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Commit ec942310 authored by 3aafea210314d192989b2a83c1ab7239's avatar 3aafea210314d192989b2a83c1ab7239
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Showing with 2192 additions and 69 deletions
module2/exo5/exo5_fr.ipynb
View file @ ec942310
...@@ -40,7 +40,7 @@ ...@@ -40,7 +40,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 43,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -261,33 +261,33 @@ ...@@ -261,33 +261,33 @@
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" Date Count Temperature Pressure Malfunction\n", " Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n", "0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n", "1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n", "2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n", "3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n", "4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n", "5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n", "6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n", "7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n", "8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n", "9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n", "10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n", "11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n", "12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n", "13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n", "14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n", "15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n", "16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n", "17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n", "18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n", "19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n", "20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n", "21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 1, "execution_count": 43,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -322,7 +322,7 @@ ...@@ -322,7 +322,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -425,7 +425,7 @@ ...@@ -425,7 +425,7 @@
"22 1/12/86 6 58 200 1" "22 1/12/86 6 58 200 1"
] ]
}, },
"execution_count": 2, "execution_count": 37,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -448,12 +448,12 @@ ...@@ -448,12 +448,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 44,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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\n", "image/png": 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -474,6 +474,129 @@ ...@@ -474,6 +474,129 @@
"plt.grid(True)" "plt.grid(True)"
] ]
}, },
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Count</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
" <th>Frequency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>23.0</td>\n",
" <td>23.000000</td>\n",
" <td>23.000000</td>\n",
" <td>23.000000</td>\n",
" <td>23.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>6.0</td>\n",
" <td>69.565217</td>\n",
" <td>152.173913</td>\n",
" <td>0.391304</td>\n",
" <td>0.065217</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.0</td>\n",
" <td>7.057080</td>\n",
" <td>68.221332</td>\n",
" <td>0.656376</td>\n",
" <td>0.109396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>6.0</td>\n",
" <td>53.000000</td>\n",
" <td>50.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>6.0</td>\n",
" <td>67.000000</td>\n",
" <td>75.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>6.0</td>\n",
" <td>70.000000</td>\n",
" <td>200.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>6.0</td>\n",
" <td>75.000000</td>\n",
" <td>200.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>6.0</td>\n",
" <td>81.000000</td>\n",
" <td>200.000000</td>\n",
" <td>2.000000</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
...@@ -500,7 +623,7 @@ ...@@ -500,7 +623,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 46,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -509,10 +632,10 @@ ...@@ -509,10 +632,10 @@
"<table class=\"simpletable\">\n", "<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n", "<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n", "<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n", " <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 23</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n", " <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 21</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n", " <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
...@@ -521,16 +644,16 @@ ...@@ -521,16 +644,16 @@
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n", " <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \n", " <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -3.9210</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Date:</th> <td>Sat, 13 Apr 2019</td> <th> Deviance: </th> <td> 0.22231</td> \n", " <th>Date:</th> <td>Fri, 14 Jan 2022</td> <th> Deviance: </th> <td> 3.0144</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Time:</th> <td>19:11:24</td> <th> Pearson chi2: </th> <td> 0.236</td> \n", " <th>Time:</th> <td>19:16:48</td> <th> Pearson chi2: </th> <td> 5.00</td> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n", " <th>No. Iterations:</th> <td>6</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n", "</tr>\n",
"</table>\n", "</table>\n",
"<table class=\"simpletable\">\n", "<table class=\"simpletable\">\n",
...@@ -538,10 +661,10 @@ ...@@ -538,10 +661,10 @@
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n", " <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Intercept</th> <td> -1.3895</td> <td> 7.828</td> <td> -0.178</td> <td> 0.859</td> <td> -16.732</td> <td> 13.953</td>\n", " <th>Intercept</th> <td> 5.0850</td> <td> 7.477</td> <td> 0.680</td> <td> 0.496</td> <td> -9.570</td> <td> 19.740</td>\n",
"</tr>\n", "</tr>\n",
"<tr>\n", "<tr>\n",
" <th>Temperature</th> <td> 0.0014</td> <td> 0.122</td> <td> 0.012</td> <td> 0.991</td> <td> -0.238</td> <td> 0.240</td>\n", " <th>Temperature</th> <td> -0.1156</td> <td> 0.115</td> <td> -1.004</td> <td> 0.316</td> <td> -0.341</td> <td> 0.110</td>\n",
"</tr>\n", "</tr>\n",
"</table>" "</table>"
], ],
...@@ -550,24 +673,24 @@ ...@@ -550,24 +673,24 @@
"\"\"\"\n", "\"\"\"\n",
" Generalized Linear Model Regression Results \n", " Generalized Linear Model Regression Results \n",
"==============================================================================\n", "==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n", "Dep. Variable: Frequency No. Observations: 23\n",
"Model: GLM Df Residuals: 5\n", "Model: GLM Df Residuals: 21\n",
"Model Family: Binomial Df Model: 1\n", "Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n", "Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.5250\n", "Method: IRLS Log-Likelihood: -3.9210\n",
"Date: Sat, 13 Apr 2019 Deviance: 0.22231\n", "Date: Fri, 14 Jan 2022 Deviance: 3.0144\n",
"Time: 19:11:24 Pearson chi2: 0.236\n", "Time: 19:16:48 Pearson chi2: 5.00\n",
"No. Iterations: 4 Covariance Type: nonrobust\n", "No. Iterations: 6 Covariance Type: nonrobust\n",
"===============================================================================\n", "===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n", " coef std err z P>|z| [0.025 0.975]\n",
"-------------------------------------------------------------------------------\n", "-------------------------------------------------------------------------------\n",
"Intercept -1.3895 7.828 -0.178 0.859 -16.732 13.953\n", "Intercept 5.0850 7.477 0.680 0.496 -9.570 19.740\n",
"Temperature 0.0014 0.122 0.012 0.991 -0.238 0.240\n", "Temperature -0.1156 0.115 -1.004 0.316 -0.341 0.110\n",
"===============================================================================\n", "===============================================================================\n",
"\"\"\"" "\"\"\""
] ]
}, },
"execution_count": 4, "execution_count": 46,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -605,12 +728,12 @@ ...@@ -605,12 +728,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 47,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
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\n", 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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -623,7 +746,7 @@ ...@@ -623,7 +746,7 @@
], ],
"source": [ "source": [
"%matplotlib inline\n", "%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['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n",
"data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n", "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
"plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n", "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
...@@ -648,20 +771,23 @@ ...@@ -648,20 +771,23 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 50,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"0.06521739130434782\n" "0.06521739130434782\n",
"[1.]\n"
] ]
} }
], ],
"source": [ "source": [
"data = pd.read_csv(\"shuttle.csv\")\n", "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 @@ ...@@ -705,7 +831,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
module3/exo1/analyse-syndrome-grippal.ipynb
View file @ ec942310
...@@ -9,7 +9,7 @@ ...@@ -9,7 +9,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -28,10 +28,8 @@ ...@@ -28,10 +28,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 2,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [], "outputs": [],
"source": [ "source": [
"data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
...@@ -61,12 +59,980 @@ ...@@ -61,12 +59,980 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 6,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
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" <th>26</th>\n",
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" <td>14548.0</td>\n",
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" <td>10.0</td>\n",
" <td>22.0</td>\n",
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" <th>27</th>\n",
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" <td>7086</td>\n",
" <td>4109.0</td>\n",
" <td>10063.0</td>\n",
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" <th>28</th>\n",
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" <td>7942</td>\n",
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" <td>10344.0</td>\n",
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" <td>8.0</td>\n",
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" <th>29</th>\n",
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" <td>4855</td>\n",
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" <tr>\n",
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" <td>3</td>\n",
" <td>26096</td>\n",
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" <td>47</td>\n",
" <td>35.0</td>\n",
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" <td>France</td>\n",
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" <tr>\n",
" <th>1912</th>\n",
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" <td>3</td>\n",
" <td>27896</td>\n",
" <td>20885.0</td>\n",
" <td>34907.0</td>\n",
" <td>51</td>\n",
" <td>38.0</td>\n",
" <td>64.0</td>\n",
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" <tr>\n",
" <th>1913</th>\n",
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" <td>53487.0</td>\n",
" <td>78</td>\n",
" <td>59.0</td>\n",
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" <th>1915</th>\n",
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" <td>44.0</td>\n",
" <td>80.0</td>\n",
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" <th>1916</th>\n",
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" <th>1917</th>\n",
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" <td>83.0</td>\n",
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" <td>France</td>\n",
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" <tr>\n",
" <th>1918</th>\n",
" <td>198514</td>\n",
" <td>3</td>\n",
" <td>134545</td>\n",
" <td>114400.0</td>\n",
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" <td>244</td>\n",
" <td>207.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
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" <tr>\n",
" <th>1919</th>\n",
" <td>198513</td>\n",
" <td>3</td>\n",
" <td>197206</td>\n",
" <td>176080.0</td>\n",
" <td>218332.0</td>\n",
" <td>357</td>\n",
" <td>319.0</td>\n",
" <td>395.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1920</th>\n",
" <td>198512</td>\n",
" <td>3</td>\n",
" <td>245240</td>\n",
" <td>223304.0</td>\n",
" <td>267176.0</td>\n",
" <td>445</td>\n",
" <td>405.0</td>\n",
" <td>485.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1921</th>\n",
" <td>198511</td>\n",
" <td>3</td>\n",
" <td>276205</td>\n",
" <td>252399.0</td>\n",
" <td>300011.0</td>\n",
" <td>501</td>\n",
" <td>458.0</td>\n",
" <td>544.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1922</th>\n",
" <td>198510</td>\n",
" <td>3</td>\n",
" <td>353231</td>\n",
" <td>326279.0</td>\n",
" <td>380183.0</td>\n",
" <td>640</td>\n",
" <td>591.0</td>\n",
" <td>689.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1923</th>\n",
" <td>198509</td>\n",
" <td>3</td>\n",
" <td>369895</td>\n",
" <td>341109.0</td>\n",
" <td>398681.0</td>\n",
" <td>670</td>\n",
" <td>618.0</td>\n",
" <td>722.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1924</th>\n",
" <td>198508</td>\n",
" <td>3</td>\n",
" <td>389886</td>\n",
" <td>359529.0</td>\n",
" <td>420243.0</td>\n",
" <td>707</td>\n",
" <td>652.0</td>\n",
" <td>762.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1925</th>\n",
" <td>198507</td>\n",
" <td>3</td>\n",
" <td>471852</td>\n",
" <td>432599.0</td>\n",
" <td>511105.0</td>\n",
" <td>855</td>\n",
" <td>784.0</td>\n",
" <td>926.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1926</th>\n",
" <td>198506</td>\n",
" <td>3</td>\n",
" <td>565825</td>\n",
" <td>518011.0</td>\n",
" <td>613639.0</td>\n",
" <td>1026</td>\n",
" <td>939.0</td>\n",
" <td>1113.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1927</th>\n",
" <td>198505</td>\n",
" <td>3</td>\n",
" <td>637302</td>\n",
" <td>592795.0</td>\n",
" <td>681809.0</td>\n",
" <td>1155</td>\n",
" <td>1074.0</td>\n",
" <td>1236.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1928</th>\n",
" <td>198504</td>\n",
" <td>3</td>\n",
" <td>424937</td>\n",
" <td>390794.0</td>\n",
" <td>459080.0</td>\n",
" <td>770</td>\n",
" <td>708.0</td>\n",
" <td>832.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1929</th>\n",
" <td>198503</td>\n",
" <td>3</td>\n",
" <td>213901</td>\n",
" <td>174689.0</td>\n",
" <td>253113.0</td>\n",
" <td>388</td>\n",
" <td>317.0</td>\n",
" <td>459.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1930</th>\n",
" <td>198502</td>\n",
" <td>3</td>\n",
" <td>97586</td>\n",
" <td>80949.0</td>\n",
" <td>114223.0</td>\n",
" <td>177</td>\n",
" <td>147.0</td>\n",
" <td>207.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1931</th>\n",
" <td>198501</td>\n",
" <td>3</td>\n",
" <td>85489</td>\n",
" <td>65918.0</td>\n",
" <td>105060.0</td>\n",
" <td>155</td>\n",
" <td>120.0</td>\n",
" <td>190.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1932</th>\n",
" <td>198452</td>\n",
" <td>3</td>\n",
" <td>84830</td>\n",
" <td>60602.0</td>\n",
" <td>109058.0</td>\n",
" <td>154</td>\n",
" <td>110.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1933</th>\n",
" <td>198451</td>\n",
" <td>3</td>\n",
" <td>101726</td>\n",
" <td>80242.0</td>\n",
" <td>123210.0</td>\n",
" <td>185</td>\n",
" <td>146.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1934</th>\n",
" <td>198450</td>\n",
" <td>3</td>\n",
" <td>123680</td>\n",
" <td>101401.0</td>\n",
" <td>145959.0</td>\n",
" <td>225</td>\n",
" <td>184.0</td>\n",
" <td>266.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1935</th>\n",
" <td>198449</td>\n",
" <td>3</td>\n",
" <td>101073</td>\n",
" <td>81684.0</td>\n",
" <td>120462.0</td>\n",
" <td>184</td>\n",
" <td>149.0</td>\n",
" <td>219.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1936</th>\n",
" <td>198448</td>\n",
" <td>3</td>\n",
" <td>78620</td>\n",
" <td>60634.0</td>\n",
" <td>96606.0</td>\n",
" <td>143</td>\n",
" <td>110.0</td>\n",
" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1937</th>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1938</th>\n",
" <td>198446</td>\n",
" <td>3</td>\n",
" <td>87330</td>\n",
" <td>67686.0</td>\n",
" <td>106974.0</td>\n",
" <td>159</td>\n",
" <td>123.0</td>\n",
" <td>195.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1939</th>\n",
" <td>198445</td>\n",
" <td>3</td>\n",
" <td>135223</td>\n",
" <td>101414.0</td>\n",
" <td>169032.0</td>\n",
" <td>246</td>\n",
" <td>184.0</td>\n",
" <td>308.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1940</th>\n",
" <td>198444</td>\n",
" <td>3</td>\n",
" <td>68422</td>\n",
" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
" <td>125</td>\n",
" <td>37.0</td>\n",
" <td>213.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1941 rows × 10 columns</p>\n",
"</div>"
],
"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",
"[1941 rows x 10 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n", "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 @@ ...@@ -78,11 +1044,75 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 4,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1704</th>\n",
" <td>198919</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low inc100_up \\\n",
"1704 198919 3 0 NaN NaN 0 NaN NaN \n",
"\n",
" geo_insee geo_name \n",
"1704 FR France "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data[raw_data.isnull().any(axis=1)]" "raw_data_copy[raw_data_copy.isnull().any(axis=1)]"
] ]
}, },
{ {
...@@ -94,9 +1124,976 @@ ...@@ -94,9 +1124,976 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 5,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>week</th>\n",
" <th>indicator</th>\n",
" <th>inc</th>\n",
" <th>inc_low</th>\n",
" <th>inc_up</th>\n",
" <th>inc100</th>\n",
" <th>inc100_low</th>\n",
" <th>inc100_up</th>\n",
" <th>geo_insee</th>\n",
" <th>geo_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>202201</td>\n",
" <td>3</td>\n",
" <td>67182</td>\n",
" <td>56533.0</td>\n",
" <td>77831.0</td>\n",
" <td>101</td>\n",
" <td>85.0</td>\n",
" <td>117.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202152</td>\n",
" <td>3</td>\n",
" <td>53141</td>\n",
" <td>45865.0</td>\n",
" <td>60417.0</td>\n",
" <td>80</td>\n",
" <td>69.0</td>\n",
" <td>91.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>202151</td>\n",
" <td>3</td>\n",
" <td>41698</td>\n",
" <td>35359.0</td>\n",
" <td>48037.0</td>\n",
" <td>63</td>\n",
" <td>53.0</td>\n",
" <td>73.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>202150</td>\n",
" <td>3</td>\n",
" <td>38117</td>\n",
" <td>32497.0</td>\n",
" <td>43737.0</td>\n",
" <td>58</td>\n",
" <td>49.0</td>\n",
" <td>67.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>202149</td>\n",
" <td>3</td>\n",
" <td>40168</td>\n",
" <td>34716.0</td>\n",
" <td>45620.0</td>\n",
" <td>61</td>\n",
" <td>53.0</td>\n",
" <td>69.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>202148</td>\n",
" <td>3</td>\n",
" <td>41842</td>\n",
" <td>36364.0</td>\n",
" <td>47320.0</td>\n",
" <td>63</td>\n",
" <td>55.0</td>\n",
" <td>71.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>202147</td>\n",
" <td>3</td>\n",
" <td>36598</td>\n",
" <td>31338.0</td>\n",
" <td>41858.0</td>\n",
" <td>55</td>\n",
" <td>47.0</td>\n",
" <td>63.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>202146</td>\n",
" <td>3</td>\n",
" <td>30059</td>\n",
" <td>25302.0</td>\n",
" <td>34816.0</td>\n",
" <td>46</td>\n",
" <td>39.0</td>\n",
" <td>53.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>202145</td>\n",
" <td>3</td>\n",
" <td>20364</td>\n",
" <td>16564.0</td>\n",
" <td>24164.0</td>\n",
" <td>31</td>\n",
" <td>25.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>202144</td>\n",
" <td>3</td>\n",
" <td>18999</td>\n",
" <td>15042.0</td>\n",
" <td>22956.0</td>\n",
" <td>29</td>\n",
" <td>23.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>202143</td>\n",
" <td>3</td>\n",
" <td>27040</td>\n",
" <td>21935.0</td>\n",
" <td>32145.0</td>\n",
" <td>41</td>\n",
" <td>33.0</td>\n",
" <td>49.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>202142</td>\n",
" <td>3</td>\n",
" <td>28343</td>\n",
" <td>23382.0</td>\n",
" <td>33304.0</td>\n",
" <td>43</td>\n",
" <td>35.0</td>\n",
" <td>51.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>202141</td>\n",
" <td>3</td>\n",
" <td>25043</td>\n",
" <td>20586.0</td>\n",
" <td>29500.0</td>\n",
" <td>38</td>\n",
" <td>31.0</td>\n",
" <td>45.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>202140</td>\n",
" <td>3</td>\n",
" <td>26286</td>\n",
" <td>21842.0</td>\n",
" <td>30730.0</td>\n",
" <td>40</td>\n",
" <td>33.0</td>\n",
" <td>47.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202139</td>\n",
" <td>3</td>\n",
" <td>22155</td>\n",
" <td>18014.0</td>\n",
" <td>26296.0</td>\n",
" <td>34</td>\n",
" <td>28.0</td>\n",
" <td>40.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>202138</td>\n",
" <td>3</td>\n",
" <td>15614</td>\n",
" <td>12310.0</td>\n",
" <td>18918.0</td>\n",
" <td>24</td>\n",
" <td>19.0</td>\n",
" <td>29.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>202137</td>\n",
" <td>3</td>\n",
" <td>13673</td>\n",
" <td>10404.0</td>\n",
" <td>16942.0</td>\n",
" <td>21</td>\n",
" <td>16.0</td>\n",
" <td>26.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>202136</td>\n",
" <td>3</td>\n",
" <td>10289</td>\n",
" <td>7505.0</td>\n",
" <td>13073.0</td>\n",
" <td>16</td>\n",
" <td>12.0</td>\n",
" <td>20.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202135</td>\n",
" <td>3</td>\n",
" <td>12609</td>\n",
" <td>9282.0</td>\n",
" <td>15936.0</td>\n",
" <td>19</td>\n",
" <td>14.0</td>\n",
" <td>24.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202134</td>\n",
" <td>3</td>\n",
" <td>13015</td>\n",
" <td>9485.0</td>\n",
" <td>16545.0</td>\n",
" <td>20</td>\n",
" <td>15.0</td>\n",
" <td>25.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202133</td>\n",
" <td>3</td>\n",
" <td>10392</td>\n",
" <td>7042.0</td>\n",
" <td>13742.0</td>\n",
" <td>16</td>\n",
" <td>11.0</td>\n",
" <td>21.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202132</td>\n",
" <td>3</td>\n",
" <td>15586</td>\n",
" <td>11009.0</td>\n",
" <td>20163.0</td>\n",
" <td>24</td>\n",
" <td>17.0</td>\n",
" <td>31.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>202131</td>\n",
" <td>3</td>\n",
" <td>18855</td>\n",
" <td>13664.0</td>\n",
" <td>24046.0</td>\n",
" <td>29</td>\n",
" <td>21.0</td>\n",
" <td>37.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202130</td>\n",
" <td>3</td>\n",
" <td>13991</td>\n",
" <td>9695.0</td>\n",
" <td>18287.0</td>\n",
" <td>21</td>\n",
" <td>14.0</td>\n",
" <td>28.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>202129</td>\n",
" <td>3</td>\n",
" <td>13626</td>\n",
" <td>9618.0</td>\n",
" <td>17634.0</td>\n",
" <td>21</td>\n",
" <td>15.0</td>\n",
" <td>27.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>202128</td>\n",
" <td>3</td>\n",
" <td>8636</td>\n",
" <td>5430.0</td>\n",
" <td>11842.0</td>\n",
" <td>13</td>\n",
" <td>8.0</td>\n",
" <td>18.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202127</td>\n",
" <td>3</td>\n",
" <td>10693</td>\n",
" <td>6838.0</td>\n",
" <td>14548.0</td>\n",
" <td>16</td>\n",
" <td>10.0</td>\n",
" <td>22.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202126</td>\n",
" <td>3</td>\n",
" <td>7086</td>\n",
" <td>4109.0</td>\n",
" <td>10063.0</td>\n",
" <td>11</td>\n",
" <td>6.0</td>\n",
" <td>16.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202125</td>\n",
" <td>3</td>\n",
" <td>7942</td>\n",
" <td>5540.0</td>\n",
" <td>10344.0</td>\n",
" <td>12</td>\n",
" <td>8.0</td>\n",
" <td>16.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202124</td>\n",
" <td>3</td>\n",
" <td>4855</td>\n",
" <td>3011.0</td>\n",
" <td>6699.0</td>\n",
" <td>7</td>\n",
" <td>4.0</td>\n",
" <td>10.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1911</th>\n",
" <td>198521</td>\n",
" <td>3</td>\n",
" <td>26096</td>\n",
" <td>19621.0</td>\n",
" <td>32571.0</td>\n",
" <td>47</td>\n",
" <td>35.0</td>\n",
" <td>59.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1912</th>\n",
" <td>198520</td>\n",
" <td>3</td>\n",
" <td>27896</td>\n",
" <td>20885.0</td>\n",
" <td>34907.0</td>\n",
" <td>51</td>\n",
" <td>38.0</td>\n",
" <td>64.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1913</th>\n",
" <td>198519</td>\n",
" <td>3</td>\n",
" <td>43154</td>\n",
" <td>32821.0</td>\n",
" <td>53487.0</td>\n",
" <td>78</td>\n",
" <td>59.0</td>\n",
" <td>97.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1914</th>\n",
" <td>198518</td>\n",
" <td>3</td>\n",
" <td>40555</td>\n",
" <td>29935.0</td>\n",
" <td>51175.0</td>\n",
" <td>74</td>\n",
" <td>55.0</td>\n",
" <td>93.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1915</th>\n",
" <td>198517</td>\n",
" <td>3</td>\n",
" <td>34053</td>\n",
" <td>24366.0</td>\n",
" <td>43740.0</td>\n",
" <td>62</td>\n",
" <td>44.0</td>\n",
" <td>80.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1916</th>\n",
" <td>198516</td>\n",
" <td>3</td>\n",
" <td>50362</td>\n",
" <td>36451.0</td>\n",
" <td>64273.0</td>\n",
" <td>91</td>\n",
" <td>66.0</td>\n",
" <td>116.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1917</th>\n",
" <td>198515</td>\n",
" <td>3</td>\n",
" <td>63881</td>\n",
" <td>45538.0</td>\n",
" <td>82224.0</td>\n",
" <td>116</td>\n",
" <td>83.0</td>\n",
" <td>149.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1918</th>\n",
" <td>198514</td>\n",
" <td>3</td>\n",
" <td>134545</td>\n",
" <td>114400.0</td>\n",
" <td>154690.0</td>\n",
" <td>244</td>\n",
" <td>207.0</td>\n",
" <td>281.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1919</th>\n",
" <td>198513</td>\n",
" <td>3</td>\n",
" <td>197206</td>\n",
" <td>176080.0</td>\n",
" <td>218332.0</td>\n",
" <td>357</td>\n",
" <td>319.0</td>\n",
" <td>395.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1920</th>\n",
" <td>198512</td>\n",
" <td>3</td>\n",
" <td>245240</td>\n",
" <td>223304.0</td>\n",
" <td>267176.0</td>\n",
" <td>445</td>\n",
" <td>405.0</td>\n",
" <td>485.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1921</th>\n",
" <td>198511</td>\n",
" <td>3</td>\n",
" <td>276205</td>\n",
" <td>252399.0</td>\n",
" <td>300011.0</td>\n",
" <td>501</td>\n",
" <td>458.0</td>\n",
" <td>544.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1922</th>\n",
" <td>198510</td>\n",
" <td>3</td>\n",
" <td>353231</td>\n",
" <td>326279.0</td>\n",
" <td>380183.0</td>\n",
" <td>640</td>\n",
" <td>591.0</td>\n",
" <td>689.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1923</th>\n",
" <td>198509</td>\n",
" <td>3</td>\n",
" <td>369895</td>\n",
" <td>341109.0</td>\n",
" <td>398681.0</td>\n",
" <td>670</td>\n",
" <td>618.0</td>\n",
" <td>722.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1924</th>\n",
" <td>198508</td>\n",
" <td>3</td>\n",
" <td>389886</td>\n",
" <td>359529.0</td>\n",
" <td>420243.0</td>\n",
" <td>707</td>\n",
" <td>652.0</td>\n",
" <td>762.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1925</th>\n",
" <td>198507</td>\n",
" <td>3</td>\n",
" <td>471852</td>\n",
" <td>432599.0</td>\n",
" <td>511105.0</td>\n",
" <td>855</td>\n",
" <td>784.0</td>\n",
" <td>926.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1926</th>\n",
" <td>198506</td>\n",
" <td>3</td>\n",
" <td>565825</td>\n",
" <td>518011.0</td>\n",
" <td>613639.0</td>\n",
" <td>1026</td>\n",
" <td>939.0</td>\n",
" <td>1113.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1927</th>\n",
" <td>198505</td>\n",
" <td>3</td>\n",
" <td>637302</td>\n",
" <td>592795.0</td>\n",
" <td>681809.0</td>\n",
" <td>1155</td>\n",
" <td>1074.0</td>\n",
" <td>1236.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1928</th>\n",
" <td>198504</td>\n",
" <td>3</td>\n",
" <td>424937</td>\n",
" <td>390794.0</td>\n",
" <td>459080.0</td>\n",
" <td>770</td>\n",
" <td>708.0</td>\n",
" <td>832.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1929</th>\n",
" <td>198503</td>\n",
" <td>3</td>\n",
" <td>213901</td>\n",
" <td>174689.0</td>\n",
" <td>253113.0</td>\n",
" <td>388</td>\n",
" <td>317.0</td>\n",
" <td>459.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1930</th>\n",
" <td>198502</td>\n",
" <td>3</td>\n",
" <td>97586</td>\n",
" <td>80949.0</td>\n",
" <td>114223.0</td>\n",
" <td>177</td>\n",
" <td>147.0</td>\n",
" <td>207.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1931</th>\n",
" <td>198501</td>\n",
" <td>3</td>\n",
" <td>85489</td>\n",
" <td>65918.0</td>\n",
" <td>105060.0</td>\n",
" <td>155</td>\n",
" <td>120.0</td>\n",
" <td>190.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1932</th>\n",
" <td>198452</td>\n",
" <td>3</td>\n",
" <td>84830</td>\n",
" <td>60602.0</td>\n",
" <td>109058.0</td>\n",
" <td>154</td>\n",
" <td>110.0</td>\n",
" <td>198.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1933</th>\n",
" <td>198451</td>\n",
" <td>3</td>\n",
" <td>101726</td>\n",
" <td>80242.0</td>\n",
" <td>123210.0</td>\n",
" <td>185</td>\n",
" <td>146.0</td>\n",
" <td>224.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1934</th>\n",
" <td>198450</td>\n",
" <td>3</td>\n",
" <td>123680</td>\n",
" <td>101401.0</td>\n",
" <td>145959.0</td>\n",
" <td>225</td>\n",
" <td>184.0</td>\n",
" <td>266.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1935</th>\n",
" <td>198449</td>\n",
" <td>3</td>\n",
" <td>101073</td>\n",
" <td>81684.0</td>\n",
" <td>120462.0</td>\n",
" <td>184</td>\n",
" <td>149.0</td>\n",
" <td>219.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1936</th>\n",
" <td>198448</td>\n",
" <td>3</td>\n",
" <td>78620</td>\n",
" <td>60634.0</td>\n",
" <td>96606.0</td>\n",
" <td>143</td>\n",
" <td>110.0</td>\n",
" <td>176.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1937</th>\n",
" <td>198447</td>\n",
" <td>3</td>\n",
" <td>72029</td>\n",
" <td>54274.0</td>\n",
" <td>89784.0</td>\n",
" <td>131</td>\n",
" <td>99.0</td>\n",
" <td>163.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1938</th>\n",
" <td>198446</td>\n",
" <td>3</td>\n",
" <td>87330</td>\n",
" <td>67686.0</td>\n",
" <td>106974.0</td>\n",
" <td>159</td>\n",
" <td>123.0</td>\n",
" <td>195.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1939</th>\n",
" <td>198445</td>\n",
" <td>3</td>\n",
" <td>135223</td>\n",
" <td>101414.0</td>\n",
" <td>169032.0</td>\n",
" <td>246</td>\n",
" <td>184.0</td>\n",
" <td>308.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1940</th>\n",
" <td>198444</td>\n",
" <td>3</td>\n",
" <td>68422</td>\n",
" <td>20056.0</td>\n",
" <td>116788.0</td>\n",
" <td>125</td>\n",
" <td>37.0</td>\n",
" <td>213.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1940 rows × 10 columns</p>\n",
"</div>"
],
"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": [ "source": [
"data = raw_data.dropna().copy()\n", "data = raw_data.dropna().copy()\n",
"data" "data"
...@@ -364,7 +2361,7 @@ ...@@ -364,7 +2361,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.6.1" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
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