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module3/exo3/exercice.ipynb
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{
"cells": [],
"cells": [
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style>.container { width:100% !important; }</style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"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>Unnamed: 0</th>\n",
" <th>Year</th>\n",
" <th>Wheat</th>\n",
" <th>Wages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1565</td>\n",
" <td>41.0</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1570</td>\n",
" <td>45.0</td>\n",
" <td>5.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1575</td>\n",
" <td>42.0</td>\n",
" <td>5.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1580</td>\n",
" <td>49.0</td>\n",
" <td>5.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1585</td>\n",
" <td>41.5</td>\n",
" <td>5.15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Year Wheat Wages\n",
"0 1 1565 41.0 5.00\n",
"1 2 1570 45.0 5.05\n",
"2 3 1575 42.0 5.08\n",
"3 4 1580 49.0 5.12\n",
"4 5 1585 41.5 5.15"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.core.display import display, HTML\n",
"display(HTML(\"<style>.container { width:100% !important; }</style>\"))\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"plt.rcParams[\"figure.figsize\"] = (20,10)\n",
"data = pd.read_csv('https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv')\n",
"data = data.dropna()\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute 'update'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-78-497220ad2742>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0max2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtwinx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myaxwages\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'g-'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0max1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_ylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m120\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myaxwheat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'b-'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# ax1 plotted twice in order to have same scale for both wages and wheat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1865\u001b[0m \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1866\u001b[0m RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1867\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1869\u001b[0m inner.__doc__ = _add_data_doc(inner.__doc__,\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbar\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2224\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2225\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2226\u001b[0;31m \u001b[0mwidth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2227\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mxerr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2228\u001b[0m \u001b[0mxerr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_xunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mconvert_xunits\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxaxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 193\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mconvert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1524\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1526\u001b[0;31m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconverter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1527\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/category.py\u001b[0m in \u001b[0;36mconvert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[0;31m# force an update so it also does type checking\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0munit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m str2idx = np.vectorize(unit._mapping.__getitem__,\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'update'"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"xax = data['Year']\n",
"yaxwages = data['Wages']\n",
"yaxwheat = data['Wheat']\n",
"\n",
"fig,ax1 = plt.subplots()\n",
"# ax1.plot(xax, yaxwheat)\n",
"\n",
"\n",
"ax2 = ax1.twinx()\n",
"ax1.plot(xax, yaxwages, 'g-')\n",
"ax1.set_ylim([0,120])\n",
"ax2.plot(xax, yaxwheat, 'b-') # ax1 plotted twice in order to have same scale for both wages and wheat\n",
"ax2.set_ylim([0,120])\n",
"\n",
"ax1.set(xlabel='Year', title='The Price of the Quarter of Wheat & \\nWages of Labor by the Week')\n",
"\n",
"ax1.set_ylabel('Y1 data', color='g')\n",
"ax2.set_ylabel('Y2 data', color='b')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +191,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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