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module3/exo1/influenza-like-illness-analysis.ipynb
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...@@ -9,10 +9,8 @@ ...@@ -9,10 +9,8 @@
}, },
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"metadata": { "metadata": {},
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"%matplotlib inline\n", "%matplotlib inline\n",
...@@ -25,15 +23,13 @@ ...@@ -25,15 +23,13 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](http://www.sentiweb.fr/). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download." "The data on the incidence of influenza-like illness are available from the Web site of the [Réseau Sentinelles](https://www.sentiweb.fr/france/en/?). We download them as a file in CSV format, in which each line corresponds to a week in the observation period. Only the complete dataset, starting in 1984 and ending with a recent week, is available for download."
] ]
}, },
{ {
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"execution_count": null, "execution_count": 2,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
...@@ -63,9 +59,976 @@ ...@@ -63,9 +59,976 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
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"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
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" <td>159155.0</td>\n",
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" <td>206.0</td>\n",
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" <td>158682.0</td>\n",
" <td>222</td>\n",
" <td>205.0</td>\n",
" <td>239.0</td>\n",
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" <td>134929.0</td>\n",
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" <td>171.0</td>\n",
" <td>203.0</td>\n",
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" <td>35.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>2040</th>\n",
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" <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",
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" <th>2041</th>\n",
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" <td>53487.0</td>\n",
" <td>78</td>\n",
" <td>59.0</td>\n",
" <td>97.0</td>\n",
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" <td>France</td>\n",
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" <td>51175.0</td>\n",
" <td>74</td>\n",
" <td>55.0</td>\n",
" <td>93.0</td>\n",
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" <th>2043</th>\n",
" <td>198517</td>\n",
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" <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",
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" <th>2044</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",
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" <tr>\n",
" <th>2045</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",
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" <tr>\n",
" <th>2046</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",
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" <tr>\n",
" <th>2047</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",
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" <tr>\n",
" <th>2048</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",
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" <tr>\n",
" <th>2049</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>2050</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>2051</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>2052</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>2053</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>2054</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>2055</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>2056</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>2057</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>2058</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>2059</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>2060</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>2061</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>2062</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>2063</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>2064</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>2065</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>2066</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>2067</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>2068</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>2069 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202425 3 50347 42176.0 58518.0 75 63.0 \n",
"1 202424 3 41414 34928.0 47900.0 62 52.0 \n",
"2 202423 3 35875 30610.0 41140.0 54 46.0 \n",
"3 202422 3 33772 28274.0 39270.0 51 43.0 \n",
"4 202421 3 21963 17556.0 26370.0 33 26.0 \n",
"5 202420 3 20057 15780.0 24334.0 30 24.0 \n",
"6 202419 3 15375 11274.0 19476.0 23 17.0 \n",
"7 202418 3 22409 17653.0 27165.0 34 27.0 \n",
"8 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"9 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"10 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"11 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"12 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"13 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"14 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"15 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"16 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"17 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"18 202407 3 138078 127050.0 149106.0 207 190.0 \n",
"19 202406 3 190062 177955.0 202169.0 285 267.0 \n",
"20 202405 3 216237 203595.0 228879.0 324 305.0 \n",
"21 202404 3 213196 200547.0 225845.0 320 301.0 \n",
"22 202403 3 163457 152276.0 174638.0 245 228.0 \n",
"23 202402 3 129436 119453.0 139419.0 194 179.0 \n",
"24 202401 3 120769 109452.0 132086.0 181 164.0 \n",
"25 202352 3 115446 103738.0 127154.0 174 156.0 \n",
"26 202351 3 148755 136546.0 160964.0 224 206.0 \n",
"27 202350 3 147971 136787.0 159155.0 223 206.0 \n",
"28 202349 3 147552 136422.0 158682.0 222 205.0 \n",
"29 202348 3 124204 113479.0 134929.0 187 171.0 \n",
"... ... ... ... ... ... ... ... \n",
"2039 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2040 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2041 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2042 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2043 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2044 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2045 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2046 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2047 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2048 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2049 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2050 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2051 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2052 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2053 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2054 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2055 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2056 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2057 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2058 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2059 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2060 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2061 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2062 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2063 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2064 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2065 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2066 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2067 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2068 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 87.0 FR France \n",
"1 72.0 FR France \n",
"2 62.0 FR France \n",
"3 59.0 FR France \n",
"4 40.0 FR France \n",
"5 36.0 FR France \n",
"6 29.0 FR France \n",
"7 41.0 FR France \n",
"8 49.0 FR France \n",
"9 51.0 FR France \n",
"10 53.0 FR France \n",
"11 56.0 FR France \n",
"12 61.0 FR France \n",
"13 70.0 FR France \n",
"14 85.0 FR France \n",
"15 101.0 FR France \n",
"16 119.0 FR France \n",
"17 172.0 FR France \n",
"18 224.0 FR France \n",
"19 303.0 FR France \n",
"20 343.0 FR France \n",
"21 339.0 FR France \n",
"22 262.0 FR France \n",
"23 209.0 FR France \n",
"24 198.0 FR France \n",
"25 192.0 FR France \n",
"26 242.0 FR France \n",
"27 240.0 FR France \n",
"28 239.0 FR France \n",
"29 203.0 FR France \n",
"... ... ... ... \n",
"2039 59.0 FR France \n",
"2040 64.0 FR France \n",
"2041 97.0 FR France \n",
"2042 93.0 FR France \n",
"2043 80.0 FR France \n",
"2044 116.0 FR France \n",
"2045 149.0 FR France \n",
"2046 281.0 FR France \n",
"2047 395.0 FR France \n",
"2048 485.0 FR France \n",
"2049 544.0 FR France \n",
"2050 689.0 FR France \n",
"2051 722.0 FR France \n",
"2052 762.0 FR France \n",
"2053 926.0 FR France \n",
"2054 1113.0 FR France \n",
"2055 1236.0 FR France \n",
"2056 832.0 FR France \n",
"2057 459.0 FR France \n",
"2058 207.0 FR France \n",
"2059 190.0 FR France \n",
"2060 198.0 FR France \n",
"2061 224.0 FR France \n",
"2062 266.0 FR France \n",
"2063 219.0 FR France \n",
"2064 176.0 FR France \n",
"2065 163.0 FR France \n",
"2066 195.0 FR France \n",
"2067 308.0 FR France \n",
"2068 213.0 FR France \n",
"\n",
"[2069 rows x 10 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n", "raw_data = pd.read_csv(data_url, skiprows=1)\n",
"raw_data" "raw_data"
...@@ -80,9 +1043,73 @@ ...@@ -80,9 +1043,73 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
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"<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>1832</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",
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],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1832 198919 3 - NaN NaN - NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1832 FR France "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data[raw_data.isnull().any(axis=1)]" "raw_data[raw_data.isnull().any(axis=1)]"
] ]
...@@ -96,9 +1123,976 @@ ...@@ -96,9 +1123,976 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": {}, "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>0</th>\n",
" <td>202425</td>\n",
" <td>3</td>\n",
" <td>50347</td>\n",
" <td>42176.0</td>\n",
" <td>58518.0</td>\n",
" <td>75</td>\n",
" <td>63.0</td>\n",
" <td>87.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202424</td>\n",
" <td>3</td>\n",
" <td>41414</td>\n",
" <td>34928.0</td>\n",
" <td>47900.0</td>\n",
" <td>62</td>\n",
" <td>52.0</td>\n",
" <td>72.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202423</td>\n",
" <td>3</td>\n",
" <td>35875</td>\n",
" <td>30610.0</td>\n",
" <td>41140.0</td>\n",
" <td>54</td>\n",
" <td>46.0</td>\n",
" <td>62.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202422</td>\n",
" <td>3</td>\n",
" <td>33772</td>\n",
" <td>28274.0</td>\n",
" <td>39270.0</td>\n",
" <td>51</td>\n",
" <td>43.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202421</td>\n",
" <td>3</td>\n",
" <td>21963</td>\n",
" <td>17556.0</td>\n",
" <td>26370.0</td>\n",
" <td>33</td>\n",
" <td>26.0</td>\n",
" <td>40.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202420</td>\n",
" <td>3</td>\n",
" <td>20057</td>\n",
" <td>15780.0</td>\n",
" <td>24334.0</td>\n",
" <td>30</td>\n",
" <td>24.0</td>\n",
" <td>36.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202419</td>\n",
" <td>3</td>\n",
" <td>15375</td>\n",
" <td>11274.0</td>\n",
" <td>19476.0</td>\n",
" <td>23</td>\n",
" <td>17.0</td>\n",
" <td>29.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202418</td>\n",
" <td>3</td>\n",
" <td>22409</td>\n",
" <td>17653.0</td>\n",
" <td>27165.0</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>8</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>9</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>10</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>11</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>12</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>13</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>14</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>15</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>16</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>17</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>18</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>19</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>20</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>21</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>22</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>23</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>24</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>25</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>26</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>27</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>28</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>29</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>...</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>2039</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>2040</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>2041</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>2042</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>2043</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>2044</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>2045</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>2046</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>2047</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>2048</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>2049</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>2050</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>2051</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>2052</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>2053</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>2054</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>2055</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>2056</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>2057</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>2058</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>2059</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>2060</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>2061</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>2062</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>2063</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>2064</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>2065</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>2066</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>2067</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>2068</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>2068 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202425 3 50347 42176.0 58518.0 75 63.0 \n",
"1 202424 3 41414 34928.0 47900.0 62 52.0 \n",
"2 202423 3 35875 30610.0 41140.0 54 46.0 \n",
"3 202422 3 33772 28274.0 39270.0 51 43.0 \n",
"4 202421 3 21963 17556.0 26370.0 33 26.0 \n",
"5 202420 3 20057 15780.0 24334.0 30 24.0 \n",
"6 202419 3 15375 11274.0 19476.0 23 17.0 \n",
"7 202418 3 22409 17653.0 27165.0 34 27.0 \n",
"8 202417 3 27042 21410.0 32674.0 41 33.0 \n",
"9 202416 3 28882 23305.0 34459.0 43 35.0 \n",
"10 202415 3 30229 24648.0 35810.0 45 37.0 \n",
"11 202414 3 31813 26529.0 37097.0 48 40.0 \n",
"12 202413 3 35090 29607.0 40573.0 53 45.0 \n",
"13 202412 3 40639 34582.0 46696.0 61 52.0 \n",
"14 202411 3 50268 43331.0 57205.0 75 65.0 \n",
"15 202410 3 60107 52623.0 67591.0 90 79.0 \n",
"16 202409 3 71121 62920.0 79322.0 107 95.0 \n",
"17 202408 3 104566 94520.0 114612.0 157 142.0 \n",
"18 202407 3 138078 127050.0 149106.0 207 190.0 \n",
"19 202406 3 190062 177955.0 202169.0 285 267.0 \n",
"20 202405 3 216237 203595.0 228879.0 324 305.0 \n",
"21 202404 3 213196 200547.0 225845.0 320 301.0 \n",
"22 202403 3 163457 152276.0 174638.0 245 228.0 \n",
"23 202402 3 129436 119453.0 139419.0 194 179.0 \n",
"24 202401 3 120769 109452.0 132086.0 181 164.0 \n",
"25 202352 3 115446 103738.0 127154.0 174 156.0 \n",
"26 202351 3 148755 136546.0 160964.0 224 206.0 \n",
"27 202350 3 147971 136787.0 159155.0 223 206.0 \n",
"28 202349 3 147552 136422.0 158682.0 222 205.0 \n",
"29 202348 3 124204 113479.0 134929.0 187 171.0 \n",
"... ... ... ... ... ... ... ... \n",
"2039 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2040 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2041 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2042 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2043 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2044 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2045 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2046 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2047 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2048 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2049 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2050 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2051 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2052 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2053 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2054 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2055 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2056 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2057 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2058 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2059 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2060 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2061 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2062 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2063 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2064 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2065 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2066 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2067 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2068 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 87.0 FR France \n",
"1 72.0 FR France \n",
"2 62.0 FR France \n",
"3 59.0 FR France \n",
"4 40.0 FR France \n",
"5 36.0 FR France \n",
"6 29.0 FR France \n",
"7 41.0 FR France \n",
"8 49.0 FR France \n",
"9 51.0 FR France \n",
"10 53.0 FR France \n",
"11 56.0 FR France \n",
"12 61.0 FR France \n",
"13 70.0 FR France \n",
"14 85.0 FR France \n",
"15 101.0 FR France \n",
"16 119.0 FR France \n",
"17 172.0 FR France \n",
"18 224.0 FR France \n",
"19 303.0 FR France \n",
"20 343.0 FR France \n",
"21 339.0 FR France \n",
"22 262.0 FR France \n",
"23 209.0 FR France \n",
"24 198.0 FR France \n",
"25 192.0 FR France \n",
"26 242.0 FR France \n",
"27 240.0 FR France \n",
"28 239.0 FR France \n",
"29 203.0 FR France \n",
"... ... ... ... \n",
"2039 59.0 FR France \n",
"2040 64.0 FR France \n",
"2041 97.0 FR France \n",
"2042 93.0 FR France \n",
"2043 80.0 FR France \n",
"2044 116.0 FR France \n",
"2045 149.0 FR France \n",
"2046 281.0 FR France \n",
"2047 395.0 FR France \n",
"2048 485.0 FR France \n",
"2049 544.0 FR France \n",
"2050 689.0 FR France \n",
"2051 722.0 FR France \n",
"2052 762.0 FR France \n",
"2053 926.0 FR France \n",
"2054 1113.0 FR France \n",
"2055 1236.0 FR France \n",
"2056 832.0 FR France \n",
"2057 459.0 FR France \n",
"2058 207.0 FR France \n",
"2059 190.0 FR France \n",
"2060 198.0 FR France \n",
"2061 224.0 FR France \n",
"2062 266.0 FR France \n",
"2063 219.0 FR France \n",
"2064 176.0 FR France \n",
"2065 163.0 FR France \n",
"2066 195.0 FR France \n",
"2067 308.0 FR France \n",
"2068 213.0 FR France \n",
"\n",
"[2068 rows x 10 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"data = raw_data.dropna().copy()\n", "data = raw_data.dropna().copy()\n",
"data" "data"
...@@ -123,10 +2117,8 @@ ...@@ -123,10 +2117,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"def convert_week(year_and_week_int):\n", "def convert_week(year_and_week_int):\n",
...@@ -154,10 +2146,8 @@ ...@@ -154,10 +2146,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 7,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sorted_data = data.set_index('period').sort_index()" "sorted_data = data.set_index('period').sort_index()"
...@@ -180,9 +2170,17 @@ ...@@ -180,9 +2170,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
]
}
],
"source": [ "source": [
"periods = sorted_data.index\n", "periods = sorted_data.index\n",
"for p1, p2 in zip(periods[:-1], periods[1:]):\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
...@@ -200,9 +2198,26 @@ ...@@ -200,9 +2198,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 9,
"metadata": {}, "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-9-0966cd984262>\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": [ "source": [
"sorted_data['inc'].plot()" "sorted_data['inc'].plot()"
] ]
...@@ -365,7 +2380,7 @@ ...@@ -365,7 +2380,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.1" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
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