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mooc-rr
Commit a01f124e authored by 1b9d5d5d82005e2b54b9ac83be96a5b8's avatar 1b9d5d5d82005e2b54b9ac83be96a5b8
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update1

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module2/exo2/exercice.ipynb
View file @ a01f124e
{
"cells": [],
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1- Données"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"donnees = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9, 12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 2 - Calculs"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"moyenne = np.mean(donnees)\n",
"ecart_type = np.std(donnees)\n",
"minimum = np.min(donnees)\n",
"maximum = np.max(donnees)\n",
"mediane = np.median(donnees)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3 - Affichage des résultats"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Moyenne : 14.113000000000001\n",
"Écart-type : 4.312369534258399\n",
"Minimum : 2.8\n",
"Médiane : 14.5\n",
"Maximum : 23.4\n"
]
}
],
"source": [
"print(\"Moyenne :\", moyenne)\n",
"print(\"Écart-type :\", ecart_type)\n",
"print(\"Minimum :\", minimum)\n",
"print(\"Médiane :\", mediane)\n",
"print(\"Maximum :\", maximum)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(donnees, bins=20, color='blue', alpha=0.7)\n",
"plt.xlabel('Valeurs')\n",
"plt.ylabel('Fréquence')\n",
"plt.title('Histogramme des données')\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tracer le graphique de séquence"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Graphique de séquence')"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x432 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 6))\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(donnees, color='blue')\n",
"plt.xlabel('Indice')\n",
"plt.ylabel('Valeurs')\n",
"plt.title('Graphique de séquence')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tracer l'histogramme"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.subplot(1, 2, 2)\n",
"plt.hist(donnees, bins=20, color='bleue', alpha=0.7)\n",
"plt.xlabel('Valeurs')\n",
"plt.ylabel('Fréquence')\n",
"plt.title('Histogramme des données')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +217,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module3/exo1/analyse-syndrome-grippal.ipynb
View file @ a01f124e
......@@ -28,10 +28,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
......@@ -61,9 +59,976 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 18,
"metadata": {},
"outputs": [],
"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>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202419</td>\n",
" <td>3</td>\n",
" <td>17551</td>\n",
" <td>11964.0</td>\n",
" <td>23138.0</td>\n",
" <td>26</td>\n",
" <td>18.0</td>\n",
" <td>34.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202418</td>\n",
" <td>3</td>\n",
" <td>22831</td>\n",
" <td>17935.0</td>\n",
" <td>27727.0</td>\n",
" <td>34</td>\n",
" <td>27.0</td>\n",
" <td>41.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202417</td>\n",
" <td>3</td>\n",
" <td>27042</td>\n",
" <td>21410.0</td>\n",
" <td>32674.0</td>\n",
" <td>41</td>\n",
" <td>33.0</td>\n",
" <td>49.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202416</td>\n",
" <td>3</td>\n",
" <td>28882</td>\n",
" <td>23305.0</td>\n",
" <td>34459.0</td>\n",
" <td>43</td>\n",
" <td>35.0</td>\n",
" <td>51.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202415</td>\n",
" <td>3</td>\n",
" <td>30229</td>\n",
" <td>24648.0</td>\n",
" <td>35810.0</td>\n",
" <td>45</td>\n",
" <td>37.0</td>\n",
" <td>53.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202414</td>\n",
" <td>3</td>\n",
" <td>31813</td>\n",
" <td>26529.0</td>\n",
" <td>37097.0</td>\n",
" <td>48</td>\n",
" <td>40.0</td>\n",
" <td>56.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202413</td>\n",
" <td>3</td>\n",
" <td>35090</td>\n",
" <td>29607.0</td>\n",
" <td>40573.0</td>\n",
" <td>53</td>\n",
" <td>45.0</td>\n",
" <td>61.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202412</td>\n",
" <td>3</td>\n",
" <td>40639</td>\n",
" <td>34582.0</td>\n",
" <td>46696.0</td>\n",
" <td>61</td>\n",
" <td>52.0</td>\n",
" <td>70.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202411</td>\n",
" <td>3</td>\n",
" <td>50268</td>\n",
" <td>43331.0</td>\n",
" <td>57205.0</td>\n",
" <td>75</td>\n",
" <td>65.0</td>\n",
" <td>85.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202410</td>\n",
" <td>3</td>\n",
" <td>60107</td>\n",
" <td>52623.0</td>\n",
" <td>67591.0</td>\n",
" <td>90</td>\n",
" <td>79.0</td>\n",
" <td>101.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202409</td>\n",
" <td>3</td>\n",
" <td>71121</td>\n",
" <td>62920.0</td>\n",
" <td>79322.0</td>\n",
" <td>107</td>\n",
" <td>95.0</td>\n",
" <td>119.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202408</td>\n",
" <td>3</td>\n",
" <td>104566</td>\n",
" <td>94520.0</td>\n",
" <td>114612.0</td>\n",
" <td>157</td>\n",
" <td>142.0</td>\n",
" <td>172.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202407</td>\n",
" <td>3</td>\n",
" <td>138078</td>\n",
" <td>127050.0</td>\n",
" <td>149106.0</td>\n",
" <td>207</td>\n",
" <td>190.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202406</td>\n",
" <td>3</td>\n",
" <td>190062</td>\n",
" <td>177955.0</td>\n",
" <td>202169.0</td>\n",
" <td>285</td>\n",
" <td>267.0</td>\n",
" <td>303.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202405</td>\n",
" <td>3</td>\n",
" <td>216237</td>\n",
" <td>203595.0</td>\n",
" <td>228879.0</td>\n",
" <td>324</td>\n",
" <td>305.0</td>\n",
" <td>343.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202404</td>\n",
" <td>3</td>\n",
" <td>213196</td>\n",
" <td>200547.0</td>\n",
" <td>225845.0</td>\n",
" <td>320</td>\n",
" <td>301.0</td>\n",
" <td>339.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202403</td>\n",
" <td>3</td>\n",
" <td>163457</td>\n",
" <td>152276.0</td>\n",
" <td>174638.0</td>\n",
" <td>245</td>\n",
" <td>228.0</td>\n",
" <td>262.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202402</td>\n",
" <td>3</td>\n",
" <td>129436</td>\n",
" <td>119453.0</td>\n",
" <td>139419.0</td>\n",
" <td>194</td>\n",
" <td>179.0</td>\n",
" <td>209.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202401</td>\n",
" <td>3</td>\n",
" <td>120769</td>\n",
" <td>109452.0</td>\n",
" <td>132086.0</td>\n",
" <td>181</td>\n",
" <td>164.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202352</td>\n",
" <td>3</td>\n",
" <td>115446</td>\n",
" <td>103738.0</td>\n",
" <td>127154.0</td>\n",
" <td>174</td>\n",
" <td>156.0</td>\n",
" <td>192.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202351</td>\n",
" <td>3</td>\n",
" <td>148755</td>\n",
" <td>136546.0</td>\n",
" <td>160964.0</td>\n",
" <td>224</td>\n",
" <td>206.0</td>\n",
" <td>242.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202350</td>\n",
" <td>3</td>\n",
" <td>147971</td>\n",
" <td>136787.0</td>\n",
" <td>159155.0</td>\n",
" <td>223</td>\n",
" <td>206.0</td>\n",
" <td>240.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>202349</td>\n",
" <td>3</td>\n",
" <td>147552</td>\n",
" <td>136422.0</td>\n",
" <td>158682.0</td>\n",
" <td>222</td>\n",
" <td>205.0</td>\n",
" <td>239.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202348</td>\n",
" <td>3</td>\n",
" <td>124204</td>\n",
" <td>113479.0</td>\n",
" <td>134929.0</td>\n",
" <td>187</td>\n",
" <td>171.0</td>\n",
" <td>203.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202347</td>\n",
" <td>3</td>\n",
" <td>110948</td>\n",
" <td>100694.0</td>\n",
" <td>121202.0</td>\n",
" <td>167</td>\n",
" <td>152.0</td>\n",
" <td>182.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202346</td>\n",
" <td>3</td>\n",
" <td>83894</td>\n",
" <td>75134.0</td>\n",
" <td>92654.0</td>\n",
" <td>126</td>\n",
" <td>113.0</td>\n",
" <td>139.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202345</td>\n",
" <td>3</td>\n",
" <td>72003</td>\n",
" <td>63178.0</td>\n",
" <td>80828.0</td>\n",
" <td>108</td>\n",
" <td>95.0</td>\n",
" <td>121.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202344</td>\n",
" <td>3</td>\n",
" <td>49952</td>\n",
" <td>42813.0</td>\n",
" <td>57091.0</td>\n",
" <td>75</td>\n",
" <td>64.0</td>\n",
" <td>86.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202343</td>\n",
" <td>3</td>\n",
" <td>44982</td>\n",
" <td>38170.0</td>\n",
" <td>51794.0</td>\n",
" <td>68</td>\n",
" <td>58.0</td>\n",
" <td>78.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202342</td>\n",
" <td>3</td>\n",
" <td>56842</td>\n",
" <td>49277.0</td>\n",
" <td>64407.0</td>\n",
" <td>86</td>\n",
" <td>75.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2033</th>\n",
" <td>198521</td>\n",
" <td>3</td>\n",
" <td>26096</td>\n",
" <td>19621.0</td>\n",
" <td>32571.0</td>\n",
" <td>47</td>\n",
" <td>35.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2034</th>\n",
" <td>198520</td>\n",
" <td>3</td>\n",
" <td>27896</td>\n",
" <td>20885.0</td>\n",
" <td>34907.0</td>\n",
" <td>51</td>\n",
" <td>38.0</td>\n",
" <td>64.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2035</th>\n",
" <td>198519</td>\n",
" <td>3</td>\n",
" <td>43154</td>\n",
" <td>32821.0</td>\n",
" <td>53487.0</td>\n",
" <td>78</td>\n",
" <td>59.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2036</th>\n",
" <td>198518</td>\n",
" <td>3</td>\n",
" <td>40555</td>\n",
" <td>29935.0</td>\n",
" <td>51175.0</td>\n",
" <td>74</td>\n",
" <td>55.0</td>\n",
" <td>93.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2037</th>\n",
" <td>198517</td>\n",
" <td>3</td>\n",
" <td>34053</td>\n",
" <td>24366.0</td>\n",
" <td>43740.0</td>\n",
" <td>62</td>\n",
" <td>44.0</td>\n",
" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2038</th>\n",
" <td>198516</td>\n",
" <td>3</td>\n",
" <td>50362</td>\n",
" <td>36451.0</td>\n",
" <td>64273.0</td>\n",
" <td>91</td>\n",
" <td>66.0</td>\n",
" <td>116.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2039</th>\n",
" <td>198515</td>\n",
" <td>3</td>\n",
" <td>63881</td>\n",
" <td>45538.0</td>\n",
" <td>82224.0</td>\n",
" <td>116</td>\n",
" <td>83.0</td>\n",
" <td>149.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2040</th>\n",
" <td>198514</td>\n",
" <td>3</td>\n",
" <td>134545</td>\n",
" <td>114400.0</td>\n",
" <td>154690.0</td>\n",
" <td>244</td>\n",
" <td>207.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2041</th>\n",
" <td>198513</td>\n",
" <td>3</td>\n",
" <td>197206</td>\n",
" <td>176080.0</td>\n",
" <td>218332.0</td>\n",
" <td>357</td>\n",
" <td>319.0</td>\n",
" <td>395.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2042</th>\n",
" <td>198512</td>\n",
" <td>3</td>\n",
" <td>245240</td>\n",
" <td>223304.0</td>\n",
" <td>267176.0</td>\n",
" <td>445</td>\n",
" <td>405.0</td>\n",
" <td>485.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2043</th>\n",
" <td>198511</td>\n",
" <td>3</td>\n",
" <td>276205</td>\n",
" <td>252399.0</td>\n",
" <td>300011.0</td>\n",
" <td>501</td>\n",
" <td>458.0</td>\n",
" <td>544.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2044</th>\n",
" <td>198510</td>\n",
" <td>3</td>\n",
" <td>353231</td>\n",
" <td>326279.0</td>\n",
" <td>380183.0</td>\n",
" <td>640</td>\n",
" <td>591.0</td>\n",
" <td>689.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2045</th>\n",
" <td>198509</td>\n",
" <td>3</td>\n",
" <td>369895</td>\n",
" <td>341109.0</td>\n",
" <td>398681.0</td>\n",
" <td>670</td>\n",
" <td>618.0</td>\n",
" <td>722.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2046</th>\n",
" <td>198508</td>\n",
" <td>3</td>\n",
" <td>389886</td>\n",
" <td>359529.0</td>\n",
" <td>420243.0</td>\n",
" <td>707</td>\n",
" <td>652.0</td>\n",
" <td>762.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2047</th>\n",
" <td>198507</td>\n",
" <td>3</td>\n",
" <td>471852</td>\n",
" <td>432599.0</td>\n",
" <td>511105.0</td>\n",
" <td>855</td>\n",
" <td>784.0</td>\n",
" <td>926.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2048</th>\n",
" <td>198506</td>\n",
" <td>3</td>\n",
" <td>565825</td>\n",
" <td>518011.0</td>\n",
" <td>613639.0</td>\n",
" <td>1026</td>\n",
" <td>939.0</td>\n",
" <td>1113.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2049</th>\n",
" <td>198505</td>\n",
" <td>3</td>\n",
" <td>637302</td>\n",
" <td>592795.0</td>\n",
" <td>681809.0</td>\n",
" <td>1155</td>\n",
" <td>1074.0</td>\n",
" <td>1236.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2050</th>\n",
" <td>198504</td>\n",
" <td>3</td>\n",
" <td>424937</td>\n",
" <td>390794.0</td>\n",
" <td>459080.0</td>\n",
" <td>770</td>\n",
" <td>708.0</td>\n",
" <td>832.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2051</th>\n",
" <td>198503</td>\n",
" <td>3</td>\n",
" <td>213901</td>\n",
" <td>174689.0</td>\n",
" <td>253113.0</td>\n",
" <td>388</td>\n",
" <td>317.0</td>\n",
" <td>459.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2052</th>\n",
" <td>198502</td>\n",
" <td>3</td>\n",
" <td>97586</td>\n",
" <td>80949.0</td>\n",
" <td>114223.0</td>\n",
" <td>177</td>\n",
" <td>147.0</td>\n",
" <td>207.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2053</th>\n",
" <td>198501</td>\n",
" <td>3</td>\n",
" <td>85489</td>\n",
" <td>65918.0</td>\n",
" <td>105060.0</td>\n",
" <td>155</td>\n",
" <td>120.0</td>\n",
" <td>190.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2054</th>\n",
" <td>198452</td>\n",
" <td>3</td>\n",
" <td>84830</td>\n",
" <td>60602.0</td>\n",
" <td>109058.0</td>\n",
" <td>154</td>\n",
" <td>110.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2055</th>\n",
" <td>198451</td>\n",
" <td>3</td>\n",
" <td>101726</td>\n",
" <td>80242.0</td>\n",
" <td>123210.0</td>\n",
" <td>185</td>\n",
" <td>146.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2056</th>\n",
" <td>198450</td>\n",
" <td>3</td>\n",
" <td>123680</td>\n",
" <td>101401.0</td>\n",
" <td>145959.0</td>\n",
" <td>225</td>\n",
" <td>184.0</td>\n",
" <td>266.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2057</th>\n",
" <td>198449</td>\n",
" <td>3</td>\n",
" <td>101073</td>\n",
" <td>81684.0</td>\n",
" <td>120462.0</td>\n",
" <td>184</td>\n",
" <td>149.0</td>\n",
" <td>219.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2058</th>\n",
" <td>198448</td>\n",
" <td>3</td>\n",
" <td>78620</td>\n",
" <td>60634.0</td>\n",
" <td>96606.0</td>\n",
" <td>143</td>\n",
" <td>110.0</td>\n",
" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>198446</td>\n",
" <td>3</td>\n",
" <td>87330</td>\n",
" <td>67686.0</td>\n",
" <td>106974.0</td>\n",
" <td>159</td>\n",
" <td>123.0</td>\n",
" <td>195.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2061</th>\n",
" <td>198445</td>\n",
" <td>3</td>\n",
" <td>135223</td>\n",
" <td>101414.0</td>\n",
" <td>169032.0</td>\n",
" <td>246</td>\n",
" <td>184.0</td>\n",
" <td>308.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td>198444</td>\n",
" <td>3</td>\n",
" <td>68422</td>\n",
" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
" <td>125</td>\n",
" <td>37.0</td>\n",
" <td>213.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2063 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202419 3 17551 11964.0 23138.0 26 18.0 \n",
"1 202418 3 22831 17935.0 27727.0 34 27.0 \n",
"2 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"3 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"4 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"5 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"6 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"7 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"8 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"9 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"10 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"11 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"12 202407 3 138078 127050.0 149106.0 207 190.0 \n",
"13 202406 3 190062 177955.0 202169.0 285 267.0 \n",
"14 202405 3 216237 203595.0 228879.0 324 305.0 \n",
"15 202404 3 213196 200547.0 225845.0 320 301.0 \n",
"16 202403 3 163457 152276.0 174638.0 245 228.0 \n",
"17 202402 3 129436 119453.0 139419.0 194 179.0 \n",
"18 202401 3 120769 109452.0 132086.0 181 164.0 \n",
"19 202352 3 115446 103738.0 127154.0 174 156.0 \n",
"20 202351 3 148755 136546.0 160964.0 224 206.0 \n",
"21 202350 3 147971 136787.0 159155.0 223 206.0 \n",
"22 202349 3 147552 136422.0 158682.0 222 205.0 \n",
"23 202348 3 124204 113479.0 134929.0 187 171.0 \n",
"24 202347 3 110948 100694.0 121202.0 167 152.0 \n",
"25 202346 3 83894 75134.0 92654.0 126 113.0 \n",
"26 202345 3 72003 63178.0 80828.0 108 95.0 \n",
"27 202344 3 49952 42813.0 57091.0 75 64.0 \n",
"28 202343 3 44982 38170.0 51794.0 68 58.0 \n",
"29 202342 3 56842 49277.0 64407.0 86 75.0 \n",
"... ... ... ... ... ... ... ... \n",
"2033 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2034 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2035 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2036 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2037 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2038 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2039 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2040 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2041 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2042 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2043 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2044 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2045 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2046 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2047 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2048 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2049 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2050 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2051 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2052 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2053 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2054 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2055 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2056 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2057 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2058 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2059 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2060 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2061 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2062 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 34.0 FR France \n",
"1 41.0 FR France \n",
"2 49.0 FR France \n",
"3 51.0 FR France \n",
"4 53.0 FR France \n",
"5 56.0 FR France \n",
"6 61.0 FR France \n",
"7 70.0 FR France \n",
"8 85.0 FR France \n",
"9 101.0 FR France \n",
"10 119.0 FR France \n",
"11 172.0 FR France \n",
"12 224.0 FR France \n",
"13 303.0 FR France \n",
"14 343.0 FR France \n",
"15 339.0 FR France \n",
"16 262.0 FR France \n",
"17 209.0 FR France \n",
"18 198.0 FR France \n",
"19 192.0 FR France \n",
"20 242.0 FR France \n",
"21 240.0 FR France \n",
"22 239.0 FR France \n",
"23 203.0 FR France \n",
"24 182.0 FR France \n",
"25 139.0 FR France \n",
"26 121.0 FR France \n",
"27 86.0 FR France \n",
"28 78.0 FR France \n",
"29 97.0 FR France \n",
"... ... ... ... \n",
"2033 59.0 FR France \n",
"2034 64.0 FR France \n",
"2035 97.0 FR France \n",
"2036 93.0 FR France \n",
"2037 80.0 FR France \n",
"2038 116.0 FR France \n",
"2039 149.0 FR France \n",
"2040 281.0 FR France \n",
"2041 395.0 FR France \n",
"2042 485.0 FR France \n",
"2043 544.0 FR France \n",
"2044 689.0 FR France \n",
"2045 722.0 FR France \n",
"2046 762.0 FR France \n",
"2047 926.0 FR France \n",
"2048 1113.0 FR France \n",
"2049 1236.0 FR France \n",
"2050 832.0 FR France \n",
"2051 459.0 FR France \n",
"2052 207.0 FR France \n",
"2053 190.0 FR France \n",
"2054 198.0 FR France \n",
"2055 224.0 FR France \n",
"2056 266.0 FR France \n",
"2057 219.0 FR France \n",
"2058 176.0 FR France \n",
"2059 163.0 FR France \n",
"2060 195.0 FR France \n",
"2061 308.0 FR France \n",
"2062 213.0 FR France \n",
"\n",
"[2063 rows x 10 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"raw_data"
......@@ -78,9 +1043,73 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 19,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1826</th>\n",
" <td>198919</td>\n",
" <td>3</td>\n",
" <td>-</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>-</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1826 198919 3 - NaN NaN - NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1826 FR France "
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
......@@ -94,9 +1123,976 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 20,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202419</td>\n",
" <td>3</td>\n",
" <td>17551</td>\n",
" <td>11964.0</td>\n",
" <td>23138.0</td>\n",
" <td>26</td>\n",
" <td>18.0</td>\n",
" <td>34.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202418</td>\n",
" <td>3</td>\n",
" <td>22831</td>\n",
" <td>17935.0</td>\n",
" <td>27727.0</td>\n",
" <td>34</td>\n",
" <td>27.0</td>\n",
" <td>41.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202417</td>\n",
" <td>3</td>\n",
" <td>27042</td>\n",
" <td>21410.0</td>\n",
" <td>32674.0</td>\n",
" <td>41</td>\n",
" <td>33.0</td>\n",
" <td>49.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202416</td>\n",
" <td>3</td>\n",
" <td>28882</td>\n",
" <td>23305.0</td>\n",
" <td>34459.0</td>\n",
" <td>43</td>\n",
" <td>35.0</td>\n",
" <td>51.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202415</td>\n",
" <td>3</td>\n",
" <td>30229</td>\n",
" <td>24648.0</td>\n",
" <td>35810.0</td>\n",
" <td>45</td>\n",
" <td>37.0</td>\n",
" <td>53.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202414</td>\n",
" <td>3</td>\n",
" <td>31813</td>\n",
" <td>26529.0</td>\n",
" <td>37097.0</td>\n",
" <td>48</td>\n",
" <td>40.0</td>\n",
" <td>56.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202413</td>\n",
" <td>3</td>\n",
" <td>35090</td>\n",
" <td>29607.0</td>\n",
" <td>40573.0</td>\n",
" <td>53</td>\n",
" <td>45.0</td>\n",
" <td>61.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202412</td>\n",
" <td>3</td>\n",
" <td>40639</td>\n",
" <td>34582.0</td>\n",
" <td>46696.0</td>\n",
" <td>61</td>\n",
" <td>52.0</td>\n",
" <td>70.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202411</td>\n",
" <td>3</td>\n",
" <td>50268</td>\n",
" <td>43331.0</td>\n",
" <td>57205.0</td>\n",
" <td>75</td>\n",
" <td>65.0</td>\n",
" <td>85.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202410</td>\n",
" <td>3</td>\n",
" <td>60107</td>\n",
" <td>52623.0</td>\n",
" <td>67591.0</td>\n",
" <td>90</td>\n",
" <td>79.0</td>\n",
" <td>101.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202409</td>\n",
" <td>3</td>\n",
" <td>71121</td>\n",
" <td>62920.0</td>\n",
" <td>79322.0</td>\n",
" <td>107</td>\n",
" <td>95.0</td>\n",
" <td>119.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202408</td>\n",
" <td>3</td>\n",
" <td>104566</td>\n",
" <td>94520.0</td>\n",
" <td>114612.0</td>\n",
" <td>157</td>\n",
" <td>142.0</td>\n",
" <td>172.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202407</td>\n",
" <td>3</td>\n",
" <td>138078</td>\n",
" <td>127050.0</td>\n",
" <td>149106.0</td>\n",
" <td>207</td>\n",
" <td>190.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202406</td>\n",
" <td>3</td>\n",
" <td>190062</td>\n",
" <td>177955.0</td>\n",
" <td>202169.0</td>\n",
" <td>285</td>\n",
" <td>267.0</td>\n",
" <td>303.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202405</td>\n",
" <td>3</td>\n",
" <td>216237</td>\n",
" <td>203595.0</td>\n",
" <td>228879.0</td>\n",
" <td>324</td>\n",
" <td>305.0</td>\n",
" <td>343.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202404</td>\n",
" <td>3</td>\n",
" <td>213196</td>\n",
" <td>200547.0</td>\n",
" <td>225845.0</td>\n",
" <td>320</td>\n",
" <td>301.0</td>\n",
" <td>339.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202403</td>\n",
" <td>3</td>\n",
" <td>163457</td>\n",
" <td>152276.0</td>\n",
" <td>174638.0</td>\n",
" <td>245</td>\n",
" <td>228.0</td>\n",
" <td>262.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202402</td>\n",
" <td>3</td>\n",
" <td>129436</td>\n",
" <td>119453.0</td>\n",
" <td>139419.0</td>\n",
" <td>194</td>\n",
" <td>179.0</td>\n",
" <td>209.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202401</td>\n",
" <td>3</td>\n",
" <td>120769</td>\n",
" <td>109452.0</td>\n",
" <td>132086.0</td>\n",
" <td>181</td>\n",
" <td>164.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202352</td>\n",
" <td>3</td>\n",
" <td>115446</td>\n",
" <td>103738.0</td>\n",
" <td>127154.0</td>\n",
" <td>174</td>\n",
" <td>156.0</td>\n",
" <td>192.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202351</td>\n",
" <td>3</td>\n",
" <td>148755</td>\n",
" <td>136546.0</td>\n",
" <td>160964.0</td>\n",
" <td>224</td>\n",
" <td>206.0</td>\n",
" <td>242.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202350</td>\n",
" <td>3</td>\n",
" <td>147971</td>\n",
" <td>136787.0</td>\n",
" <td>159155.0</td>\n",
" <td>223</td>\n",
" <td>206.0</td>\n",
" <td>240.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>202349</td>\n",
" <td>3</td>\n",
" <td>147552</td>\n",
" <td>136422.0</td>\n",
" <td>158682.0</td>\n",
" <td>222</td>\n",
" <td>205.0</td>\n",
" <td>239.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202348</td>\n",
" <td>3</td>\n",
" <td>124204</td>\n",
" <td>113479.0</td>\n",
" <td>134929.0</td>\n",
" <td>187</td>\n",
" <td>171.0</td>\n",
" <td>203.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202347</td>\n",
" <td>3</td>\n",
" <td>110948</td>\n",
" <td>100694.0</td>\n",
" <td>121202.0</td>\n",
" <td>167</td>\n",
" <td>152.0</td>\n",
" <td>182.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202346</td>\n",
" <td>3</td>\n",
" <td>83894</td>\n",
" <td>75134.0</td>\n",
" <td>92654.0</td>\n",
" <td>126</td>\n",
" <td>113.0</td>\n",
" <td>139.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202345</td>\n",
" <td>3</td>\n",
" <td>72003</td>\n",
" <td>63178.0</td>\n",
" <td>80828.0</td>\n",
" <td>108</td>\n",
" <td>95.0</td>\n",
" <td>121.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202344</td>\n",
" <td>3</td>\n",
" <td>49952</td>\n",
" <td>42813.0</td>\n",
" <td>57091.0</td>\n",
" <td>75</td>\n",
" <td>64.0</td>\n",
" <td>86.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202343</td>\n",
" <td>3</td>\n",
" <td>44982</td>\n",
" <td>38170.0</td>\n",
" <td>51794.0</td>\n",
" <td>68</td>\n",
" <td>58.0</td>\n",
" <td>78.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202342</td>\n",
" <td>3</td>\n",
" <td>56842</td>\n",
" <td>49277.0</td>\n",
" <td>64407.0</td>\n",
" <td>86</td>\n",
" <td>75.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2033</th>\n",
" <td>198521</td>\n",
" <td>3</td>\n",
" <td>26096</td>\n",
" <td>19621.0</td>\n",
" <td>32571.0</td>\n",
" <td>47</td>\n",
" <td>35.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2034</th>\n",
" <td>198520</td>\n",
" <td>3</td>\n",
" <td>27896</td>\n",
" <td>20885.0</td>\n",
" <td>34907.0</td>\n",
" <td>51</td>\n",
" <td>38.0</td>\n",
" <td>64.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2035</th>\n",
" <td>198519</td>\n",
" <td>3</td>\n",
" <td>43154</td>\n",
" <td>32821.0</td>\n",
" <td>53487.0</td>\n",
" <td>78</td>\n",
" <td>59.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2036</th>\n",
" <td>198518</td>\n",
" <td>3</td>\n",
" <td>40555</td>\n",
" <td>29935.0</td>\n",
" <td>51175.0</td>\n",
" <td>74</td>\n",
" <td>55.0</td>\n",
" <td>93.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2037</th>\n",
" <td>198517</td>\n",
" <td>3</td>\n",
" <td>34053</td>\n",
" <td>24366.0</td>\n",
" <td>43740.0</td>\n",
" <td>62</td>\n",
" <td>44.0</td>\n",
" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2038</th>\n",
" <td>198516</td>\n",
" <td>3</td>\n",
" <td>50362</td>\n",
" <td>36451.0</td>\n",
" <td>64273.0</td>\n",
" <td>91</td>\n",
" <td>66.0</td>\n",
" <td>116.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2039</th>\n",
" <td>198515</td>\n",
" <td>3</td>\n",
" <td>63881</td>\n",
" <td>45538.0</td>\n",
" <td>82224.0</td>\n",
" <td>116</td>\n",
" <td>83.0</td>\n",
" <td>149.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2040</th>\n",
" <td>198514</td>\n",
" <td>3</td>\n",
" <td>134545</td>\n",
" <td>114400.0</td>\n",
" <td>154690.0</td>\n",
" <td>244</td>\n",
" <td>207.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2041</th>\n",
" <td>198513</td>\n",
" <td>3</td>\n",
" <td>197206</td>\n",
" <td>176080.0</td>\n",
" <td>218332.0</td>\n",
" <td>357</td>\n",
" <td>319.0</td>\n",
" <td>395.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2042</th>\n",
" <td>198512</td>\n",
" <td>3</td>\n",
" <td>245240</td>\n",
" <td>223304.0</td>\n",
" <td>267176.0</td>\n",
" <td>445</td>\n",
" <td>405.0</td>\n",
" <td>485.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2043</th>\n",
" <td>198511</td>\n",
" <td>3</td>\n",
" <td>276205</td>\n",
" <td>252399.0</td>\n",
" <td>300011.0</td>\n",
" <td>501</td>\n",
" <td>458.0</td>\n",
" <td>544.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2044</th>\n",
" <td>198510</td>\n",
" <td>3</td>\n",
" <td>353231</td>\n",
" <td>326279.0</td>\n",
" <td>380183.0</td>\n",
" <td>640</td>\n",
" <td>591.0</td>\n",
" <td>689.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2045</th>\n",
" <td>198509</td>\n",
" <td>3</td>\n",
" <td>369895</td>\n",
" <td>341109.0</td>\n",
" <td>398681.0</td>\n",
" <td>670</td>\n",
" <td>618.0</td>\n",
" <td>722.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2046</th>\n",
" <td>198508</td>\n",
" <td>3</td>\n",
" <td>389886</td>\n",
" <td>359529.0</td>\n",
" <td>420243.0</td>\n",
" <td>707</td>\n",
" <td>652.0</td>\n",
" <td>762.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2047</th>\n",
" <td>198507</td>\n",
" <td>3</td>\n",
" <td>471852</td>\n",
" <td>432599.0</td>\n",
" <td>511105.0</td>\n",
" <td>855</td>\n",
" <td>784.0</td>\n",
" <td>926.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2048</th>\n",
" <td>198506</td>\n",
" <td>3</td>\n",
" <td>565825</td>\n",
" <td>518011.0</td>\n",
" <td>613639.0</td>\n",
" <td>1026</td>\n",
" <td>939.0</td>\n",
" <td>1113.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2049</th>\n",
" <td>198505</td>\n",
" <td>3</td>\n",
" <td>637302</td>\n",
" <td>592795.0</td>\n",
" <td>681809.0</td>\n",
" <td>1155</td>\n",
" <td>1074.0</td>\n",
" <td>1236.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2050</th>\n",
" <td>198504</td>\n",
" <td>3</td>\n",
" <td>424937</td>\n",
" <td>390794.0</td>\n",
" <td>459080.0</td>\n",
" <td>770</td>\n",
" <td>708.0</td>\n",
" <td>832.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2051</th>\n",
" <td>198503</td>\n",
" <td>3</td>\n",
" <td>213901</td>\n",
" <td>174689.0</td>\n",
" <td>253113.0</td>\n",
" <td>388</td>\n",
" <td>317.0</td>\n",
" <td>459.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2052</th>\n",
" <td>198502</td>\n",
" <td>3</td>\n",
" <td>97586</td>\n",
" <td>80949.0</td>\n",
" <td>114223.0</td>\n",
" <td>177</td>\n",
" <td>147.0</td>\n",
" <td>207.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2053</th>\n",
" <td>198501</td>\n",
" <td>3</td>\n",
" <td>85489</td>\n",
" <td>65918.0</td>\n",
" <td>105060.0</td>\n",
" <td>155</td>\n",
" <td>120.0</td>\n",
" <td>190.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2054</th>\n",
" <td>198452</td>\n",
" <td>3</td>\n",
" <td>84830</td>\n",
" <td>60602.0</td>\n",
" <td>109058.0</td>\n",
" <td>154</td>\n",
" <td>110.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2055</th>\n",
" <td>198451</td>\n",
" <td>3</td>\n",
" <td>101726</td>\n",
" <td>80242.0</td>\n",
" <td>123210.0</td>\n",
" <td>185</td>\n",
" <td>146.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2056</th>\n",
" <td>198450</td>\n",
" <td>3</td>\n",
" <td>123680</td>\n",
" <td>101401.0</td>\n",
" <td>145959.0</td>\n",
" <td>225</td>\n",
" <td>184.0</td>\n",
" <td>266.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2057</th>\n",
" <td>198449</td>\n",
" <td>3</td>\n",
" <td>101073</td>\n",
" <td>81684.0</td>\n",
" <td>120462.0</td>\n",
" <td>184</td>\n",
" <td>149.0</td>\n",
" <td>219.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2058</th>\n",
" <td>198448</td>\n",
" <td>3</td>\n",
" <td>78620</td>\n",
" <td>60634.0</td>\n",
" <td>96606.0</td>\n",
" <td>143</td>\n",
" <td>110.0</td>\n",
" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2059</th>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2060</th>\n",
" <td>198446</td>\n",
" <td>3</td>\n",
" <td>87330</td>\n",
" <td>67686.0</td>\n",
" <td>106974.0</td>\n",
" <td>159</td>\n",
" <td>123.0</td>\n",
" <td>195.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2061</th>\n",
" <td>198445</td>\n",
" <td>3</td>\n",
" <td>135223</td>\n",
" <td>101414.0</td>\n",
" <td>169032.0</td>\n",
" <td>246</td>\n",
" <td>184.0</td>\n",
" <td>308.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2062</th>\n",
" <td>198444</td>\n",
" <td>3</td>\n",
" <td>68422</td>\n",
" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
" <td>125</td>\n",
" <td>37.0</td>\n",
" <td>213.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2062 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202419 3 17551 11964.0 23138.0 26 18.0 \n",
"1 202418 3 22831 17935.0 27727.0 34 27.0 \n",
"2 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"3 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"4 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"5 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"6 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"7 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"8 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"9 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"10 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"11 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"12 202407 3 138078 127050.0 149106.0 207 190.0 \n",
"13 202406 3 190062 177955.0 202169.0 285 267.0 \n",
"14 202405 3 216237 203595.0 228879.0 324 305.0 \n",
"15 202404 3 213196 200547.0 225845.0 320 301.0 \n",
"16 202403 3 163457 152276.0 174638.0 245 228.0 \n",
"17 202402 3 129436 119453.0 139419.0 194 179.0 \n",
"18 202401 3 120769 109452.0 132086.0 181 164.0 \n",
"19 202352 3 115446 103738.0 127154.0 174 156.0 \n",
"20 202351 3 148755 136546.0 160964.0 224 206.0 \n",
"21 202350 3 147971 136787.0 159155.0 223 206.0 \n",
"22 202349 3 147552 136422.0 158682.0 222 205.0 \n",
"23 202348 3 124204 113479.0 134929.0 187 171.0 \n",
"24 202347 3 110948 100694.0 121202.0 167 152.0 \n",
"25 202346 3 83894 75134.0 92654.0 126 113.0 \n",
"26 202345 3 72003 63178.0 80828.0 108 95.0 \n",
"27 202344 3 49952 42813.0 57091.0 75 64.0 \n",
"28 202343 3 44982 38170.0 51794.0 68 58.0 \n",
"29 202342 3 56842 49277.0 64407.0 86 75.0 \n",
"... ... ... ... ... ... ... ... \n",
"2033 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2034 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2035 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2036 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2037 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2038 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2039 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2040 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2041 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2042 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2043 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2044 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2045 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2046 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2047 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2048 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2049 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2050 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2051 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2052 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2053 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2054 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2055 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2056 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2057 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2058 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2059 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2060 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2061 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2062 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 34.0 FR France \n",
"1 41.0 FR France \n",
"2 49.0 FR France \n",
"3 51.0 FR France \n",
"4 53.0 FR France \n",
"5 56.0 FR France \n",
"6 61.0 FR France \n",
"7 70.0 FR France \n",
"8 85.0 FR France \n",
"9 101.0 FR France \n",
"10 119.0 FR France \n",
"11 172.0 FR France \n",
"12 224.0 FR France \n",
"13 303.0 FR France \n",
"14 343.0 FR France \n",
"15 339.0 FR France \n",
"16 262.0 FR France \n",
"17 209.0 FR France \n",
"18 198.0 FR France \n",
"19 192.0 FR France \n",
"20 242.0 FR France \n",
"21 240.0 FR France \n",
"22 239.0 FR France \n",
"23 203.0 FR France \n",
"24 182.0 FR France \n",
"25 139.0 FR France \n",
"26 121.0 FR France \n",
"27 86.0 FR France \n",
"28 78.0 FR France \n",
"29 97.0 FR France \n",
"... ... ... ... \n",
"2033 59.0 FR France \n",
"2034 64.0 FR France \n",
"2035 97.0 FR France \n",
"2036 93.0 FR France \n",
"2037 80.0 FR France \n",
"2038 116.0 FR France \n",
"2039 149.0 FR France \n",
"2040 281.0 FR France \n",
"2041 395.0 FR France \n",
"2042 485.0 FR France \n",
"2043 544.0 FR France \n",
"2044 689.0 FR France \n",
"2045 722.0 FR France \n",
"2046 762.0 FR France \n",
"2047 926.0 FR France \n",
"2048 1113.0 FR France \n",
"2049 1236.0 FR France \n",
"2050 832.0 FR France \n",
"2051 459.0 FR France \n",
"2052 207.0 FR France \n",
"2053 190.0 FR France \n",
"2054 198.0 FR France \n",
"2055 224.0 FR France \n",
"2056 266.0 FR France \n",
"2057 219.0 FR France \n",
"2058 176.0 FR France \n",
"2059 163.0 FR France \n",
"2060 195.0 FR France \n",
"2061 308.0 FR France \n",
"2062 213.0 FR France \n",
"\n",
"[2062 rows x 10 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
......@@ -122,7 +2118,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
......@@ -152,10 +2148,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"sorted_data = data.set_index('period').sort_index()"
......@@ -179,9 +2173,17 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 30,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
}
],
"source": [
"periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n",
......@@ -199,9 +2201,26 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 31,
"metadata": {},
"outputs": [],
"outputs": [
{
"ename": "TypeError",
"evalue": "Empty 'DataFrame': no numeric data to plot",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-31-0966cd984262>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 2501\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolormap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2502\u001b[0m \u001b[0mxerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2503\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 2504\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_series\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2505\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mplot_series\u001b[0;34m(data, kind, ax, figsize, use_index, title, grid, legend, style, logx, logy, loglog, xticks, yticks, xlim, ylim, rot, fontsize, colormap, table, yerr, xerr, label, secondary_y, **kwds)\u001b[0m\n\u001b[1;32m 1925\u001b[0m \u001b[0myerr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0myerr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxerr\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[1;32m 1926\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msecondary_y\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecondary_y\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1927\u001b[0;31m **kwds)\n\u001b[0m\u001b[1;32m 1928\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_plot\u001b[0;34m(data, x, y, subplots, ax, kind, **kwds)\u001b[0m\n\u001b[1;32m 1727\u001b[0m \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mklass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msubplots\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1729\u001b[0;31m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1730\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 248\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 249\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 251\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 252\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\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/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 363\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 364\u001b[0m raise TypeError('Empty {0!r}: no numeric data to '\n\u001b[0;32m--> 365\u001b[0;31m 'plot'.format(numeric_data.__class__.__name__))\n\u001b[0m\u001b[1;32m 366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 367\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: Empty 'DataFrame': no numeric data to plot"
]
}
],
"source": [
"sorted_data['inc'].plot()"
]
......@@ -364,7 +2383,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.4"
}
},
"nbformat": 4,
......
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