{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Concentration de $CO_2$ dans l'atmosphère depuis 1958" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous étudions l'évolution de la concentration de $CO_2$ dans l'atmosphère depuis 1958 à partir des données de l'[Institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html). L'étude a été réalisée avec les données au 23 octobre 2020. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chargement et pré-traitement du jeu de données" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On importe les librairies python adéquates pour étudier le jeu de données, disponible au format CSV." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le jeu de données sont récupérées sur le site de l'institut Scripps, les données recouvrent la période de janvier 1958 à décembre 2020." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les 53 premières lignes sont une présentation du jeu de données, on les passe pour ne traiter que le jeu de données à proprement parler. Voici ci-dessous un extrait de ce jeu de données." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
0adjustedadjusted fitfilledadjusted filled
1Excel[ppm][ppm][ppm][ppm][ppm][ppm]
2195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99
3195802212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99
4195803212591958.2027315.70314.44316.18314.90315.70314.44
5195804212901958.2877317.45315.16317.29314.98317.45315.16
6195805213201958.3699317.51314.71317.86315.06317.51314.71
7195806213511958.4548-99.99-99.99317.24315.14317.24315.14
8195807213811958.5370315.86315.19315.86315.21315.86315.19
9195808214121958.6219314.93316.19313.99315.28314.93316.19
10195809214431958.7068313.21316.08312.45315.35313.21316.08
11195810214731958.7890-99.99-99.99312.43315.40312.43315.40
12195811215041958.8740313.33315.20313.61315.46313.33315.20
13195812215341958.9562314.67315.43314.76315.51314.67315.43
14195901215651959.0411315.58315.54315.62315.57315.58315.54
15195902215961959.1260316.49315.86316.26315.63316.49315.86
16195903216241959.2027316.65315.38316.97315.69316.65315.38
17195904216551959.2877317.72315.42318.08315.76317.72315.42
18195905216851959.3699318.29315.49318.65315.84318.29315.49
19195906217161959.4548318.15316.03318.04315.93318.15316.03
20195907217461959.5370316.54315.86316.67316.02316.54315.86
21195908217771959.6219314.80316.06314.82316.12314.80316.06
22195909218081959.7068313.84316.73313.31316.21313.84316.73
23195910218381959.7890313.33316.33313.32316.30313.33316.33
24195911218691959.8740314.81316.68314.54316.39314.81316.68
25195912218991959.9562315.58316.35315.72316.47315.58316.35
26196001219301960.0410316.43316.39316.61316.55316.43316.39
27196002219611960.1257316.98316.35317.27316.64316.98316.35
28196003219901960.2049317.58316.28318.02316.71317.58316.28
29196004220211960.2896319.03316.70319.14316.79319.03316.70
.................................
728201807432962018.5370408.90408.08409.43408.65408.90408.08
729201808433272018.6219407.10408.63407.33408.90407.10408.63
730201809433582018.7068405.59409.08405.66409.18405.59409.08
731201810433882018.7890405.99409.61405.84409.44405.99409.61
732201811434192018.8740408.12410.38407.48409.72408.12410.38
733201812434492018.9562409.23410.15409.07409.98409.23410.15
734201901434802019.0411410.92410.87410.30410.24410.92410.87
735201902435112019.1260411.66410.90411.25410.48411.66410.90
736201903435392019.2027412.00410.46412.25410.69412.00410.46
737201904435702019.2877413.52410.72413.73410.92413.52410.72
738201905436002019.3699414.83411.42414.54411.14414.83411.42
739201906436312019.4548413.96411.38413.91411.36413.96411.38
740201907436612019.5370411.85411.03412.36411.57411.85411.03
741201908436922019.6219410.08411.62410.22411.79410.08411.62
742201909437232019.7068408.55412.06408.49412.02408.55412.06
743201910437532019.7890408.43412.06408.62412.23408.43412.06
744201911437842019.8740410.29412.56410.21412.46410.29412.56
745201912438142019.9562411.85412.78411.76412.67411.85412.78
746202001438452020.0410413.37413.32412.95412.89413.37413.32
747202002438762020.1257414.09413.33413.87413.10414.09413.33
748202003439052020.2049414.51412.94414.89413.30414.51412.94
749202004439362020.2896416.18413.35416.35413.50416.18413.35
750202005439662020.3716417.16413.75-99.99-99.99417.16413.75
751202006439972020.4563-99.99-99.99-99.99-99.99-99.99-99.99
752202007440272020.5383-99.99-99.99-99.99-99.99-99.99-99.99
753202008440582020.6230-99.99-99.99-99.99-99.99-99.99-99.99
754202009440892020.7077-99.99-99.99-99.99-99.99-99.99-99.99
755202010441192020.7896-99.99-99.99-99.99-99.99-99.99-99.99
756202011441502020.8743-99.99-99.99-99.99-99.99-99.99-99.99
757202012441802020.9563-99.99-99.99-99.99-99.99-99.99-99.99
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758 rows × 10 columns

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" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "0 adjusted \n", "1 Excel [ppm] [ppm] [ppm] \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 315.70 314.44 316.18 \n", "5 1958 04 21290 1958.2877 317.45 315.16 317.29 \n", "6 1958 05 21320 1958.3699 317.51 314.71 317.86 \n", "7 1958 06 21351 1958.4548 -99.99 -99.99 317.24 \n", "8 1958 07 21381 1958.5370 315.86 315.19 315.86 \n", "9 1958 08 21412 1958.6219 314.93 316.19 313.99 \n", "10 1958 09 21443 1958.7068 313.21 316.08 312.45 \n", "11 1958 10 21473 1958.7890 -99.99 -99.99 312.43 \n", "12 1958 11 21504 1958.8740 313.33 315.20 313.61 \n", "13 1958 12 21534 1958.9562 314.67 315.43 314.76 \n", "14 1959 01 21565 1959.0411 315.58 315.54 315.62 \n", "15 1959 02 21596 1959.1260 316.49 315.86 316.26 \n", "16 1959 03 21624 1959.2027 316.65 315.38 316.97 \n", "17 1959 04 21655 1959.2877 317.72 315.42 318.08 \n", "18 1959 05 21685 1959.3699 318.29 315.49 318.65 \n", "19 1959 06 21716 1959.4548 318.15 316.03 318.04 \n", "20 1959 07 21746 1959.5370 316.54 315.86 316.67 \n", "21 1959 08 21777 1959.6219 314.80 316.06 314.82 \n", "22 1959 09 21808 1959.7068 313.84 316.73 313.31 \n", "23 1959 10 21838 1959.7890 313.33 316.33 313.32 \n", "24 1959 11 21869 1959.8740 314.81 316.68 314.54 \n", "25 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", "26 1960 01 21930 1960.0410 316.43 316.39 316.61 \n", "27 1960 02 21961 1960.1257 316.98 316.35 317.27 \n", "28 1960 03 21990 1960.2049 317.58 316.28 318.02 \n", "29 1960 04 22021 1960.2896 319.03 316.70 319.14 \n", ".. ... ... ... ... ... ... ... \n", "728 2018 07 43296 2018.5370 408.90 408.08 409.43 \n", "729 2018 08 43327 2018.6219 407.10 408.63 407.33 \n", "730 2018 09 43358 2018.7068 405.59 409.08 405.66 \n", "731 2018 10 43388 2018.7890 405.99 409.61 405.84 \n", "732 2018 11 43419 2018.8740 408.12 410.38 407.48 \n", "733 2018 12 43449 2018.9562 409.23 410.15 409.07 \n", "734 2019 01 43480 2019.0411 410.92 410.87 410.30 \n", "735 2019 02 43511 2019.1260 411.66 410.90 411.25 \n", "736 2019 03 43539 2019.2027 412.00 410.46 412.25 \n", "737 2019 04 43570 2019.2877 413.52 410.72 413.73 \n", "738 2019 05 43600 2019.3699 414.83 411.42 414.54 \n", "739 2019 06 43631 2019.4548 413.96 411.38 413.91 \n", "740 2019 07 43661 2019.5370 411.85 411.03 412.36 \n", "741 2019 08 43692 2019.6219 410.08 411.62 410.22 \n", "742 2019 09 43723 2019.7068 408.55 412.06 408.49 \n", "743 2019 10 43753 2019.7890 408.43 412.06 408.62 \n", "744 2019 11 43784 2019.8740 410.29 412.56 410.21 \n", "745 2019 12 43814 2019.9562 411.85 412.78 411.76 \n", "746 2020 01 43845 2020.0410 413.37 413.32 412.95 \n", "747 2020 02 43876 2020.1257 414.09 413.33 413.87 \n", "748 2020 03 43905 2020.2049 414.51 412.94 414.89 \n", "749 2020 04 43936 2020.2896 416.18 413.35 416.35 \n", "750 2020 05 43966 2020.3716 417.16 413.75 -99.99 \n", "751 2020 06 43997 2020.4563 -99.99 -99.99 -99.99 \n", "752 2020 07 44027 2020.5383 -99.99 -99.99 -99.99 \n", "753 2020 08 44058 2020.6230 -99.99 -99.99 -99.99 \n", "754 2020 09 44089 2020.7077 -99.99 -99.99 -99.99 \n", "755 2020 10 44119 2020.7896 -99.99 -99.99 -99.99 \n", "756 2020 11 44150 2020.8743 -99.99 -99.99 -99.99 \n", "757 2020 12 44180 2020.9563 -99.99 -99.99 -99.99 \n", "\n", " seasonally CO2 seasonally \n", "0 adjusted fit filled adjusted filled \n", "1 [ppm] [ppm] [ppm] \n", "2 -99.99 -99.99 -99.99 \n", "3 -99.99 -99.99 -99.99 \n", "4 314.90 315.70 314.44 \n", "5 314.98 317.45 315.16 \n", "6 315.06 317.51 314.71 \n", "7 315.14 317.24 315.14 \n", "8 315.21 315.86 315.19 \n", "9 315.28 314.93 316.19 \n", "10 315.35 313.21 316.08 \n", "11 315.40 312.43 315.40 \n", "12 315.46 313.33 315.20 \n", "13 315.51 314.67 315.43 \n", "14 315.57 315.58 315.54 \n", "15 315.63 316.49 315.86 \n", "16 315.69 316.65 315.38 \n", "17 315.76 317.72 315.42 \n", "18 315.84 318.29 315.49 \n", "19 315.93 318.15 316.03 \n", "20 316.02 316.54 315.86 \n", "21 316.12 314.80 316.06 \n", "22 316.21 313.84 316.73 \n", "23 316.30 313.33 316.33 \n", "24 316.39 314.81 316.68 \n", "25 316.47 315.58 316.35 \n", "26 316.55 316.43 316.39 \n", "27 316.64 316.98 316.35 \n", "28 316.71 317.58 316.28 \n", "29 316.79 319.03 316.70 \n", ".. ... ... ... \n", "728 408.65 408.90 408.08 \n", "729 408.90 407.10 408.63 \n", "730 409.18 405.59 409.08 \n", "731 409.44 405.99 409.61 \n", "732 409.72 408.12 410.38 \n", "733 409.98 409.23 410.15 \n", "734 410.24 410.92 410.87 \n", "735 410.48 411.66 410.90 \n", "736 410.69 412.00 410.46 \n", "737 410.92 413.52 410.72 \n", "738 411.14 414.83 411.42 \n", "739 411.36 413.96 411.38 \n", "740 411.57 411.85 411.03 \n", "741 411.79 410.08 411.62 \n", "742 412.02 408.55 412.06 \n", "743 412.23 408.43 412.06 \n", "744 412.46 410.29 412.56 \n", "745 412.67 411.85 412.78 \n", "746 412.89 413.37 413.32 \n", "747 413.10 414.09 413.33 \n", "748 413.30 414.51 412.94 \n", "749 413.50 416.18 413.35 \n", "750 -99.99 417.16 413.75 \n", "751 -99.99 -99.99 -99.99 \n", "752 -99.99 -99.99 -99.99 \n", "753 -99.99 -99.99 -99.99 \n", "754 -99.99 -99.99 -99.99 \n", "755 -99.99 -99.99 -99.99 \n", "756 -99.99 -99.99 -99.99 \n", "757 -99.99 -99.99 -99.99 \n", "\n", "[758 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_url, skiprows=54)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifie les clés de la table:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index([' Yr', ' Mn', ' Date', ' Date', ' CO2', 'seasonally',\n", " ' fit', ' seasonally', ' CO2', ' seasonally'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On constate des espaces intempestifs dans le nom des clés, il faudra les prendre en compte pour les requetes. on observe également que quand certaines données sont manquantes, la valeur -99,99 est mise à la place. On va donc filtrer les lignes où les valeurs sont manquantes pour le taux de CO2." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [ Yr, Mn, Date, Date, CO2, seasonally, fit, seasonally, CO2, seasonally]\n", "Index: []" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data[' CO2'] == ' -99.99']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On drop ces lignes et également les deux premières lignes qui contiennent les unités de mesures mais pas des données en tant que telles." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
4195803212591958.2027315.70314.44316.18314.90315.70314.44
5195804212901958.2877317.45315.16317.29314.98317.45315.16
6195805213201958.3699317.51314.71317.86315.06317.51314.71
7195806213511958.4548-99.99-99.99317.24315.14317.24315.14
8195807213811958.5370315.86315.19315.86315.21315.86315.19
9195808214121958.6219314.93316.19313.99315.28314.93316.19
10195809214431958.7068313.21316.08312.45315.35313.21316.08
11195810214731958.7890-99.99-99.99312.43315.40312.43315.40
12195811215041958.8740313.33315.20313.61315.46313.33315.20
13195812215341958.9562314.67315.43314.76315.51314.67315.43
14195901215651959.0411315.58315.54315.62315.57315.58315.54
15195902215961959.1260316.49315.86316.26315.63316.49315.86
16195903216241959.2027316.65315.38316.97315.69316.65315.38
17195904216551959.2877317.72315.42318.08315.76317.72315.42
18195905216851959.3699318.29315.49318.65315.84318.29315.49
19195906217161959.4548318.15316.03318.04315.93318.15316.03
20195907217461959.5370316.54315.86316.67316.02316.54315.86
21195908217771959.6219314.80316.06314.82316.12314.80316.06
22195909218081959.7068313.84316.73313.31316.21313.84316.73
23195910218381959.7890313.33316.33313.32316.30313.33316.33
24195911218691959.8740314.81316.68314.54316.39314.81316.68
25195912218991959.9562315.58316.35315.72316.47315.58316.35
26196001219301960.0410316.43316.39316.61316.55316.43316.39
27196002219611960.1257316.98316.35317.27316.64316.98316.35
28196003219901960.2049317.58316.28318.02316.71317.58316.28
29196004220211960.2896319.03316.70319.14316.79319.03316.70
30196005220511960.3716320.04317.22319.68316.86320.04317.22
31196006220821960.4563319.58317.48319.01316.92319.58317.48
32196007221121960.5383318.18317.52317.60316.97318.18317.52
33196008221431960.6230315.90317.20315.68317.01315.90317.20
.................................
721201712430842017.9562406.75407.68406.46407.36406.75407.68
722201801431152018.0411408.05408.00407.58407.51408.05408.00
723201802431462018.1260408.34407.59408.44407.67408.34407.59
724201803431742018.2027409.25407.72409.37407.82409.25407.72
725201804432052018.2877410.30407.52410.80408.00410.30407.52
726201805432352018.3699411.30407.91411.59408.19411.30407.91
727201806432662018.4548410.88408.31410.96408.41410.88408.31
728201807432962018.5370408.90408.08409.43408.65408.90408.08
729201808433272018.6219407.10408.63407.33408.90407.10408.63
730201809433582018.7068405.59409.08405.66409.18405.59409.08
731201810433882018.7890405.99409.61405.84409.44405.99409.61
732201811434192018.8740408.12410.38407.48409.72408.12410.38
733201812434492018.9562409.23410.15409.07409.98409.23410.15
734201901434802019.0411410.92410.87410.30410.24410.92410.87
735201902435112019.1260411.66410.90411.25410.48411.66410.90
736201903435392019.2027412.00410.46412.25410.69412.00410.46
737201904435702019.2877413.52410.72413.73410.92413.52410.72
738201905436002019.3699414.83411.42414.54411.14414.83411.42
739201906436312019.4548413.96411.38413.91411.36413.96411.38
740201907436612019.5370411.85411.03412.36411.57411.85411.03
741201908436922019.6219410.08411.62410.22411.79410.08411.62
742201909437232019.7068408.55412.06408.49412.02408.55412.06
743201910437532019.7890408.43412.06408.62412.23408.43412.06
744201911437842019.8740410.29412.56410.21412.46410.29412.56
745201912438142019.9562411.85412.78411.76412.67411.85412.78
746202001438452020.0410413.37413.32412.95412.89413.37413.32
747202002438762020.1257414.09413.33413.87413.10414.09413.33
748202003439052020.2049414.51412.94414.89413.30414.51412.94
749202004439362020.2896416.18413.35416.35413.50416.18413.35
750202005439662020.3716417.16413.75-99.99-99.99417.16413.75
\n", "

747 rows × 10 columns

\n", "
" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "4 1958 03 21259 1958.2027 315.70 314.44 316.18 \n", "5 1958 04 21290 1958.2877 317.45 315.16 317.29 \n", "6 1958 05 21320 1958.3699 317.51 314.71 317.86 \n", "7 1958 06 21351 1958.4548 -99.99 -99.99 317.24 \n", "8 1958 07 21381 1958.5370 315.86 315.19 315.86 \n", "9 1958 08 21412 1958.6219 314.93 316.19 313.99 \n", "10 1958 09 21443 1958.7068 313.21 316.08 312.45 \n", "11 1958 10 21473 1958.7890 -99.99 -99.99 312.43 \n", "12 1958 11 21504 1958.8740 313.33 315.20 313.61 \n", "13 1958 12 21534 1958.9562 314.67 315.43 314.76 \n", "14 1959 01 21565 1959.0411 315.58 315.54 315.62 \n", "15 1959 02 21596 1959.1260 316.49 315.86 316.26 \n", "16 1959 03 21624 1959.2027 316.65 315.38 316.97 \n", "17 1959 04 21655 1959.2877 317.72 315.42 318.08 \n", "18 1959 05 21685 1959.3699 318.29 315.49 318.65 \n", "19 1959 06 21716 1959.4548 318.15 316.03 318.04 \n", "20 1959 07 21746 1959.5370 316.54 315.86 316.67 \n", "21 1959 08 21777 1959.6219 314.80 316.06 314.82 \n", "22 1959 09 21808 1959.7068 313.84 316.73 313.31 \n", "23 1959 10 21838 1959.7890 313.33 316.33 313.32 \n", "24 1959 11 21869 1959.8740 314.81 316.68 314.54 \n", "25 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", "26 1960 01 21930 1960.0410 316.43 316.39 316.61 \n", "27 1960 02 21961 1960.1257 316.98 316.35 317.27 \n", "28 1960 03 21990 1960.2049 317.58 316.28 318.02 \n", "29 1960 04 22021 1960.2896 319.03 316.70 319.14 \n", "30 1960 05 22051 1960.3716 320.04 317.22 319.68 \n", "31 1960 06 22082 1960.4563 319.58 317.48 319.01 \n", "32 1960 07 22112 1960.5383 318.18 317.52 317.60 \n", "33 1960 08 22143 1960.6230 315.90 317.20 315.68 \n", ".. ... ... ... ... ... ... ... \n", "721 2017 12 43084 2017.9562 406.75 407.68 406.46 \n", "722 2018 01 43115 2018.0411 408.05 408.00 407.58 \n", "723 2018 02 43146 2018.1260 408.34 407.59 408.44 \n", "724 2018 03 43174 2018.2027 409.25 407.72 409.37 \n", "725 2018 04 43205 2018.2877 410.30 407.52 410.80 \n", "726 2018 05 43235 2018.3699 411.30 407.91 411.59 \n", "727 2018 06 43266 2018.4548 410.88 408.31 410.96 \n", "728 2018 07 43296 2018.5370 408.90 408.08 409.43 \n", "729 2018 08 43327 2018.6219 407.10 408.63 407.33 \n", "730 2018 09 43358 2018.7068 405.59 409.08 405.66 \n", "731 2018 10 43388 2018.7890 405.99 409.61 405.84 \n", "732 2018 11 43419 2018.8740 408.12 410.38 407.48 \n", "733 2018 12 43449 2018.9562 409.23 410.15 409.07 \n", "734 2019 01 43480 2019.0411 410.92 410.87 410.30 \n", "735 2019 02 43511 2019.1260 411.66 410.90 411.25 \n", "736 2019 03 43539 2019.2027 412.00 410.46 412.25 \n", "737 2019 04 43570 2019.2877 413.52 410.72 413.73 \n", "738 2019 05 43600 2019.3699 414.83 411.42 414.54 \n", "739 2019 06 43631 2019.4548 413.96 411.38 413.91 \n", "740 2019 07 43661 2019.5370 411.85 411.03 412.36 \n", "741 2019 08 43692 2019.6219 410.08 411.62 410.22 \n", "742 2019 09 43723 2019.7068 408.55 412.06 408.49 \n", "743 2019 10 43753 2019.7890 408.43 412.06 408.62 \n", "744 2019 11 43784 2019.8740 410.29 412.56 410.21 \n", "745 2019 12 43814 2019.9562 411.85 412.78 411.76 \n", "746 2020 01 43845 2020.0410 413.37 413.32 412.95 \n", "747 2020 02 43876 2020.1257 414.09 413.33 413.87 \n", "748 2020 03 43905 2020.2049 414.51 412.94 414.89 \n", "749 2020 04 43936 2020.2896 416.18 413.35 416.35 \n", "750 2020 05 43966 2020.3716 417.16 413.75 -99.99 \n", "\n", " seasonally CO2 seasonally \n", "4 314.90 315.70 314.44 \n", "5 314.98 317.45 315.16 \n", "6 315.06 317.51 314.71 \n", "7 315.14 317.24 315.14 \n", "8 315.21 315.86 315.19 \n", "9 315.28 314.93 316.19 \n", "10 315.35 313.21 316.08 \n", "11 315.40 312.43 315.40 \n", "12 315.46 313.33 315.20 \n", "13 315.51 314.67 315.43 \n", "14 315.57 315.58 315.54 \n", "15 315.63 316.49 315.86 \n", "16 315.69 316.65 315.38 \n", "17 315.76 317.72 315.42 \n", "18 315.84 318.29 315.49 \n", "19 315.93 318.15 316.03 \n", "20 316.02 316.54 315.86 \n", "21 316.12 314.80 316.06 \n", "22 316.21 313.84 316.73 \n", "23 316.30 313.33 316.33 \n", "24 316.39 314.81 316.68 \n", "25 316.47 315.58 316.35 \n", "26 316.55 316.43 316.39 \n", "27 316.64 316.98 316.35 \n", "28 316.71 317.58 316.28 \n", "29 316.79 319.03 316.70 \n", "30 316.86 320.04 317.22 \n", "31 316.92 319.58 317.48 \n", "32 316.97 318.18 317.52 \n", "33 317.01 315.90 317.20 \n", ".. ... ... ... \n", "721 407.36 406.75 407.68 \n", "722 407.51 408.05 408.00 \n", "723 407.67 408.34 407.59 \n", "724 407.82 409.25 407.72 \n", "725 408.00 410.30 407.52 \n", "726 408.19 411.30 407.91 \n", "727 408.41 410.88 408.31 \n", "728 408.65 408.90 408.08 \n", "729 408.90 407.10 408.63 \n", "730 409.18 405.59 409.08 \n", "731 409.44 405.99 409.61 \n", "732 409.72 408.12 410.38 \n", "733 409.98 409.23 410.15 \n", "734 410.24 410.92 410.87 \n", "735 410.48 411.66 410.90 \n", "736 410.69 412.00 410.46 \n", "737 410.92 413.52 410.72 \n", "738 411.14 414.83 411.42 \n", "739 411.36 413.96 411.38 \n", "740 411.57 411.85 411.03 \n", "741 411.79 410.08 411.62 \n", "742 412.02 408.55 412.06 \n", "743 412.23 408.43 412.06 \n", "744 412.46 410.29 412.56 \n", "745 412.67 411.85 412.78 \n", "746 412.89 413.37 413.32 \n", "747 413.10 414.09 413.33 \n", "748 413.30 414.51 412.94 \n", "749 413.50 416.18 413.35 \n", "750 -99.99 417.16 413.75 \n", "\n", "[747 rows x 10 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.drop(raw_data[raw_data[' CO2'] == ' -99.99'].index)\n", "data = data.drop([0,1])\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'évolution du niveau de $CO_2$ dans l'atmosphère" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ayons un premier aperçu de nos données sur l'intégralité de la periode considérée: les dates de relevé sont données au format \"nombre de jours depuis le 1er Janvier 1900\". " ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['days'] = [int(date) for date in data[' Date']]\n", "sorted_data = data.astype(float).set_index('days').sort_index()\n", "CO2_data = sorted_data[' CO2'].astype(float)\n", "CO2_data.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si on regarde sur une plus petite periode de temps:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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j7Kgt4f2HGzLudu2bCjIVWOJo20o/+JbK4qTUjTGGl7oneOv2qngOP1HsNKeXuq1Z/85N1GXyZtFAr5S6JS6P+TnSUs72mhKmA0tMB5ZSxrxyxdo8eTRh41JbVXFS6mbEt8jw7AL37ahO+3Pq7QNCXuq2vpfO6DXQK6VuAV9wGa8/xM7aknh3yFgTsRhjDH//aj/7GkqTdrK2VbrxLSzjs/vXXLVfl6lNcF1pESJwYXTO6l2Tw4us66WBXil10/VMWK2Cd9SWsKPGCuJXV6VvjvdO0z0+zy/d157UbybWv2bATt9cs1+XqW98Yb6DKrcV3HfVeXKyd80bpYFeKXXTXbF3nu6s9dBU7sKZ74jPzGN+eHUKh8B7DiZXybTZfWxi7QyuTQbwOLPP1CfnQwD8zNHmG/YMm5mWVyqlbroe7zxFBQ6aKlw4HEJThYsR30LSmFP90+xtKE3Z3NSaMqMPsK3GnXWm/psP7eDCyBwfPtqSccxWooFeKXVTGWN4pWeSPfWl5NkHiDSVuxi2NzWBda7rmYFZPnRX6gzc7cynuqSQgamV1M0926qy/sx//e7dN/AJNj9N3Sil3pTh2QUCoXDGr7/YPcGlMT+/cG9b/FpTuSve0wbg8rifwFIkY1Ox1kqr8ia4FGbEt8i2NXa7qmQa6JVS121ucZl3/skL3PH7z3FhZC7tmK+fHqbSXchPHm6MX2ssdzE5H2Jx2ep32DlsdaI83Fye9nu0VbkZmA5yze5Gme1cV5VKA71S6ro92zXO4nKUpUiUr5waTPl6NGr4Yc8kb9tZTWH+SriJbWoa81npm4ujftyFefF8/GotlcWM+ha4bB/0vS1DxY1KTwO9Uuq6PXV2hJZKF4eay+JBONHFsTkm55d4YGfy4UKN5damphG7U+WF0Tl213syHgLeVmmd7frSlQlEVk6GUuujgV4pdV3CkSivXZviXXvr2VPvSRvoX75itSG4f2fyLtbmcvskqNkFjDFcHJ1jb0P6c11hpcTyB5e8NFe4KCpIPehbZaaBXil1XXonA4TCUQ42l7K7vpSpwBIT/lDSmJevTLC7zkOd3ZYgpr6sCIfA0HSQ4dkF/IvhrIE+ltKZWwyzrVrz82+UBnqlVBJjDF8/PZS1kgagy1583ddQxp56qx1B4qx+YSnCib4ZHtiZ2pOmMN9Ba2UxPRPzXBy1XpMt0Cdujso2TqWngV4plaRzeI6Pf/ksf/NKb9ZxF0bnKMx3sK3GzQ67CqZ3cmW36/G+aZbC0ZS0TczOOg/d4/NcHJ1DhPibRToiwkfuaeWn72rm19+x/TqeamvTQK+USnJm0OoV/40zwxiTeih3TNeIjz31HgryHFSXOHEIeBNSN2cHZwG4u70y7et31ZXQNxng3JCPtkrroJFs/tMHD/LHP3OY0qKCrONUKg30SqkkZ4esmvZrE4F4ema1gakgr12b5p4OK4jnOYTqEifjcyu7XfumAtSXFmUM4LvqPISjhucvjrOnXtMxN5MGeqVUkrODs/Ee7ufsoL/an/3gCnkO4Vcf2Ba/VlvqTJrRD0wF49Uy6SQeCKJ595tLA71SKm42uETPxDyPHmigMM9B31QgZYxvYZmnzo7wobuaqU2opqn1FOGdWwn0fVPBrPXu22vd7K6zulm+Y09NxnHqzdOmZkqpuG+fH8MYePe+Or59fpS+ydRA/9SZYRaXo/zc3a1J1+tKnfHfAOZDYSbnQ7RVZ57RO/Pz+O7H33ZjH0ClpTN6pVTcN88Ms63Gzf7GUtqr3Gln9M9d9LKjtoQDTcnplhpPEVOBEOFIlH77dbqDdWPQQK/UFmCMYXA6mLWKZtS3wPG+aR473ISI0FFtdYyMRk3S9zk3NMvRtoqUfvC1HifGwOT8UrylcKbeNerW0kCvVI4LhSP86v88yQOf/QG/8oWTLIWjacc9fXYUY+Anj1hdJtuq3ITCUUYTKmkGpoPMBpc5lKbLZGz3q9e/SJ8d6LMtxqpbRwO9Ujnupe5Jnr/o5YGd1Xz/kpfzw+kraZ4+N8Kh5jI67F7vsbRLf0L6JlZ6ebilLOX1tfbu1VHfIv1TAapLCvFozfuGoIFeqU3I61/kv33vCt6E2XYmrw/MkO8QPvW+vUDqodxgtSvoHJnjwV0r1S/NFVYr4cQDQs4NzuLMd7CrLnUX6866EooKHPyoZ5K+qQBtmp/fMDTQK7UJfeXkEH/yXDfv+txLa/akOT0ww77GUnbUlFCQJ/HDOxJdGPURiRoONq3M1BvsVsLDsyuBvts7z866EgryUkNHcWE+D+6q4btd4/RNZq+hV7fWmoFeRIpE5LiInBWRLhH5tH39yyJyxv7TJyJnEl7zSRHpEZHLIvLIzXwApbaiHq81K/ctLPOt86MZx4UjUc4O+riztYL8PAftVe60M/qzg7GUzEru3ZmfR63HmTSjv+qdZ3tN5u6Rjx6oZ2xukbG5RdoqdUa/UaxnRh8CHjLGHAaOAI+KyL3GmJ81xhwxxhwBvgp8DUBE9gGPA/uBR4E/FxFtHq3UDXTF6+eBndVsq3Hz5InUk51iLo/7WViOcEerFcC31bi5libQnx/2UVfqTGkn3FThis/oF5YiDM8uZA30j+yvj3+sM/qNY81AbyyxfxkF9p94vZVYNVYfBv6Xfekx4EvGmJAxphfoAY7d0LtWaguLRg1XvQF21Jbw2OEmTvbP4Asupx37+oDVWOzOVuvQ7e01JfRPBVmOrFTeGGM43jud9rzWpvKVQH/N7kyZLdAXF+bzLx7aARDvaKluv3Xl6EUkz07NeIHnjDGvJXz5AWDcGHPF/rwJSJxiDNnXVn/Pj4nISRE5OTExcX13r9QWNDy7wMJyhJ21Hu5utwL4abvj5GqnB2aoLimML6y2V7sJRw2jsyuLuOeGfAzPLvDuhNl4TFOFi9HZRevNxc7trxXAP/6uXTz/r97GgabUyhx1e6wr0BtjInaKphk4JiIHEr78c6zM5gHSHfqYskvDGPN5Y8xRY8zRmhrtc6HUesXy8ztqSzjcUo5D4PX+TIF+ljtaVzY3xQL+0EwwPubb50cpyBPetbcu5fXN5S6WIlEm5kNc9c7jkLVTMiLCjtrMveXVrfeGqm6MMbPAC1i5d0QkH/gp4MsJw4aAloTPm4GRN3WXSm0RL3VP8I+v9Wcfc2WCwjwHexs8uJ357G0o5dRAaqB/5cokvZMBjrZVxK+1VNhntSYssL50ZZJ7OqooK06teW+xd7b2TwW5OjFPS2Wxnte6Ca2n6qZGRMrtj13Aw8Al+8sPA5eMMUMJL3kKeFxEnCLSAewEjt/Y21Yq9xhj+P2nL/Cpr3dyOk3gBis//0znGG/bVR3fjHSwqSzlYO5wJMpvffkMu+pK+MW3tMWvx89qtWf0i8sRroz7026AgpV8/NWJea5OBLLm59XGtZ4ZfQPwAxE5B5zAytE/bX/tcZLTNhhjuoAngQvAM8ATxpjIjbtlpXLTxVE/Pd55ROCPvns57ZjTg7OM+hZ578GG+LWGMheT80tJrQ0ujfmZnA/xxDt2UFy40qS2IM9BQ5krPqO/NOYnvKp+PlFTuYuiAgfd436uTcyzvUZLJjejNdsUG2POAXdk+No/y3D9M8Bn3tSdKbXFfOv8CPkO4f2HGnima4xI1JDnSF7y+vb5UQrzHDy8byWf3lBmlUSOzy3GUy2x3whi1TaJmitcDNoz+k67HcL+xvSB3uEQtlWX8FL3BKFwVGf0m5TujFVqgzg9MMvehlIe2FnD4nI0pd49GjV85/woD+ysTjo3td4O9GMJ7RBOD8xSXeKML74maq4ojs/oO4d9lBcXpB0Xs722JF5xs11LJjclDfRKbQDGGLpG5jjQVMrBZmt23TmS3Hzs0pifEd8ijx5ILoOMzehHfQmBfnCWO1vLU1oJgzWjH5tbZHE5wuVxP3vqPWnHxSSma3bojH5T0kCv1AYwNLOAb2GZ/Y1lbKt2U1Tg4PxQ8sHcZwatzU/H7AO5Y+piqRs70M+HwvROBjLm3bfXlmCMdfh3j3d+zbr4d+yuZU+9h8/+9CEq3IXX9Xzq9tKjBJXaALpGYrnyUvLzHOyuL+XS2OpAP0NFcUHKYR4eZz7uwrz4jP7KuFWBs7s+fS177ODvH12dxL8YTjqkO53DLeU881t65N9mpjN6pTaArpE58hzC3gbreL6OqmIGpoNJY84O+jjckpqOERHqy4oYm7Py7t1rBPqOajd5DuHbdjM0bVWQ+zTQK3WTzS0us7CUvcK4c9jHjpqS+Gak1spiRmYX4iWTgVCYbq8/bT8asBZkYzP6y2PzFBU44pujVnPm59FeVRzvg6OBPvdpoFfqJgqFIxz6D8/ysb8/mXVc58gc+xtXDttuqSwmamBkdmWWbgzsayxN+/r6UhdjdqDvHvezq86Dw5F5gTUW3D3O/PjJUCp3aaBX6ib6h1cHAHj5ymTGg7m9c4tM+EPsT1g8jeXhY+mbWH+bnRlm3w1lRXj9ISJRw2U70Gfzzj11NJW7+PRj+7NW3KjcoIuxSt1E3zq30uZpxLdIU3lqvXqsjPJAwmw9dgxffyzQT8xTmOdIWYiNqS8rIhI1dI/7mfCH2L1GoP/w3S18+O6WrGNU7tAZvVI3Uf9UMJ6SOWuXR672ypUpCvMdSW19az1OCvMdDNqB/qp3nvbqYvLTHOEHUG8fGPJSt9Xye1eGhVi1NWmgV+ommVtcZiqwxKP76ynMc6QN9MYYnrs4xn3bq3A7V37BdjiElgoXA1MrqZtsi6ax3bEv2oF+rRm92lo00Cv1BoUjUd7/317mb1/pzTouFqR31pWwr7GU02kC/RXvPIPTC0m9a2Laqtz0TwdZXI4wMB3Muis1tjv2R1enKC3Kp65UF1jVCg30Sr1BP7w6RefwHH/87GW8/sWM4/rtQN9W5eZISznnh3yEE47wA2uRFqzdp6u1VhYzOB2ka2SOqCFpsXa1SnchhXZaZ1dd9pYGauvRQK/UG/TNM8O4C/NYWI7w5eOZD+bum7IagbVWFnOkpZyF5Qg9qxqVneidprnCRWOaRdqWymLmQ+F4OiZTDT1Ym6aW7DeRdL8dqK1NA71Sb8DCUoTvdo7xvkMNtFe5UxqPJeqbDFDjceJ25nO4xQrSZwZW0jfGGE70TXOsvTLt69vsCpunz41Q43GuOx3zi/e2rT1IbSlaXqnUG/C9S+MEliJ84EgTgVCE88PpA/1yJMoPLk9wR6sV4NurivE487kwutK/5tpkgKnAEnd3pA/0rfbZrNcmArxzT+2a6Zhv/Yv7WY6YpEVdpUBn9Eq9Id88M0Ktx8k926rY2+BhYDqIf3E5Zdz3Lo4zOR/i545ZteoiQoudc4+5NGr1pDnUnD73ntjC4C3bq9a8t/2NZRxpyZzeUVuXBnqlgOHZBT76t8e5MDKXcUxwycqXv/dgQ1IDstXntQL809lRajxOHty1ssjaUulKOpQ7tus10yYoV2EeDoHCPAcffWv79TyWUoAGeqUwxvCJr5zlxe4J/stz6c9qBXjlyiRL4SgP77UWO/fEAv14cqBfCkd5sXuCh/fWJR0FGDvZKdYKYXAmSEVxQfyQ73Re/rcPcfr33kVBho1SSq2H/utRW97QzAI/ujpFa2Uxz1/0cnVVZUzM9y958Tjz4wd/1JcWke8QhhNm6QCv9U4xHwrz8N7kksmWChcLyxGmAksADE4HM87mY5rKXZpzV2+aBnq15cXq3X/joR0AvHZtOu24k/0zHOuopDDf+r9NnsPqAz88mxzov3fRS1GBg/t2VCddb7Zz7rE8/eB0kOY1Ar1SN4IGerXl9U9b9e737aimxJnP5bHUPH0gFObqxHxSPxqwZtwjCYHeGMNzF8a5f0dNvLd8TIsd1IdmFohEDcOzC2vO6JW6ETTQqy1vYCpIYb6DhtIidtWVcCnN4urF0TmMIeUc1qZyV1Lq5vK4n+HZhZS0DViHcoOVmx+bW2Q5YjTQq1tCA73a8vqmArRWFuNwCLvrS7k87k/pHR+rlz+4qhSyqcLF2NxivLXBK7GWBntSA73bmU+Zq4Ax3yJ9k9ZvEW0a6NUtoIFebXn9U8F4wN3b4GE2uMz4XChpTOfwHNUlzpTTmJrKXUQNjM1ZPW+4JXc8AAAgAElEQVSuTsxT6S6kzm4bvFqDfeTfNXvBd1uWRmVK3Sga6FXOOjs4ywf+/x8y4Q9lHGOMYWA6GD/oY1u1FXhjfWpiOod9HGwqTdmdGutRMzJrB3pvgO017ow/r76siDHfIlcnAhQX5mmXSXVLaKBXOSkaNfzuNzs5MzjL0wmnPK3WPT5PcCnCHvugjvoyK/COz610pVxYinDF609ZiAUrdQMwPGtV0lybnI+/WaQTn9FPBuiodmuXSXVLaKBXOem13mnODfkozHPwrXOjGce92O0F4IFdVilkrZ1y8Sakbi6OWW2C0wX6xjI70M8s4AsuMzm/xPbaLDP6UheT8yG6x/yatlG3jAZ6lZPO2Id8/MK9bZzsn8mYvnmxe4LddR4a7IDtceZTXJgXz7kDdMUWYtMEeldhHlXuQoZnF7k6aefd15jRg5XT31ad+Q1BqRtpzUAvIkUiclxEzopIl4h8OuFrvykil+3rn024/kkR6bG/9sjNunmlMjk/PEtLpYt32b3ZL46mr40/0TvDg7tr4tdEhLrSoqTUzflhH5XuwniQXq2pwsXw7EK85816jvwD4r1ylLrZ1rO3OgQ8ZIyZF5EC4BUR+Q7gAh4DDhljQiJSCyAi+4DHgf1AI/C8iOwyxkRuziOorWRwOsgPeyZ5/Fhr1nHnhnwcbi5nt517vzzm5227apLGvHptiqVIlAdXXa/1OJNSN2cGZzncXJYxn95Y5qJnYp4zA7OUFxfQVpW5ZDIx0L9jT03GcUrdSGvO6I0l1vyjwP5jgF8D/tAYE7LHee0xjwFfMsaEjDG9QA9w7IbfudqS/uz7PfzO186nnaHHTAeWGJpZ4GBzGZXuQmo8zrSboF7snsBVkMfR9oqk63WlRfHUzXwozBXvfPzgkHSaKqxNU68PzHCkpTzrAmusSufhvXU48/MyjlPqRlpXjl5E8kTkDOAFnjPGvAbsAh4QkddE5EURudse3gQknq82ZF9b/T0/JiInReTkxMTEm3sKtakthaPMpenpvlo0avj+ZWs+8bXXhzKOi21uOmTn1PfUe7g8nvrG8GL3BG/dXpUScOvLrNSNMYZzQ7MYQ9Y+703lVrOyK9557mipyDgOoMSZz/P/6kH+4hfuzDpOqRtpXYHeGBMxxhwBmoFjInIAK+1TAdwLfAJ4UqypTLrpjEm5YMznjTFHjTFHa2r0V9it7De++Drv+i8v4lvIHuy7RuaY8IfwOPP55pnMJZPnh6yF2Nhh2rvrPFwZnycSXfln2DcZoH8qmJSfj6n1OAmFo/gWljk7aL1pZAv0iee9xk6UymZHbYm2HVa31Bv612aMmQVeAB7Fmql/zU7tHAeiQLV9vSXhZc1A5v9Xqpz0f3/hBH/03Utrjusa8fHshXHG50J87rnurGNf7rF+8/vIPa14/aGMbwznh310VLspc1l93rfVlBAKR5MWWGMHbq/Oz8NK4B6aWaDHO099aRHlxYUZ72t/YymF+Q4+eEdTSsdKpTaC9VTd1IhIuf2xC3gYuAR8A3jIvr4LKAQmgaeAx0XEKSIdwE7g+M25fbURDU4Hef6iN2v9esyTJwZxFeTx1u1VvHDZm3Xsyb4Ztte44/1mVveBjzk/5EsqhVzZ1LQy/qXuCdqriuM7YhPtqrMWcC+N+emfCmRdXAWrK+Xl//gon/vZI0kHjSi1UaxnRt8A/EBEzgEnsHL0TwN/C2wTkU7gS8BH7dl9F/AkcAF4BnhCK262lu90WgG+byrI5Hzm9gMAnSNzHGwuY39jKWN2XjydaNRwqn+Gu9sraYq3HUgN9F0jPkZ8i9yZkEKJjU98Y+gc8XFnW/p8entVMc58B5dG5+ifDq4Z6AHd4ao2tDXLK40x54A70lxfAn4hw2s+A3zmTd+d2pS+f8lLcWEewaUIp/pneGR/fdpx0ajh4ugcHz7aQn2Zi8VlKy+eLk3SMzGPb2GZu9oq0s7QY/7yxWuUOPP54J3N8WuN5UVJ4+cWraZlO2s9ae8rP8/BrjoPpwasjVbpZv1KbSa6IqRuKGMMl8b8vOdAA4V5Dl7vn8k4tn86SHApwr6GUurt1gOjvsW0Y08PWN/nrrYKqt1OCvMdKYF+cTnCt8+P8jNHm+P5eYDiwnwq3YUMzVj9aHq8VrVwto1Ne+o9nB6wFnXXM6NXaiPTQK9uqIn5ELPBZQ40ldJeXcy1yUDGsRdGrJLHfY2l8Y1EYxkC/YWROdyFebRXuXE4JOXAD7A6ToajhjtaU1MyTeUuhuzxsUC/M0ug39e4smu1XWf0apPTQK9uqCvjVhDdVeehcdUxe6t1jvjIdwg7akuSesCkc2F0jr0NpTjsxc6mchdDq7537GfvSNMsrKncFf8NoMc7T2G+I360Xzofumsl9dOqM3q1yWmgVzdU97i1A3VnXcmagf7MwCx7G0opKsijxuPEIelTN1Yu3580y043o+/xziMC29L0g2+2d68aY+jxzrOt2p21Qqa0qIBX/u07+NPHj1BaVJBxnFKbgQZ6tS7GGH73G528dm0q67ju8XnKiwuoKXHSVO5iJrjMwlJq0VUkau06jW1EKshzUONxMuZLfWPonQowHwqzL6EJWFOF1e53cXnle/dMzNNSUZxyKDdAW7WbUDjK2NwiV7z+rPn5mOaKYh47krKpW6lNRwO9WpdrkwH+/tV+/seP+rKOuzLuZ2dtCSISr3YZSRO8e7zzBJYiSTtO68tcaWf0f/3yNQryhAcSNjelK7G86p3PGMC32y2Bu4bnGJpZyFhxo1Qu0kCv1uXHV62Z/I+uTiW1EkhkjKF73M9Oe8NR7FCOdOmbE33TABxJqHdvTpOOGZ9b5MmTQ/z8PW3x4A6pm6DCkSjXJjMf4xc75OO5C+MYk73iRqlco4FercurdsrGt7Acbxq2mtcfYm4xzC47iDZm2dj0nc5ROqrdSYdvNFdYC6zRhDeS0wOzRKKGD9yRnEJZvQmqbyrAUjjK7vr0Pd7rSp24C/N49sIYYK0hKLVVaKBXa4pEDT++OsUDO60+Lpny9LGF2FgLgfqyIkRgeDY5HeP1L/Ljq1P8xKGGpB2lzRUulsLRpN20XSM+8hwSP9M1pr6sCIeszOhjbYhXj4sRETpq3MwEl8lziJZMqi1FA/0Wl7iYmcnrAzNMBZasHaylRfGTlFbrtssbY6mbgjwHdZ6ilBn99y96iRp436HGpOvNFVYZ42Biq4JhHztrS1IWWAvyHNSXFsVn9JfH/Dgke0qmudz6/geayijM13/6auvQf+1b2NdPD7Hv955Zs/nYM51jFOY5ePvuGnbVe7g8nj7Qd434qHIXUl2y0sKgsTw10P/42hQ1Hie7VqVPmitiXSOD8WudI3Psb0w9qxWsPP1Qwoy+vdqdtuIm5pfua+eX7mvnbz56NMvTKpV7NNBvUb7gMp/4yjmiBv70e90Zm4kZY/hu1xj376zGU1TA7roSrnjn0y7IHu+d5u72yqR0zOpaemMMr16b4t5tVSmNwJoqVtoDA3jnFpnwh9jfmD7v3lxRzNC09aZwecyfMW0Tc8+2Kv79T+ynusSZdZxSuUYD/RbVMzFPOGp478F6usfnOd47nXZc14hVjvjIfuuQ7V11HpbCUfqnklsbDM8uMDSzwLGOyqTrjeUuRnwrXSn7poKMz4W4d1vyOLB60lQl9KTpslskHGhKP6NvrSxmdG6RmcASA9NBdtfpYdtKpaOBfovqs3vQ/Mr92wB43W7gtdqzXWM4xDrjFIgftt29Kn1zvNdaoL1nVQBvLCtiKRxlKrAEwCm7ydnd7amBHuzKG3tG32lX9+zLMKNvqyrGGKtbZuK9KaWSaaDfovqmAjgEDjaV0VLpigfV1V7snuCutgqq7HRHu10O2TcVTBr3lZND1Hqc7FlV3ri6xLJz2IerII/tafrRgJ2OiQX6ER/bqt2UONN30451lYyVTK6VulFqq9JAn2O8/kX+49MXmA+Fs47rmwrSVOGiMN/BwaaytLXxwaUwnSNzSbPv0qICyosLGJxeCfSn+qf50dUpPva2bSn9Y1YH+q4RH/saSzP2mYn1pIlGDZ3Dcxln8wCtldabzrMXxnEV5NGapUmZUluZBvoc87ev9PE3r/Typ89nP3+1bzIQryU/0FTGwHQQXzD5DNazgz4iUcPR9uS2vy0VxUklkM9d8FKQJ3zkntaUn9OUcP5qNGroGpnjQJbg3VzhYikS5fywj+HZBQ43Zz5su7qkkOLCPIyBXXUl8c6WSqlkGuhzSDgS5auvD5HnEP7uh31M+NMf42eMoW8yQIedhok1C+v2JufdT/VbC7R3rurv3lpZnDSjvzw2x/aaEooLU1Ms5cUFuAvzGJpZoHcqQHApwv4Mi6uwUkv/P3/cD8Dbd6ce3h0jIgTthmk/cbgx4ziltjoN9DnkZL919N3/9ZY2wlFD10j6vPvZIR/+UJgDdn16pjNYT/bPsLO2JOVov+bKlfQKWKWNmRZCRYTWKjcD00GujGffvQortfRffX2I5grXmj1pPvmePXz0LW38yv0dWccptZVpoM8hsXLEjxyzUiirK2NivnpqCGe+g0cPWme5NtiBPrFzZDRqeL1/JiVtA1bqZikSZdy/iG9hmRHfYtaKl9ZKlx3o1z7CL1ZLD1alz1qHbv8/D27n048d0MO5lcpizcPB1eZxcXSO6hInO+s81Hic8ZYEibpGfHzl1CDvOVAfP1CjxJmPpyif0YQZ/RXvPHOLYe5qSy2DjC16DkwF43nxbLP0tio3L1ye4PK4n+YKV9oUT0zi137joR1rPLFSaj000OeQi6Nz7G2wAu7uOk/aGf2/+3on5a5C/t379iZdb1zVC/6knZ8/2pZmRl+50pMmFLZy5LFGZum0VBYTCkf58dUpDjVnzs/HfPXX3kqtx6k7WJW6QTR1kyOWI1GujM/HF1Z31pXQPe5Pavm7HIlyYcTHB+9sotZTlPT6+rKipED/ev8sVe7CeK16oqZyFyIwOB3kqjdAUYEj3ns+nTb7jWEqsBRveJbNXW0VWc9zVUq9MRroc8T5YR9LkSh77UC/vaaExeVo0mHb1yYCLEdM2jRLY3kRowknQXWN+DjUXJY2912Y76ChtIjBmSDXJufpqM5e2phY364Hfih162mg3wROD8zwn5+5lLHxGMBfvXQNT1E+D+2tBVbSK0MJ9e6XxqzF2nQLpw1lLibnlwiFIywuR7jinc/YYwag2S6xvDYRSHsYd6LWymL++YPb+cQju3nfwYasY5VSN54G+k3gr1/u5S9euMqr19I3HpuaD/FM1xi/cG9bfIG1xa5eSax3vzTmJ98hbKtOnVU3lFmpnNHZRS6N+YlETcb2wGAF72sTAYZmgvHzWDNxOITfec8ennjHDtwZ2hkopW4eDfQbXDgS5eUrEwB8IcPB3BdH/RgD9++ojl9rqrDz6DOJG5v8bK8pSXvoRuJvALG+NweaMu9gbakoZiqwRNSsnMeqlNqYNNBvcGeHfMwthmkqd/H9y960feBjKZnE3LszP486TxGD08mnNWXqHRPLo/dPB+ga8VFeXJB0GPdqLZUrX9PzV5Xa2NYM9CJSJCLHReSsiHSJyKft6/9BRIZF5Iz9570Jr/mkiPSIyGUReeRmPkCuO9FnpWs+ck8rS+HkxdWYi6N+ajzOeIfJmJZKV3xG751bxOsPZcy715cWUZjnYGA6SOfwHPsbS7NuQop1qfzIPa3xSh+l1Ma0noRpCHjIGDMvIgXAKyLyHftrnzPG/HHiYBHZBzwO7AcagedFZJcxZu3DSVWKK+Pz1HqcHGmxmnv1TwVSZtpW/XxqsG2pKOZV+yDvWHfKTHXsDofQXOniqjfA5TE/v3Rfe9b72tdYyunffRcV7sKs45RSt9+aM3pjiW2xLLD/ZC7/gMeALxljQsaYXqAHOPam7zTHnB6Y4fuXxtcc1zMxz47akng9e/+qPvDLkSg93nn2pqmkabFPYFpcjnBuyIcIWWffbZXFfO/SOEuRaNbGYzEa5JXaHNaVoxeRPBE5A3iB54wxr9lf+g0ROScifysisS2UTcBgwsuH7GvKZozh3371HL/5xdMElzL3jTfGcNU7z87aEhrKXBTkSUqg750MsBSJsqchNdB3VLsxxqq86Rz2sb2mJGvVS2uldWITkLWVsFJqc1lXoDfGRIwxR4Bm4JiIHAD+AtgOHAFGgT+xh6dL7Kb8BiAiHxORkyJycmJi4rpufrPqGpmje3yewFKEZzrHMo4bnwsxHwqzo7aEPIfQUlmcclbrxVFrITZd6ibWhrh3MsD5YR+H1pilt9r96fc2lMZ71SulNr83VHVjjJkFXgAeNcaM228AUeCvWEnPDAEtCS9rBkbSfK/PG2OOGmOO1tRk7jmei751fpSCPKGxrIhvnEn5q4m7YveH327vJm2rLE45wu/iqJ+CvPS18bFj/169Np11ITbmw0eb+eyHDvH1X3+rHuKhVA5ZT9VNjYiU2x+7gIeBSyKSuMXxg0Cn/fFTwOMi4hSRDmAncPzG3vbmdml0jp21Hu7bUR2fkacTazu82+4Ps6O2hGsT80kllpfG5thR60lbG1/mKqC6pJCnzlpvJgfXaCjmKSrgw3e3UFSQ94afSSm1ca1nRt8A/EBEzgEnsHL0TwOfFZHz9vV3AB8HMMZ0AU8CF4BngCe2SsXNF18b4PkLay+w9k4G6Khxs62mhAl/CP/ictpxx3un2V7jjpdN7q4vJRSO0peQvrk4Opd2ITamo9rN5HwIxxoLsUqp3LVmeaUx5hxwR5rrv5jlNZ8BPvPmbm1z8foX+fdPdVJXWsRDe2ozpj6WwlEGZxb4icONSTn0Q6vORo1EDSf6pnn/oZVfnGIboi6NWjtcvXOLjM+Fsh6g3VxRzIm+GY51VGr7AaW2KN0Ze4N86fggyxHD0MxCfJNTOoMzQSJRQ0e1m+12M7BrE4GUcWeHZvEvhrm7feXgj9iibGwn7NkhqzY+VmOfzq+/fTu/+/59/N0/0wpXpbYqDfQ3yFNnR7iztZziwjy+dX4047i+SSuot1e7aa0qxiFwbSL1JKjPPddNeXEB79xbF79WVJBHR7WbS2PWIu3ZwVnyHJJ1kXVnnYdfub8DV6Hm3ZXaqjTQ3wCjvgV6vPO850ADe+rTn+wU02sH+m3Vbpz5eTRXFHNtMnlGf2FkjpevTPLE23dQ5ipI+lriyVFnh2bZU+/RxVOlVFYa6G+AV65MAnD/zmraq930TQYzjj0/7KPG46S82NpV2lpZzGBCz3hYOcbvPfbh3Ym21bgZnA4SCkes2vh1HM2nlNraNNBn4Qsu884/eYF/eLU/67gfXZ2iusTJnnoPHVVuxuYWWVhKLTQyxvDqtSnu3VYVv9Zc4WJ4JvmN4fX+GWo9zrTdI7fVuIkaONk3w2xwOd5cTCmlMtFAn8UffOciVycC/NXL15LOXl3t4ugch+1j92KblPqmUhdYeycDjM+FuHfbygJrU7l1stPi8sobw6mBGe5srUjbPTK2Meo7ndY6QLZDuZVSCjTQZ2SM4ZtnRmgsK6J/KsiPrk6lHReJGq5NBuK7V2Mlk32TqYH+pW6r1cNbEmf0dl/34VkrfTPhDzE4vcCdbekraTrsSp1Y64Rd2gteKbUGDfQZeP0hFpYj/PL9HRTkCa/0TKYdNzyzwFI4yg77lKXYjL43zYz+m2dH2FPvSTqRqak8+WzX2OlOh5vTB/rSogKqS5xMzi9RXZLag14ppVbTQJ9BrDpmd72H7TUlXMlQSdMzEetHYwX4Emc+1SXOlBl972SA0wOzfOCO5EaeTfbZrsOrAn22TVCxuvkdtdp4TCm1Nt0qmUGsS2R7lZtddR5O9c+kHdfjtWrgd9Ss5Mo7qotTKm++cXoYEXjsSGPS9TqPk3yHMDxrje8c8dFR7cZTlFxWmehPfuYwT50d5nCWjVJKKRWz5Wb0waUwv/uNznhOPJO+qSAFeUJDWRG76z0Mzy6k7UlzfniO+tIiyopXAnN7lTspdWOM4RtnhnnLtioaypIrafLzHDSWu+K/QXQOz63ZZbKsuIBffEt7StsEpZRKZ8sF+ucvevn7V/v5/X/qyjqubzJAS2Ux+XmOeGXLFW/yDlZjDK9dm+JYR2XS9fZqNxN+q5c8wLkhH/1TQT54R/rzV460lHOqfwZfcJnh2QX266EfSqkbaMsF+u9ftLpLfrdrPJ4PT6d3MkBbpbVQGqts6RlPDvR9U0G8/hD3bEsO9Ksrb84MzgLwwM70fffv7qhkfC7E8/a97daSSaXUDbSlAn0kanihe4K3brfKG18fSJ939/oXuTzu545W63TEhjIXIqSke16zD96+Z/WMviq5lv7S2BzlxQXUlaavkDlmNy77h9esjVk7tWRSKXUDbalA3zcVYDa4zE/d2Ux5cQEXR9NX0nz/ohdj4F37rIZihfkOaj1ORn3Jgf75i14ayorYXpMcmGMHecdm9JfG/Oyp96TdAAWws7aEiuICTg/M4i7MS7sjVimlrteWCvQD9jF8HdXF7Kn3xNv9rvbchXGayl3x/u8AjeUuRmYX45/PLS7zUvcE7z3YkBLA3c58aj1OeieDRKOGy2P+rK0KHA7h0QNWX5umClfGNwSllLoeORHoZwJLfP6lq4Qj0azjYiWTrZVu9jaUcnnMn9LaILgU5pWeSd61ry4p4DaWuRhJSN18/6KXpUiU9x5sIJ32ajd9UwEGZ4IElyLsbcied//AEWuh1r8YzjpOKaXeqJwI9H/x4lX+07cv8aLdYiCT/ukgxYV5VJcUsre+lOBShIHp5Hr3l69MEgpHefe+uqTrjeVFDM8uYIz1xnCyfxqPM587MtSyd1S56ZsMxNNDazUfu7u9kg/e0cRnf/pQ1nFKKfVGbfpAv7AU4csnBgH4btdY1rEDU0FaK4sRkfiCZ8+qkslnu8YpLcrn7lULrI3lLkLhKDNBq5a+c3iOfY2lGY8MbK92MxVY4kTfNCJrNx9zOITP/eyRjJU5Sil1vTZ9oH/+4ji+hWU6qt08f9GbNX3TPx2ML5TG+s1cTTjdaXE5wrNdYzy8r46CvOS/mkZ7gXRkdoFwJMrF0ewbmzqqrZ/zTOcY7VVuPeFJKXXbbPpA/4PLXsqLC/iNd+xgOrBE93jqsXwA0ahhcDpIm136WOYqoMbjTAr0L1z24g+F4/nyRI1lK10mr04ECIWjHGjKnI6JNTcbnl1IWtRVSqlbbVMH+mjU8FL3BA/uqon3fbk4mr6S5tXeKULhaNKu0+017qTUzbMXxql0F8br7BM1lhcB1oz+wqi10Wp/Y7YZvRtPUb79c7QuXil1+2zqQN854mNyfol37K6lo9pNUYGDCxkC/ZdPDFJalM8j+1eO59teU8LViUB8gbV3MsDuOg/5eal/LZXuQpz5DkZ9i/R458l3SHwHbDrO/Dz+xy8do8bj5MHdmndXSt0+m7p7ZUtFMX/wUwd5cFcNeQ5hT30pXSOpbQ3CkSjPXRjnsSNNSQdpb68pwbewzFTA6u0+OL3AO/fUpv1ZIkJTuYvh2QWiUUNrZXFKHn+1u9oqOPGph9/cQyql1Ju0qQN9hbuQnzvWGv98X2MpT58dwRiTVAN/edxPcCmSdIQfWOe1AozOLlJcmMfkfIhWe7E2nYbyIkZmFwiEwmyr0V7wSqnNYVOnblbbUVPC3GI4XgIZc3rAaip2R0tF0vXaUivvPj63yOC0tRmqpTJzoG8sczE0s0DfVFDz7kqpTWNTz+hXiy2YjvoWqHQXxq+fHpiluqSQlsrkHjKxJmNefyh+rTVboC93MWGP1Rm9UmqzyKkZfX3ZSiom0an+aY60lKf0kKkucSJizehjO2SzBfrEZmM6o1dKbRY5Fegby+wZ/dxKoB+YCtI3FeS+HdUp4wvyHFS5C/H6F+mdDOBx5lNRnPkIv9imqYayovi5rUoptdGtGehFpEhEjovIWRHpEpFPr/r6b4uIEZHqhGufFJEeEbksIo/cjBtPp6rEOn91NKH52IvdXgAe3JW+xLHWU4R3LsTZoVkONJVl7Rx5rKOS333/Pp75rbelLcFUSqmNaD3RKgQ8ZIw5DBwBHhWRewFEpAV4FzAQGywi+4DHgf3Ao8Cfi8gt2f+f5xDqSosY9a3M6F/snqC1sjhjzXttqZP+6SAXRuY40pp9ll6Y7+BX7u+gzJV51q+UUhvNmoHeWGLbRwvsP7Hevp8D/k3C5wCPAV8yxoSMMb1AD3Dsxt1ydg1lRfEDQowxnB6Y5VhHZcaZep2niB7vPOGoydiJUimlNrN15R9EJE9EzgBe4DljzGsi8pPAsDHm7KrhTcBgwudD9rVboqHcFZ/RT/hDTAWWsh62XZtwvN9aM3qllNqM1hXojTERY8wRoBk4JiKHgE8Bv5dmeLqps0kZJPIxETkpIicnJrL3kX8jGsus1E00augasdoh7GvIHOgP2h0of/3t26n1FN2w+1BKqY3iDdXRG2NmReQFrPRMB3DWTok0A6+LyDGsGXxLwsuagZE03+vzwOcBjh49mvJGcL2aK4tZCkcZ9y/G+97szTKjf/f+evr+8H036scrpdSGs56qmxoRKbc/dgEPA6eNMbXGmHZjTDtWcL/TGDMGPAU8LiJOEekAdgLHb9oTrNJm18H3T1kLrK2VxZQW6eKpUmrrWs+MvgH4gl054wCeNMY8nWmwMaZLRJ4ELgBh4AljTOSG3O06xA4WGZgKcmF0LmvaRimltoI1A70x5hxwxxpj2ld9/hngM2/qzq5TY7mLPIdwYXSOvqkAH7zjlq0DK6XUhpRzu34K8hw0V7h4tmsMY7IvxCql1FaQc4EerH41I3aJ5b4sC7FKKbUV5GSgTzzir6FMSyaVUltbTrUpjvmX79xJlbuQsuKCrL1rlFJqK8jJQO8qzONX37btdt+GUkptCDmZulFKKbVCA71SSuU4DfRKKc4msOEAAAZjSURBVJXjNNArpVSO00CvlFI5TgO9UkrlOA30SimV4zTQK6VUjhNjbtiZH9d/EyITQP91vrwamLyBt7MZ6DNvDfrMW8f1PnebMaZmrUEbItC/GSJy0hhz9Hbfx62kz7w16DNvHTf7uTV1o5RSOU4DvVJK5bhcCPSfv903cBvoM28N+sxbx0197k2fo1dKKZVdLszolVJKZbEhAr2IFInIcRE5KyJdIvJp+3qliDwnIlfs/1YkvOaTItIjIpdF5JGE63eJyHn7a/9V7JNHRMQpIl+2r78mIu23+jkTZXnmPxKRSyJyTkS+LiLlCa/JyWdO+Ppvi4gRkeqEazn7zCLym/ZzdYnIZxOu5+Qzi8gREXlVRM6IyEkROZbwmk39zDEikicip0XkafvzjRHDjDG3/Q8gQIn9cQHwGnAv8Fngd+zrvwP8Z/vjfcBZwAl0AFeBPPtrx4G32N/zO8B77Ou/Dvx3++PHgS9v0Gd+N5BvX//PW+GZ7c9bgO9i7aeozvVnBt4BPA847a/VboFnfjbhnt8LvJArz5zw7P8K+CLwtP35hohhG2JGbyzz9qcF9h8DPAZ8wb7+BeAD9sePAV8yxoSMMb1AD3BMRBqAUmPMj431t/E/V70m9r3+N/DO2Dvl7ZDpmY0xzxpjwvb1V4Fm++OcfWb7888B/ybhc8jtZ/414A+NMSF7nNcek8vPbIBS+3oZMGJ/vOmfGUBEmoH3AX+dcHlDxLANEegh/ivPGcALPGeMeQ2oM8aMAtj/rbWHNwGDCS8fsq812R+vvp70GjuQ+oCqm/M065PhmRP9MtY7OuTwM4vITwLDxpizq4bn7DMDu4AH7F/BXxSRu+3hufzMvwX8kYgMAn8MfNIenhPPDPx/WJOVaMK1DRHDNkygN8ZEjDFHsGawx0TkQJbh6d7FTJbr2V5z22R7ZhH5FBAG/jF2Kd23yHI922tumzTPfAj4FPB7aYbn6jMfwDqvuQIrpfEJ4El7dpbLz/xrwMeNMS3Ax4G/sYdv+mcWkfcDXmPMqfW+JM21m/bMGybQxxhjZoEXgEeBcftXGez/xn69HcLK6cY0Y/0aOMRKqiPxetJrRCQf61fH6ZvyEG/QqmdGRD4KvB/4efvXN8jdZ34MK0d5VkT6sO7/dRGpJ3ef+VGs+/yaneY4jjULrCa3n/mjwNfsL30FiC3G5sIz3wf8pP1v+EvAQyLyD2yUGPZGFxtuxh+gBii3P3YBL2MFuj8ieSHjs/bH+0leyLjGykLGCaxZUmwh47329SdIXsh4coM+86PABaBm1ficfeZVY/pYWYzN2WcG/jnw+/b1XVi/kkuOP/NF4O329XcCp3Llf+dVz/92VhZjN0QMu+1/KfZNHwJOA+eATuD37OtVwPeAK/Z/KxNe8ymslerL2KvS9vWj9ve4CvwZK5vCirBmET1Yq9rbNugz99j/pz9j//nvuf7Mq8b0YQf6XH5moBD4B/va68BDW+CZ7wdOYQW414C7cuWZVz3/21kJ9BsihunOWKWUynEbLkevlFLqxtJAr5RSOU4DvVJK5TgN9EopleM00CulVI7TQK+2PBH5DyLy27f7PpS6WTTQK6VUjtNAr7YkEfmU3Qf8eWC3fe1XReSE3Uf9qyJSLCIeEekVkQJ7TKmI9IlIgYj8CxG5INbZAV+6rQ+kVBYa6NX/ae/+XSkK4ziOv7825ZaRQfkD/EgyysxNIoO6iKz+CZtdZhKZUP6BW2S0uHYpZRWxGD6G57FIuddw7+2cz2s6nZ5Tz1k+53ROz+cpnYiYJC0hnwCWgO/myHNJU5LGScv1tyS9kbpaqnnMCnAm6ZO0pH1C0hip0sCsKznorYymgQtJH5Jegct8fiQiriOiAdRIfSSQ+sU38/EmcJCP74CTiFglNY2adSUHvZXVb90fh8C2pFFgh9QtgqQbYDgiZkjFU/d5fBXYByaB29woaNZ1HPRWRlfAYkT0RkQFmM/nK8Bz/h5f+3HNEXBKfpuPiB5gSFKdtNlEP9DXjsmbtcqlZlZKeWOXddIetU+kauh3Umg/Ag2gImkjjx8AHoBBSS/5YVAndYIHcCxpt933YdYMB71ZEyJiGViQtNbpuZi1yt8Uzf4QEXvALDDX6bmY/Yff6M3MCs4/Y83MCs5Bb2ZWcA56M7OCc9CbmRWcg97MrOAc9GZmBfcFOiIA18gLTcAAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "CO2_data[30000:40000].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On constate une forme de periodicité des emissions sur une periode d'un an. Observons le comportement sur l'année 2019:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "requeteCO2_data[43480:43845].plot()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonallymonthsdays
734201901434802019.0411410.92410.87410.30410.24410.92410.874348043480
735201902435112019.1260411.66410.90411.25410.48411.66410.904351143511
736201903435392019.2027412.00410.46412.25410.69412.00410.464353943539
737201904435702019.2877413.52410.72413.73410.92413.52410.724357043570
738201905436002019.3699414.83411.42414.54411.14414.83411.424360043600
739201906436312019.4548413.96411.38413.91411.36413.96411.384363143631
740201907436612019.5370411.85411.03412.36411.57411.85411.034366143661
741201908436922019.6219410.08411.62410.22411.79410.08411.624369243692
742201909437232019.7068408.55412.06408.49412.02408.55412.064372343723
743201910437532019.7890408.43412.06408.62412.23408.43412.064375343753
744201911437842019.8740410.29412.56410.21412.46410.29412.564378443784
745201912438142019.9562411.85412.78411.76412.67411.85412.784381443814
\n", "
" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "734 2019 01 43480 2019.0411 410.92 410.87 410.30 \n", "735 2019 02 43511 2019.1260 411.66 410.90 411.25 \n", "736 2019 03 43539 2019.2027 412.00 410.46 412.25 \n", "737 2019 04 43570 2019.2877 413.52 410.72 413.73 \n", "738 2019 05 43600 2019.3699 414.83 411.42 414.54 \n", "739 2019 06 43631 2019.4548 413.96 411.38 413.91 \n", "740 2019 07 43661 2019.5370 411.85 411.03 412.36 \n", "741 2019 08 43692 2019.6219 410.08 411.62 410.22 \n", "742 2019 09 43723 2019.7068 408.55 412.06 408.49 \n", "743 2019 10 43753 2019.7890 408.43 412.06 408.62 \n", "744 2019 11 43784 2019.8740 410.29 412.56 410.21 \n", "745 2019 12 43814 2019.9562 411.85 412.78 411.76 \n", "\n", " seasonally CO2 seasonally months days \n", "734 410.24 410.92 410.87 43480 43480 \n", "735 410.48 411.66 410.90 43511 43511 \n", "736 410.69 412.00 410.46 43539 43539 \n", "737 410.92 413.52 410.72 43570 43570 \n", "738 411.14 414.83 411.42 43600 43600 \n", "739 411.36 413.96 411.38 43631 43631 \n", "740 411.57 411.85 411.03 43661 43661 \n", "741 411.79 410.08 411.62 43692 43692 \n", "742 412.02 408.55 412.06 43723 43723 \n", "743 412.23 408.43 412.06 43753 43753 \n", "744 412.46 410.29 412.56 43784 43784 \n", "745 412.67 411.85 412.78 43814 43814 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data[' Yr'] == '2019']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On constate, en observant les valeurs numériques dans la table, un maximum lors du mois de mai et un minumum lors du mois d'octobre. Observons, de manière plus générale, la distribution des maxima et minima de $CO_2$ sur l'année." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:5: 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", " \"\"\"\n" ] } ], "source": [ "mois_mini = []\n", "mois_maxi = []\n", "for yr in range(1958,2020):\n", " fenetre = data[data[' Yr'] == str(yr)]\n", " fenetre['mois'] = [int(mois) for mois in fenetre[' Mn']]\n", " fenetre_triee = fenetre.astype(float).set_index('mois').sort_index()\n", " mois_mini.append(int(fenetre_triee[' CO2'].idxmin()))\n", " mois_maxi.append(int(fenetre_triee[' CO2'].idxmax()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'histogramme correspondant pour les mois ayant les emissions minimales:" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 0., 1., 0., 0., 0., 1., 0., 0., 33., 27., 0.]),\n", " array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),\n", " )" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(mois_mini, bins=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Et l'histogramme pour les emissions maximales:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 0., 0., 0., 2., 60., 0., 0., 0., 0., 0., 0.]),\n", " array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]),\n", " )" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(mois_maxi, bins=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "D'après ces résultats, les mois de mai et de septembre correspondent aux maximums d'emission en général.\n", "Pour observer l'évolution globale des niveaux de $CO_2$, nous allons nous concentrer sur ces deux mois." ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [], "source": [ "frame_septembre = data[data[' Mn'] == ' 09'] \n", "frame_septembre = frame_septembre.astype(float).set_index(' Yr').sort_index()\n", "frame_mai = data[data[' Mn'] == ' 05'] \n", "frame_mai = frame_mai.astype(float).set_index(' Yr').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici le graphe pour l'évolution du niveau de CO2 à chaque mois de septembre:" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "frame_septembre[' CO2'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Et pour le mois de mai:" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "frame_mai[' CO2'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe dans les deux cas une évolution globale du niveau de $CO_2$ dans l'atmosphère d'environ $100 ppm$ sur la periode considérée." ] } ], "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 }