diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..07647478eeb5b07a208c6570d2fdfbc082a1c2b7 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,14 +9,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/jovyan/work/module3/exo1'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import isoweek" + "import isoweek\n", + "import os\n", + "os.getcwd()" ] }, { @@ -28,10 +41,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -61,11 +72,917 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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284 rows × 9 columns

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weekgeo_inseeindicatorincinc100inc_upinc_lowinc100_upinc100_low
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weekgeo_inseeindicatorincinc100inc_upinc_lowinc100_upinc100_low
0202016FR25417266344257391956760
1202017FR25459037051774400327961
2202018FR25346185339737294996045
3202019FR25273474231814228804835
4202020FR25284164332957238755036
5202021FR25146512217949113532717
6202022FR25303974632550282444943
7202023FR25216923323520198643630
8202024FR25207833222565190013429
9202025FR25153522316904138002621
10202026FR2510251161152289801714
11202027FR258978141017977771512
12202028FR2510079151137587831713
13202029FR2510061151139787251713
14202030FR25132782014843117132318
15202031FR25120181813527105092116
16202032FR25142072215941124732419
17202033FR25142192216007124312419
18202034FR25143572216049126652419
19202035FR25156942417305140832621
20202036FR25171432618779155072924
21202037FR25331365035395308775447
22202038FR25508587753662480548173
23202039FR25495667552295468377971
24202040FR25570328759978540869182
25202041FR25718761097516768585114104
26202042FR25819481248542978467130119
27202043FR25824201258602178819131120
28202044FR25825441258617978909131120
29202045FR25799411218340376479127116
..............................
254202509FR25124057185135588112526202168
255202510FR259594414310523286656157129
256202511FR259820214710777688628161132
257202512FR259426414110374184787155126
258202513FR2573745110819916549912298
259202514FR2574759112837866573212598
260202515FR25795301198864170419132105
261202516FR256534497744225626611184
262202517FR25488197356035416038462
263202518FR25403966046566342266951
264202519FR25390195845001330376749
265202520FR25531127960442457829068
266202521FR255862387667475049910075
267202522FR25458756852839389117958
268202523FR256038690684015237110278
269202524FR25549078261896479189271
270202525FR25511617658159441638766
271202526FR25483647254996417328262
272202527FR25457526852041394637859
273202528FR25465836953778393888059
274202529FR25413326248532341327251
275202530FR25452106752705377157956
276202531FR25391555847757305537146
277202532FR25471517055928383748357
278202533FR25369355543957299136645
279202534FR25445886752167370097855
280202535FR25462346953668388008058
281202536FR25511957658092442988766
282202537FR25889061339815479658146119
283202538FR25128661192141022116300210174
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284 rows × 9 columns

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" + ], + "text/plain": [ + " week geo_insee indicator inc inc100 inc_up inc_low inc100_up \\\n", + "0 202016 FR 25 41726 63 44257 39195 67 \n", + "1 202017 FR 25 45903 70 51774 40032 79 \n", + "2 202018 FR 25 34618 53 39737 29499 60 \n", + "3 202019 FR 25 27347 42 31814 22880 48 \n", + "4 202020 FR 25 28416 43 32957 23875 50 \n", + "5 202021 FR 25 14651 22 17949 11353 27 \n", + "6 202022 FR 25 30397 46 32550 28244 49 \n", + "7 202023 FR 25 21692 33 23520 19864 36 \n", + "8 202024 FR 25 20783 32 22565 19001 34 \n", + "9 202025 FR 25 15352 23 16904 13800 26 \n", + "10 202026 FR 25 10251 16 11522 8980 17 \n", + "11 202027 FR 25 8978 14 10179 7777 15 \n", + "12 202028 FR 25 10079 15 11375 8783 17 \n", + "13 202029 FR 25 10061 15 11397 8725 17 \n", + "14 202030 FR 25 13278 20 14843 11713 23 \n", + "15 202031 FR 25 12018 18 13527 10509 21 \n", + "16 202032 FR 25 14207 22 15941 12473 24 \n", + "17 202033 FR 25 14219 22 16007 12431 24 \n", + "18 202034 FR 25 14357 22 16049 12665 24 \n", + "19 202035 FR 25 15694 24 17305 14083 26 \n", + "20 202036 FR 25 17143 26 18779 15507 29 \n", + "21 202037 FR 25 33136 50 35395 30877 54 \n", + "22 202038 FR 25 50858 77 53662 48054 81 \n", + "23 202039 FR 25 49566 75 52295 46837 79 \n", + "24 202040 FR 25 57032 87 59978 54086 91 \n", + "25 202041 FR 25 71876 109 75167 68585 114 \n", + "26 202042 FR 25 81948 124 85429 78467 130 \n", + "27 202043 FR 25 82420 125 86021 78819 131 \n", + "28 202044 FR 25 82544 125 86179 78909 131 \n", + "29 202045 FR 25 79941 121 83403 76479 127 \n", + ".. ... ... ... ... ... ... ... ... \n", + "254 202509 FR 25 124057 185 135588 112526 202 \n", + "255 202510 FR 25 95944 143 105232 86656 157 \n", + "256 202511 FR 25 98202 147 107776 88628 161 \n", + "257 202512 FR 25 94264 141 103741 84787 155 \n", + "258 202513 FR 25 73745 110 81991 65499 122 \n", + "259 202514 FR 25 74759 112 83786 65732 125 \n", + "260 202515 FR 25 79530 119 88641 70419 132 \n", + "261 202516 FR 25 65344 97 74422 56266 111 \n", + "262 202517 FR 25 48819 73 56035 41603 84 \n", + "263 202518 FR 25 40396 60 46566 34226 69 \n", + "264 202519 FR 25 39019 58 45001 33037 67 \n", + "265 202520 FR 25 53112 79 60442 45782 90 \n", + "266 202521 FR 25 58623 87 66747 50499 100 \n", + "267 202522 FR 25 45875 68 52839 38911 79 \n", + "268 202523 FR 25 60386 90 68401 52371 102 \n", + "269 202524 FR 25 54907 82 61896 47918 92 \n", + "270 202525 FR 25 51161 76 58159 44163 87 \n", + "271 202526 FR 25 48364 72 54996 41732 82 \n", + "272 202527 FR 25 45752 68 52041 39463 78 \n", + "273 202528 FR 25 46583 69 53778 39388 80 \n", + "274 202529 FR 25 41332 62 48532 34132 72 \n", + "275 202530 FR 25 45210 67 52705 37715 79 \n", + "276 202531 FR 25 39155 58 47757 30553 71 \n", + "277 202532 FR 25 47151 70 55928 38374 83 \n", + "278 202533 FR 25 36935 55 43957 29913 66 \n", + "279 202534 FR 25 44588 67 52167 37009 78 \n", + "280 202535 FR 25 46234 69 53668 38800 80 \n", + "281 202536 FR 25 51195 76 58092 44298 87 \n", + "282 202537 FR 25 88906 133 98154 79658 146 \n", + "283 202538 FR 25 128661 192 141022 116300 210 \n", + "\n", + " inc100_low \n", + "0 60 \n", + "1 61 \n", + "2 45 \n", + "3 35 \n", + "4 36 \n", + "5 17 \n", + "6 43 \n", + "7 30 \n", + "8 29 \n", + "9 21 \n", + "10 14 \n", + "11 12 \n", + "12 13 \n", + "13 13 \n", + "14 18 \n", + "15 16 \n", + "16 19 \n", + "17 19 \n", + "18 19 \n", + "19 21 \n", + "20 24 \n", + "21 47 \n", + "22 73 \n", + "23 71 \n", + "24 82 \n", + "25 104 \n", + "26 119 \n", + "27 120 \n", + "28 120 \n", + "29 116 \n", + ".. ... \n", + "254 168 \n", + "255 129 \n", + "256 132 \n", + "257 126 \n", + "258 98 \n", + "259 98 \n", + "260 105 \n", + "261 84 \n", + "262 62 \n", + "263 51 \n", + "264 49 \n", + "265 68 \n", + "266 75 \n", + "267 58 \n", + "268 78 \n", + "269 71 \n", + "270 66 \n", + "271 62 \n", + "272 59 \n", + "273 59 \n", + "274 51 \n", + "275 56 \n", + "276 46 \n", + "277 57 \n", + "278 45 \n", + "279 55 \n", + "280 58 \n", + "281 66 \n", + "282 119 \n", + "283 174 \n", + "\n", + "[284 rows x 9 columns]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +1992,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2022,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 15, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,7 +2047,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -199,9 +2067,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sorted_data['inc'][-200:].plot()" ] @@ -252,10 +2166,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", @@ -274,9 +2186,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "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", @@ -341,9 +2265,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -364,7 +2286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/inc-25-PAY-ds2.csv b/module3/exo1/inc-25-PAY-ds2.csv new file mode 100644 index 0000000000000000000000000000000000000000..8854898e3066362dc5b867d9d3a4ed1954ca205a --- /dev/null +++ b/module3/exo1/inc-25-PAY-ds2.csv @@ -0,0 +1,286 @@ +# 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