From ec9423100d9e4a23a72b7658047791a2cf68a47d Mon Sep 17 00:00:00 2001 From: 3aafea210314d192989b2a83c1ab7239 <3aafea210314d192989b2a83c1ab7239@app-learninglab.inria.fr> Date: Sat, 15 Jan 2022 11:33:25 +0000 Subject: [PATCH] update data --- module2/exo5/exo5_fr.ipynb | 236 ++- module3/exo1/analyse-syndrome-grippal.ipynb | 2025 ++++++++++++++++++- 2 files changed, 2192 insertions(+), 69 deletions(-) diff --git a/module2/exo5/exo5_fr.ipynb b/module2/exo5/exo5_fr.ipynb index 26ad6d9..d15068c 100644 --- a/module2/exo5/exo5_fr.ipynb +++ b/module2/exo5/exo5_fr.ipynb @@ -40,7 +40,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -261,33 +261,33 @@ "" ], "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 7/29/85 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" + " 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 7/29/85 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": 1, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" } @@ -322,7 +322,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -425,7 +425,7 @@ "22 1/12/86 6 58 200 1" ] }, - "execution_count": 2, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -448,12 +448,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 44, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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CountTemperaturePressureMalfunctionFrequency
count23.023.00000023.00000023.00000023.000000
mean6.069.565217152.1739130.3913040.065217
std0.07.05708068.2213320.6563760.109396
min6.053.00000050.0000000.0000000.000000
25%6.067.00000075.0000000.0000000.000000
50%6.070.000000200.0000000.0000000.000000
75%6.075.000000200.0000001.0000000.166667
max6.081.000000200.0000002.0000000.333333
\n", + "
" + ], + "text/plain": [ + " Count Temperature Pressure Malfunction Frequency\n", + "count 23.0 23.000000 23.000000 23.000000 23.000000\n", + "mean 6.0 69.565217 152.173913 0.391304 0.065217\n", + "std 0.0 7.057080 68.221332 0.656376 0.109396\n", + "min 6.0 53.000000 50.000000 0.000000 0.000000\n", + "25% 6.0 67.000000 75.000000 0.000000 0.000000\n", + "50% 6.0 70.000000 200.000000 0.000000 0.000000\n", + "75% 6.0 75.000000 200.000000 1.000000 0.166667\n", + "max 6.0 81.000000 200.000000 2.000000 0.333333" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.describe()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -500,7 +623,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -509,10 +632,10 @@ "\n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", " \n", @@ -521,16 +644,16 @@ " \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: 7Dep. Variable: Frequency No. Observations: 23
Model: GLM Df Residuals: 5Model: GLM Df Residuals: 21
Model Family: Binomial Df Model: 1Link Function: logit Scale: 1.0000
Method: IRLS Log-Likelihood: -2.5250Method: IRLS Log-Likelihood: -3.9210
Date: Sat, 13 Apr 2019 Deviance: 0.22231Date: Fri, 14 Jan 2022 Deviance: 3.0144
Time: 19:11:24 Pearson chi2: 0.236Time: 19:16:48 Pearson chi2: 5.00
No. Iterations: 4 Covariance Type: nonrobustNo. Iterations: 6 Covariance Type: nonrobust
\n", "\n", @@ -538,10 +661,10 @@ " \n", "\n", "\n", - " \n", + " \n", "\n", "\n", - " \n", + " \n", "\n", "
coef std err z P>|z| [0.025 0.975]
Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740
Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110
" ], @@ -550,24 +673,24 @@ "\"\"\"\n", " Generalized Linear Model Regression Results \n", "==============================================================================\n", - "Dep. Variable: Frequency No. Observations: 7\n", - "Model: GLM Df Residuals: 5\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: -2.5250\n", - "Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", - "Time: 19:11:24 Pearson chi2: 0.236\n", - "No. Iterations: 4 Covariance Type: nonrobust\n", + "Method: IRLS Log-Likelihood: -3.9210\n", + "Date: Fri, 14 Jan 2022 Deviance: 3.0144\n", + "Time: 19:16:48 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 -1.3895 7.828 -0.178 0.859 -16.732 13.953\n", - "Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\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, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -605,12 +728,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { - "image/png": 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5KgqSDz7YzSHPPLnf9RSHS1tfE2awbHy57KYS1ErUSNTUQI1ErYQENTUdt0WNoLZmb7u9j8nuqylaRo0K5teI2hoxvOPxUr4sAFFbky1b+bq7PK5omWv68HSUFfSSLgC+B9QCd0fEXxbd/3VgPtAKvAH8SUT8qg/19Nk+r/qljgAKjhpKvcruEzzFO0vnEUL2uP0dtaz/TSvvvbRjnzCIotvdBl3Rq3rbfu7b35HBPvOLQ6rLkcq+Ry7dHYE1N3/AyFVPdd7X2ZcSwVNYb9fneyD/Qnpp3UsH9PDC8FBhQOThURwEtTXZziyU7/D7Pr62RojC8MkeO6y2hkOG7Q2cjmDqWEbn8vL17vztbzn6qCP3CaWOdvvczutUPr+wPqlrqGX9UNewLFFDTan11AhREKI1RcuVgL3PRZdw7QjfGvHsM88we/YZBbUL1RQ9FwXPrfLlVotb+vCYHoNeUi1wB3Ae0ASskrQsIjYWNFsDnBoRH0j6r8BfAVfsb7nbm9s596//uXQQlArtff716Ro8Q9Lavrz2ltbxh93lFb9oR+g86ug4AqnpLmz2hkoWGnuPHrJ1lA6O2jwUJHizpoWjfv/wLjt58Tpqa9Sl9pJHPwV9KWyX9TN7LHmfawtrrum6jNqa7LkR+4ZNjYAuobdvUBTWvnrV85x+2qx9gqXrkV/R9ig6EhzK4dHY2EhDw8mDXUa/+djIGj4+ZuRglzGklHNEPxPYGhGvAkh6ELgY6Az6iHiqoP2zwBd6WujwGlH/e2M6d8CSQdBNKOzdWfNQoGvoFR/xFP6bpOLbRUc1xUddxQGyN/T2hlvx0UiNxIurVzNr1oyCYN7fkR37HAWpKJiGmiwspg12Gf2iaXQNnxg3erDLMKuYcoJ+AvB6wXQTMGs/7b8E/GOpOyQtABYAjB8/nssmvFdmmX0Q+U8FteU/5RirD2ja+EJlCxhCmpubaWxsHOwy+kXKfQP372BUTtCXOpwsGaGSvgCcCvxhqfsjYjGwGKC+vj4aGhrKq7IKZUe8DYNdRr9JuX8p9w3cv4NROUHfBBxTMD0R2FHcSNK5wP8E/jAiPqxMeWZmdqDK+cDUKmCKpMmSRgBXAssKG0iaBtwFzI2InZUv08zM+qrHoI+IVuBaYAWwCVgSERsk3Sxpbt5sEXAo8BNJayUt62ZxZmY2wMq6jj4ilgPLi+Z9q+D2uRWuy6zXlq7ZzqIVm9nxTgtHH17Hwjn1APvMu2TahAGtoT/X1xvfXLqeB557na+dsIcv3bCcebOO4ZZLThzssmwA+JOxloSla7ZzwyPradmTXRe1/Z0WFv7kJRDsaYvOeTc8sh6gX8K3VA39ub7e+ObS9fzw2dc6p9siOqcd9unzoGaWhEUrNncGbIc97dEZ8h1a9rSxaMXmAauhP9fXGw8893qv5ltaHPSWhB3vtPRL20rU0F/r6422bsab6G6+pcVBb0k4+vC6fmlbiRr6a329UdvNp6u7m29pcdBbEhbOqadueG2XecNrxPDarkFWN7y2803agaihP9fXG/NmHdOr+ZYWvxlrSeh4s3Mwr7rprobBfiMW9r7h2nFOvlbyVTcHEQe9JeOSaRNKhupABm13NQwFt1xyIrdcciKNjY3821UNg12ODSCfujEzS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBLnoDczS5yD3swscQ56M7PEOejNzBJXVtBLukDSZklbJX2jxP2HSHoov/85SZMqXaiZmfVNj0EvqRa4A7gQOB6YJ+n4omZfAt6OiH8HfBf4X5Uu1MzM+qacI/qZwNaIeDUiPgIeBC4uanMx8A/57YeBz0hS5co0M7O+GlZGmwnA6wXTTcCs7tpERKukd4FxwJuFjSQtABbkkx9KerkvRVeJIynqf2JS7l/KfQP3r9rV9/YB5QR9qSPz6EMbImIxsBhA0uqIOLWM9Vcl9696pdw3cP+qnaTVvX1MOadumoBjCqYnAju6ayNpGDAW+F1vizEzs8orJ+hXAVMkTZY0ArgSWFbUZhnwx/ntS4F/ioh9jujNzGzg9XjqJj/nfi2wAqgF7o2IDZJuBlZHxDLgHuB+SVvJjuSvLGPdiw+g7mrg/lWvlPsG7l+163X/5ANvM7O0+ZOxZmaJc9CbmSVuQIJe0khJz0t6SdIGSd/O50/Oh0zYkg+hMGIg6ukPkmolrZH0s3w6pb5tk7Re0tqOS7skHSHp8bx/j0v62GDX2VeSDpf0sKT/J2mTpNNT6Z+k+ny7dfy8J+lrCfXvv+WZ8rKkB/KsSWnf+/O8bxskfS2f1+ttN1BH9B8C50TEycBU4AJJp5ENlfDdiJgCvE02lEK1+nNgU8F0Sn0DODsiphZcn/wN4Mm8f0/m09Xqe8DPI+I44GSy7ZhE/yJic77dpgKnAB8Aj5JA/yRNAK4DTo2IE8guFrmSRPY9SScAXyYbneBk4HOSptCXbRcRA/oDjAJeJPt07ZvAsHz+6cCKga6nQn2amD/h5wA/I/sAWRJ9y+vfBhxZNG8zcFR++yhg82DX2ce+HQb8kvzChNT6V9Sn84GnU+kfez+RfwTZFYQ/A+aksu8BlwF3F0z/BfA/+rLtBuwcfX5qYy2wE3gc+DfgnYhozZs0kW24anQ72QZoz6fHkU7fIPuU80pJL+TDWAD8XkT8GiD//fFBq+7AfBJ4A/j7/NTb3ZJGk07/Cl0JPJDfrvr+RcR24DbgNeDXwLvAC6Sz770MnCVpnKRRwGfJPpja6203YEEfEW2R/fs4kexfkU+XajZQ9VSKpM8BOyPihcLZJZpWXd8KzI6I6WQjmP6ZpLMGu6AKGgZMB+6MiGnA+1ThaYye5Oep5wI/GexaKiU/N30xMBk4GhhN9jdarCr3vYjYRHYa6nHg58BLQOt+H9SNAb/qJiLeARqB04DD8yEToPTQCtVgNjBX0jaykT3PITvCT6FvAETEjvz3TrLzuzOB30o6CiD/vXPwKjwgTUBTRDyXTz9MFvyp9K/DhcCLEfHbfDqF/p0L/DIi3oiIPcAjwBmkte/dExHTI+Issg+jbqEP226grroZL+nw/HYd2QbaBDxFNmQCZEMoPDYQ9VRSRNwQERMjYhLZv8b/FBFXkUDfACSNljSm4zbZed6X6TrsRdX2LyJ+A7wuqWNEwM8AG0mkfwXmsfe0DaTRv9eA0ySNyodF79h2Sex7AJI+nv8+FviPZNuw19tuQD4ZK+kksvHqa8leXJZExM2SPkl2FHwEsAb4QkR82O8F9RNJDcD1EfG5VPqW9+PRfHIY8OOIuFXSOGAJcCzZDndZRFTlQHaSpgJ3AyOAV4Evkv+dkkb/RpG9afnJiHg3n5fE9ssv1b6C7JTGGmA+2Tn5qt/3ACT9C9l7fnuAr0fEk33Zdh4Cwcwscf5krJlZ4hz0ZmaJc9CbmSXOQW9mljgHvZlZ4sr5cnCzAZVfPvZkPvn7QBvZMAUAMyPio0EpbD8k/QmwPL8u32xI8eWVNqRJuglojojbhkAttRHR1s19/wpcGxFre7G8YQVjspj1G5+6saoi6Y+VfbfBWkl/I6lG0jBJ70haJOlFSSskzZL0z5JelfTZ/LHzJT2a379Z0jfLXO4tkp4HZkr6tqRV+Rjhf6vMFWTDbz+UP36EpKaCT4OfJumJ/PYtku6S9DjZQGrDJP11vu51kuYP/LNqqXPQW9XIx+f+PHBGPkDeMPZ+Ef1YYGU++NpHwE1kH4m/DLi5YDEz88dMB/6zpKllLPfFiJgZEc8A34uIGcCJ+X0XRMRDwFrgisjGfu/p1NI04KKI+CNgAdmgeDOBGWSDxh3bl+fHrDs+R2/V5FyyMFydDW1CHdlH+wFaIuLx/PZ64N2IaJW0HphUsIwVEfE2gKSlwH8g2w+6W+5H7B0CAuAzkhYCI4EjyYbF/cde9uOxiNid3z4f+LSkwheWKWQfbTerCAe9VRMB90bEX3SZmY1UWHgU3U72rWYdtwv/zovflIoeltsS+RtZ+Zgx/xeYHhHbJd1CFviltLL3P+biNu8X9elPI+JJzPqJT91YNXkCuFzSkZBdndOH0xznK/uO2FFkY5k/3Yvl1pG9cLyZj+j5nwru2wWMKZjeRvbVfRS1K7YC+NOOYXWVfcdrXS/7ZLZfPqK3qhER6/PRCp+QVEM2ot9/oXfjjf8r8GPgD4D7O66SKWe5EfGWpH8gG6b5V8BzBXf/PXC3pBay9wFuAv5O0m+A5/dTz11koxCuzU8b7SR7ATKrGF9eaQeN/IqWEyLia4Ndi9lA8qkbM7PE+YjezCxxPqI3M0ucg97MLHEOejOzxDnozcwS56A3M0vc/wcowwoTqhaBUgAAAABJRU5ErkJggg==\n", 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" ] @@ -623,7 +746,7 @@ ], "source": [ "%matplotlib inline\n", - "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n", + "data_pred = pd.DataFrame({'Temperature': np.linspace(start=0, stop=90, num=121), 'Intercept': 1})\n", "data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n", "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", @@ -648,20 +771,23 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.06521739130434782\n" + "0.06521739130434782\n", + "[1.]\n" ] } ], "source": [ "data = pd.read_csv(\"shuttle.csv\")\n", - "print(np.sum(data.Malfunction)/np.sum(data.Count))" + "print(np.sum(data.Malfunction)/np.sum(data.Count))\n", + "freq = logmodel.predict([5,31])\n", + "print(freq)" ] }, { @@ -705,7 +831,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..5855a3c 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": 1, "metadata": {}, "outputs": [], "source": [ @@ -28,10 +28,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" @@ -61,12 +59,980 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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220215134169835359.048037.06353.073.0FRFrance
320215033811732497.043737.05849.067.0FRFrance
420214934016834716.045620.06153.069.0FRFrance
520214834184236364.047320.06355.071.0FRFrance
620214733659831338.041858.05547.063.0FRFrance
720214633005925302.034816.04639.053.0FRFrance
820214532036416564.024164.03125.037.0FRFrance
920214431899915042.022956.02923.035.0FRFrance
1020214332704021935.032145.04133.049.0FRFrance
1120214232834323382.033304.04335.051.0FRFrance
1220214132504320586.029500.03831.045.0FRFrance
1320214032628621842.030730.04033.047.0FRFrance
1420213932215518014.026296.03428.040.0FRFrance
1520213831561412310.018918.02419.029.0FRFrance
1620213731367310404.016942.02116.026.0FRFrance
172021363102897505.013073.01612.020.0FRFrance
182021353126099282.015936.01914.024.0FRFrance
192021343130159485.016545.02015.025.0FRFrance
202021333103927042.013742.01611.021.0FRFrance
2120213231558611009.020163.02417.031.0FRFrance
2220213131885513664.024046.02921.037.0FRFrance
232021303139919695.018287.02114.028.0FRFrance
242021293136269618.017634.02115.027.0FRFrance
25202128386365430.011842.0138.018.0FRFrance
262021273106936838.014548.01610.022.0FRFrance
27202126370864109.010063.0116.016.0FRFrance
28202125379425540.010344.0128.016.0FRFrance
29202124348553011.06699.074.010.0FRFrance
.................................
191119852132609619621.032571.04735.059.0FRFrance
191219852032789620885.034907.05138.064.0FRFrance
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191419851834055529935.051175.07455.093.0FRFrance
191519851733405324366.043740.06244.080.0FRFrance
191619851635036236451.064273.09166.0116.0FRFrance
191719851536388145538.082224.011683.0149.0FRFrance
19181985143134545114400.0154690.0244207.0281.0FRFrance
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19241985083389886359529.0420243.0707652.0762.0FRFrance
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193219845238483060602.0109058.0154110.0198.0FRFrance
1933198451310172680242.0123210.0185146.0224.0FRFrance
19341984503123680101401.0145959.0225184.0266.0FRFrance
1935198449310107381684.0120462.0184149.0219.0FRFrance
193619844837862060634.096606.0143110.0176.0FRFrance
193719844737202954274.089784.013199.0163.0FRFrance
193819844638733067686.0106974.0159123.0195.0FRFrance
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1941 rows × 10 columns

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9282.0 15936.0 19 14.0 \n", + "19 202134 3 13015 9485.0 16545.0 20 15.0 \n", + "20 202133 3 10392 7042.0 13742.0 16 11.0 \n", + "21 202132 3 15586 11009.0 20163.0 24 17.0 \n", + "22 202131 3 18855 13664.0 24046.0 29 21.0 \n", + "23 202130 3 13991 9695.0 18287.0 21 14.0 \n", + "24 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "25 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "26 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "27 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "28 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "29 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "... ... ... ... ... ... ... ... \n", + "1911 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1912 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1913 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1914 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1915 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1916 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1917 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1918 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1919 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1920 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1921 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1922 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1923 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1924 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1925 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1926 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1927 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1928 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1929 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1930 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1931 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1932 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1933 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1934 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1935 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1936 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1937 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1938 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1939 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1940 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 117.0 FR France \n", + "1 91.0 FR France \n", + "2 73.0 FR France \n", + "3 67.0 FR France \n", + "4 69.0 FR France \n", + "5 71.0 FR France \n", + "6 63.0 FR France \n", + "7 53.0 FR France \n", + "8 37.0 FR France \n", + "9 35.0 FR France \n", + "10 49.0 FR France \n", + "11 51.0 FR France \n", + "12 45.0 FR France \n", + "13 47.0 FR France \n", + "14 40.0 FR France \n", + "15 29.0 FR France \n", + "16 26.0 FR France \n", + "17 20.0 FR France \n", + "18 24.0 FR France \n", + "19 25.0 FR France \n", + "20 21.0 FR France \n", + "21 31.0 FR France \n", + "22 37.0 FR France \n", + "23 28.0 FR France \n", + "24 27.0 FR France \n", + "25 18.0 FR France \n", + "26 22.0 FR France \n", + "27 16.0 FR France \n", + "28 16.0 FR France \n", + "29 10.0 FR France \n", + "... ... ... ... \n", + "1911 59.0 FR France \n", + "1912 64.0 FR France \n", + "1913 97.0 FR France \n", + "1914 93.0 FR France \n", + "1915 80.0 FR France \n", + "1916 116.0 FR France \n", + "1917 149.0 FR France \n", + "1918 281.0 FR France \n", + "1919 395.0 FR France \n", + "1920 485.0 FR France \n", + "1921 544.0 FR France \n", + "1922 689.0 FR France \n", + "1923 722.0 FR France \n", + "1924 762.0 FR France \n", + "1925 926.0 FR France \n", + "1926 1113.0 FR France \n", + "1927 1236.0 FR France \n", + "1928 832.0 FR France \n", + "1929 459.0 FR France \n", + "1930 207.0 FR France \n", + "1931 190.0 FR France \n", + "1932 198.0 FR France \n", + "1933 224.0 FR France \n", + "1934 266.0 FR France \n", + "1935 219.0 FR France \n", + "1936 176.0 FR France \n", + "1937 163.0 FR France \n", + "1938 195.0 FR France \n", + "1939 308.0 FR France \n", + "1940 213.0 FR France \n", + "\n", + "[1941 rows x 10 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=1)\n", - "raw_data" + "raw_data_copy = raw_data.copy(deep=True)\n", + "raw_data_copy" ] }, { @@ -78,11 +1044,75 @@ }, { "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
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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320215033811732497.043737.05849.067.0FRFrance
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520214834184236364.047320.06355.071.0FRFrance
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920214431899915042.022956.02923.035.0FRFrance
1020214332704021935.032145.04133.049.0FRFrance
1120214232834323382.033304.04335.051.0FRFrance
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182021353126099282.015936.01914.024.0FRFrance
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202021333103927042.013742.01611.021.0FRFrance
2120213231558611009.020163.02417.031.0FRFrance
2220213131885513664.024046.02921.037.0FRFrance
232021303139919695.018287.02114.028.0FRFrance
242021293136269618.017634.02115.027.0FRFrance
25202128386365430.011842.0138.018.0FRFrance
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27202126370864109.010063.0116.016.0FRFrance
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.................................
191119852132609619621.032571.04735.059.0FRFrance
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1940 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202201 3 67182 56533.0 77831.0 101 85.0 \n", + "1 202152 3 53141 45865.0 60417.0 80 69.0 \n", + "2 202151 3 41698 35359.0 48037.0 63 53.0 \n", + "3 202150 3 38117 32497.0 43737.0 58 49.0 \n", + "4 202149 3 40168 34716.0 45620.0 61 53.0 \n", + "5 202148 3 41842 36364.0 47320.0 63 55.0 \n", + "6 202147 3 36598 31338.0 41858.0 55 47.0 \n", + "7 202146 3 30059 25302.0 34816.0 46 39.0 \n", + "8 202145 3 20364 16564.0 24164.0 31 25.0 \n", + "9 202144 3 18999 15042.0 22956.0 29 23.0 \n", + "10 202143 3 27040 21935.0 32145.0 41 33.0 \n", + "11 202142 3 28343 23382.0 33304.0 43 35.0 \n", + "12 202141 3 25043 20586.0 29500.0 38 31.0 \n", + "13 202140 3 26286 21842.0 30730.0 40 33.0 \n", + "14 202139 3 22155 18014.0 26296.0 34 28.0 \n", + "15 202138 3 15614 12310.0 18918.0 24 19.0 \n", + "16 202137 3 13673 10404.0 16942.0 21 16.0 \n", + "17 202136 3 10289 7505.0 13073.0 16 12.0 \n", + "18 202135 3 12609 9282.0 15936.0 19 14.0 \n", + "19 202134 3 13015 9485.0 16545.0 20 15.0 \n", + "20 202133 3 10392 7042.0 13742.0 16 11.0 \n", + "21 202132 3 15586 11009.0 20163.0 24 17.0 \n", + "22 202131 3 18855 13664.0 24046.0 29 21.0 \n", + "23 202130 3 13991 9695.0 18287.0 21 14.0 \n", + "24 202129 3 13626 9618.0 17634.0 21 15.0 \n", + "25 202128 3 8636 5430.0 11842.0 13 8.0 \n", + "26 202127 3 10693 6838.0 14548.0 16 10.0 \n", + "27 202126 3 7086 4109.0 10063.0 11 6.0 \n", + "28 202125 3 7942 5540.0 10344.0 12 8.0 \n", + "29 202124 3 4855 3011.0 6699.0 7 4.0 \n", + "... ... ... ... ... ... ... ... \n", + "1911 198521 3 26096 19621.0 32571.0 47 35.0 \n", + "1912 198520 3 27896 20885.0 34907.0 51 38.0 \n", + "1913 198519 3 43154 32821.0 53487.0 78 59.0 \n", + "1914 198518 3 40555 29935.0 51175.0 74 55.0 \n", + "1915 198517 3 34053 24366.0 43740.0 62 44.0 \n", + "1916 198516 3 50362 36451.0 64273.0 91 66.0 \n", + "1917 198515 3 63881 45538.0 82224.0 116 83.0 \n", + "1918 198514 3 134545 114400.0 154690.0 244 207.0 \n", + "1919 198513 3 197206 176080.0 218332.0 357 319.0 \n", + "1920 198512 3 245240 223304.0 267176.0 445 405.0 \n", + "1921 198511 3 276205 252399.0 300011.0 501 458.0 \n", + "1922 198510 3 353231 326279.0 380183.0 640 591.0 \n", + "1923 198509 3 369895 341109.0 398681.0 670 618.0 \n", + "1924 198508 3 389886 359529.0 420243.0 707 652.0 \n", + "1925 198507 3 471852 432599.0 511105.0 855 784.0 \n", + "1926 198506 3 565825 518011.0 613639.0 1026 939.0 \n", + "1927 198505 3 637302 592795.0 681809.0 1155 1074.0 \n", + "1928 198504 3 424937 390794.0 459080.0 770 708.0 \n", + "1929 198503 3 213901 174689.0 253113.0 388 317.0 \n", + "1930 198502 3 97586 80949.0 114223.0 177 147.0 \n", + "1931 198501 3 85489 65918.0 105060.0 155 120.0 \n", + "1932 198452 3 84830 60602.0 109058.0 154 110.0 \n", + "1933 198451 3 101726 80242.0 123210.0 185 146.0 \n", + "1934 198450 3 123680 101401.0 145959.0 225 184.0 \n", + "1935 198449 3 101073 81684.0 120462.0 184 149.0 \n", + "1936 198448 3 78620 60634.0 96606.0 143 110.0 \n", + "1937 198447 3 72029 54274.0 89784.0 131 99.0 \n", + "1938 198446 3 87330 67686.0 106974.0 159 123.0 \n", + "1939 198445 3 135223 101414.0 169032.0 246 184.0 \n", + "1940 198444 3 68422 20056.0 116788.0 125 37.0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 117.0 FR France \n", + "1 91.0 FR France \n", + "2 73.0 FR France \n", + "3 67.0 FR France \n", + "4 69.0 FR France \n", + "5 71.0 FR France \n", + "6 63.0 FR France \n", + "7 53.0 FR France \n", + "8 37.0 FR France \n", + "9 35.0 FR France \n", + "10 49.0 FR France \n", + "11 51.0 FR France \n", + "12 45.0 FR France \n", + "13 47.0 FR France \n", + "14 40.0 FR France \n", + "15 29.0 FR France \n", + "16 26.0 FR France \n", + "17 20.0 FR France \n", + "18 24.0 FR France \n", + "19 25.0 FR France \n", + "20 21.0 FR France \n", + "21 31.0 FR France \n", + "22 37.0 FR France \n", + "23 28.0 FR France \n", + "24 27.0 FR France \n", + "25 18.0 FR France \n", + "26 22.0 FR France \n", + "27 16.0 FR France \n", + "28 16.0 FR France \n", + "29 10.0 FR France \n", + "... ... ... ... \n", + "1911 59.0 FR France \n", + "1912 64.0 FR France \n", + "1913 97.0 FR France \n", + "1914 93.0 FR France \n", + "1915 80.0 FR France \n", + "1916 116.0 FR France \n", + "1917 149.0 FR France \n", + "1918 281.0 FR France \n", + "1919 395.0 FR France \n", + "1920 485.0 FR France \n", + "1921 544.0 FR France \n", + "1922 689.0 FR France \n", + "1923 722.0 FR France \n", + "1924 762.0 FR France \n", + "1925 926.0 FR France \n", + "1926 1113.0 FR France \n", + "1927 1236.0 FR France \n", + "1928 832.0 FR France \n", + "1929 459.0 FR France \n", + "1930 207.0 FR France \n", + "1931 190.0 FR France \n", + "1932 198.0 FR France \n", + "1933 224.0 FR France \n", + "1934 266.0 FR France \n", + "1935 219.0 FR France \n", + "1936 176.0 FR France \n", + "1937 163.0 FR France \n", + "1938 195.0 FR France \n", + "1939 308.0 FR France \n", + "1940 213.0 FR France \n", + "\n", + "[1940 rows x 10 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -364,7 +2361,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, -- 2.18.1