{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "# Concentration de CO2 dans l'atmosphère depuis 1958" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hideCode": false, "hideOutput": true, "hidePrompt": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Les données sont disponibles du site Web du [l'Institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 et se termine avec une semaine récente." ] }, { "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": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Réseau Sentinelles, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"concentration-co2.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": "markdown", "metadata": {}, "source": [ "Le fichier de données contient 10 colonnes : \n", "1. Les colonnes 1 à 4 indiquent les dates dans plusieurs formats redondants. \n", "2. La colonne 5 ci-dessous indique les concentrations mensuelles de CO2 du Mauna Loa en micromoles de CO2 par mole (ppm), rapportées sur l'échelle de fraction molaire manométrique SIO 2008A. Il s'agit de la version standard des données les plus recherchées. Les valeurs mensuelles ont été ajustées à minuit le 15 de chaque mois. \n", "3. La colonne 6 donne les mêmes données après un ajustement saisonnier pour supprimer le cycle saisonnier quasi régulier. L'ajustement consiste à soustraire des données un ajustement à 4 harmoniques avec un facteur de gain linéaire. \n", "4. La colonne 7 est une version lissée des données générées à partir d'une fonction spline cubique rigide et de fonctions 4 harmoniques avec gain linéaire. \n", "5. La colonne 8 est la même version lissée avec le cycle saisonnier supprimé. \n", "6. La colonne 9 est identique à la colonne 5, sauf que les valeurs manquantes de la colonne 5 ont été remplies avec des valeurs de la colonne 7. \n", "7. La colonne 10 est identique à la colonne 6, sauf que les valeurs manquantes ont été remplies avec des valeurs de la colonne 8. \n", "\n", "Les valeurs manquantes sont indiquées par -99,99 \n", "\n", "Les 54 premières lignes du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=54`." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "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
4195803212591958.2027315.70314.44316.19314.91315.70314.44
5195804212901958.2877317.45315.16317.30314.99317.45315.16
6195805213201958.3699317.51314.71317.86315.06317.51314.71
7195806213511958.4548-99.99-99.99317.24315.14317.24315.14
8195807213811958.5370315.86315.19315.86315.22315.86315.19
9195808214121958.6219314.93316.19314.00315.29314.93316.19
10195809214431958.7068313.21316.08312.46315.35313.21316.08
11195810214731958.7890-99.99-99.99312.44315.40312.44315.40
12195811215041958.8740313.33315.20313.62315.46313.33315.20
13195812215341958.9562314.67315.43314.77315.51314.67315.43
14195901215651959.0411315.58315.54315.62315.57315.58315.54
15195902215961959.1260316.49315.86316.27315.63316.49315.86
16195903216241959.2027316.65315.38316.98315.69316.65315.38
17195904216551959.2877317.72315.42318.09315.77317.72315.42
18195905216851959.3699318.29315.49318.65315.85318.29315.49
19195906217161959.4548318.15316.03318.04315.94318.15316.03
20195907217461959.5370316.54315.86316.67316.03316.54315.86
21195908217771959.6219314.80316.06314.83316.12314.80316.06
22195909218081959.7068313.84316.72313.32316.22313.84316.72
23195910218381959.7890313.33316.32313.33316.30313.33316.32
24195911218691959.8740314.81316.68314.54316.39314.81316.68
25195912218991959.9562315.58316.35315.72316.47315.58316.35
26196001219301960.0410316.43316.39316.61316.56316.43316.39
27196002219611960.1257316.98316.35317.27316.64316.98316.35
28196003219901960.2049317.58316.28318.03316.71317.58316.28
29196004220211960.2896319.03316.70319.14316.79319.03316.70
.................................
728201807432962018.5370408.90408.08409.43408.65408.90408.08
729201808433272018.6219407.10408.63407.33408.91407.10408.63
730201809433582018.7068405.59409.09405.66409.18405.59409.09
731201810433882018.7890405.99409.62405.83409.44405.99409.62
732201811434192018.8740408.12410.39407.47409.72408.12410.39
733201812434492018.9562409.23410.16409.07409.97409.23410.16
734201901434802019.0411410.92410.87410.29410.23410.92410.87
735201902435112019.1260411.66410.90411.24410.47411.66410.90
736201903435392019.2027412.00410.45412.25410.68412.00410.45
737201904435702019.2877413.52410.72413.73410.91413.52410.72
738201905436002019.3699414.83411.42414.54411.13414.83411.42
739201906436312019.4548413.96411.38413.91411.35413.96411.38
740201907436612019.5370411.85411.03412.36411.57411.85411.03
741201908436922019.6219410.08411.62410.23411.81410.08411.62
742201909437232019.7068408.55412.06408.52412.05408.55412.06
743201910437532019.7890408.43412.07408.67412.29408.43412.07
744201911437842019.8740410.28412.56410.29412.54410.28412.56
745201912438142019.9562411.85412.78411.88412.79411.85412.78
746202001438452020.0410413.37413.33413.11413.05413.37413.33
747202002438762020.1257-99.99-99.99-99.99-99.99-99.99-99.99
748202003439052020.2049-99.99-99.99-99.99-99.99-99.99-99.99
749202004439362020.2896-99.99-99.99-99.99-99.99-99.99-99.99
750202005439662020.3716-99.99-99.99-99.99-99.99-99.99-99.99
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
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758 rows × 10 columns

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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 314.80 316.06 314.83 \n", "22 1959 09 21808 1959.7068 313.84 316.72 313.32 \n", "23 1959 10 21838 1959.7890 313.33 316.32 313.33 \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 317.58 316.28 318.03 \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 405.59 409.09 405.66 \n", "731 2018 10 43388 2018.7890 405.99 409.62 405.83 \n", "732 2018 11 43419 2018.8740 408.12 410.39 407.47 \n", "733 2018 12 43449 2018.9562 409.23 410.16 409.07 \n", "734 2019 01 43480 2019.0411 410.92 410.87 410.29 \n", "735 2019 02 43511 2019.1260 411.66 410.90 411.24 \n", "736 2019 03 43539 2019.2027 412.00 410.45 412.25 \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 410.08 411.62 410.23 \n", "742 2019 09 43723 2019.7068 408.55 412.06 408.52 \n", "743 2019 10 43753 2019.7890 408.43 412.07 408.67 \n", "744 2019 11 43784 2019.8740 410.28 412.56 410.29 \n", "745 2019 12 43814 2019.9562 411.85 412.78 411.88 \n", "746 2020 01 43845 2020.0410 413.37 413.33 413.11 \n", "747 2020 02 43876 2020.1257 -99.99 -99.99 -99.99 \n", "748 2020 03 43905 2020.2049 -99.99 -99.99 -99.99 \n", "749 2020 04 43936 2020.2896 -99.99 -99.99 -99.99 \n", "750 2020 05 43966 2020.3716 -99.99 -99.99 -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", "4 314.91 315.70 314.44 \n", "5 314.99 317.45 315.16 \n", "6 315.06 317.51 314.71 \n", "7 315.14 317.24 315.14 \n", "8 315.22 315.86 315.19 \n", "9 315.29 314.93 316.19 \n", "10 315.35 313.21 316.08 \n", "11 315.40 312.44 315.40 \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", "17 315.77 317.72 315.42 \n", "18 315.85 318.29 315.49 \n", "19 315.94 318.15 316.03 \n", "20 316.03 316.54 315.86 \n", "21 316.12 314.80 316.06 \n", "22 316.22 313.84 316.72 \n", "23 316.30 313.33 316.32 \n", "24 316.39 314.81 316.68 \n", "25 316.47 315.58 316.35 \n", "26 316.56 316.43 316.39 \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", "729 408.91 407.10 408.63 \n", "730 409.18 405.59 409.09 \n", "731 409.44 405.99 409.62 \n", "732 409.72 408.12 410.39 \n", "733 409.97 409.23 410.16 \n", "734 410.23 410.92 410.87 \n", "735 410.47 411.66 410.90 \n", "736 410.68 412.00 410.45 \n", "737 410.91 413.52 410.72 \n", "738 411.13 414.83 411.42 \n", "739 411.35 413.96 411.38 \n", "740 411.57 411.85 411.03 \n", "741 411.81 410.08 411.62 \n", "742 412.05 408.55 412.06 \n", "743 412.29 408.43 412.07 \n", "744 412.54 410.28 412.56 \n", "745 412.79 411.85 412.78 \n", "746 413.05 413.37 413.33 \n", "747 -99.99 -99.99 -99.99 \n", "748 -99.99 -99.99 -99.99 \n", "749 -99.99 -99.99 -99.99 \n", "750 -99.99 -99.99 -99.99 \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": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=54)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérifions si nous avons des points manquants dans ce jeu de données." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonally
0adjustedadjusted fitfilledadjusted filled
1Excel[ppm][ppm][ppm][ppm][ppm][ppm]
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5195804212901958.2877317.45315.16317.30314.99317.45315.16
6195805213201958.3699317.51314.71317.86315.06317.51314.71
7195806213511958.4548-99.99-99.99317.24315.14317.24315.14
8195807213811958.5370315.86315.19315.86315.22315.86315.19
9195808214121958.6219314.93316.19314.00315.29314.93316.19
10195809214431958.7068313.21316.08312.46315.35313.21316.08
11195810214731958.7890-99.99-99.99312.44315.40312.44315.40
12195811215041958.8740313.33315.20313.62315.46313.33315.20
13195812215341958.9562314.67315.43314.77315.51314.67315.43
14195901215651959.0411315.58315.54315.62315.57315.58315.54
15195902215961959.1260316.49315.86316.27315.63316.49315.86
16195903216241959.2027316.65315.38316.98315.69316.65315.38
17195904216551959.2877317.72315.42318.09315.77317.72315.42
18195905216851959.3699318.29315.49318.65315.85318.29315.49
19195906217161959.4548318.15316.03318.04315.94318.15316.03
20195907217461959.5370316.54315.86316.67316.03316.54315.86
21195908217771959.6219314.80316.06314.83316.12314.80316.06
22195909218081959.7068313.84316.72313.32316.22313.84316.72
23195910218381959.7890313.33316.32313.33316.30313.33316.32
24195911218691959.8740314.81316.68314.54316.39314.81316.68
25195912218991959.9562315.58316.35315.72316.47315.58316.35
26196001219301960.0410316.43316.39316.61316.56316.43316.39
27196002219611960.1257316.98316.35317.27316.64316.98316.35
28196003219901960.2049317.58316.28318.03316.71317.58316.28
29196004220211960.2896319.03316.70319.14316.79319.03316.70
.................................
728201807432962018.5370408.90408.08409.43408.65408.90408.08
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730201809433582018.7068405.59409.09405.66409.18405.59409.09
731201810433882018.7890405.99409.62405.83409.44405.99409.62
732201811434192018.8740408.12410.39407.47409.72408.12410.39
733201812434492018.9562409.23410.16409.07409.97409.23410.16
734201901434802019.0411410.92410.87410.29410.23410.92410.87
735201902435112019.1260411.66410.90411.24410.47411.66410.90
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737201904435702019.2877413.52410.72413.73410.91413.52410.72
738201905436002019.3699414.83411.42414.54411.13414.83411.42
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740201907436612019.5370411.85411.03412.36411.57411.85411.03
741201908436922019.6219410.08411.62410.23411.81410.08411.62
742201909437232019.7068408.55412.06408.52412.05408.55412.06
743201910437532019.7890408.43412.07408.67412.29408.43412.07
744201911437842019.8740410.28412.56410.29412.54410.28412.56
745201912438142019.9562411.85412.78411.88412.79411.85412.78
746202001438452020.0410413.37413.33413.11413.05413.37413.33
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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 315.70 314.44 316.19 \n", "5 1958 04 21290 1958.2877 317.45 315.16 317.30 \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 314.93 316.19 314.00 \n", "10 1958 09 21443 1958.7068 313.21 316.08 312.46 \n", "11 1958 10 21473 1958.7890 -99.99 -99.99 312.44 \n", "12 1958 11 21504 1958.8740 313.33 315.20 313.62 \n", "13 1958 12 21534 1958.9562 314.67 315.43 314.77 \n", "14 1959 01 21565 1959.0411 315.58 315.54 315.62 \n", "15 1959 02 21596 1959.1260 316.49 315.86 316.27 \n", "16 1959 03 21624 1959.2027 316.65 315.38 316.98 \n", "17 1959 04 21655 1959.2877 317.72 315.42 318.09 \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 314.80 316.06 314.83 \n", "22 1959 09 21808 1959.7068 313.84 316.72 313.32 \n", "23 1959 10 21838 1959.7890 313.33 316.32 313.33 \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 317.58 316.28 318.03 \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 405.59 409.09 405.66 \n", "731 2018 10 43388 2018.7890 405.99 409.62 405.83 \n", "732 2018 11 43419 2018.8740 408.12 410.39 407.47 \n", "733 2018 12 43449 2018.9562 409.23 410.16 409.07 \n", "734 2019 01 43480 2019.0411 410.92 410.87 410.29 \n", "735 2019 02 43511 2019.1260 411.66 410.90 411.24 \n", "736 2019 03 43539 2019.2027 412.00 410.45 412.25 \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 410.08 411.62 410.23 \n", "742 2019 09 43723 2019.7068 408.55 412.06 408.52 \n", "743 2019 10 43753 2019.7890 408.43 412.07 408.67 \n", "744 2019 11 43784 2019.8740 410.28 412.56 410.29 \n", "745 2019 12 43814 2019.9562 411.85 412.78 411.88 \n", "746 2020 01 43845 2020.0410 413.37 413.33 413.11 \n", "747 2020 02 43876 2020.1257 -99.99 -99.99 -99.99 \n", "748 2020 03 43905 2020.2049 -99.99 -99.99 -99.99 \n", "749 2020 04 43936 2020.2896 -99.99 -99.99 -99.99 \n", "750 2020 05 43966 2020.3716 -99.99 -99.99 -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", "4 314.91 315.70 314.44 \n", "5 314.99 317.45 315.16 \n", "6 315.06 317.51 314.71 \n", "7 315.14 317.24 315.14 \n", "8 315.22 315.86 315.19 \n", "9 315.29 314.93 316.19 \n", "10 315.35 313.21 316.08 \n", "11 315.40 312.44 315.40 \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", "17 315.77 317.72 315.42 \n", "18 315.85 318.29 315.49 \n", "19 315.94 318.15 316.03 \n", "20 316.03 316.54 315.86 \n", "21 316.12 314.80 316.06 \n", "22 316.22 313.84 316.72 \n", "23 316.30 313.33 316.32 \n", "24 316.39 314.81 316.68 \n", "25 316.47 315.58 316.35 \n", "26 316.56 316.43 316.39 \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", "729 408.91 407.10 408.63 \n", "730 409.18 405.59 409.09 \n", "731 409.44 405.99 409.62 \n", "732 409.72 408.12 410.39 \n", "733 409.97 409.23 410.16 \n", "734 410.23 410.92 410.87 \n", "735 410.47 411.66 410.90 \n", "736 410.68 412.00 410.45 \n", "737 410.91 413.52 410.72 \n", "738 411.13 414.83 411.42 \n", "739 411.35 413.96 411.38 \n", "740 411.57 411.85 411.03 \n", "741 411.81 410.08 411.62 \n", "742 412.05 408.55 412.06 \n", "743 412.29 408.43 412.07 \n", "744 412.54 410.28 412.56 \n", "745 412.79 411.85 412.78 \n", "746 413.05 413.37 413.33 \n", "747 -99.99 -99.99 -99.99 \n", "748 -99.99 -99.99 -99.99 \n", "749 -99.99 -99.99 -99.99 \n", "750 -99.99 -99.99 -99.99 \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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'sorted_data' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msorted_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'inc'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'sorted_data' is not defined" ] } ], "source": [ "sorted_data['inc'].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "hide_code_all_hidden": false, "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 }