{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#

Evaluation par les pairs : Concentration de CO2 dans l'atmosphère depuis 1958

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426.35 MLO \n", "2025 427.00 426.21 MLO \n", "2025 427.73 426.19 MLO \n", "2025 429.24 426.47 MLO \n", "2025 430.21 426.80 MLO \n", "2025 429.52 426.93 MLO \n", "2025 427.56 426.76 MLO \n", "2025 -99.99 -99.99 MLO \n", "2025 -99.99 -99.99 MLO \n", "2025 -99.99 -99.99 MLO \n", "2025 -99.99 -99.99 MLO \n", "2025 -99.99 -99.99 MLO \n", "\n", "[816 rows x 10 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=63)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ensuite on modifie la première colonne afin de bien les identifier\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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moisdate decimaldate?CO2(ppm)seasonally adjusted(ppm)fit(ppm)seasonally adjusted fit(ppm)CO2 filled(ppm)seasonally adjusted filled(ppm)institut qui fait les mesures
19581212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99MLO
19582212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99MLO
19583212591958.2027315.71314.43316.20314.91315.71314.43MLO
19584212901958.2877317.45315.15317.31314.99317.45315.15MLO
19585213201958.3699317.51314.68317.89315.07317.51314.68MLO
19586213511958.4548-99.99-99.99317.27315.14317.27315.14MLO
19587213811958.5370315.87315.20315.85315.22315.87315.20MLO
19588214121958.6219314.93316.23313.95315.29314.93316.23MLO
19589214431958.7068313.21316.12312.42315.35313.21316.12MLO
195810214731958.7890-99.99-99.99312.41315.41312.41315.41MLO
195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
19591215651959.0411315.58315.52315.64315.57315.58315.52MLO
19592215961959.1260316.49315.83316.30315.64316.49315.83MLO
19593216241959.2027316.65315.37317.00315.70316.65315.37MLO
19594216551959.2877317.72315.41318.10315.77317.72315.41MLO
19595216851959.3699318.29315.46318.69315.85318.29315.46MLO
19596217161959.4548318.15316.00318.08315.94318.15316.00MLO
19597217461959.5370316.54315.87316.67316.03316.54315.87MLO
19598217771959.6219314.79316.10314.79316.13314.79316.10MLO
19599218081959.7068313.84316.76313.28316.22313.84316.76MLO
195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
19601219301960.0410316.43316.37316.63316.56316.43316.37MLO
19602219611960.1257316.98316.33317.30316.64316.98316.33MLO
19603219901960.2049317.58316.27318.04316.71317.58316.27MLO
19604220211960.2896319.03316.69319.14316.79319.03316.69MLO
19605220511960.3716320.03317.19319.70316.86320.03317.19MLO
19606220821960.4563319.58317.44319.05316.92319.58317.44MLO
.................................
20237451222023.5370421.62420.83421.72420.96421.62420.83MLO
20238451532023.6219419.56421.12419.67421.27419.56421.12MLO
20239451842023.7068418.06421.56418.07421.58418.06421.56MLO
202310452142023.7890418.41422.01418.30421.89418.41422.01MLO
202311452452023.8740420.11422.37419.97422.20420.11422.37MLO
202312452752023.9562421.65422.57421.60422.50421.65422.57MLO
20241453062024.0410422.62422.55422.88422.80422.62422.55MLO
20242453372024.1257424.34423.56423.89423.10424.34423.56MLO
20243453662024.2049425.22423.65424.95423.37425.22423.65MLO
20244453972024.2896426.30423.50426.47423.66426.30423.50MLO
20245454272024.3716426.70423.30427.33423.93426.70423.30MLO
20246454582024.4563426.62424.07426.75424.21426.62424.07MLO
20247454882024.5383425.40424.63425.22424.48425.40424.63MLO
20248455192024.6230422.70424.30423.13424.76422.70424.30MLO
20249455502024.7077421.60425.11421.50425.03421.60425.11MLO
202410455802024.7896422.05425.66421.70425.29422.05425.66MLO
202411456112024.8743423.61425.87423.31425.54423.61425.87MLO
202412456412024.9563425.01425.93424.87425.76425.01425.93MLO
20251456722025.0411426.42426.35426.07425.98426.42426.35MLO
20252457032025.1260427.00426.21426.99426.19427.00426.21MLO
20253457312025.2027427.73426.19427.92426.36427.73426.19MLO
20254457622025.2877429.24426.47429.34426.55429.24426.47MLO
20255457922025.3699430.21426.80430.13426.72430.21426.80MLO
20256458232025.4548429.52426.93429.46426.90429.52426.93MLO
20257458532025.5370427.56426.76427.83427.06427.56426.76MLO
20258458842025.6219-99.99-99.99-99.99-99.99-99.99-99.99MLO
20259459152025.7068-99.99-99.99-99.99-99.99-99.99-99.99MLO
202510459452025.7890-99.99-99.99-99.99-99.99-99.99-99.99MLO
202511459762025.8740-99.99-99.99-99.99-99.99-99.99-99.99MLO
202512460062025.9562-99.99-99.99-99.99-99.99-99.99-99.99MLO
\n", "

816 rows × 10 columns

\n", "
" ], "text/plain": [ " mois date decimal date? CO2(ppm) seasonally adjusted(ppm) \\\n", "1958 1 21200 1958.0411 -99.99 -99.99 \n", "1958 2 21231 1958.1260 -99.99 -99.99 \n", "1958 3 21259 1958.2027 315.71 314.43 \n", "1958 4 21290 1958.2877 317.45 315.15 \n", "1958 5 21320 1958.3699 317.51 314.68 \n", "1958 6 21351 1958.4548 -99.99 -99.99 \n", "1958 7 21381 1958.5370 315.87 315.20 \n", "1958 8 21412 1958.6219 314.93 316.23 \n", "1958 9 21443 1958.7068 313.21 316.12 \n", "1958 10 21473 1958.7890 -99.99 -99.99 \n", "1958 11 21504 1958.8740 313.33 315.21 \n", "1958 12 21534 1958.9562 314.67 315.43 \n", "1959 1 21565 1959.0411 315.58 315.52 \n", "1959 2 21596 1959.1260 316.49 315.83 \n", "1959 3 21624 1959.2027 316.65 315.37 \n", "1959 4 21655 1959.2877 317.72 315.41 \n", "1959 5 21685 1959.3699 318.29 315.46 \n", "1959 6 21716 1959.4548 318.15 316.00 \n", "1959 7 21746 1959.5370 316.54 315.87 \n", "1959 8 21777 1959.6219 314.79 316.10 \n", "1959 9 21808 1959.7068 313.84 316.76 \n", "1959 10 21838 1959.7890 313.33 316.35 \n", "1959 11 21869 1959.8740 314.81 316.69 \n", "1959 12 21899 1959.9562 315.58 316.35 \n", "1960 1 21930 1960.0410 316.43 316.37 \n", "1960 2 21961 1960.1257 316.98 316.33 \n", "1960 3 21990 1960.2049 317.58 316.27 \n", "1960 4 22021 1960.2896 319.03 316.69 \n", "1960 5 22051 1960.3716 320.03 317.19 \n", "1960 6 22082 1960.4563 319.58 317.44 \n", "... ... ... ... ... ... \n", "2023 7 45122 2023.5370 421.62 420.83 \n", "2023 8 45153 2023.6219 419.56 421.12 \n", "2023 9 45184 2023.7068 418.06 421.56 \n", "2023 10 45214 2023.7890 418.41 422.01 \n", "2023 11 45245 2023.8740 420.11 422.37 \n", "2023 12 45275 2023.9562 421.65 422.57 \n", "2024 1 45306 2024.0410 422.62 422.55 \n", "2024 2 45337 2024.1257 424.34 423.56 \n", "2024 3 45366 2024.2049 425.22 423.65 \n", "2024 4 45397 2024.2896 426.30 423.50 \n", "2024 5 45427 2024.3716 426.70 423.30 \n", "2024 6 45458 2024.4563 426.62 424.07 \n", "2024 7 45488 2024.5383 425.40 424.63 \n", "2024 8 45519 2024.6230 422.70 424.30 \n", "2024 9 45550 2024.7077 421.60 425.11 \n", "2024 10 45580 2024.7896 422.05 425.66 \n", "2024 11 45611 2024.8743 423.61 425.87 \n", "2024 12 45641 2024.9563 425.01 425.93 \n", "2025 1 45672 2025.0411 426.42 426.35 \n", "2025 2 45703 2025.1260 427.00 426.21 \n", "2025 3 45731 2025.2027 427.73 426.19 \n", "2025 4 45762 2025.2877 429.24 426.47 \n", "2025 5 45792 2025.3699 430.21 426.80 \n", "2025 6 45823 2025.4548 429.52 426.93 \n", "2025 7 45853 2025.5370 427.56 426.76 \n", "2025 8 45884 2025.6219 -99.99 -99.99 \n", "2025 9 45915 2025.7068 -99.99 -99.99 \n", "2025 10 45945 2025.7890 -99.99 -99.99 \n", "2025 11 45976 2025.8740 -99.99 -99.99 \n", "2025 12 46006 2025.9562 -99.99 -99.99 \n", "\n", " fit(ppm) seasonally adjusted fit(ppm) CO2 filled(ppm) \\\n", "1958 -99.99 -99.99 -99.99 \n", "1958 -99.99 -99.99 -99.99 \n", "1958 316.20 314.91 315.71 \n", "1958 317.31 314.99 317.45 \n", "1958 317.89 315.07 317.51 \n", "1958 317.27 315.14 317.27 \n", "1958 315.85 315.22 315.87 \n", "1958 313.95 315.29 314.93 \n", "1958 312.42 315.35 313.21 \n", "1958 312.41 315.41 312.41 \n", "1958 313.60 315.46 313.33 \n", "1958 314.77 315.52 314.67 \n", "1959 315.64 315.57 315.58 \n", "1959 316.30 315.64 316.49 \n", "1959 317.00 315.70 316.65 \n", "1959 318.10 315.77 317.72 \n", "1959 318.69 315.85 318.29 \n", "1959 318.08 315.94 318.15 \n", "1959 316.67 316.03 316.54 \n", "1959 314.79 316.13 314.79 \n", "1959 313.28 316.22 313.84 \n", "1959 313.31 316.31 313.33 \n", "1959 314.53 316.40 314.81 \n", "1959 315.72 316.48 315.58 \n", "1960 316.63 316.56 316.43 \n", "1960 317.30 316.64 316.98 \n", "1960 318.04 316.71 317.58 \n", "1960 319.14 316.79 319.03 \n", "1960 319.70 316.86 320.03 \n", "1960 319.05 316.92 319.58 \n", "... ... ... ... \n", "2023 421.72 420.96 421.62 \n", "2023 419.67 421.27 419.56 \n", "2023 418.07 421.58 418.06 \n", "2023 418.30 421.89 418.41 \n", "2023 419.97 422.20 420.11 \n", "2023 421.60 422.50 421.65 \n", "2024 422.88 422.80 422.62 \n", "2024 423.89 423.10 424.34 \n", "2024 424.95 423.37 425.22 \n", "2024 426.47 423.66 426.30 \n", "2024 427.33 423.93 426.70 \n", "2024 426.75 424.21 426.62 \n", "2024 425.22 424.48 425.40 \n", "2024 423.13 424.76 422.70 \n", "2024 421.50 425.03 421.60 \n", "2024 421.70 425.29 422.05 \n", "2024 423.31 425.54 423.61 \n", "2024 424.87 425.76 425.01 \n", "2025 426.07 425.98 426.42 \n", "2025 426.99 426.19 427.00 \n", "2025 427.92 426.36 427.73 \n", "2025 429.34 426.55 429.24 \n", "2025 430.13 426.72 430.21 \n", "2025 429.46 426.90 429.52 \n", "2025 427.83 427.06 427.56 \n", "2025 -99.99 -99.99 -99.99 \n", "2025 -99.99 -99.99 -99.99 \n", "2025 -99.99 -99.99 -99.99 \n", "2025 -99.99 -99.99 -99.99 \n", "2025 -99.99 -99.99 -99.99 \n", "\n", " seasonally adjusted filled(ppm) institut qui fait les mesures \n", "1958 -99.99 MLO \n", "1958 -99.99 MLO \n", "1958 314.43 MLO \n", "1958 315.15 MLO \n", "1958 314.68 MLO \n", "1958 315.14 MLO \n", "1958 315.20 MLO \n", "1958 316.23 MLO \n", "1958 316.12 MLO \n", "1958 315.41 MLO \n", "1958 315.21 MLO \n", "1958 315.43 MLO \n", "1959 315.52 MLO \n", "1959 315.83 MLO \n", "1959 315.37 MLO \n", "1959 315.41 MLO \n", "1959 315.46 MLO \n", "1959 316.00 MLO \n", "1959 315.87 MLO \n", "1959 316.10 MLO \n", "1959 316.76 MLO \n", "1959 316.35 MLO \n", "1959 316.69 MLO \n", "1959 316.35 MLO \n", "1960 316.37 MLO \n", "1960 316.33 MLO \n", "1960 316.27 MLO \n", "1960 316.69 MLO \n", "1960 317.19 MLO \n", "1960 317.44 MLO \n", "... ... ... \n", "2023 420.83 MLO \n", "2023 421.12 MLO \n", "2023 421.56 MLO \n", "2023 422.01 MLO \n", "2023 422.37 MLO \n", "2023 422.57 MLO \n", "2024 422.55 MLO \n", "2024 423.56 MLO \n", "2024 423.65 MLO \n", "2024 423.50 MLO \n", "2024 423.30 MLO \n", "2024 424.07 MLO \n", "2024 424.63 MLO \n", "2024 424.30 MLO \n", "2024 425.11 MLO \n", "2024 425.66 MLO \n", "2024 425.87 MLO \n", "2024 425.93 MLO \n", "2025 426.35 MLO \n", "2025 426.21 MLO \n", "2025 426.19 MLO \n", "2025 426.47 MLO \n", "2025 426.80 MLO \n", "2025 426.93 MLO \n", "2025 426.76 MLO \n", "2025 -99.99 MLO \n", "2025 -99.99 MLO \n", "2025 -99.99 MLO \n", "2025 -99.99 MLO \n", "2025 -99.99 MLO \n", "\n", "[816 rows x 10 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.columns = \"mois\", \"date decimal\", \"date?\", \"CO2(ppm)\", \"seasonally adjusted(ppm)\", \"fit(ppm)\",\"seasonally adjusted fit(ppm)\",\"CO2 filled(ppm)\",\"seasonally adjusted filled(ppm)\", \"institut qui fait les mesures\"\n", "\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On décale ensuite les années afin qu'elles ne soit plus en indice" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmoisdate decimaldate?CO2(ppm)seasonally adjusted(ppm)fit(ppm)seasonally adjusted fit(ppm)CO2 filled(ppm)seasonally adjusted filled(ppm)institut qui fait les mesures
019581212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99MLO
119582212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99MLO
219583212591958.2027315.71314.43316.20314.91315.71314.43MLO
319584212901958.2877317.45315.15317.31314.99317.45315.15MLO
419585213201958.3699317.51314.68317.89315.07317.51314.68MLO
519586213511958.4548-99.99-99.99317.27315.14317.27315.14MLO
619587213811958.5370315.87315.20315.85315.22315.87315.20MLO
719588214121958.6219314.93316.23313.95315.29314.93316.23MLO
819589214431958.7068313.21316.12312.42315.35313.21316.12MLO
9195810214731958.7890-99.99-99.99312.41315.41312.41315.41MLO
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
11195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
1219591215651959.0411315.58315.52315.64315.57315.58315.52MLO
1319592215961959.1260316.49315.83316.30315.64316.49315.83MLO
1419593216241959.2027316.65315.37317.00315.70316.65315.37MLO
1519594216551959.2877317.72315.41318.10315.77317.72315.41MLO
1619595216851959.3699318.29315.46318.69315.85318.29315.46MLO
1719596217161959.4548318.15316.00318.08315.94318.15316.00MLO
1819597217461959.5370316.54315.87316.67316.03316.54315.87MLO
1919598217771959.6219314.79316.10314.79316.13314.79316.10MLO
2019599218081959.7068313.84316.76313.28316.22313.84316.76MLO
21195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
22195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
23195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
2419601219301960.0410316.43316.37316.63316.56316.43316.37MLO
2519602219611960.1257316.98316.33317.30316.64316.98316.33MLO
2619603219901960.2049317.58316.27318.04316.71317.58316.27MLO
2719604220211960.2896319.03316.69319.14316.79319.03316.69MLO
2819605220511960.3716320.03317.19319.70316.86320.03317.19MLO
2919606220821960.4563319.58317.44319.05316.92319.58317.44MLO
....................................
78620237451222023.5370421.62420.83421.72420.96421.62420.83MLO
78720238451532023.6219419.56421.12419.67421.27419.56421.12MLO
78820239451842023.7068418.06421.56418.07421.58418.06421.56MLO
789202310452142023.7890418.41422.01418.30421.89418.41422.01MLO
790202311452452023.8740420.11422.37419.97422.20420.11422.37MLO
791202312452752023.9562421.65422.57421.60422.50421.65422.57MLO
79220241453062024.0410422.62422.55422.88422.80422.62422.55MLO
79320242453372024.1257424.34423.56423.89423.10424.34423.56MLO
79420243453662024.2049425.22423.65424.95423.37425.22423.65MLO
79520244453972024.2896426.30423.50426.47423.66426.30423.50MLO
79620245454272024.3716426.70423.30427.33423.93426.70423.30MLO
79720246454582024.4563426.62424.07426.75424.21426.62424.07MLO
79820247454882024.5383425.40424.63425.22424.48425.40424.63MLO
79920248455192024.6230422.70424.30423.13424.76422.70424.30MLO
80020249455502024.7077421.60425.11421.50425.03421.60425.11MLO
801202410455802024.7896422.05425.66421.70425.29422.05425.66MLO
802202411456112024.8743423.61425.87423.31425.54423.61425.87MLO
803202412456412024.9563425.01425.93424.87425.76425.01425.93MLO
80420251456722025.0411426.42426.35426.07425.98426.42426.35MLO
80520252457032025.1260427.00426.21426.99426.19427.00426.21MLO
80620253457312025.2027427.73426.19427.92426.36427.73426.19MLO
80720254457622025.2877429.24426.47429.34426.55429.24426.47MLO
80820255457922025.3699430.21426.80430.13426.72430.21426.80MLO
80920256458232025.4548429.52426.93429.46426.90429.52426.93MLO
81020257458532025.5370427.56426.76427.83427.06427.56426.76MLO
81120258458842025.6219-99.99-99.99-99.99-99.99-99.99-99.99MLO
81220259459152025.7068-99.99-99.99-99.99-99.99-99.99-99.99MLO
813202510459452025.7890-99.99-99.99-99.99-99.99-99.99-99.99MLO
814202511459762025.8740-99.99-99.99-99.99-99.99-99.99-99.99MLO
815202512460062025.9562-99.99-99.99-99.99-99.99-99.99-99.99MLO
\n", "

816 rows × 11 columns

\n", "
" ], "text/plain": [ " year mois date decimal date? CO2(ppm) seasonally adjusted(ppm) \\\n", "0 1958 1 21200 1958.0411 -99.99 -99.99 \n", "1 1958 2 21231 1958.1260 -99.99 -99.99 \n", "2 1958 3 21259 1958.2027 315.71 314.43 \n", "3 1958 4 21290 1958.2877 317.45 315.15 \n", "4 1958 5 21320 1958.3699 317.51 314.68 \n", "5 1958 6 21351 1958.4548 -99.99 -99.99 \n", "6 1958 7 21381 1958.5370 315.87 315.20 \n", "7 1958 8 21412 1958.6219 314.93 316.23 \n", "8 1958 9 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 1 21565 1959.0411 315.58 315.52 \n", "13 1959 2 21596 1959.1260 316.49 315.83 \n", "14 1959 3 21624 1959.2027 316.65 315.37 \n", "15 1959 4 21655 1959.2877 317.72 315.41 \n", "16 1959 5 21685 1959.3699 318.29 315.46 \n", "17 1959 6 21716 1959.4548 318.15 316.00 \n", "18 1959 7 21746 1959.5370 316.54 315.87 \n", "19 1959 8 21777 1959.6219 314.79 316.10 \n", "20 1959 9 21808 1959.7068 313.84 316.76 \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 1 21930 1960.0410 316.43 316.37 \n", "25 1960 2 21961 1960.1257 316.98 316.33 \n", "26 1960 3 21990 1960.2049 317.58 316.27 \n", "27 1960 4 22021 1960.2896 319.03 316.69 \n", "28 1960 5 22051 1960.3716 320.03 317.19 \n", "29 1960 6 22082 1960.4563 319.58 317.44 \n", ".. ... ... ... ... ... ... \n", "786 2023 7 45122 2023.5370 421.62 420.83 \n", "787 2023 8 45153 2023.6219 419.56 421.12 \n", "788 2023 9 45184 2023.7068 418.06 421.56 \n", "789 2023 10 45214 2023.7890 418.41 422.01 \n", "790 2023 11 45245 2023.8740 420.11 422.37 \n", "791 2023 12 45275 2023.9562 421.65 422.57 \n", "792 2024 1 45306 2024.0410 422.62 422.55 \n", "793 2024 2 45337 2024.1257 424.34 423.56 \n", "794 2024 3 45366 2024.2049 425.22 423.65 \n", "795 2024 4 45397 2024.2896 426.30 423.50 \n", "796 2024 5 45427 2024.3716 426.70 423.30 \n", "797 2024 6 45458 2024.4563 426.62 424.07 \n", "798 2024 7 45488 2024.5383 425.40 424.63 \n", "799 2024 8 45519 2024.6230 422.70 424.30 \n", "800 2024 9 45550 2024.7077 421.60 425.11 \n", "801 2024 10 45580 2024.7896 422.05 425.66 \n", "802 2024 11 45611 2024.8743 423.61 425.87 \n", "803 2024 12 45641 2024.9563 425.01 425.93 \n", "804 2025 1 45672 2025.0411 426.42 426.35 \n", "805 2025 2 45703 2025.1260 427.00 426.21 \n", "806 2025 3 45731 2025.2027 427.73 426.19 \n", "807 2025 4 45762 2025.2877 429.24 426.47 \n", "808 2025 5 45792 2025.3699 430.21 426.80 \n", "809 2025 6 45823 2025.4548 429.52 426.93 \n", "810 2025 7 45853 2025.5370 427.56 426.76 \n", "811 2025 8 45884 2025.6219 -99.99 -99.99 \n", "812 2025 9 45915 2025.7068 -99.99 -99.99 \n", "813 2025 10 45945 2025.7890 -99.99 -99.99 \n", "814 2025 11 45976 2025.8740 -99.99 -99.99 \n", "815 2025 12 46006 2025.9562 -99.99 -99.99 \n", "\n", " fit(ppm) seasonally adjusted fit(ppm) CO2 filled(ppm) \\\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", "3 317.31 314.99 317.45 \n", "4 317.89 315.07 317.51 \n", "5 317.27 315.14 317.27 \n", "6 315.85 315.22 315.87 \n", "7 313.95 315.29 314.93 \n", "8 312.42 315.35 313.21 \n", "9 312.41 315.41 312.41 \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", "14 317.00 315.70 316.65 \n", "15 318.10 315.77 317.72 \n", "16 318.69 315.85 318.29 \n", "17 318.08 315.94 318.15 \n", "18 316.67 316.03 316.54 \n", "19 314.79 316.13 314.79 \n", "20 313.28 316.22 313.84 \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", "24 316.63 316.56 316.43 \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", "29 319.05 316.92 319.58 \n", ".. ... ... ... \n", "786 421.72 420.96 421.62 \n", "787 419.67 421.27 419.56 \n", "788 418.07 421.58 418.06 \n", "789 418.30 421.89 418.41 \n", "790 419.97 422.20 420.11 \n", "791 421.60 422.50 421.65 \n", "792 422.88 422.80 422.62 \n", "793 423.89 423.10 424.34 \n", "794 424.95 423.37 425.22 \n", "795 426.47 423.66 426.30 \n", "796 427.33 423.93 426.70 \n", "797 426.75 424.21 426.62 \n", "798 425.22 424.48 425.40 \n", "799 423.13 424.76 422.70 \n", "800 421.50 425.03 421.60 \n", "801 421.70 425.29 422.05 \n", "802 423.31 425.54 423.61 \n", "803 424.87 425.76 425.01 \n", "804 426.07 425.98 426.42 \n", "805 426.99 426.19 427.00 \n", "806 427.92 426.36 427.73 \n", "807 429.34 426.55 429.24 \n", "808 430.13 426.72 430.21 \n", "809 429.46 426.90 429.52 \n", "810 427.83 427.06 427.56 \n", "811 -99.99 -99.99 -99.99 \n", "812 -99.99 -99.99 -99.99 \n", "813 -99.99 -99.99 -99.99 \n", "814 -99.99 -99.99 -99.99 \n", "815 -99.99 -99.99 -99.99 \n", "\n", " seasonally adjusted filled(ppm) institut qui fait les mesures \n", "0 -99.99 MLO \n", "1 -99.99 MLO \n", "2 314.43 MLO \n", "3 315.15 MLO \n", "4 314.68 MLO \n", "5 315.14 MLO \n", "6 315.20 MLO \n", "7 316.23 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", "13 315.83 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", "19 316.10 MLO \n", "20 316.76 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", "27 316.69 MLO \n", "28 317.19 MLO \n", "29 317.44 MLO \n", ".. ... ... \n", "786 420.83 MLO \n", "787 421.12 MLO \n", "788 421.56 MLO \n", "789 422.01 MLO \n", "790 422.37 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", "796 423.30 MLO \n", "797 424.07 MLO \n", "798 424.63 MLO \n", "799 424.30 MLO \n", "800 425.11 MLO \n", "801 425.66 MLO \n", "802 425.87 MLO \n", "803 425.93 MLO \n", "804 426.35 MLO \n", "805 426.21 MLO \n", "806 426.19 MLO \n", "807 426.47 MLO \n", "808 426.80 MLO \n", "809 426.93 MLO \n", "810 426.76 MLO \n", "811 -99.99 MLO \n", "812 -99.99 MLO \n", "813 -99.99 MLO \n", "814 -99.99 MLO \n", "815 -99.99 MLO \n", "\n", "[816 rows x 11 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.reset_index() # l'année devient une vraie colonne\n", "data.rename(columns={'index': 'year'}, inplace=True) # renomme l'index en \"year\"\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les cases de ce tableau de données remplient avec la valeur -99.99 correspondent a des cases vide il faut donc modifier cela avec des cases vide :\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmoisdate decimaldate?CO2(ppm)seasonally adjusted(ppm)fit(ppm)seasonally adjusted fit(ppm)CO2 filled(ppm)seasonally adjusted filled(ppm)institut qui fait les mesures
019581212001958.0411NaNNaNNaNNaNNaNNaNMLO
119582212311958.1260NaNNaNNaNNaNNaNNaNMLO
219583212591958.2027315.71314.43316.20314.91315.71314.43MLO
319584212901958.2877317.45315.15317.31314.99317.45315.15MLO
419585213201958.3699317.51314.68317.89315.07317.51314.68MLO
519586213511958.4548NaNNaN317.27315.14317.27315.14MLO
619587213811958.5370315.87315.20315.85315.22315.87315.20MLO
719588214121958.6219314.93316.23313.95315.29314.93316.23MLO
819589214431958.7068313.21316.12312.42315.35313.21316.12MLO
9195810214731958.7890NaNNaN312.41315.41312.41315.41MLO
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
11195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
1219591215651959.0411315.58315.52315.64315.57315.58315.52MLO
1319592215961959.1260316.49315.83316.30315.64316.49315.83MLO
1419593216241959.2027316.65315.37317.00315.70316.65315.37MLO
1519594216551959.2877317.72315.41318.10315.77317.72315.41MLO
1619595216851959.3699318.29315.46318.69315.85318.29315.46MLO
1719596217161959.4548318.15316.00318.08315.94318.15316.00MLO
1819597217461959.5370316.54315.87316.67316.03316.54315.87MLO
1919598217771959.6219314.79316.10314.79316.13314.79316.10MLO
2019599218081959.7068313.84316.76313.28316.22313.84316.76MLO
21195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
22195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
23195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
2419601219301960.0410316.43316.37316.63316.56316.43316.37MLO
2519602219611960.1257316.98316.33317.30316.64316.98316.33MLO
2619603219901960.2049317.58316.27318.04316.71317.58316.27MLO
2719604220211960.2896319.03316.69319.14316.79319.03316.69MLO
2819605220511960.3716320.03317.19319.70316.86320.03317.19MLO
2919606220821960.4563319.58317.44319.05316.92319.58317.44MLO
....................................
78620237451222023.5370421.62420.83421.72420.96421.62420.83MLO
78720238451532023.6219419.56421.12419.67421.27419.56421.12MLO
78820239451842023.7068418.06421.56418.07421.58418.06421.56MLO
789202310452142023.7890418.41422.01418.30421.89418.41422.01MLO
790202311452452023.8740420.11422.37419.97422.20420.11422.37MLO
791202312452752023.9562421.65422.57421.60422.50421.65422.57MLO
79220241453062024.0410422.62422.55422.88422.80422.62422.55MLO
79320242453372024.1257424.34423.56423.89423.10424.34423.56MLO
79420243453662024.2049425.22423.65424.95423.37425.22423.65MLO
79520244453972024.2896426.30423.50426.47423.66426.30423.50MLO
79620245454272024.3716426.70423.30427.33423.93426.70423.30MLO
79720246454582024.4563426.62424.07426.75424.21426.62424.07MLO
79820247454882024.5383425.40424.63425.22424.48425.40424.63MLO
79920248455192024.6230422.70424.30423.13424.76422.70424.30MLO
80020249455502024.7077421.60425.11421.50425.03421.60425.11MLO
801202410455802024.7896422.05425.66421.70425.29422.05425.66MLO
802202411456112024.8743423.61425.87423.31425.54423.61425.87MLO
803202412456412024.9563425.01425.93424.87425.76425.01425.93MLO
80420251456722025.0411426.42426.35426.07425.98426.42426.35MLO
80520252457032025.1260427.00426.21426.99426.19427.00426.21MLO
80620253457312025.2027427.73426.19427.92426.36427.73426.19MLO
80720254457622025.2877429.24426.47429.34426.55429.24426.47MLO
80820255457922025.3699430.21426.80430.13426.72430.21426.80MLO
80920256458232025.4548429.52426.93429.46426.90429.52426.93MLO
81020257458532025.5370427.56426.76427.83427.06427.56426.76MLO
81120258458842025.6219NaNNaNNaNNaNNaNNaNMLO
81220259459152025.7068NaNNaNNaNNaNNaNNaNMLO
813202510459452025.7890NaNNaNNaNNaNNaNNaNMLO
814202511459762025.8740NaNNaNNaNNaNNaNNaNMLO
815202512460062025.9562NaNNaNNaNNaNNaNNaNMLO
\n", "

816 rows × 11 columns

\n", "
" ], "text/plain": [ " year mois date decimal date? CO2(ppm) seasonally adjusted(ppm) \\\n", "0 1958 1 21200 1958.0411 NaN NaN \n", "1 1958 2 21231 1958.1260 NaN NaN \n", "2 1958 3 21259 1958.2027 315.71 314.43 \n", "3 1958 4 21290 1958.2877 317.45 315.15 \n", "4 1958 5 21320 1958.3699 317.51 314.68 \n", "5 1958 6 21351 1958.4548 NaN NaN \n", "6 1958 7 21381 1958.5370 315.87 315.20 \n", "7 1958 8 21412 1958.6219 314.93 316.23 \n", "8 1958 9 21443 1958.7068 313.21 316.12 \n", "9 1958 10 21473 1958.7890 NaN NaN \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 1 21565 1959.0411 315.58 315.52 \n", "13 1959 2 21596 1959.1260 316.49 315.83 \n", "14 1959 3 21624 1959.2027 316.65 315.37 \n", "15 1959 4 21655 1959.2877 317.72 315.41 \n", "16 1959 5 21685 1959.3699 318.29 315.46 \n", "17 1959 6 21716 1959.4548 318.15 316.00 \n", "18 1959 7 21746 1959.5370 316.54 315.87 \n", "19 1959 8 21777 1959.6219 314.79 316.10 \n", "20 1959 9 21808 1959.7068 313.84 316.76 \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 1 21930 1960.0410 316.43 316.37 \n", "25 1960 2 21961 1960.1257 316.98 316.33 \n", "26 1960 3 21990 1960.2049 317.58 316.27 \n", "27 1960 4 22021 1960.2896 319.03 316.69 \n", "28 1960 5 22051 1960.3716 320.03 317.19 \n", "29 1960 6 22082 1960.4563 319.58 317.44 \n", ".. ... ... ... ... ... ... \n", "786 2023 7 45122 2023.5370 421.62 420.83 \n", "787 2023 8 45153 2023.6219 419.56 421.12 \n", "788 2023 9 45184 2023.7068 418.06 421.56 \n", "789 2023 10 45214 2023.7890 418.41 422.01 \n", "790 2023 11 45245 2023.8740 420.11 422.37 \n", "791 2023 12 45275 2023.9562 421.65 422.57 \n", "792 2024 1 45306 2024.0410 422.62 422.55 \n", "793 2024 2 45337 2024.1257 424.34 423.56 \n", "794 2024 3 45366 2024.2049 425.22 423.65 \n", "795 2024 4 45397 2024.2896 426.30 423.50 \n", "796 2024 5 45427 2024.3716 426.70 423.30 \n", "797 2024 6 45458 2024.4563 426.62 424.07 \n", "798 2024 7 45488 2024.5383 425.40 424.63 \n", "799 2024 8 45519 2024.6230 422.70 424.30 \n", "800 2024 9 45550 2024.7077 421.60 425.11 \n", "801 2024 10 45580 2024.7896 422.05 425.66 \n", "802 2024 11 45611 2024.8743 423.61 425.87 \n", "803 2024 12 45641 2024.9563 425.01 425.93 \n", "804 2025 1 45672 2025.0411 426.42 426.35 \n", "805 2025 2 45703 2025.1260 427.00 426.21 \n", "806 2025 3 45731 2025.2027 427.73 426.19 \n", "807 2025 4 45762 2025.2877 429.24 426.47 \n", "808 2025 5 45792 2025.3699 430.21 426.80 \n", "809 2025 6 45823 2025.4548 429.52 426.93 \n", "810 2025 7 45853 2025.5370 427.56 426.76 \n", "811 2025 8 45884 2025.6219 NaN NaN \n", "812 2025 9 45915 2025.7068 NaN NaN \n", "813 2025 10 45945 2025.7890 NaN NaN \n", "814 2025 11 45976 2025.8740 NaN NaN \n", "815 2025 12 46006 2025.9562 NaN NaN \n", "\n", " fit(ppm) seasonally adjusted fit(ppm) CO2 filled(ppm) \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 316.20 314.91 315.71 \n", "3 317.31 314.99 317.45 \n", "4 317.89 315.07 317.51 \n", "5 317.27 315.14 317.27 \n", "6 315.85 315.22 315.87 \n", "7 313.95 315.29 314.93 \n", "8 312.42 315.35 313.21 \n", "9 312.41 315.41 312.41 \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", "14 317.00 315.70 316.65 \n", "15 318.10 315.77 317.72 \n", "16 318.69 315.85 318.29 \n", "17 318.08 315.94 318.15 \n", "18 316.67 316.03 316.54 \n", "19 314.79 316.13 314.79 \n", "20 313.28 316.22 313.84 \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", "24 316.63 316.56 316.43 \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", "29 319.05 316.92 319.58 \n", ".. ... ... ... \n", "786 421.72 420.96 421.62 \n", "787 419.67 421.27 419.56 \n", "788 418.07 421.58 418.06 \n", "789 418.30 421.89 418.41 \n", "790 419.97 422.20 420.11 \n", "791 421.60 422.50 421.65 \n", "792 422.88 422.80 422.62 \n", "793 423.89 423.10 424.34 \n", "794 424.95 423.37 425.22 \n", "795 426.47 423.66 426.30 \n", "796 427.33 423.93 426.70 \n", "797 426.75 424.21 426.62 \n", "798 425.22 424.48 425.40 \n", "799 423.13 424.76 422.70 \n", "800 421.50 425.03 421.60 \n", "801 421.70 425.29 422.05 \n", "802 423.31 425.54 423.61 \n", "803 424.87 425.76 425.01 \n", "804 426.07 425.98 426.42 \n", "805 426.99 426.19 427.00 \n", "806 427.92 426.36 427.73 \n", "807 429.34 426.55 429.24 \n", "808 430.13 426.72 430.21 \n", "809 429.46 426.90 429.52 \n", "810 427.83 427.06 427.56 \n", "811 NaN NaN NaN \n", "812 NaN NaN NaN \n", "813 NaN NaN NaN \n", "814 NaN NaN NaN \n", "815 NaN NaN NaN \n", "\n", " seasonally adjusted filled(ppm) institut qui fait les mesures \n", "0 NaN MLO \n", "1 NaN MLO \n", "2 314.43 MLO \n", "3 315.15 MLO \n", "4 314.68 MLO \n", "5 315.14 MLO \n", "6 315.20 MLO \n", "7 316.23 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", "13 315.83 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", "19 316.10 MLO \n", "20 316.76 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", "27 316.69 MLO \n", "28 317.19 MLO \n", "29 317.44 MLO \n", ".. ... ... \n", "786 420.83 MLO \n", "787 421.12 MLO \n", "788 421.56 MLO \n", "789 422.01 MLO \n", "790 422.37 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", "796 423.30 MLO \n", "797 424.07 MLO \n", "798 424.63 MLO \n", "799 424.30 MLO \n", "800 425.11 MLO \n", "801 425.66 MLO \n", "802 425.87 MLO \n", "803 425.93 MLO \n", "804 426.35 MLO \n", "805 426.21 MLO \n", "806 426.19 MLO \n", "807 426.47 MLO \n", "808 426.80 MLO \n", "809 426.93 MLO \n", "810 426.76 MLO \n", "811 NaN MLO \n", "812 NaN MLO \n", "813 NaN MLO \n", "814 NaN MLO \n", "815 NaN MLO \n", "\n", "[816 rows x 11 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "data = data.replace(-99.99, np.nan)\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour la suite, comme c'est demandé dans la question 1 on trace un graphique qui vous montrera une oscillation périodique superposée à une évolution systématique plus lente :" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Créer la figure\n", "plt.figure(figsize=(12, 6))\n", "\n", "# Courbe brute : oscillations saisonnières\n", "plt.plot(data[\"date?\"], data[\"CO2(ppm)\"], label=\"CO₂ oscillation périodique\", color=\"blue\")\n", "\n", "# Courbe lissée : tendance à long terme\n", "plt.plot(data[\"date?\"], data[\"seasonally adjusted(ppm)\"], label=\"CO₂ (évolution systématique plus lente)\", color=\"red\", linewidth=2)\n", "\n", "# Mise en forme\n", "plt.xlabel(\"Année\")\n", "plt.xticks(np.arange(1955, 2025, 5))\n", "plt.ylabel(\"CO₂ (ppm)\")\n", "plt.title(\"Évolution du CO₂ atmosphérique à Mauna Loa\\nOscillation saisonnière et tendance à long terme\")\n", "plt.legend()\n", "plt.grid(True)\n", "\n", "plt.show()" ] }, { "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 }