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Commit 5a1b5d50 authored by ea193d10612bf244e34514d3b8080b04's avatar ea193d10612bf244e34514d3b8080b04
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Exo sur Jupyter

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module2/exo2/exercice.ipynb
View file @ 5a1b5d50
{
"cells": [],
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8\n",
" , 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6\n",
" , 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5\n",
" , 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7\n",
" , 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2\n",
" , 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.mean(data)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.min(data)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(data)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.median(data)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"4.334094455301447"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.std(data, ddof=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +157,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module2/exo3/exercice.ipynb
View file @ 5a1b5d50
{
"cells": [],
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9\n",
",12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9\n",
", 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9\n",
", 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7\n",
", 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3\n",
", 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.hist(data)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
......@@ -16,10 +95,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module2/exo4/donnees-hospitalieres-classe-age-hebdo-covid19-2021-09-09-19h05_Paris.csv 0 → 100644
View file @ 5a1b5d50
reg;Semaine;cl_age90;NewAdmHospit
75;2020-S09;0;0
75;2020-S10;0;7
75;2020-S11;0;41
75;2020-S12;0;214
75;2020-S13;0;583
75;2020-S14;0;495
75;2020-S15;0;406
75;2020-S16;0;264
75;2020-S17;0;172
75;2020-S18;0;88
75;2020-S19;0;65
75;2020-S20;0;60
75;2020-S21;0;44
75;2020-S22;0;33
75;2020-S23;0;22
75;2020-S24;0;12
75;2020-S25;0;5
75;2020-S26;0;9
75;2020-S27;0;7
75;2020-S28;0;8
75;2020-S29;0;21
75;2020-S30;0;14
75;2020-S31;0;9
75;2020-S32;0;15
75;2020-S33;0;31
75;2020-S34;0;37
75;2020-S35;0;48
75;2020-S36;0;91
75;2020-S37;0;161
75;2020-S38;0;208
75;2020-S39;0;191
75;2020-S40;0;224
75;2020-S41;0;229
75;2020-S42;0;332
75;2020-S43;0;551
75;2020-S44;0;836
75;2020-S45;0;875
75;2020-S46;0;863
75;2020-S47;0;632
75;2020-S48;0;531
75;2020-S49;0;472
75;2020-S50;0;442
75;2020-S51;0;395
75;2020-S52;0;303
75;2020-S53;0;370
75;2021-S01;0;491
75;2021-S02;0;638
75;2021-S03;0;675
75;2021-S04;0;664
75;2021-S05;0;624
75;2021-S06;0;482
75;2021-S07;0;368
75;2021-S08;0;389
75;2021-S09;0;381
75;2021-S10;0;427
75;2021-S11;0;421
75;2021-S12;0;457
75;2021-S13;0;492
75;2021-S14;0;525
75;2021-S15;0;570
75;2021-S16;0;600
75;2021-S17;0;509
75;2021-S18;0;374
75;2021-S19;0;228
75;2021-S20;0;186
75;2021-S21;0;149
75;2021-S22;0;118
75;2021-S23;0;103
75;2021-S24;0;65
75;2021-S25;0;39
75;2021-S26;0;32
75;2021-S27;0;36
75;2021-S28;0;50
75;2021-S29;0;123
75;2021-S30;0;214
75;2021-S31;0;267
75;2021-S32;0;312
75;2021-S33;0;285
75;2021-S34;0;224
75;2021-S35;0;181
75;2020-S09;9;0
75;2020-S10;9;0
75;2020-S11;9;0
75;2020-S12;9;2
75;2020-S13;9;2
75;2020-S14;9;1
75;2020-S15;9;2
75;2020-S16;9;2
75;2020-S17;9;1
75;2020-S18;9;0
75;2020-S19;9;0
75;2020-S20;9;0
75;2020-S21;9;1
75;2020-S22;9;0
75;2020-S23;9;0
75;2020-S24;9;0
75;2020-S25;9;0
75;2020-S26;9;1
75;2020-S27;9;0
75;2020-S28;9;0
75;2020-S29;9;0
75;2020-S30;9;0
75;2020-S31;9;0
75;2020-S32;9;0
75;2020-S33;9;0
75;2020-S34;9;2
75;2020-S35;9;0
75;2020-S36;9;0
75;2020-S37;9;2
75;2020-S38;9;4
75;2020-S39;9;0
75;2020-S40;9;1
75;2020-S41;9;2
75;2020-S42;9;7
75;2020-S43;9;4
75;2020-S44;9;7
75;2020-S45;9;4
75;2020-S46;9;2
75;2020-S47;9;3
75;2020-S48;9;2
75;2020-S49;9;1
75;2020-S50;9;0
75;2020-S51;9;0
75;2020-S52;9;2
75;2020-S53;9;1
75;2021-S01;9;0
75;2021-S02;9;3
75;2021-S03;9;2
75;2021-S04;9;3
75;2021-S05;9;0
75;2021-S06;9;3
75;2021-S07;9;0
75;2021-S08;9;1
75;2021-S09;9;1
75;2021-S10;9;3
75;2021-S11;9;1
75;2021-S12;9;3
75;2021-S13;9;1
75;2021-S14;9;4
75;2021-S15;9;4
75;2021-S16;9;2
75;2021-S17;9;2
75;2021-S18;9;1
75;2021-S19;9;4
75;2021-S20;9;2
75;2021-S21;9;2
75;2021-S22;9;1
75;2021-S23;9;2
75;2021-S24;9;1
75;2021-S25;9;1
75;2021-S26;9;0
75;2021-S27;9;4
75;2021-S28;9;0
75;2021-S29;9;7
75;2021-S30;9;3
75;2021-S31;9;5
75;2021-S32;9;5
75;2021-S33;9;4
75;2021-S34;9;4
75;2021-S35;9;2
75;2020-S09;19;0
75;2020-S10;19;0
75;2020-S11;19;1
75;2020-S12;19;0
75;2020-S13;19;1
75;2020-S14;19;4
75;2020-S15;19;0
75;2020-S16;19;3
75;2020-S17;19;0
75;2020-S18;19;0
75;2020-S19;19;1
75;2020-S20;19;0
75;2020-S21;19;0
75;2020-S22;19;3
75;2020-S23;19;0
75;2020-S24;19;0
75;2020-S25;19;0
75;2020-S26;19;0
75;2020-S27;19;0
75;2020-S28;19;0
75;2020-S29;19;1
75;2020-S30;19;0
75;2020-S31;19;0
75;2020-S32;19;0
75;2020-S33;19;1
75;2020-S34;19;3
75;2020-S35;19;1
75;2020-S36;19;4
75;2020-S37;19;1
75;2020-S38;19;1
75;2020-S39;19;3
75;2020-S40;19;1
75;2020-S41;19;0
75;2020-S42;19;5
75;2020-S43;19;6
75;2020-S44;19;3
75;2020-S45;19;3
75;2020-S46;19;7
75;2020-S47;19;2
75;2020-S48;19;1
75;2020-S49;19;0
75;2020-S50;19;3
75;2020-S51;19;1
75;2020-S52;19;2
75;2020-S53;19;1
75;2021-S01;19;2
75;2021-S02;19;2
75;2021-S03;19;3
75;2021-S04;19;3
75;2021-S05;19;2
75;2021-S06;19;1
75;2021-S07;19;3
75;2021-S08;19;0
75;2021-S09;19;1
75;2021-S10;19;1
75;2021-S11;19;3
75;2021-S12;19;3
75;2021-S13;19;2
75;2021-S14;19;3
75;2021-S15;19;4
75;2021-S16;19;4
75;2021-S17;19;4
75;2021-S18;19;1
75;2021-S19;19;0
75;2021-S20;19;1
75;2021-S21;19;3
75;2021-S22;19;3
75;2021-S23;19;0
75;2021-S24;19;2
75;2021-S25;19;0
75;2021-S26;19;0
75;2021-S27;19;0
75;2021-S28;19;6
75;2021-S29;19;2
75;2021-S30;19;6
75;2021-S31;19;4
75;2021-S32;19;7
75;2021-S33;19;4
75;2021-S34;19;3
75;2021-S35;19;4
75;2020-S09;29;0
75;2020-S10;29;0
75;2020-S11;29;2
75;2020-S12;29;5
75;2020-S13;29;13
75;2020-S14;29;5
75;2020-S15;29;10
75;2020-S16;29;7
75;2020-S17;29;3
75;2020-S18;29;1
75;2020-S19;29;1
75;2020-S20;29;3
75;2020-S21;29;0
75;2020-S22;29;0
75;2020-S23;29;2
75;2020-S24;29;0
75;2020-S25;29;0
75;2020-S26;29;1
75;2020-S27;29;2
75;2020-S28;29;0
75;2020-S29;29;3
75;2020-S30;29;0
75;2020-S31;29;0
75;2020-S32;29;1
75;2020-S33;29;3
75;2020-S34;29;4
75;2020-S35;29;3
75;2020-S36;29;4
75;2020-S37;29;9
75;2020-S38;29;12
75;2020-S39;29;7
75;2020-S40;29;3
75;2020-S41;29;5
75;2020-S42;29;3
75;2020-S43;29;10
75;2020-S44;29;11
75;2020-S45;29;18
75;2020-S46;29;10
75;2020-S47;29;3
75;2020-S48;29;5
75;2020-S49;29;4
75;2020-S50;29;5
75;2020-S51;29;5
75;2020-S52;29;2
75;2020-S53;29;3
75;2021-S01;29;10
75;2021-S02;29;8
75;2021-S03;29;14
75;2021-S04;29;10
75;2021-S05;29;10
75;2021-S06;29;5
75;2021-S07;29;4
75;2021-S08;29;3
75;2021-S09;29;7
75;2021-S10;29;10
75;2021-S11;29;13
75;2021-S12;29;15
75;2021-S13;29;13
75;2021-S14;29;18
75;2021-S15;29;10
75;2021-S16;29;10
75;2021-S17;29;5
75;2021-S18;29;14
75;2021-S19;29;9
75;2021-S20;29;12
75;2021-S21;29;4
75;2021-S22;29;5
75;2021-S23;29;1
75;2021-S24;29;6
75;2021-S25;29;0
75;2021-S26;29;0
75;2021-S27;29;0
75;2021-S28;29;6
75;2021-S29;29;10
75;2021-S30;29;21
75;2021-S31;29;17
75;2021-S32;29;10
75;2021-S33;29;9
75;2021-S34;29;8
75;2021-S35;29;8
75;2020-S09;39;0
75;2020-S10;39;1
75;2020-S11;39;2
75;2020-S12;39;8
75;2020-S13;39;24
75;2020-S14;39;18
75;2020-S15;39;18
75;2020-S16;39;7
75;2020-S17;39;7
75;2020-S18;39;3
75;2020-S19;39;4
75;2020-S20;39;5
75;2020-S21;39;2
75;2020-S22;39;3
75;2020-S23;39;3
75;2020-S24;39;1
75;2020-S25;39;0
75;2020-S26;39;0
75;2020-S27;39;1
75;2020-S28;39;0
75;2020-S29;39;2
75;2020-S30;39;1
75;2020-S31;39;0
75;2020-S32;39;1
75;2020-S33;39;3
75;2020-S34;39;2
75;2020-S35;39;1
75;2020-S36;39;6
75;2020-S37;39;11
75;2020-S38;39;10
75;2020-S39;39;5
75;2020-S40;39;3
75;2020-S41;39;8
75;2020-S42;39;15
75;2020-S43;39;9
75;2020-S44;39;20
75;2020-S45;39;21
75;2020-S46;39;16
75;2020-S47;39;12
75;2020-S48;39;8
75;2020-S49;39;5
75;2020-S50;39;7
75;2020-S51;39;11
75;2020-S52;39;6
75;2020-S53;39;8
75;2021-S01;39;10
75;2021-S02;39;19
75;2021-S03;39;9
75;2021-S04;39;9
75;2021-S05;39;13
75;2021-S06;39;4
75;2021-S07;39;10
75;2021-S08;39;15
75;2021-S09;39;5
75;2021-S10;39;7
75;2021-S11;39;16
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\ No newline at end of file
module2/exo4/exercice.ipynb
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module2/exo5/exo5_fr.ipynb
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......@@ -40,7 +40,7 @@
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......@@ -261,33 +261,33 @@
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" 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,
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}
......@@ -322,8 +322,270 @@
},
{
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" <td>72</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8/30/83</td>\n",
" <td>6</td>\n",
" <td>73</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11/28/83</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10/03/85</td>\n",
" <td>6</td>\n",
" <td>79</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8/27/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7/29/85</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6/17/85</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4/29/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10/05/84</td>\n",
" <td>6</td>\n",
" <td>78</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11/08/84</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>11/26/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8/30/84</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4/06/84</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4/12/85</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"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",
"20 10/30/85 6 75 200 2\n",
"19 10/03/85 6 79 200 0\n",
"18 8/27/85 6 76 200 0\n",
"17 7/29/85 6 81 200 0\n",
"16 6/17/85 6 70 200 0\n",
"15 4/29/85 6 75 200 0\n",
"11 10/05/84 6 78 200 0\n",
"13 1/24/85 6 53 200 2\n",
"12 11/08/84 6 67 200 0\n",
"21 11/26/85 6 76 200 0\n",
"10 8/30/84 6 70 200 1\n",
"9 4/06/84 6 63 200 1\n",
"8 2/03/84 6 57 200 1\n",
"14 4/12/85 6 67 200 0\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.sort_values('Pressure')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
......@@ -351,6 +613,7 @@
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
" <th>Frequency</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
......@@ -361,6 +624,7 @@
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
......@@ -369,6 +633,7 @@
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
......@@ -377,6 +642,7 @@
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
......@@ -385,6 +651,7 @@
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
......@@ -393,6 +660,7 @@
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
......@@ -401,6 +669,7 @@
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
......@@ -409,23 +678,24 @@
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"1 11/12/81 6 70 50 1\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",
"13 1/24/85 6 53 200 2\n",
"20 10/30/85 6 75 200 2\n",
"22 1/12/86 6 58 200 1"
" Date Count Temperature Pressure Malfunction Frequency\n",
"1 11/12/81 6 70 50 1 0.166667\n",
"8 2/03/84 6 57 200 1 0.166667\n",
"9 4/06/84 6 63 200 1 0.166667\n",
"10 8/30/84 6 70 200 1 0.166667\n",
"13 1/24/85 6 53 200 2 0.333333\n",
"20 10/30/85 6 75 200 2 0.333333\n",
"22 1/12/86 6 58 200 1 0.166667"
]
},
"execution_count": 2,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
......@@ -448,12 +718,12 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -500,7 +770,131 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 14,
"metadata": {
"scrolled": true
},
"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>Date</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>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4/06/84</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8/30/84</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" <td>0.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" <td>0.166667</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction Frequency\n",
"1 11/12/81 6 70 50 1 0.166667\n",
"8 2/03/84 6 57 200 1 0.166667\n",
"9 4/06/84 6 63 200 1 0.166667\n",
"10 8/30/84 6 70 200 1 0.166667\n",
"13 1/24/85 6 53 200 2 0.333333\n",
"20 10/30/85 6 75 200 2 0.333333\n",
"22 1/12/86 6 58 200 1 0.166667"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
......@@ -524,10 +918,10 @@
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5250</td> \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>Tue, 14 Sep 2021</td> <th> Deviance: </th> <td> 0.22231</td> \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>15:23:08</td> <th> Pearson chi2: </th> <td> 0.236</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
......@@ -555,8 +949,8 @@
"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",
"Date: Tue, 14 Sep 2021 Deviance: 0.22231\n",
"Time: 15:23:08 Pearson chi2: 0.236\n",
"No. Iterations: 4 Covariance Type: nonrobust\n",
"===============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
......@@ -567,7 +961,7 @@
"\"\"\""
]
},
"execution_count": 4,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
......@@ -610,7 +1004,7 @@
"outputs": [
{
"data": {
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\n",
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -705,7 +1099,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.6.4"
}
},
"nbformat": 4,
......
module3/exo1/analyse-syndrome-grippal.ipynb
View file @ 5a1b5d50
......@@ -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\""
......@@ -61,11 +59,1023 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data_path = 'incidence-PAY-3.csv'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"raw_data = pd.read_csv(data_url, skiprows=1)\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On vérifie l'existence du fichier et on le télécharge si besoin, puis sauvegare sous csv"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"File incidence-PAY-3.csv downloaded\n"
]
}
],
"source": [
"if data_path not in os.listdir():\n",
" raw_data = pd.read_csv(data_url, skiprows=1)\n",
" raw_data.to_csv(data_path, index=False)\n",
" print('File ' + data_path + \" downloaded\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"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>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>202136</td>\n",
" <td>3</td>\n",
" <td>13068</td>\n",
" <td>9214.0</td>\n",
" <td>16922.0</td>\n",
" <td>20</td>\n",
" <td>14.0</td>\n",
" <td>26.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>202135</td>\n",
" <td>3</td>\n",
" <td>12672</td>\n",
" <td>9277.0</td>\n",
" <td>16067.0</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>2</th>\n",
" <td>202134</td>\n",
" <td>3</td>\n",
" <td>13013</td>\n",
" <td>9481.0</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>3</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>4</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>5</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>6</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>7</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>8</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>9</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>10</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>11</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>12</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>13</th>\n",
" <td>202123</td>\n",
" <td>3</td>\n",
" <td>6710</td>\n",
" <td>4455.0</td>\n",
" <td>8965.0</td>\n",
" <td>10</td>\n",
" <td>7.0</td>\n",
" <td>13.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>202122</td>\n",
" <td>3</td>\n",
" <td>7879</td>\n",
" <td>5495.0</td>\n",
" <td>10263.0</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>15</th>\n",
" <td>202121</td>\n",
" <td>3</td>\n",
" <td>7827</td>\n",
" <td>5403.0</td>\n",
" <td>10251.0</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>16</th>\n",
" <td>202120</td>\n",
" <td>3</td>\n",
" <td>10278</td>\n",
" <td>7540.0</td>\n",
" <td>13016.0</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>17</th>\n",
" <td>202119</td>\n",
" <td>3</td>\n",
" <td>9539</td>\n",
" <td>6860.0</td>\n",
" <td>12218.0</td>\n",
" <td>14</td>\n",
" <td>10.0</td>\n",
" <td>18.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>202118</td>\n",
" <td>3</td>\n",
" <td>12135</td>\n",
" <td>9165.0</td>\n",
" <td>15105.0</td>\n",
" <td>18</td>\n",
" <td>14.0</td>\n",
" <td>22.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>202117</td>\n",
" <td>3</td>\n",
" <td>12058</td>\n",
" <td>8891.0</td>\n",
" <td>15225.0</td>\n",
" <td>18</td>\n",
" <td>13.0</td>\n",
" <td>23.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>202116</td>\n",
" <td>3</td>\n",
" <td>16505</td>\n",
" <td>12735.0</td>\n",
" <td>20275.0</td>\n",
" <td>25</td>\n",
" <td>19.0</td>\n",
" <td>31.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>202115</td>\n",
" <td>3</td>\n",
" <td>19306</td>\n",
" <td>15398.0</td>\n",
" <td>23214.0</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>22</th>\n",
" <td>202114</td>\n",
" <td>3</td>\n",
" <td>21073</td>\n",
" <td>17099.0</td>\n",
" <td>25047.0</td>\n",
" <td>32</td>\n",
" <td>26.0</td>\n",
" <td>38.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>202113</td>\n",
" <td>3</td>\n",
" <td>26413</td>\n",
" <td>22094.0</td>\n",
" <td>30732.0</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>24</th>\n",
" <td>202112</td>\n",
" <td>3</td>\n",
" <td>30658</td>\n",
" <td>25919.0</td>\n",
" <td>35397.0</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>25</th>\n",
" <td>202111</td>\n",
" <td>3</td>\n",
" <td>24988</td>\n",
" <td>20718.0</td>\n",
" <td>29258.0</td>\n",
" <td>38</td>\n",
" <td>32.0</td>\n",
" <td>44.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>202110</td>\n",
" <td>3</td>\n",
" <td>19539</td>\n",
" <td>15951.0</td>\n",
" <td>23127.0</td>\n",
" <td>30</td>\n",
" <td>25.0</td>\n",
" <td>35.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>202109</td>\n",
" <td>3</td>\n",
" <td>17572</td>\n",
" <td>13926.0</td>\n",
" <td>21218.0</td>\n",
" <td>27</td>\n",
" <td>21.0</td>\n",
" <td>33.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>202108</td>\n",
" <td>3</td>\n",
" <td>20882</td>\n",
" <td>16907.0</td>\n",
" <td>24857.0</td>\n",
" <td>32</td>\n",
" <td>26.0</td>\n",
" <td>38.0</td>\n",
" <td>FR</td>\n",
" <td>France</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>202107</td>\n",
" <td>3</td>\n",
" <td>22393</td>\n",
" <td>18303.0</td>\n",
" <td>26483.0</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>...</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>1894</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>1895</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>1896</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>1897</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>1898</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>1899</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>1900</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>1901</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>1902</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>1903</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>1904</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>1905</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>1906</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>1907</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>1908</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>1909</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>1910</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>1911</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>1912</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>1913</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>1914</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>1915</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>1916</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>1917</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>1918</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>1919</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>1920</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>1921</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>1922</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>1923</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>1924 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" week indicator inc inc_low inc_up inc100 inc100_low \\\n",
"0 202136 3 13068 9214.0 16922.0 20 14.0 \n",
"1 202135 3 12672 9277.0 16067.0 19 14.0 \n",
"2 202134 3 13013 9481.0 16545.0 20 15.0 \n",
"3 202133 3 10392 7042.0 13742.0 16 11.0 \n",
"4 202132 3 15586 11009.0 20163.0 24 17.0 \n",
"5 202131 3 18855 13664.0 24046.0 29 21.0 \n",
"6 202130 3 13991 9695.0 18287.0 21 14.0 \n",
"7 202129 3 13626 9618.0 17634.0 21 15.0 \n",
"8 202128 3 8636 5430.0 11842.0 13 8.0 \n",
"9 202127 3 10693 6838.0 14548.0 16 10.0 \n",
"10 202126 3 7086 4109.0 10063.0 11 6.0 \n",
"11 202125 3 7942 5540.0 10344.0 12 8.0 \n",
"12 202124 3 4855 3011.0 6699.0 7 4.0 \n",
"13 202123 3 6710 4455.0 8965.0 10 7.0 \n",
"14 202122 3 7879 5495.0 10263.0 12 8.0 \n",
"15 202121 3 7827 5403.0 10251.0 12 8.0 \n",
"16 202120 3 10278 7540.0 13016.0 16 12.0 \n",
"17 202119 3 9539 6860.0 12218.0 14 10.0 \n",
"18 202118 3 12135 9165.0 15105.0 18 14.0 \n",
"19 202117 3 12058 8891.0 15225.0 18 13.0 \n",
"20 202116 3 16505 12735.0 20275.0 25 19.0 \n",
"21 202115 3 19306 15398.0 23214.0 29 23.0 \n",
"22 202114 3 21073 17099.0 25047.0 32 26.0 \n",
"23 202113 3 26413 22094.0 30732.0 40 33.0 \n",
"24 202112 3 30658 25919.0 35397.0 46 39.0 \n",
"25 202111 3 24988 20718.0 29258.0 38 32.0 \n",
"26 202110 3 19539 15951.0 23127.0 30 25.0 \n",
"27 202109 3 17572 13926.0 21218.0 27 21.0 \n",
"28 202108 3 20882 16907.0 24857.0 32 26.0 \n",
"29 202107 3 22393 18303.0 26483.0 34 28.0 \n",
"... ... ... ... ... ... ... ... \n",
"1894 198521 3 26096 19621.0 32571.0 47 35.0 \n",
"1895 198520 3 27896 20885.0 34907.0 51 38.0 \n",
"1896 198519 3 43154 32821.0 53487.0 78 59.0 \n",
"1897 198518 3 40555 29935.0 51175.0 74 55.0 \n",
"1898 198517 3 34053 24366.0 43740.0 62 44.0 \n",
"1899 198516 3 50362 36451.0 64273.0 91 66.0 \n",
"1900 198515 3 63881 45538.0 82224.0 116 83.0 \n",
"1901 198514 3 134545 114400.0 154690.0 244 207.0 \n",
"1902 198513 3 197206 176080.0 218332.0 357 319.0 \n",
"1903 198512 3 245240 223304.0 267176.0 445 405.0 \n",
"1904 198511 3 276205 252399.0 300011.0 501 458.0 \n",
"1905 198510 3 353231 326279.0 380183.0 640 591.0 \n",
"1906 198509 3 369895 341109.0 398681.0 670 618.0 \n",
"1907 198508 3 389886 359529.0 420243.0 707 652.0 \n",
"1908 198507 3 471852 432599.0 511105.0 855 784.0 \n",
"1909 198506 3 565825 518011.0 613639.0 1026 939.0 \n",
"1910 198505 3 637302 592795.0 681809.0 1155 1074.0 \n",
"1911 198504 3 424937 390794.0 459080.0 770 708.0 \n",
"1912 198503 3 213901 174689.0 253113.0 388 317.0 \n",
"1913 198502 3 97586 80949.0 114223.0 177 147.0 \n",
"1914 198501 3 85489 65918.0 105060.0 155 120.0 \n",
"1915 198452 3 84830 60602.0 109058.0 154 110.0 \n",
"1916 198451 3 101726 80242.0 123210.0 185 146.0 \n",
"1917 198450 3 123680 101401.0 145959.0 225 184.0 \n",
"1918 198449 3 101073 81684.0 120462.0 184 149.0 \n",
"1919 198448 3 78620 60634.0 96606.0 143 110.0 \n",
"1920 198447 3 72029 54274.0 89784.0 131 99.0 \n",
"1921 198446 3 87330 67686.0 106974.0 159 123.0 \n",
"1922 198445 3 135223 101414.0 169032.0 246 184.0 \n",
"1923 198444 3 68422 20056.0 116788.0 125 37.0 \n",
"\n",
" inc100_up geo_insee geo_name \n",
"0 26.0 FR France \n",
"1 24.0 FR France \n",
"2 25.0 FR France \n",
"3 21.0 FR France \n",
"4 31.0 FR France \n",
"5 37.0 FR France \n",
"6 28.0 FR France \n",
"7 27.0 FR France \n",
"8 18.0 FR France \n",
"9 22.0 FR France \n",
"10 16.0 FR France \n",
"11 16.0 FR France \n",
"12 10.0 FR France \n",
"13 13.0 FR France \n",
"14 16.0 FR France \n",
"15 16.0 FR France \n",
"16 20.0 FR France \n",
"17 18.0 FR France \n",
"18 22.0 FR France \n",
"19 23.0 FR France \n",
"20 31.0 FR France \n",
"21 35.0 FR France \n",
"22 38.0 FR France \n",
"23 47.0 FR France \n",
"24 53.0 FR France \n",
"25 44.0 FR France \n",
"26 35.0 FR France \n",
"27 33.0 FR France \n",
"28 38.0 FR France \n",
"29 40.0 FR France \n",
"... ... ... ... \n",
"1894 59.0 FR France \n",
"1895 64.0 FR France \n",
"1896 97.0 FR France \n",
"1897 93.0 FR France \n",
"1898 80.0 FR France \n",
"1899 116.0 FR France \n",
"1900 149.0 FR France \n",
"1901 281.0 FR France \n",
"1902 395.0 FR France \n",
"1903 485.0 FR France \n",
"1904 544.0 FR France \n",
"1905 689.0 FR France \n",
"1906 722.0 FR France \n",
"1907 762.0 FR France \n",
"1908 926.0 FR France \n",
"1909 1113.0 FR France \n",
"1910 1236.0 FR France \n",
"1911 832.0 FR France \n",
"1912 459.0 FR France \n",
"1913 207.0 FR France \n",
"1914 190.0 FR France \n",
"1915 198.0 FR France \n",
"1916 224.0 FR France \n",
"1917 266.0 FR France \n",
"1918 219.0 FR France \n",
"1919 176.0 FR France \n",
"1920 163.0 FR France \n",
"1921 195.0 FR France \n",
"1922 308.0 FR France \n",
"1923 213.0 FR France \n",
"\n",
"[1924 rows x 10 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_path)\n",
"raw_data"
]
},
......@@ -364,7 +1374,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
"version": "3.6.4"
}
},
"nbformat": 4,
......
module3/exo1/incidence-PAY-3.csv 0 → 100644
View file @ 5a1b5d50
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