\nL 351.476028 192.060082 \r\nL 351.619325 194.462679 \r\nL 351.667091 194.330829 \r\nL 351.858154 196.689476 \r\nL 351.90592 195.869077 \r\nL 351.953686 197.128976 \r\nL 352.001452 196.264627 \r\nL 352.144749 196.660176 \r\nL 352.192515 196.337877 \r\nL 352.335812 197.216876 \r\nL 352.526875 197.964025 \r\nL 352.670173 195.488178 \r\nL 352.81347 195.370978 \r\nL 352.861236 196.279277 \r\nL 352.909002 195.971627 \r\nL 352.956768 195.898377 \r\nL 353.004533 196.059527 \r\nL 353.338894 194.184329 \r\nL 353.577723 194.140379 \r\nL 353.816552 195.136578 \r\nL 353.864318 195.107278 \r\nL 354.007615 193.17348 \r\nL 354.150912 193.53973 \r\nL 354.246444 192.587481 \r\nL 354.485273 194.448029 \r\nL 354.533039 194.448029 \r\nL 354.580805 194.198979 \r\nL 354.62857 194.360129 \r\nL 354.676336 194.535929 \r\nL 354.819634 194.550579 \r\nL 354.867399 193.24673 \r\nL 354.915165 193.84738 \r\nL 354.962931 194.594529 \r\nL 355.010697 194.067129 \r\nL 355.153994 194.535929 \r\nL 355.20176 193.61298 \r\nL 355.249526 194.491979 \r\nL 355.345057 194.067129 \r\nL 355.918247 188.939635 \r\nL 356.252607 191.957532 \r\nL 356.300373 191.195733 \r\nL 356.348139 190.990633 \r\nL 356.539202 192.763281 \r\nL 356.825797 189.774684 \r\nL 356.969094 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193.37858 \r\nL 360.933655 194.169679 \r\nL 360.981421 193.48113 \r\nL 361.029187 193.83273 \r\nL 361.172484 193.45183 \r\nL 361.602376 188.881035 \r\nL 361.697908 188.646636 \r\nL 361.984503 185.658039 \r\nL 362.032268 185.555489 \r\nL 362.223332 187.709037 \r\nL 362.318863 186.595638 \r\nL 362.366629 184.70579 \r\nL 362.509926 186.126838 \r\nL 362.605458 188.514786 \r\nL 362.653224 186.624938 \r\nL 362.70099 187.152337 \r\nL 362.892053 185.936389 \r\nL 363.03535 183.797491 \r\nL 363.226413 182.713392 \r\nL 363.321945 183.768191 \r\nL 363.369711 182.185993 \r\nL 363.513008 185.921739 \r\nL 363.560774 185.526189 \r\nL 363.656305 183.943991 \r\nL 363.704071 184.33954 \r\nL 363.847369 185.321089 \r\nL 364.038432 182.083443 \r\nL 364.181729 182.947792 \r\nL 364.325026 179.768746 \r\nL 364.372792 179.798046 \r\nL 364.611621 179.197396 \r\nL 364.659387 180.032445 \r\nL 364.707153 179.578296 \r\nL 364.85045 178.801847 \r\nL 364.898216 180.603795 \r\nL 364.945982 177.820298 \r\nL 364.993748 179.475746 \r\nL 365.041513 179.578296 \r\nL 365.184811 180.545195 \r\nL 365.375874 178.113298 \r\nL 365.566937 179.505046 \r\nL 365.614703 177.952148 \r\nL 365.710234 181.556044 \r\nL 365.853532 182.273893 \r\nL 366.044595 193.61298 \r\nL 366.187892 190.199534 \r\nL 366.235658 184.47139 \r\nL 366.283424 191.371532 \r\nL 366.33119 189.027535 \r\nL 366.378956 189.174035 \r\nL 366.522253 187.811587 \r\nL 366.617785 191.605932 \r\nL 366.66555 190.873433 \r\nL 366.856614 192.763281 \r\nL 366.904379 192.455631 \r\nL 366.952145 191.049233 \r\nL 366.999911 193.58368 \r\nL 367.047677 192.001482 \r\nL 367.190974 191.811032 \r\nL 367.334271 189.335185 \r\nL 367.525335 188.368286 \r\nL 367.859695 196.440427 \r\nL 367.907461 196.337877 \r\nL 367.955227 195.209828 \r\nL 368.002993 195.268428 \r\nL 368.050758 195.722578 \r\nL 368.194056 194.711729 \r\nL 368.241822 195.854427 \r\nL 368.289587 194.990078 \r\nL 368.385119 192.924431 \r\nL 368.528416 193.37858 \r\nL 368.623948 190.639033 \r\nL 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179.021597 \r\nL 384.577723 178.069348 \r\nL 384.673255 179.783396 \r\nL 384.72102 178.142598 \r\nL 384.768786 178.201197 \r\nL 384.912083 178.552797 \r\nL 384.959849 178.333047 \r\nL 385.007615 179.798046 \r\nL 385.055381 178.201197 \r\nL 385.103147 178.904397 \r\nL 385.246444 179.197396 \r\nL 385.389741 178.494197 \r\nL 385.437507 178.772547 \r\nL 385.62857 176.501799 \r\nL 385.771868 174.861001 \r\nL 385.915165 176.443199 \r\nL 386.106228 172.751404 \r\nL 386.249526 172.590254 \r\nL 386.297292 174.362902 \r\nL 386.345057 172.473054 \r\nL 386.440589 175.080751 \r\nL 386.583886 171.857755 \r\nL 386.631652 172.707454 \r\nL 386.727184 175.93045 \r\nL 386.774949 174.978201 \r\nL 386.966013 171.418255 \r\nL 387.10931 177.117099 \r\nL 387.252607 176.706899 \r\nL 387.300373 178.962997 \r\nL 387.348139 178.347697 \r\nL 387.395905 177.102449 \r\nL 387.634734 178.962997 \r\nL 387.6825 178.743247 \r\nL 387.778031 176.765499 \r\nL 388.01686 178.406297 \r\nL 388.064626 178.040048 \r\nL 388.303455 181.277694 \r\nL 388.398986 176.941299 \r\nL 388.446752 177.219649 \r\nL 388.733347 174.436152 \r\nL 388.781113 174.978201 \r\nL 388.92441 175.8572 \r\nL 388.972176 175.490951 \r\nL 389.115473 173.220203 \r\nL 389.258771 172.560954 \r\nL 389.354302 173.908752 \r\nL 389.449834 172.165404 \r\nL 389.593131 172.707454 \r\nL 389.975258 168.781258 \r\nL 390.023023 170.832256 \r\nL 390.070789 168.400359 \r\nL 390.118555 169.044958 \r\nL 390.261852 167.711809 \r\nL 390.309618 168.224559 \r\nL 390.357384 167.916909 \r\nL 390.452916 165.148062 \r\nL 390.596213 166.012411 \r\nL 390.73951 162.144816 \r\nL 390.787276 163.697714 \r\nL 391.026105 169.220758 \r\nL 391.073871 170.348806 \r\nL 391.121637 170.275557 \r\nL 391.3127 171.564755 \r\nL 391.408231 168.913108 \r\nL 391.455997 169.059608 \r\nL 391.694826 170.773656 \r\nL 391.742592 168.195259 \r\nL 391.790358 170.671106 \r\nL 391.981421 168.063409 \r\nL 392.029187 169.220758 \r\nL 392.076953 168.927758 \r\nL 392.124718 166.583761 \r\nL 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134.676097 \r\nL 408.699449 131.013601 \r\nL 408.79498 131.614251 \r\nL 408.842746 135.408596 \r\nL 409.033809 136.990794 \r\nL 409.177107 139.364092 \r\nL 409.36817 140.87304 \r\nL 409.415936 137.913743 \r\nL 409.463701 138.734143 \r\nL 409.511467 141.26859 \r\nL 409.654765 134.969097 \r\nL 409.750296 135.335346 \r\nL 409.798062 135.174197 \r\nL 409.845828 138.690193 \r\nL 409.989125 136.771045 \r\nL 410.036891 138.294643 \r\nL 410.132422 135.525796 \r\nL 410.180188 136.434095 \r\nL 410.323486 137.679344 \r\nL 410.514549 142.880088 \r\nL 410.657846 144.491586 \r\nL 410.753378 139.481292 \r\nL 410.801144 141.06349 \r\nL 410.848909 141.708089 \r\nL 410.992207 139.290842 \r\nL 411.135504 144.008136 \r\nL 411.18327 143.993487 \r\nL 411.326567 144.608786 \r\nL 411.374333 141.385789 \r\nL 411.422099 144.286486 \r\nL 411.51763 143.627237 \r\nL 411.756459 145.897984 \r\nL 411.804225 143.041238 \r\nL 411.851991 144.652736 \r\nL 411.995288 146.410734 \r\nL 412.043054 145.971234 \r\nL 412.09082 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133.284349 \r\nL 416.103147 135.848096 \r\nL 416.150912 138.631593 \r\nL 416.198678 137.606094 \r\nL 416.341976 138.045593 \r\nL 416.437507 141.854589 \r\nL 416.485273 141.634839 \r\nL 416.771868 149.033081 \r\nL 416.819634 147.568082 \r\nL 416.867399 145.883334 \r\nL 417.058463 143.759087 \r\nL 417.153994 148.417781 \r\nL 417.20176 143.392837 \r\nL 417.345057 144.052086 \r\nL 417.440589 142.587088 \r\nL 417.53612 140.243091 \r\nL 417.727184 143.319587 \r\nL 417.870481 145.663585 \r\nL 418.157076 140.462841 \r\nL 418.443671 145.487785 \r\nL 418.539202 139.818241 \r\nL 418.6825 140.037991 \r\nL 418.825797 135.716246 \r\nL 418.873563 136.375495 \r\nL 419.01686 136.639195 \r\nL 419.064626 137.107994 \r\nL 419.160157 134.954447 \r\nL 419.207923 135.247446 \r\nL 419.876644 125.417308 \r\nL 420.067708 134.646797 \r\nL 420.211005 128.244754 \r\nL 420.402068 124.113459 \r\nL 420.4976 130.911051 \r\nL 420.545365 130.749902 \r\nL 420.688663 131.248001 \r\nL 420.83196 126.486756 \r\nL 420.879726 128.142205 \r\nL 421.118555 132.859499 \r\nL 421.166321 131.423801 \r\nL 421.214087 133.167149 \r\nL 421.357384 132.0098 \r\nL 421.500681 129.533953 \r\nL 421.548447 128.904004 \r\nL 421.73951 128.171505 \r\nL 421.835042 130.720602 \r\nL 421.882808 126.882306 \r\nL 422.073871 127.321806 \r\nL 422.169402 130.515502 \r\nL 422.217168 128.508454 \r\nL 422.360466 130.471552 \r\nL 422.408231 133.108549 \r\nL 422.455997 132.17095 \r\nL 422.503763 131.204051 \r\nL 422.551529 131.248001 \r\nL 422.694826 132.0391 \r\nL 422.742592 131.90725 \r\nL 422.838124 129.973452 \r\nL 422.885889 130.149252 \r\nL 423.459079 123.26376 \r\nL 423.506845 123.55676 \r\nL 423.55461 125.373358 \r\nL 423.697908 125.754257 \r\nL 423.793439 123.57141 \r\nL 423.888971 120.919763 \r\nL 424.032268 122.531261 \r\nL 424.1278 126.794406 \r\nL 424.175566 125.021758 \r\nL 424.223332 125.724957 \r\nL 424.366629 124.596909 \r\nL 424.414395 125.812857 \r\nL 424.462161 125.285458 \r\nL 424.509926 124.289259 \r\nL 424.557692 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145.634285 \r\nL 428.140127 149.106331 \r\nL 428.187892 148.124782 \r\nL 428.378956 145.663585 \r\nL 428.570019 149.41398 \r\nL 428.713316 150.307629 \r\nL 428.904379 144.432986 \r\nL 429.190974 148.652181 \r\nL 429.382037 145.370585 \r\nL 429.429803 147.084633 \r\nL 429.477569 145.634285 \r\nL 429.525335 143.803037 \r\nL 429.5731 146.029834 \r\nL 429.764164 148.051532 \r\nL 429.811929 147.304383 \r\nL 429.907461 151.538228 \r\nL 430.050758 150.776429 \r\nL 430.098524 148.447081 \r\nL 430.14629 149.51653 \r\nL 430.576182 140.50679 \r\nL 430.815011 142.308738 \r\nL 430.862777 141.810639 \r\nL 431.101606 143.978837 \r\nL 431.149372 143.568637 \r\nL 431.244903 140.55074 \r\nL 431.388201 142.323388 \r\nL 431.483732 141.01954 \r\nL 431.579264 142.880088 \r\nL 431.770327 143.964187 \r\nL 431.818093 145.077585 \r\nL 431.865859 144.960385 \r\nL 431.913624 144.286486 \r\nL 432.104687 144.403686 \r\nL 432.200219 151.904477 \r\nL 432.247985 150.966879 \r\nL 432.391282 151.274528 \r\nL 432.439048 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\n"
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
}
|
||
}
|
||
],
|
||
"source": [
|
||
"mydata.plot(figsize=(12,6))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"Notez que vous devez connaître le \"code Quandl\" de chaque ensemble de données que vous téléchargez. Dans l'exemple ci-dessus, il s'agit de \"EIA/PET_RWTC_D\"."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Modifier les formats\n",
|
||
"Vous pouvez obtenir les mêmes données dans un tableau NumPy:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"mydata = quandl.get(\"EIA/PET_RWTC_D\", returns=\"numpy\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Spécification des données\n",
|
||
"\n",
|
||
"Pour définir les dates de début et de fin:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"mydata = quandl.get(\"FRED/GDP\", start_date=\"2001-12-31\", end_date=\"2005-12-31\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" Value\n",
|
||
"Date \n",
|
||
"2002-01-01 10788.952\n",
|
||
"2002-04-01 10893.207\n",
|
||
"2002-07-01 10992.051\n",
|
||
"2002-10-01 11071.463\n",
|
||
"2003-01-01 11183.507"
|
||
],
|
||
"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>Value</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2002-01-01</th>\n <td>10788.952</td>\n </tr>\n <tr>\n <th>2002-04-01</th>\n <td>10893.207</td>\n </tr>\n <tr>\n <th>2002-07-01</th>\n <td>10992.051</td>\n </tr>\n <tr>\n <th>2002-10-01</th>\n <td>11071.463</td>\n </tr>\n <tr>\n <th>2003-01-01</th>\n <td>11183.507</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 8
|
||
}
|
||
],
|
||
"source": [
|
||
"mydata.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"mydata = quandl.get([\"NSE/OIL.1\", \"WIKI/AAPL.4\"])"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" NSE/OIL - Open WIKI/AAPL - Close\n",
|
||
"Date \n",
|
||
"1980-12-12 NaN 28.75\n",
|
||
"1980-12-15 NaN 27.25\n",
|
||
"1980-12-16 NaN 25.25\n",
|
||
"1980-12-17 NaN 25.87\n",
|
||
"1980-12-18 NaN 26.63"
|
||
],
|
||
"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>NSE/OIL - Open</th>\n <th>WIKI/AAPL - Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1980-12-12</th>\n <td>NaN</td>\n <td>28.75</td>\n </tr>\n <tr>\n <th>1980-12-15</th>\n <td>NaN</td>\n <td>27.25</td>\n </tr>\n <tr>\n <th>1980-12-16</th>\n <td>NaN</td>\n <td>25.25</td>\n </tr>\n <tr>\n <th>1980-12-17</th>\n <td>NaN</td>\n <td>25.87</td>\n </tr>\n <tr>\n <th>1980-12-18</th>\n <td>NaN</td>\n <td>26.63</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 10
|
||
}
|
||
],
|
||
"source": [
|
||
"mydata.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Limites d'utilisation\n",
|
||
"Le module Quandl Python est gratuit. Si vous souhaitez passer plus de 50 requêtes par jour, vous devrez créer un compte Quandl gratuit et définir votre clé API:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# EXEMPLE\n",
|
||
"quandl.ApiConfig.api_key = \"A remplacer par votre clé API (API KEY)\"\n",
|
||
"mydata = quandl.get(\"FRED/GDP\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Codes de base de données\n",
|
||
"\n",
|
||
"Chaque base de données sur Quandl a un ID de base de données court (3 à 6 caractères). Par exemple :\n",
|
||
"\n",
|
||
"* CFTC Commitment of Traders Data: CFTC\n",
|
||
"* Actions américaines (Core US Stock Fundamentals): SF1\n",
|
||
"* Données économiques de la Réserve fédérale américaine (Federal Reserve Economic Data): FRED\n",
|
||
"\n",
|
||
"Chaque base de données contient de nombreux ensembles de données. Les ensembles de données ont leurs propres ID qui sont ajoutés à l'ID de leur base de données mère, comme ceci :\n",
|
||
"\n",
|
||
"* CFTC pour le blé: CFTC/W_F_ALL\n",
|
||
"* Capitalisation d'Apple: SF1/AAPL_MARKETCAP\n",
|
||
"* Taux de chômage américain: FRED/UNRATE\n",
|
||
"\n",
|
||
"Vous pouvez télécharger tous les codes d'un ensemble de données dans une base de données en ue seule requête API, en ajoutant '/codes' à votre demande de base de données. La requête retourne un fichier ZIP contenant un CSV.\n",
|
||
"\n",
|
||
"### Bases de Données\n",
|
||
"\n",
|
||
"\n",
|
||
"Chaque code Quandl comporte 2 parties: le code de la base de données (\"WIKI\") qui spécifie d'où proviennent les données, et le code du jeu de données (\"FB\") qui identifie la série temporelle spécifique que vous voulez.\n",
|
||
"\n",
|
||
"Vous pouvez trouver les codes Quandl sur leur site Web, en utilisant leur navigateur de données.\n",
|
||
"\n",
|
||
"https://www.quandl.com/search"
|
||
]
|
||
},
|
||
{
|
||
"source": [
|
||
"# POUR LES ACTIONS (STOCKS)"
|
||
],
|
||
"cell_type": "code",
|
||
"metadata": {},
|
||
"execution_count": 12,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"mydata = quandl.get('WIKI/FB',start_date='2015-01-01',end_date='2017-01-01')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" Open High Low Close Volume Ex-Dividend \\\n",
|
||
"Date \n",
|
||
"2015-01-02 78.58 78.9300 77.700 78.450 18177475.0 0.0 \n",
|
||
"2015-01-05 77.98 79.2455 76.860 77.190 26452191.0 0.0 \n",
|
||
"2015-01-06 77.23 77.5900 75.365 76.150 27399288.0 0.0 \n",
|
||
"2015-01-07 76.76 77.3600 75.820 76.150 22045333.0 0.0 \n",
|
||
"2015-01-08 76.74 78.2300 76.080 78.175 23960953.0 0.0 \n",
|
||
"\n",
|
||
" Split Ratio Adj. Open Adj. High Adj. Low Adj. Close \\\n",
|
||
"Date \n",
|
||
"2015-01-02 1.0 78.58 78.9300 77.700 78.450 \n",
|
||
"2015-01-05 1.0 77.98 79.2455 76.860 77.190 \n",
|
||
"2015-01-06 1.0 77.23 77.5900 75.365 76.150 \n",
|
||
"2015-01-07 1.0 76.76 77.3600 75.820 76.150 \n",
|
||
"2015-01-08 1.0 76.74 78.2300 76.080 78.175 \n",
|
||
"\n",
|
||
" Adj. Volume \n",
|
||
"Date \n",
|
||
"2015-01-02 18177475.0 \n",
|
||
"2015-01-05 26452191.0 \n",
|
||
"2015-01-06 27399288.0 \n",
|
||
"2015-01-07 22045333.0 \n",
|
||
"2015-01-08 23960953.0 "
|
||
],
|
||
"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>Ex-Dividend</th>\n <th>Split Ratio</th>\n <th>Adj. Open</th>\n <th>Adj. High</th>\n <th>Adj. Low</th>\n <th>Adj. Close</th>\n <th>Adj. Volume</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 <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>2015-01-02</th>\n <td>78.58</td>\n <td>78.9300</td>\n <td>77.700</td>\n <td>78.450</td>\n <td>18177475.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>78.58</td>\n <td>78.9300</td>\n <td>77.700</td>\n <td>78.450</td>\n <td>18177475.0</td>\n </tr>\n <tr>\n <th>2015-01-05</th>\n <td>77.98</td>\n <td>79.2455</td>\n <td>76.860</td>\n <td>77.190</td>\n <td>26452191.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>77.98</td>\n <td>79.2455</td>\n <td>76.860</td>\n <td>77.190</td>\n <td>26452191.0</td>\n </tr>\n <tr>\n <th>2015-01-06</th>\n <td>77.23</td>\n <td>77.5900</td>\n <td>75.365</td>\n <td>76.150</td>\n <td>27399288.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>77.23</td>\n <td>77.5900</td>\n <td>75.365</td>\n <td>76.150</td>\n <td>27399288.0</td>\n </tr>\n <tr>\n <th>2015-01-07</th>\n <td>76.76</td>\n <td>77.3600</td>\n <td>75.820</td>\n <td>76.150</td>\n <td>22045333.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>76.76</td>\n <td>77.3600</td>\n <td>75.820</td>\n <td>76.150</td>\n <td>22045333.0</td>\n </tr>\n <tr>\n <th>2015-01-08</th>\n <td>76.74</td>\n <td>78.2300</td>\n <td>76.080</td>\n <td>78.175</td>\n <td>23960953.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>76.74</td>\n <td>78.2300</td>\n <td>76.080</td>\n <td>78.175</td>\n <td>23960953.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 14
|
||
}
|
||
],
|
||
"source": [
|
||
"mydata.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"mydata = quandl.get('WIKI/FB.1',start_date='2015-01-01',end_date='2017-01-01')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
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||
"execution_count": 16,
|
||
"metadata": {},
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||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
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||
"data": {
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||
"text/plain": [
|
||
" Open\n",
|
||
"Date \n",
|
||
"2015-01-02 78.58\n",
|
||
"2015-01-05 77.98\n",
|
||
"2015-01-06 77.23\n",
|
||
"2015-01-07 76.76\n",
|
||
"2015-01-08 76.74"
|
||
],
|
||
"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 </tr>\n <tr>\n <th>Date</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2015-01-02</th>\n <td>78.58</td>\n </tr>\n <tr>\n <th>2015-01-05</th>\n <td>77.98</td>\n </tr>\n <tr>\n <th>2015-01-06</th>\n <td>77.23</td>\n </tr>\n <tr>\n <th>2015-01-07</th>\n <td>76.76</td>\n </tr>\n <tr>\n <th>2015-01-08</th>\n <td>76.74</td>\n </tr>\n </tbody>\n</table>\n</div>"
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||
},
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||
"metadata": {},
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||
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||
}
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||
],
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||
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|
||
"mydata.head()"
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||
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||
},
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||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"mydata = quandl.get('WIKI/FB.7',start_date='2015-01-01',end_date='2017-01-01')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
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|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
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||
"data": {
|
||
"text/plain": [
|
||
" Split Ratio\n",
|
||
"Date \n",
|
||
"2015-01-02 1.0\n",
|
||
"2015-01-05 1.0\n",
|
||
"2015-01-06 1.0\n",
|
||
"2015-01-07 1.0\n",
|
||
"2015-01-08 1.0"
|
||
],
|
||
"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>Split Ratio</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2015-01-02</th>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2015-01-05</th>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2015-01-06</th>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2015-01-07</th>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2015-01-08</th>\n <td>1.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 18
|
||
}
|
||
],
|
||
"source": [
|
||
"mydata.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### Exemple Prix Logement\n",
|
||
"\n",
|
||
"**Zillow Home Value Index (Metro: Indice Zillow des loyers - Tous les logements - San Francisco, CA**\n",
|
||
"\n",
|
||
"L'indice Zillow 'Home Value Index' est l'estimation par Zillow de la valeur marchande médiane de l'indice locatif Zillow pour toutes les maisons dans la zone de San Francisco, CA. Ces données sont calculées par Zillow Real Estate Research (www.zillow.com/research) à partir de leur base de données de 110 millions de foyers."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"houses = quandl.get('ZILLOW/M11_ZRIAH')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" Value\n",
|
||
"Date \n",
|
||
"2010-11-30 2454.0\n",
|
||
"2010-12-31 2461.0\n",
|
||
"2011-01-31 2484.0\n",
|
||
"2011-02-28 2506.0\n",
|
||
"2011-03-31 2526.0"
|
||
],
|
||
"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>Value</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2010-11-30</th>\n <td>2454.0</td>\n </tr>\n <tr>\n <th>2010-12-31</th>\n <td>2461.0</td>\n </tr>\n <tr>\n <th>2011-01-31</th>\n <td>2484.0</td>\n </tr>\n <tr>\n <th>2011-02-28</th>\n <td>2506.0</td>\n </tr>\n <tr>\n <th>2011-03-31</th>\n <td>2526.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 20
|
||
}
|
||
],
|
||
"source": [
|
||
"houses.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"<AxesSubplot:xlabel='Date'>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 21
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||
},
|
||
{
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||
"output_type": "display_data",
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||
"data": {
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||
"text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
|
||
},
|
||
"metadata": {
|
||
"needs_background": "light"
|
||
}
|
||
}
|
||
],
|
||
"source": [
|
||
"houses.plot()"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.7.9-final"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
} |