{ "cells": [ { "cell_type": "code", "execution_count": 6, "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", "\n", "C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and oceans from 1978 to 2000. I. Global aspects, SIO Reference Series, No. 01-06, Scripps Institution of Oceanography, San Diego, 88 pages, 2001." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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806 rows × 11 columns

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44941 2023.0411 419.13 419.06 419.45 \n", "783 2023 02 44972 2023.1260 420.33 419.55 420.40 \n", "784 2023 03 45000 2023.2027 420.51 418.97 421.39 \n", "785 2023 04 45031 2023.2877 422.73 419.96 422.89 \n", "786 2023 05 45061 2023.3699 423.78 420.38 423.77 \n", "787 2023 06 45092 2023.4548 423.39 420.81 423.23 \n", "788 2023 07 45122 2023.5370 421.62 420.82 421.73 \n", "789 2023 08 45153 2023.6219 419.56 421.12 419.67 \n", "790 2023 09 45184 2023.7068 418.06 421.56 418.06 \n", "791 2023 10 45214 2023.7890 418.41 422.02 418.28 \n", "792 2023 11 45245 2023.8740 420.11 422.38 419.95 \n", "793 2023 12 45275 2023.9562 421.65 422.57 421.58 \n", "794 2024 01 45306 2024.0410 422.62 422.55 422.85 \n", "795 2024 02 45337 2024.1257 424.34 423.56 423.85 \n", "796 2024 03 45366 2024.2049 425.22 423.65 424.91 \n", "797 2024 04 45397 2024.2896 426.30 423.50 426.41 \n", "798 2024 05 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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_file = 'module3_exo3_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 en têtes. On concatene les deux premieres lignes et enleve celle de l'unité." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDate_excelDateCO2seasonally_adjustedfitseasonally_adjusted_fitCO2_filledseasonally_adjusted_filledSta
0195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99MLO
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
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
801202410455802024.7896-99.99-99.99-99.99-99.99-99.99-99.99MLO
802202411456112024.8743-99.99-99.99-99.99-99.99-99.99-99.99MLO
803202412456412024.9563-99.99-99.99-99.99-99.99-99.99-99.99MLO
\n", "

804 rows × 11 columns

\n", "
" ], "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": 11, "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. Elles ne nous interessent pas. On les enlève de l'analyse." ] }, { "cell_type": "code", "execution_count": 12, "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
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794 rows × 11 columns

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" ], "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": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = data[data['CO2'].astype(float)>0]\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On crée une collonne avec les dates compreensibles par pandas" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:357: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self.obj[key] = _infer_fill_value(value)\n", "/opt/conda/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " self.obj[item] = s\n" ] }, { "data": { "text/html": [ "
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YrMnDate_excelDateCO2seasonally_adjustedfitseasonally_adjusted_fitCO2_filledseasonally_adjusted_filledStaperiod
2195803212591958.2027315.71314.43316.20314.91315.71314.43MLO1958-03-01
3195804212901958.2877317.45315.16317.30314.99317.45315.16MLO1958-04-01
4195805213201958.3699317.51314.69317.89315.07317.51314.69MLO1958-05-01
6195807213811958.5370315.87315.20315.86315.22315.87315.20MLO1958-07-01
7195808214121958.6219314.93316.22313.96315.29314.93316.22MLO1958-08-01
8195809214431958.7068313.21316.12312.43315.35313.21316.12MLO1958-09-01
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO1958-11-01
11195812215341958.9562314.67315.43314.77315.52314.67315.43MLO1958-12-01
12195901215651959.0411315.58315.52315.64315.57315.58315.52MLO1959-01-01
13195902215961959.1260316.49315.84316.30315.64316.49315.84MLO1959-02-01
14195903216241959.2027316.65315.37316.99315.70316.65315.37MLO1959-03-01
15195904216551959.2877317.72315.41318.09315.77317.72315.41MLO1959-04-01
16195905216851959.3699318.29315.46318.68315.85318.29315.46MLO1959-05-01
17195906217161959.4548318.15316.00318.07315.94318.15316.00MLO1959-06-01
18195907217461959.5370316.54315.87316.67316.03316.54315.87MLO1959-07-01
19195908217771959.6219314.80316.09314.80316.13314.80316.09MLO1959-08-01
20195909218081959.7068313.84316.75313.29316.22313.84316.75MLO1959-09-01
21195910218381959.7890313.33316.35313.31316.31313.33316.35MLO1959-10-01
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\n", "

794 rows × 12 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 period \n", "2 314.43 MLO 1958-03-01 \n", "3 315.16 MLO 1958-04-01 \n", "4 314.69 MLO 1958-05-01 \n", "6 315.20 MLO 1958-07-01 \n", "7 316.22 MLO 1958-08-01 \n", "8 316.12 MLO 1958-09-01 \n", "10 315.21 MLO 1958-11-01 \n", "11 315.43 MLO 1958-12-01 \n", "12 315.52 MLO 1959-01-01 \n", "13 315.84 MLO 1959-02-01 \n", "14 315.37 MLO 1959-03-01 \n", "15 315.41 MLO 1959-04-01 \n", "16 315.46 MLO 1959-05-01 \n", "17 316.00 MLO 1959-06-01 \n", "18 315.87 MLO 1959-07-01 \n", "19 316.09 MLO 1959-08-01 \n", "20 316.75 MLO 1959-09-01 \n", "21 316.35 MLO 1959-10-01 \n", "22 316.69 MLO 1959-11-01 \n", "23 316.35 MLO 1959-12-01 \n", "24 316.37 MLO 1960-01-01 \n", "25 316.33 MLO 1960-02-01 \n", "26 316.27 MLO 1960-03-01 \n", "27 316.70 MLO 1960-04-01 \n", "28 317.20 MLO 1960-05-01 \n", "29 317.45 MLO 1960-06-01 \n", "30 317.53 MLO 1960-07-01 \n", "31 317.23 MLO 1960-08-01 \n", "32 317.10 MLO 1960-09-01 \n", "33 316.85 MLO 1960-10-01 \n", ".. ... ... ... \n", "771 417.25 MLO 2022-04-01 \n", "772 417.39 MLO 2022-05-01 \n", "773 418.10 MLO 2022-06-01 \n", "774 417.91 MLO 2022-07-01 \n", "775 418.30 MLO 2022-08-01 \n", "776 418.91 MLO 2022-09-01 \n", "777 418.92 MLO 2022-10-01 \n", "778 419.29 MLO 2022-11-01 \n", "779 419.38 MKO 2022-12-01 \n", "780 419.06 MKO 2023-01-01 \n", "781 419.55 MKO 2023-02-01 \n", "782 418.97 MLO 2023-03-01 \n", "783 419.96 MLO 2023-04-01 \n", "784 420.38 MLO 2023-05-01 \n", "785 420.81 MLO 2023-06-01 \n", "786 420.82 MLO 2023-07-01 \n", "787 421.12 MLO 2023-08-01 \n", "788 421.56 MLO 2023-09-01 \n", "789 422.02 MLO 2023-10-01 \n", "790 422.38 MLO 2023-11-01 \n", "791 422.57 MLO 2023-12-01 \n", "792 422.55 MLO 2024-01-01 \n", "793 423.56 MLO 2024-02-01 \n", "794 423.65 MLO 2024-03-01 \n", "795 423.50 MLO 2024-04-01 \n", "796 423.29 MLO 2024-05-01 \n", "797 424.06 MLO 2024-06-01 \n", "798 424.62 MLO 2024-07-01 \n", "799 424.30 MLO 2024-08-01 \n", "800 425.12 MLO 2024-09-01 \n", "\n", "[794 rows x 12 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "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", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On fait le plot de la tendance historique\n", "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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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": "markdown", "metadata": {}, "source": [ "On plot les données" ] }, { "cell_type": "code", "execution_count": 16, "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": 17, "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", 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id(linestyle=\":\")\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", "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 }