diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0427d55f300bed5c50058860abbb459034739d81 100644 --- a/module3/exo2/exercice.ipynb +++ b/module3/exo2/exercice.ipynb @@ -1,5 +1,2368 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Incidence of chickenpox illness in France" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek\n", + "import os.path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We check if the file is already downloaded and store to a local file to prevent to download every time we run the program. If it is not, we use the link and download it and save it locally." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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.................................
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1559 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202042 7 2993 1283 4703 5 2 \n", + "1 202041 7 3974 2106 5842 6 3 \n", + "2 202040 7 2078 675 3481 3 1 \n", + "3 202039 7 1049 237 1861 2 1 \n", + "4 202038 7 2253 782 3724 3 1 \n", + "5 202037 7 1584 405 2763 2 0 \n", + "6 202036 7 919 100 1738 1 0 \n", + "7 202035 7 828 0 1694 1 0 \n", + "8 202034 7 2272 371 4173 3 0 \n", + "9 202033 7 1284 177 2391 2 0 \n", + "10 202032 7 2650 689 4611 4 1 \n", + "11 202031 7 1303 100 2506 2 0 \n", + "12 202030 7 1385 75 2695 2 0 \n", + "13 202029 7 841 10 1672 1 0 \n", + "14 202028 7 728 0 1515 1 0 \n", + "15 202027 7 986 149 1823 1 0 \n", + "16 202026 7 694 0 1454 1 0 \n", + "17 202025 7 228 0 597 0 0 \n", + "18 202024 7 388 0 959 1 0 \n", + "19 202023 7 558 1 1115 1 0 \n", + "20 202022 7 277 0 633 0 0 \n", + "21 202021 7 602 36 1168 1 0 \n", + "22 202020 7 824 20 1628 1 0 \n", + "23 202019 7 310 0 753 0 0 \n", + "24 202018 7 849 98 1600 1 0 \n", + "25 202017 7 272 0 658 0 0 \n", + "26 202016 7 758 78 1438 1 0 \n", + "27 202015 7 1918 675 3161 3 1 \n", + "28 202014 7 3879 2227 5531 6 3 \n", + "29 202013 7 7326 5236 9416 11 8 \n", + "... ... ... ... ... ... ... ... \n", + "1529 199126 7 17608 11304 23912 31 20 \n", + "1530 199125 7 16169 10700 21638 28 18 \n", + "1531 199124 7 16171 10071 22271 28 17 \n", + "1532 199123 7 11947 7671 16223 21 13 \n", + "1533 199122 7 15452 9953 20951 27 17 \n", + "1534 199121 7 14903 8975 20831 26 16 \n", + "1535 199120 7 19053 12742 25364 34 23 \n", + "1536 199119 7 16739 11246 22232 29 19 \n", + "1537 199118 7 21385 13882 28888 38 25 \n", + "1538 199117 7 13462 8877 18047 24 16 \n", + "1539 199116 7 14857 10068 19646 26 18 \n", + "1540 199115 7 13975 9781 18169 25 18 \n", + "1541 199114 7 12265 7684 16846 22 14 \n", + "1542 199113 7 9567 6041 13093 17 11 \n", + "1543 199112 7 10864 7331 14397 19 13 \n", + "1544 199111 7 15574 11184 19964 27 19 \n", + "1545 199110 7 16643 11372 21914 29 20 \n", + "1546 199109 7 13741 8780 18702 24 15 \n", + "1547 199108 7 13289 8813 17765 23 15 \n", + "1548 199107 7 12337 8077 16597 22 15 \n", + "1549 199106 7 10877 7013 14741 19 12 \n", + "1550 199105 7 10442 6544 14340 18 11 \n", + "1551 199104 7 7913 4563 11263 14 8 \n", + "1552 199103 7 15387 10484 20290 27 18 \n", + "1553 199102 7 16277 11046 21508 29 20 \n", + "1554 199101 7 15565 10271 20859 27 18 \n", + "1555 199052 7 19375 13295 25455 34 23 \n", + "1556 199051 7 19080 13807 24353 34 25 \n", + "1557 199050 7 11079 6660 15498 20 12 \n", + "1558 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 8 FR France \n", + "1 9 FR France \n", + "2 5 FR France \n", + "3 3 FR France \n", + "4 5 FR France \n", + "5 4 FR France \n", + "6 2 FR France \n", + "7 2 FR France \n", + "8 6 FR France \n", + "9 4 FR France \n", + "10 7 FR France \n", + "11 4 FR France \n", + "12 4 FR France \n", + "13 2 FR France \n", + "14 2 FR France \n", + "15 2 FR France \n", + "16 2 FR France \n", + "17 1 FR France \n", + "18 2 FR France \n", + "19 2 FR France \n", + "20 1 FR France \n", + "21 2 FR France \n", + "22 2 FR France \n", + "23 1 FR France \n", + "24 2 FR France \n", + "25 1 FR France \n", + "26 2 FR France \n", + "27 5 FR France \n", + "28 9 FR France \n", + "29 14 FR France \n", + "... ... ... ... \n", + "1529 42 FR France \n", + "1530 38 FR France \n", + "1531 39 FR France \n", + "1532 29 FR France \n", + "1533 37 FR France \n", + "1534 36 FR France \n", + "1535 45 FR France \n", + "1536 39 FR France \n", + "1537 51 FR France \n", + "1538 32 FR France \n", + "1539 34 FR France \n", + "1540 32 FR France \n", + "1541 30 FR France \n", + "1542 23 FR France \n", + "1543 25 FR France \n", + "1544 35 FR France \n", + "1545 38 FR France \n", + "1546 33 FR France \n", + "1547 31 FR France \n", + "1548 29 FR France \n", + "1549 26 FR France \n", + "1550 25 FR France \n", + "1551 20 FR France \n", + "1552 36 FR France \n", + "1553 38 FR France \n", + "1554 36 FR France \n", + "1555 45 FR France \n", + "1556 43 FR France \n", + "1557 28 FR France \n", + "1558 5 FR France \n", + "\n", + "[1559 rows x 10 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"\n", + "file = \"incidence-PAY-7.csv\"\n", + "import urllib.request\n", + "if not os.path.exists(file):\n", + " urllib.request.urlretrieve(data_url, file)\n", + "raw_data = pd.read_csv(file, skiprows=1)\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Are there missing data points?" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n", + "Index: []" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data[raw_data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this case we are not missing anything. But in case we are we should remove this." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
02020427299312834703528FRFrance
12020417397421065842639FRFrance
2202040720786753481315FRFrance
3202039710492371861213FRFrance
4202038722537823724315FRFrance
5202037715844052763204FRFrance
620203679191001738102FRFrance
7202035782801694102FRFrance
8202034722723714173306FRFrance
9202033712841772391204FRFrance
10202032726506894611417FRFrance
11202031713031002506204FRFrance
1220203071385752695204FRFrance
132020297841101672102FRFrance
14202028772801515102FRFrance
1520202779861491823102FRFrance
16202026769401454102FRFrance
1720202572280597001FRFrance
1820202473880959102FRFrance
19202023755811115102FRFrance
2020202272770633001FRFrance
212020217602361168102FRFrance
222020207824201628102FRFrance
2320201973100753001FRFrance
242020187849981600102FRFrance
2520201772720658001FRFrance
262020167758781438102FRFrance
27202015719186753161315FRFrance
282020147387922275531639FRFrance
29202013773265236941611814FRFrance
.................................
15291991267176081130423912312042FRFrance
15301991257161691070021638281838FRFrance
15311991247161711007122271281739FRFrance
1532199123711947767116223211329FRFrance
1533199122715452995320951271737FRFrance
1534199121714903897520831261636FRFrance
15351991207190531274225364342345FRFrance
15361991197167391124622232291939FRFrance
15371991187213851388228888382551FRFrance
1538199117713462887718047241632FRFrance
15391991167148571006819646261834FRFrance
1540199115713975978118169251832FRFrance
1541199114712265768416846221430FRFrance
154219911379567604113093171123FRFrance
1543199112710864733114397191325FRFrance
15441991117155741118419964271935FRFrance
15451991107166431137221914292038FRFrance
1546199109713741878018702241533FRFrance
1547199108713289881317765231531FRFrance
1548199107712337807716597221529FRFrance
1549199106710877701314741191226FRFrance
1550199105710442654414340181125FRFrance
15511991047791345631126314820FRFrance
15521991037153871048420290271836FRFrance
15531991027162771104621508292038FRFrance
15541991017155651027120859271836FRFrance
15551990527193751329525455342345FRFrance
15561990517190801380724353342543FRFrance
1557199050711079666015498201228FRFrance
15581990497114302610205FRFrance
\n", + "

1559 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202042 7 2993 1283 4703 5 2 \n", + "1 202041 7 3974 2106 5842 6 3 \n", + "2 202040 7 2078 675 3481 3 1 \n", + "3 202039 7 1049 237 1861 2 1 \n", + "4 202038 7 2253 782 3724 3 1 \n", + "5 202037 7 1584 405 2763 2 0 \n", + "6 202036 7 919 100 1738 1 0 \n", + "7 202035 7 828 0 1694 1 0 \n", + "8 202034 7 2272 371 4173 3 0 \n", + "9 202033 7 1284 177 2391 2 0 \n", + "10 202032 7 2650 689 4611 4 1 \n", + "11 202031 7 1303 100 2506 2 0 \n", + "12 202030 7 1385 75 2695 2 0 \n", + "13 202029 7 841 10 1672 1 0 \n", + "14 202028 7 728 0 1515 1 0 \n", + "15 202027 7 986 149 1823 1 0 \n", + "16 202026 7 694 0 1454 1 0 \n", + "17 202025 7 228 0 597 0 0 \n", + "18 202024 7 388 0 959 1 0 \n", + "19 202023 7 558 1 1115 1 0 \n", + "20 202022 7 277 0 633 0 0 \n", + "21 202021 7 602 36 1168 1 0 \n", + "22 202020 7 824 20 1628 1 0 \n", + "23 202019 7 310 0 753 0 0 \n", + "24 202018 7 849 98 1600 1 0 \n", + "25 202017 7 272 0 658 0 0 \n", + "26 202016 7 758 78 1438 1 0 \n", + "27 202015 7 1918 675 3161 3 1 \n", + "28 202014 7 3879 2227 5531 6 3 \n", + "29 202013 7 7326 5236 9416 11 8 \n", + "... ... ... ... ... ... ... ... \n", + "1529 199126 7 17608 11304 23912 31 20 \n", + "1530 199125 7 16169 10700 21638 28 18 \n", + "1531 199124 7 16171 10071 22271 28 17 \n", + "1532 199123 7 11947 7671 16223 21 13 \n", + "1533 199122 7 15452 9953 20951 27 17 \n", + "1534 199121 7 14903 8975 20831 26 16 \n", + "1535 199120 7 19053 12742 25364 34 23 \n", + "1536 199119 7 16739 11246 22232 29 19 \n", + "1537 199118 7 21385 13882 28888 38 25 \n", + "1538 199117 7 13462 8877 18047 24 16 \n", + "1539 199116 7 14857 10068 19646 26 18 \n", + "1540 199115 7 13975 9781 18169 25 18 \n", + "1541 199114 7 12265 7684 16846 22 14 \n", + "1542 199113 7 9567 6041 13093 17 11 \n", + "1543 199112 7 10864 7331 14397 19 13 \n", + "1544 199111 7 15574 11184 19964 27 19 \n", + "1545 199110 7 16643 11372 21914 29 20 \n", + "1546 199109 7 13741 8780 18702 24 15 \n", + "1547 199108 7 13289 8813 17765 23 15 \n", + "1548 199107 7 12337 8077 16597 22 15 \n", + "1549 199106 7 10877 7013 14741 19 12 \n", + "1550 199105 7 10442 6544 14340 18 11 \n", + "1551 199104 7 7913 4563 11263 14 8 \n", + "1552 199103 7 15387 10484 20290 27 18 \n", + "1553 199102 7 16277 11046 21508 29 20 \n", + "1554 199101 7 15565 10271 20859 27 18 \n", + "1555 199052 7 19375 13295 25455 34 23 \n", + "1556 199051 7 19080 13807 24353 34 25 \n", + "1557 199050 7 11079 6660 15498 20 12 \n", + "1558 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 8 FR France \n", + "1 9 FR France \n", + "2 5 FR France \n", + "3 3 FR France \n", + "4 5 FR France \n", + "5 4 FR France \n", + "6 2 FR France \n", + "7 2 FR France \n", + "8 6 FR France \n", + "9 4 FR France \n", + "10 7 FR France \n", + "11 4 FR France \n", + "12 4 FR France \n", + "13 2 FR France \n", + "14 2 FR France \n", + "15 2 FR France \n", + "16 2 FR France \n", + "17 1 FR France \n", + "18 2 FR France \n", + "19 2 FR France \n", + "20 1 FR France \n", + "21 2 FR France \n", + "22 2 FR France \n", + "23 1 FR France \n", + "24 2 FR France \n", + "25 1 FR France \n", + "26 2 FR France \n", + "27 5 FR France \n", + "28 9 FR France \n", + "29 14 FR France \n", + "... ... ... ... \n", + "1529 42 FR France \n", + "1530 38 FR France \n", + "1531 39 FR France \n", + "1532 29 FR France \n", + "1533 37 FR France \n", + "1534 36 FR France \n", + "1535 45 FR France \n", + "1536 39 FR France \n", + "1537 51 FR France \n", + "1538 32 FR France \n", + "1539 34 FR France \n", + "1540 32 FR France \n", + "1541 30 FR France \n", + "1542 23 FR France \n", + "1543 25 FR France \n", + "1544 35 FR France \n", + "1545 38 FR France \n", + "1546 33 FR France \n", + "1547 31 FR France \n", + "1548 29 FR France \n", + "1549 26 FR France \n", + "1550 25 FR France \n", + "1551 20 FR France \n", + "1552 36 FR France \n", + "1553 38 FR France \n", + "1554 36 FR France \n", + "1555 45 FR France \n", + "1556 43 FR France \n", + "1557 28 FR France \n", + "1558 5 FR France \n", + "\n", + "[1559 rows x 10 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.dropna().copy()\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our dataset uses an uncommon encoding; the week number is attached to the year number, leaving the impression of a six-digit integer. That is how Pandas interprets it.\n", + "\n", + "A second problem is that Pandas does not know about week numbers. It needs to be given the dates of the beginning and end of the week. We use the library isoweek for that.\n", + "\n", + "Since the conversion is a bit lengthy, we write a small Python function for doing it. Then we apply it to all points in our dataset. The results go into a new column 'period'." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_week(year_and_week_int):\n", + " year_and_week_str = str(year_and_week_int)\n", + " year = int(year_and_week_str[:4])\n", + " 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']]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are two more small changes to make.\n", + "\n", + "First, we define the observation periods as the new index of our dataset. That turns it into a time series, which will be convenient later on.\n", + "\n", + "Second, we sort the points chronologically." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "sorted_data = data.set_index('period').sort_index()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We check the consistency of the data. Between the end of a period and the beginning of the next one, the difference should be zero, or very small. We tolerate an error of one second.\n", + "\n", + "All is OK in our case." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now lets look at the data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "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'].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Study of the annual incidence" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we want to know the years in which the annual epidemy was strongest and weakest we need to choose September 1st as the beginning of each annual period.\n", + "\n", + "we define the reference period for the annual incidence from September 1st of year $N$ to September 1st of year $N+1$. \n", + "Our task is a bit complicated by the fact that a year does not have an integer number of weeks. Therefore we modify our reference period a bit: instead of September 1st, we use the first day of the week containing September 1st.\n", + "\n", + "A final detail: the dataset starts in December 1990, the first year is thus incomplete, We start the analysis with the first full peak." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "first_sept_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n", + " for y in range(1991,\n", + " sorted_data.index[-1].year)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Starting from this list of weeks that contain September 1st, we obtain intervals of approximately one year as the periods between two adjacent weeks in this list. We compute the sums of weekly incidences for all these periods.\n", + "\n", + "We also check that our periods contain between 51 and 52 weeks, as a safeguard against potential mistakes in our code." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "year = []\n", + "yearly_incidence = []\n", + "for week1, week2 in zip(first_sept_week[:-1],\n", + " first_sept_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": "markdown", + "metadata": {}, + "source": [ + "And here are the annual incidences." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.plot(style='*')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A sorted list makes it easier to find the highest values (at the end)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2002 516689\n", + "2018 542312\n", + "2017 551041\n", + "1996 564901\n", + "2019 584066\n", + "2015 604382\n", + "2000 617597\n", + "2001 619041\n", + "2012 624573\n", + "2005 628464\n", + "2006 632833\n", + "2011 642368\n", + "1993 643387\n", + "1995 652478\n", + "1994 661409\n", + "1998 677775\n", + "1997 683434\n", + "2014 685769\n", + "2013 698332\n", + "2007 717352\n", + "2008 749478\n", + "1999 756456\n", + "2003 758363\n", + "2004 777388\n", + "2016 782114\n", + "2010 829911\n", + "1992 832939\n", + "2009 842373\n", + "dtype: int64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yearly_incidence.sort_values()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, a histogram clearly shows the epidemic ocurences." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "yearly_incidence.hist(xrot=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +2379,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo2/incidence-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv new file mode 100644 index 0000000000000000000000000000000000000000..e7a5487f8f7c3d4d6fc8c4f2465b9000fa71eea9 --- /dev/null +++ b/module3/exo2/incidence-PAY-7.csv @@ -0,0 +1,1561 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202042],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, 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