476 lines
207 KiB
Plaintext
476 lines
207 KiB
Plaintext
|
{
|
||
|
|
"cells": [
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Exercices Visualisation Pandas - Solutions\n",
|
||
|
|
"\n",
|
||
|
|
"C'est un exercice rapide pour vous permettre de passer en revue les différentes graphiques que nous avons montrées plus tôt. Utilisez **df3** pour reproduire les tracés suivants. "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 1,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"df3 = pd.read_csv('df3')\n",
|
||
|
|
"%matplotlib inline"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
||
|
|
"RangeIndex: 500 entries, 0 to 499\n",
|
||
|
|
"Data columns (total 4 columns):\n",
|
||
|
|
"a 500 non-null float64\n",
|
||
|
|
"b 500 non-null float64\n",
|
||
|
|
"c 500 non-null float64\n",
|
||
|
|
"d 500 non-null float64\n",
|
||
|
|
"dtypes: float64(4)\n",
|
||
|
|
"memory usage: 15.8 KB\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df3.info()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"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>a</th>\n",
|
||
|
|
" <th>b</th>\n",
|
||
|
|
" <th>c</th>\n",
|
||
|
|
" <th>d</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>0.336272</td>\n",
|
||
|
|
" <td>0.325011</td>\n",
|
||
|
|
" <td>0.001020</td>\n",
|
||
|
|
" <td>0.401402</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>0.980265</td>\n",
|
||
|
|
" <td>0.831835</td>\n",
|
||
|
|
" <td>0.772288</td>\n",
|
||
|
|
" <td>0.076485</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>0.480387</td>\n",
|
||
|
|
" <td>0.686839</td>\n",
|
||
|
|
" <td>0.000575</td>\n",
|
||
|
|
" <td>0.746758</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>0.502106</td>\n",
|
||
|
|
" <td>0.305142</td>\n",
|
||
|
|
" <td>0.768608</td>\n",
|
||
|
|
" <td>0.654685</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>0.856602</td>\n",
|
||
|
|
" <td>0.171448</td>\n",
|
||
|
|
" <td>0.157971</td>\n",
|
||
|
|
" <td>0.321231</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" a b c d\n",
|
||
|
|
"0 0.336272 0.325011 0.001020 0.401402\n",
|
||
|
|
"1 0.980265 0.831835 0.772288 0.076485\n",
|
||
|
|
"2 0.480387 0.686839 0.000575 0.746758\n",
|
||
|
|
"3 0.502106 0.305142 0.768608 0.654685\n",
|
||
|
|
"4 0.856602 0.171448 0.157971 0.321231"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df3.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Recréez ce nuage de points de b en fonction de a. Notez la couleur et la taille des points. Notez également la taille des chiffres. Voyez si vous pouvez trouver un moyen de l'étirer de la même façon. Si besoin revoyez le notebook Matplotlib...**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11b398390>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 864x216 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df3.plot.scatter(x='a',y='b',figsize=(12,3),s=50,c='red')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Créer un histogramme de la colonne 'a'.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11b4b3410>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df3['a'].plot.hist()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Ces graphiques sont biens, mais les finitions ne sont pas extras. Utilisez les feuilles de style pour définir le style sur 'ggplot' et refaire le même histogramme qu'au-dessus. Trouvez aussi comment y ajouter des bandes supplémentaires.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11b7e2150>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.style.use('ggplot')\n",
|
||
|
|
"df3['a'].plot.hist(bins=20,alpha=0.5)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Créer une boîte à moustache comparant les colonnes 'a' et 'b'.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x11b908910>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAQGUlEQVR4nO3db2xTdd/H8U9pcREGZJzK5mDR2ECMmoijGtgDwrIGExOXxQRIjEqyiNFF+RMJCgGdkcVKQLwFTQzOhSt4J4u5IvEPaFIIIW4hGYGp6AM2wcSx6tLWP5MJbju9HnDdva1rd3q6dRs/3q9HnJ5f6dfm8PZwdko9yWQyKQDAdW/aZA8AABgfBB0ADEHQAcAQBB0ADEHQAcAQBB0ADOGbzBfv7e2dzJc3it/vVywWm+wxgBE4NsdXeXl51n2coQOAIQg6ABiCoAOAIQg6ABiCoAOAIRzvcnnnnXd05swZzZkzR3v27BmxP5lMqqWlRWfPnlVRUZEaGhp0xx13FGRYAEB2jmfoK1as0LZt27LuP3v2rH766Se99dZbeuqpp/Tee++N64AAgNw4Bv2uu+5ScXFx1v2nT5/W8uXL5fF4tGjRIl2+fFm//PLLuA4JAHA25g8WJRIJ+f3+1LZlWUokEiopKRmxNhKJKBKJSJLC4XDa8+CsqKgor+ddvXp1nCcBcufz+fizPkHGHPRM34/h8Xgyrg2FQgqFQqltPj3mzqVLl7LuG15XK++BjzPu433GRJg/f77r54x2TCOz0T4pOuagW5aVFox4PJ7x7ByA2bLFebSTDYyvMd+2GAwGdfLkSSWTSZ0/f14zZswg6AAwCRzP0N98801999136u/v19NPP63Vq1draGhIkrRy5Urdd999OnPmjNavX6+bbrpJDQ0NBR8aADCSY9A3btw46n6Px6Mnn3xy3AYCAOSHT4oCgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYwpfLos7OTrW0tMi2bdXU1Kiuri5tfywW09tvv63Lly/Ltm09+uijqqysLMjAAIDMHINu27aam5u1fft2WZalrVu3KhgMasGCBak1//73v7Vs2TKtXLlSPT09eu211wg6AEwwx0su3d3dKisrU2lpqXw+n6qqqtTR0ZG2xuPxaGBgQJI0MDCgkpKSwkwLAMjK8Qw9kUjIsqzUtmVZ6urqSluzatUq7dy5U59//rmuXr2qHTt2ZPy9IpGIIpGIJCkcDsvv949ldvzNzxLvJ6Ykjs2J4xj0ZDI54jGPx5O23dbWphUrVujhhx/W+fPntW/fPu3Zs0fTpqX/BSAUCikUCqW2Y7FYvnMjA95PTFUcm+OnvLw86z7HSy6WZSkej6e24/H4iEsqx48f17JlyyRJixYt0uDgoPr7+/OdFwCQB8egBwIBRaNR9fX1aWhoSO3t7QoGg2lr/H6/zp07J0nq6enR4OCgZs+eXZiJAQAZOV5y8Xq9qq+vV1NTk2zbVnV1tSoqKtTa2qpAIKBgMKgnnnhC7777rj777DNJUkNDw4jLMgCAwvIkM10knyC9vb2T9dLGGV5XK++Bjyd7DGAEjs3xNaZr6ACA6wNBBwBDEHQAMERO/5YLJtbwhkelgT/cP29dbe6LZxTL+z//6/o1AExdBH0qGvjD9Q+R/H6/qw9vuIo/gOsCl1wAwBAEHQAMQdABwBAEHQAMwQ9FAbiSz11Yrn8Iz11YeSHoANxxeReW2zuwJO7CyheXXADAEAQdAAxB0AHAEAQdAAxB0AHAEAQdAAxB0AHAEAQdAAxB0AHAEAQdAAxB0AHAEAQdAAxB0AHAEAQdAAxB0AHAEAQdAAxB0AHAEAQdAAxB0AHAEAQdAAyR05dEd3Z2qqWlRbZtq6amRnV1dSPWtLe368MPP5TH49Ftt92mDRs2jPuwAIDsHINu27aam5u1fft2WZalrVu3KhgMasGCBak10WhUhw8f1quvvqri4mL99ttvBR0aADCS4yWX7u5ulZWVqbS0VD6fT1VVVero6Ehbc+zYMT344IMqLi6WJM2ZM6cw0wIAsnI8Q08kErIsK7VtWZa6urrS1vT29kqSduzYIdu2tWrVKi1evHjE7xWJRBSJRCRJ4XBYfr9/TMOb6mfJ9Xvj8/lcPSef1wAk98eO22Mzn9fANY5BTyaTIx7zeDxp27ZtKxqN6uWXX1YikdBLL72kPXv2aObMmWnrQqGQQqFQajsWi+U7t/Hcvjd+v9/1c3j/kS83x04+x6bb17iRlJeXZ93neMnFsizF4/HUdjweV0lJSdqauXPn6v7775fP59O8efNUXl6uaDQ6hpEBAG45Bj0QCCgajaqvr09DQ0Nqb29XMBhMW/PAAw/o3LlzkqTff/9d0WhUpaWlhZkYAJCR4yUXr9er+vp6NTU1ybZtVVdXq6KiQq2trQoEAgoGg7r33nv11VdfadOmTZo2bZoee+wxzZo1ayLmBwD8V073oVdWVqqysjLtsTVr1qR+7fF4tHbtWq1du3Z8pwMA5IxPigKAIQg6ABiCoAOAIQg6ABiCoAOAIQg6ABiCoAOAIQg6ABiCoAOAIQg6ABiCoAOAIQg6ABgip3+cCwD+z5HQv6TWX108w83a/wr9Sw+7f9YNj6ADcOWhyBPyHvg45/X5fGPR8LpaaU3ur4FruOQCAIYg6ABgCIIOAIYg6ABgCH4oOgW5v4tAcn0nAXcRAMYh6FOQ27sIJPd3EnAXAWAeLrkAgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCEIOgAYgqADgCFyCnpnZ6c2bNig5557TocPH8667tSpU1q9erW+//77cRsQAJAbx6Dbtq3m5mZt27ZNe/fuVVtbm3p6ekas+/PPP3X06FEtXLiwIIMCAEbnGPTu7m6VlZWptLRUPp9PVVVV6ujoGLGutbVVtbW1mj59ekEGBQCMzvGfz00kErIsK7VtWZa6urrS1ly8eFGxWExLlizRJ598kvX3ikQiikQikqRwOCy/35/v3Eb7WXL93vh8PlfPyec1AMn9seP22MznNXCNY9CTyeSIxzweT+rXtm3r4MGDamhocHyxUCikUCiU2nb7TeA3ErfvTT7frM77j3y5OXbyOTbdvsaNpLy8POs+x6BblqV4PJ7ajsfjKikpSW1fuXJFP/74o1555RVJ0q+//qpdu3Zpy5YtCgQCY5kbAOCCY9ADgYCi0aj6+vo0d+5ctbe3a/369an9M2bMUHNzc2q7sbFRjz/+ODEHgAnmGHSv16v6+no1NTXJtm1VV1eroqJCra2tCgQCCgaDEzEnAMBBTt8pWllZqcrKyrTH1qxZk3FtY2PjmIcCALjHJ0UBwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBA5facoAPzd8LranNf+nM8LzCjO51k3PIIOwBXvgY9drR9eV+v6OcgPl1wAwBAEHQAMQdABwBAEHQAMQdABwBAEHQAMQdABwBDchz5FufnghpTHhzf44AZgHII+BeXzIQw+vAEgp6B3dnaqpaVFtm2rpqZGdXV1afs//fRTHTt2TF6vV7Nnz9YzzzyjW265pSADAwAyc7yGbtu2mpubtW3bNu3du1dtbW3q6elJW3P77bcrHA5r9+7dWrp0qQ4dOlSwgQEAmTkGvbu7W2VlZSotLZXP51NVVZU6OjrS1txzzz0qKiqSJC1cuFCJRKIw0wIAsnK85JJIJGRZVmrbsix1dXVlXX/8+HEtXrw4475IJKJIJCJJCofD8vv9budFFj9LvJ+Ykjg2J45j0JPJ5IjHPB5PxrUnT57UhQsX1NjYmHF/KBRSKBRKbcdisRzHRC5
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df3[['a','b']].plot.box()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Créer un diagramme d'estimation de la densité du noyau (kde) de la colonne 'd'**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1d6e6c90>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD8CAYAAACYebj1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO3dfVxUZf7/8dc1A4KIIgwIgiCKeENuKpI3VKZJbvvdtnX9btl2n+uWmbXVtrX2qO+2te7629VsS2vdIrvd1u7c7natyFLTNEzx/g4Vb1ECvAERBc71+2N0EoUYYOacw8zn+XhYM3POmfPmAPPhXNc516W01hohhBACcFgdQAghhH1IURBCCOEhRUEIIYSHFAUhhBAeUhSEEEJ4SFEQQgjhEWLGTp599llWr15NVFQUM2fOPG/50qVLee+99wAIDw9n4sSJpKammhFNCCHEWZQZ9yls2rSJ8PBw5syZ02BR2Lp1K0lJSURGRrJmzRreeust/vSnP/k7lhBCiHOYcqaQkZFBSUlJo8v79OnjeZyenk5ZWZkZsYQQQpzDdn0KixYtYtCgQVbHEEKIoGTKmYK3NmzYwOeff87jjz/e6Dp5eXnk5eUBMH36dE6dOtXs/YSEhFBbW9vinP5k12x2zQX2zWbXXGDfbHbNBfbN1pJc7dq1a/z9WhvIV3bv3s3cuXOZOnUqHTt2bHS9nJwccnJyPM9LS0ubva/Y2NgWbWcGu2azay6wbza75gL7ZrNrLrBvtpbkSkxMbHSZLZqPSktLmTFjBlOmTPnesEIIIfzLlDOFp556ik2bNlFRUcGkSZO49tprPac7Y8aM4e2336ayspIXXngBAKfTyfTp082IJoQQ4iymFIV77733e5dPmjSJSZMmmRFFCCHE97BF85EQQgh7kKIghBDCQ4qCEEIID9tckiqEsCd98iR6XT4c2g/RLtTAYagOkVbHEn4iRUEI0SCtNfrrJeh//QMqK757Pfx51I2TcQy9zMJ0wl+kKAghzqMNA/1mLvqzD6BnHxyTboEe6VC8F+NfL6BfmIlRW4vj4tFWRxU+Jn0KQoh6tNbo155Ff/YBKudqHA9NR/Xpj2oXhureC8f9T0C/AehX56D37rI6rvAxKQpCiHr0B/9CL/0E9aOfo679JcrhrLdchYbi+NVvIbIjxotPoY06i5IKf5CiIITw0GtWoD94AzX8ctTPbkIp1eB6qmMn1LW/hH270F99YW5I4VdSFIQQAOiyEoyX/gbde6FuuqvRgnCGuuhSSE1H/+dNOVsIIFIUhBDo2lqM52eAYeC4/beo0NAmt1FK4fjhz6CkGNblm5BSmEGKghAC/f7rsGOL+wyhS1fvNxw0HFxdMD59z3/hhKmkKAgR5PSG1ej/voMa8UMcQ0Y0a1vldKIuuxK2bUSXFPspoTCTFAUhgpg+Uobx4ixI6o4aP7FF76GGXgZKoVcu9nE6YQUpCkIEKW3UYbzwJJysdvcjtAtr0fuomDjo3R+94gu01j5OKcwmRUGIIKU/+BdsXY+6fhIqMaVV76WGXgYlB2DPTh+lE1aRoiBEEDq5ZgX6ozdR2aN9MlSFGjDE3YS09msfpBNWkqIgRJDR5aUcfepxSExBXe+bGQ9Vp87Qs497NFXRpklRECKI6OoTGHP+CKdO4bjjIVRYy/oRGqIGDIHdhejDZT57T2E+KQpCBAl3x/JM2FtE1AOPo7p28+n7qwuHuPez4Rufvq8wlxQFIYKANgz0a8/B2q9Rv7idsMHZvt9JYjJERcOW9b5/b2EaKQpCBDh3QXjWPfLp/1yDY9T/+GU/SilUnx+gt66TS1PbMCkKQgQwfbIaY+5fTheEa1Fjb/TvDvteCEcPw8H9/t2P8BuZeU2IAKWL92L8Ywbs3+2eFyHn6iZHPm0t1fdCNKC3rPN5n4UwhxQFIQKMPnUS/el76A/nQ1g4jrsfRf1gsDk7j42HmDj01nXgp2Yq4V9SFIQIEPp4BfqrReiFC+BoOWRm47jhDlSnaNMyePoVNnyD1trvZybC90wpCs8++yyrV68mKiqKmTNnnrdca828efNYs2YNYWFhTJ48mZ49e5oRTYg2TVccQ29cDevy0WtWQG0NpGfguP0BVO/+1oTq1Re+WgTfHoTmDMMtbMGUojBy5EiuvPJK5syZ0+DyNWvWcPDgQZ5++mm2b9/OCy+8wJ/+9CczognRpuhTJ2HHFvSW9ejNBVC0HbSGyE6oS65AXToGlWLtH1SqZx93v8LOLc2bm0HYgilFISMjg5KSkkaXr1q1ihEjRqCUonfv3hw/fpzDhw8THW3eaa8QdqWL96G/WYbesg52bHGfDTgckJqOuuo61A+yoHsaymGTiwkTUyCsPezcCsNGWZ1GNJMt+hTKy8uJjY31PHe5XJSXlzdYFPLy8sjLywNg+vTp9bbzVkhISIu2M4Nds9k1F/g2W+3eXVQvzaNm63rqDpdBbS2OqGhCUtMI7XshYYOH44js5Pdcdd8epPrLPKqXfkrdru2gFCE90mn345/T7geDCe03AEdEhxa9d2uzeeNw7wyM3TtwNXMfwfJz5ku+zmWLotDQjS6NdVDl5OSQk5PjeV5aWtrs/cXGxrZoOzPYNZtdc4FvsunSQxjzc6FgBSgHpPSEuK4op5O6o+XUfL6QEwsXgDMELhiEGnoZauDQ752DoLm59LEj7jOCr5dA4Wb3iz16o8ZPRGVdgu4cw0ngJEDVCfe/FvL399NITkMvfJtv9+9DhYXbJldr2DVbS3IlJiY2uswWRcHlctX7osrKyqTpSJhGr/oSY97fAFBXX4+67Er3qJ9nr2PUQVGh+0M7/0v0unx0+w6orItR2ZdDWr8WXWmjqyrRa1a6C8GWtWAY7lnQxt6IGjICFZfgk6/RbCqtD9owYHchWNXhLVrEFkUhKyuLhQsXcvHFF7N9+3YiIiKkKAhTGP99G/3uK5DWF8evfotyxTW4nnI4oWcfdyfq/94KW9ejly9Cr1yMXvoJxCWghl/unmwmLqHRAqG1hrIS9Oa16NXLYfM6qKuF2HjUlf/rLgRJ3f34FZukRx8A9K5t1l0FJVrElKLw1FNPsWnTJioqKpg0aRLXXnsttbW1AIwZM4ZBgwaxevVq7rnnHtq1a8fkyZPNiCWCnPHxu+h3X0ENuQx16z2o0FCvtlMOB/QbgOo3AH3DJPTq5e4C8f4/0e//0z0oXPdeHEtMxnCGuK8OqqpElx6CfbvhyOmhpeMSUKN/ghqc7W4mCqBr+lXHThATKzOxtUGmFIV77733e5crpZg4sWWThgvREsbKxei3X0JddCnql/e6zwRaQIW3R2WPhuzR6LIS98xju7ah9+ykeucWdGUFOJ3uq3Fi41G9L4Be/dx/PSemBFQhOE9KGlqKQptji+YjIcyk9+xEv/IMpGegJrS8IJxLubqgLr/K89yuHZNmUSlp6LVfo6tPoMLbWx1HeMkmFzYLYQ59ogrjuT9Dh044Jj2ECvGuyUg0n0rp6W4621dkdRTRDFIURFDR81+Asm9x3PGgqWMCBaWUNAD0nh0WBxHNIUVBBA299mv0sjzUleNQaX2tjhP4OsdAxyjpbG5jpCiIoKCPV2K8Mhu6paJ+8gur4wQFpRSk9JQzhTZGioIICvq916HiGI7bfu31paei9VRKGhzYg66psTqK8JIUBRHw9J6d6C/+ixr5I/eHlDCNSukJdXVwYI/VUYSXpCiIgKa1xnhjLnSIRP30BqvjBJ9uqQDo/butzSG8JkVBBDS94gso3IwadzOqQ6TVcYJPl64QEgpSFNoMKQoiYOkTVei357mHkLg4p+kNhM8phxMSk9EHpCi0FVIURMDS778BFUdx/OIO+0xAE4RUYnfYL30KbYX8poiApPfvQS/6wD1FZY90q+MEt6QUOFyKrqq0OonwghQFEXA8ncvhEaif3Wx1nKDnGQpcrkBqE6QoiICj85fC1vWon93oHsJZWCvRXRT0PulXaAukKIiAoqur0G+9CClpqBE/tDqOAPe8CuHtQTqb2wQpCiKg6A/nw5FyHNff4bMhsUX
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df3['d'].plot.density()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Déterminez comment augmenter la largeur de la ligne et faire en sorte que le style de ligne soit en pointillés. (Note: En général, on ne trace pas de kde)**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1d7d6ed0>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df3['d'].plot.kde(lw=5,ls='--')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"**Créer un graphique de surface de toutes les colonnes pour les lignes seulement jusqu'à la 30ème.**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x1a1d7a8110>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df3.loc[0:30].plot.area(alpha=0.4)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Défi Bonus !\n",
|
||
|
|
"Il est possible que cela vous semble compliqué, référez-vous aux solutions si vous n'arrivez pas à trouver !\n",
|
||
|
|
"**Remarquez comment la légende de notre figure précédente recouvrait une partie du diagramme. Savez-vous comment afficher la légende à l'extérieur du tracé comme indiqué ci-dessous?**\n",
|
||
|
|
"\n",
|
||
|
|
"**Essayez de faire une recherche sur Google pour trouver un bon lien sur stackoverflow à ce sujet. Si vous n'arrivez pas à en trouver, utilisez celui-ci comme indice. - [Lien Stackoverflow.](http://stackoverflow.com/questions/23556153/how-to-put-legend-outside-the-plot-with-pandas)**"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 0 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"f = plt.figure()\n",
|
||
|
|
"df3.loc[0:30].plot.area(alpha=0.4)\n",
|
||
|
|
"plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
|
||
|
|
"plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Bon travail!"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.7.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 1
|
||
|
|
}
|