{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "data_file = \"co2monthly.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDaysYrDecimalCO2CO2_seasonallyCO2_fitCO2_fit_wo_cycleCO2_filledCO2_seasonally_filled
2195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99
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4195803212591958.2027315.71314.43316.20314.90315.71314.43
5195804212901958.2877317.45315.15317.30314.98317.45315.15
6195805213201958.3699317.51314.68317.89315.06317.51314.68
7195806213511958.4548-99.99-99.99317.27315.14317.27315.14
8195807213811958.5370315.87315.20315.85315.21315.87315.20
9195808214121958.6219314.93316.23313.95315.28314.93316.23
10195809214431958.7068313.21316.13312.42315.35313.21316.13
11195810214731958.7890-99.99-99.99312.41315.40312.41315.40
12195811215041958.8740313.33315.21313.60315.46313.33315.21
13195812215341958.9562314.67315.43314.76315.51314.67315.43
14195901215651959.0411315.58315.52315.64315.57315.58315.52
15195902215961959.1260316.49315.83316.29315.63316.49315.83
16195903216241959.2027316.65315.37316.99315.69316.65315.37
17195904216551959.2877317.72315.41318.09315.77317.72315.41
18195905216851959.3699318.29315.46318.68315.85318.29315.46
19195906217161959.4548318.15316.00318.08315.94318.15316.00
20195907217461959.5370316.54315.87316.67316.03316.54315.87
21195908217771959.6219314.79316.10314.79316.12314.79316.10
22195909218081959.7068313.84316.76313.28316.22313.84316.76
23195910218381959.7890313.33316.35313.30316.31313.33316.35
24195911218691959.8740314.81316.70314.53316.39314.81316.70
25195912218991959.9562315.58316.35315.72316.47315.58316.35
26196001219301960.0410316.43316.37316.62316.55316.43316.37
27196002219611960.1257316.98316.33317.30316.64316.98316.33
28196003219901960.2049317.58316.27318.04316.71317.58316.27
29196004220211960.2896319.03316.69319.14316.79319.03316.69
30196005220511960.3716320.03317.19319.70316.86320.03317.19
31196006220821960.4563319.59317.44319.04316.92319.59317.44
.................................
788202307451222023.5370421.62420.82421.71420.95421.62420.82
789202308451532023.6219419.56421.11419.66421.26419.56421.11
790202309451842023.7068418.06421.56418.06421.57418.06421.56
791202310452142023.7890418.41422.01418.29421.88418.41422.01
792202311452452023.8740420.11422.37419.96422.19420.11422.37
793202312452752023.9562421.65422.57421.59422.49421.65422.57
794202401453062024.0410422.62422.55422.87422.79422.62422.55
795202402453372024.1257424.34423.56423.87423.08424.34423.56
796202403453662024.2049425.22423.66424.93423.35425.22423.66
797202404453972024.2896426.30423.51426.45423.64426.30423.51
798202405454272024.3716426.70423.31427.30423.91426.70423.31
799202406454582024.4563426.62424.07426.72424.19426.62424.07
800202407454882024.5383425.40424.63425.20424.46425.40424.63
801202408455192024.6230422.70424.29423.11424.74422.70424.29
802202409455502024.7077421.60425.11421.49425.02421.60425.11
803202410455802024.7896422.05425.65421.69425.28422.05425.65
804202411456112024.8743423.61425.86423.31425.53423.61425.86
805202412456412024.9563425.01425.93424.87425.77425.01425.93
806202501456722025.0411426.42426.35426.09426.01426.42426.35
807202502457032025.1260427.00426.21427.03426.24427.00426.21
808202503457312025.2027427.73426.19427.99426.44427.73426.19
809202504457622025.2877429.24426.47429.44426.65429.24426.47
810202505457922025.3699430.20426.80-99.99-99.99430.20426.80
811202506458232025.4548-99.99-99.99-99.99-99.99-99.99-99.99
812202507458532025.5370-99.99-99.99-99.99-99.99-99.99-99.99
813202508458842025.6219-99.99-99.99-99.99-99.99-99.99-99.99
814202509459152025.7068-99.99-99.99-99.99-99.99-99.99-99.99
815202510459452025.7890-99.99-99.99-99.99-99.99-99.99-99.99
816202511459762025.8740-99.99-99.99-99.99-99.99-99.99-99.99
817202512460062025.9562-99.99-99.99-99.99-99.99-99.99-99.99
\n", "

816 rows × 10 columns

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" ], "text/plain": [ " Yr Mn Days YrDecimal CO2 CO2_seasonally CO2_fit \\\n", "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", "4 1958 03 21259 1958.2027 315.71 314.43 316.20 \n", "5 1958 04 21290 1958.2877 317.45 315.15 317.30 \n", "6 1958 05 21320 1958.3699 317.51 314.68 317.89 \n", "7 1958 06 21351 1958.4548 -99.99 -99.99 317.27 \n", "8 1958 07 21381 1958.5370 315.87 315.20 315.85 \n", "9 1958 08 21412 1958.6219 314.93 316.23 313.95 \n", "10 1958 09 21443 1958.7068 313.21 316.13 312.42 \n", "11 1958 10 21473 1958.7890 -99.99 -99.99 312.41 \n", "12 1958 11 21504 1958.8740 313.33 315.21 313.60 \n", "13 1958 12 21534 1958.9562 314.67 315.43 314.76 \n", "14 1959 01 21565 1959.0411 315.58 315.52 315.64 \n", "15 1959 02 21596 1959.1260 316.49 315.83 316.29 \n", "16 1959 03 21624 1959.2027 316.65 315.37 316.99 \n", "17 1959 04 21655 1959.2877 317.72 315.41 318.09 \n", "18 1959 05 21685 1959.3699 318.29 315.46 318.68 \n", "19 1959 06 21716 1959.4548 318.15 316.00 318.08 \n", "20 1959 07 21746 1959.5370 316.54 315.87 316.67 \n", "21 1959 08 21777 1959.6219 314.79 316.10 314.79 \n", "22 1959 09 21808 1959.7068 313.84 316.76 313.28 \n", "23 1959 10 21838 1959.7890 313.33 316.35 313.30 \n", "24 1959 11 21869 1959.8740 314.81 316.70 314.53 \n", "25 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", "26 1960 01 21930 1960.0410 316.43 316.37 316.62 \n", "27 1960 02 21961 1960.1257 316.98 316.33 317.30 \n", "28 1960 03 21990 1960.2049 317.58 316.27 318.04 \n", "29 1960 04 22021 1960.2896 319.03 316.69 319.14 \n", "30 1960 05 22051 1960.3716 320.03 317.19 319.70 \n", "31 1960 06 22082 1960.4563 319.59 317.44 319.04 \n", ".. ... ... ... ... ... ... ... \n", "788 2023 07 45122 2023.5370 421.62 420.82 421.71 \n", "789 2023 08 45153 2023.6219 419.56 421.11 419.66 \n", "790 2023 09 45184 2023.7068 418.06 421.56 418.06 \n", "791 2023 10 45214 2023.7890 418.41 422.01 418.29 \n", "792 2023 11 45245 2023.8740 420.11 422.37 419.96 \n", "793 2023 12 45275 2023.9562 421.65 422.57 421.59 \n", "794 2024 01 45306 2024.0410 422.62 422.55 422.87 \n", "795 2024 02 45337 2024.1257 424.34 423.56 423.87 \n", "796 2024 03 45366 2024.2049 425.22 423.66 424.93 \n", "797 2024 04 45397 2024.2896 426.30 423.51 426.45 \n", "798 2024 05 45427 2024.3716 426.70 423.31 427.30 \n", "799 2024 06 45458 2024.4563 426.62 424.07 426.72 \n", "800 2024 07 45488 2024.5383 425.40 424.63 425.20 \n", "801 2024 08 45519 2024.6230 422.70 424.29 423.11 \n", "802 2024 09 45550 2024.7077 421.60 425.11 421.49 \n", "803 2024 10 45580 2024.7896 422.05 425.65 421.69 \n", "804 2024 11 45611 2024.8743 423.61 425.86 423.31 \n", "805 2024 12 45641 2024.9563 425.01 425.93 424.87 \n", "806 2025 01 45672 2025.0411 426.42 426.35 426.09 \n", "807 2025 02 45703 2025.1260 427.00 426.21 427.03 \n", "808 2025 03 45731 2025.2027 427.73 426.19 427.99 \n", "809 2025 04 45762 2025.2877 429.24 426.47 429.44 \n", "810 2025 05 45792 2025.3699 430.20 426.80 -99.99 \n", "811 2025 06 45823 2025.4548 -99.99 -99.99 -99.99 \n", "812 2025 07 45853 2025.5370 -99.99 -99.99 -99.99 \n", "813 2025 08 45884 2025.6219 -99.99 -99.99 -99.99 \n", "814 2025 09 45915 2025.7068 -99.99 -99.99 -99.99 \n", "815 2025 10 45945 2025.7890 -99.99 -99.99 -99.99 \n", "816 2025 11 45976 2025.8740 -99.99 -99.99 -99.99 \n", "817 2025 12 46006 2025.9562 -99.99 -99.99 -99.99 \n", "\n", " CO2_fit_wo_cycle CO2_filled CO2_seasonally_filled \n", "2 -99.99 -99.99 -99.99 \n", "3 -99.99 -99.99 -99.99 \n", "4 314.90 315.71 314.43 \n", "5 314.98 317.45 315.15 \n", "6 315.06 317.51 314.68 \n", "7 315.14 317.27 315.14 \n", "8 315.21 315.87 315.20 \n", "9 315.28 314.93 316.23 \n", "10 315.35 313.21 316.13 \n", "11 315.40 312.41 315.40 \n", "12 315.46 313.33 315.21 \n", "13 315.51 314.67 315.43 \n", "14 315.57 315.58 315.52 \n", "15 315.63 316.49 315.83 \n", "16 315.69 316.65 315.37 \n", "17 315.77 317.72 315.41 \n", "18 315.85 318.29 315.46 \n", "19 315.94 318.15 316.00 \n", "20 316.03 316.54 315.87 \n", "21 316.12 314.79 316.10 \n", "22 316.22 313.84 316.76 \n", "23 316.31 313.33 316.35 \n", "24 316.39 314.81 316.70 \n", "25 316.47 315.58 316.35 \n", "26 316.55 316.43 316.37 \n", "27 316.64 316.98 316.33 \n", "28 316.71 317.58 316.27 \n", "29 316.79 319.03 316.69 \n", "30 316.86 320.03 317.19 \n", "31 316.92 319.59 317.44 \n", ".. ... ... ... \n", "788 420.95 421.62 420.82 \n", "789 421.26 419.56 421.11 \n", "790 421.57 418.06 421.56 \n", "791 421.88 418.41 422.01 \n", "792 422.19 420.11 422.37 \n", "793 422.49 421.65 422.57 \n", "794 422.79 422.62 422.55 \n", "795 423.08 424.34 423.56 \n", "796 423.35 425.22 423.66 \n", "797 423.64 426.30 423.51 \n", "798 423.91 426.70 423.31 \n", "799 424.19 426.62 424.07 \n", "800 424.46 425.40 424.63 \n", "801 424.74 422.70 424.29 \n", "802 425.02 421.60 425.11 \n", "803 425.28 422.05 425.65 \n", "804 425.53 423.61 425.86 \n", "805 425.77 425.01 425.93 \n", "806 426.01 426.42 426.35 \n", "807 426.24 427.00 426.21 \n", "808 426.44 427.73 426.19 \n", "809 426.65 429.24 426.47 \n", "810 -99.99 430.20 426.80 \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", "816 -99.99 -99.99 -99.99 \n", "817 -99.99 -99.99 -99.99 \n", "\n", "[816 rows x 10 columns]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "df = pd.read_csv(data_file, skiprows=61)\n", "\n", "df = df.drop([0,1])\n", "df = df.rename(columns={' Yr':'Yr', ' Mn':'Mn', ' Date':'Days', ' Date':'YrDecimal', ' CO2':'CO2', 'seasonally':'CO2_seasonally', ' fit':'CO2_fit', ' seasonally':'CO2_fit_wo_cycle', ' CO2':'CO2_filled', ' seasonally':'CO2_seasonally_filled', ' Sta':'Sta'})\n", "df = df.drop('Sta', axis=1)\n", "\n", "df\n", "\n" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDaysYrDecimalCO2CO2_seasonallyCO2_fitCO2_fit_wo_cycleCO2_filledCO2_seasonally_filled
41958.03.021259.01958.2027315.71314.43316.20314.90315.71314.43
51958.04.021290.01958.2877317.45315.15317.30314.98317.45315.15
61958.05.021320.01958.3699317.51314.68317.89315.06317.51314.68
81958.07.021381.01958.5370315.87315.20315.85315.21315.87315.20
91958.08.021412.01958.6219314.93316.23313.95315.28314.93316.23
101958.09.021443.01958.7068313.21316.13312.42315.35313.21316.13
121958.011.021504.01958.8740313.33315.21313.60315.46313.33315.21
131958.012.021534.01958.9562314.67315.43314.76315.51314.67315.43
141959.01.021565.01959.0411315.58315.52315.64315.57315.58315.52
151959.02.021596.01959.1260316.49315.83316.29315.63316.49315.83
161959.03.021624.01959.2027316.65315.37316.99315.69316.65315.37
171959.04.021655.01959.2877317.72315.41318.09315.77317.72315.41
181959.05.021685.01959.3699318.29315.46318.68315.85318.29315.46
191959.06.021716.01959.4548318.15316.00318.08315.94318.15316.00
201959.07.021746.01959.5370316.54315.87316.67316.03316.54315.87
211959.08.021777.01959.6219314.79316.10314.79316.12314.79316.10
221959.09.021808.01959.7068313.84316.76313.28316.22313.84316.76
231959.010.021838.01959.7890313.33316.35313.30316.31313.33316.35
241959.011.021869.01959.8740314.81316.70314.53316.39314.81316.70
251959.012.021899.01959.9562315.58316.35315.72316.47315.58316.35
261960.01.021930.01960.0410316.43316.37316.62316.55316.43316.37
271960.02.021961.01960.1257316.98316.33317.30316.64316.98316.33
281960.03.021990.01960.2049317.58316.27318.04316.71317.58316.27
291960.04.022021.01960.2896319.03316.69319.14316.79319.03316.69
301960.05.022051.01960.3716320.03317.19319.70316.86320.03317.19
311960.06.022082.01960.4563319.59317.44319.04316.92319.59317.44
321960.07.022112.01960.5383318.18317.53317.59316.97318.18317.53
331960.08.022143.01960.6230315.90317.24315.65317.01315.90317.24
341960.09.022174.01960.7077314.17317.11314.09317.05314.17317.11
351960.010.022204.01960.7896313.83316.85314.07317.08313.83316.85
.................................
7812022.012.044910.02022.9562418.46419.38418.25419.15418.46419.38
7822023.01.044941.02023.0411419.13419.06419.45419.37419.13419.06
7832023.02.044972.02023.1260420.33419.56420.39419.60420.33419.56
7842023.03.045000.02023.2027420.51418.98421.37419.83420.51418.98
7852023.04.045031.02023.2877422.73419.98422.87420.09422.73419.98
7862023.05.045061.02023.3699423.78420.39423.75420.36423.78420.39
7872023.06.045092.02023.4548423.39420.82423.21420.66423.39420.82
7882023.07.045122.02023.5370421.62420.82421.71420.95421.62420.82
7892023.08.045153.02023.6219419.56421.11419.66421.26419.56421.11
7902023.09.045184.02023.7068418.06421.56418.06421.57418.06421.56
7912023.010.045214.02023.7890418.41422.01418.29421.88418.41422.01
7922023.011.045245.02023.8740420.11422.37419.96422.19420.11422.37
7932023.012.045275.02023.9562421.65422.57421.59422.49421.65422.57
7942024.01.045306.02024.0410422.62422.55422.87422.79422.62422.55
7952024.02.045337.02024.1257424.34423.56423.87423.08424.34423.56
7962024.03.045366.02024.2049425.22423.66424.93423.35425.22423.66
7972024.04.045397.02024.2896426.30423.51426.45423.64426.30423.51
7982024.05.045427.02024.3716426.70423.31427.30423.91426.70423.31
7992024.06.045458.02024.4563426.62424.07426.72424.19426.62424.07
8002024.07.045488.02024.5383425.40424.63425.20424.46425.40424.63
8012024.08.045519.02024.6230422.70424.29423.11424.74422.70424.29
8022024.09.045550.02024.7077421.60425.11421.49425.02421.60425.11
8032024.010.045580.02024.7896422.05425.65421.69425.28422.05425.65
8042024.011.045611.02024.8743423.61425.86423.31425.53423.61425.86
8052024.012.045641.02024.9563425.01425.93424.87425.77425.01425.93
8062025.01.045672.02025.0411426.42426.35426.09426.01426.42426.35
8072025.02.045703.02025.1260427.00426.21427.03426.24427.00426.21
8082025.03.045731.02025.2027427.73426.19427.99426.44427.73426.19
8092025.04.045762.02025.2877429.24426.47429.44426.65429.24426.47
8102025.05.045792.02025.3699430.20426.80-99.99-99.99430.20426.80
\n", "

802 rows × 10 columns

\n", "
" ], "text/plain": [ " Yr Mn Days YrDecimal CO2 CO2_seasonally CO2_fit \\\n", "4 1958.0 3.0 21259.0 1958.2027 315.71 314.43 316.20 \n", "5 1958.0 4.0 21290.0 1958.2877 317.45 315.15 317.30 \n", "6 1958.0 5.0 21320.0 1958.3699 317.51 314.68 317.89 \n", "8 1958.0 7.0 21381.0 1958.5370 315.87 315.20 315.85 \n", "9 1958.0 8.0 21412.0 1958.6219 314.93 316.23 313.95 \n", "10 1958.0 9.0 21443.0 1958.7068 313.21 316.13 312.42 \n", "12 1958.0 11.0 21504.0 1958.8740 313.33 315.21 313.60 \n", "13 1958.0 12.0 21534.0 1958.9562 314.67 315.43 314.76 \n", "14 1959.0 1.0 21565.0 1959.0411 315.58 315.52 315.64 \n", "15 1959.0 2.0 21596.0 1959.1260 316.49 315.83 316.29 \n", "16 1959.0 3.0 21624.0 1959.2027 316.65 315.37 316.99 \n", "17 1959.0 4.0 21655.0 1959.2877 317.72 315.41 318.09 \n", "18 1959.0 5.0 21685.0 1959.3699 318.29 315.46 318.68 \n", "19 1959.0 6.0 21716.0 1959.4548 318.15 316.00 318.08 \n", "20 1959.0 7.0 21746.0 1959.5370 316.54 315.87 316.67 \n", "21 1959.0 8.0 21777.0 1959.6219 314.79 316.10 314.79 \n", "22 1959.0 9.0 21808.0 1959.7068 313.84 316.76 313.28 \n", "23 1959.0 10.0 21838.0 1959.7890 313.33 316.35 313.30 \n", "24 1959.0 11.0 21869.0 1959.8740 314.81 316.70 314.53 \n", "25 1959.0 12.0 21899.0 1959.9562 315.58 316.35 315.72 \n", "26 1960.0 1.0 21930.0 1960.0410 316.43 316.37 316.62 \n", "27 1960.0 2.0 21961.0 1960.1257 316.98 316.33 317.30 \n", "28 1960.0 3.0 21990.0 1960.2049 317.58 316.27 318.04 \n", "29 1960.0 4.0 22021.0 1960.2896 319.03 316.69 319.14 \n", "30 1960.0 5.0 22051.0 1960.3716 320.03 317.19 319.70 \n", "31 1960.0 6.0 22082.0 1960.4563 319.59 317.44 319.04 \n", "32 1960.0 7.0 22112.0 1960.5383 318.18 317.53 317.59 \n", "33 1960.0 8.0 22143.0 1960.6230 315.90 317.24 315.65 \n", "34 1960.0 9.0 22174.0 1960.7077 314.17 317.11 314.09 \n", "35 1960.0 10.0 22204.0 1960.7896 313.83 316.85 314.07 \n", ".. ... ... ... ... ... ... ... \n", "781 2022.0 12.0 44910.0 2022.9562 418.46 419.38 418.25 \n", "782 2023.0 1.0 44941.0 2023.0411 419.13 419.06 419.45 \n", "783 2023.0 2.0 44972.0 2023.1260 420.33 419.56 420.39 \n", "784 2023.0 3.0 45000.0 2023.2027 420.51 418.98 421.37 \n", "785 2023.0 4.0 45031.0 2023.2877 422.73 419.98 422.87 \n", "786 2023.0 5.0 45061.0 2023.3699 423.78 420.39 423.75 \n", "787 2023.0 6.0 45092.0 2023.4548 423.39 420.82 423.21 \n", "788 2023.0 7.0 45122.0 2023.5370 421.62 420.82 421.71 \n", "789 2023.0 8.0 45153.0 2023.6219 419.56 421.11 419.66 \n", "790 2023.0 9.0 45184.0 2023.7068 418.06 421.56 418.06 \n", "791 2023.0 10.0 45214.0 2023.7890 418.41 422.01 418.29 \n", "792 2023.0 11.0 45245.0 2023.8740 420.11 422.37 419.96 \n", "793 2023.0 12.0 45275.0 2023.9562 421.65 422.57 421.59 \n", "794 2024.0 1.0 45306.0 2024.0410 422.62 422.55 422.87 \n", "795 2024.0 2.0 45337.0 2024.1257 424.34 423.56 423.87 \n", "796 2024.0 3.0 45366.0 2024.2049 425.22 423.66 424.93 \n", "797 2024.0 4.0 45397.0 2024.2896 426.30 423.51 426.45 \n", "798 2024.0 5.0 45427.0 2024.3716 426.70 423.31 427.30 \n", "799 2024.0 6.0 45458.0 2024.4563 426.62 424.07 426.72 \n", "800 2024.0 7.0 45488.0 2024.5383 425.40 424.63 425.20 \n", "801 2024.0 8.0 45519.0 2024.6230 422.70 424.29 423.11 \n", "802 2024.0 9.0 45550.0 2024.7077 421.60 425.11 421.49 \n", "803 2024.0 10.0 45580.0 2024.7896 422.05 425.65 421.69 \n", "804 2024.0 11.0 45611.0 2024.8743 423.61 425.86 423.31 \n", "805 2024.0 12.0 45641.0 2024.9563 425.01 425.93 424.87 \n", "806 2025.0 1.0 45672.0 2025.0411 426.42 426.35 426.09 \n", "807 2025.0 2.0 45703.0 2025.1260 427.00 426.21 427.03 \n", "808 2025.0 3.0 45731.0 2025.2027 427.73 426.19 427.99 \n", "809 2025.0 4.0 45762.0 2025.2877 429.24 426.47 429.44 \n", "810 2025.0 5.0 45792.0 2025.3699 430.20 426.80 -99.99 \n", "\n", " CO2_fit_wo_cycle CO2_filled CO2_seasonally_filled \n", "4 314.90 315.71 314.43 \n", "5 314.98 317.45 315.15 \n", "6 315.06 317.51 314.68 \n", "8 315.21 315.87 315.20 \n", "9 315.28 314.93 316.23 \n", "10 315.35 313.21 316.13 \n", "12 315.46 313.33 315.21 \n", "13 315.51 314.67 315.43 \n", "14 315.57 315.58 315.52 \n", "15 315.63 316.49 315.83 \n", "16 315.69 316.65 315.37 \n", "17 315.77 317.72 315.41 \n", "18 315.85 318.29 315.46 \n", "19 315.94 318.15 316.00 \n", "20 316.03 316.54 315.87 \n", "21 316.12 314.79 316.10 \n", "22 316.22 313.84 316.76 \n", "23 316.31 313.33 316.35 \n", "24 316.39 314.81 316.70 \n", "25 316.47 315.58 316.35 \n", "26 316.55 316.43 316.37 \n", "27 316.64 316.98 316.33 \n", "28 316.71 317.58 316.27 \n", "29 316.79 319.03 316.69 \n", "30 316.86 320.03 317.19 \n", "31 316.92 319.59 317.44 \n", "32 316.97 318.18 317.53 \n", "33 317.01 315.90 317.24 \n", "34 317.05 314.17 317.11 \n", "35 317.08 313.83 316.85 \n", ".. ... ... ... \n", "781 419.15 418.46 419.38 \n", "782 419.37 419.13 419.06 \n", "783 419.60 420.33 419.56 \n", "784 419.83 420.51 418.98 \n", "785 420.09 422.73 419.98 \n", "786 420.36 423.78 420.39 \n", "787 420.66 423.39 420.82 \n", "788 420.95 421.62 420.82 \n", "789 421.26 419.56 421.11 \n", "790 421.57 418.06 421.56 \n", "791 421.88 418.41 422.01 \n", "792 422.19 420.11 422.37 \n", "793 422.49 421.65 422.57 \n", "794 422.79 422.62 422.55 \n", "795 423.08 424.34 423.56 \n", "796 423.35 425.22 423.66 \n", "797 423.64 426.30 423.51 \n", "798 423.91 426.70 423.31 \n", "799 424.19 426.62 424.07 \n", "800 424.46 425.40 424.63 \n", "801 424.74 422.70 424.29 \n", "802 425.02 421.60 425.11 \n", "803 425.28 422.05 425.65 \n", "804 425.53 423.61 425.86 \n", "805 425.77 425.01 425.93 \n", "806 426.01 426.42 426.35 \n", "807 426.24 427.00 426.21 \n", "808 426.44 427.73 426.19 \n", "809 426.65 429.24 426.47 \n", "810 -99.99 430.20 426.80 \n", "\n", "[802 rows x 10 columns]" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\n", "df = df.astype(float)\n", "df = df[df['CO2'] != -99.99]\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Suppose df is your DataFrame\n", "# Example: df = pd.DataFrame({'Day': [...], 'CO2': [...]})\n", "\n", "# Plotting\n", "plt.figure(figsize=(10, 5))\n", "plt.plot(df['Days'], df['CO2'], color='blue')\n", "plt.title('Évolution du CO2 au fil du temps')\n", "plt.xlabel('Jour')\n", "plt.ylabel('Concentration de CO2')\n", "plt.grid(True)\n", "plt.xticks(rotation=45) # utile si les dates sont longues\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [], "source": [ "\n", "# Create a new 'Date' column from 'Yr' and 'Mn'\n", "df['Date'] = pd.to_datetime({'year': df['Yr'], 'month': df['Mn'], 'day': 1})\n" ] }, { "cell_type": "code", "execution_count": 92, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "plt.figure(figsize=(10, 5))\n", "plt.plot(df['Date'], df['CO2'], linestyle='-', color='blue')\n", "plt.title('Évolution du CO2 par mois')\n", "plt.xlabel('Date')\n", "plt.ylabel('Concentration de CO2')\n", "plt.grid(True)\n", "plt.xticks(rotation=45)\n", "plt.tight_layout()\n", "plt.show()" ] } ], "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 }