{ "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": 2, "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": 4, "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": 41, "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": 41, "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": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index([' Yr', ' Mn', ' Date', ' Date', ' CO2', 'seasonally',\n", " ' fit', ' seasonally', ' CO2', ' seasonally'],\n", " dtype='object')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.keys()\n" ] }, { "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": 43, "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": 43, "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": 44, "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": 44, "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": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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 }