diff --git a/module2/exo1/.ipynb b/module2/exo1/.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..0b9222b1a99a78a9be11960d31692f210281a3cf --- /dev/null +++ b/module2/exo1/.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " # À propos du calcul de π" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## En demandant à la lib maths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mon ordinateur m’indique que π vaut *approximativement*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.141592653589793\n" + ] + } + ], + "source": [ + "from math import *\n", + "print(pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## En utilisant la méthode des aiguilles de Buffon" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mais calculé avec **la méthode** des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon), on obtiendrait comme **approximation** :\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.128911138923655" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "np.random.seed(seed=42)\n", + "N = 10000\n", + "x = np.random.uniform(size=N, low=0, high=1)\n", + "theta = np.random.uniform(size=N, low=0, high=pi/2)\n", + "2/(sum((x+np.sin(theta))>1)/N)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Avec un argument \"fréquentiel\" de surface" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d’appel à la fonction\n", + "sinus se base sur le fait que si X ∼ U(0, 1) et Y ∼ U(0, 1) alors P[X^2 + Y^2 ≤ 1] = π/4 [voir\n", + "méthode de Monte Carlo sur Wikipedia](https://fr.wikipedia.org/wiki/M%C].3%A9thode_de_Monte-Carlo#D%C3%A9termination_de_la_valeur_de_%CF%8) Le code suivant illustre ce fait :" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "np.random.seed(seed=42)\n", + "N = 1000\n", + "x = np.random.uniform(size=N, low=0, high=1)\n", + "y = np.random.uniform(size=N, low=0, high=1)\n", + "accept = (x*x+y*y) <= 1\n", + "reject = np.logical_not(accept)\n", + "fig, ax = plt.subplots(1)\n", + "ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)\n", + "ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)\n", + "ax.set_aspect('equal')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il est alors aisé d’obtenir une approximation (pas terrible) de π en comptant combien de fois,\n", + "en moyenne, X^2 + Y^2\n", + "est inférieur à 1 :" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.112" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "4*np.mean(accept)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb index 9bb89338cb612403cd224d9edb566664f4f5dc2b..65793b0c4beac61f008d7ce1102e2da4b026ce96 100644 --- a/module2/exo1/toy_notebook_fr.ipynb +++ b/module2/exo1/toy_notebook_fr.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -16,7 +16,7 @@ "source": [ "# À propos du calcul de \\pi\n", "## En demandant à la lib maths\n", - "Mon ordinateur m’indique que \\pi vaut approximativement\n", + "#Mon ordinateur m’indique que \\pi vaut approximativement\n", "\n", "from math import *\n", "print(pi)\n" @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -33,14 +33,14 @@ "3.128911138923655" ] }, - "execution_count": 2, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## En utilisant la méthode des aiguilles de Buffon\n", - "Mais calculé avec la méthode des aiguilles de Buffon, on obtiendrait comme *approximation* :\n", + "#Mais calculé avec la méthode des aiguilles de Buffon, on obtiendrait comme *approximation* :\n", "\n", "import numpy as np\n", "np.random.seed(seed=42)\n", @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -61,7 +61,7 @@ "3.112" ] }, - "execution_count": 3, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -79,9 +79,9 @@ } ], "source": [ - " Avec un argument \"fréquentiel\" de surface\n", - "Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d’appel à la fonction\n", - "sinus se base sur le fait que si \n", + " #Avec un argument \"fréquentiel\" de surface\n", + "#Sinon, une méthode plus simple à comprendre et ne faisant pas intervenir d’appel à la fonction\n", + "#sinus se base sur le fait que si \n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", @@ -103,6 +103,13 @@ "cell_type": "markdown", "metadata": {}, "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5b58a3ae26346460dd39e62a39c55243d7..f9f6c7a6d54dc6e1124dcc67958bae0a1a1bcc5c 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -28,13 +28,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { - "collapsed": true + "scrolled": true }, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\" " ] }, { @@ -61,11 +61,978 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202145 3 21052 17090.0 25014.0 32 26.0 \n", + "1 202144 3 19014 15044.0 22984.0 29 23.0 \n", + "2 202143 3 27040 21935.0 32145.0 41 33.0 \n", + "3 202142 3 28343 23382.0 33304.0 43 35.0 \n", + "4 202141 3 25043 20586.0 29500.0 38 31.0 \n", + "5 202140 3 26286 21842.0 30730.0 40 33.0 \n", + "6 202139 3 22155 18014.0 26296.0 34 28.0 \n", + "7 202138 3 15614 12310.0 18918.0 24 19.0 \n", + "8 202137 3 13673 10404.0 16942.0 21 16.0 \n", + "9 202136 3 10289 7505.0 13073.0 16 12.0 \n", + "10 202135 3 12609 9282.0 15936.0 19 14.0 \n", + "11 202134 3 13015 9485.0 16545.0 20 15.0 \n", + "12 202133 3 10392 7042.0 13742.0 16 11.0 \n", + "13 202132 3 15586 11009.0 20163.0 24 17.0 \n", + "14 202131 3 18855 13664.0 24046.0 29 21.0 \n", + "15 202130 3 13991 9695.0 18287.0 21 14.0 \n", + "16 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "17 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "18 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "19 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "20 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "21 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "22 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "23 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "24 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "25 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "26 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "27 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "28 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "29 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "... ... ... ... ... ... ... ... \n", + "1903 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1904 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1905 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1906 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1907 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1908 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1909 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1910 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1911 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1912 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1913 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1914 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1915 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1916 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1917 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1918 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1919 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1920 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1921 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1922 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1923 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1924 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1925 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1926 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1927 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1928 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1929 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1930 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1931 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1932 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 38.0 FR France \n", + "1 35.0 FR France \n", + "2 49.0 FR France \n", + "3 51.0 FR France \n", + "4 45.0 FR France \n", + "5 47.0 FR France \n", + "6 40.0 FR France \n", + "7 29.0 FR France \n", + "8 26.0 FR France \n", + "9 20.0 FR France \n", + "10 24.0 FR France \n", + "11 25.0 FR France \n", + "12 21.0 FR France \n", + "13 31.0 FR France \n", + "14 37.0 FR France \n", + "15 28.0 FR France \n", + "16 27.0 FR France \n", + "17 18.0 FR France \n", + "18 22.0 FR France \n", + "19 16.0 FR France \n", + "20 16.0 FR France \n", + "21 10.0 FR France \n", + "22 13.0 FR France \n", + "23 16.0 FR France \n", + "24 16.0 FR France \n", + "25 20.0 FR France \n", + "26 18.0 FR France \n", + "27 22.0 FR France \n", + "28 23.0 FR France \n", + "29 31.0 FR France \n", + "... ... ... ... \n", + "1903 59.0 FR France \n", + "1904 64.0 FR France \n", + "1905 97.0 FR France \n", + "1906 93.0 FR France \n", + "1907 80.0 FR France \n", + "1908 116.0 FR France \n", + "1909 149.0 FR France \n", + "1910 281.0 FR France \n", + "1911 395.0 FR France \n", + "1912 485.0 FR France \n", + "1913 544.0 FR France \n", + "1914 689.0 FR France \n", + "1915 722.0 FR France \n", + "1916 762.0 FR France \n", + "1917 926.0 FR France \n", + "1918 1113.0 FR France \n", + "1919 1236.0 FR France \n", + "1920 832.0 FR France \n", + "1921 459.0 FR France \n", + "1922 207.0 FR France \n", + "1923 190.0 FR France \n", + "1924 198.0 FR France \n", + "1925 224.0 FR France \n", + "1926 266.0 FR France \n", + "1927 219.0 FR France \n", + "1928 176.0 FR France \n", + "1929 163.0 FR France \n", + "1930 195.0 FR France \n", + "1931 308.0 FR France \n", + "1932 213.0 FR France \n", + "\n", + "[1933 rows x 10 columns]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(data_url,encoding = 'iso-8859-1', skiprows=1)\n", "raw_data" ] }, @@ -78,9 +1045,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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.................................
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192419845238483060602.0109058.0154110.0198.0FRFrance
1925198451310172680242.0123210.0185146.0224.0FRFrance
19261984503123680101401.0145959.0225184.0266.0FRFrance
1927198449310107381684.0120462.0184149.0219.0FRFrance
192819844837862060634.096606.0143110.0176.0FRFrance
192919844737202954274.089784.013199.0163.0FRFrance
193019844638733067686.0106974.0159123.0195.0FRFrance
19311984453135223101414.0169032.0246184.0308.0FRFrance
193219844436842220056.0116788.012537.0213.0FRFrance
\n", + "

1932 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202145 3 21052 17090.0 25014.0 32 26.0 \n", + "1 202144 3 19014 15044.0 22984.0 29 23.0 \n", + "2 202143 3 27040 21935.0 32145.0 41 33.0 \n", + "3 202142 3 28343 23382.0 33304.0 43 35.0 \n", + "4 202141 3 25043 20586.0 29500.0 38 31.0 \n", + "5 202140 3 26286 21842.0 30730.0 40 33.0 \n", + "6 202139 3 22155 18014.0 26296.0 34 28.0 \n", + "7 202138 3 15614 12310.0 18918.0 24 19.0 \n", + "8 202137 3 13673 10404.0 16942.0 21 16.0 \n", + "9 202136 3 10289 7505.0 13073.0 16 12.0 \n", + "10 202135 3 12609 9282.0 15936.0 19 14.0 \n", + "11 202134 3 13015 9485.0 16545.0 20 15.0 \n", + "12 202133 3 10392 7042.0 13742.0 16 11.0 \n", + "13 202132 3 15586 11009.0 20163.0 24 17.0 \n", + "14 202131 3 18855 13664.0 24046.0 29 21.0 \n", + "15 202130 3 13991 9695.0 18287.0 21 14.0 \n", + "16 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "17 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "18 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "19 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "20 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "21 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "22 202123 3 6710 4455.0 8965.0 10 7.0 \n", + "23 202122 3 7879 5495.0 10263.0 12 8.0 \n", + "24 202121 3 7827 5403.0 10251.0 12 8.0 \n", + "25 202120 3 10278 7540.0 13016.0 16 12.0 \n", + "26 202119 3 9539 6860.0 12218.0 14 10.0 \n", + "27 202118 3 12135 9165.0 15105.0 18 14.0 \n", + "28 202117 3 12058 8891.0 15225.0 18 13.0 \n", + "29 202116 3 16505 12735.0 20275.0 25 19.0 \n", + "... ... ... ... ... ... ... ... \n", + "1903 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1904 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1905 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1906 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1907 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1908 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1909 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1910 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1911 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1912 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1913 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1914 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1915 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1916 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1917 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1918 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1919 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1920 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1921 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1922 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1923 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1924 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1925 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1926 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1927 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1928 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1929 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1930 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1931 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1932 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 38.0 FR France \n", + "1 35.0 FR France \n", + "2 49.0 FR France \n", + "3 51.0 FR France \n", + "4 45.0 FR France \n", + "5 47.0 FR France \n", + "6 40.0 FR France \n", + "7 29.0 FR France \n", + "8 26.0 FR France \n", + "9 20.0 FR France \n", + "10 24.0 FR France \n", + "11 25.0 FR France \n", + "12 21.0 FR France \n", + "13 31.0 FR France \n", + "14 37.0 FR France \n", + "15 28.0 FR France \n", + "16 27.0 FR France \n", + "17 18.0 FR France \n", + "18 22.0 FR France \n", + "19 16.0 FR France \n", + "20 16.0 FR France \n", + "21 10.0 FR France \n", + "22 13.0 FR France \n", + "23 16.0 FR France \n", + "24 16.0 FR France \n", + "25 20.0 FR France \n", + "26 18.0 FR France \n", + "27 22.0 FR France \n", + "28 23.0 FR France \n", + "29 31.0 FR France \n", + "... ... ... ... \n", + "1903 59.0 FR France \n", + "1904 64.0 FR France \n", + "1905 97.0 FR France \n", + "1906 93.0 FR France \n", + "1907 80.0 FR France \n", + "1908 116.0 FR France \n", + "1909 149.0 FR France \n", + "1910 281.0 FR France \n", + "1911 395.0 FR France \n", + "1912 485.0 FR France \n", + "1913 544.0 FR France \n", + "1914 689.0 FR France \n", + "1915 722.0 FR France \n", + "1916 762.0 FR France \n", + "1917 926.0 FR France \n", + "1918 1113.0 FR France \n", + "1919 1236.0 FR France \n", + "1920 832.0 FR France \n", + "1921 459.0 FR France \n", + "1922 207.0 FR France \n", + "1923 190.0 FR France \n", + "1924 198.0 FR France \n", + "1925 224.0 FR France \n", + "1926 266.0 FR France \n", + "1927 219.0 FR France \n", + "1928 176.0 FR France \n", + "1929 163.0 FR France \n", + "1930 195.0 FR France \n", + "1931 308.0 FR France \n", + "1932 213.0 FR France \n", + "\n", + "[1932 rows x 10 columns]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2120,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2150,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2175,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n" + ] + } + ], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", @@ -199,9 +2203,34 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 29, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.plot(style='*')" ] @@ -314,9 +2387,55 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\n", + "2020 2053781\n", + "2012 2175217\n", + "2003 2234584\n", + "2019 2254386\n", + "2006 2307352\n", + "2017 2321583\n", + "2001 2529279\n", + "1992 2574578\n", + "1993 2703886\n", + "2018 2705325\n", + "1988 2765617\n", + "2007 2780164\n", + "1987 2855570\n", + "2016 2856393\n", + "2011 2857040\n", + "2008 2973918\n", + "1998 3034904\n", + "2002 3125418\n", + "2009 3444020\n", + "1994 3514763\n", + "1996 3539413\n", + "2004 3567744\n", + "1997 3620066\n", + "2015 3654892\n", + "2000 3826372\n", + "2005 3835025\n", + "1999 3908112\n", + "2010 4111392\n", + "2013 4182691\n", + "1986 5115251\n", + "1990 5235827\n", + "1989 5466192\n", + "dtype: int64" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2450,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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