From b172b58e70ecd120b01365d6218840626cf2a180 Mon Sep 17 00:00:00 2001
From: 1954346cc8b06b34603a827d87ca92b1
<1954346cc8b06b34603a827d87ca92b1@app-learninglab.inria.fr>
Date: Wed, 9 Sep 2020 10:58:29 +0000
Subject: [PATCH] too many errors to fix
---
module3/exo3/exercice.ipynb | 3884 ++++++++++++++++++++++-------------
1 file changed, 2475 insertions(+), 1409 deletions(-)
diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 489fb5d..045b365 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -14,16 +14,16 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
- " data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
+ " data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
]
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 28,
"metadata": {},
"outputs": [
{
@@ -62,391 +62,391 @@
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+ " 13882 | \n",
+ " 28888 | \n",
+ " 38 | \n",
+ " 25 | \n",
+ " 51 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1825 | \n",
- " 198512 | \n",
- " 3 | \n",
- " 245240 | \n",
- " 223304.0 | \n",
- " 267176.0 | \n",
- " 445 | \n",
- " 405.0 | \n",
- " 485.0 | \n",
+ " 1531 | \n",
+ " 199117 | \n",
+ " 7 | \n",
+ " 13462 | \n",
+ " 8877 | \n",
+ " 18047 | \n",
+ " 24 | \n",
+ " 16 | \n",
+ " 32 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1826 | \n",
- " 198511 | \n",
- " 3 | \n",
- " 276205 | \n",
- " 252399.0 | \n",
- " 300011.0 | \n",
- " 501 | \n",
- " 458.0 | \n",
- " 544.0 | \n",
+ " 1532 | \n",
+ " 199116 | \n",
+ " 7 | \n",
+ " 14857 | \n",
+ " 10068 | \n",
+ " 19646 | \n",
+ " 26 | \n",
+ " 18 | \n",
+ " 34 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1827 | \n",
- " 198510 | \n",
- " 3 | \n",
- " 353231 | \n",
- " 326279.0 | \n",
- " 380183.0 | \n",
- " 640 | \n",
- " 591.0 | \n",
- " 689.0 | \n",
+ " 1533 | \n",
+ " 199115 | \n",
+ " 7 | \n",
+ " 13975 | \n",
+ " 9781 | \n",
+ " 18169 | \n",
+ " 25 | \n",
+ " 18 | \n",
+ " 32 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1828 | \n",
- " 198509 | \n",
- " 3 | \n",
- " 369895 | \n",
- " 341109.0 | \n",
- " 398681.0 | \n",
- " 670 | \n",
- " 618.0 | \n",
- " 722.0 | \n",
+ " 1534 | \n",
+ " 199114 | \n",
+ " 7 | \n",
+ " 12265 | \n",
+ " 7684 | \n",
+ " 16846 | \n",
+ " 22 | \n",
+ " 14 | \n",
+ " 30 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1829 | \n",
- " 198508 | \n",
- " 3 | \n",
- " 389886 | \n",
- " 359529.0 | \n",
- " 420243.0 | \n",
- " 707 | \n",
- " 652.0 | \n",
- " 762.0 | \n",
+ " 1535 | \n",
+ " 199113 | \n",
+ " 7 | \n",
+ " 9567 | \n",
+ " 6041 | \n",
+ " 13093 | \n",
+ " 17 | \n",
+ " 11 | \n",
+ " 23 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1830 | \n",
- " 198507 | \n",
- " 3 | \n",
- " 471852 | \n",
- " 432599.0 | \n",
- " 511105.0 | \n",
- " 855 | \n",
- " 784.0 | \n",
- " 926.0 | \n",
+ " 1536 | \n",
+ " 199112 | \n",
+ " 7 | \n",
+ " 10864 | \n",
+ " 7331 | \n",
+ " 14397 | \n",
+ " 19 | \n",
+ " 13 | \n",
+ " 25 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1831 | \n",
- " 198506 | \n",
- " 3 | \n",
- " 565825 | \n",
- " 518011.0 | \n",
- " 613639.0 | \n",
- " 1026 | \n",
- " 939.0 | \n",
- " 1113.0 | \n",
+ " 1537 | \n",
+ " 199111 | \n",
+ " 7 | \n",
+ " 15574 | \n",
+ " 11184 | \n",
+ " 19964 | \n",
+ " 27 | \n",
+ " 19 | \n",
+ " 35 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1832 | \n",
- " 198505 | \n",
- " 3 | \n",
- " 637302 | \n",
- " 592795.0 | \n",
- " 681809.0 | \n",
- " 1155 | \n",
- " 1074.0 | \n",
- " 1236.0 | \n",
+ " 1538 | \n",
+ " 199110 | \n",
+ " 7 | \n",
+ " 16643 | \n",
+ " 11372 | \n",
+ " 21914 | \n",
+ " 29 | \n",
+ " 20 | \n",
+ " 38 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1833 | \n",
- " 198504 | \n",
- " 3 | \n",
- " 424937 | \n",
- " 390794.0 | \n",
- " 459080.0 | \n",
- " 770 | \n",
- " 708.0 | \n",
- " 832.0 | \n",
+ " 1539 | \n",
+ " 199109 | \n",
+ " 7 | \n",
+ " 13741 | \n",
+ " 8780 | \n",
+ " 18702 | \n",
+ " 24 | \n",
+ " 15 | \n",
+ " 33 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1834 | \n",
- " 198503 | \n",
- " 3 | \n",
- " 213901 | \n",
- " 174689.0 | \n",
- " 253113.0 | \n",
- " 388 | \n",
- " 317.0 | \n",
- " 459.0 | \n",
+ " 1540 | \n",
+ " 199108 | \n",
+ " 7 | \n",
+ " 13289 | \n",
+ " 8813 | \n",
+ " 17765 | \n",
+ " 23 | \n",
+ " 15 | \n",
+ " 31 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1835 | \n",
- " 198502 | \n",
- " 3 | \n",
- " 97586 | \n",
- " 80949.0 | \n",
- " 114223.0 | \n",
- " 177 | \n",
- " 147.0 | \n",
- " 207.0 | \n",
+ " 1541 | \n",
+ " 199107 | \n",
+ " 7 | \n",
+ " 12337 | \n",
+ " 8077 | \n",
+ " 16597 | \n",
+ " 22 | \n",
+ " 15 | \n",
+ " 29 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1836 | \n",
- " 198501 | \n",
- " 3 | \n",
- " 85489 | \n",
- " 65918.0 | \n",
- " 105060.0 | \n",
- " 155 | \n",
- " 120.0 | \n",
- " 190.0 | \n",
+ " 1542 | \n",
+ " 199106 | \n",
+ " 7 | \n",
+ " 10877 | \n",
+ " 7013 | \n",
+ " 14741 | \n",
+ " 19 | \n",
+ " 12 | \n",
+ " 26 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1837 | \n",
- " 198452 | \n",
- " 3 | \n",
- " 84830 | \n",
- " 60602.0 | \n",
- " 109058.0 | \n",
- " 154 | \n",
- " 110.0 | \n",
- " 198.0 | \n",
+ " 1543 | \n",
+ " 199105 | \n",
+ " 7 | \n",
+ " 10442 | \n",
+ " 6544 | \n",
+ " 14340 | \n",
+ " 18 | \n",
+ " 11 | \n",
+ " 25 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1838 | \n",
- " 198451 | \n",
- " 3 | \n",
- " 101726 | \n",
- " 80242.0 | \n",
- " 123210.0 | \n",
- " 185 | \n",
- " 146.0 | \n",
- " 224.0 | \n",
+ " 1544 | \n",
+ " 199104 | \n",
+ " 7 | \n",
+ " 7913 | \n",
+ " 4563 | \n",
+ " 11263 | \n",
+ " 14 | \n",
+ " 8 | \n",
+ " 20 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1839 | \n",
- " 198450 | \n",
- " 3 | \n",
- " 123680 | \n",
- " 101401.0 | \n",
- " 145959.0 | \n",
- " 225 | \n",
- " 184.0 | \n",
- " 266.0 | \n",
+ " 1545 | \n",
+ " 199103 | \n",
+ " 7 | \n",
+ " 15387 | \n",
+ " 10484 | \n",
+ " 20290 | \n",
+ " 27 | \n",
+ " 18 | \n",
+ " 36 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1840 | \n",
- " 198449 | \n",
- " 3 | \n",
- " 101073 | \n",
- " 81684.0 | \n",
- " 120462.0 | \n",
- " 184 | \n",
- " 149.0 | \n",
- " 219.0 | \n",
+ " 1546 | \n",
+ " 199102 | \n",
+ " 7 | \n",
+ " 16277 | \n",
+ " 11046 | \n",
+ " 21508 | \n",
+ " 29 | \n",
+ " 20 | \n",
+ " 38 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1841 | \n",
- " 198448 | \n",
- " 3 | \n",
- " 78620 | \n",
- " 60634.0 | \n",
- " 96606.0 | \n",
- " 143 | \n",
- " 110.0 | \n",
- " 176.0 | \n",
+ " 1547 | \n",
+ " 199101 | \n",
+ " 7 | \n",
+ " 15565 | \n",
+ " 10271 | \n",
+ " 20859 | \n",
+ " 27 | \n",
+ " 18 | \n",
+ " 36 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1842 | \n",
- " 198447 | \n",
- " 3 | \n",
- " 72029 | \n",
- " 54274.0 | \n",
- " 89784.0 | \n",
- " 131 | \n",
- " 99.0 | \n",
- " 163.0 | \n",
+ " 1548 | \n",
+ " 199052 | \n",
+ " 7 | \n",
+ " 19375 | \n",
+ " 13295 | \n",
+ " 25455 | \n",
+ " 34 | \n",
+ " 23 | \n",
+ " 45 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1843 | \n",
- " 198446 | \n",
- " 3 | \n",
- " 87330 | \n",
- " 67686.0 | \n",
- " 106974.0 | \n",
- " 159 | \n",
- " 123.0 | \n",
- " 195.0 | \n",
+ " 1549 | \n",
+ " 199051 | \n",
+ " 7 | \n",
+ " 19080 | \n",
+ " 13807 | \n",
+ " 24353 | \n",
+ " 34 | \n",
+ " 25 | \n",
+ " 43 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1844 | \n",
- " 198445 | \n",
- " 3 | \n",
- " 135223 | \n",
- " 101414.0 | \n",
- " 169032.0 | \n",
- " 246 | \n",
- " 184.0 | \n",
- " 308.0 | \n",
+ " 1550 | \n",
+ " 199050 | \n",
+ " 7 | \n",
+ " 11079 | \n",
+ " 6660 | \n",
+ " 15498 | \n",
+ " 20 | \n",
+ " 12 | \n",
+ " 28 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1845 | \n",
- " 198444 | \n",
- " 3 | \n",
- " 68422 | \n",
- " 20056.0 | \n",
- " 116788.0 | \n",
- " 125 | \n",
- " 37.0 | \n",
- " 213.0 | \n",
+ " 1551 | \n",
+ " 199049 | \n",
+ " 7 | \n",
+ " 1143 | \n",
+ " 0 | \n",
+ " 2610 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ " 5 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
"\n",
- "1846 rows × 10 columns
\n",
+ "1552 rows × 10 columns
\n",
""
],
"text/plain": [
- " week indicator inc inc_low inc_up inc100 inc100_low \\\n",
- "0 202011 3 101704 93652.0 109756.0 154 142.0 \n",
- "1 202010 3 104977 96650.0 113304.0 159 146.0 \n",
- "2 202009 3 110696 102066.0 119326.0 168 155.0 \n",
- "3 202008 3 143753 133984.0 153522.0 218 203.0 \n",
- "4 202007 3 183610 172812.0 194408.0 279 263.0 \n",
- "5 202006 3 206669 195481.0 217857.0 314 297.0 \n",
- "6 202005 3 187957 177445.0 198469.0 285 269.0 \n",
- "7 202004 3 122331 113492.0 131170.0 186 173.0 \n",
- "8 202003 3 78413 71330.0 85496.0 119 108.0 \n",
- "9 202002 3 53614 47654.0 59574.0 81 72.0 \n",
- "10 202001 3 36850 31608.0 42092.0 56 48.0 \n",
- "11 201952 3 28135 23220.0 33050.0 43 36.0 \n",
- "12 201951 3 29786 25042.0 34530.0 45 38.0 \n",
- "13 201950 3 34223 29156.0 39290.0 52 44.0 \n",
- "14 201949 3 25662 21414.0 29910.0 39 33.0 \n",
- "15 201948 3 22367 18055.0 26679.0 34 27.0 \n",
- "16 201947 3 18669 14759.0 22579.0 28 22.0 \n",
- "17 201946 3 16030 12567.0 19493.0 24 19.0 \n",
- "18 201945 3 10138 7160.0 13116.0 15 10.0 \n",
- "19 201944 3 7822 5010.0 10634.0 12 8.0 \n",
- "20 201943 3 9487 6448.0 12526.0 14 9.0 \n",
- "21 201942 3 7747 5243.0 10251.0 12 8.0 \n",
- "22 201941 3 7122 4720.0 9524.0 11 7.0 \n",
- "23 201940 3 8505 5784.0 11226.0 13 9.0 \n",
- "24 201939 3 7091 4462.0 9720.0 11 7.0 \n",
- "25 201938 3 4897 2891.0 6903.0 7 4.0 \n",
- "26 201937 3 3172 1367.0 4977.0 5 2.0 \n",
- "27 201936 3 2295 728.0 3862.0 3 1.0 \n",
- "28 201935 3 1010 2.0 2018.0 2 0.0 \n",
- "29 201934 3 1672 279.0 3065.0 3 1.0 \n",
- "... ... ... ... ... ... ... ... \n",
- "1816 198521 3 26096 19621.0 32571.0 47 35.0 \n",
- "1817 198520 3 27896 20885.0 34907.0 51 38.0 \n",
- "1818 198519 3 43154 32821.0 53487.0 78 59.0 \n",
- "1819 198518 3 40555 29935.0 51175.0 74 55.0 \n",
- "1820 198517 3 34053 24366.0 43740.0 62 44.0 \n",
- "1821 198516 3 50362 36451.0 64273.0 91 66.0 \n",
- "1822 198515 3 63881 45538.0 82224.0 116 83.0 \n",
- "1823 198514 3 134545 114400.0 154690.0 244 207.0 \n",
- "1824 198513 3 197206 176080.0 218332.0 357 319.0 \n",
- "1825 198512 3 245240 223304.0 267176.0 445 405.0 \n",
- "1826 198511 3 276205 252399.0 300011.0 501 458.0 \n",
- "1827 198510 3 353231 326279.0 380183.0 640 591.0 \n",
- "1828 198509 3 369895 341109.0 398681.0 670 618.0 \n",
- "1829 198508 3 389886 359529.0 420243.0 707 652.0 \n",
- "1830 198507 3 471852 432599.0 511105.0 855 784.0 \n",
- "1831 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
- "1832 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
- "1833 198504 3 424937 390794.0 459080.0 770 708.0 \n",
- "1834 198503 3 213901 174689.0 253113.0 388 317.0 \n",
- "1835 198502 3 97586 80949.0 114223.0 177 147.0 \n",
- "1836 198501 3 85489 65918.0 105060.0 155 120.0 \n",
- "1837 198452 3 84830 60602.0 109058.0 154 110.0 \n",
- "1838 198451 3 101726 80242.0 123210.0 185 146.0 \n",
- "1839 198450 3 123680 101401.0 145959.0 225 184.0 \n",
- "1840 198449 3 101073 81684.0 120462.0 184 149.0 \n",
- "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n",
- "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n",
- "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n",
- "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n",
- "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n",
+ " week indicator inc inc_low inc_up inc100 inc100_low \\\n",
+ "0 202035 7 837 0 1712 1 0 \n",
+ "1 202034 7 2275 373 4177 3 0 \n",
+ "2 202033 7 1284 177 2391 2 0 \n",
+ "3 202032 7 2650 689 4611 4 1 \n",
+ "4 202031 7 1303 100 2506 2 0 \n",
+ "5 202030 7 1385 75 2695 2 0 \n",
+ "6 202029 7 841 10 1672 1 0 \n",
+ "7 202028 7 728 0 1515 1 0 \n",
+ "8 202027 7 986 149 1823 1 0 \n",
+ "9 202026 7 694 0 1454 1 0 \n",
+ "10 202025 7 228 0 597 0 0 \n",
+ "11 202024 7 388 0 959 1 0 \n",
+ "12 202023 7 558 1 1115 1 0 \n",
+ "13 202022 7 277 0 633 0 0 \n",
+ "14 202021 7 602 36 1168 1 0 \n",
+ "15 202020 7 824 20 1628 1 0 \n",
+ "16 202019 7 310 0 753 0 0 \n",
+ "17 202018 7 849 98 1600 1 0 \n",
+ "18 202017 7 272 0 658 0 0 \n",
+ "19 202016 7 758 78 1438 1 0 \n",
+ "20 202015 7 1918 675 3161 3 1 \n",
+ "21 202014 7 3879 2227 5531 6 3 \n",
+ "22 202013 7 7326 5236 9416 11 8 \n",
+ "23 202012 7 8123 5790 10456 12 8 \n",
+ "24 202011 7 10198 7568 12828 15 11 \n",
+ "25 202010 7 9011 6691 11331 14 10 \n",
+ "26 202009 7 13631 10544 16718 21 16 \n",
+ "27 202008 7 10424 7708 13140 16 12 \n",
+ "28 202007 7 8959 6574 11344 14 10 \n",
+ "29 202006 7 9264 6925 11603 14 10 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "1522 199126 7 17608 11304 23912 31 20 \n",
+ "1523 199125 7 16169 10700 21638 28 18 \n",
+ "1524 199124 7 16171 10071 22271 28 17 \n",
+ "1525 199123 7 11947 7671 16223 21 13 \n",
+ "1526 199122 7 15452 9953 20951 27 17 \n",
+ "1527 199121 7 14903 8975 20831 26 16 \n",
+ "1528 199120 7 19053 12742 25364 34 23 \n",
+ "1529 199119 7 16739 11246 22232 29 19 \n",
+ "1530 199118 7 21385 13882 28888 38 25 \n",
+ "1531 199117 7 13462 8877 18047 24 16 \n",
+ "1532 199116 7 14857 10068 19646 26 18 \n",
+ "1533 199115 7 13975 9781 18169 25 18 \n",
+ "1534 199114 7 12265 7684 16846 22 14 \n",
+ "1535 199113 7 9567 6041 13093 17 11 \n",
+ "1536 199112 7 10864 7331 14397 19 13 \n",
+ "1537 199111 7 15574 11184 19964 27 19 \n",
+ "1538 199110 7 16643 11372 21914 29 20 \n",
+ "1539 199109 7 13741 8780 18702 24 15 \n",
+ "1540 199108 7 13289 8813 17765 23 15 \n",
+ "1541 199107 7 12337 8077 16597 22 15 \n",
+ "1542 199106 7 10877 7013 14741 19 12 \n",
+ "1543 199105 7 10442 6544 14340 18 11 \n",
+ "1544 199104 7 7913 4563 11263 14 8 \n",
+ "1545 199103 7 15387 10484 20290 27 18 \n",
+ "1546 199102 7 16277 11046 21508 29 20 \n",
+ "1547 199101 7 15565 10271 20859 27 18 \n",
+ "1548 199052 7 19375 13295 25455 34 23 \n",
+ "1549 199051 7 19080 13807 24353 34 25 \n",
+ "1550 199050 7 11079 6660 15498 20 12 \n",
+ "1551 199049 7 1143 0 2610 2 0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
- "0 166.0 FR France \n",
- "1 172.0 FR France \n",
- "2 181.0 FR France \n",
- "3 233.0 FR France \n",
- "4 295.0 FR France \n",
- "5 331.0 FR France \n",
- "6 301.0 FR France \n",
- "7 199.0 FR France \n",
- "8 130.0 FR France \n",
- "9 90.0 FR France \n",
- "10 64.0 FR France \n",
- "11 50.0 FR France \n",
- "12 52.0 FR France \n",
- "13 60.0 FR France \n",
- "14 45.0 FR France \n",
- "15 41.0 FR France \n",
- "16 34.0 FR France \n",
- "17 29.0 FR France \n",
- "18 20.0 FR France \n",
- "19 16.0 FR France \n",
- "20 19.0 FR France \n",
- "21 16.0 FR France \n",
- "22 15.0 FR France \n",
- "23 17.0 FR France \n",
- "24 15.0 FR France \n",
- "25 10.0 FR France \n",
- "26 8.0 FR France \n",
- "27 5.0 FR France \n",
- "28 4.0 FR France \n",
- "29 5.0 FR France \n",
+ "0 2 FR France \n",
+ "1 6 FR France \n",
+ "2 4 FR France \n",
+ "3 7 FR France \n",
+ "4 4 FR France \n",
+ "5 4 FR France \n",
+ "6 2 FR France \n",
+ "7 2 FR France \n",
+ "8 2 FR France \n",
+ "9 2 FR France \n",
+ "10 1 FR France \n",
+ "11 2 FR France \n",
+ "12 2 FR France \n",
+ "13 1 FR France \n",
+ "14 2 FR France \n",
+ "15 2 FR France \n",
+ "16 1 FR France \n",
+ "17 2 FR France \n",
+ "18 1 FR France \n",
+ "19 2 FR France \n",
+ "20 5 FR France \n",
+ "21 9 FR France \n",
+ "22 14 FR France \n",
+ "23 16 FR France \n",
+ "24 19 FR France \n",
+ "25 18 FR France \n",
+ "26 26 FR France \n",
+ "27 20 FR France \n",
+ "28 18 FR France \n",
+ "29 18 FR France \n",
"... ... ... ... \n",
- "1816 59.0 FR France \n",
- "1817 64.0 FR France \n",
- "1818 97.0 FR France \n",
- "1819 93.0 FR France \n",
- "1820 80.0 FR France \n",
- "1821 116.0 FR France \n",
- "1822 149.0 FR France \n",
- "1823 281.0 FR France \n",
- "1824 395.0 FR France \n",
- "1825 485.0 FR France \n",
- "1826 544.0 FR France \n",
- "1827 689.0 FR France \n",
- "1828 722.0 FR France \n",
- "1829 762.0 FR France \n",
- "1830 926.0 FR France \n",
- "1831 1113.0 FR France \n",
- "1832 1236.0 FR France \n",
- "1833 832.0 FR France \n",
- "1834 459.0 FR France \n",
- "1835 207.0 FR France \n",
- "1836 190.0 FR France \n",
- "1837 198.0 FR France \n",
- "1838 224.0 FR France \n",
- "1839 266.0 FR France \n",
- "1840 219.0 FR France \n",
- "1841 176.0 FR France \n",
- "1842 163.0 FR France \n",
- "1843 195.0 FR France \n",
- "1844 308.0 FR France \n",
- "1845 213.0 FR France \n",
+ "1522 42 FR France \n",
+ "1523 38 FR France \n",
+ "1524 39 FR France \n",
+ "1525 29 FR France \n",
+ "1526 37 FR France \n",
+ "1527 36 FR France \n",
+ "1528 45 FR France \n",
+ "1529 39 FR France \n",
+ "1530 51 FR France \n",
+ "1531 32 FR France \n",
+ "1532 34 FR France \n",
+ "1533 32 FR France \n",
+ "1534 30 FR France \n",
+ "1535 23 FR France \n",
+ "1536 25 FR France \n",
+ "1537 35 FR France \n",
+ "1538 38 FR France \n",
+ "1539 33 FR France \n",
+ "1540 31 FR France \n",
+ "1541 29 FR France \n",
+ "1542 26 FR France \n",
+ "1543 25 FR France \n",
+ "1544 20 FR France \n",
+ "1545 36 FR France \n",
+ "1546 38 FR France \n",
+ "1547 36 FR France \n",
+ "1548 45 FR France \n",
+ "1549 43 FR France \n",
+ "1550 28 FR France \n",
+ "1551 5 FR France \n",
"\n",
- "[1846 rows x 10 columns]"
+ "[1552 rows x 10 columns]"
]
},
- "execution_count": 4,
+ "execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
@@ -1000,7 +1000,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -1037,32 +1037,17 @@
" \n",
" \n",
" \n",
- " \n",
- " 1609 | \n",
- " 198919 | \n",
- " 3 | \n",
- " 0 | \n",
- " NaN | \n",
- " NaN | \n",
- " 0 | \n",
- " NaN | \n",
- " NaN | \n",
- " FR | \n",
- " France | \n",
- "
\n",
" \n",
"\n",
""
],
"text/plain": [
- " week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
- "1609 198919 3 0 NaN NaN 0 NaN NaN \n",
- "\n",
- " geo_insee geo_name \n",
- "1609 FR France "
+ "Empty DataFrame\n",
+ "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
+ "Index: []"
]
},
- "execution_count": 5,
+ "execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
@@ -1073,7 +1058,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -1112,391 +1097,391 @@
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"
\n",
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"
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"
\n",
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\n",
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"
\n",
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"
\n",
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"
\n",
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\n",
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\n",
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"
\n",
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"
\n",
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"
\n",
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" 11 | \n",
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" France | \n",
"
\n",
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" FR | \n",
" France | \n",
"
\n",
" \n",
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" 11 | \n",
- " 7.0 | \n",
- " 15.0 | \n",
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" FR | \n",
" France | \n",
"
\n",
" \n",
" 25 | \n",
- " 201938 | \n",
- " 3 | \n",
- " 4897 | \n",
- " 2891.0 | \n",
- " 6903.0 | \n",
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" 7 | \n",
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+ " 6691 | \n",
+ " 11331 | \n",
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+ " 10 | \n",
+ " 18 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
" 26 | \n",
- " 201937 | \n",
- " 3 | \n",
- " 3172 | \n",
- " 1367.0 | \n",
- " 4977.0 | \n",
- " 5 | \n",
- " 2.0 | \n",
- " 8.0 | \n",
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+ " 10544 | \n",
+ " 16718 | \n",
+ " 21 | \n",
+ " 16 | \n",
+ " 26 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
" 27 | \n",
- " 201936 | \n",
- " 3 | \n",
- " 2295 | \n",
- " 728.0 | \n",
- " 3862.0 | \n",
- " 3 | \n",
- " 1.0 | \n",
- " 5.0 | \n",
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+ " 7708 | \n",
+ " 13140 | \n",
+ " 16 | \n",
+ " 12 | \n",
+ " 20 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
" 28 | \n",
- " 201935 | \n",
- " 3 | \n",
- " 1010 | \n",
- " 2.0 | \n",
- " 2018.0 | \n",
- " 2 | \n",
- " 0.0 | \n",
- " 4.0 | \n",
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+ " 8959 | \n",
+ " 6574 | \n",
+ " 11344 | \n",
+ " 14 | \n",
+ " 10 | \n",
+ " 18 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
" 29 | \n",
- " 201934 | \n",
- " 3 | \n",
- " 1672 | \n",
- " 279.0 | \n",
- " 3065.0 | \n",
- " 3 | \n",
- " 1.0 | \n",
- " 5.0 | \n",
+ " 202006 | \n",
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+ " 9264 | \n",
+ " 6925 | \n",
+ " 11603 | \n",
+ " 14 | \n",
+ " 10 | \n",
+ " 18 | \n",
" FR | \n",
" France | \n",
"
\n",
@@ -1514,531 +1499,531 @@
" ... | \n",
" \n",
" \n",
- " 1816 | \n",
- " 198521 | \n",
- " 3 | \n",
- " 26096 | \n",
- " 19621.0 | \n",
- " 32571.0 | \n",
- " 47 | \n",
- " 35.0 | \n",
- " 59.0 | \n",
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+ " 199126 | \n",
+ " 7 | \n",
+ " 17608 | \n",
+ " 11304 | \n",
+ " 23912 | \n",
+ " 31 | \n",
+ " 20 | \n",
+ " 42 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1817 | \n",
- " 198520 | \n",
- " 3 | \n",
- " 27896 | \n",
- " 20885.0 | \n",
- " 34907.0 | \n",
- " 51 | \n",
- " 38.0 | \n",
- " 64.0 | \n",
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+ " 7 | \n",
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+ " 10700 | \n",
+ " 21638 | \n",
+ " 28 | \n",
+ " 18 | \n",
+ " 38 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1818 | \n",
- " 198519 | \n",
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- " 43154 | \n",
- " 32821.0 | \n",
- " 53487.0 | \n",
- " 78 | \n",
- " 59.0 | \n",
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+ " 16171 | \n",
+ " 10071 | \n",
+ " 22271 | \n",
+ " 28 | \n",
+ " 17 | \n",
+ " 39 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1819 | \n",
- " 198518 | \n",
- " 3 | \n",
- " 40555 | \n",
- " 29935.0 | \n",
- " 51175.0 | \n",
- " 74 | \n",
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- " 93.0 | \n",
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+ " 11947 | \n",
+ " 7671 | \n",
+ " 16223 | \n",
+ " 21 | \n",
+ " 13 | \n",
+ " 29 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1820 | \n",
- " 198517 | \n",
- " 3 | \n",
- " 34053 | \n",
- " 24366.0 | \n",
- " 43740.0 | \n",
- " 62 | \n",
- " 44.0 | \n",
- " 80.0 | \n",
+ " 1526 | \n",
+ " 199122 | \n",
+ " 7 | \n",
+ " 15452 | \n",
+ " 9953 | \n",
+ " 20951 | \n",
+ " 27 | \n",
+ " 17 | \n",
+ " 37 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1821 | \n",
- " 198516 | \n",
- " 3 | \n",
- " 50362 | \n",
- " 36451.0 | \n",
- " 64273.0 | \n",
- " 91 | \n",
- " 66.0 | \n",
- " 116.0 | \n",
+ " 1527 | \n",
+ " 199121 | \n",
+ " 7 | \n",
+ " 14903 | \n",
+ " 8975 | \n",
+ " 20831 | \n",
+ " 26 | \n",
+ " 16 | \n",
+ " 36 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1822 | \n",
- " 198515 | \n",
- " 3 | \n",
- " 63881 | \n",
- " 45538.0 | \n",
- " 82224.0 | \n",
- " 116 | \n",
- " 83.0 | \n",
- " 149.0 | \n",
+ " 1528 | \n",
+ " 199120 | \n",
+ " 7 | \n",
+ " 19053 | \n",
+ " 12742 | \n",
+ " 25364 | \n",
+ " 34 | \n",
+ " 23 | \n",
+ " 45 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1823 | \n",
- " 198514 | \n",
- " 3 | \n",
- " 134545 | \n",
- " 114400.0 | \n",
- " 154690.0 | \n",
- " 244 | \n",
- " 207.0 | \n",
- " 281.0 | \n",
+ " 1529 | \n",
+ " 199119 | \n",
+ " 7 | \n",
+ " 16739 | \n",
+ " 11246 | \n",
+ " 22232 | \n",
+ " 29 | \n",
+ " 19 | \n",
+ " 39 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1824 | \n",
- " 198513 | \n",
- " 3 | \n",
- " 197206 | \n",
- " 176080.0 | \n",
- " 218332.0 | \n",
- " 357 | \n",
- " 319.0 | \n",
- " 395.0 | \n",
+ " 1530 | \n",
+ " 199118 | \n",
+ " 7 | \n",
+ " 21385 | \n",
+ " 13882 | \n",
+ " 28888 | \n",
+ " 38 | \n",
+ " 25 | \n",
+ " 51 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1825 | \n",
- " 198512 | \n",
- " 3 | \n",
- " 245240 | \n",
- " 223304.0 | \n",
- " 267176.0 | \n",
- " 445 | \n",
- " 405.0 | \n",
- " 485.0 | \n",
+ " 1531 | \n",
+ " 199117 | \n",
+ " 7 | \n",
+ " 13462 | \n",
+ " 8877 | \n",
+ " 18047 | \n",
+ " 24 | \n",
+ " 16 | \n",
+ " 32 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1826 | \n",
- " 198511 | \n",
- " 3 | \n",
- " 276205 | \n",
- " 252399.0 | \n",
- " 300011.0 | \n",
- " 501 | \n",
- " 458.0 | \n",
- " 544.0 | \n",
+ " 1532 | \n",
+ " 199116 | \n",
+ " 7 | \n",
+ " 14857 | \n",
+ " 10068 | \n",
+ " 19646 | \n",
+ " 26 | \n",
+ " 18 | \n",
+ " 34 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1827 | \n",
- " 198510 | \n",
- " 3 | \n",
- " 353231 | \n",
- " 326279.0 | \n",
- " 380183.0 | \n",
- " 640 | \n",
- " 591.0 | \n",
- " 689.0 | \n",
+ " 1533 | \n",
+ " 199115 | \n",
+ " 7 | \n",
+ " 13975 | \n",
+ " 9781 | \n",
+ " 18169 | \n",
+ " 25 | \n",
+ " 18 | \n",
+ " 32 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1828 | \n",
- " 198509 | \n",
- " 3 | \n",
- " 369895 | \n",
- " 341109.0 | \n",
- " 398681.0 | \n",
- " 670 | \n",
- " 618.0 | \n",
- " 722.0 | \n",
+ " 1534 | \n",
+ " 199114 | \n",
+ " 7 | \n",
+ " 12265 | \n",
+ " 7684 | \n",
+ " 16846 | \n",
+ " 22 | \n",
+ " 14 | \n",
+ " 30 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1829 | \n",
- " 198508 | \n",
- " 3 | \n",
- " 389886 | \n",
- " 359529.0 | \n",
- " 420243.0 | \n",
- " 707 | \n",
- " 652.0 | \n",
- " 762.0 | \n",
+ " 1535 | \n",
+ " 199113 | \n",
+ " 7 | \n",
+ " 9567 | \n",
+ " 6041 | \n",
+ " 13093 | \n",
+ " 17 | \n",
+ " 11 | \n",
+ " 23 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1830 | \n",
- " 198507 | \n",
- " 3 | \n",
- " 471852 | \n",
- " 432599.0 | \n",
- " 511105.0 | \n",
- " 855 | \n",
- " 784.0 | \n",
- " 926.0 | \n",
+ " 1536 | \n",
+ " 199112 | \n",
+ " 7 | \n",
+ " 10864 | \n",
+ " 7331 | \n",
+ " 14397 | \n",
+ " 19 | \n",
+ " 13 | \n",
+ " 25 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1831 | \n",
- " 198506 | \n",
- " 3 | \n",
- " 565825 | \n",
- " 518011.0 | \n",
- " 613639.0 | \n",
- " 1026 | \n",
- " 939.0 | \n",
- " 1113.0 | \n",
+ " 1537 | \n",
+ " 199111 | \n",
+ " 7 | \n",
+ " 15574 | \n",
+ " 11184 | \n",
+ " 19964 | \n",
+ " 27 | \n",
+ " 19 | \n",
+ " 35 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1832 | \n",
- " 198505 | \n",
- " 3 | \n",
- " 637302 | \n",
- " 592795.0 | \n",
- " 681809.0 | \n",
- " 1155 | \n",
- " 1074.0 | \n",
- " 1236.0 | \n",
+ " 1538 | \n",
+ " 199110 | \n",
+ " 7 | \n",
+ " 16643 | \n",
+ " 11372 | \n",
+ " 21914 | \n",
+ " 29 | \n",
+ " 20 | \n",
+ " 38 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1833 | \n",
- " 198504 | \n",
- " 3 | \n",
- " 424937 | \n",
- " 390794.0 | \n",
- " 459080.0 | \n",
- " 770 | \n",
- " 708.0 | \n",
- " 832.0 | \n",
+ " 1539 | \n",
+ " 199109 | \n",
+ " 7 | \n",
+ " 13741 | \n",
+ " 8780 | \n",
+ " 18702 | \n",
+ " 24 | \n",
+ " 15 | \n",
+ " 33 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1834 | \n",
- " 198503 | \n",
- " 3 | \n",
- " 213901 | \n",
- " 174689.0 | \n",
- " 253113.0 | \n",
- " 388 | \n",
- " 317.0 | \n",
- " 459.0 | \n",
+ " 1540 | \n",
+ " 199108 | \n",
+ " 7 | \n",
+ " 13289 | \n",
+ " 8813 | \n",
+ " 17765 | \n",
+ " 23 | \n",
+ " 15 | \n",
+ " 31 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1835 | \n",
- " 198502 | \n",
- " 3 | \n",
- " 97586 | \n",
- " 80949.0 | \n",
- " 114223.0 | \n",
- " 177 | \n",
- " 147.0 | \n",
- " 207.0 | \n",
+ " 1541 | \n",
+ " 199107 | \n",
+ " 7 | \n",
+ " 12337 | \n",
+ " 8077 | \n",
+ " 16597 | \n",
+ " 22 | \n",
+ " 15 | \n",
+ " 29 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1836 | \n",
- " 198501 | \n",
- " 3 | \n",
- " 85489 | \n",
- " 65918.0 | \n",
- " 105060.0 | \n",
- " 155 | \n",
- " 120.0 | \n",
- " 190.0 | \n",
+ " 1542 | \n",
+ " 199106 | \n",
+ " 7 | \n",
+ " 10877 | \n",
+ " 7013 | \n",
+ " 14741 | \n",
+ " 19 | \n",
+ " 12 | \n",
+ " 26 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1837 | \n",
- " 198452 | \n",
- " 3 | \n",
- " 84830 | \n",
- " 60602.0 | \n",
- " 109058.0 | \n",
- " 154 | \n",
- " 110.0 | \n",
- " 198.0 | \n",
+ " 1543 | \n",
+ " 199105 | \n",
+ " 7 | \n",
+ " 10442 | \n",
+ " 6544 | \n",
+ " 14340 | \n",
+ " 18 | \n",
+ " 11 | \n",
+ " 25 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1838 | \n",
- " 198451 | \n",
- " 3 | \n",
- " 101726 | \n",
- " 80242.0 | \n",
- " 123210.0 | \n",
- " 185 | \n",
- " 146.0 | \n",
- " 224.0 | \n",
+ " 1544 | \n",
+ " 199104 | \n",
+ " 7 | \n",
+ " 7913 | \n",
+ " 4563 | \n",
+ " 11263 | \n",
+ " 14 | \n",
+ " 8 | \n",
+ " 20 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1839 | \n",
- " 198450 | \n",
- " 3 | \n",
- " 123680 | \n",
- " 101401.0 | \n",
- " 145959.0 | \n",
- " 225 | \n",
- " 184.0 | \n",
- " 266.0 | \n",
+ " 1545 | \n",
+ " 199103 | \n",
+ " 7 | \n",
+ " 15387 | \n",
+ " 10484 | \n",
+ " 20290 | \n",
+ " 27 | \n",
+ " 18 | \n",
+ " 36 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1840 | \n",
- " 198449 | \n",
- " 3 | \n",
- " 101073 | \n",
- " 81684.0 | \n",
- " 120462.0 | \n",
- " 184 | \n",
- " 149.0 | \n",
- " 219.0 | \n",
+ " 1546 | \n",
+ " 199102 | \n",
+ " 7 | \n",
+ " 16277 | \n",
+ " 11046 | \n",
+ " 21508 | \n",
+ " 29 | \n",
+ " 20 | \n",
+ " 38 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1841 | \n",
- " 198448 | \n",
- " 3 | \n",
- " 78620 | \n",
- " 60634.0 | \n",
- " 96606.0 | \n",
- " 143 | \n",
- " 110.0 | \n",
- " 176.0 | \n",
+ " 1547 | \n",
+ " 199101 | \n",
+ " 7 | \n",
+ " 15565 | \n",
+ " 10271 | \n",
+ " 20859 | \n",
+ " 27 | \n",
+ " 18 | \n",
+ " 36 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1842 | \n",
- " 198447 | \n",
- " 3 | \n",
- " 72029 | \n",
- " 54274.0 | \n",
- " 89784.0 | \n",
- " 131 | \n",
- " 99.0 | \n",
- " 163.0 | \n",
+ " 1548 | \n",
+ " 199052 | \n",
+ " 7 | \n",
+ " 19375 | \n",
+ " 13295 | \n",
+ " 25455 | \n",
+ " 34 | \n",
+ " 23 | \n",
+ " 45 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1843 | \n",
- " 198446 | \n",
- " 3 | \n",
- " 87330 | \n",
- " 67686.0 | \n",
- " 106974.0 | \n",
- " 159 | \n",
- " 123.0 | \n",
- " 195.0 | \n",
+ " 1549 | \n",
+ " 199051 | \n",
+ " 7 | \n",
+ " 19080 | \n",
+ " 13807 | \n",
+ " 24353 | \n",
+ " 34 | \n",
+ " 25 | \n",
+ " 43 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1844 | \n",
- " 198445 | \n",
- " 3 | \n",
- " 135223 | \n",
- " 101414.0 | \n",
- " 169032.0 | \n",
- " 246 | \n",
- " 184.0 | \n",
- " 308.0 | \n",
+ " 1550 | \n",
+ " 199050 | \n",
+ " 7 | \n",
+ " 11079 | \n",
+ " 6660 | \n",
+ " 15498 | \n",
+ " 20 | \n",
+ " 12 | \n",
+ " 28 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
- " 1845 | \n",
- " 198444 | \n",
- " 3 | \n",
- " 68422 | \n",
- " 20056.0 | \n",
- " 116788.0 | \n",
- " 125 | \n",
- " 37.0 | \n",
- " 213.0 | \n",
+ " 1551 | \n",
+ " 199049 | \n",
+ " 7 | \n",
+ " 1143 | \n",
+ " 0 | \n",
+ " 2610 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ " 5 | \n",
" FR | \n",
" France | \n",
"
\n",
" \n",
"\n",
- "1845 rows × 10 columns
\n",
+ "1552 rows × 10 columns
\n",
""
],
"text/plain": [
- " week indicator inc inc_low inc_up inc100 inc100_low \\\n",
- "0 202011 3 101704 93652.0 109756.0 154 142.0 \n",
- "1 202010 3 104977 96650.0 113304.0 159 146.0 \n",
- "2 202009 3 110696 102066.0 119326.0 168 155.0 \n",
- "3 202008 3 143753 133984.0 153522.0 218 203.0 \n",
- "4 202007 3 183610 172812.0 194408.0 279 263.0 \n",
- "5 202006 3 206669 195481.0 217857.0 314 297.0 \n",
- "6 202005 3 187957 177445.0 198469.0 285 269.0 \n",
- "7 202004 3 122331 113492.0 131170.0 186 173.0 \n",
- "8 202003 3 78413 71330.0 85496.0 119 108.0 \n",
- "9 202002 3 53614 47654.0 59574.0 81 72.0 \n",
- "10 202001 3 36850 31608.0 42092.0 56 48.0 \n",
- "11 201952 3 28135 23220.0 33050.0 43 36.0 \n",
- "12 201951 3 29786 25042.0 34530.0 45 38.0 \n",
- "13 201950 3 34223 29156.0 39290.0 52 44.0 \n",
- "14 201949 3 25662 21414.0 29910.0 39 33.0 \n",
- "15 201948 3 22367 18055.0 26679.0 34 27.0 \n",
- "16 201947 3 18669 14759.0 22579.0 28 22.0 \n",
- "17 201946 3 16030 12567.0 19493.0 24 19.0 \n",
- "18 201945 3 10138 7160.0 13116.0 15 10.0 \n",
- "19 201944 3 7822 5010.0 10634.0 12 8.0 \n",
- "20 201943 3 9487 6448.0 12526.0 14 9.0 \n",
- "21 201942 3 7747 5243.0 10251.0 12 8.0 \n",
- "22 201941 3 7122 4720.0 9524.0 11 7.0 \n",
- "23 201940 3 8505 5784.0 11226.0 13 9.0 \n",
- "24 201939 3 7091 4462.0 9720.0 11 7.0 \n",
- "25 201938 3 4897 2891.0 6903.0 7 4.0 \n",
- "26 201937 3 3172 1367.0 4977.0 5 2.0 \n",
- "27 201936 3 2295 728.0 3862.0 3 1.0 \n",
- "28 201935 3 1010 2.0 2018.0 2 0.0 \n",
- "29 201934 3 1672 279.0 3065.0 3 1.0 \n",
- "... ... ... ... ... ... ... ... \n",
- "1816 198521 3 26096 19621.0 32571.0 47 35.0 \n",
- "1817 198520 3 27896 20885.0 34907.0 51 38.0 \n",
- "1818 198519 3 43154 32821.0 53487.0 78 59.0 \n",
- "1819 198518 3 40555 29935.0 51175.0 74 55.0 \n",
- "1820 198517 3 34053 24366.0 43740.0 62 44.0 \n",
- "1821 198516 3 50362 36451.0 64273.0 91 66.0 \n",
- "1822 198515 3 63881 45538.0 82224.0 116 83.0 \n",
- "1823 198514 3 134545 114400.0 154690.0 244 207.0 \n",
- "1824 198513 3 197206 176080.0 218332.0 357 319.0 \n",
- "1825 198512 3 245240 223304.0 267176.0 445 405.0 \n",
- "1826 198511 3 276205 252399.0 300011.0 501 458.0 \n",
- "1827 198510 3 353231 326279.0 380183.0 640 591.0 \n",
- "1828 198509 3 369895 341109.0 398681.0 670 618.0 \n",
- "1829 198508 3 389886 359529.0 420243.0 707 652.0 \n",
- "1830 198507 3 471852 432599.0 511105.0 855 784.0 \n",
- "1831 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
- "1832 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
- "1833 198504 3 424937 390794.0 459080.0 770 708.0 \n",
- "1834 198503 3 213901 174689.0 253113.0 388 317.0 \n",
- "1835 198502 3 97586 80949.0 114223.0 177 147.0 \n",
- "1836 198501 3 85489 65918.0 105060.0 155 120.0 \n",
- "1837 198452 3 84830 60602.0 109058.0 154 110.0 \n",
- "1838 198451 3 101726 80242.0 123210.0 185 146.0 \n",
- "1839 198450 3 123680 101401.0 145959.0 225 184.0 \n",
- "1840 198449 3 101073 81684.0 120462.0 184 149.0 \n",
- "1841 198448 3 78620 60634.0 96606.0 143 110.0 \n",
- "1842 198447 3 72029 54274.0 89784.0 131 99.0 \n",
- "1843 198446 3 87330 67686.0 106974.0 159 123.0 \n",
- "1844 198445 3 135223 101414.0 169032.0 246 184.0 \n",
- "1845 198444 3 68422 20056.0 116788.0 125 37.0 \n",
+ " week indicator inc inc_low inc_up inc100 inc100_low \\\n",
+ "0 202035 7 837 0 1712 1 0 \n",
+ "1 202034 7 2275 373 4177 3 0 \n",
+ "2 202033 7 1284 177 2391 2 0 \n",
+ "3 202032 7 2650 689 4611 4 1 \n",
+ "4 202031 7 1303 100 2506 2 0 \n",
+ "5 202030 7 1385 75 2695 2 0 \n",
+ "6 202029 7 841 10 1672 1 0 \n",
+ "7 202028 7 728 0 1515 1 0 \n",
+ "8 202027 7 986 149 1823 1 0 \n",
+ "9 202026 7 694 0 1454 1 0 \n",
+ "10 202025 7 228 0 597 0 0 \n",
+ "11 202024 7 388 0 959 1 0 \n",
+ "12 202023 7 558 1 1115 1 0 \n",
+ "13 202022 7 277 0 633 0 0 \n",
+ "14 202021 7 602 36 1168 1 0 \n",
+ "15 202020 7 824 20 1628 1 0 \n",
+ "16 202019 7 310 0 753 0 0 \n",
+ "17 202018 7 849 98 1600 1 0 \n",
+ "18 202017 7 272 0 658 0 0 \n",
+ "19 202016 7 758 78 1438 1 0 \n",
+ "20 202015 7 1918 675 3161 3 1 \n",
+ "21 202014 7 3879 2227 5531 6 3 \n",
+ "22 202013 7 7326 5236 9416 11 8 \n",
+ "23 202012 7 8123 5790 10456 12 8 \n",
+ "24 202011 7 10198 7568 12828 15 11 \n",
+ "25 202010 7 9011 6691 11331 14 10 \n",
+ "26 202009 7 13631 10544 16718 21 16 \n",
+ "27 202008 7 10424 7708 13140 16 12 \n",
+ "28 202007 7 8959 6574 11344 14 10 \n",
+ "29 202006 7 9264 6925 11603 14 10 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "1522 199126 7 17608 11304 23912 31 20 \n",
+ "1523 199125 7 16169 10700 21638 28 18 \n",
+ "1524 199124 7 16171 10071 22271 28 17 \n",
+ "1525 199123 7 11947 7671 16223 21 13 \n",
+ "1526 199122 7 15452 9953 20951 27 17 \n",
+ "1527 199121 7 14903 8975 20831 26 16 \n",
+ "1528 199120 7 19053 12742 25364 34 23 \n",
+ "1529 199119 7 16739 11246 22232 29 19 \n",
+ "1530 199118 7 21385 13882 28888 38 25 \n",
+ "1531 199117 7 13462 8877 18047 24 16 \n",
+ "1532 199116 7 14857 10068 19646 26 18 \n",
+ "1533 199115 7 13975 9781 18169 25 18 \n",
+ "1534 199114 7 12265 7684 16846 22 14 \n",
+ "1535 199113 7 9567 6041 13093 17 11 \n",
+ "1536 199112 7 10864 7331 14397 19 13 \n",
+ "1537 199111 7 15574 11184 19964 27 19 \n",
+ "1538 199110 7 16643 11372 21914 29 20 \n",
+ "1539 199109 7 13741 8780 18702 24 15 \n",
+ "1540 199108 7 13289 8813 17765 23 15 \n",
+ "1541 199107 7 12337 8077 16597 22 15 \n",
+ "1542 199106 7 10877 7013 14741 19 12 \n",
+ "1543 199105 7 10442 6544 14340 18 11 \n",
+ "1544 199104 7 7913 4563 11263 14 8 \n",
+ "1545 199103 7 15387 10484 20290 27 18 \n",
+ "1546 199102 7 16277 11046 21508 29 20 \n",
+ "1547 199101 7 15565 10271 20859 27 18 \n",
+ "1548 199052 7 19375 13295 25455 34 23 \n",
+ "1549 199051 7 19080 13807 24353 34 25 \n",
+ "1550 199050 7 11079 6660 15498 20 12 \n",
+ "1551 199049 7 1143 0 2610 2 0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
- "0 166.0 FR France \n",
- "1 172.0 FR France \n",
- "2 181.0 FR France \n",
- "3 233.0 FR France \n",
- "4 295.0 FR France \n",
- "5 331.0 FR France \n",
- "6 301.0 FR France \n",
- "7 199.0 FR France \n",
- "8 130.0 FR France \n",
- "9 90.0 FR France \n",
- "10 64.0 FR France \n",
- "11 50.0 FR France \n",
- "12 52.0 FR France \n",
- "13 60.0 FR France \n",
- "14 45.0 FR France \n",
- "15 41.0 FR France \n",
- "16 34.0 FR France \n",
- "17 29.0 FR France \n",
- "18 20.0 FR France \n",
- "19 16.0 FR France \n",
- "20 19.0 FR France \n",
- "21 16.0 FR France \n",
- "22 15.0 FR France \n",
- "23 17.0 FR France \n",
- "24 15.0 FR France \n",
- "25 10.0 FR France \n",
- "26 8.0 FR France \n",
- "27 5.0 FR France \n",
- "28 4.0 FR France \n",
- "29 5.0 FR France \n",
+ "0 2 FR France \n",
+ "1 6 FR France \n",
+ "2 4 FR France \n",
+ "3 7 FR France \n",
+ "4 4 FR France \n",
+ "5 4 FR France \n",
+ "6 2 FR France \n",
+ "7 2 FR France \n",
+ "8 2 FR France \n",
+ "9 2 FR France \n",
+ "10 1 FR France \n",
+ "11 2 FR France \n",
+ "12 2 FR France \n",
+ "13 1 FR France \n",
+ "14 2 FR France \n",
+ "15 2 FR France \n",
+ "16 1 FR France \n",
+ "17 2 FR France \n",
+ "18 1 FR France \n",
+ "19 2 FR France \n",
+ "20 5 FR France \n",
+ "21 9 FR France \n",
+ "22 14 FR France \n",
+ "23 16 FR France \n",
+ "24 19 FR France \n",
+ "25 18 FR France \n",
+ "26 26 FR France \n",
+ "27 20 FR France \n",
+ "28 18 FR France \n",
+ "29 18 FR France \n",
"... ... ... ... \n",
- "1816 59.0 FR France \n",
- "1817 64.0 FR France \n",
- "1818 97.0 FR France \n",
- "1819 93.0 FR France \n",
- "1820 80.0 FR France \n",
- "1821 116.0 FR France \n",
- "1822 149.0 FR France \n",
- "1823 281.0 FR France \n",
- "1824 395.0 FR France \n",
- "1825 485.0 FR France \n",
- "1826 544.0 FR France \n",
- "1827 689.0 FR France \n",
- "1828 722.0 FR France \n",
- "1829 762.0 FR France \n",
- "1830 926.0 FR France \n",
- "1831 1113.0 FR France \n",
- "1832 1236.0 FR France \n",
- "1833 832.0 FR France \n",
- "1834 459.0 FR France \n",
- "1835 207.0 FR France \n",
- "1836 190.0 FR France \n",
- "1837 198.0 FR France \n",
- "1838 224.0 FR France \n",
- "1839 266.0 FR France \n",
- "1840 219.0 FR France \n",
- "1841 176.0 FR France \n",
- "1842 163.0 FR France \n",
- "1843 195.0 FR France \n",
- "1844 308.0 FR France \n",
- "1845 213.0 FR France \n",
+ "1522 42 FR France \n",
+ "1523 38 FR France \n",
+ "1524 39 FR France \n",
+ "1525 29 FR France \n",
+ "1526 37 FR France \n",
+ "1527 36 FR France \n",
+ "1528 45 FR France \n",
+ "1529 39 FR France \n",
+ "1530 51 FR France \n",
+ "1531 32 FR France \n",
+ "1532 34 FR France \n",
+ "1533 32 FR France \n",
+ "1534 30 FR France \n",
+ "1535 23 FR France \n",
+ "1536 25 FR France \n",
+ "1537 35 FR France \n",
+ "1538 38 FR France \n",
+ "1539 33 FR France \n",
+ "1540 31 FR France \n",
+ "1541 29 FR France \n",
+ "1542 26 FR France \n",
+ "1543 25 FR France \n",
+ "1544 20 FR France \n",
+ "1545 36 FR France \n",
+ "1546 38 FR France \n",
+ "1547 36 FR France \n",
+ "1548 45 FR France \n",
+ "1549 43 FR France \n",
+ "1550 28 FR France \n",
+ "1551 5 FR France \n",
"\n",
- "[1845 rows x 10 columns]"
+ "[1552 rows x 10 columns]"
]
},
- "execution_count": 6,
+ "execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
@@ -2050,7 +2035,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
@@ -2060,62 +2045,1093 @@
" week = int(year_and_week_str[4:])\n",
" w = isoweek.Week(year, week)\n",
" return pd.Period(w.day(0), 'W')\n",
- "\n",
- "data['period'] = [convert_week(yw) for yw in data['week']]"
+ "\n"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
- "sorted_data = data.set_index('period').sort_index()"
+ "data['period'] = [convert_week(yw) for yw in data['week']]\n"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 41,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "periods = sorted_data.index\n",
- "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
- " delta = p2.to_timestamp() - p1.end_time\n",
- " if delta > pd.Timedelta('1s'):\n",
- " print(p1, p2)"
+ "sorted_data = data.set_index('period').sort_index()"
]
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {},
+ "execution_count": 42,
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [
{
"data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 16,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
+ " \n",
+ " 1991-05-06/1991-05-12 | \n",
+ " 199119 | \n",
+ " 7 | \n",
+ " 16739 | \n",
+ " 11246 | \n",
+ " 22232 | \n",
+ " 29 | \n",
+ " 19 | \n",
+ " 39 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 1991-05-13/1991-05-19 | \n",
+ " 199120 | \n",
+ " 7 | \n",
+ " 19053 | \n",
+ " 12742 | \n",
+ " 25364 | \n",
+ " 34 | \n",
+ " 23 | \n",
+ " 45 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 1991-05-20/1991-05-26 | \n",
+ " 199121 | \n",
+ " 7 | \n",
+ " 14903 | \n",
+ " 8975 | \n",
+ " 20831 | \n",
+ " 26 | \n",
+ " 16 | \n",
+ " 36 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 1991-05-27/1991-06-02 | \n",
+ " 199122 | \n",
+ " 7 | \n",
+ " 15452 | \n",
+ " 9953 | \n",
+ " 20951 | \n",
+ " 27 | \n",
+ " 17 | \n",
+ " 37 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 1991-06-03/1991-06-09 | \n",
+ " 199123 | \n",
+ " 7 | \n",
+ " 11947 | \n",
+ " 7671 | \n",
+ " 16223 | \n",
+ " 21 | \n",
+ " 13 | \n",
+ " 29 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 1991-06-10/1991-06-16 | \n",
+ " 199124 | \n",
+ " 7 | \n",
+ " 16171 | \n",
+ " 10071 | \n",
+ " 22271 | \n",
+ " 28 | \n",
+ " 17 | \n",
+ " 39 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 1991-06-17/1991-06-23 | \n",
+ " 199125 | \n",
+ " 7 | \n",
+ " 16169 | \n",
+ " 10700 | \n",
+ " 21638 | \n",
+ " 28 | \n",
+ " 18 | \n",
+ " 38 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 1991-06-24/1991-06-30 | \n",
+ " 199126 | \n",
+ " 7 | \n",
+ " 17608 | \n",
+ " 11304 | \n",
+ " 23912 | \n",
+ " 31 | \n",
+ " 20 | \n",
+ " 42 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ " ... | \n",
+ "
\n",
+ " \n",
+ " 2020-02-03/2020-02-09 | \n",
+ " 202006 | \n",
+ " 7 | \n",
+ " 9264 | \n",
+ " 6925 | \n",
+ " 11603 | \n",
+ " 14 | \n",
+ " 10 | \n",
+ " 18 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-02-10/2020-02-16 | \n",
+ " 202007 | \n",
+ " 7 | \n",
+ " 8959 | \n",
+ " 6574 | \n",
+ " 11344 | \n",
+ " 14 | \n",
+ " 10 | \n",
+ " 18 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-02-17/2020-02-23 | \n",
+ " 202008 | \n",
+ " 7 | \n",
+ " 10424 | \n",
+ " 7708 | \n",
+ " 13140 | \n",
+ " 16 | \n",
+ " 12 | \n",
+ " 20 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-02-24/2020-03-01 | \n",
+ " 202009 | \n",
+ " 7 | \n",
+ " 13631 | \n",
+ " 10544 | \n",
+ " 16718 | \n",
+ " 21 | \n",
+ " 16 | \n",
+ " 26 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-03-02/2020-03-08 | \n",
+ " 202010 | \n",
+ " 7 | \n",
+ " 9011 | \n",
+ " 6691 | \n",
+ " 11331 | \n",
+ " 14 | \n",
+ " 10 | \n",
+ " 18 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-03-09/2020-03-15 | \n",
+ " 202011 | \n",
+ " 7 | \n",
+ " 10198 | \n",
+ " 7568 | \n",
+ " 12828 | \n",
+ " 15 | \n",
+ " 11 | \n",
+ " 19 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-03-16/2020-03-22 | \n",
+ " 202012 | \n",
+ " 7 | \n",
+ " 8123 | \n",
+ " 5790 | \n",
+ " 10456 | \n",
+ " 12 | \n",
+ " 8 | \n",
+ " 16 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-03-23/2020-03-29 | \n",
+ " 202013 | \n",
+ " 7 | \n",
+ " 7326 | \n",
+ " 5236 | \n",
+ " 9416 | \n",
+ " 11 | \n",
+ " 8 | \n",
+ " 14 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-03-30/2020-04-05 | \n",
+ " 202014 | \n",
+ " 7 | \n",
+ " 3879 | \n",
+ " 2227 | \n",
+ " 5531 | \n",
+ " 6 | \n",
+ " 3 | \n",
+ " 9 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-04-06/2020-04-12 | \n",
+ " 202015 | \n",
+ " 7 | \n",
+ " 1918 | \n",
+ " 675 | \n",
+ " 3161 | \n",
+ " 3 | \n",
+ " 1 | \n",
+ " 5 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-04-13/2020-04-19 | \n",
+ " 202016 | \n",
+ " 7 | \n",
+ " 758 | \n",
+ " 78 | \n",
+ " 1438 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-04-20/2020-04-26 | \n",
+ " 202017 | \n",
+ " 7 | \n",
+ " 272 | \n",
+ " 0 | \n",
+ " 658 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-04-27/2020-05-03 | \n",
+ " 202018 | \n",
+ " 7 | \n",
+ " 849 | \n",
+ " 98 | \n",
+ " 1600 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-05-04/2020-05-10 | \n",
+ " 202019 | \n",
+ " 7 | \n",
+ " 310 | \n",
+ " 0 | \n",
+ " 753 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-05-11/2020-05-17 | \n",
+ " 202020 | \n",
+ " 7 | \n",
+ " 824 | \n",
+ " 20 | \n",
+ " 1628 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-05-18/2020-05-24 | \n",
+ " 202021 | \n",
+ " 7 | \n",
+ " 602 | \n",
+ " 36 | \n",
+ " 1168 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-05-25/2020-05-31 | \n",
+ " 202022 | \n",
+ " 7 | \n",
+ " 277 | \n",
+ " 0 | \n",
+ " 633 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-06-01/2020-06-07 | \n",
+ " 202023 | \n",
+ " 7 | \n",
+ " 558 | \n",
+ " 1 | \n",
+ " 1115 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-06-08/2020-06-14 | \n",
+ " 202024 | \n",
+ " 7 | \n",
+ " 388 | \n",
+ " 0 | \n",
+ " 959 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-06-15/2020-06-21 | \n",
+ " 202025 | \n",
+ " 7 | \n",
+ " 228 | \n",
+ " 0 | \n",
+ " 597 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-06-22/2020-06-28 | \n",
+ " 202026 | \n",
+ " 7 | \n",
+ " 694 | \n",
+ " 0 | \n",
+ " 1454 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-06-29/2020-07-05 | \n",
+ " 202027 | \n",
+ " 7 | \n",
+ " 986 | \n",
+ " 149 | \n",
+ " 1823 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-07-06/2020-07-12 | \n",
+ " 202028 | \n",
+ " 7 | \n",
+ " 728 | \n",
+ " 0 | \n",
+ " 1515 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-07-13/2020-07-19 | \n",
+ " 202029 | \n",
+ " 7 | \n",
+ " 841 | \n",
+ " 10 | \n",
+ " 1672 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-07-20/2020-07-26 | \n",
+ " 202030 | \n",
+ " 7 | \n",
+ " 1385 | \n",
+ " 75 | \n",
+ " 2695 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-07-27/2020-08-02 | \n",
+ " 202031 | \n",
+ " 7 | \n",
+ " 1303 | \n",
+ " 100 | \n",
+ " 2506 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-08-03/2020-08-09 | \n",
+ " 202032 | \n",
+ " 7 | \n",
+ " 2650 | \n",
+ " 689 | \n",
+ " 4611 | \n",
+ " 4 | \n",
+ " 1 | \n",
+ " 7 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-08-10/2020-08-16 | \n",
+ " 202033 | \n",
+ " 7 | \n",
+ " 1284 | \n",
+ " 177 | \n",
+ " 2391 | \n",
+ " 2 | \n",
+ " 0 | \n",
+ " 4 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-08-17/2020-08-23 | \n",
+ " 202034 | \n",
+ " 7 | \n",
+ " 2275 | \n",
+ " 373 | \n",
+ " 4177 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 6 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ " 2020-08-24/2020-08-30 | \n",
+ " 202035 | \n",
+ " 7 | \n",
+ " 837 | \n",
+ " 0 | \n",
+ " 1712 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 2 | \n",
+ " FR | \n",
+ " France | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
1552 rows × 10 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " week indicator inc inc_low inc_up inc100 \\\n",
+ "period \n",
+ "1990-12-03/1990-12-09 199049 7 1143 0 2610 2 \n",
+ "1990-12-10/1990-12-16 199050 7 11079 6660 15498 20 \n",
+ "1990-12-17/1990-12-23 199051 7 19080 13807 24353 34 \n",
+ "1990-12-24/1990-12-30 199052 7 19375 13295 25455 34 \n",
+ "1990-12-31/1991-01-06 199101 7 15565 10271 20859 27 \n",
+ "1991-01-07/1991-01-13 199102 7 16277 11046 21508 29 \n",
+ "1991-01-14/1991-01-20 199103 7 15387 10484 20290 27 \n",
+ "1991-01-21/1991-01-27 199104 7 7913 4563 11263 14 \n",
+ "1991-01-28/1991-02-03 199105 7 10442 6544 14340 18 \n",
+ "1991-02-04/1991-02-10 199106 7 10877 7013 14741 19 \n",
+ "1991-02-11/1991-02-17 199107 7 12337 8077 16597 22 \n",
+ "1991-02-18/1991-02-24 199108 7 13289 8813 17765 23 \n",
+ "1991-02-25/1991-03-03 199109 7 13741 8780 18702 24 \n",
+ "1991-03-04/1991-03-10 199110 7 16643 11372 21914 29 \n",
+ "1991-03-11/1991-03-17 199111 7 15574 11184 19964 27 \n",
+ "1991-03-18/1991-03-24 199112 7 10864 7331 14397 19 \n",
+ "1991-03-25/1991-03-31 199113 7 9567 6041 13093 17 \n",
+ "1991-04-01/1991-04-07 199114 7 12265 7684 16846 22 \n",
+ "1991-04-08/1991-04-14 199115 7 13975 9781 18169 25 \n",
+ "1991-04-15/1991-04-21 199116 7 14857 10068 19646 26 \n",
+ "1991-04-22/1991-04-28 199117 7 13462 8877 18047 24 \n",
+ "1991-04-29/1991-05-05 199118 7 21385 13882 28888 38 \n",
+ "1991-05-06/1991-05-12 199119 7 16739 11246 22232 29 \n",
+ "1991-05-13/1991-05-19 199120 7 19053 12742 25364 34 \n",
+ "1991-05-20/1991-05-26 199121 7 14903 8975 20831 26 \n",
+ "1991-05-27/1991-06-02 199122 7 15452 9953 20951 27 \n",
+ "1991-06-03/1991-06-09 199123 7 11947 7671 16223 21 \n",
+ "1991-06-10/1991-06-16 199124 7 16171 10071 22271 28 \n",
+ "1991-06-17/1991-06-23 199125 7 16169 10700 21638 28 \n",
+ "1991-06-24/1991-06-30 199126 7 17608 11304 23912 31 \n",
+ "... ... ... ... ... ... ... \n",
+ "2020-02-03/2020-02-09 202006 7 9264 6925 11603 14 \n",
+ "2020-02-10/2020-02-16 202007 7 8959 6574 11344 14 \n",
+ "2020-02-17/2020-02-23 202008 7 10424 7708 13140 16 \n",
+ "2020-02-24/2020-03-01 202009 7 13631 10544 16718 21 \n",
+ "2020-03-02/2020-03-08 202010 7 9011 6691 11331 14 \n",
+ "2020-03-09/2020-03-15 202011 7 10198 7568 12828 15 \n",
+ "2020-03-16/2020-03-22 202012 7 8123 5790 10456 12 \n",
+ "2020-03-23/2020-03-29 202013 7 7326 5236 9416 11 \n",
+ "2020-03-30/2020-04-05 202014 7 3879 2227 5531 6 \n",
+ "2020-04-06/2020-04-12 202015 7 1918 675 3161 3 \n",
+ "2020-04-13/2020-04-19 202016 7 758 78 1438 1 \n",
+ "2020-04-20/2020-04-26 202017 7 272 0 658 0 \n",
+ "2020-04-27/2020-05-03 202018 7 849 98 1600 1 \n",
+ "2020-05-04/2020-05-10 202019 7 310 0 753 0 \n",
+ "2020-05-11/2020-05-17 202020 7 824 20 1628 1 \n",
+ "2020-05-18/2020-05-24 202021 7 602 36 1168 1 \n",
+ "2020-05-25/2020-05-31 202022 7 277 0 633 0 \n",
+ "2020-06-01/2020-06-07 202023 7 558 1 1115 1 \n",
+ "2020-06-08/2020-06-14 202024 7 388 0 959 1 \n",
+ "2020-06-15/2020-06-21 202025 7 228 0 597 0 \n",
+ "2020-06-22/2020-06-28 202026 7 694 0 1454 1 \n",
+ "2020-06-29/2020-07-05 202027 7 986 149 1823 1 \n",
+ "2020-07-06/2020-07-12 202028 7 728 0 1515 1 \n",
+ "2020-07-13/2020-07-19 202029 7 841 10 1672 1 \n",
+ "2020-07-20/2020-07-26 202030 7 1385 75 2695 2 \n",
+ "2020-07-27/2020-08-02 202031 7 1303 100 2506 2 \n",
+ "2020-08-03/2020-08-09 202032 7 2650 689 4611 4 \n",
+ "2020-08-10/2020-08-16 202033 7 1284 177 2391 2 \n",
+ "2020-08-17/2020-08-23 202034 7 2275 373 4177 3 \n",
+ "2020-08-24/2020-08-30 202035 7 837 0 1712 1 \n",
+ "\n",
+ " inc100_low inc100_up geo_insee geo_name \n",
+ "period \n",
+ "1990-12-03/1990-12-09 0 5 FR France \n",
+ "1990-12-10/1990-12-16 12 28 FR France \n",
+ "1990-12-17/1990-12-23 25 43 FR France \n",
+ "1990-12-24/1990-12-30 23 45 FR France \n",
+ "1990-12-31/1991-01-06 18 36 FR France \n",
+ "1991-01-07/1991-01-13 20 38 FR France \n",
+ "1991-01-14/1991-01-20 18 36 FR France \n",
+ "1991-01-21/1991-01-27 8 20 FR France \n",
+ "1991-01-28/1991-02-03 11 25 FR France \n",
+ "1991-02-04/1991-02-10 12 26 FR France \n",
+ "1991-02-11/1991-02-17 15 29 FR France \n",
+ "1991-02-18/1991-02-24 15 31 FR France \n",
+ "1991-02-25/1991-03-03 15 33 FR France \n",
+ "1991-03-04/1991-03-10 20 38 FR France \n",
+ "1991-03-11/1991-03-17 19 35 FR France \n",
+ "1991-03-18/1991-03-24 13 25 FR France \n",
+ "1991-03-25/1991-03-31 11 23 FR France \n",
+ "1991-04-01/1991-04-07 14 30 FR France \n",
+ "1991-04-08/1991-04-14 18 32 FR France \n",
+ "1991-04-15/1991-04-21 18 34 FR France \n",
+ "1991-04-22/1991-04-28 16 32 FR France \n",
+ "1991-04-29/1991-05-05 25 51 FR France \n",
+ "1991-05-06/1991-05-12 19 39 FR France \n",
+ "1991-05-13/1991-05-19 23 45 FR France \n",
+ "1991-05-20/1991-05-26 16 36 FR France \n",
+ "1991-05-27/1991-06-02 17 37 FR France \n",
+ "1991-06-03/1991-06-09 13 29 FR France \n",
+ "1991-06-10/1991-06-16 17 39 FR France \n",
+ "1991-06-17/1991-06-23 18 38 FR France \n",
+ "1991-06-24/1991-06-30 20 42 FR France \n",
+ "... ... ... ... ... \n",
+ "2020-02-03/2020-02-09 10 18 FR France \n",
+ "2020-02-10/2020-02-16 10 18 FR France \n",
+ "2020-02-17/2020-02-23 12 20 FR France \n",
+ "2020-02-24/2020-03-01 16 26 FR France \n",
+ "2020-03-02/2020-03-08 10 18 FR France \n",
+ "2020-03-09/2020-03-15 11 19 FR France \n",
+ "2020-03-16/2020-03-22 8 16 FR France \n",
+ "2020-03-23/2020-03-29 8 14 FR France \n",
+ "2020-03-30/2020-04-05 3 9 FR France \n",
+ "2020-04-06/2020-04-12 1 5 FR France \n",
+ "2020-04-13/2020-04-19 0 2 FR France \n",
+ "2020-04-20/2020-04-26 0 1 FR France \n",
+ "2020-04-27/2020-05-03 0 2 FR France \n",
+ "2020-05-04/2020-05-10 0 1 FR France \n",
+ "2020-05-11/2020-05-17 0 2 FR France \n",
+ "2020-05-18/2020-05-24 0 2 FR France \n",
+ "2020-05-25/2020-05-31 0 1 FR France \n",
+ "2020-06-01/2020-06-07 0 2 FR France \n",
+ "2020-06-08/2020-06-14 0 2 FR France \n",
+ "2020-06-15/2020-06-21 0 1 FR France \n",
+ "2020-06-22/2020-06-28 0 2 FR France \n",
+ "2020-06-29/2020-07-05 0 2 FR France \n",
+ "2020-07-06/2020-07-12 0 2 FR France \n",
+ "2020-07-13/2020-07-19 0 2 FR France \n",
+ "2020-07-20/2020-07-26 0 4 FR France \n",
+ "2020-07-27/2020-08-02 0 4 FR France \n",
+ "2020-08-03/2020-08-09 1 7 FR France \n",
+ "2020-08-10/2020-08-16 0 4 FR France \n",
+ "2020-08-17/2020-08-23 0 6 FR France \n",
+ "2020-08-24/2020-08-30 0 2 FR France \n",
+ "\n",
+ "[1552 rows x 10 columns]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "periods = sorted_data.index\n",
+ "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+ " delta = p2.to_timestamp() - p1.end_time\n",
+ " if delta > pd.Timedelta('1s'):\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2020-08-17/2020-08-23 2020-08-24/2020-08-30\n"
+ ]
+ }
+ ],
+ "source": [
+ " print(p1, p2)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "1143"
+ ]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sorted_data['inc'][0]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
"metadata": {
"needs_background": "light"
},
@@ -2129,15 +3145,15 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 53,
"metadata": {},
"outputs": [
{
"ename": "SyntaxError",
- "evalue": "invalid syntax (, line 1)",
+ "evalue": "invalid syntax (, line 1)",
"output_type": "error",
"traceback": [
- "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+ "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
]
}
],
@@ -2147,20 +3163,35 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 52,
"metadata": {},
"outputs": [
{
- "ename": "SyntaxError",
- "evalue": "invalid syntax (, line 1)",
- "output_type": "error",
- "traceback": [
- "\u001b[0;36m File \u001b[0;32m\"\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m \u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
- ]
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
}
],
"source": [
- " sorted_data['inc'][-200:].plot()"
+ " sorted_data['inc'][-200:].plot()\n",
+ " "
]
},
{
@@ -2183,22 +3214,22 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ ""
]
},
- "execution_count": 19,
+ "execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -2215,64 +3246,132 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 98,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Timestamp('1990-08-01 00:00:00')"
+ ]
+ },
+ "execution_count": 98,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
- " for y in range(1985,\n",
- " sorted_data.index[-1].year)]"
+ "pd.Timestamp(1990, 8, 1)"
]
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 99,
"metadata": {},
"outputs": [],
+ "source": [
+ "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+ " for y in range(1990,\n",
+ " sorted_data.index[-1].year)]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 101,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AssertionError",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m first_august_week[1:]):\n\u001b[1;32m 5\u001b[0m \u001b[0mone_year\u001b[0m \u001b[0;34m=\u001b[0m \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[0mweek1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+ "\u001b[0;31mAssertionError\u001b[0m: "
+ ]
+ }
+ ],
"source": [
"year = []\n",
"yearly_incidence = []\n",
- "for week1, week2 in zip(first_sept_week[:-1],\n",
- " first_sept_week[1:]):\n",
+ "for week1, week2 in zip(first_august_week[:-1],\n",
+ " first_august_week[1:]):\n",
" one_year = sorted_data['inc'][week1:week2-1]\n",
" assert abs(len(one_year)-52) < 2\n",
" yearly_incidence.append(one_year.sum())\n",
" year.append(week2.year)\n",
- "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+ " "
]
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 88,
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- ""
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
+ "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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\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": [
- "yearly_incidence.plot(style='*')"
+ "yearly_incidence.plot(style='*')\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 102,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "yearly_incidence = pd.Series(data=yearly_incidence, index=year)\n",
+ " "
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {},
+ "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\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\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": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
{
"cell_type": "code",
"execution_count": 23,
@@ -2293,52 +3392,19 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 97,
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "2014 1601698\n",
- "1991 1663610\n",
- "1995 1828304\n",
- "2012 2183912\n",
- "2003 2234514\n",
- "2019 2254363\n",
- "2006 2297262\n",
- "2017 2322818\n",
- "2001 2540826\n",
- "1992 2590314\n",
- "1993 2699482\n",
- "2018 2701716\n",
- "1988 2759663\n",
- "2007 2786458\n",
- "2011 2852504\n",
- "2016 2859019\n",
- "1987 2867464\n",
- "2008 2984311\n",
- "1998 3047298\n",
- "2002 3115484\n",
- "1994 3514133\n",
- "1996 3540251\n",
- "2009 3558474\n",
- "2004 3572810\n",
- "1997 3624129\n",
- "2015 3647492\n",
- "2000 3808190\n",
- "2005 3831409\n",
- "1999 3914003\n",
- "2010 3992174\n",
- "2013 4176872\n",
- "1986 5050543\n",
- "1990 5214494\n",
- "1989 5461328\n",
- "dtype: int64"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "AttributeError",
+ "evalue": "'list' object has no attribute 'sort_values'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'sort_values'"
+ ]
}
],
"source": [
--
2.18.1