From 3aff4e3e079a43c7ed335d7653b0de4a672b5a17 Mon Sep 17 00:00:00 2001 From: 2da59b5ab1bcae563013413663de99e5 <2da59b5ab1bcae563013413663de99e5@app-learninglab.inria.fr> Date: Sat, 11 Dec 2021 16:12:43 +0000 Subject: [PATCH] Test CO2 --- module2/exo1/toy_notebook_fr.ipynb | 61 +- module3/exo1/analyse-syndrome-grippal.ipynb | 2162 +++++++++++++- module3/exo3/Test CO2.ipynb | 2966 +++++++++++++++++++ module3/exo3/exercice.ipynb | 2931 +++++++++++++++++- 4 files changed, 8075 insertions(+), 45 deletions(-) create mode 100644 module3/exo3/Test CO2.ipynb diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb index 0bbbe37..70ee48e 100644 --- a/module2/exo1/toy_notebook_fr.ipynb +++ b/module2/exo1/toy_notebook_fr.ipynb @@ -1,5 +1,61 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. A propos du calcul de $\\pi$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 En demandant à la lib maths" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Mon ordinateur m'indique de $\\pi$ vaut approximatvement" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.141592653589793\n" + ] + } + ], + "source": [ + "from math import *\n", + "print(pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 En utilisant la méthode des aiguilles de Buffon" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Mais calculé avec la méthode des [aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguille_de_Buffon)" + ] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +72,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - diff --git a/module3/exo1/analyse-syndrome-grippal.ipynb b/module3/exo1/analyse-syndrome-grippal.ipynb index 59d72b5..71b4eff 100644 --- a/module3/exo1/analyse-syndrome-grippal.ipynb +++ b/module3/exo1/analyse-syndrome-grippal.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -28,13 +28,11 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 18, + "metadata": {}, "outputs": [], "source": [ - "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\"" + "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\"" ] }, { @@ -61,9 +59,976 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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2620212274817275268827410FRFrance
2720212176092345887269513FRFrance
282021207748546011036911715FRFrance
29202119766544370893810713FRFrance
.................................
15881991267176081130423912312042FRFrance
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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weekindicatorincinc_lowinc_upinc100inc100_lowinc100_upgeo_inseegeo_name
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1202147711322828214362171222FRFrance
22021467821657241070812816FRFrance
320214578965646811462141018FRFrance
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62021427944360371284914919FRFrance
72021417402122395803639FRFrance
820214074441245464287410FRFrance
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1020213874325226763837410FRFrance
11202137719647543174315FRFrance
122021367344117305152528FRFrance
132021357256211074017426FRFrance
14202134714293782480204FRFrance
152021337382918305828639FRFrance
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182021307719041911018911616FRFrance
19202129768004109949110614FRFrance
202021287973402173115033FRFrance
212021277902643161373614721FRFrance
222021267728441081046011616FRFrance
2320212579351654012162141018FRFrance
24202124712034893715131181323FRFrance
2520212379116642011812141018FRFrance
2620212274817275268827410FRFrance
2720212176092345887269513FRFrance
282021207748546011036911715FRFrance
29202119766544370893810713FRFrance
.................................
15881991267176081130423912312042FRFrance
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1618 rows × 10 columns

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" + ], + "text/plain": [ + " week indicator inc inc_low inc_up inc100 inc100_low \\\n", + "0 202148 7 10905 7371 14439 17 12 \n", + "1 202147 7 11322 8282 14362 17 12 \n", + "2 202146 7 8216 5724 10708 12 8 \n", + "3 202145 7 8965 6468 11462 14 10 \n", + "4 202144 7 8736 5636 11836 13 8 \n", + "5 202143 7 8145 5164 11126 12 7 \n", + "6 202142 7 9443 6037 12849 14 9 \n", + "7 202141 7 4021 2239 5803 6 3 \n", + "8 202140 7 4441 2454 6428 7 4 \n", + "9 202139 7 2291 1056 3526 3 1 \n", + "10 202138 7 4325 2267 6383 7 4 \n", + "11 202137 7 1964 754 3174 3 1 \n", + "12 202136 7 3441 1730 5152 5 2 \n", + "13 202135 7 2562 1107 4017 4 2 \n", + "14 202134 7 1429 378 2480 2 0 \n", + "15 202133 7 3829 1830 5828 6 3 \n", + "16 202132 7 4108 1895 6321 6 3 \n", + "17 202131 7 4793 2301 7285 7 3 \n", + "18 202130 7 7190 4191 10189 11 6 \n", + "19 202129 7 6800 4109 9491 10 6 \n", + "20 202128 7 9734 0 21731 15 0 \n", + "21 202127 7 9026 4316 13736 14 7 \n", + "22 202126 7 7284 4108 10460 11 6 \n", + "23 202125 7 9351 6540 12162 14 10 \n", + "24 202124 7 12034 8937 15131 18 13 \n", + "25 202123 7 9116 6420 11812 14 10 \n", + "26 202122 7 4817 2752 6882 7 4 \n", + "27 202121 7 6092 3458 8726 9 5 \n", + "28 202120 7 7485 4601 10369 11 7 \n", + "29 202119 7 6654 4370 8938 10 7 \n", + "... ... ... ... ... ... ... ... \n", + "1588 199126 7 17608 11304 23912 31 20 \n", + "1589 199125 7 16169 10700 21638 28 18 \n", + "1590 199124 7 16171 10071 22271 28 17 \n", + "1591 199123 7 11947 7671 16223 21 13 \n", + "1592 199122 7 15452 9953 20951 27 17 \n", + "1593 199121 7 14903 8975 20831 26 16 \n", + "1594 199120 7 19053 12742 25364 34 23 \n", + "1595 199119 7 16739 11246 22232 29 19 \n", + "1596 199118 7 21385 13882 28888 38 25 \n", + "1597 199117 7 13462 8877 18047 24 16 \n", + "1598 199116 7 14857 10068 19646 26 18 \n", + "1599 199115 7 13975 9781 18169 25 18 \n", + "1600 199114 7 12265 7684 16846 22 14 \n", + "1601 199113 7 9567 6041 13093 17 11 \n", + "1602 199112 7 10864 7331 14397 19 13 \n", + "1603 199111 7 15574 11184 19964 27 19 \n", + "1604 199110 7 16643 11372 21914 29 20 \n", + "1605 199109 7 13741 8780 18702 24 15 \n", + "1606 199108 7 13289 8813 17765 23 15 \n", + "1607 199107 7 12337 8077 16597 22 15 \n", + "1608 199106 7 10877 7013 14741 19 12 \n", + "1609 199105 7 10442 6544 14340 18 11 \n", + "1610 199104 7 7913 4563 11263 14 8 \n", + "1611 199103 7 15387 10484 20290 27 18 \n", + "1612 199102 7 16277 11046 21508 29 20 \n", + "1613 199101 7 15565 10271 20859 27 18 \n", + "1614 199052 7 19375 13295 25455 34 23 \n", + "1615 199051 7 19080 13807 24353 34 25 \n", + "1616 199050 7 11079 6660 15498 20 12 \n", + "1617 199049 7 1143 0 2610 2 0 \n", + "\n", + " inc100_up geo_insee geo_name \n", + "0 22 FR France \n", + "1 22 FR France \n", + "2 16 FR France \n", + "3 18 FR France \n", + "4 18 FR France \n", + "5 17 FR France \n", + "6 19 FR France \n", + "7 9 FR France \n", + "8 10 FR France \n", + "9 5 FR France \n", + "10 10 FR France \n", + "11 5 FR France \n", + "12 8 FR France \n", + "13 6 FR France \n", + "14 4 FR France \n", + "15 9 FR France \n", + "16 9 FR France \n", + "17 11 FR France \n", + "18 16 FR France \n", + "19 14 FR France \n", + "20 33 FR France \n", + "21 21 FR France \n", + "22 16 FR France \n", + "23 18 FR France \n", + "24 23 FR France \n", + "25 18 FR France \n", + "26 10 FR France \n", + "27 13 FR France \n", + "28 15 FR France \n", + "29 13 FR France \n", + "... ... ... ... \n", + "1588 42 FR France \n", + "1589 38 FR France \n", + "1590 39 FR France \n", + "1591 29 FR France \n", + "1592 37 FR France \n", + "1593 36 FR France \n", + "1594 45 FR France \n", + "1595 39 FR France \n", + "1596 51 FR France \n", + "1597 32 FR France \n", + "1598 34 FR France \n", + "1599 32 FR France \n", + "1600 30 FR France \n", + "1601 23 FR France \n", + "1602 25 FR France \n", + "1603 35 FR France \n", + "1604 38 FR France \n", + "1605 33 FR France \n", + "1606 31 FR France \n", + "1607 29 FR France \n", + "1608 26 FR France \n", + "1609 25 FR France \n", + "1610 20 FR France \n", + "1611 36 FR France \n", + "1612 38 FR France \n", + "1613 36 FR France \n", + "1614 45 FR France \n", + "1615 43 FR France \n", + "1616 28 FR France \n", + "1617 5 FR France \n", + "\n", + "[1618 rows x 10 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "data = raw_data.dropna().copy()\n", "data" @@ -122,7 +2103,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -152,10 +2133,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" @@ -179,7 +2158,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -199,9 +2178,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "sorted_data['inc'][-200:].plot()" ] @@ -252,11 +2277,17 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[Period('1985-07-29/1985-08-04', 'W-SUN'), Period('1986-07-28/1986-08-03', 'W-SUN'), Period('1987-07-27/1987-08-02', 'W-SUN'), Period('1988-08-01/1988-08-07', 'W-SUN'), Period('1989-07-31/1989-08-06', 'W-SUN'), Period('1990-07-30/1990-08-05', 'W-SUN'), Period('1991-07-29/1991-08-04', 'W-SUN'), Period('1992-07-27/1992-08-02', 'W-SUN'), Period('1993-07-26/1993-08-01', 'W-SUN'), Period('1994-08-01/1994-08-07', 'W-SUN'), Period('1995-07-31/1995-08-06', 'W-SUN'), Period('1996-07-29/1996-08-04', 'W-SUN'), Period('1997-07-28/1997-08-03', 'W-SUN'), Period('1998-07-27/1998-08-02', 'W-SUN'), Period('1999-07-26/1999-08-01', 'W-SUN'), Period('2000-07-31/2000-08-06', 'W-SUN'), Period('2001-07-30/2001-08-05', 'W-SUN'), Period('2002-07-29/2002-08-04', 'W-SUN'), Period('2003-07-28/2003-08-03', 'W-SUN'), Period('2004-07-26/2004-08-01', 'W-SUN'), Period('2005-08-01/2005-08-07', 'W-SUN'), Period('2006-07-31/2006-08-06', 'W-SUN'), Period('2007-07-30/2007-08-05', 'W-SUN'), Period('2008-07-28/2008-08-03', 'W-SUN'), Period('2009-07-27/2009-08-02', 'W-SUN'), Period('2010-07-26/2010-08-01', 'W-SUN'), Period('2011-08-01/2011-08-07', 'W-SUN'), Period('2012-07-30/2012-08-05', 'W-SUN'), Period('2013-07-29/2013-08-04', 'W-SUN'), Period('2014-07-28/2014-08-03', 'W-SUN'), Period('2015-07-27/2015-08-02', 'W-SUN'), Period('2016-08-01/2016-08-07', 'W-SUN'), Period('2017-07-31/2017-08-06', 'W-SUN'), Period('2018-07-30/2018-08-05', 'W-SUN'), Period('2019-07-29/2019-08-04', 'W-SUN'), Period('2020-07-27/2020-08-02', 'W-SUN')]\n" + ] + } + ], "source": [ "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n", " for y in range(1985,\n", @@ -274,9 +2305,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AssertionError", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m first_august_week[1:]):\n\u001b[1;32m 5\u001b[0m \u001b[0mone_year\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mweek1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m52\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mone_year\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0myear\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweek2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAssertionError\u001b[0m: " + ] + } + ], "source": [ "year = []\n", "yearly_incidence = []\n", @@ -298,9 +2341,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'plot'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'*'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'plot'" + ] + } + ], "source": [ "yearly_incidence.plot(style='*')" ] @@ -314,9 +2369,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'sort_values'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'sort_values'" + ] + } + ], "source": [ "yearly_incidence.sort_values()" ] @@ -331,9 +2398,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'hist'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0myearly_incidence\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxrot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'hist'" + ] + } + ], "source": [ "yearly_incidence.hist(xrot=20)" ] @@ -341,9 +2420,14 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [], "source": [] } @@ -364,7 +2448,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.1" + "version": "3.6.4" } }, "nbformat": 4, diff --git a/module3/exo3/Test CO2.ipynb b/module3/exo3/Test CO2.ipynb new file mode 100644 index 0000000..5dad474 --- /dev/null +++ b/module3/exo3/Test CO2.ipynb @@ -0,0 +1,2966 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sujet 1 : Concentration de CO2 dans l'atmosphère depuis 1958" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Présentation du sujet\n", + "\n", + "En 1958, Charles David Keeling a initié une mesure de la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis qui continue jusqu'à aujourd'hui. L'objectif initial était d'étudier la variation saisonnière, mais l'intérêt s'est déplacé plus tard vers l'étude de la tendance croissante dans le contexte du changement climatique. En honneur à Keeling, ce jeu de données est souvent appelé [\"Keeling Curve\"](https://fr.wikipedia.org/wiki/Courbe_de_Keeling). L'exploitation des données sera réalisée avec le langage Python 3." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chargement des données" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# On importe les lib nécessaires au chargement, au traitement et à la présentation des données.\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données que nous utilisons pour étudier l'évolution du taux de CO2 dans l'atmosphère sont disponibles sur le [site web de l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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410.15 \n", + "745 2019 12 43814 2019.9562 411.81 412.74 411.70 \n", + "746 2020 01 43845 2020.0410 413.30 413.25 412.90 \n", + "747 2020 02 43876 2020.1257 414.05 413.28 413.82 \n", + "748 2020 03 43905 2020.2049 414.45 412.87 414.83 \n", + "749 2020 04 43936 2020.2896 416.11 413.29 416.28 \n", + "750 2020 05 43966 2020.3716 417.15 413.74 417.05 \n", + "751 2020 06 43997 2020.4563 416.29 413.73 416.38 \n", + "752 2020 07 44027 2020.5383 414.42 413.64 414.79 \n", + "753 2020 08 44058 2020.6230 412.52 414.10 412.63 \n", + "754 2020 09 44089 2020.7077 411.18 414.70 410.91 \n", + "755 2020 10 44119 2020.7896 411.12 414.75 411.02 \n", + "756 2020 11 44150 2020.8743 412.88 415.16 412.57 \n", + "757 2020 12 44180 2020.9563 413.89 414.82 414.08 \n", + "758 2021 01 44211 2021.0411 415.15 415.10 415.22 \n", + "759 2021 02 44242 2021.1260 416.47 415.70 416.10 \n", + "760 2021 03 44270 2021.2027 417.16 415.61 417.02 \n", + "761 2021 04 44301 2021.2877 418.24 415.44 418.41 \n", + "762 2021 05 44331 2021.3699 418.95 415.53 419.14 \n", + "763 2021 06 44362 2021.4548 418.70 416.11 418.42 \n", + "764 2021 07 44392 2021.5370 416.65 415.84 416.76 \n", + "765 2021 08 44423 2021.6219 414.34 415.90 414.53 \n", + "766 2021 09 44454 2021.7068 412.90 416.42 -99.99 \n", + "767 2021 10 44484 2021.7890 -99.99 -99.99 -99.99 \n", + "768 2021 11 44515 2021.8740 -99.99 -99.99 -99.99 \n", + "769 2021 12 44545 2021.9562 -99.99 -99.99 -99.99 \n", + "\n", + " seasonally CO2 seasonally \n", + "0 adjusted fit filled adjusted filled \n", + "1 [ppm] [ppm] [ppm] \n", + "2 -99.99 -99.99 -99.99 \n", + "3 -99.99 -99.99 -99.99 \n", + "4 314.91 315.71 314.43 \n", + "5 314.99 317.45 315.16 \n", + "6 315.07 317.51 314.71 \n", + "7 315.15 317.25 315.15 \n", + "8 315.22 315.86 315.20 \n", + "9 315.29 314.93 316.20 \n", + "10 315.35 313.21 316.09 \n", + "11 315.41 312.44 315.41 \n", + "12 315.46 313.33 315.20 \n", + "13 315.52 314.67 315.43 \n", + "14 315.57 315.58 315.54 \n", + "15 315.64 316.49 315.85 \n", + "16 315.70 316.65 315.37 \n", + "17 315.77 317.72 315.42 \n", + "18 315.85 318.29 315.48 \n", + "19 315.94 318.15 316.02 \n", + "20 316.03 316.54 315.87 \n", + "21 316.13 314.80 316.07 \n", + "22 316.22 313.84 316.73 \n", + "23 316.31 313.33 316.33 \n", + "24 316.40 314.81 316.69 \n", + "25 316.48 315.58 316.35 \n", + "26 316.56 316.43 316.39 \n", + "27 316.64 316.98 316.34 \n", + "28 316.72 317.58 316.27 \n", + "29 316.80 319.03 316.70 \n", + ".. ... ... ... \n", + "740 411.51 411.78 410.97 \n", + "741 411.73 410.01 411.56 \n", + "742 411.96 408.48 411.98 \n", + "743 412.17 408.40 412.02 \n", + "744 412.40 410.16 412.44 \n", + "745 412.61 411.81 412.74 \n", + "746 412.83 413.30 413.25 \n", + "747 413.04 414.05 413.28 \n", + "748 413.23 414.45 412.87 \n", + "749 413.44 416.11 413.29 \n", + "750 413.64 417.15 413.74 \n", + "751 413.84 416.29 413.73 \n", + "752 414.04 414.42 413.64 \n", + "753 414.25 412.52 414.10 \n", + "754 414.45 411.18 414.70 \n", + "755 414.63 411.12 414.75 \n", + "756 414.82 412.88 415.16 \n", + "757 414.99 413.89 414.82 \n", + "758 415.16 415.15 415.10 \n", + "759 415.31 416.47 415.70 \n", + "760 415.45 417.16 415.61 \n", + "761 415.59 418.24 415.44 \n", + "762 415.72 418.95 415.53 \n", + "763 415.86 418.70 416.11 \n", + "764 415.98 416.65 415.84 \n", + "765 416.12 414.34 415.90 \n", + "766 -99.99 412.90 416.42 \n", + "767 -99.99 -99.99 -99.99 \n", + "768 -99.99 -99.99 -99.99 \n", + "769 -99.99 -99.99 -99.99 \n", + "\n", + "[770 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# On charge les données disponibles dans le fichier \".csv\" (ov enlève l'entête qui fait 54 lignes)\n", + "raw_data = pd.read_csv(data_url, skiprows=54, sep = ',')\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utilisation et mise en forme des données\n", + "\n", + "- Nous l'exploitation des données numériques, nous décidons d'enlever les lignes 0 et 1 car elles contiennent seulement des informations textuelles, ou des unités de mesure. On retiendra le point important qui est l'unité utilisée pour exprimer les quantités de CO2 : le ppm ou partie par million.\n", + "\n", + "- Pour ce qui est des dates, on garde uniquement les colonnes associées au mois (Mn) et à l'année (Yr) de la mesure. \n", + "\n", + "- De plus, on garde seulement les premières colonnes appelées \"CO2\" et \"seasonally\" puisqu'elles correspondent aux données non lissées. Les données \"CO2\" sont les données brutes et les données \"seasonally\" corresondent aux données brutes auqelles on a soustrait les variations saisonières. \n", + "\n", + "- Finalement, il faut enlever les espaces inutiles dans le nom des colonnes qui gènent le dépouillement. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Yr Mn CO2 seasonally\n", + "2 1958 01 -99.99 -99.99\n", + "3 1958 02 -99.99 -99.99\n", + "4 1958 03 315.71 314.43\n", + "5 1958 04 317.45 315.16\n", + "6 1958 05 317.51 314.71\n", + "7 1958 06 -99.99 -99.99\n", + "8 1958 07 315.86 315.20\n", + "9 1958 08 314.93 316.20\n", + "10 1958 09 313.21 316.09\n", + "11 1958 10 -99.99 -99.99\n", + "12 1958 11 313.33 315.20\n", + "13 1958 12 314.67 315.43\n", + "14 1959 01 315.58 315.54\n", + "15 1959 02 316.49 315.85\n", + "16 1959 03 316.65 315.37\n", + "17 1959 04 317.72 315.42\n", + "18 1959 05 318.29 315.48\n", + "19 1959 06 318.15 316.02\n", + "20 1959 07 316.54 315.87\n", + "21 1959 08 314.80 316.07\n", + "22 1959 09 313.84 316.73\n", + "23 1959 10 313.33 316.33\n", + "24 1959 11 314.81 316.69\n", + "25 1959 12 315.58 316.35\n", + "26 1960 01 316.43 316.39\n", + "27 1960 02 316.98 316.34\n", + "28 1960 03 317.58 316.27\n", + "29 1960 04 319.03 316.70\n", + "30 1960 05 320.03 317.22\n", + "31 1960 06 319.59 317.47\n", + ".. ... ... ... ...\n", + "740 2019 07 411.78 410.97\n", + "741 2019 08 410.01 411.56\n", + "742 2019 09 408.48 411.98\n", + "743 2019 10 408.40 412.02\n", + "744 2019 11 410.16 412.44\n", + "745 2019 12 411.81 412.74\n", + "746 2020 01 413.30 413.25\n", + "747 2020 02 414.05 413.28\n", + "748 2020 03 414.45 412.87\n", + "749 2020 04 416.11 413.29\n", + "750 2020 05 417.15 413.74\n", + "751 2020 06 416.29 413.73\n", + "752 2020 07 414.42 413.64\n", + "753 2020 08 412.52 414.10\n", + "754 2020 09 411.18 414.70\n", + "755 2020 10 411.12 414.75\n", + "756 2020 11 412.88 415.16\n", + "757 2020 12 413.89 414.82\n", + "758 2021 01 415.15 415.10\n", + "759 2021 02 416.47 415.70\n", + "760 2021 03 417.16 415.61\n", + "761 2021 04 418.24 415.44\n", + "762 2021 05 418.95 415.53\n", + "763 2021 06 418.70 416.11\n", + "764 2021 07 416.65 415.84\n", + "765 2021 08 414.34 415.90\n", + "766 2021 09 412.90 416.42\n", + "767 2021 10 -99.99 -99.99\n", + "768 2021 11 -99.99 -99.99\n", + "769 2021 12 -99.99 -99.99\n", + "\n", + "[768 rows x 4 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# On enlève les deux premières lignes\n", + "num_data = raw_data[2:]\n", + "# On supprime les deux colonnes de données \"Date\"\n", + "num_data = num_data.drop(num_data.columns[[2,3,6,7,8,9]], axis=1).copy()\n", + "# On enlève les espaces pour le nom des colonnes\n", + "new_columns = [y.replace(\" \",\"\") for y in num_data.columns]\n", + "num_data.columns = new_columns\n", + "num_data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A partir des colonnes Yr (année) et Mn (mois), on construit la colonne période au format approprié pour la représentation graphique des mesures." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YrMnCO2seasonallyperiod
2195801-99.99-99.991958-01
3195802-99.99-99.991958-02
4195803315.71314.431958-03
5195804317.45315.161958-04
6195805317.51314.711958-05
7195806-99.99-99.991958-06
8195807315.86315.201958-07
9195808314.93316.201958-08
10195809313.21316.091958-09
11195810-99.99-99.991958-10
12195811313.33315.201958-11
13195812314.67315.431958-12
14195901315.58315.541959-01
15195902316.49315.851959-02
16195903316.65315.371959-03
17195904317.72315.421959-04
18195905318.29315.481959-05
19195906318.15316.021959-06
20195907316.54315.871959-07
21195908314.80316.071959-08
22195909313.84316.731959-09
23195910313.33316.331959-10
24195911314.81316.691959-11
25195912315.58316.351959-12
26196001316.43316.391960-01
27196002316.98316.341960-02
28196003317.58316.271960-03
29196004319.03316.701960-04
30196005320.03317.221960-05
31196006319.59317.471960-06
..................
740201907411.78410.972019-07
741201908410.01411.562019-08
742201909408.48411.982019-09
743201910408.40412.022019-10
744201911410.16412.442019-11
745201912411.81412.742019-12
746202001413.30413.252020-01
747202002414.05413.282020-02
748202003414.45412.872020-03
749202004416.11413.292020-04
750202005417.15413.742020-05
751202006416.29413.732020-06
752202007414.42413.642020-07
753202008412.52414.102020-08
754202009411.18414.702020-09
755202010411.12414.752020-10
756202011412.88415.162020-11
757202012413.89414.822020-12
758202101415.15415.102021-01
759202102416.47415.702021-02
760202103417.16415.612021-03
761202104418.24415.442021-04
762202105418.95415.532021-05
763202106418.70416.112021-06
764202107416.65415.842021-07
765202108414.34415.902021-08
766202109412.90416.422021-09
767202110-99.99-99.992021-10
768202111-99.99-99.992021-11
769202112-99.99-99.992021-12
\n", + "

768 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn CO2 seasonally period\n", + "2 1958 01 -99.99 -99.99 1958-01\n", + "3 1958 02 -99.99 -99.99 1958-02\n", + "4 1958 03 315.71 314.43 1958-03\n", + "5 1958 04 317.45 315.16 1958-04\n", + "6 1958 05 317.51 314.71 1958-05\n", + "7 1958 06 -99.99 -99.99 1958-06\n", + "8 1958 07 315.86 315.20 1958-07\n", + "9 1958 08 314.93 316.20 1958-08\n", + "10 1958 09 313.21 316.09 1958-09\n", + "11 1958 10 -99.99 -99.99 1958-10\n", + "12 1958 11 313.33 315.20 1958-11\n", + "13 1958 12 314.67 315.43 1958-12\n", + "14 1959 01 315.58 315.54 1959-01\n", + "15 1959 02 316.49 315.85 1959-02\n", + "16 1959 03 316.65 315.37 1959-03\n", + "17 1959 04 317.72 315.42 1959-04\n", + "18 1959 05 318.29 315.48 1959-05\n", + "19 1959 06 318.15 316.02 1959-06\n", + "20 1959 07 316.54 315.87 1959-07\n", + "21 1959 08 314.80 316.07 1959-08\n", + "22 1959 09 313.84 316.73 1959-09\n", + "23 1959 10 313.33 316.33 1959-10\n", + "24 1959 11 314.81 316.69 1959-11\n", + "25 1959 12 315.58 316.35 1959-12\n", + "26 1960 01 316.43 316.39 1960-01\n", + "27 1960 02 316.98 316.34 1960-02\n", + "28 1960 03 317.58 316.27 1960-03\n", + "29 1960 04 319.03 316.70 1960-04\n", + "30 1960 05 320.03 317.22 1960-05\n", + "31 1960 06 319.59 317.47 1960-06\n", + ".. ... ... ... ... ...\n", + "740 2019 07 411.78 410.97 2019-07\n", + "741 2019 08 410.01 411.56 2019-08\n", + "742 2019 09 408.48 411.98 2019-09\n", + "743 2019 10 408.40 412.02 2019-10\n", + "744 2019 11 410.16 412.44 2019-11\n", + "745 2019 12 411.81 412.74 2019-12\n", + "746 2020 01 413.30 413.25 2020-01\n", + "747 2020 02 414.05 413.28 2020-02\n", + "748 2020 03 414.45 412.87 2020-03\n", + "749 2020 04 416.11 413.29 2020-04\n", + "750 2020 05 417.15 413.74 2020-05\n", + "751 2020 06 416.29 413.73 2020-06\n", + "752 2020 07 414.42 413.64 2020-07\n", + "753 2020 08 412.52 414.10 2020-08\n", + "754 2020 09 411.18 414.70 2020-09\n", + "755 2020 10 411.12 414.75 2020-10\n", + "756 2020 11 412.88 415.16 2020-11\n", + "757 2020 12 413.89 414.82 2020-12\n", + "758 2021 01 415.15 415.10 2021-01\n", + "759 2021 02 416.47 415.70 2021-02\n", + "760 2021 03 417.16 415.61 2021-03\n", + "761 2021 04 418.24 415.44 2021-04\n", + "762 2021 05 418.95 415.53 2021-05\n", + "763 2021 06 418.70 416.11 2021-06\n", + "764 2021 07 416.65 415.84 2021-07\n", + "765 2021 08 414.34 415.90 2021-08\n", + "766 2021 09 412.90 416.42 2021-09\n", + "767 2021 10 -99.99 -99.99 2021-10\n", + "768 2021 11 -99.99 -99.99 2021-11\n", + "769 2021 12 -99.99 -99.99 2021-12\n", + "\n", + "[768 rows x 5 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Avec isoweek, on définit une période pour chaque date de mesure.\n", + "\n", + "final_data = num_data.copy()\n", + "\n", + "ind_min = min(final_data.index)\n", + "ind_max = max(final_data.index)\n", + "\n", + "\n", + "final_data['period'] = [pd.Period(freq ='M', year=int(final_data['Yr'][k]), month=int(final_data['Mn'][k])) for k in range(ind_min, ind_max+1)]\n", + "final_data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YrMnCO2seasonally
period
1958-01195801-99.99-99.99
1958-02195802-99.99-99.99
1958-03195803315.71314.43
1958-04195804317.45315.16
1958-05195805317.51314.71
1958-06195806-99.99-99.99
1958-07195807315.86315.20
1958-08195808314.93316.20
1958-09195809313.21316.09
1958-10195810-99.99-99.99
1958-11195811313.33315.20
1958-12195812314.67315.43
1959-01195901315.58315.54
1959-02195902316.49315.85
1959-03195903316.65315.37
1959-04195904317.72315.42
1959-05195905318.29315.48
1959-06195906318.15316.02
1959-07195907316.54315.87
1959-08195908314.80316.07
1959-09195909313.84316.73
1959-10195910313.33316.33
1959-11195911314.81316.69
1959-12195912315.58316.35
1960-01196001316.43316.39
1960-02196002316.98316.34
1960-03196003317.58316.27
1960-04196004319.03316.70
1960-05196005320.03317.22
1960-06196006319.59317.47
...............
2019-07201907411.78410.97
2019-08201908410.01411.56
2019-09201909408.48411.98
2019-10201910408.40412.02
2019-11201911410.16412.44
2019-12201912411.81412.74
2020-01202001413.30413.25
2020-02202002414.05413.28
2020-03202003414.45412.87
2020-04202004416.11413.29
2020-05202005417.15413.74
2020-06202006416.29413.73
2020-07202007414.42413.64
2020-08202008412.52414.10
2020-09202009411.18414.70
2020-10202010411.12414.75
2020-11202011412.88415.16
2020-12202012413.89414.82
2021-01202101415.15415.10
2021-02202102416.47415.70
2021-03202103417.16415.61
2021-04202104418.24415.44
2021-05202105418.95415.53
2021-06202106418.70416.11
2021-07202107416.65415.84
2021-08202108414.34415.90
2021-09202109412.90416.42
2021-10202110-99.99-99.99
2021-11202111-99.99-99.99
2021-12202112-99.99-99.99
\n", + "

768 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn CO2 seasonally\n", + "period \n", + "1958-01 1958 01 -99.99 -99.99\n", + "1958-02 1958 02 -99.99 -99.99\n", + "1958-03 1958 03 315.71 314.43\n", + "1958-04 1958 04 317.45 315.16\n", + "1958-05 1958 05 317.51 314.71\n", + "1958-06 1958 06 -99.99 -99.99\n", + "1958-07 1958 07 315.86 315.20\n", + "1958-08 1958 08 314.93 316.20\n", + "1958-09 1958 09 313.21 316.09\n", + "1958-10 1958 10 -99.99 -99.99\n", + "1958-11 1958 11 313.33 315.20\n", + "1958-12 1958 12 314.67 315.43\n", + "1959-01 1959 01 315.58 315.54\n", + "1959-02 1959 02 316.49 315.85\n", + "1959-03 1959 03 316.65 315.37\n", + "1959-04 1959 04 317.72 315.42\n", + "1959-05 1959 05 318.29 315.48\n", + "1959-06 1959 06 318.15 316.02\n", + "1959-07 1959 07 316.54 315.87\n", + "1959-08 1959 08 314.80 316.07\n", + "1959-09 1959 09 313.84 316.73\n", + "1959-10 1959 10 313.33 316.33\n", + "1959-11 1959 11 314.81 316.69\n", + "1959-12 1959 12 315.58 316.35\n", + "1960-01 1960 01 316.43 316.39\n", + "1960-02 1960 02 316.98 316.34\n", + "1960-03 1960 03 317.58 316.27\n", + "1960-04 1960 04 319.03 316.70\n", + "1960-05 1960 05 320.03 317.22\n", + "1960-06 1960 06 319.59 317.47\n", + "... ... ... ... ...\n", + "2019-07 2019 07 411.78 410.97\n", + "2019-08 2019 08 410.01 411.56\n", + "2019-09 2019 09 408.48 411.98\n", + "2019-10 2019 10 408.40 412.02\n", + "2019-11 2019 11 410.16 412.44\n", + "2019-12 2019 12 411.81 412.74\n", + "2020-01 2020 01 413.30 413.25\n", + "2020-02 2020 02 414.05 413.28\n", + "2020-03 2020 03 414.45 412.87\n", + "2020-04 2020 04 416.11 413.29\n", + "2020-05 2020 05 417.15 413.74\n", + "2020-06 2020 06 416.29 413.73\n", + "2020-07 2020 07 414.42 413.64\n", + "2020-08 2020 08 412.52 414.10\n", + "2020-09 2020 09 411.18 414.70\n", + "2020-10 2020 10 411.12 414.75\n", + "2020-11 2020 11 412.88 415.16\n", + "2020-12 2020 12 413.89 414.82\n", + "2021-01 2021 01 415.15 415.10\n", + "2021-02 2021 02 416.47 415.70\n", + "2021-03 2021 03 417.16 415.61\n", + "2021-04 2021 04 418.24 415.44\n", + "2021-05 2021 05 418.95 415.53\n", + "2021-06 2021 06 418.70 416.11\n", + "2021-07 2021 07 416.65 415.84\n", + "2021-08 2021 08 414.34 415.90\n", + "2021-09 2021 09 412.90 416.42\n", + "2021-10 2021 10 -99.99 -99.99\n", + "2021-11 2021 11 -99.99 -99.99\n", + "2021-12 2021 12 -99.99 -99.99\n", + "\n", + "[768 rows x 4 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_data = final_data.set_index('period').sort_index()\n", + "final_data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "for k in [2,3]:\n", + " final_data[final_data.columns[k]] = final_data[final_data.columns[k]].astype(float) \n", + "final_data.drop( final_data[ final_data['CO2'] <0 ].index, inplace=True)\n", + "final_data.drop( final_data[ final_data['seasonally'] <0 ].index, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Présentation graphique des données\n", + "\n", + "Cette section présente les variations brutes de CO2 mesurées, ainsi que les mêmes données corrigées des variations saisonières.\n", + "\n", + "- On observe une tendance globale à la hausse de la quantité de CO2 dans l'atmosphère\n", + "- On observe des variations saisonières autour de cette tendance globale" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "final_data['CO2'].plot(label = 'Données brutes')\n", + "final_data['seasonally'].plot(label = 'Données corrigées des variations saisonières')\n", + "plt.xlabel('Période')\n", + "plt.ylabel('Quantité de CO2 en ppm')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caractérisation de la période des oscillations\n", + "\n", + "On cherche à caractériser la période de ces oscillations. Pour ce faire, on divise les données CO2 brutes par les données corrigées des variations saisonières. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "oscillations = final_data['CO2']/final_data['seasonally']\n", + "oscillations.plot(label = 'Données brutes')\n", + "plt.xlabel('Période')\n", + "plt.ylabel('Oscillations saisonières : variations relatives')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut estimer la période caractéristiques des variations saisonières" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "oscillations = final_data['CO2']/final_data['seasonally']\n", + "oscillations[200:250].plot(label = 'Données brutes')\n", + "plt.xlabel('Période')\n", + "plt.ylabel('Oscillations saisonières : variations relatives')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On observe que la période des oscillations est de 1 an, ce qui est cohérent avec le côté saisonier de ces variations. Une analyse par transformée de Fourier Rapide (FFT) permettrait d'identifier plus précisément cette période. Nous considérons qu'un analyse visuelle suffit en premier lieu." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 0bbbe37..47c217f 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -1,5 +1,2931 @@ { - "cells": [], + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sujet 1 : Concentration de CO2 dans l'atmosphère depuis 1958" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Présentation du sujet\n", + "\n", + "En 1958, Charles David Keeling a initié une mesure de la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis qui continue jusqu'à aujourd'hui. L'objectif initial était d'étudier la variation saisonnière, mais l'intérêt s'est déplacé plus tard vers l'étude de la tendance croissante dans le contexte du changement climatique. En honneur à Keeling, ce jeu de données est souvent appelé [\"Keeling Curve\"](https://fr.wikipedia.org/wiki/Courbe_de_Keeling). L'exploitation des données sera réalisée avec le langage Python 3." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Chargement des données" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# On importe les lib nécessaires au chargement, au traitement et à la présentation des données.\n", + "%matplotlib inline\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import isoweek" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données que nous utilisons pour étudier l'évolution du taux de CO2 dans l'atmosphère sont disponibles sur le [site web de l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
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1Excel[ppm][ppm][ppm][ppm][ppm][ppm]
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7195806213511958.4548-99.99-99.99317.25315.15317.25315.15
8195807213811958.5370315.86315.20315.86315.22315.86315.20
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10195809214431958.7068313.21316.09312.46315.35313.21316.09
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17195904216551959.2877317.72315.42318.09315.77317.72315.42
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20195907217461959.5370316.54315.87316.67316.03316.54315.87
21195908217771959.6219314.80316.07314.82316.13314.80316.07
22195909218081959.7068313.84316.73313.32316.22313.84316.73
23195910218381959.7890313.33316.33313.33316.31313.33316.33
24195911218691959.8740314.81316.69314.54316.40314.81316.69
25195912218991959.9562315.58316.35315.72316.48315.58316.35
26196001219301960.0410316.43316.39316.61316.56316.43316.39
27196002219611960.1257316.98316.34317.28316.64316.98316.34
28196003219901960.2049317.58316.27318.04316.72317.58316.27
29196004220211960.2896319.03316.70319.14316.80319.03316.70
.................................
740201907436612019.5370411.78410.97412.29411.51411.78410.97
741201908436922019.6219410.01411.56410.15411.73410.01411.56
742201909437232019.7068408.48411.98408.44411.96408.48411.98
743201910437532019.7890408.40412.02408.57412.17408.40412.02
744201911437842019.8740410.16412.44410.15412.40410.16412.44
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747202002438762020.1257414.05413.28413.82413.04414.05413.28
748202003439052020.2049414.45412.87414.83413.23414.45412.87
749202004439362020.2896416.11413.29416.28413.44416.11413.29
750202005439662020.3716417.15413.74417.05413.64417.15413.74
751202006439972020.4563416.29413.73416.38413.84416.29413.73
752202007440272020.5383414.42413.64414.79414.04414.42413.64
753202008440582020.6230412.52414.10412.63414.25412.52414.10
754202009440892020.7077411.18414.70410.91414.45411.18414.70
755202010441192020.7896411.12414.75411.02414.63411.12414.75
756202011441502020.8743412.88415.16412.57414.82412.88415.16
757202012441802020.9563413.89414.82414.08414.99413.89414.82
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770 rows × 10 columns

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44331 2021.3699 418.95 415.53 419.14 \n", + "763 2021 06 44362 2021.4548 418.70 416.11 418.42 \n", + "764 2021 07 44392 2021.5370 416.65 415.84 416.76 \n", + "765 2021 08 44423 2021.6219 414.34 415.90 414.53 \n", + "766 2021 09 44454 2021.7068 412.90 416.42 -99.99 \n", + "767 2021 10 44484 2021.7890 -99.99 -99.99 -99.99 \n", + "768 2021 11 44515 2021.8740 -99.99 -99.99 -99.99 \n", + "769 2021 12 44545 2021.9562 -99.99 -99.99 -99.99 \n", + "\n", + " seasonally CO2 seasonally \n", + "0 adjusted fit filled adjusted filled \n", + "1 [ppm] [ppm] [ppm] \n", + "2 -99.99 -99.99 -99.99 \n", + "3 -99.99 -99.99 -99.99 \n", + "4 314.91 315.71 314.43 \n", + "5 314.99 317.45 315.16 \n", + "6 315.07 317.51 314.71 \n", + "7 315.15 317.25 315.15 \n", + "8 315.22 315.86 315.20 \n", + "9 315.29 314.93 316.20 \n", + "10 315.35 313.21 316.09 \n", + "11 315.41 312.44 315.41 \n", + "12 315.46 313.33 315.20 \n", + "13 315.52 314.67 315.43 \n", + "14 315.57 315.58 315.54 \n", + "15 315.64 316.49 315.85 \n", + "16 315.70 316.65 315.37 \n", + "17 315.77 317.72 315.42 \n", + "18 315.85 318.29 315.48 \n", + "19 315.94 318.15 316.02 \n", + "20 316.03 316.54 315.87 \n", + "21 316.13 314.80 316.07 \n", + "22 316.22 313.84 316.73 \n", + "23 316.31 313.33 316.33 \n", + "24 316.40 314.81 316.69 \n", + "25 316.48 315.58 316.35 \n", + "26 316.56 316.43 316.39 \n", + "27 316.64 316.98 316.34 \n", + "28 316.72 317.58 316.27 \n", + "29 316.80 319.03 316.70 \n", + ".. ... ... ... \n", + "740 411.51 411.78 410.97 \n", + "741 411.73 410.01 411.56 \n", + "742 411.96 408.48 411.98 \n", + "743 412.17 408.40 412.02 \n", + "744 412.40 410.16 412.44 \n", + "745 412.61 411.81 412.74 \n", + "746 412.83 413.30 413.25 \n", + "747 413.04 414.05 413.28 \n", + "748 413.23 414.45 412.87 \n", + "749 413.44 416.11 413.29 \n", + "750 413.64 417.15 413.74 \n", + "751 413.84 416.29 413.73 \n", + "752 414.04 414.42 413.64 \n", + "753 414.25 412.52 414.10 \n", + "754 414.45 411.18 414.70 \n", + "755 414.63 411.12 414.75 \n", + "756 414.82 412.88 415.16 \n", + "757 414.99 413.89 414.82 \n", + "758 415.16 415.15 415.10 \n", + "759 415.31 416.47 415.70 \n", + "760 415.45 417.16 415.61 \n", + "761 415.59 418.24 415.44 \n", + "762 415.72 418.95 415.53 \n", + "763 415.86 418.70 416.11 \n", + "764 415.98 416.65 415.84 \n", + "765 416.12 414.34 415.90 \n", + "766 -99.99 412.90 416.42 \n", + "767 -99.99 -99.99 -99.99 \n", + "768 -99.99 -99.99 -99.99 \n", + "769 -99.99 -99.99 -99.99 \n", + "\n", + "[770 rows x 10 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# On charge les données disponibles dans le fichier \".csv\" (ov enlève l'entête qui fait 54 lignes)\n", + "raw_data = pd.read_csv(data_url, skiprows=54, sep = ',')\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Utilisation et mise en forme des données\n", + "\n", + "- Nous l'exploitation des données numériques, nous décidons d'enlever les lignes 0 et 1 car elles contiennent seulement des informations textuelles, ou des unités de mesure. On retiendra le point important qui est l'unité utilisée pour exprimer les quantités de CO2 : le ppm ou partie par million.\n", + "\n", + "- Pour ce qui est des dates, on garde uniquement les colonnes associées au mois (Mn) et à l'année (Yr) de la mesure. \n", + "\n", + "- De plus, on garde seulement les premières colonnes appelées \"CO2\" et \"seasonally\" puisqu'elles correspondent aux données non lissées. Les données \"CO2\" sont les données brutes et les données \"seasonally\" corresondent aux données brutes auqelles on a soustrait les variations saisonières. \n", + "\n", + "- Finalement, il faut enlever les espaces inutiles dans le nom des colonnes qui gènent le dépouillement. " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Yr Mn CO2 seasonally\n", + "2 1958 01 -99.99 -99.99\n", + "3 1958 02 -99.99 -99.99\n", + "4 1958 03 315.71 314.43\n", + "5 1958 04 317.45 315.16\n", + "6 1958 05 317.51 314.71\n", + "7 1958 06 -99.99 -99.99\n", + "8 1958 07 315.86 315.20\n", + "9 1958 08 314.93 316.20\n", + "10 1958 09 313.21 316.09\n", + "11 1958 10 -99.99 -99.99\n", + "12 1958 11 313.33 315.20\n", + "13 1958 12 314.67 315.43\n", + "14 1959 01 315.58 315.54\n", + "15 1959 02 316.49 315.85\n", + "16 1959 03 316.65 315.37\n", + "17 1959 04 317.72 315.42\n", + "18 1959 05 318.29 315.48\n", + "19 1959 06 318.15 316.02\n", + "20 1959 07 316.54 315.87\n", + "21 1959 08 314.80 316.07\n", + "22 1959 09 313.84 316.73\n", + "23 1959 10 313.33 316.33\n", + "24 1959 11 314.81 316.69\n", + "25 1959 12 315.58 316.35\n", + "26 1960 01 316.43 316.39\n", + "27 1960 02 316.98 316.34\n", + "28 1960 03 317.58 316.27\n", + "29 1960 04 319.03 316.70\n", + "30 1960 05 320.03 317.22\n", + "31 1960 06 319.59 317.47\n", + ".. ... ... ... ...\n", + "740 2019 07 411.78 410.97\n", + "741 2019 08 410.01 411.56\n", + "742 2019 09 408.48 411.98\n", + "743 2019 10 408.40 412.02\n", + "744 2019 11 410.16 412.44\n", + "745 2019 12 411.81 412.74\n", + "746 2020 01 413.30 413.25\n", + "747 2020 02 414.05 413.28\n", + "748 2020 03 414.45 412.87\n", + "749 2020 04 416.11 413.29\n", + "750 2020 05 417.15 413.74\n", + "751 2020 06 416.29 413.73\n", + "752 2020 07 414.42 413.64\n", + "753 2020 08 412.52 414.10\n", + "754 2020 09 411.18 414.70\n", + "755 2020 10 411.12 414.75\n", + "756 2020 11 412.88 415.16\n", + "757 2020 12 413.89 414.82\n", + "758 2021 01 415.15 415.10\n", + "759 2021 02 416.47 415.70\n", + "760 2021 03 417.16 415.61\n", + "761 2021 04 418.24 415.44\n", + "762 2021 05 418.95 415.53\n", + "763 2021 06 418.70 416.11\n", + "764 2021 07 416.65 415.84\n", + "765 2021 08 414.34 415.90\n", + "766 2021 09 412.90 416.42\n", + "767 2021 10 -99.99 -99.99\n", + "768 2021 11 -99.99 -99.99\n", + "769 2021 12 -99.99 -99.99\n", + "\n", + "[768 rows x 4 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# On enlève les deux premières lignes\n", + "num_data = raw_data[2:]\n", + "# On supprime les deux colonnes de données \"Date\"\n", + "num_data = num_data.drop(num_data.columns[[2,3,6,7,8,9]], axis=1).copy()\n", + "# On enlève les espaces pour le nom des colonnes\n", + "new_columns = [y.replace(\" \",\"\") for y in num_data.columns]\n", + "num_data.columns = new_columns\n", + "num_data\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A partir des colonnes Yr (année) et Mn (mois), on construit la colonne période au format approprié pour la représentation graphique des mesures." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YrMnCO2seasonallyperiod
2195801-99.99-99.991958-01
3195802-99.99-99.991958-02
4195803315.71314.431958-03
5195804317.45315.161958-04
6195805317.51314.711958-05
7195806-99.99-99.991958-06
8195807315.86315.201958-07
9195808314.93316.201958-08
10195809313.21316.091958-09
11195810-99.99-99.991958-10
12195811313.33315.201958-11
13195812314.67315.431958-12
14195901315.58315.541959-01
15195902316.49315.851959-02
16195903316.65315.371959-03
17195904317.72315.421959-04
18195905318.29315.481959-05
19195906318.15316.021959-06
20195907316.54315.871959-07
21195908314.80316.071959-08
22195909313.84316.731959-09
23195910313.33316.331959-10
24195911314.81316.691959-11
25195912315.58316.351959-12
26196001316.43316.391960-01
27196002316.98316.341960-02
28196003317.58316.271960-03
29196004319.03316.701960-04
30196005320.03317.221960-05
31196006319.59317.471960-06
..................
740201907411.78410.972019-07
741201908410.01411.562019-08
742201909408.48411.982019-09
743201910408.40412.022019-10
744201911410.16412.442019-11
745201912411.81412.742019-12
746202001413.30413.252020-01
747202002414.05413.282020-02
748202003414.45412.872020-03
749202004416.11413.292020-04
750202005417.15413.742020-05
751202006416.29413.732020-06
752202007414.42413.642020-07
753202008412.52414.102020-08
754202009411.18414.702020-09
755202010411.12414.752020-10
756202011412.88415.162020-11
757202012413.89414.822020-12
758202101415.15415.102021-01
759202102416.47415.702021-02
760202103417.16415.612021-03
761202104418.24415.442021-04
762202105418.95415.532021-05
763202106418.70416.112021-06
764202107416.65415.842021-07
765202108414.34415.902021-08
766202109412.90416.422021-09
767202110-99.99-99.992021-10
768202111-99.99-99.992021-11
769202112-99.99-99.992021-12
\n", + "

768 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn CO2 seasonally period\n", + "2 1958 01 -99.99 -99.99 1958-01\n", + "3 1958 02 -99.99 -99.99 1958-02\n", + "4 1958 03 315.71 314.43 1958-03\n", + "5 1958 04 317.45 315.16 1958-04\n", + "6 1958 05 317.51 314.71 1958-05\n", + "7 1958 06 -99.99 -99.99 1958-06\n", + "8 1958 07 315.86 315.20 1958-07\n", + "9 1958 08 314.93 316.20 1958-08\n", + "10 1958 09 313.21 316.09 1958-09\n", + "11 1958 10 -99.99 -99.99 1958-10\n", + "12 1958 11 313.33 315.20 1958-11\n", + "13 1958 12 314.67 315.43 1958-12\n", + "14 1959 01 315.58 315.54 1959-01\n", + "15 1959 02 316.49 315.85 1959-02\n", + "16 1959 03 316.65 315.37 1959-03\n", + "17 1959 04 317.72 315.42 1959-04\n", + "18 1959 05 318.29 315.48 1959-05\n", + "19 1959 06 318.15 316.02 1959-06\n", + "20 1959 07 316.54 315.87 1959-07\n", + "21 1959 08 314.80 316.07 1959-08\n", + "22 1959 09 313.84 316.73 1959-09\n", + "23 1959 10 313.33 316.33 1959-10\n", + "24 1959 11 314.81 316.69 1959-11\n", + "25 1959 12 315.58 316.35 1959-12\n", + "26 1960 01 316.43 316.39 1960-01\n", + "27 1960 02 316.98 316.34 1960-02\n", + "28 1960 03 317.58 316.27 1960-03\n", + "29 1960 04 319.03 316.70 1960-04\n", + "30 1960 05 320.03 317.22 1960-05\n", + "31 1960 06 319.59 317.47 1960-06\n", + ".. ... ... ... ... ...\n", + "740 2019 07 411.78 410.97 2019-07\n", + "741 2019 08 410.01 411.56 2019-08\n", + "742 2019 09 408.48 411.98 2019-09\n", + "743 2019 10 408.40 412.02 2019-10\n", + "744 2019 11 410.16 412.44 2019-11\n", + "745 2019 12 411.81 412.74 2019-12\n", + "746 2020 01 413.30 413.25 2020-01\n", + "747 2020 02 414.05 413.28 2020-02\n", + "748 2020 03 414.45 412.87 2020-03\n", + "749 2020 04 416.11 413.29 2020-04\n", + "750 2020 05 417.15 413.74 2020-05\n", + "751 2020 06 416.29 413.73 2020-06\n", + "752 2020 07 414.42 413.64 2020-07\n", + "753 2020 08 412.52 414.10 2020-08\n", + "754 2020 09 411.18 414.70 2020-09\n", + "755 2020 10 411.12 414.75 2020-10\n", + "756 2020 11 412.88 415.16 2020-11\n", + "757 2020 12 413.89 414.82 2020-12\n", + "758 2021 01 415.15 415.10 2021-01\n", + "759 2021 02 416.47 415.70 2021-02\n", + "760 2021 03 417.16 415.61 2021-03\n", + "761 2021 04 418.24 415.44 2021-04\n", + "762 2021 05 418.95 415.53 2021-05\n", + "763 2021 06 418.70 416.11 2021-06\n", + "764 2021 07 416.65 415.84 2021-07\n", + "765 2021 08 414.34 415.90 2021-08\n", + "766 2021 09 412.90 416.42 2021-09\n", + "767 2021 10 -99.99 -99.99 2021-10\n", + "768 2021 11 -99.99 -99.99 2021-11\n", + "769 2021 12 -99.99 -99.99 2021-12\n", + "\n", + "[768 rows x 5 columns]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Avec isoweek, on définit une période pour chaque date de mesure.\n", + "\n", + "final_data = num_data.copy()\n", + "\n", + "ind_min = min(final_data.index)\n", + "ind_max = max(final_data.index)\n", + "\n", + "\n", + "final_data['period'] = [pd.Period(freq ='M', year=int(final_data['Yr'][k]), month=int(final_data['Mn'][k])) for k in range(ind_min, ind_max+1)]\n", + "final_data" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YrMnCO2seasonally
period
1958-01195801-99.99-99.99
1958-02195802-99.99-99.99
1958-03195803315.71314.43
1958-04195804317.45315.16
1958-05195805317.51314.71
1958-06195806-99.99-99.99
1958-07195807315.86315.20
1958-08195808314.93316.20
1958-09195809313.21316.09
1958-10195810-99.99-99.99
1958-11195811313.33315.20
1958-12195812314.67315.43
1959-01195901315.58315.54
1959-02195902316.49315.85
1959-03195903316.65315.37
1959-04195904317.72315.42
1959-05195905318.29315.48
1959-06195906318.15316.02
1959-07195907316.54315.87
1959-08195908314.80316.07
1959-09195909313.84316.73
1959-10195910313.33316.33
1959-11195911314.81316.69
1959-12195912315.58316.35
1960-01196001316.43316.39
1960-02196002316.98316.34
1960-03196003317.58316.27
1960-04196004319.03316.70
1960-05196005320.03317.22
1960-06196006319.59317.47
...............
2019-07201907411.78410.97
2019-08201908410.01411.56
2019-09201909408.48411.98
2019-10201910408.40412.02
2019-11201911410.16412.44
2019-12201912411.81412.74
2020-01202001413.30413.25
2020-02202002414.05413.28
2020-03202003414.45412.87
2020-04202004416.11413.29
2020-05202005417.15413.74
2020-06202006416.29413.73
2020-07202007414.42413.64
2020-08202008412.52414.10
2020-09202009411.18414.70
2020-10202010411.12414.75
2020-11202011412.88415.16
2020-12202012413.89414.82
2021-01202101415.15415.10
2021-02202102416.47415.70
2021-03202103417.16415.61
2021-04202104418.24415.44
2021-05202105418.95415.53
2021-06202106418.70416.11
2021-07202107416.65415.84
2021-08202108414.34415.90
2021-09202109412.90416.42
2021-10202110-99.99-99.99
2021-11202111-99.99-99.99
2021-12202112-99.99-99.99
\n", + "

768 rows × 4 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn CO2 seasonally\n", + "period \n", + "1958-01 1958 01 -99.99 -99.99\n", + "1958-02 1958 02 -99.99 -99.99\n", + "1958-03 1958 03 315.71 314.43\n", + "1958-04 1958 04 317.45 315.16\n", + "1958-05 1958 05 317.51 314.71\n", + "1958-06 1958 06 -99.99 -99.99\n", + "1958-07 1958 07 315.86 315.20\n", + "1958-08 1958 08 314.93 316.20\n", + "1958-09 1958 09 313.21 316.09\n", + "1958-10 1958 10 -99.99 -99.99\n", + "1958-11 1958 11 313.33 315.20\n", + "1958-12 1958 12 314.67 315.43\n", + "1959-01 1959 01 315.58 315.54\n", + "1959-02 1959 02 316.49 315.85\n", + "1959-03 1959 03 316.65 315.37\n", + "1959-04 1959 04 317.72 315.42\n", + "1959-05 1959 05 318.29 315.48\n", + "1959-06 1959 06 318.15 316.02\n", + "1959-07 1959 07 316.54 315.87\n", + "1959-08 1959 08 314.80 316.07\n", + "1959-09 1959 09 313.84 316.73\n", + "1959-10 1959 10 313.33 316.33\n", + "1959-11 1959 11 314.81 316.69\n", + "1959-12 1959 12 315.58 316.35\n", + "1960-01 1960 01 316.43 316.39\n", + "1960-02 1960 02 316.98 316.34\n", + "1960-03 1960 03 317.58 316.27\n", + "1960-04 1960 04 319.03 316.70\n", + "1960-05 1960 05 320.03 317.22\n", + "1960-06 1960 06 319.59 317.47\n", + "... ... ... ... ...\n", + "2019-07 2019 07 411.78 410.97\n", + "2019-08 2019 08 410.01 411.56\n", + "2019-09 2019 09 408.48 411.98\n", + "2019-10 2019 10 408.40 412.02\n", + "2019-11 2019 11 410.16 412.44\n", + "2019-12 2019 12 411.81 412.74\n", + "2020-01 2020 01 413.30 413.25\n", + "2020-02 2020 02 414.05 413.28\n", + "2020-03 2020 03 414.45 412.87\n", + "2020-04 2020 04 416.11 413.29\n", + "2020-05 2020 05 417.15 413.74\n", + "2020-06 2020 06 416.29 413.73\n", + "2020-07 2020 07 414.42 413.64\n", + "2020-08 2020 08 412.52 414.10\n", + "2020-09 2020 09 411.18 414.70\n", + "2020-10 2020 10 411.12 414.75\n", + "2020-11 2020 11 412.88 415.16\n", + "2020-12 2020 12 413.89 414.82\n", + "2021-01 2021 01 415.15 415.10\n", + "2021-02 2021 02 416.47 415.70\n", + "2021-03 2021 03 417.16 415.61\n", + "2021-04 2021 04 418.24 415.44\n", + "2021-05 2021 05 418.95 415.53\n", + "2021-06 2021 06 418.70 416.11\n", + "2021-07 2021 07 416.65 415.84\n", + "2021-08 2021 08 414.34 415.90\n", + "2021-09 2021 09 412.90 416.42\n", + "2021-10 2021 10 -99.99 -99.99\n", + "2021-11 2021 11 -99.99 -99.99\n", + "2021-12 2021 12 -99.99 -99.99\n", + "\n", + "[768 rows x 4 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "final_data = final_data.set_index('period').sort_index()\n", + "final_data" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "for k in [2,3]:\n", + " final_data[final_data.columns[k]] = final_data[final_data.columns[k]].astype(float) \n", + "final_data.drop( final_data[ final_data['CO2'] <0 ].index, inplace=True)\n", + "final_data.drop( final_data[ final_data['seasonally'] <0 ].index, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Présentation graphique des données\n", + "\n", + "Cette section présente les variations brutes de CO2 mesurées, ainsi que les mêmes données corrigées des variations saisonières.\n", + "\n", + "- On observe une tendance globale à la hausse de la quantité de CO2 dans l'atmosphère\n", + "- On observe des variations saisonières autour de cette tendance globale" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "final_data['CO2'].plot(label = 'Données brutes')\n", + "final_data['seasonally'].plot(label = 'Données corrigées des variations saisonières')\n", + "plt.xlabel('Période')\n", + "plt.ylabel('Quantité de CO2 en ppm')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Caractérisation de la période des oscillations\n", + "\n", + "On cherche à caractériser la période de ces oscillations. Pour ce faire, on divise les données CO2 brutes par les données corrigées des variations saisonières. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "oscillations = final_data['CO2']/final_data['seasonally']\n", + "oscillations.plot(label = 'Données brutes')\n", + "plt.xlabel('Période')\n", + "plt.ylabel('Oscillations saisonières : variations relatives')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On peut estimer la période caractéristiques des variations saisonières" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "oscillations = final_data['CO2']/final_data['seasonally']\n", + "oscillations[200:250].plot(label = 'Données brutes')\n", + "plt.xlabel('Période')\n", + "plt.ylabel('Oscillations saisonières : variations relatives')\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On observe que la période des oscillations est de 1 an, ce qui est cohérent avec le côté saisonier de ces variations. Une analyse par transformée de Fourier Rapide (FFT) permettrait d'identifier plus précisément cette période. Nous considérons qu'un analyse visuelle suffit en premier lieu." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +2942,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } - -- 2.18.1