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update analyse-syndrome-grippal and csv file

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module3/exo1/analyse-syndrome-grippal.ipynb
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...@@ -9,7 +9,7 @@ ...@@ -9,7 +9,7 @@
}, },
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"source": [ "source": [
...@@ -26,17 +26,6 @@ ...@@ -26,17 +26,6 @@
"Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
] ]
}, },
{
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"source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
]
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
...@@ -56,16 +45,1053 @@ ...@@ -56,16 +45,1053 @@
"| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n", "| geo_insee | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/ |\n",
"| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n", "| geo_name | Libellé de la zone géographique (ce libellé peut être modifié sans préavis) |\n",
"\n", "\n",
"La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`." "Nous vérifions si le fichier csv contenant les données est déjà téléchargé. Si tel est le cas, nous chargeons simplement les données en mémoire. Sinon, nous téléchargeons le fichier csv avant de charger les données en mémoire depuis celui-ci. La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`. Nous précisons aussi l'encodage des données et nous ne chargeons que les 11 premières colonnes du tableau, qui sont celles présentées ci-dessus."
] ]
}, },
{ {
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" <td>370.0</td>\n",
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" <td>38.0</td>\n",
" <td>64.0</td>\n",
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" <th>2098</th>\n",
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" <td>3</td>\n",
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" <td>53487.0</td>\n",
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" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>2099</th>\n",
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" <td>3</td>\n",
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" <td>93.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>2100</th>\n",
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" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>2101</th>\n",
" <td>2102</td>\n",
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" <td>66.0</td>\n",
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" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>2102</th>\n",
" <td>2103</td>\n",
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" <td>83.0</td>\n",
" <td>149.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <th>2103</th>\n",
" <td>2104</td>\n",
" <td>198514</td>\n",
" <td>3</td>\n",
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" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2104</th>\n",
" <td>2105</td>\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>2105</th>\n",
" <td>2106</td>\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>2106</th>\n",
" <td>2107</td>\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>2107</th>\n",
" <td>2108</td>\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>2108</th>\n",
" <td>2109</td>\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>2109</th>\n",
" <td>2110</td>\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>2110</th>\n",
" <td>2111</td>\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>2111</th>\n",
" <td>2112</td>\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>2112</th>\n",
" <td>2113</td>\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>2113</th>\n",
" <td>2114</td>\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>2114</th>\n",
" <td>2115</td>\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>2115</th>\n",
" <td>2116</td>\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>2116</th>\n",
" <td>2117</td>\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>2117</th>\n",
" <td>2118</td>\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>2118</th>\n",
" <td>2119</td>\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>2119</th>\n",
" <td>2120</td>\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>2120</th>\n",
" <td>2121</td>\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>2121</th>\n",
" <td>2122</td>\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>2122</th>\n",
" <td>2123</td>\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>2123</th>\n",
" <td>2124</td>\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>2124</th>\n",
" <td>2125</td>\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>2125</th>\n",
" <td>2126</td>\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>2126 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 1 202530 3 21219 14914.0 27524.0 32 23.0 \n",
"1 2 202529 3 18648 13750.0 23546.0 28 21.0 \n",
"2 3 202528 3 23285 18131.0 28439.0 35 27.0 \n",
"3 4 202527 3 21453 17129.0 25777.0 32 26.0 \n",
"4 5 202526 3 21945 17422.0 26468.0 33 26.0 \n",
"5 6 202525 3 23323 18546.0 28100.0 35 28.0 \n",
"6 7 202524 3 23154 18577.0 27731.0 35 28.0 \n",
"7 8 202523 3 24391 19307.0 29475.0 36 28.0 \n",
"8 9 202522 3 18755 14333.0 23177.0 28 21.0 \n",
"9 10 202521 3 23760 18671.0 28849.0 35 27.0 \n",
"10 11 202520 3 20265 15814.0 24716.0 30 23.0 \n",
"11 12 202519 3 16264 12394.0 20134.0 24 18.0 \n",
"12 13 202518 3 18115 13975.0 22255.0 27 21.0 \n",
"13 14 202517 3 22150 17291.0 27009.0 33 26.0 \n",
"14 15 202516 3 28564 22550.0 34578.0 43 34.0 \n",
"15 16 202515 3 35721 29592.0 41850.0 53 44.0 \n",
"16 17 202514 3 37579 31232.0 43926.0 56 47.0 \n",
"17 18 202513 3 39673 33686.0 45660.0 59 50.0 \n",
"18 19 202512 3 52543 45627.0 59459.0 78 68.0 \n",
"19 20 202511 3 59469 52154.0 66784.0 89 78.0 \n",
"20 21 202510 3 60334 53048.0 67620.0 90 79.0 \n",
"21 22 202509 3 84531 74994.0 94068.0 126 112.0 \n",
"22 23 202508 3 136020 124824.0 147216.0 203 186.0 \n",
"23 24 202507 3 208952 195988.0 221916.0 312 293.0 \n",
"24 25 202506 3 273519 258159.0 288879.0 408 385.0 \n",
"25 26 202505 3 334395 318416.0 350374.0 499 475.0 \n",
"26 27 202504 3 350043 332885.0 367201.0 522 496.0 \n",
"27 28 202503 3 252772 238917.0 266627.0 377 356.0 \n",
"28 29 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"29 30 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"... ... ... ... ... ... ... ... ... \n",
"2096 2097 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2097 2098 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2098 2099 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2099 2100 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2100 2101 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2101 2102 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2102 2103 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2103 2104 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2104 2105 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2105 2106 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2106 2107 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2107 2108 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2108 2109 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2109 2110 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2110 2111 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2111 2112 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2112 2113 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2113 2114 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2114 2115 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2115 2116 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2116 2117 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2117 2118 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2118 2119 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2119 2120 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2120 2121 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2121 2122 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2122 2123 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2123 2124 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2124 2125 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2125 2126 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 41.0 FR France \n",
"1 35.0 FR France \n",
"2 43.0 FR France \n",
"3 38.0 FR France \n",
"4 40.0 FR France \n",
"5 42.0 FR France \n",
"6 42.0 FR France \n",
"7 44.0 FR France \n",
"8 35.0 FR France \n",
"9 43.0 FR France \n",
"10 37.0 FR France \n",
"11 30.0 FR France \n",
"12 33.0 FR France \n",
"13 40.0 FR France \n",
"14 52.0 FR France \n",
"15 62.0 FR France \n",
"16 65.0 FR France \n",
"17 68.0 FR France \n",
"18 88.0 FR France \n",
"19 100.0 FR France \n",
"20 101.0 FR France \n",
"21 140.0 FR France \n",
"22 220.0 FR France \n",
"23 331.0 FR France \n",
"24 431.0 FR France \n",
"25 523.0 FR France \n",
"26 548.0 FR France \n",
"27 398.0 FR France \n",
"28 405.0 FR France \n",
"29 370.0 FR France \n",
"... ... ... ... \n",
"2096 59.0 FR France \n",
"2097 64.0 FR France \n",
"2098 97.0 FR France \n",
"2099 93.0 FR France \n",
"2100 80.0 FR France \n",
"2101 116.0 FR France \n",
"2102 149.0 FR France \n",
"2103 281.0 FR France \n",
"2104 395.0 FR France \n",
"2105 485.0 FR France \n",
"2106 544.0 FR France \n",
"2107 689.0 FR France \n",
"2108 722.0 FR France \n",
"2109 762.0 FR France \n",
"2110 926.0 FR France \n",
"2111 1113.0 FR France \n",
"2112 1236.0 FR France \n",
"2113 832.0 FR France \n",
"2114 459.0 FR France \n",
"2115 207.0 FR France \n",
"2116 190.0 FR France \n",
"2117 198.0 FR France \n",
"2118 224.0 FR France \n",
"2119 266.0 FR France \n",
"2120 219.0 FR France \n",
"2121 176.0 FR France \n",
"2122 163.0 FR France \n",
"2123 195.0 FR France \n",
"2124 308.0 FR France \n",
"2125 213.0 FR France \n",
"\n",
"[2126 rows x 11 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n", "try:\n",
" data_path = \"incidence-PAY-3.csv\"\n",
" raw_data = pd.read_csv(data_path, encoding = 'iso-8859-1', skiprows=1, usecols=[i for i in range(11)])\n",
"except:\n",
" data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n",
" tmp_data = pd.read_csv(data_url, encoding = 'iso-8859-1')\n",
" tmp_data.to_csv(\"incidence-PAY-3.csv\")\n",
" data_path = \"incidence-PAY-3.csv\"\n",
" raw_data = pd.read_csv(data_path, encoding = 'iso-8859-1', skiprows=1, usecols=[i for i in range(11)])\n",
"raw_data" "raw_data"
] ]
}, },
...@@ -78,9 +1104,75 @@ ...@@ -78,9 +1104,75 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
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" <th></th>\n",
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" <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>1889</th>\n",
" <td>1890</td>\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": [
" 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"1889 1890 198919 3 - NaN NaN - NaN \n",
"\n",
" inc100_up geo_insee geo_name \n",
"1889 NaN FR France "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data[raw_data.isnull().any(axis=1)]" "raw_data[raw_data.isnull().any(axis=1)]"
] ]
...@@ -94,9 +1186,1038 @@ ...@@ -94,9 +1186,1038 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
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" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
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" <th>geo_name</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>202530</td>\n",
" <td>3</td>\n",
" <td>21219</td>\n",
" <td>14914.0</td>\n",
" <td>27524.0</td>\n",
" <td>32</td>\n",
" <td>23.0</td>\n",
" <td>41.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>202529</td>\n",
" <td>3</td>\n",
" <td>18648</td>\n",
" <td>13750.0</td>\n",
" <td>23546.0</td>\n",
" <td>28</td>\n",
" <td>21.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>202528</td>\n",
" <td>3</td>\n",
" <td>23285</td>\n",
" <td>18131.0</td>\n",
" <td>28439.0</td>\n",
" <td>35</td>\n",
" <td>27.0</td>\n",
" <td>43.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>202527</td>\n",
" <td>3</td>\n",
" <td>21453</td>\n",
" <td>17129.0</td>\n",
" <td>25777.0</td>\n",
" <td>32</td>\n",
" <td>26.0</td>\n",
" <td>38.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>202526</td>\n",
" <td>3</td>\n",
" <td>21945</td>\n",
" <td>17422.0</td>\n",
" <td>26468.0</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>6</td>\n",
" <td>202525</td>\n",
" <td>3</td>\n",
" <td>23323</td>\n",
" <td>18546.0</td>\n",
" <td>28100.0</td>\n",
" <td>35</td>\n",
" <td>28.0</td>\n",
" <td>42.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>202524</td>\n",
" <td>3</td>\n",
" <td>23154</td>\n",
" <td>18577.0</td>\n",
" <td>27731.0</td>\n",
" <td>35</td>\n",
" <td>28.0</td>\n",
" <td>42.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>202523</td>\n",
" <td>3</td>\n",
" <td>24391</td>\n",
" <td>19307.0</td>\n",
" <td>29475.0</td>\n",
" <td>36</td>\n",
" <td>28.0</td>\n",
" <td>44.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>202522</td>\n",
" <td>3</td>\n",
" <td>18755</td>\n",
" <td>14333.0</td>\n",
" <td>23177.0</td>\n",
" <td>28</td>\n",
" <td>21.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>202521</td>\n",
" <td>3</td>\n",
" <td>23760</td>\n",
" <td>18671.0</td>\n",
" <td>28849.0</td>\n",
" <td>35</td>\n",
" <td>27.0</td>\n",
" <td>43.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>202520</td>\n",
" <td>3</td>\n",
" <td>20265</td>\n",
" <td>15814.0</td>\n",
" <td>24716.0</td>\n",
" <td>30</td>\n",
" <td>23.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>202519</td>\n",
" <td>3</td>\n",
" <td>16264</td>\n",
" <td>12394.0</td>\n",
" <td>20134.0</td>\n",
" <td>24</td>\n",
" <td>18.0</td>\n",
" <td>30.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>202518</td>\n",
" <td>3</td>\n",
" <td>18115</td>\n",
" <td>13975.0</td>\n",
" <td>22255.0</td>\n",
" <td>27</td>\n",
" <td>21.0</td>\n",
" <td>33.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>202517</td>\n",
" <td>3</td>\n",
" <td>22150</td>\n",
" <td>17291.0</td>\n",
" <td>27009.0</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>14</th>\n",
" <td>15</td>\n",
" <td>202516</td>\n",
" <td>3</td>\n",
" <td>28564</td>\n",
" <td>22550.0</td>\n",
" <td>34578.0</td>\n",
" <td>43</td>\n",
" <td>34.0</td>\n",
" <td>52.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>202515</td>\n",
" <td>3</td>\n",
" <td>35721</td>\n",
" <td>29592.0</td>\n",
" <td>41850.0</td>\n",
" <td>53</td>\n",
" <td>44.0</td>\n",
" <td>62.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>202514</td>\n",
" <td>3</td>\n",
" <td>37579</td>\n",
" <td>31232.0</td>\n",
" <td>43926.0</td>\n",
" <td>56</td>\n",
" <td>47.0</td>\n",
" <td>65.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>202513</td>\n",
" <td>3</td>\n",
" <td>39673</td>\n",
" <td>33686.0</td>\n",
" <td>45660.0</td>\n",
" <td>59</td>\n",
" <td>50.0</td>\n",
" <td>68.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>202512</td>\n",
" <td>3</td>\n",
" <td>52543</td>\n",
" <td>45627.0</td>\n",
" <td>59459.0</td>\n",
" <td>78</td>\n",
" <td>68.0</td>\n",
" <td>88.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>202511</td>\n",
" <td>3</td>\n",
" <td>59469</td>\n",
" <td>52154.0</td>\n",
" <td>66784.0</td>\n",
" <td>89</td>\n",
" <td>78.0</td>\n",
" <td>100.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>202510</td>\n",
" <td>3</td>\n",
" <td>60334</td>\n",
" <td>53048.0</td>\n",
" <td>67620.0</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>21</th>\n",
" <td>22</td>\n",
" <td>202509</td>\n",
" <td>3</td>\n",
" <td>84531</td>\n",
" <td>74994.0</td>\n",
" <td>94068.0</td>\n",
" <td>126</td>\n",
" <td>112.0</td>\n",
" <td>140.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>202508</td>\n",
" <td>3</td>\n",
" <td>136020</td>\n",
" <td>124824.0</td>\n",
" <td>147216.0</td>\n",
" <td>203</td>\n",
" <td>186.0</td>\n",
" <td>220.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>202507</td>\n",
" <td>3</td>\n",
" <td>208952</td>\n",
" <td>195988.0</td>\n",
" <td>221916.0</td>\n",
" <td>312</td>\n",
" <td>293.0</td>\n",
" <td>331.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>202506</td>\n",
" <td>3</td>\n",
" <td>273519</td>\n",
" <td>258159.0</td>\n",
" <td>288879.0</td>\n",
" <td>408</td>\n",
" <td>385.0</td>\n",
" <td>431.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>202505</td>\n",
" <td>3</td>\n",
" <td>334395</td>\n",
" <td>318416.0</td>\n",
" <td>350374.0</td>\n",
" <td>499</td>\n",
" <td>475.0</td>\n",
" <td>523.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>202504</td>\n",
" <td>3</td>\n",
" <td>350043</td>\n",
" <td>332885.0</td>\n",
" <td>367201.0</td>\n",
" <td>522</td>\n",
" <td>496.0</td>\n",
" <td>548.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>202503</td>\n",
" <td>3</td>\n",
" <td>252772</td>\n",
" <td>238917.0</td>\n",
" <td>266627.0</td>\n",
" <td>377</td>\n",
" <td>356.0</td>\n",
" <td>398.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>202502</td>\n",
" <td>3</td>\n",
" <td>257247</td>\n",
" <td>242991.0</td>\n",
" <td>271503.0</td>\n",
" <td>384</td>\n",
" <td>363.0</td>\n",
" <td>405.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>202501</td>\n",
" <td>3</td>\n",
" <td>231549</td>\n",
" <td>214627.0</td>\n",
" <td>248471.0</td>\n",
" <td>345</td>\n",
" <td>320.0</td>\n",
" <td>370.0</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",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2096</th>\n",
" <td>2097</td>\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>2097</th>\n",
" <td>2098</td>\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>2098</th>\n",
" <td>2099</td>\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>2099</th>\n",
" <td>2100</td>\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>2100</th>\n",
" <td>2101</td>\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>2101</th>\n",
" <td>2102</td>\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>2102</th>\n",
" <td>2103</td>\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>2103</th>\n",
" <td>2104</td>\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>2104</th>\n",
" <td>2105</td>\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>2105</th>\n",
" <td>2106</td>\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>2106</th>\n",
" <td>2107</td>\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>2107</th>\n",
" <td>2108</td>\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>2108</th>\n",
" <td>2109</td>\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>2109</th>\n",
" <td>2110</td>\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>2110</th>\n",
" <td>2111</td>\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>2111</th>\n",
" <td>2112</td>\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>2112</th>\n",
" <td>2113</td>\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>2113</th>\n",
" <td>2114</td>\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>2114</th>\n",
" <td>2115</td>\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>2115</th>\n",
" <td>2116</td>\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>2116</th>\n",
" <td>2117</td>\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>2117</th>\n",
" <td>2118</td>\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>2118</th>\n",
" <td>2119</td>\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>2119</th>\n",
" <td>2120</td>\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>2120</th>\n",
" <td>2121</td>\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>2121</th>\n",
" <td>2122</td>\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>2122</th>\n",
" <td>2123</td>\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>2123</th>\n",
" <td>2124</td>\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>2124</th>\n",
" <td>2125</td>\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>2125</th>\n",
" <td>2126</td>\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>2125 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 1 202530 3 21219 14914.0 27524.0 32 23.0 \n",
"1 2 202529 3 18648 13750.0 23546.0 28 21.0 \n",
"2 3 202528 3 23285 18131.0 28439.0 35 27.0 \n",
"3 4 202527 3 21453 17129.0 25777.0 32 26.0 \n",
"4 5 202526 3 21945 17422.0 26468.0 33 26.0 \n",
"5 6 202525 3 23323 18546.0 28100.0 35 28.0 \n",
"6 7 202524 3 23154 18577.0 27731.0 35 28.0 \n",
"7 8 202523 3 24391 19307.0 29475.0 36 28.0 \n",
"8 9 202522 3 18755 14333.0 23177.0 28 21.0 \n",
"9 10 202521 3 23760 18671.0 28849.0 35 27.0 \n",
"10 11 202520 3 20265 15814.0 24716.0 30 23.0 \n",
"11 12 202519 3 16264 12394.0 20134.0 24 18.0 \n",
"12 13 202518 3 18115 13975.0 22255.0 27 21.0 \n",
"13 14 202517 3 22150 17291.0 27009.0 33 26.0 \n",
"14 15 202516 3 28564 22550.0 34578.0 43 34.0 \n",
"15 16 202515 3 35721 29592.0 41850.0 53 44.0 \n",
"16 17 202514 3 37579 31232.0 43926.0 56 47.0 \n",
"17 18 202513 3 39673 33686.0 45660.0 59 50.0 \n",
"18 19 202512 3 52543 45627.0 59459.0 78 68.0 \n",
"19 20 202511 3 59469 52154.0 66784.0 89 78.0 \n",
"20 21 202510 3 60334 53048.0 67620.0 90 79.0 \n",
"21 22 202509 3 84531 74994.0 94068.0 126 112.0 \n",
"22 23 202508 3 136020 124824.0 147216.0 203 186.0 \n",
"23 24 202507 3 208952 195988.0 221916.0 312 293.0 \n",
"24 25 202506 3 273519 258159.0 288879.0 408 385.0 \n",
"25 26 202505 3 334395 318416.0 350374.0 499 475.0 \n",
"26 27 202504 3 350043 332885.0 367201.0 522 496.0 \n",
"27 28 202503 3 252772 238917.0 266627.0 377 356.0 \n",
"28 29 202502 3 257247 242991.0 271503.0 384 363.0 \n",
"29 30 202501 3 231549 214627.0 248471.0 345 320.0 \n",
"... ... ... ... ... ... ... ... ... \n",
"2096 2097 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"2097 2098 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"2098 2099 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"2099 2100 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"2100 2101 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"2101 2102 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"2102 2103 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"2103 2104 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"2104 2105 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"2105 2106 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"2106 2107 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"2107 2108 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"2108 2109 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"2109 2110 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"2110 2111 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"2111 2112 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"2112 2113 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"2113 2114 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"2114 2115 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"2115 2116 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"2116 2117 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"2117 2118 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"2118 2119 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"2119 2120 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"2120 2121 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"2121 2122 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"2122 2123 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"2123 2124 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"2124 2125 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"2125 2126 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 41.0 FR France \n",
"1 35.0 FR France \n",
"2 43.0 FR France \n",
"3 38.0 FR France \n",
"4 40.0 FR France \n",
"5 42.0 FR France \n",
"6 42.0 FR France \n",
"7 44.0 FR France \n",
"8 35.0 FR France \n",
"9 43.0 FR France \n",
"10 37.0 FR France \n",
"11 30.0 FR France \n",
"12 33.0 FR France \n",
"13 40.0 FR France \n",
"14 52.0 FR France \n",
"15 62.0 FR France \n",
"16 65.0 FR France \n",
"17 68.0 FR France \n",
"18 88.0 FR France \n",
"19 100.0 FR France \n",
"20 101.0 FR France \n",
"21 140.0 FR France \n",
"22 220.0 FR France \n",
"23 331.0 FR France \n",
"24 431.0 FR France \n",
"25 523.0 FR France \n",
"26 548.0 FR France \n",
"27 398.0 FR France \n",
"28 405.0 FR France \n",
"29 370.0 FR France \n",
"... ... ... ... \n",
"2096 59.0 FR France \n",
"2097 64.0 FR France \n",
"2098 97.0 FR France \n",
"2099 93.0 FR France \n",
"2100 80.0 FR France \n",
"2101 116.0 FR France \n",
"2102 149.0 FR France \n",
"2103 281.0 FR France \n",
"2104 395.0 FR France \n",
"2105 485.0 FR France \n",
"2106 544.0 FR France \n",
"2107 689.0 FR France \n",
"2108 722.0 FR France \n",
"2109 762.0 FR France \n",
"2110 926.0 FR France \n",
"2111 1113.0 FR France \n",
"2112 1236.0 FR France \n",
"2113 832.0 FR France \n",
"2114 459.0 FR France \n",
"2115 207.0 FR France \n",
"2116 190.0 FR France \n",
"2117 198.0 FR France \n",
"2118 224.0 FR France \n",
"2119 266.0 FR France \n",
"2120 219.0 FR France \n",
"2121 176.0 FR France \n",
"2122 163.0 FR France \n",
"2123 195.0 FR France \n",
"2124 308.0 FR France \n",
"2125 213.0 FR France \n",
"\n",
"[2125 rows x 11 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"data = raw_data.dropna().copy()\n", "data = raw_data.dropna().copy()\n",
"data" "data"
...@@ -122,9 +2243,81 @@ ...@@ -122,9 +2243,81 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"0 2025-07-21/2025-07-27\n",
"1 2025-07-14/2025-07-20\n",
"2 2025-07-07/2025-07-13\n",
"3 2025-06-30/2025-07-06\n",
"4 2025-06-23/2025-06-29\n",
"5 2025-06-16/2025-06-22\n",
"6 2025-06-09/2025-06-15\n",
"7 2025-06-02/2025-06-08\n",
"8 2025-05-26/2025-06-01\n",
"9 2025-05-19/2025-05-25\n",
"10 2025-05-12/2025-05-18\n",
"11 2025-05-05/2025-05-11\n",
"12 2025-04-28/2025-05-04\n",
"13 2025-04-21/2025-04-27\n",
"14 2025-04-14/2025-04-20\n",
"15 2025-04-07/2025-04-13\n",
"16 2025-03-31/2025-04-06\n",
"17 2025-03-24/2025-03-30\n",
"18 2025-03-17/2025-03-23\n",
"19 2025-03-10/2025-03-16\n",
"20 2025-03-03/2025-03-09\n",
"21 2025-02-24/2025-03-02\n",
"22 2025-02-17/2025-02-23\n",
"23 2025-02-10/2025-02-16\n",
"24 2025-02-03/2025-02-09\n",
"25 2025-01-27/2025-02-02\n",
"26 2025-01-20/2025-01-26\n",
"27 2025-01-13/2025-01-19\n",
"28 2025-01-06/2025-01-12\n",
"29 2024-12-30/2025-01-05\n",
" ... \n",
"2096 1985-05-20/1985-05-26\n",
"2097 1985-05-13/1985-05-19\n",
"2098 1985-05-06/1985-05-12\n",
"2099 1985-04-29/1985-05-05\n",
"2100 1985-04-22/1985-04-28\n",
"2101 1985-04-15/1985-04-21\n",
"2102 1985-04-08/1985-04-14\n",
"2103 1985-04-01/1985-04-07\n",
"2104 1985-03-25/1985-03-31\n",
"2105 1985-03-18/1985-03-24\n",
"2106 1985-03-11/1985-03-17\n",
"2107 1985-03-04/1985-03-10\n",
"2108 1985-02-25/1985-03-03\n",
"2109 1985-02-18/1985-02-24\n",
"2110 1985-02-11/1985-02-17\n",
"2111 1985-02-04/1985-02-10\n",
"2112 1985-01-28/1985-02-03\n",
"2113 1985-01-21/1985-01-27\n",
"2114 1985-01-14/1985-01-20\n",
"2115 1985-01-07/1985-01-13\n",
"2116 1984-12-31/1985-01-06\n",
"2117 1984-12-24/1984-12-30\n",
"2118 1984-12-17/1984-12-23\n",
"2119 1984-12-10/1984-12-16\n",
"2120 1984-12-03/1984-12-09\n",
"2121 1984-11-26/1984-12-02\n",
"2122 1984-11-19/1984-11-25\n",
"2123 1984-11-12/1984-11-18\n",
"2124 1984-11-05/1984-11-11\n",
"2125 1984-10-29/1984-11-04\n",
"Name: period, Length: 2125, dtype: object"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"def convert_week(year_and_week_int):\n", "def convert_week(year_and_week_int):\n",
" year_and_week_str = str(year_and_week_int)\n", " year_and_week_str = str(year_and_week_int)\n",
...@@ -133,7 +2326,8 @@ ...@@ -133,7 +2326,8 @@
" w = isoweek.Week(year, week)\n", " w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n", " return pd.Period(w.day(0), 'W')\n",
"\n", "\n",
"data['period'] = [convert_week(yw) for yw in data['week']]" "data['period'] = [convert_week(yw) for yw in data['week']]\n",
"data['period']"
] ]
}, },
{ {
...@@ -152,13 +2346,85 @@ ...@@ -152,13 +2346,85 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 16,
"metadata": { "metadata": {},
"collapsed": true "outputs": [
}, {
"outputs": [], "data": {
"text/plain": [
"period\n",
"1984-10-29/1984-11-04 68422\n",
"1984-11-05/1984-11-11 135223\n",
"1984-11-12/1984-11-18 87330\n",
"1984-11-19/1984-11-25 72029\n",
"1984-11-26/1984-12-02 78620\n",
"1984-12-03/1984-12-09 101073\n",
"1984-12-10/1984-12-16 123680\n",
"1984-12-17/1984-12-23 101726\n",
"1984-12-24/1984-12-30 84830\n",
"1984-12-31/1985-01-06 85489\n",
"1985-01-07/1985-01-13 97586\n",
"1985-01-14/1985-01-20 213901\n",
"1985-01-21/1985-01-27 424937\n",
"1985-01-28/1985-02-03 637302\n",
"1985-02-04/1985-02-10 565825\n",
"1985-02-11/1985-02-17 471852\n",
"1985-02-18/1985-02-24 389886\n",
"1985-02-25/1985-03-03 369895\n",
"1985-03-04/1985-03-10 353231\n",
"1985-03-11/1985-03-17 276205\n",
"1985-03-18/1985-03-24 245240\n",
"1985-03-25/1985-03-31 197206\n",
"1985-04-01/1985-04-07 134545\n",
"1985-04-08/1985-04-14 63881\n",
"1985-04-15/1985-04-21 50362\n",
"1985-04-22/1985-04-28 34053\n",
"1985-04-29/1985-05-05 40555\n",
"1985-05-06/1985-05-12 43154\n",
"1985-05-13/1985-05-19 27896\n",
"1985-05-20/1985-05-26 26096\n",
" ... \n",
"2024-12-30/2025-01-05 231549\n",
"2025-01-06/2025-01-12 257247\n",
"2025-01-13/2025-01-19 252772\n",
"2025-01-20/2025-01-26 350043\n",
"2025-01-27/2025-02-02 334395\n",
"2025-02-03/2025-02-09 273519\n",
"2025-02-10/2025-02-16 208952\n",
"2025-02-17/2025-02-23 136020\n",
"2025-02-24/2025-03-02 84531\n",
"2025-03-03/2025-03-09 60334\n",
"2025-03-10/2025-03-16 59469\n",
"2025-03-17/2025-03-23 52543\n",
"2025-03-24/2025-03-30 39673\n",
"2025-03-31/2025-04-06 37579\n",
"2025-04-07/2025-04-13 35721\n",
"2025-04-14/2025-04-20 28564\n",
"2025-04-21/2025-04-27 22150\n",
"2025-04-28/2025-05-04 18115\n",
"2025-05-05/2025-05-11 16264\n",
"2025-05-12/2025-05-18 20265\n",
"2025-05-19/2025-05-25 23760\n",
"2025-05-26/2025-06-01 18755\n",
"2025-06-02/2025-06-08 24391\n",
"2025-06-09/2025-06-15 23154\n",
"2025-06-16/2025-06-22 23323\n",
"2025-06-23/2025-06-29 21945\n",
"2025-06-30/2025-07-06 21453\n",
"2025-07-07/2025-07-13 23285\n",
"2025-07-14/2025-07-20 18648\n",
"2025-07-21/2025-07-27 21219\n",
"Freq: W-SUN, Name: inc, Length: 2125, dtype: object"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"sorted_data = data.set_index('period').sort_index()" "sorted_data = data.set_index('period').sort_index()\n",
"sorted_data['inc']"
] ]
}, },
{ {
...@@ -179,9 +2445,17 @@ ...@@ -179,9 +2445,17 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 12,
"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",
...@@ -199,9 +2473,26 @@ ...@@ -199,9 +2473,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 17,
"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-17-0966cd984262>\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()"
] ]
...@@ -215,9 +2506,26 @@ ...@@ -215,9 +2506,26 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 18,
"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-18-495b7092a92e>\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[0;34m-\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m:\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'][-200:].plot()" "sorted_data['inc'][-200:].plot()"
] ]
...@@ -364,7 +2672,7 @@ ...@@ -364,7 +2672,7 @@
"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,
......
module3/exo1/incidence-PAY-3.csv 0 → 100644
View file @ 487a241b
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