{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Concentration de CO2 dans l'atmosphère depuis 1958" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de concentration de CO2 dans l'atmosphère depuis 1958 sont disponibles du site Web de [l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à un mois.\n", "Nous téléchargeons un dataset nommé \"Primary Mauna Loa CO2 Record\" le 25 novembre 2022.\n", "Le jeu de données commence en Mars 1958 et se termine avec le mois précédent." ] }, { "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": "markdown", "metadata": {}, "source": [ "On télécharge le dataset (s'il n'existe pas déjà) afin de pouvoir travailler en local et de figer les résultat (en cas de mise à jour des données)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib.request\n", "\n", "local_file = 'monthly_in_situ_co2_mlo.csv'\n", "\n", "if not os.path.exists(local_file):\n", " urllib.request.urlretrieve(data_url, local_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les 54 premières lignes du fichier CSV sont des commentaires, les 3 suivantes constituent le header.\n", "Nous les ignorons en précisant `skiprows=57`.\n", "Nous renommons également les colonnes." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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519586213511958.4548-99.99-99.99317.26315.14317.26315.14
619587213811958.5370315.87315.20315.85315.22315.87315.20
719588214121958.6219314.93316.21313.97315.29314.93316.21
819589214431958.7068313.21316.11312.44315.35313.21316.11
9195810214731958.7890-99.99-99.99312.42315.41312.42315.41
10195811215041958.8740313.33315.21313.60315.46313.33315.21
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1319592215961959.1260316.49315.84316.29315.63316.49315.84
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1519594216551959.2877317.72315.41318.09315.77317.72315.41
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1919598217771959.6219314.80316.09314.80316.12314.80316.09
2019599218081959.7068313.84316.74313.30316.22313.84316.74
21195910218381959.7890313.33316.34313.31316.31313.33316.34
22195911218691959.8740314.81316.70314.53316.39314.81316.70
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2919606220821960.4563319.59317.46319.03316.93319.59317.46
.................................
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780 rows × 10 columns

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318.15 316.01 318.06 \n", "18 1959 7 21746 1959.5370 316.54 315.87 316.67 \n", "19 1959 8 21777 1959.6219 314.80 316.09 314.80 \n", "20 1959 9 21808 1959.7068 313.84 316.74 313.30 \n", "21 1959 10 21838 1959.7890 313.33 316.34 313.31 \n", "22 1959 11 21869 1959.8740 314.81 316.70 314.53 \n", "23 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", "24 1960 1 21930 1960.0410 316.43 316.37 316.62 \n", "25 1960 2 21961 1960.1257 316.98 316.33 317.29 \n", "26 1960 3 21990 1960.2049 317.58 316.27 318.04 \n", "27 1960 4 22021 1960.2896 319.03 316.70 319.14 \n", "28 1960 5 22051 1960.3716 320.03 317.20 319.69 \n", "29 1960 6 22082 1960.4563 319.59 317.46 319.03 \n", ".. ... .. ... ... ... ... ... \n", "750 2020 7 44027 2020.5383 414.42 413.65 414.75 \n", "751 2020 8 44058 2020.6230 412.52 414.09 412.60 \n", "752 2020 9 44089 2020.7077 411.18 414.68 410.91 \n", "753 2020 10 44119 2020.7896 411.12 414.72 411.02 \n", "754 2020 11 44150 2020.8743 412.88 415.14 412.56 \n", "755 2020 12 44180 2020.9563 413.89 414.81 414.07 \n", "756 2021 1 44211 2021.0411 415.15 415.08 415.23 \n", "757 2021 2 44242 2021.1260 416.47 415.69 416.12 \n", "758 2021 3 44270 2021.2027 417.16 415.62 417.04 \n", "759 2021 4 44301 2021.2877 418.24 415.47 418.44 \n", "760 2021 5 44331 2021.3699 418.95 415.56 419.21 \n", "761 2021 6 44362 2021.4548 418.70 416.13 418.55 \n", "762 2021 7 44392 2021.5370 416.65 415.85 416.94 \n", "763 2021 8 44423 2021.6219 414.34 415.89 414.76 \n", "764 2021 9 44454 2021.7068 412.91 416.40 413.03 \n", "765 2021 10 44484 2021.7890 413.55 417.16 413.13 \n", "766 2021 11 44515 2021.8740 414.82 417.09 414.67 \n", "767 2021 12 44545 2021.9562 416.43 417.36 416.18 \n", "768 2022 1 44576 2022.0411 418.01 417.94 417.34 \n", "769 2022 2 44607 2022.1260 418.99 418.20 418.20 \n", "770 2022 3 44635 2022.2027 418.45 416.91 419.11 \n", "771 2022 4 44666 2022.2877 420.02 417.24 420.49 \n", "772 2022 5 44696 2022.3699 420.77 417.37 421.25 \n", "773 2022 6 44727 2022.4548 420.68 418.10 420.58 \n", "774 2022 7 44757 2022.5370 418.68 417.87 418.96 \n", "775 2022 8 44788 2022.6219 416.76 418.31 416.78 \n", "776 2022 9 44819 2022.7068 415.41 418.91 415.04 \n", "777 2022 10 44849 2022.7890 415.31 418.93 -99.99 \n", "778 2022 11 44880 2022.8740 -99.99 -99.99 -99.99 \n", "779 2022 12 44910 2022.9562 -99.99 -99.99 -99.99 \n", "\n", " SeasonallyAdjustedFit CO2Filled SeasonallyAdjustedFilled \n", "0 -99.99 -99.99 -99.99 \n", "1 -99.99 -99.99 -99.99 \n", "2 314.91 315.71 314.43 \n", "3 314.99 317.45 315.16 \n", "4 315.06 317.51 314.70 \n", "5 315.14 317.26 315.14 \n", "6 315.22 315.87 315.20 \n", "7 315.29 314.93 316.21 \n", "8 315.35 313.21 316.11 \n", "9 315.41 312.42 315.41 \n", "10 315.46 313.33 315.21 \n", "11 315.51 314.67 315.44 \n", "12 315.57 315.58 315.52 \n", "13 315.63 316.49 315.84 \n", "14 315.69 316.65 315.37 \n", "15 315.77 317.72 315.41 \n", "16 315.85 318.29 315.47 \n", "17 315.94 318.15 316.01 \n", "18 316.03 316.54 315.87 \n", "19 316.12 314.80 316.09 \n", "20 316.22 313.84 316.74 \n", "21 316.31 313.33 316.34 \n", "22 316.39 314.81 316.70 \n", "23 316.47 315.58 316.35 \n", "24 316.56 316.43 316.37 \n", "25 316.64 316.98 316.33 \n", "26 316.71 317.58 316.27 \n", "27 316.79 319.03 316.70 \n", "28 316.86 320.03 317.20 \n", "29 316.93 319.59 317.46 \n", ".. ... ... ... \n", "750 414.02 414.42 413.65 \n", "751 414.22 412.52 414.09 \n", "752 414.42 411.18 414.68 \n", "753 414.61 411.12 414.72 \n", "754 414.80 412.88 415.14 \n", "755 414.97 413.89 414.81 \n", "756 415.15 415.15 415.08 \n", "757 415.33 416.47 415.69 \n", "758 415.48 417.16 415.62 \n", "759 415.65 418.24 415.47 \n", "760 415.82 418.95 415.56 \n", "761 415.99 418.70 416.13 \n", "762 416.17 416.65 415.85 \n", "763 416.36 414.34 415.89 \n", "764 416.55 412.91 416.40 \n", "765 416.73 413.55 417.16 \n", "766 416.92 414.82 417.09 \n", "767 417.09 416.43 417.36 \n", "768 417.25 418.01 417.94 \n", "769 417.41 418.99 418.20 \n", "770 417.55 418.45 416.91 \n", "771 417.70 420.02 417.24 \n", "772 417.85 420.77 417.37 \n", "773 418.02 420.68 418.10 \n", "774 418.19 418.68 417.87 \n", "775 418.37 416.76 418.31 \n", "776 418.56 415.41 418.91 \n", "777 -99.99 415.31 418.93 \n", "778 -99.99 -99.99 -99.99 \n", "779 -99.99 -99.99 -99.99 \n", "\n", "[780 rows x 10 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(\n", " local_file,\n", " skiprows=57,\n", " names=[\n", " 'Yr', 'Mn', 'ExcelDate', 'Date',\n", " 'CO2', 'SeasonallyAdjusted', 'Fit',\n", " 'SeasonallyAdjustedFit', 'CO2Filled',\n", " 'SeasonallyAdjustedFilled',\n", " ],\n", ")\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour quelques premières et dernières lignes, on n'a pas de donnée (valeur à -99.99).\n", "On cherche donc à retirer ces données." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Removing row number 0\n", "Removing row number 1\n", "Removing row number 778\n", "Removing row number 779\n" ] }, { "data": { "text/html": [ "
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YrMnExcelDateDateCO2SeasonallyAdjustedFitSeasonallyAdjustedFitCO2FilledSeasonallyAdjustedFilled
219583212591958.2027315.71314.43316.20314.91315.71314.43
319584212901958.2877317.45315.16317.30314.99317.45315.16
419585213201958.3699317.51314.70317.88315.06317.51314.70
519586213511958.4548-99.99-99.99317.26315.14317.26315.14
619587213811958.5370315.87315.20315.85315.22315.87315.20
719588214121958.6219314.93316.21313.97315.29314.93316.21
819589214431958.7068313.21316.11312.44315.35313.21316.11
9195810214731958.7890-99.99-99.99312.42315.41312.42315.41
10195811215041958.8740313.33315.21313.60315.46313.33315.21
11195812215341958.9562314.67315.44314.76315.51314.67315.44
1219591215651959.0411315.58315.52315.64315.57315.58315.52
1319592215961959.1260316.49315.84316.29315.63316.49315.84
1419593216241959.2027316.65315.37316.99315.69316.65315.37
1519594216551959.2877317.72315.41318.09315.77317.72315.41
1619595216851959.3699318.29315.47318.67315.85318.29315.47
1719596217161959.4548318.15316.01318.06315.94318.15316.01
1819597217461959.5370316.54315.87316.67316.03316.54315.87
1919598217771959.6219314.80316.09314.80316.12314.80316.09
2019599218081959.7068313.84316.74313.30316.22313.84316.74
21195910218381959.7890313.33316.34313.31316.31313.33316.34
22195911218691959.8740314.81316.70314.53316.39314.81316.70
23195912218991959.9562315.58316.35315.72316.47315.58316.35
2419601219301960.0410316.43316.37316.62316.56316.43316.37
2519602219611960.1257316.98316.33317.29316.64316.98316.33
2619603219901960.2049317.58316.27318.04316.71317.58316.27
2719604220211960.2896319.03316.70319.14316.79319.03316.70
2819605220511960.3716320.03317.20319.69316.86320.03317.20
2919606220821960.4563319.59317.46319.03316.93319.59317.46
3019607221121960.5383318.18317.53317.59316.98318.18317.53
3119608221431960.6230315.90317.22315.66317.01315.90317.22
.................................
74820205439662020.3716417.15413.77417.00413.62417.15413.77
74920206439972020.4563416.29413.75416.34413.82416.29413.75
75020207440272020.5383414.42413.65414.75414.02414.42413.65
75120208440582020.6230412.52414.09412.60414.22412.52414.09
75220209440892020.7077411.18414.68410.91414.42411.18414.68
753202010441192020.7896411.12414.72411.02414.61411.12414.72
754202011441502020.8743412.88415.14412.56414.80412.88415.14
755202012441802020.9563413.89414.81414.07414.97413.89414.81
75620211442112021.0411415.15415.08415.23415.15415.15415.08
75720212442422021.1260416.47415.69416.12415.33416.47415.69
75820213442702021.2027417.16415.62417.04415.48417.16415.62
75920214443012021.2877418.24415.47418.44415.65418.24415.47
76020215443312021.3699418.95415.56419.21415.82418.95415.56
76120216443622021.4548418.70416.13418.55415.99418.70416.13
76220217443922021.5370416.65415.85416.94416.17416.65415.85
76320218444232021.6219414.34415.89414.76416.36414.34415.89
76420219444542021.7068412.91416.40413.03416.55412.91416.40
765202110444842021.7890413.55417.16413.13416.73413.55417.16
766202111445152021.8740414.82417.09414.67416.92414.82417.09
767202112445452021.9562416.43417.36416.18417.09416.43417.36
76820221445762022.0411418.01417.94417.34417.25418.01417.94
76920222446072022.1260418.99418.20418.20417.41418.99418.20
77020223446352022.2027418.45416.91419.11417.55418.45416.91
77120224446662022.2877420.02417.24420.49417.70420.02417.24
77220225446962022.3699420.77417.37421.25417.85420.77417.37
77320226447272022.4548420.68418.10420.58418.02420.68418.10
77420227447572022.5370418.68417.87418.96418.19418.68417.87
77520228447882022.6219416.76418.31416.78418.37416.76418.31
77620229448192022.7068415.41418.91415.04418.56415.41418.91
777202210448492022.7890415.31418.93-99.99-99.99415.31418.93
\n", "

776 rows × 10 columns

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" ], "text/plain": [ " Yr Mn ExcelDate Date CO2 SeasonallyAdjusted Fit \\\n", "2 1958 3 21259 1958.2027 315.71 314.43 316.20 \n", "3 1958 4 21290 1958.2877 317.45 315.16 317.30 \n", "4 1958 5 21320 1958.3699 317.51 314.70 317.88 \n", "5 1958 6 21351 1958.4548 -99.99 -99.99 317.26 \n", "6 1958 7 21381 1958.5370 315.87 315.20 315.85 \n", "7 1958 8 21412 1958.6219 314.93 316.21 313.97 \n", "8 1958 9 21443 1958.7068 313.21 316.11 312.44 \n", "9 1958 10 21473 1958.7890 -99.99 -99.99 312.42 \n", "10 1958 11 21504 1958.8740 313.33 315.21 313.60 \n", "11 1958 12 21534 1958.9562 314.67 315.44 314.76 \n", "12 1959 1 21565 1959.0411 315.58 315.52 315.64 \n", "13 1959 2 21596 1959.1260 316.49 315.84 316.29 \n", "14 1959 3 21624 1959.2027 316.65 315.37 316.99 \n", "15 1959 4 21655 1959.2877 317.72 315.41 318.09 \n", "16 1959 5 21685 1959.3699 318.29 315.47 318.67 \n", "17 1959 6 21716 1959.4548 318.15 316.01 318.06 \n", "18 1959 7 21746 1959.5370 316.54 315.87 316.67 \n", "19 1959 8 21777 1959.6219 314.80 316.09 314.80 \n", "20 1959 9 21808 1959.7068 313.84 316.74 313.30 \n", "21 1959 10 21838 1959.7890 313.33 316.34 313.31 \n", "22 1959 11 21869 1959.8740 314.81 316.70 314.53 \n", "23 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", "24 1960 1 21930 1960.0410 316.43 316.37 316.62 \n", "25 1960 2 21961 1960.1257 316.98 316.33 317.29 \n", "26 1960 3 21990 1960.2049 317.58 316.27 318.04 \n", "27 1960 4 22021 1960.2896 319.03 316.70 319.14 \n", "28 1960 5 22051 1960.3716 320.03 317.20 319.69 \n", "29 1960 6 22082 1960.4563 319.59 317.46 319.03 \n", "30 1960 7 22112 1960.5383 318.18 317.53 317.59 \n", "31 1960 8 22143 1960.6230 315.90 317.22 315.66 \n", ".. ... .. ... ... ... ... ... \n", "748 2020 5 43966 2020.3716 417.15 413.77 417.00 \n", "749 2020 6 43997 2020.4563 416.29 413.75 416.34 \n", "750 2020 7 44027 2020.5383 414.42 413.65 414.75 \n", "751 2020 8 44058 2020.6230 412.52 414.09 412.60 \n", "752 2020 9 44089 2020.7077 411.18 414.68 410.91 \n", "753 2020 10 44119 2020.7896 411.12 414.72 411.02 \n", "754 2020 11 44150 2020.8743 412.88 415.14 412.56 \n", "755 2020 12 44180 2020.9563 413.89 414.81 414.07 \n", "756 2021 1 44211 2021.0411 415.15 415.08 415.23 \n", "757 2021 2 44242 2021.1260 416.47 415.69 416.12 \n", "758 2021 3 44270 2021.2027 417.16 415.62 417.04 \n", "759 2021 4 44301 2021.2877 418.24 415.47 418.44 \n", "760 2021 5 44331 2021.3699 418.95 415.56 419.21 \n", "761 2021 6 44362 2021.4548 418.70 416.13 418.55 \n", "762 2021 7 44392 2021.5370 416.65 415.85 416.94 \n", "763 2021 8 44423 2021.6219 414.34 415.89 414.76 \n", "764 2021 9 44454 2021.7068 412.91 416.40 413.03 \n", "765 2021 10 44484 2021.7890 413.55 417.16 413.13 \n", "766 2021 11 44515 2021.8740 414.82 417.09 414.67 \n", "767 2021 12 44545 2021.9562 416.43 417.36 416.18 \n", "768 2022 1 44576 2022.0411 418.01 417.94 417.34 \n", "769 2022 2 44607 2022.1260 418.99 418.20 418.20 \n", "770 2022 3 44635 2022.2027 418.45 416.91 419.11 \n", "771 2022 4 44666 2022.2877 420.02 417.24 420.49 \n", "772 2022 5 44696 2022.3699 420.77 417.37 421.25 \n", "773 2022 6 44727 2022.4548 420.68 418.10 420.58 \n", "774 2022 7 44757 2022.5370 418.68 417.87 418.96 \n", "775 2022 8 44788 2022.6219 416.76 418.31 416.78 \n", "776 2022 9 44819 2022.7068 415.41 418.91 415.04 \n", "777 2022 10 44849 2022.7890 415.31 418.93 -99.99 \n", "\n", " SeasonallyAdjustedFit CO2Filled SeasonallyAdjustedFilled \n", "2 314.91 315.71 314.43 \n", "3 314.99 317.45 315.16 \n", "4 315.06 317.51 314.70 \n", "5 315.14 317.26 315.14 \n", "6 315.22 315.87 315.20 \n", "7 315.29 314.93 316.21 \n", "8 315.35 313.21 316.11 \n", "9 315.41 312.42 315.41 \n", "10 315.46 313.33 315.21 \n", "11 315.51 314.67 315.44 \n", "12 315.57 315.58 315.52 \n", "13 315.63 316.49 315.84 \n", "14 315.69 316.65 315.37 \n", "15 315.77 317.72 315.41 \n", "16 315.85 318.29 315.47 \n", "17 315.94 318.15 316.01 \n", "18 316.03 316.54 315.87 \n", "19 316.12 314.80 316.09 \n", "20 316.22 313.84 316.74 \n", "21 316.31 313.33 316.34 \n", "22 316.39 314.81 316.70 \n", "23 316.47 315.58 316.35 \n", "24 316.56 316.43 316.37 \n", "25 316.64 316.98 316.33 \n", "26 316.71 317.58 316.27 \n", "27 316.79 319.03 316.70 \n", "28 316.86 320.03 317.20 \n", "29 316.93 319.59 317.46 \n", "30 316.98 318.18 317.53 \n", "31 317.01 315.90 317.22 \n", ".. ... ... ... \n", "748 413.62 417.15 413.77 \n", "749 413.82 416.29 413.75 \n", "750 414.02 414.42 413.65 \n", "751 414.22 412.52 414.09 \n", "752 414.42 411.18 414.68 \n", "753 414.61 411.12 414.72 \n", "754 414.80 412.88 415.14 \n", "755 414.97 413.89 414.81 \n", "756 415.15 415.15 415.08 \n", "757 415.33 416.47 415.69 \n", "758 415.48 417.16 415.62 \n", "759 415.65 418.24 415.47 \n", "760 415.82 418.95 415.56 \n", "761 415.99 418.70 416.13 \n", "762 416.17 416.65 415.85 \n", "763 416.36 414.34 415.89 \n", "764 416.55 412.91 416.40 \n", "765 416.73 413.55 417.16 \n", "766 416.92 414.82 417.09 \n", "767 417.09 416.43 417.36 \n", "768 417.25 418.01 417.94 \n", "769 417.41 418.99 418.20 \n", "770 417.55 418.45 416.91 \n", "771 417.70 420.02 417.24 \n", "772 417.85 420.77 417.37 \n", "773 418.02 420.68 418.10 \n", "774 418.19 418.68 417.87 \n", "775 418.37 416.76 418.31 \n", "776 418.56 415.41 418.91 \n", "777 -99.99 415.31 418.93 \n", "\n", "[776 rows x 10 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for i, row in data.iterrows():\n", " if data['CO2Filled'][i] < 0:\n", " print('Removing row number', i)\n", " data = data.drop(i)\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous devons adapter les colonnes de dates pour être compréhensible par Pandas.\n", "\n", "On fait donc une fonction `convert_month` qui renvoie une date dans un format que Pandas peut comprendre." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def convert_month(i):\n", " y = data['Yr'][i]\n", " m = data['Mn'][i]\n", " return pd.Period(f\"%s-%s\" % (y, m))\n", "\n", "data['period'] = [convert_month(i) for i, row in data.iterrows()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On définit les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n", "\n", "Ensuite, on trie les points par période, dans le sens chronologique." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "sorted_data = data.set_index('period').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifie la cohérence des données. Entre deux périodes il doit y avoir maximum 31 jours.\n", "\n", "Ceci se révèle tout à fait juste pour l'entierté du dataset car aucun message n'est print." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "periods = sorted_data.index\n", "for p1, p2 in zip(periods[:-1], periods[1:]):\n", " delta = p2.to_timestamp() - p1.to_timestamp()\n", " if delta > pd.Timedelta('31d'):\n", " print(p1, p2, delta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On affiche la courbe de concentration de CO2 sur toute la période." ] }, { "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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sorted_data['CO2Filled'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe deux phénomènes:\n", "- La courbe oscille périodiquement chaque année\n", "- La courbe à une allure générale exponentielle\n", "\n", "Pour isoler le mouvement lent, mouvement étant la cause du réchauffement climatique, on tente de plot une courbe exponentielle avec des paramètres au hasard.\n", "On ajuste ensuite ces paramètres manuellement jusqu'à ce que la courbe se supperpose à la courbe de concentration de CO2." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Paramètres de la courbe exponentielle\n", "r = 1.00125\n", "a = 0.196\n", "b = 250\n", "\n", "v = sorted_data['CO2Filled'][0]\n", "m = pd.Series()\n", "i = 0\n", "\n", "def predict(date):\n", " global i\n", " m[date] = v * (r ** i) * a + b\n", " i += 1\n", "\n", "for date in sorted_data['CO2Filled'].keys():\n", " predict(date)\n", "\n", "# On fait des prédictions sur les années suivantes jusqu'à 2035\n", "lastPeriod = m.index[-1]\n", "year = lastPeriod.year\n", "month = lastPeriod.month\n", "\n", "while (year < 2035):\n", " while (month < 12):\n", " month += 1\n", " date = pd.Period(f\"%s-%s\" % (year, month))\n", " predict(date)\n", " month = 0\n", " year += 1\n", "\n", "plt.title('Évolution de concentration du CO2 (en ppm) et prévisions')\n", "sorted_data['CO2Filled'].plot()\n", "m.plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On zoom sur les 300 derniers mois de la courbe..." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "nbrVal = 300\n", "\n", "iStart = len(sorted_data['CO2Filled']) - nbrVal\n", "sorted_data['CO2Filled'][iStart:].plot()\n", "m[iStart:].plot();" ] } ], "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 }