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Commit c12294ab authored by 4cd1f95f8ecb711eb9c4bf11b7d47192's avatar 4cd1f95f8ecb711eb9c4bf11b7d47192
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Download and use local version if available

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module3/exo1/analyse-syndrome-grippal.ipynb
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...@@ -9,14 +9,15 @@ ...@@ -9,14 +9,15 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pandas as pd\n", "import pandas as pd\n",
"import isoweek" "import isoweek\n",
"import os"
] ]
}, },
{ {
...@@ -28,10 +29,8 @@ ...@@ -28,10 +29,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"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\""
...@@ -56,16 +55,1059 @@ ...@@ -56,16 +55,1059 @@
"| 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`." "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`. Si le fichier existe déjà en version locale, nous utilisons la version locale et nous la sauvegardons."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Reading local version\n"
]
},
{
"data": {
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" </tr>\n",
" <tr>\n",
" <th>1824</th>\n",
" <td>1825</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>1825</th>\n",
" <td>1826</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>1826</th>\n",
" <td>1827</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>1827</th>\n",
" <td>1828</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>1828</th>\n",
" <td>1829</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>1829</th>\n",
" <td>1830</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>1830</th>\n",
" <td>1831</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>1831</th>\n",
" <td>1832</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>1832</th>\n",
" <td>1833</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>1833</th>\n",
" <td>1834</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>1834</th>\n",
" <td>1835</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>1835</th>\n",
" <td>1836</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>1836</th>\n",
" <td>1837</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>1837</th>\n",
" <td>1838</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>1838</th>\n",
" <td>1839</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>1839</th>\n",
" <td>1840</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>1840</th>\n",
" <td>1841</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>1841</th>\n",
" <td>1842</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>1842</th>\n",
" <td>1843</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>1843</th>\n",
" <td>1844</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>1844</th>\n",
" <td>1845</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>1845 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 202011 3 101704 93652.0 109756.0 154 142.0 166.0 FR \\\n",
"0 1 202010 3 104977 96650.0 113304.0 159 146.0 172.0 FR \n",
"1 2 202009 3 110696 102066.0 119326.0 168 155.0 181.0 FR \n",
"2 3 202008 3 143753 133984.0 153522.0 218 203.0 233.0 FR \n",
"3 4 202007 3 183610 172812.0 194408.0 279 263.0 295.0 FR \n",
"4 5 202006 3 206669 195481.0 217857.0 314 297.0 331.0 FR \n",
"5 6 202005 3 187957 177445.0 198469.0 285 269.0 301.0 FR \n",
"6 7 202004 3 122331 113492.0 131170.0 186 173.0 199.0 FR \n",
"7 8 202003 3 78413 71330.0 85496.0 119 108.0 130.0 FR \n",
"8 9 202002 3 53614 47654.0 59574.0 81 72.0 90.0 FR \n",
"9 10 202001 3 36850 31608.0 42092.0 56 48.0 64.0 FR \n",
"10 11 201952 3 28135 23220.0 33050.0 43 36.0 50.0 FR \n",
"11 12 201951 3 29786 25042.0 34530.0 45 38.0 52.0 FR \n",
"12 13 201950 3 34223 29156.0 39290.0 52 44.0 60.0 FR \n",
"13 14 201949 3 25662 21414.0 29910.0 39 33.0 45.0 FR \n",
"14 15 201948 3 22367 18055.0 26679.0 34 27.0 41.0 FR \n",
"15 16 201947 3 18669 14759.0 22579.0 28 22.0 34.0 FR \n",
"16 17 201946 3 16030 12567.0 19493.0 24 19.0 29.0 FR \n",
"17 18 201945 3 10138 7160.0 13116.0 15 10.0 20.0 FR \n",
"18 19 201944 3 7822 5010.0 10634.0 12 8.0 16.0 FR \n",
"19 20 201943 3 9487 6448.0 12526.0 14 9.0 19.0 FR \n",
"20 21 201942 3 7747 5243.0 10251.0 12 8.0 16.0 FR \n",
"21 22 201941 3 7122 4720.0 9524.0 11 7.0 15.0 FR \n",
"22 23 201940 3 8505 5784.0 11226.0 13 9.0 17.0 FR \n",
"23 24 201939 3 7091 4462.0 9720.0 11 7.0 15.0 FR \n",
"24 25 201938 3 4897 2891.0 6903.0 7 4.0 10.0 FR \n",
"25 26 201937 3 3172 1367.0 4977.0 5 2.0 8.0 FR \n",
"26 27 201936 3 2295 728.0 3862.0 3 1.0 5.0 FR \n",
"27 28 201935 3 1010 2.0 2018.0 2 0.0 4.0 FR \n",
"28 29 201934 3 1672 279.0 3065.0 3 1.0 5.0 FR \n",
"29 30 201933 3 1593 68.0 3118.0 2 0.0 4.0 FR \n",
"... ... ... .. ... ... ... ... ... ... .. \n",
"1815 1816 198521 3 26096 19621.0 32571.0 47 35.0 59.0 FR \n",
"1816 1817 198520 3 27896 20885.0 34907.0 51 38.0 64.0 FR \n",
"1817 1818 198519 3 43154 32821.0 53487.0 78 59.0 97.0 FR \n",
"1818 1819 198518 3 40555 29935.0 51175.0 74 55.0 93.0 FR \n",
"1819 1820 198517 3 34053 24366.0 43740.0 62 44.0 80.0 FR \n",
"1820 1821 198516 3 50362 36451.0 64273.0 91 66.0 116.0 FR \n",
"1821 1822 198515 3 63881 45538.0 82224.0 116 83.0 149.0 FR \n",
"1822 1823 198514 3 134545 114400.0 154690.0 244 207.0 281.0 FR \n",
"1823 1824 198513 3 197206 176080.0 218332.0 357 319.0 395.0 FR \n",
"1824 1825 198512 3 245240 223304.0 267176.0 445 405.0 485.0 FR \n",
"1825 1826 198511 3 276205 252399.0 300011.0 501 458.0 544.0 FR \n",
"1826 1827 198510 3 353231 326279.0 380183.0 640 591.0 689.0 FR \n",
"1827 1828 198509 3 369895 341109.0 398681.0 670 618.0 722.0 FR \n",
"1828 1829 198508 3 389886 359529.0 420243.0 707 652.0 762.0 FR \n",
"1829 1830 198507 3 471852 432599.0 511105.0 855 784.0 926.0 FR \n",
"1830 1831 198506 3 565825 518011.0 613639.0 1026 939.0 1113.0 FR \n",
"1831 1832 198505 3 637302 592795.0 681809.0 1155 1074.0 1236.0 FR \n",
"1832 1833 198504 3 424937 390794.0 459080.0 770 708.0 832.0 FR \n",
"1833 1834 198503 3 213901 174689.0 253113.0 388 317.0 459.0 FR \n",
"1834 1835 198502 3 97586 80949.0 114223.0 177 147.0 207.0 FR \n",
"1835 1836 198501 3 85489 65918.0 105060.0 155 120.0 190.0 FR \n",
"1836 1837 198452 3 84830 60602.0 109058.0 154 110.0 198.0 FR \n",
"1837 1838 198451 3 101726 80242.0 123210.0 185 146.0 224.0 FR \n",
"1838 1839 198450 3 123680 101401.0 145959.0 225 184.0 266.0 FR \n",
"1839 1840 198449 3 101073 81684.0 120462.0 184 149.0 219.0 FR \n",
"1840 1841 198448 3 78620 60634.0 96606.0 143 110.0 176.0 FR \n",
"1841 1842 198447 3 72029 54274.0 89784.0 131 99.0 163.0 FR \n",
"1842 1843 198446 3 87330 67686.0 106974.0 159 123.0 195.0 FR \n",
"1843 1844 198445 3 135223 101414.0 169032.0 246 184.0 308.0 FR \n",
"1844 1845 198444 3 68422 20056.0 116788.0 125 37.0 213.0 FR \n",
"\n",
" France \n",
"0 France \n",
"1 France \n",
"2 France \n",
"3 France \n",
"4 France \n",
"5 France \n",
"6 France \n",
"7 France \n",
"8 France \n",
"9 France \n",
"10 France \n",
"11 France \n",
"12 France \n",
"13 France \n",
"14 France \n",
"15 France \n",
"16 France \n",
"17 France \n",
"18 France \n",
"19 France \n",
"20 France \n",
"21 France \n",
"22 France \n",
"23 France \n",
"24 France \n",
"25 France \n",
"26 France \n",
"27 France \n",
"28 France \n",
"29 France \n",
"... ... \n",
"1815 France \n",
"1816 France \n",
"1817 France \n",
"1818 France \n",
"1819 France \n",
"1820 France \n",
"1821 France \n",
"1822 France \n",
"1823 France \n",
"1824 France \n",
"1825 France \n",
"1826 France \n",
"1827 France \n",
"1828 France \n",
"1829 France \n",
"1830 France \n",
"1831 France \n",
"1832 France \n",
"1833 France \n",
"1834 France \n",
"1835 France \n",
"1836 France \n",
"1837 France \n",
"1838 France \n",
"1839 France \n",
"1840 France \n",
"1841 France \n",
"1842 France \n",
"1843 France \n",
"1844 France \n",
"\n",
"[1845 rows x 11 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n", "fname = data_url.split('/')[-1]\n",
"if os.path.isfile(fname):\n",
" print(\"Reading local version\")\n",
" raw_data = pd.read_csv(fname, skiprows=1)\n",
"else:\n",
" print(\"Downloading remote version at\", data_url)\n",
" raw_data = pd.read_csv(data_url, skiprows=1)\n",
" raw_data.to_csv(fname)\n",
"raw_data" "raw_data"
] ]
}, },
...@@ -78,9 +1120,75 @@ ...@@ -78,9 +1120,75 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
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"text/plain": [
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},
"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)]"
] ]
...@@ -94,9 +1202,1038 @@ ...@@ -94,9 +1202,1038 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
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" <td>181.0</td>\n",
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" <td>202008</td>\n",
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" <td>153522.0</td>\n",
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" <td>203.0</td>\n",
" <td>233.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>202007</td>\n",
" <td>3</td>\n",
" <td>183610</td>\n",
" <td>172812.0</td>\n",
" <td>194408.0</td>\n",
" <td>279</td>\n",
" <td>263.0</td>\n",
" <td>295.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>202006</td>\n",
" <td>3</td>\n",
" <td>206669</td>\n",
" <td>195481.0</td>\n",
" <td>217857.0</td>\n",
" <td>314</td>\n",
" <td>297.0</td>\n",
" <td>331.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>202005</td>\n",
" <td>3</td>\n",
" <td>187957</td>\n",
" <td>177445.0</td>\n",
" <td>198469.0</td>\n",
" <td>285</td>\n",
" <td>269.0</td>\n",
" <td>301.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>202004</td>\n",
" <td>3</td>\n",
" <td>122331</td>\n",
" <td>113492.0</td>\n",
" <td>131170.0</td>\n",
" <td>186</td>\n",
" <td>173.0</td>\n",
" <td>199.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>202003</td>\n",
" <td>3</td>\n",
" <td>78413</td>\n",
" <td>71330.0</td>\n",
" <td>85496.0</td>\n",
" <td>119</td>\n",
" <td>108.0</td>\n",
" <td>130.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>202002</td>\n",
" <td>3</td>\n",
" <td>53614</td>\n",
" <td>47654.0</td>\n",
" <td>59574.0</td>\n",
" <td>81</td>\n",
" <td>72.0</td>\n",
" <td>90.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>202001</td>\n",
" <td>3</td>\n",
" <td>36850</td>\n",
" <td>31608.0</td>\n",
" <td>42092.0</td>\n",
" <td>56</td>\n",
" <td>48.0</td>\n",
" <td>64.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>201952</td>\n",
" <td>3</td>\n",
" <td>28135</td>\n",
" <td>23220.0</td>\n",
" <td>33050.0</td>\n",
" <td>43</td>\n",
" <td>36.0</td>\n",
" <td>50.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>201951</td>\n",
" <td>3</td>\n",
" <td>29786</td>\n",
" <td>25042.0</td>\n",
" <td>34530.0</td>\n",
" <td>45</td>\n",
" <td>38.0</td>\n",
" <td>52.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>201950</td>\n",
" <td>3</td>\n",
" <td>34223</td>\n",
" <td>29156.0</td>\n",
" <td>39290.0</td>\n",
" <td>52</td>\n",
" <td>44.0</td>\n",
" <td>60.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>201949</td>\n",
" <td>3</td>\n",
" <td>25662</td>\n",
" <td>21414.0</td>\n",
" <td>29910.0</td>\n",
" <td>39</td>\n",
" <td>33.0</td>\n",
" <td>45.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>201948</td>\n",
" <td>3</td>\n",
" <td>22367</td>\n",
" <td>18055.0</td>\n",
" <td>26679.0</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>15</th>\n",
" <td>16</td>\n",
" <td>201947</td>\n",
" <td>3</td>\n",
" <td>18669</td>\n",
" <td>14759.0</td>\n",
" <td>22579.0</td>\n",
" <td>28</td>\n",
" <td>22.0</td>\n",
" <td>34.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>201946</td>\n",
" <td>3</td>\n",
" <td>16030</td>\n",
" <td>12567.0</td>\n",
" <td>19493.0</td>\n",
" <td>24</td>\n",
" <td>19.0</td>\n",
" <td>29.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>201945</td>\n",
" <td>3</td>\n",
" <td>10138</td>\n",
" <td>7160.0</td>\n",
" <td>13116.0</td>\n",
" <td>15</td>\n",
" <td>10.0</td>\n",
" <td>20.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>19</td>\n",
" <td>201944</td>\n",
" <td>3</td>\n",
" <td>7822</td>\n",
" <td>5010.0</td>\n",
" <td>10634.0</td>\n",
" <td>12</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>201943</td>\n",
" <td>3</td>\n",
" <td>9487</td>\n",
" <td>6448.0</td>\n",
" <td>12526.0</td>\n",
" <td>14</td>\n",
" <td>9.0</td>\n",
" <td>19.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>201942</td>\n",
" <td>3</td>\n",
" <td>7747</td>\n",
" <td>5243.0</td>\n",
" <td>10251.0</td>\n",
" <td>12</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>201941</td>\n",
" <td>3</td>\n",
" <td>7122</td>\n",
" <td>4720.0</td>\n",
" <td>9524.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>201940</td>\n",
" <td>3</td>\n",
" <td>8505</td>\n",
" <td>5784.0</td>\n",
" <td>11226.0</td>\n",
" <td>13</td>\n",
" <td>9.0</td>\n",
" <td>17.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>201939</td>\n",
" <td>3</td>\n",
" <td>7091</td>\n",
" <td>4462.0</td>\n",
" <td>9720.0</td>\n",
" <td>11</td>\n",
" <td>7.0</td>\n",
" <td>15.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>201938</td>\n",
" <td>3</td>\n",
" <td>4897</td>\n",
" <td>2891.0</td>\n",
" <td>6903.0</td>\n",
" <td>7</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>201937</td>\n",
" <td>3</td>\n",
" <td>3172</td>\n",
" <td>1367.0</td>\n",
" <td>4977.0</td>\n",
" <td>5</td>\n",
" <td>2.0</td>\n",
" <td>8.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>201936</td>\n",
" <td>3</td>\n",
" <td>2295</td>\n",
" <td>728.0</td>\n",
" <td>3862.0</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>201935</td>\n",
" <td>3</td>\n",
" <td>1010</td>\n",
" <td>2.0</td>\n",
" <td>2018.0</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>201934</td>\n",
" <td>3</td>\n",
" <td>1672</td>\n",
" <td>279.0</td>\n",
" <td>3065.0</td>\n",
" <td>3</td>\n",
" <td>1.0</td>\n",
" <td>5.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>201933</td>\n",
" <td>3</td>\n",
" <td>1593</td>\n",
" <td>68.0</td>\n",
" <td>3118.0</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>4.0</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>1815</th>\n",
" <td>1816</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>1816</th>\n",
" <td>1817</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>1817</th>\n",
" <td>1818</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>1818</th>\n",
" <td>1819</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>1819</th>\n",
" <td>1820</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>1820</th>\n",
" <td>1821</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>1821</th>\n",
" <td>1822</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>1822</th>\n",
" <td>1823</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>1823</th>\n",
" <td>1824</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>1824</th>\n",
" <td>1825</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>1825</th>\n",
" <td>1826</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>1826</th>\n",
" <td>1827</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>1827</th>\n",
" <td>1828</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>1828</th>\n",
" <td>1829</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>1829</th>\n",
" <td>1830</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>1830</th>\n",
" <td>1831</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>1831</th>\n",
" <td>1832</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>1832</th>\n",
" <td>1833</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>1833</th>\n",
" <td>1834</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>1834</th>\n",
" <td>1835</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>1835</th>\n",
" <td>1836</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>1836</th>\n",
" <td>1837</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>1837</th>\n",
" <td>1838</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>1838</th>\n",
" <td>1839</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>1839</th>\n",
" <td>1840</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>1840</th>\n",
" <td>1841</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>1841</th>\n",
" <td>1842</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>1842</th>\n",
" <td>1843</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>1843</th>\n",
" <td>1844</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>1844</th>\n",
" <td>1845</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>1844 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 202011 3 101704 93652.0 109756.0 154 142.0 166.0 FR \\\n",
"0 1 202010 3 104977 96650.0 113304.0 159 146.0 172.0 FR \n",
"1 2 202009 3 110696 102066.0 119326.0 168 155.0 181.0 FR \n",
"2 3 202008 3 143753 133984.0 153522.0 218 203.0 233.0 FR \n",
"3 4 202007 3 183610 172812.0 194408.0 279 263.0 295.0 FR \n",
"4 5 202006 3 206669 195481.0 217857.0 314 297.0 331.0 FR \n",
"5 6 202005 3 187957 177445.0 198469.0 285 269.0 301.0 FR \n",
"6 7 202004 3 122331 113492.0 131170.0 186 173.0 199.0 FR \n",
"7 8 202003 3 78413 71330.0 85496.0 119 108.0 130.0 FR \n",
"8 9 202002 3 53614 47654.0 59574.0 81 72.0 90.0 FR \n",
"9 10 202001 3 36850 31608.0 42092.0 56 48.0 64.0 FR \n",
"10 11 201952 3 28135 23220.0 33050.0 43 36.0 50.0 FR \n",
"11 12 201951 3 29786 25042.0 34530.0 45 38.0 52.0 FR \n",
"12 13 201950 3 34223 29156.0 39290.0 52 44.0 60.0 FR \n",
"13 14 201949 3 25662 21414.0 29910.0 39 33.0 45.0 FR \n",
"14 15 201948 3 22367 18055.0 26679.0 34 27.0 41.0 FR \n",
"15 16 201947 3 18669 14759.0 22579.0 28 22.0 34.0 FR \n",
"16 17 201946 3 16030 12567.0 19493.0 24 19.0 29.0 FR \n",
"17 18 201945 3 10138 7160.0 13116.0 15 10.0 20.0 FR \n",
"18 19 201944 3 7822 5010.0 10634.0 12 8.0 16.0 FR \n",
"19 20 201943 3 9487 6448.0 12526.0 14 9.0 19.0 FR \n",
"20 21 201942 3 7747 5243.0 10251.0 12 8.0 16.0 FR \n",
"21 22 201941 3 7122 4720.0 9524.0 11 7.0 15.0 FR \n",
"22 23 201940 3 8505 5784.0 11226.0 13 9.0 17.0 FR \n",
"23 24 201939 3 7091 4462.0 9720.0 11 7.0 15.0 FR \n",
"24 25 201938 3 4897 2891.0 6903.0 7 4.0 10.0 FR \n",
"25 26 201937 3 3172 1367.0 4977.0 5 2.0 8.0 FR \n",
"26 27 201936 3 2295 728.0 3862.0 3 1.0 5.0 FR \n",
"27 28 201935 3 1010 2.0 2018.0 2 0.0 4.0 FR \n",
"28 29 201934 3 1672 279.0 3065.0 3 1.0 5.0 FR \n",
"29 30 201933 3 1593 68.0 3118.0 2 0.0 4.0 FR \n",
"... ... ... .. ... ... ... ... ... ... .. \n",
"1815 1816 198521 3 26096 19621.0 32571.0 47 35.0 59.0 FR \n",
"1816 1817 198520 3 27896 20885.0 34907.0 51 38.0 64.0 FR \n",
"1817 1818 198519 3 43154 32821.0 53487.0 78 59.0 97.0 FR \n",
"1818 1819 198518 3 40555 29935.0 51175.0 74 55.0 93.0 FR \n",
"1819 1820 198517 3 34053 24366.0 43740.0 62 44.0 80.0 FR \n",
"1820 1821 198516 3 50362 36451.0 64273.0 91 66.0 116.0 FR \n",
"1821 1822 198515 3 63881 45538.0 82224.0 116 83.0 149.0 FR \n",
"1822 1823 198514 3 134545 114400.0 154690.0 244 207.0 281.0 FR \n",
"1823 1824 198513 3 197206 176080.0 218332.0 357 319.0 395.0 FR \n",
"1824 1825 198512 3 245240 223304.0 267176.0 445 405.0 485.0 FR \n",
"1825 1826 198511 3 276205 252399.0 300011.0 501 458.0 544.0 FR \n",
"1826 1827 198510 3 353231 326279.0 380183.0 640 591.0 689.0 FR \n",
"1827 1828 198509 3 369895 341109.0 398681.0 670 618.0 722.0 FR \n",
"1828 1829 198508 3 389886 359529.0 420243.0 707 652.0 762.0 FR \n",
"1829 1830 198507 3 471852 432599.0 511105.0 855 784.0 926.0 FR \n",
"1830 1831 198506 3 565825 518011.0 613639.0 1026 939.0 1113.0 FR \n",
"1831 1832 198505 3 637302 592795.0 681809.0 1155 1074.0 1236.0 FR \n",
"1832 1833 198504 3 424937 390794.0 459080.0 770 708.0 832.0 FR \n",
"1833 1834 198503 3 213901 174689.0 253113.0 388 317.0 459.0 FR \n",
"1834 1835 198502 3 97586 80949.0 114223.0 177 147.0 207.0 FR \n",
"1835 1836 198501 3 85489 65918.0 105060.0 155 120.0 190.0 FR \n",
"1836 1837 198452 3 84830 60602.0 109058.0 154 110.0 198.0 FR \n",
"1837 1838 198451 3 101726 80242.0 123210.0 185 146.0 224.0 FR \n",
"1838 1839 198450 3 123680 101401.0 145959.0 225 184.0 266.0 FR \n",
"1839 1840 198449 3 101073 81684.0 120462.0 184 149.0 219.0 FR \n",
"1840 1841 198448 3 78620 60634.0 96606.0 143 110.0 176.0 FR \n",
"1841 1842 198447 3 72029 54274.0 89784.0 131 99.0 163.0 FR \n",
"1842 1843 198446 3 87330 67686.0 106974.0 159 123.0 195.0 FR \n",
"1843 1844 198445 3 135223 101414.0 169032.0 246 184.0 308.0 FR \n",
"1844 1845 198444 3 68422 20056.0 116788.0 125 37.0 213.0 FR \n",
"\n",
" France \n",
"0 France \n",
"1 France \n",
"2 France \n",
"3 France \n",
"4 France \n",
"5 France \n",
"6 France \n",
"7 France \n",
"8 France \n",
"9 France \n",
"10 France \n",
"11 France \n",
"12 France \n",
"13 France \n",
"14 France \n",
"15 France \n",
"16 France \n",
"17 France \n",
"18 France \n",
"19 France \n",
"20 France \n",
"21 France \n",
"22 France \n",
"23 France \n",
"24 France \n",
"25 France \n",
"26 France \n",
"27 France \n",
"28 France \n",
"29 France \n",
"... ... \n",
"1815 France \n",
"1816 France \n",
"1817 France \n",
"1818 France \n",
"1819 France \n",
"1820 France \n",
"1821 France \n",
"1822 France \n",
"1823 France \n",
"1824 France \n",
"1825 France \n",
"1826 France \n",
"1827 France \n",
"1828 France \n",
"1829 France \n",
"1830 France \n",
"1831 France \n",
"1832 France \n",
"1833 France \n",
"1834 France \n",
"1835 France \n",
"1836 France \n",
"1837 France \n",
"1838 France \n",
"1839 France \n",
"1840 France \n",
"1841 France \n",
"1842 France \n",
"1843 France \n",
"1844 France \n",
"\n",
"[1844 rows x 11 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"
...@@ -122,9 +2259,38 @@ ...@@ -122,9 +2259,38 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"ename": "KeyError",
"evalue": "'week'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2524\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2525\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'week'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-6-4f9c04a6e476>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPeriod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mday\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'W'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'period'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mconvert_week\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myw\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0myw\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'week'\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/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2137\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2139\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2141\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\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/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 2144\u001b[0m \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2145\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2148\u001b[0m \u001b[0;31m# duplicate columns & possible reduce dimensionality\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/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m 1840\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1841\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1842\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1843\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1844\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m 3841\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3842\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3843\u001b[0;31m \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3844\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3845\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 2525\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2526\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2527\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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 2528\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2529\u001b[0m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'week'"
]
}
],
"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",
...@@ -153,9 +2319,7 @@ ...@@ -153,9 +2319,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"sorted_data = data.set_index('period').sort_index()" "sorted_data = data.set_index('period').sort_index()"
...@@ -253,9 +2417,7 @@ ...@@ -253,9 +2417,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
...@@ -341,9 +2503,7 @@ ...@@ -341,9 +2503,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [] "source": []
} }
...@@ -364,7 +2524,7 @@ ...@@ -364,7 +2524,7 @@
"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 @ c12294ab
This source diff could not be displayed because it is too large. You can view the blob instead.
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