From 780d3ecc4b2d585c7159b40c9f02f8392e59573d Mon Sep 17 00:00:00 2001 From: 9d26ff392e3a3b9ec48275a74df5354e <9d26ff392e3a3b9ec48275a74df5354e@app-learninglab.inria.fr> Date: Thu, 23 Apr 2020 14:37:27 +0000 Subject: [PATCH] fichier en local --- module2/challenger.ipynb | 780 +++++++ module3/exo1/analyse-syndrome-grippal.ipynb | 2222 ++++++++++++++++++- module3/exo1/incidence-PAY-3.csv | 1852 ++++++++++++++++ 3 files changed, 4813 insertions(+), 41 deletions(-) create mode 100644 module2/challenger.ipynb create mode 100644 module3/exo1/incidence-PAY-3.csv diff --git a/module2/challenger.ipynb b/module2/challenger.ipynb new file mode 100644 index 0000000..da173e6 --- /dev/null +++ b/module2/challenger.ipynb @@ -0,0 +1,780 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this document we reperform some of the analysis provided in \n", + "*Risk Analysis of the Space Shuttle: Pre-Challenger Prediction of Failure* by *Siddhartha R. Dalal, Edward B. Fowlkes, Bruce Hoadley* published in *Journal of the American Statistical Association*, Vol. 84, No. 408 (Dec., 1989), pp. 945-957 and available at http://www.jstor.org/stable/2290069. \n", + "\n", + "On the fourth page of this article, they indicate that the maximum likelihood estimates of the logistic regression using only temperature are: $\\hat{\\alpha}=5.085$ and $\\hat{\\beta}=-0.1156$ and their asymptotic standard errors are $s_{\\hat{\\alpha}}=3.052$ and $s_{\\hat{\\beta}}=0.047$. The Goodness of fit indicated for this model was $G^2=18.086$ with 21 degrees of freedom. Our goal is to reproduce the computation behind these values and the Figure 4 of this article, possibly in a nicer looking way." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Technical information on the computer on which the analysis is run" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will be using the python3 language using the pandas, statsmodels, numpy, matplotlib and seaborn libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.6.4 |Anaconda, Inc.| (default, Jan 16 2018, 18:10:19) \n", + "[GCC 7.2.0]\n", + "uname_result(system='Linux', node='3a716011d2b6', release='4.4.0-116-generic', version='#140-Ubuntu SMP Mon Feb 12 21:23:04 UTC 2018', machine='x86_64', processor='x86_64')\n", + "IPython 6.4.0\n", + "IPython.core.release 6.4.0\n", + "PIL 5.2.0\n", + "PIL.Image 5.2.0\n", + "PIL._version 5.2.0\n", + "_csv 1.0\n", + "_ctypes 1.1.0\n", + "_curses b'2.2'\n", + "decimal 1.70\n", + "argparse 1.1\n", + "backcall 0.1.0\n", + "cffi 1.11.5\n", + "csv 1.0\n", + "ctypes 1.1.0\n", + "cycler 0.10.0\n", + "dateutil 2.7.3\n", + "decimal 1.70\n", + "decorator 4.3.0\n", + "distutils 3.6.4\n", + "ipaddress 1.0\n", + "ipykernel 4.8.2\n", + "ipykernel._version 4.8.2\n", + "ipython_genutils 0.2.0\n", + "ipython_genutils._version 0.2.0\n", + "ipywidgets 7.2.1\n", + "ipywidgets._version 7.2.1\n", + "jedi 0.12.1\n", + "json 2.0.9\n", + "jupyter_client 5.2.3\n", + "jupyter_client._version 5.2.3\n", + "jupyter_core 4.4.0\n", + "jupyter_core.version 4.4.0\n", + "kiwisolver 1.0.1\n", + "logging 0.5.1.2\n", + "matplotlib 2.2.2\n", + "matplotlib.backends.backend_agg 2.2.2\n", + "numpy 1.13.3\n", + "numpy.core 1.13.3\n", + "numpy.core.multiarray 3.1\n", + "numpy.core.umath b'0.4.0'\n", + "numpy.lib 1.13.3\n", + "numpy.linalg._umath_linalg b'0.1.5'\n", + "numpy.matlib 1.13.3\n", + "optparse 1.5.3\n", + "pandas 0.22.0\n", + "_libjson 1.33\n", + "parso 0.3.0\n", + "patsy 0.5.0\n", + "patsy.version 0.5.0\n", + "pexpect 4.6.0\n", + "pickleshare 0.7.4\n", + "platform 1.0.8\n", + "prompt_toolkit 1.0.15\n", + "ptyprocess 0.6.0\n", + "pygments 2.2.0\n", + "pyparsing 2.2.0\n", + "pytz 2018.5\n", + "re 2.2.1\n", + "scipy 1.1.0\n", + "scipy._lib.decorator 4.0.5\n", + "scipy._lib.six 1.2.0\n", + "scipy.fftpack._fftpack b'$Revision: $'\n", + "scipy.fftpack.convolve b'$Revision: $'\n", + "scipy.integrate._dop b'$Revision: $'\n", + "scipy.integrate._ode $Id$\n", + "scipy.integrate._odepack 1.9 \n", + "scipy.integrate._quadpack 1.13 \n", + "scipy.integrate.lsoda b'$Revision: $'\n", + "scipy.integrate.vode b'$Revision: $'\n", + "scipy.interpolate._fitpack 1.7 \n", + "scipy.interpolate.dfitpack b'$Revision: $'\n", + "scipy.linalg 0.4.9\n", + "scipy.linalg._fblas b'$Revision: $'\n", + "scipy.linalg._flapack b'$Revision: $'\n", + "scipy.linalg._flinalg b'$Revision: $'\n", + "scipy.ndimage 2.0\n", + "scipy.optimize._cobyla b'$Revision: $'\n", + "scipy.optimize._lbfgsb b'$Revision: $'\n", + "scipy.optimize._minpack 1.10 \n", + "scipy.optimize._nnls b'$Revision: $'\n", + "scipy.optimize._slsqp b'$Revision: $'\n", + "scipy.optimize.minpack2 b'$Revision: $'\n", + "scipy.signal.spline 0.2\n", + "scipy.sparse.linalg.eigen.arpack._arpack b'$Revision: $'\n", + "scipy.sparse.linalg.isolve._iterative b'$Revision: $'\n", + "scipy.special.specfun b'$Revision: $'\n", + "scipy.stats.mvn b'$Revision: $'\n", + "scipy.stats.statlib b'$Revision: $'\n", + "seaborn 0.8.1\n", + "seaborn.external.husl 2.1.0\n", + "seaborn.external.six 1.10.0\n", + "six 1.11.0\n", + "statsmodels 0.9.0\n", + "statsmodels.__init__ 0.9.0\n", + "traitlets 4.3.2\n", + "traitlets._version 4.3.2\n", + "urllib.request 3.6\n", + "zlib 1.0\n", + "zmq 17.1.0\n", + "zmq.sugar 17.1.0\n", + "zmq.sugar.version 17.1.0\n" + ] + } + ], + "source": [ + "def print_imported_modules():\n", + " import sys\n", + " for name, val in sorted(sys.modules.items()):\n", + " if(hasattr(val, '__version__')): \n", + " print(val.__name__, val.__version__)\n", + "# else:\n", + "# print(val.__name__, \"(unknown version)\")\n", + "def print_sys_info():\n", + " import sys\n", + " import platform\n", + " print(sys.version)\n", + " print(platform.uname())\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import statsmodels.api as sm\n", + "import seaborn as sns\n", + "\n", + "print_sys_info()\n", + "print_imported_modules()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading and inspecting data\n", + "Let's start by reading data." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DateCountTemperaturePressureMalfunction
04/12/81666500
111/12/81670501
23/22/82669500
311/11/82668500
44/04/83667500
56/18/82672500
68/30/836731000
711/28/836701000
82/03/846572001
94/06/846632001
108/30/846702001
1110/05/846782000
1211/08/846672000
131/24/856532002
144/12/856672000
154/29/856752000
166/17/856702000
177/2903/856812000
188/27/856762000
1910/03/856792000
2010/30/856752002
2111/26/856762000
221/12/866582001
\n", + "
" + ], + "text/plain": [ + " Date Count Temperature Pressure Malfunction\n", + "0 4/12/81 6 66 50 0\n", + "1 11/12/81 6 70 50 1\n", + "2 3/22/82 6 69 50 0\n", + "3 11/11/82 6 68 50 0\n", + "4 4/04/83 6 67 50 0\n", + "5 6/18/82 6 72 50 0\n", + "6 8/30/83 6 73 100 0\n", + "7 11/28/83 6 70 100 0\n", + "8 2/03/84 6 57 200 1\n", + "9 4/06/84 6 63 200 1\n", + "10 8/30/84 6 70 200 1\n", + "11 10/05/84 6 78 200 0\n", + "12 11/08/84 6 67 200 0\n", + "13 1/24/85 6 53 200 2\n", + "14 4/12/85 6 67 200 0\n", + "15 4/29/85 6 75 200 0\n", + "16 6/17/85 6 70 200 0\n", + "17 7/2903/85 6 81 200 0\n", + "18 8/27/85 6 76 200 0\n", + "19 10/03/85 6 79 200 0\n", + "20 10/30/85 6 75 200 2\n", + "21 11/26/85 6 76 200 0\n", + "22 1/12/86 6 58 200 1" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(\"https://app-learninglab.inria.fr/gitlab/moocrr-session1/moocrr-reproducibility-study/raw/master/data/shuttle.csv\")\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We know from our previous experience on this data set that filtering data is a really bad idea. We will therefore process it as such." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n", + "import matplotlib.pyplot as plt\n", + "\n", + "data[\"Frequency\"]=data.Malfunction/data.Count\n", + "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Logistic regression\n", + "\n", + "Let's assume O-rings independently fail with the same probability which solely depends on temperature. A logistic regression should allow us to estimate the influence of temperature." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -3.9210
Date: Wed, 24 Oct 2018 Deviance: 3.0144
Time: 11:05:55 Pearson chi2: 5.00
No. Iterations: 6 Covariance Type: nonrobust
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coef std err z P>|z| [0.025 0.975]
Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740
Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Generalized Linear Model Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Frequency No. Observations: 23\n", + "Model: GLM Df Residuals: 21\n", + "Model Family: Binomial Df Model: 1\n", + "Link Function: logit Scale: 1.0000\n", + "Method: IRLS Log-Likelihood: -3.9210\n", + "Date: Wed, 24 Oct 2018 Deviance: 3.0144\n", + "Time: 11:05:55 Pearson chi2: 5.00\n", + "No. Iterations: 6 Covariance Type: nonrobust\n", + "===============================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------\n", + "Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n", + "Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n", + "===============================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "data[\"Success\"]=data.Count-data.Malfunction\n", + "data[\"Intercept\"]=1\n", + "\n", + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n", + " family=sm.families.Binomial(sm.families.links.logit)).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The maximum likelyhood estimator of the intercept and of Temperature are thus $\\hat{\\alpha}=5.0849$ and $\\hat{\\beta}=-0.1156$. This **corresponds** to the values from the article of Dalal *et al.* The standard errors are $s_{\\hat{\\alpha}} = 7.477$ and $s_{\\hat{\\beta}} = 0.115$, which is **different** from the $3.052$ and $0.04702$ reported by Dallal *et al.* The deviance is $3.01444$ with 21 degrees of freedom. I cannot find any value similar to the Goodness of fit ($G^2=18.086$) reported by Dalal *et al.* There seems to be something wrong. Oh I know, I haven't indicated that my observations are actually the result of 6 observations for each rocket launch. Let's indicate these weights (since the weights are always the same throughout all experiments, it does not change the estimates of the fit but it does influence the variance estimates)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Generalized Linear Model Regression Results
Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1
Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -23.526
Date: Wed, 24 Oct 2018 Deviance: 18.086
Time: 11:05:55 Pearson chi2: 30.0
No. Iterations: 6 Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068
Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Generalized Linear Model Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Frequency No. Observations: 23\n", + "Model: GLM Df Residuals: 21\n", + "Model Family: Binomial Df Model: 1\n", + "Link Function: logit Scale: 1.0000\n", + "Method: IRLS Log-Likelihood: -23.526\n", + "Date: Wed, 24 Oct 2018 Deviance: 18.086\n", + "Time: 11:05:55 Pearson chi2: 30.0\n", + "No. Iterations: 6 Covariance Type: nonrobust\n", + "===============================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------\n", + "Intercept 5.0850 3.052 1.666 0.096 -0.898 11.068\n", + "Temperature -0.1156 0.047 -2.458 0.014 -0.208 -0.023\n", + "===============================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "logmodel=sm.GLM(data['Frequency'], data[['Intercept','Temperature']], \n", + " family=sm.families.Binomial(sm.families.links.logit),\n", + " var_weights=data['Count']).fit()\n", + "\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good, now I have recovered the asymptotic standard errors $s_{\\hat{\\alpha}}=3.052$ and $s_{\\hat{\\beta}}=0.047$.\n", + "The Goodness of fit (Deviance) indicated for this model is $G^2=18.086$ with 21 degrees of freedom (Df Residuals).\n", + "\n", + "**I have therefore managed to fully replicate the results of the Dalal *et al.* article**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Predicting failure probability\n", + "The temperature when launching the shuttle was 31°F. Let's try to estimate the failure probability for such temperature using our model.:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n", + "data_pred['Frequency'] = logmodel.predict(data_pred)\n", + "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", + "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false, + "scrolled": true + }, + "source": [ + "This figure is very similar to the Figure 4 of Dalal *et al.* **I have managed to replicate the Figure 4 of the Dalal *et al.* article.**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Computing and plotting uncertainty" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Following the documentation of [Seaborn](https://seaborn.pydata.org/generated/seaborn.regplot.html), I use regplot." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set(color_codes=True)\n", + "plt.xlim(30,90)\n", + "plt.ylim(0,1)\n", + "sns.regplot(x='Temperature', y='Frequency', data=data, logistic=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**I think I have managed to correctly compute and plot the uncertainty of my prediction.** Although the shaded area seems very similar to [the one obtained by with R](https://app-learninglab.inria.fr/gitlab/moocrr-session1/moocrr-reproducibility-study/raw/5c9dbef11b4d7638b7ddf2ea71026e7bf00fcfb0/challenger.pdf), I can spot a few differences (e.g., the blue point for temperature 63 is outside)... Could this be a numerical error ? Or a difference in the statistical method ? It is not clear which one is \"right\"." + ] + } + ], + "metadata": { + "celltoolbar": "Hide code", + "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": "3.6.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..50c143a 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,32 +9,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import isoweek" + "import isoweek\n", + "from os import path\n", + "from urllib import request" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente." + "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Le jeu de données complet commence en 1984 et se termine avec une semaine récente. Le fichier est téléchargé si il n'existe pas." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"\n", + "file_name = \"incidence-PAY-3.csv\"\n", + "if not path.exists(file_name):\n", + " request.urlretrieve(data_url, file_name)\n" ] }, { @@ -61,11 +64,978 @@ }, { "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
0202015300.00.000.00.0FRFrance
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23201944378225010.010634.0128.016.0FRFrance
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.................................
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1850 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202015 3 0 0.0 0.0 0 0.0 \n", + "1 202014 3 0 0.0 0.0 0 0.0 \n", + "2 202013 3 0 0.0 0.0 0 0.0 \n", + "3 202012 3 8321 5873.0 10769.0 13 9.0 \n", + "4 202011 3 101704 93652.0 109756.0 154 142.0 \n", + "5 202010 3 104977 96650.0 113304.0 159 146.0 \n", + "6 202009 3 110696 102066.0 119326.0 168 155.0 \n", + "7 202008 3 143753 133984.0 153522.0 218 203.0 \n", + "8 202007 3 183610 172812.0 194408.0 279 263.0 \n", + "9 202006 3 206669 195481.0 217857.0 314 297.0 \n", + "10 202005 3 187957 177445.0 198469.0 285 269.0 \n", + "11 202004 3 122331 113492.0 131170.0 186 173.0 \n", + "12 202003 3 78413 71330.0 85496.0 119 108.0 \n", + "13 202002 3 53614 47654.0 59574.0 81 72.0 \n", + "14 202001 3 36850 31608.0 42092.0 56 48.0 \n", + "15 201952 3 28135 23220.0 33050.0 43 36.0 \n", + "16 201951 3 29786 25042.0 34530.0 45 38.0 \n", + "17 201950 3 34223 29156.0 39290.0 52 44.0 \n", + "18 201949 3 25662 21414.0 29910.0 39 33.0 \n", + "19 201948 3 22367 18055.0 26679.0 34 27.0 \n", + "20 201947 3 18669 14759.0 22579.0 28 22.0 \n", + "21 201946 3 16030 12567.0 19493.0 24 19.0 \n", + "22 201945 3 10138 7160.0 13116.0 15 10.0 \n", + "23 201944 3 7822 5010.0 10634.0 12 8.0 \n", + "24 201943 3 9487 6448.0 12526.0 14 9.0 \n", + "25 201942 3 7747 5243.0 10251.0 12 8.0 \n", + "26 201941 3 7122 4720.0 9524.0 11 7.0 \n", + "27 201940 3 8505 5784.0 11226.0 13 9.0 \n", + "28 201939 3 7091 4462.0 9720.0 11 7.0 \n", + "29 201938 3 4897 2891.0 6903.0 7 4.0 \n", + "... ... ... ... ... ... ... ... \n", + "1820 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1821 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1822 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1823 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1824 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1825 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1826 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1827 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1828 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1829 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1830 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1831 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1832 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1833 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1834 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1835 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1836 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1837 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1838 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1839 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1840 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1841 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1842 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1843 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1844 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1845 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1846 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1847 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1848 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1849 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 0.0 FR France \n", + "1 0.0 FR France \n", + "2 0.0 FR France \n", + "3 17.0 FR France \n", + "4 166.0 FR France \n", + "5 172.0 FR France \n", + "6 181.0 FR France \n", + "7 233.0 FR France \n", + "8 295.0 FR France \n", + "9 331.0 FR France \n", + "10 301.0 FR France \n", + "11 199.0 FR France \n", + "12 130.0 FR France \n", + "13 90.0 FR France \n", + "14 64.0 FR France \n", + "15 50.0 FR France \n", + "16 52.0 FR France \n", + "17 60.0 FR France \n", + "18 45.0 FR France \n", + "19 41.0 FR France \n", + "20 34.0 FR France \n", + "21 29.0 FR France \n", + "22 20.0 FR France \n", + "23 16.0 FR France \n", + "24 19.0 FR France \n", + "25 16.0 FR France \n", + "26 15.0 FR France \n", + "27 17.0 FR France \n", + "28 15.0 FR France \n", + "29 10.0 FR France \n", + "... ... ... ... \n", + "1820 59.0 FR France \n", + "1821 64.0 FR France \n", + "1822 97.0 FR France \n", + "1823 93.0 FR France \n", + "1824 80.0 FR France \n", + "1825 116.0 FR France \n", + "1826 149.0 FR France \n", + "1827 281.0 FR France \n", + "1828 395.0 FR France \n", + "1829 485.0 FR France \n", + "1830 544.0 FR France \n", + "1831 689.0 FR France \n", + "1832 722.0 FR France \n", + "1833 762.0 FR France \n", + "1834 926.0 FR France \n", + "1835 1113.0 FR France \n", + "1836 1236.0 FR France \n", + "1837 832.0 FR France \n", + "1838 459.0 FR France \n", + "1839 207.0 FR France \n", + "1840 190.0 FR France \n", + "1841 198.0 FR France \n", + "1842 224.0 FR France \n", + "1843 266.0 FR France \n", + "1844 219.0 FR France \n", + "1845 176.0 FR France \n", + "1846 163.0 FR France \n", + "1847 195.0 FR France \n", + "1848 308.0 FR France \n", + "1849 213.0 FR France \n", + "\n", + "[1850 rows x 10 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "raw_data = pd.read_csv(data_url, skiprows=1)\n", + "raw_data = pd.read_csv(file_name, skiprows=1)\n", "raw_data" ] }, @@ -78,9 +1048,73 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
161319891930NaNNaN0NaNNaNFRFrance
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
0202015300.00.000.00.0FRFrance
1202014300.00.000.00.0FRFrance
2202013300.00.000.00.0FRFrance
3202012383215873.010769.0139.017.0FRFrance
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5202010310497796650.0113304.0159146.0172.0FRFrance
62020093110696102066.0119326.0168155.0181.0FRFrance
72020083143753133984.0153522.0218203.0233.0FRFrance
82020073183610172812.0194408.0279263.0295.0FRFrance
92020063206669195481.0217857.0314297.0331.0FRFrance
102020053187957177445.0198469.0285269.0301.0FRFrance
112020043122331113492.0131170.0186173.0199.0FRFrance
1220200337841371330.085496.0119108.0130.0FRFrance
1320200235361447654.059574.08172.090.0FRFrance
1420200133685031608.042092.05648.064.0FRFrance
1520195232813523220.033050.04336.050.0FRFrance
1620195132978625042.034530.04538.052.0FRFrance
1720195033422329156.039290.05244.060.0FRFrance
1820194932566221414.029910.03933.045.0FRFrance
1920194832236718055.026679.03427.041.0FRFrance
2020194731866914759.022579.02822.034.0FRFrance
2120194631603012567.019493.02419.029.0FRFrance
222019453101387160.013116.01510.020.0FRFrance
23201944378225010.010634.0128.016.0FRFrance
24201943394876448.012526.0149.019.0FRFrance
25201942377475243.010251.0128.016.0FRFrance
26201941371224720.09524.0117.015.0FRFrance
27201940385055784.011226.0139.017.0FRFrance
28201939370914462.09720.0117.015.0FRFrance
29201938348972891.06903.074.010.0FRFrance
.................................
182019852132609619621.032571.04735.059.0FRFrance
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18301985113276205252399.0300011.0501458.0544.0FRFrance
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1849 rows × 10 columns

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78620 60634.0 96606.0 143 110.0 \n", + "1846 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1847 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1848 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1849 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 0.0 FR France \n", + "1 0.0 FR France \n", + "2 0.0 FR France \n", + "3 17.0 FR France \n", + "4 166.0 FR France \n", + "5 172.0 FR France \n", + "6 181.0 FR France \n", + "7 233.0 FR France \n", + "8 295.0 FR France \n", + "9 331.0 FR France \n", + "10 301.0 FR France \n", + "11 199.0 FR France \n", + "12 130.0 FR France \n", + "13 90.0 FR France \n", + "14 64.0 FR France \n", + "15 50.0 FR France \n", + "16 52.0 FR France \n", + "17 60.0 FR France \n", + "18 45.0 FR France \n", + "19 41.0 FR France \n", + "20 34.0 FR France \n", + "21 29.0 FR France \n", + "22 20.0 FR France \n", + "23 16.0 FR France \n", + "24 19.0 FR France \n", + "25 16.0 FR France \n", + "26 15.0 FR France \n", + "27 17.0 FR France \n", + "28 15.0 FR France \n", + "29 10.0 FR France \n", + "... ... ... ... \n", + "1820 59.0 FR France \n", + "1821 64.0 FR France \n", + "1822 97.0 FR France \n", + "1823 93.0 FR France \n", + "1824 80.0 FR France \n", + "1825 116.0 FR France \n", + "1826 149.0 FR France \n", + "1827 281.0 FR France \n", + "1828 395.0 FR France \n", + "1829 485.0 FR France \n", + "1830 544.0 FR France \n", + "1831 689.0 FR France \n", + "1832 722.0 FR France \n", + "1833 762.0 FR France \n", + "1834 926.0 FR France \n", + "1835 1113.0 FR France \n", + "1836 1236.0 FR France \n", + "1837 832.0 FR France \n", + "1838 459.0 FR France \n", + "1839 207.0 FR France \n", + "1840 190.0 FR France \n", + "1841 198.0 FR France \n", + "1842 224.0 FR France \n", + "1843 266.0 FR France \n", + "1844 219.0 FR France \n", + "1845 176.0 FR France \n", + "1846 163.0 FR France \n", + "1847 195.0 FR France \n", + "1848 308.0 FR France \n", + "1849 213.0 FR France \n", + "\n", + "[1849 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2123,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2153,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,9 +2178,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "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 +2206,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "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 +2388,54 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "2014 1600941\n", + "1991 1659249\n", + "1995 1840410\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": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2450,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] @@ -341,9 +2483,7 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -364,7 +2504,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/incidence-PAY-3.csv b/module3/exo1/incidence-PAY-3.csv new file mode 100644 index 0000000..1398d38 --- /dev/null +++ b/module3/exo1/incidence-PAY-3.csv @@ -0,0 +1,1852 @@ +# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202015],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2020-04-23T16:15:17+02:00 +week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name 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