diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb index 0bbbe371b01e359e381e43239412d77bf53fb1fb..fbaa7c04bd838fe5331ef121cc04c9496430bf76 100644 --- a/module3/exo3/exercice_fr.ipynb +++ b/module3/exo3/exercice_fr.ipynb @@ -1,5 +1,4526 @@ { - "cells": [], + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On récupère les données. Elles ont été trouvées dans ce lien:\n", + "https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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45427 2024.3716 426.70 423.29 427.25 \n", + "799 2024 06 45458 2024.4563 426.63 424.06 426.65 \n", + "800 2024 07 45488 2024.5383 425.40 424.62 425.10 \n", + "801 2024 08 45519 2024.6230 422.71 424.30 423.00 \n", + "802 2024 09 45550 2024.7077 421.60 425.12 -99.99 \n", + "803 2024 10 45580 2024.7896 -99.99 -99.99 -99.99 \n", + "804 2024 11 45611 2024.8743 -99.99 -99.99 -99.99 \n", + "805 2024 12 45641 2024.9563 -99.99 -99.99 -99.99 \n", + "\n", + " seasonally CO2 seasonally Sta \n", + "0 adjusted fit filled adjusted filled NaN \n", + "1 [ppm] [ppm] [ppm] NaN \n", + "2 -99.99 -99.99 -99.99 MLO \n", + "3 -99.99 -99.99 -99.99 MLO \n", + "4 314.91 315.71 314.43 MLO \n", + "5 314.99 317.45 315.16 MLO \n", + "6 315.07 317.51 314.69 MLO \n", + "7 315.15 317.27 315.15 MLO \n", + "8 315.22 315.87 315.20 MLO \n", + "9 315.29 314.93 316.22 MLO \n", + "10 315.35 313.21 316.12 MLO \n", + "11 315.41 312.42 315.41 MLO \n", + "12 315.46 313.33 315.21 MLO \n", + "13 315.52 314.67 315.43 MLO \n", + "14 315.57 315.58 315.52 MLO \n", + "15 315.64 316.49 315.84 MLO \n", + "16 315.70 316.65 315.37 MLO \n", + "17 315.77 317.72 315.41 MLO \n", + "18 315.85 318.29 315.46 MLO \n", + "19 315.94 318.15 316.00 MLO \n", + "20 316.03 316.54 315.87 MLO \n", + "21 316.13 314.80 316.09 MLO \n", + "22 316.22 313.84 316.75 MLO \n", + "23 316.31 313.33 316.35 MLO \n", + "24 316.40 314.81 316.69 MLO \n", + "25 316.48 315.58 316.35 MLO \n", + "26 316.56 316.43 316.37 MLO \n", + "27 316.64 316.98 316.33 MLO \n", + "28 316.71 317.58 316.27 MLO \n", + "29 316.79 319.03 316.70 MLO \n", + ".. ... ... ... ... \n", + "776 418.18 418.71 417.91 MLO \n", + "777 418.36 416.75 418.30 MLO \n", + "778 418.55 415.42 418.91 MLO \n", + "779 418.74 415.31 418.92 MLO \n", + "780 418.95 417.03 419.29 MLO \n", + "781 419.15 418.46 419.38 MKO \n", + "782 419.37 419.13 419.06 MKO \n", + "783 419.61 420.33 419.55 MKO \n", + "784 419.83 420.51 418.97 MLO \n", + "785 420.10 422.73 419.96 MLO \n", + "786 420.37 423.78 420.38 MLO \n", + "787 420.66 423.39 420.81 MLO \n", + "788 420.96 421.62 420.82 MLO \n", + "789 421.27 419.56 421.12 MLO \n", + "790 421.58 418.06 421.56 MLO \n", + "791 421.88 418.41 422.02 MLO \n", + "792 422.19 420.11 422.38 MLO \n", + "793 422.48 421.65 422.57 MLO \n", + "794 422.77 422.62 422.55 MLO \n", + "795 423.06 424.34 423.56 MLO \n", + "796 423.31 425.22 423.65 MLO \n", + "797 423.58 426.30 423.50 MLO \n", + "798 423.84 426.70 423.29 MLO \n", + "799 424.11 426.63 424.06 MLO \n", + "800 424.36 425.40 424.62 MLO \n", + "801 424.63 422.71 424.30 MLO \n", + "802 -99.99 421.60 425.12 MLO \n", + "803 -99.99 -99.99 -99.99 MLO \n", + "804 -99.99 -99.99 -99.99 MLO \n", + "805 -99.99 -99.99 -99.99 MLO \n", + "\n", + "[806 rows x 11 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_file = 'monthly_in_situ_co2_mlo.csv'\n", + "data = pd.read_csv(data_file, skiprows=61)\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Il y a un problème avec les entêtes. On concatene les deux premieres lignes et enleve celle de l'unité." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YrMnDate_excelDateCO2seasonally_adjustedfitseasonally_adjusted_fitCO2_filledseasonally_adjusted_filledSta
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1195802212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99MLO
2195803212591958.2027315.71314.43316.20314.91315.71314.43MLO
3195804212901958.2877317.45315.16317.30314.99317.45315.16MLO
4195805213201958.3699317.51314.69317.89315.07317.51314.69MLO
5195806213511958.4548-99.99-99.99317.27315.15317.27315.15MLO
6195807213811958.5370315.87315.20315.86315.22315.87315.20MLO
7195808214121958.6219314.93316.22313.96315.29314.93316.22MLO
8195809214431958.7068313.21316.12312.43315.35313.21316.12MLO
9195810214731958.7890-99.99-99.99312.42315.41312.42315.41MLO
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
11195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
12195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
13195902215961959.1260316.49315.84316.30315.64316.49315.84MLO
14195903216241959.2027316.65315.37316.99315.70316.65315.37MLO
15195904216551959.2877317.72315.41318.09315.77317.72315.41MLO
16195905216851959.3699318.29315.46318.68315.85318.29315.46MLO
17195906217161959.4548318.15316.00318.07315.94318.15316.00MLO
18195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
19195908217771959.6219314.80316.09314.80316.13314.80316.09MLO
20195909218081959.7068313.84316.75313.29316.22313.84316.75MLO
21195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
22195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
23195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
24196001219301960.0410316.43316.37316.62316.56316.43316.37MLO
25196002219611960.1257316.98316.33317.30316.64316.98316.33MLO
26196003219901960.2049317.58316.27318.04316.71317.58316.27MLO
27196004220211960.2896319.03316.70319.14316.79319.03316.70MLO
28196005220511960.3716320.03317.20319.70316.86320.03317.20MLO
29196006220821960.4563319.59317.45319.04316.93319.59317.45MLO
....................................
774202207447572022.5370418.71417.91418.94418.18418.71417.91MLO
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776202209448192022.7068415.42418.91415.04418.55415.42418.91MLO
777202210448492022.7890415.31418.92415.15418.74415.31418.92MLO
778202211448802022.8740417.03419.29416.71418.95417.03419.29MLO
779202212449102022.9562418.46419.38418.25419.15418.46419.38MKO
780202301449412023.0411419.13419.06419.45419.37419.13419.06MKO
781202302449722023.1260420.33419.55420.40419.61420.33419.55MKO
782202303450002023.2027420.51418.97421.39419.83420.51418.97MLO
783202304450312023.2877422.73419.96422.89420.10422.73419.96MLO
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804 rows × 11 columns

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" + ], + "text/plain": [ + " Yr Mn Date_excel Date CO2 seasonally_adjusted \\\n", + "0 1958 01 21200 1958.0411 -99.99 -99.99 \n", + "1 1958 02 21231 1958.1260 -99.99 -99.99 \n", + "2 1958 03 21259 1958.2027 315.71 314.43 \n", + "3 1958 04 21290 1958.2877 317.45 315.16 \n", + "4 1958 05 21320 1958.3699 317.51 314.69 \n", + "5 1958 06 21351 1958.4548 -99.99 -99.99 \n", + "6 1958 07 21381 1958.5370 315.87 315.20 \n", + "7 1958 08 21412 1958.6219 314.93 316.22 \n", + "8 1958 09 21443 1958.7068 313.21 316.12 \n", + "9 1958 10 21473 1958.7890 -99.99 -99.99 \n", + "10 1958 11 21504 1958.8740 313.33 315.21 \n", + "11 1958 12 21534 1958.9562 314.67 315.43 \n", + "12 1959 01 21565 1959.0411 315.58 315.52 \n", + "13 1959 02 21596 1959.1260 316.49 315.84 \n", + "14 1959 03 21624 1959.2027 316.65 315.37 \n", + "15 1959 04 21655 1959.2877 317.72 315.41 \n", + "16 1959 05 21685 1959.3699 318.29 315.46 \n", + "17 1959 06 21716 1959.4548 318.15 316.00 \n", + "18 1959 07 21746 1959.5370 316.54 315.87 \n", + "19 1959 08 21777 1959.6219 314.80 316.09 \n", + "20 1959 09 21808 1959.7068 313.84 316.75 \n", + "21 1959 10 21838 1959.7890 313.33 316.35 \n", + "22 1959 11 21869 1959.8740 314.81 316.69 \n", + "23 1959 12 21899 1959.9562 315.58 316.35 \n", + "24 1960 01 21930 1960.0410 316.43 316.37 \n", + "25 1960 02 21961 1960.1257 316.98 316.33 \n", + "26 1960 03 21990 1960.2049 317.58 316.27 \n", + "27 1960 04 22021 1960.2896 319.03 316.70 \n", + "28 1960 05 22051 1960.3716 320.03 317.20 \n", + "29 1960 06 22082 1960.4563 319.59 317.45 \n", + ".. ... ... ... ... ... ... \n", + "774 2022 07 44757 2022.5370 418.71 417.91 \n", + "775 2022 08 44788 2022.6219 416.75 418.30 \n", + "776 2022 09 44819 2022.7068 415.42 418.91 \n", + "777 2022 10 44849 2022.7890 415.31 418.92 \n", + "778 2022 11 44880 2022.8740 417.03 419.29 \n", + "779 2022 12 44910 2022.9562 418.46 419.38 \n", + "780 2023 01 44941 2023.0411 419.13 419.06 \n", + "781 2023 02 44972 2023.1260 420.33 419.55 \n", + "782 2023 03 45000 2023.2027 420.51 418.97 \n", + "783 2023 04 45031 2023.2877 422.73 419.96 \n", + "784 2023 05 45061 2023.3699 423.78 420.38 \n", + "785 2023 06 45092 2023.4548 423.39 420.81 \n", + "786 2023 07 45122 2023.5370 421.62 420.82 \n", + "787 2023 08 45153 2023.6219 419.56 421.12 \n", + "788 2023 09 45184 2023.7068 418.06 421.56 \n", + "789 2023 10 45214 2023.7890 418.41 422.02 \n", + "790 2023 11 45245 2023.8740 420.11 422.38 \n", + "791 2023 12 45275 2023.9562 421.65 422.57 \n", + "792 2024 01 45306 2024.0410 422.62 422.55 \n", + "793 2024 02 45337 2024.1257 424.34 423.56 \n", + "794 2024 03 45366 2024.2049 425.22 423.65 \n", + "795 2024 04 45397 2024.2896 426.30 423.50 \n", + "796 2024 05 45427 2024.3716 426.70 423.29 \n", + "797 2024 06 45458 2024.4563 426.63 424.06 \n", + "798 2024 07 45488 2024.5383 425.40 424.62 \n", + "799 2024 08 45519 2024.6230 422.71 424.30 \n", + "800 2024 09 45550 2024.7077 421.60 425.12 \n", + "801 2024 10 45580 2024.7896 -99.99 -99.99 \n", + "802 2024 11 45611 2024.8743 -99.99 -99.99 \n", + "803 2024 12 45641 2024.9563 -99.99 -99.99 \n", + "\n", + " fit seasonally_adjusted_fit CO2_filled \\\n", + "0 -99.99 -99.99 -99.99 \n", + "1 -99.99 -99.99 -99.99 \n", + "2 316.20 314.91 315.71 \n", + "3 317.30 314.99 317.45 \n", + "4 317.89 315.07 317.51 \n", + "5 317.27 315.15 317.27 \n", + "6 315.86 315.22 315.87 \n", + "7 313.96 315.29 314.93 \n", + "8 312.43 315.35 313.21 \n", + "9 312.42 315.41 312.42 \n", + "10 313.60 315.46 313.33 \n", + "11 314.77 315.52 314.67 \n", + "12 315.64 315.57 315.58 \n", + "13 316.30 315.64 316.49 \n", + "14 316.99 315.70 316.65 \n", + "15 318.09 315.77 317.72 \n", + "16 318.68 315.85 318.29 \n", + "17 318.07 315.94 318.15 \n", + "18 316.67 316.03 316.54 \n", + "19 314.80 316.13 314.80 \n", + "20 313.29 316.22 313.84 \n", + "21 313.31 316.31 313.33 \n", + "22 314.53 316.40 314.81 \n", + "23 315.72 316.48 315.58 \n", + "24 316.62 316.56 316.43 \n", + "25 317.30 316.64 316.98 \n", + "26 318.04 316.71 317.58 \n", + "27 319.14 316.79 319.03 \n", + "28 319.70 316.86 320.03 \n", + "29 319.04 316.93 319.59 \n", + ".. ... ... ... \n", + "774 418.94 418.18 418.71 \n", + "775 416.77 418.36 416.75 \n", + "776 415.04 418.55 415.42 \n", + "777 415.15 418.74 415.31 \n", + "778 416.71 418.95 417.03 \n", + "779 418.25 419.15 418.46 \n", + "780 419.45 419.37 419.13 \n", + "781 420.40 419.61 420.33 \n", + "782 421.39 419.83 420.51 \n", + "783 422.89 420.10 422.73 \n", + "784 423.77 420.37 423.78 \n", + "785 423.23 420.66 423.39 \n", + "786 421.73 420.96 421.62 \n", + "787 419.67 421.27 419.56 \n", + "788 418.06 421.58 418.06 \n", + "789 418.28 421.88 418.41 \n", + "790 419.95 422.19 420.11 \n", + "791 421.58 422.48 421.65 \n", + "792 422.85 422.77 422.62 \n", + "793 423.85 423.06 424.34 \n", + "794 424.91 423.31 425.22 \n", + "795 426.41 423.58 426.30 \n", + "796 427.25 423.84 426.70 \n", + "797 426.65 424.11 426.63 \n", + "798 425.10 424.36 425.40 \n", + "799 423.00 424.63 422.71 \n", + "800 -99.99 -99.99 421.60 \n", + "801 -99.99 -99.99 -99.99 \n", + "802 -99.99 -99.99 -99.99 \n", + "803 -99.99 -99.99 -99.99 \n", + "\n", + " seasonally_adjusted_filled Sta \n", + "0 -99.99 MLO \n", + "1 -99.99 MLO \n", + "2 314.43 MLO \n", + "3 315.16 MLO \n", + "4 314.69 MLO \n", + "5 315.15 MLO \n", + "6 315.20 MLO \n", + "7 316.22 MLO \n", + "8 316.12 MLO \n", + "9 315.41 MLO \n", + "10 315.21 MLO \n", + "11 315.43 MLO \n", + "12 315.52 MLO \n", + "13 315.84 MLO \n", + "14 315.37 MLO \n", + "15 315.41 MLO \n", + "16 315.46 MLO \n", + "17 316.00 MLO \n", + "18 315.87 MLO \n", + "19 316.09 MLO \n", + "20 316.75 MLO \n", + "21 316.35 MLO \n", + "22 316.69 MLO \n", + "23 316.35 MLO \n", + "24 316.37 MLO \n", + "25 316.33 MLO \n", + "26 316.27 MLO \n", + "27 316.70 MLO \n", + "28 317.20 MLO \n", + "29 317.45 MLO \n", + ".. ... ... \n", + "774 417.91 MLO \n", + "775 418.30 MLO \n", + "776 418.91 MLO \n", + "777 418.92 MLO \n", + "778 419.29 MLO \n", + "779 419.38 MKO \n", + "780 419.06 MKO \n", + "781 419.55 MKO \n", + "782 418.97 MLO \n", + "783 419.96 MLO \n", + "784 420.38 MLO \n", + "785 420.81 MLO \n", + "786 420.82 MLO \n", + "787 421.12 MLO \n", + "788 421.56 MLO \n", + "789 422.02 MLO \n", + "790 422.38 MLO \n", + "791 422.57 MLO \n", + "792 422.55 MLO \n", + "793 423.56 MLO \n", + "794 423.65 MLO \n", + "795 423.50 MLO \n", + "796 423.29 MLO \n", + "797 424.06 MLO \n", + "798 424.62 MLO \n", + "799 424.30 MLO \n", + "800 425.12 MLO \n", + "801 -99.99 MLO \n", + "802 -99.99 MLO \n", + "803 -99.99 MLO \n", + "\n", + "[804 rows x 11 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns_label = ['Yr', 'Mn', 'Date_excel', 'Date', 'CO2', 'seasonally_adjusted',\n", + " 'fit', 'seasonally_adjusted_fit', 'CO2_filled', 'seasonally_adjusted_filled', 'Sta']\n", + "\n", + "data.columns = columns_label\n", + "data = data.drop([0, 1]).reset_index().drop('index',axis=1)\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Les données manquantes dans le fichier de base ont été remplacées par -99.99.\n", + "Elles ne nous interessent pas. On les enlève de l'analyse." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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YrMnDate_excelDateCO2seasonally_adjustedfitseasonally_adjusted_fitCO2_filledseasonally_adjusted_filledSta
2195803212591958.2027315.71314.43316.20314.91315.71314.43MLO
3195804212901958.2877317.45315.16317.30314.99317.45315.16MLO
4195805213201958.3699317.51314.69317.89315.07317.51314.69MLO
6195807213811958.5370315.87315.20315.86315.22315.87315.20MLO
7195808214121958.6219314.93316.22313.96315.29314.93316.22MLO
8195809214431958.7068313.21316.12312.43315.35313.21316.12MLO
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
11195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
12195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
13195902215961959.1260316.49315.84316.30315.64316.49315.84MLO
14195903216241959.2027316.65315.37316.99315.70316.65315.37MLO
15195904216551959.2877317.72315.41318.09315.77317.72315.41MLO
16195905216851959.3699318.29315.46318.68315.85318.29315.46MLO
17195906217161959.4548318.15316.00318.07315.94318.15316.00MLO
18195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
19195908217771959.6219314.80316.09314.80316.13314.80316.09MLO
20195909218081959.7068313.84316.75313.29316.22313.84316.75MLO
21195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
22195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
23195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
24196001219301960.0410316.43316.37316.62316.56316.43316.37MLO
25196002219611960.1257316.98316.33317.30316.64316.98316.33MLO
26196003219901960.2049317.58316.27318.04316.71317.58316.27MLO
27196004220211960.2896319.03316.70319.14316.79319.03316.70MLO
28196005220511960.3716320.03317.20319.70316.86320.03317.20MLO
29196006220821960.4563319.59317.45319.04316.93319.59317.45MLO
30196007221121960.5383318.18317.53317.59316.98318.18317.53MLO
31196008221431960.6230315.90317.23315.66317.02315.90317.23MLO
32196009221741960.7077314.17317.10314.10317.05314.17317.10MLO
33196010222041960.7896313.83316.85314.08317.08313.83316.85MLO
....................................
771202204446662022.2877420.01417.25420.47417.69420.01417.25MLO
772202205446962022.3699420.78417.39421.23417.84420.78417.39MLO
773202206447272022.4548420.68418.10420.56418.01420.68418.10MLO
774202207447572022.5370418.71417.91418.94418.18418.71417.91MLO
775202208447882022.6219416.75418.30416.77418.36416.75418.30MLO
776202209448192022.7068415.42418.91415.04418.55415.42418.91MLO
777202210448492022.7890415.31418.92415.15418.74415.31418.92MLO
778202211448802022.8740417.03419.29416.71418.95417.03419.29MLO
779202212449102022.9562418.46419.38418.25419.15418.46419.38MKO
780202301449412023.0411419.13419.06419.45419.37419.13419.06MKO
781202302449722023.1260420.33419.55420.40419.61420.33419.55MKO
782202303450002023.2027420.51418.97421.39419.83420.51418.97MLO
783202304450312023.2877422.73419.96422.89420.10422.73419.96MLO
784202305450612023.3699423.78420.38423.77420.37423.78420.38MLO
785202306450922023.4548423.39420.81423.23420.66423.39420.81MLO
786202307451222023.5370421.62420.82421.73420.96421.62420.82MLO
787202308451532023.6219419.56421.12419.67421.27419.56421.12MLO
788202309451842023.7068418.06421.56418.06421.58418.06421.56MLO
789202310452142023.7890418.41422.02418.28421.88418.41422.02MLO
790202311452452023.8740420.11422.38419.95422.19420.11422.38MLO
791202312452752023.9562421.65422.57421.58422.48421.65422.57MLO
792202401453062024.0410422.62422.55422.85422.77422.62422.55MLO
793202402453372024.1257424.34423.56423.85423.06424.34423.56MLO
794202403453662024.2049425.22423.65424.91423.31425.22423.65MLO
795202404453972024.2896426.30423.50426.41423.58426.30423.50MLO
796202405454272024.3716426.70423.29427.25423.84426.70423.29MLO
797202406454582024.4563426.63424.06426.65424.11426.63424.06MLO
798202407454882024.5383425.40424.62425.10424.36425.40424.62MLO
799202408455192024.6230422.71424.30423.00424.63422.71424.30MLO
800202409455502024.7077421.60425.12-99.99-99.99421.60425.12MLO
\n", + "

794 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn Date_excel Date CO2 seasonally_adjusted \\\n", + "2 1958 03 21259 1958.2027 315.71 314.43 \n", + "3 1958 04 21290 1958.2877 317.45 315.16 \n", + "4 1958 05 21320 1958.3699 317.51 314.69 \n", + "6 1958 07 21381 1958.5370 315.87 315.20 \n", + "7 1958 08 21412 1958.6219 314.93 316.22 \n", + "8 1958 09 21443 1958.7068 313.21 316.12 \n", + "10 1958 11 21504 1958.8740 313.33 315.21 \n", + "11 1958 12 21534 1958.9562 314.67 315.43 \n", + "12 1959 01 21565 1959.0411 315.58 315.52 \n", + "13 1959 02 21596 1959.1260 316.49 315.84 \n", + "14 1959 03 21624 1959.2027 316.65 315.37 \n", + "15 1959 04 21655 1959.2877 317.72 315.41 \n", + "16 1959 05 21685 1959.3699 318.29 315.46 \n", + "17 1959 06 21716 1959.4548 318.15 316.00 \n", + "18 1959 07 21746 1959.5370 316.54 315.87 \n", + "19 1959 08 21777 1959.6219 314.80 316.09 \n", + "20 1959 09 21808 1959.7068 313.84 316.75 \n", + "21 1959 10 21838 1959.7890 313.33 316.35 \n", + "22 1959 11 21869 1959.8740 314.81 316.69 \n", + "23 1959 12 21899 1959.9562 315.58 316.35 \n", + "24 1960 01 21930 1960.0410 316.43 316.37 \n", + "25 1960 02 21961 1960.1257 316.98 316.33 \n", + "26 1960 03 21990 1960.2049 317.58 316.27 \n", + "27 1960 04 22021 1960.2896 319.03 316.70 \n", + "28 1960 05 22051 1960.3716 320.03 317.20 \n", + "29 1960 06 22082 1960.4563 319.59 317.45 \n", + "30 1960 07 22112 1960.5383 318.18 317.53 \n", + "31 1960 08 22143 1960.6230 315.90 317.23 \n", + "32 1960 09 22174 1960.7077 314.17 317.10 \n", + "33 1960 10 22204 1960.7896 313.83 316.85 \n", + ".. ... ... ... ... ... ... \n", + "771 2022 04 44666 2022.2877 420.01 417.25 \n", + "772 2022 05 44696 2022.3699 420.78 417.39 \n", + "773 2022 06 44727 2022.4548 420.68 418.10 \n", + "774 2022 07 44757 2022.5370 418.71 417.91 \n", + "775 2022 08 44788 2022.6219 416.75 418.30 \n", + "776 2022 09 44819 2022.7068 415.42 418.91 \n", + "777 2022 10 44849 2022.7890 415.31 418.92 \n", + "778 2022 11 44880 2022.8740 417.03 419.29 \n", + "779 2022 12 44910 2022.9562 418.46 419.38 \n", + "780 2023 01 44941 2023.0411 419.13 419.06 \n", + "781 2023 02 44972 2023.1260 420.33 419.55 \n", + "782 2023 03 45000 2023.2027 420.51 418.97 \n", + "783 2023 04 45031 2023.2877 422.73 419.96 \n", + "784 2023 05 45061 2023.3699 423.78 420.38 \n", + "785 2023 06 45092 2023.4548 423.39 420.81 \n", + "786 2023 07 45122 2023.5370 421.62 420.82 \n", + "787 2023 08 45153 2023.6219 419.56 421.12 \n", + "788 2023 09 45184 2023.7068 418.06 421.56 \n", + "789 2023 10 45214 2023.7890 418.41 422.02 \n", + "790 2023 11 45245 2023.8740 420.11 422.38 \n", + "791 2023 12 45275 2023.9562 421.65 422.57 \n", + "792 2024 01 45306 2024.0410 422.62 422.55 \n", + "793 2024 02 45337 2024.1257 424.34 423.56 \n", + "794 2024 03 45366 2024.2049 425.22 423.65 \n", + "795 2024 04 45397 2024.2896 426.30 423.50 \n", + "796 2024 05 45427 2024.3716 426.70 423.29 \n", + "797 2024 06 45458 2024.4563 426.63 424.06 \n", + "798 2024 07 45488 2024.5383 425.40 424.62 \n", + "799 2024 08 45519 2024.6230 422.71 424.30 \n", + "800 2024 09 45550 2024.7077 421.60 425.12 \n", + "\n", + " fit seasonally_adjusted_fit CO2_filled \\\n", + "2 316.20 314.91 315.71 \n", + "3 317.30 314.99 317.45 \n", + "4 317.89 315.07 317.51 \n", + "6 315.86 315.22 315.87 \n", + "7 313.96 315.29 314.93 \n", + "8 312.43 315.35 313.21 \n", + "10 313.60 315.46 313.33 \n", + "11 314.77 315.52 314.67 \n", + "12 315.64 315.57 315.58 \n", + "13 316.30 315.64 316.49 \n", + "14 316.99 315.70 316.65 \n", + "15 318.09 315.77 317.72 \n", + "16 318.68 315.85 318.29 \n", + "17 318.07 315.94 318.15 \n", + "18 316.67 316.03 316.54 \n", + "19 314.80 316.13 314.80 \n", + "20 313.29 316.22 313.84 \n", + "21 313.31 316.31 313.33 \n", + "22 314.53 316.40 314.81 \n", + "23 315.72 316.48 315.58 \n", + "24 316.62 316.56 316.43 \n", + "25 317.30 316.64 316.98 \n", + "26 318.04 316.71 317.58 \n", + "27 319.14 316.79 319.03 \n", + "28 319.70 316.86 320.03 \n", + "29 319.04 316.93 319.59 \n", + "30 317.59 316.98 318.18 \n", + "31 315.66 317.02 315.90 \n", + "32 314.10 317.05 314.17 \n", + "33 314.08 317.08 313.83 \n", + ".. ... ... ... \n", + "771 420.47 417.69 420.01 \n", + "772 421.23 417.84 420.78 \n", + "773 420.56 418.01 420.68 \n", + "774 418.94 418.18 418.71 \n", + "775 416.77 418.36 416.75 \n", + "776 415.04 418.55 415.42 \n", + "777 415.15 418.74 415.31 \n", + "778 416.71 418.95 417.03 \n", + "779 418.25 419.15 418.46 \n", + "780 419.45 419.37 419.13 \n", + "781 420.40 419.61 420.33 \n", + "782 421.39 419.83 420.51 \n", + "783 422.89 420.10 422.73 \n", + "784 423.77 420.37 423.78 \n", + "785 423.23 420.66 423.39 \n", + "786 421.73 420.96 421.62 \n", + "787 419.67 421.27 419.56 \n", + "788 418.06 421.58 418.06 \n", + "789 418.28 421.88 418.41 \n", + "790 419.95 422.19 420.11 \n", + "791 421.58 422.48 421.65 \n", + "792 422.85 422.77 422.62 \n", + "793 423.85 423.06 424.34 \n", + "794 424.91 423.31 425.22 \n", + "795 426.41 423.58 426.30 \n", + "796 427.25 423.84 426.70 \n", + "797 426.65 424.11 426.63 \n", + "798 425.10 424.36 425.40 \n", + "799 423.00 424.63 422.71 \n", + "800 -99.99 -99.99 421.60 \n", + "\n", + " seasonally_adjusted_filled Sta \n", + "2 314.43 MLO \n", + "3 315.16 MLO \n", + "4 314.69 MLO \n", + "6 315.20 MLO \n", + "7 316.22 MLO \n", + "8 316.12 MLO \n", + "10 315.21 MLO \n", + "11 315.43 MLO \n", + "12 315.52 MLO \n", + "13 315.84 MLO \n", + "14 315.37 MLO \n", + "15 315.41 MLO \n", + "16 315.46 MLO \n", + "17 316.00 MLO \n", + "18 315.87 MLO \n", + "19 316.09 MLO \n", + "20 316.75 MLO \n", + "21 316.35 MLO \n", + "22 316.69 MLO \n", + "23 316.35 MLO \n", + "24 316.37 MLO \n", + "25 316.33 MLO \n", + "26 316.27 MLO \n", + "27 316.70 MLO \n", + "28 317.20 MLO \n", + "29 317.45 MLO \n", + "30 317.53 MLO \n", + "31 317.23 MLO \n", + "32 317.10 MLO \n", + "33 316.85 MLO \n", + ".. ... ... \n", + "771 417.25 MLO \n", + "772 417.39 MLO \n", + "773 418.10 MLO \n", + "774 417.91 MLO \n", + "775 418.30 MLO \n", + "776 418.91 MLO \n", + "777 418.92 MLO \n", + "778 419.29 MLO \n", + "779 419.38 MKO \n", + "780 419.06 MKO \n", + "781 419.55 MKO \n", + "782 418.97 MLO \n", + "783 419.96 MLO \n", + "784 420.38 MLO \n", + "785 420.81 MLO \n", + "786 420.82 MLO \n", + "787 421.12 MLO \n", + "788 421.56 MLO \n", + "789 422.02 MLO \n", + "790 422.38 MLO \n", + "791 422.57 MLO \n", + "792 422.55 MLO \n", + "793 423.56 MLO \n", + "794 423.65 MLO \n", + "795 423.50 MLO \n", + "796 423.29 MLO \n", + "797 424.06 MLO \n", + "798 424.62 MLO \n", + "799 424.30 MLO \n", + "800 425.12 MLO \n", + "\n", + "[794 rows x 11 columns]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = data[data['CO2'].astype(float)>0]\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On crée une colonne avec les dates compréensibles par pandas" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_to_month_start(year, month):\n", + " return pd.Timestamp(year=year, month=month, day=1)\n", + "\n", + "data.loc[:, 'period'] = [convert_to_month_start(y, m) for y, m in zip(data['Yr'].astype(int), data['Mn'].astype(int))]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On fait le plot de la tendance historique. On utilise les periodes en tant qu'index et on les sorte de manière croissante" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
YrMnDate_excelDateCO2seasonally_adjustedfitseasonally_adjusted_fitCO2_filledseasonally_adjusted_filledSta
period
1958-03-01195803212591958.2027315.71314.43316.20314.91315.71314.43MLO
1958-04-01195804212901958.2877317.45315.16317.30314.99317.45315.16MLO
1958-05-01195805213201958.3699317.51314.69317.89315.07317.51314.69MLO
1958-07-01195807213811958.5370315.87315.20315.86315.22315.87315.20MLO
1958-08-01195808214121958.6219314.93316.22313.96315.29314.93316.22MLO
1958-09-01195809214431958.7068313.21316.12312.43315.35313.21316.12MLO
1958-11-01195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
1958-12-01195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
1959-01-01195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
1959-02-01195902215961959.1260316.49315.84316.30315.64316.49315.84MLO
1959-03-01195903216241959.2027316.65315.37316.99315.70316.65315.37MLO
1959-04-01195904216551959.2877317.72315.41318.09315.77317.72315.41MLO
1959-05-01195905216851959.3699318.29315.46318.68315.85318.29315.46MLO
1959-06-01195906217161959.4548318.15316.00318.07315.94318.15316.00MLO
1959-07-01195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
1959-08-01195908217771959.6219314.80316.09314.80316.13314.80316.09MLO
1959-09-01195909218081959.7068313.84316.75313.29316.22313.84316.75MLO
1959-10-01195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
1959-11-01195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
1959-12-01195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
1960-01-01196001219301960.0410316.43316.37316.62316.56316.43316.37MLO
1960-02-01196002219611960.1257316.98316.33317.30316.64316.98316.33MLO
1960-03-01196003219901960.2049317.58316.27318.04316.71317.58316.27MLO
1960-04-01196004220211960.2896319.03316.70319.14316.79319.03316.70MLO
1960-05-01196005220511960.3716320.03317.20319.70316.86320.03317.20MLO
1960-06-01196006220821960.4563319.59317.45319.04316.93319.59317.45MLO
1960-07-01196007221121960.5383318.18317.53317.59316.98318.18317.53MLO
1960-08-01196008221431960.6230315.90317.23315.66317.02315.90317.23MLO
1960-09-01196009221741960.7077314.17317.10314.10317.05314.17317.10MLO
1960-10-01196010222041960.7896313.83316.85314.08317.08313.83316.85MLO
....................................
2022-04-01202204446662022.2877420.01417.25420.47417.69420.01417.25MLO
2022-05-01202205446962022.3699420.78417.39421.23417.84420.78417.39MLO
2022-06-01202206447272022.4548420.68418.10420.56418.01420.68418.10MLO
2022-07-01202207447572022.5370418.71417.91418.94418.18418.71417.91MLO
2022-08-01202208447882022.6219416.75418.30416.77418.36416.75418.30MLO
2022-09-01202209448192022.7068415.42418.91415.04418.55415.42418.91MLO
2022-10-01202210448492022.7890415.31418.92415.15418.74415.31418.92MLO
2022-11-01202211448802022.8740417.03419.29416.71418.95417.03419.29MLO
2022-12-01202212449102022.9562418.46419.38418.25419.15418.46419.38MKO
2023-01-01202301449412023.0411419.13419.06419.45419.37419.13419.06MKO
2023-02-01202302449722023.1260420.33419.55420.40419.61420.33419.55MKO
2023-03-01202303450002023.2027420.51418.97421.39419.83420.51418.97MLO
2023-04-01202304450312023.2877422.73419.96422.89420.10422.73419.96MLO
2023-05-01202305450612023.3699423.78420.38423.77420.37423.78420.38MLO
2023-06-01202306450922023.4548423.39420.81423.23420.66423.39420.81MLO
2023-07-01202307451222023.5370421.62420.82421.73420.96421.62420.82MLO
2023-08-01202308451532023.6219419.56421.12419.67421.27419.56421.12MLO
2023-09-01202309451842023.7068418.06421.56418.06421.58418.06421.56MLO
2023-10-01202310452142023.7890418.41422.02418.28421.88418.41422.02MLO
2023-11-01202311452452023.8740420.11422.38419.95422.19420.11422.38MLO
2023-12-01202312452752023.9562421.65422.57421.58422.48421.65422.57MLO
2024-01-01202401453062024.0410422.62422.55422.85422.77422.62422.55MLO
2024-02-01202402453372024.1257424.34423.56423.85423.06424.34423.56MLO
2024-03-01202403453662024.2049425.22423.65424.91423.31425.22423.65MLO
2024-04-01202404453972024.2896426.30423.50426.41423.58426.30423.50MLO
2024-05-01202405454272024.3716426.70423.29427.25423.84426.70423.29MLO
2024-06-01202406454582024.4563426.63424.06426.65424.11426.63424.06MLO
2024-07-01202407454882024.5383425.40424.62425.10424.36425.40424.62MLO
2024-08-01202408455192024.6230422.71424.30423.00424.63422.71424.30MLO
2024-09-01202409455502024.7077421.60425.12-99.99-99.99421.60425.12MLO
\n", + "

794 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn Date_excel Date CO2 seasonally_adjusted \\\n", + "period \n", + "1958-03-01 1958 03 21259 1958.2027 315.71 314.43 \n", + "1958-04-01 1958 04 21290 1958.2877 317.45 315.16 \n", + "1958-05-01 1958 05 21320 1958.3699 317.51 314.69 \n", + "1958-07-01 1958 07 21381 1958.5370 315.87 315.20 \n", + "1958-08-01 1958 08 21412 1958.6219 314.93 316.22 \n", + "1958-09-01 1958 09 21443 1958.7068 313.21 316.12 \n", + "1958-11-01 1958 11 21504 1958.8740 313.33 315.21 \n", + "1958-12-01 1958 12 21534 1958.9562 314.67 315.43 \n", + "1959-01-01 1959 01 21565 1959.0411 315.58 315.52 \n", + "1959-02-01 1959 02 21596 1959.1260 316.49 315.84 \n", + "1959-03-01 1959 03 21624 1959.2027 316.65 315.37 \n", + "1959-04-01 1959 04 21655 1959.2877 317.72 315.41 \n", + "1959-05-01 1959 05 21685 1959.3699 318.29 315.46 \n", + "1959-06-01 1959 06 21716 1959.4548 318.15 316.00 \n", + "1959-07-01 1959 07 21746 1959.5370 316.54 315.87 \n", + "1959-08-01 1959 08 21777 1959.6219 314.80 316.09 \n", + "1959-09-01 1959 09 21808 1959.7068 313.84 316.75 \n", + "1959-10-01 1959 10 21838 1959.7890 313.33 316.35 \n", + "1959-11-01 1959 11 21869 1959.8740 314.81 316.69 \n", + "1959-12-01 1959 12 21899 1959.9562 315.58 316.35 \n", + "1960-01-01 1960 01 21930 1960.0410 316.43 316.37 \n", + "1960-02-01 1960 02 21961 1960.1257 316.98 316.33 \n", + "1960-03-01 1960 03 21990 1960.2049 317.58 316.27 \n", + "1960-04-01 1960 04 22021 1960.2896 319.03 316.70 \n", + "1960-05-01 1960 05 22051 1960.3716 320.03 317.20 \n", + "1960-06-01 1960 06 22082 1960.4563 319.59 317.45 \n", + "1960-07-01 1960 07 22112 1960.5383 318.18 317.53 \n", + "1960-08-01 1960 08 22143 1960.6230 315.90 317.23 \n", + "1960-09-01 1960 09 22174 1960.7077 314.17 317.10 \n", + "1960-10-01 1960 10 22204 1960.7896 313.83 316.85 \n", + "... ... ... ... ... ... ... \n", + "2022-04-01 2022 04 44666 2022.2877 420.01 417.25 \n", + "2022-05-01 2022 05 44696 2022.3699 420.78 417.39 \n", + "2022-06-01 2022 06 44727 2022.4548 420.68 418.10 \n", + "2022-07-01 2022 07 44757 2022.5370 418.71 417.91 \n", + "2022-08-01 2022 08 44788 2022.6219 416.75 418.30 \n", + "2022-09-01 2022 09 44819 2022.7068 415.42 418.91 \n", + "2022-10-01 2022 10 44849 2022.7890 415.31 418.92 \n", + "2022-11-01 2022 11 44880 2022.8740 417.03 419.29 \n", + "2022-12-01 2022 12 44910 2022.9562 418.46 419.38 \n", + "2023-01-01 2023 01 44941 2023.0411 419.13 419.06 \n", + "2023-02-01 2023 02 44972 2023.1260 420.33 419.55 \n", + "2023-03-01 2023 03 45000 2023.2027 420.51 418.97 \n", + "2023-04-01 2023 04 45031 2023.2877 422.73 419.96 \n", + "2023-05-01 2023 05 45061 2023.3699 423.78 420.38 \n", + "2023-06-01 2023 06 45092 2023.4548 423.39 420.81 \n", + "2023-07-01 2023 07 45122 2023.5370 421.62 420.82 \n", + "2023-08-01 2023 08 45153 2023.6219 419.56 421.12 \n", + "2023-09-01 2023 09 45184 2023.7068 418.06 421.56 \n", + "2023-10-01 2023 10 45214 2023.7890 418.41 422.02 \n", + "2023-11-01 2023 11 45245 2023.8740 420.11 422.38 \n", + "2023-12-01 2023 12 45275 2023.9562 421.65 422.57 \n", + "2024-01-01 2024 01 45306 2024.0410 422.62 422.55 \n", + "2024-02-01 2024 02 45337 2024.1257 424.34 423.56 \n", + "2024-03-01 2024 03 45366 2024.2049 425.22 423.65 \n", + "2024-04-01 2024 04 45397 2024.2896 426.30 423.50 \n", + "2024-05-01 2024 05 45427 2024.3716 426.70 423.29 \n", + "2024-06-01 2024 06 45458 2024.4563 426.63 424.06 \n", + "2024-07-01 2024 07 45488 2024.5383 425.40 424.62 \n", + "2024-08-01 2024 08 45519 2024.6230 422.71 424.30 \n", + "2024-09-01 2024 09 45550 2024.7077 421.60 425.12 \n", + "\n", + " fit seasonally_adjusted_fit CO2_filled \\\n", + "period \n", + "1958-03-01 316.20 314.91 315.71 \n", + "1958-04-01 317.30 314.99 317.45 \n", + "1958-05-01 317.89 315.07 317.51 \n", + "1958-07-01 315.86 315.22 315.87 \n", + "1958-08-01 313.96 315.29 314.93 \n", + "1958-09-01 312.43 315.35 313.21 \n", + "1958-11-01 313.60 315.46 313.33 \n", + "1958-12-01 314.77 315.52 314.67 \n", + "1959-01-01 315.64 315.57 315.58 \n", + "1959-02-01 316.30 315.64 316.49 \n", + "1959-03-01 316.99 315.70 316.65 \n", + "1959-04-01 318.09 315.77 317.72 \n", + "1959-05-01 318.68 315.85 318.29 \n", + "1959-06-01 318.07 315.94 318.15 \n", + "1959-07-01 316.67 316.03 316.54 \n", + "1959-08-01 314.80 316.13 314.80 \n", + "1959-09-01 313.29 316.22 313.84 \n", + "1959-10-01 313.31 316.31 313.33 \n", + "1959-11-01 314.53 316.40 314.81 \n", + "1959-12-01 315.72 316.48 315.58 \n", + "1960-01-01 316.62 316.56 316.43 \n", + "1960-02-01 317.30 316.64 316.98 \n", + "1960-03-01 318.04 316.71 317.58 \n", + "1960-04-01 319.14 316.79 319.03 \n", + "1960-05-01 319.70 316.86 320.03 \n", + "1960-06-01 319.04 316.93 319.59 \n", + "1960-07-01 317.59 316.98 318.18 \n", + "1960-08-01 315.66 317.02 315.90 \n", + "1960-09-01 314.10 317.05 314.17 \n", + "1960-10-01 314.08 317.08 313.83 \n", + "... ... ... ... \n", + "2022-04-01 420.47 417.69 420.01 \n", + "2022-05-01 421.23 417.84 420.78 \n", + "2022-06-01 420.56 418.01 420.68 \n", + "2022-07-01 418.94 418.18 418.71 \n", + "2022-08-01 416.77 418.36 416.75 \n", + "2022-09-01 415.04 418.55 415.42 \n", + "2022-10-01 415.15 418.74 415.31 \n", + "2022-11-01 416.71 418.95 417.03 \n", + "2022-12-01 418.25 419.15 418.46 \n", + "2023-01-01 419.45 419.37 419.13 \n", + "2023-02-01 420.40 419.61 420.33 \n", + "2023-03-01 421.39 419.83 420.51 \n", + "2023-04-01 422.89 420.10 422.73 \n", + "2023-05-01 423.77 420.37 423.78 \n", + "2023-06-01 423.23 420.66 423.39 \n", + "2023-07-01 421.73 420.96 421.62 \n", + "2023-08-01 419.67 421.27 419.56 \n", + "2023-09-01 418.06 421.58 418.06 \n", + "2023-10-01 418.28 421.88 418.41 \n", + "2023-11-01 419.95 422.19 420.11 \n", + "2023-12-01 421.58 422.48 421.65 \n", + "2024-01-01 422.85 422.77 422.62 \n", + "2024-02-01 423.85 423.06 424.34 \n", + "2024-03-01 424.91 423.31 425.22 \n", + "2024-04-01 426.41 423.58 426.30 \n", + "2024-05-01 427.25 423.84 426.70 \n", + "2024-06-01 426.65 424.11 426.63 \n", + "2024-07-01 425.10 424.36 425.40 \n", + "2024-08-01 423.00 424.63 422.71 \n", + "2024-09-01 -99.99 -99.99 421.60 \n", + "\n", + " seasonally_adjusted_filled Sta \n", + "period \n", + "1958-03-01 314.43 MLO \n", + "1958-04-01 315.16 MLO \n", + "1958-05-01 314.69 MLO \n", + "1958-07-01 315.20 MLO \n", + "1958-08-01 316.22 MLO \n", + "1958-09-01 316.12 MLO \n", + "1958-11-01 315.21 MLO \n", + "1958-12-01 315.43 MLO \n", + "1959-01-01 315.52 MLO \n", + "1959-02-01 315.84 MLO \n", + "1959-03-01 315.37 MLO \n", + "1959-04-01 315.41 MLO \n", + "1959-05-01 315.46 MLO \n", + "1959-06-01 316.00 MLO \n", + "1959-07-01 315.87 MLO \n", + "1959-08-01 316.09 MLO \n", + "1959-09-01 316.75 MLO \n", + "1959-10-01 316.35 MLO \n", + "1959-11-01 316.69 MLO \n", + "1959-12-01 316.35 MLO \n", + "1960-01-01 316.37 MLO \n", + "1960-02-01 316.33 MLO \n", + "1960-03-01 316.27 MLO \n", + "1960-04-01 316.70 MLO \n", + "1960-05-01 317.20 MLO \n", + "1960-06-01 317.45 MLO \n", + "1960-07-01 317.53 MLO \n", + "1960-08-01 317.23 MLO \n", + "1960-09-01 317.10 MLO \n", + "1960-10-01 316.85 MLO \n", + "... ... ... \n", + "2022-04-01 417.25 MLO \n", + "2022-05-01 417.39 MLO \n", + "2022-06-01 418.10 MLO \n", + "2022-07-01 417.91 MLO \n", + "2022-08-01 418.30 MLO \n", + "2022-09-01 418.91 MLO \n", + "2022-10-01 418.92 MLO \n", + "2022-11-01 419.29 MLO \n", + "2022-12-01 419.38 MKO \n", + "2023-01-01 419.06 MKO \n", + "2023-02-01 419.55 MKO \n", + "2023-03-01 418.97 MLO \n", + "2023-04-01 419.96 MLO \n", + "2023-05-01 420.38 MLO \n", + "2023-06-01 420.81 MLO \n", + "2023-07-01 420.82 MLO \n", + "2023-08-01 421.12 MLO \n", + "2023-09-01 421.56 MLO \n", + "2023-10-01 422.02 MLO \n", + "2023-11-01 422.38 MLO \n", + "2023-12-01 422.57 MLO \n", + "2024-01-01 422.55 MLO \n", + "2024-02-01 423.56 MLO \n", + "2024-03-01 423.65 MLO \n", + "2024-04-01 423.50 MLO \n", + "2024-05-01 423.29 MLO \n", + "2024-06-01 424.06 MLO \n", + "2024-07-01 424.62 MLO \n", + "2024-08-01 424.30 MLO \n", + "2024-09-01 425.12 MLO \n", + "\n", + "[794 rows x 11 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data = data.set_index('period').sort_index()\n", + "sorted_data" + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "source": [ + "On plot les données" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['CO2'] = sorted_data['CO2'].astype(float)\n", + "sorted_data['CO2'].plot(color='blue')\n", + "plt.ylabel(r'Concentration en $CO_2$ [ppm]')\n", + "plt.xlabel('Année')\n", + "plt.grid(linestyle=':')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On observe deux phénomène couplés : une oscillation périodique annuelle et une contribution plus lente. On essaye de fitter cette contribution plus lente par une exponentielle croissante, selon $$[CO_2] = a \\cdot \\exp\\left(b \\cdot t \\right) + c$$" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parametres fittés: a = 55.02, b = 0.0166, c = 258.55\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from scipy.optimize import curve_fit\n", + "\n", + "def exponential_func(t, a, b, c):\n", + " return a * np.exp(b * t) + c\n", + "\n", + "start_year = sorted_data.index.min().year\n", + "sorted_data[\"period_fractional\"] = (sorted_data.index.year - start_year) + (sorted_data.index.month - 1) / 12\n", + "initial_guess = [1, 0.03, 300]\n", + "popt, pcov = curve_fit(exponential_func, sorted_data[\"period_fractional\"], sorted_data[\"CO2\"], p0=initial_guess)\n", + "fitted_values = exponential_func(sorted_data[\"period_fractional\"], *popt)\n", + "plt.plot(sorted_data.index, sorted_data['CO2'], color='blue', label='Concentration mésurée')\n", + "plt.plot(sorted_data.index, fitted_values, color='red', label='Augumentation lente')\n", + "plt.legend()\n", + "plt.ylabel(r\"Concentration en $CO_2$ [ppm]\")\n", + "plt.xlabel(\"Année\")\n", + "plt.show()\n", + "print(f\"Parametres fittés: a = {popt[0]:.2f}, b = {popt[1]:.4f}, c = {popt[2]:.2f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], "metadata": { "kernelspec": { "display_name": "Python 3", @@ -16,10 +4537,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 } -