{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Concentration de CO2 dans l'atmosphère depuis 1958" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "raw_data = pd.read_csv(\"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\", skiprows = 54, sep=r'\\s*,\\s*', engine='python')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données ont été extraites le 11/05/2020. \n", "Les 54 premières lignes correspondent à du texte contenant les références à citer, des explications sur la forme des données ... On les supprime donc pour permettre à Pandas de lire les données sous forme de tableau. " ] }, { "cell_type": "code", "execution_count": 3, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YrMnDateDate.1CO2seasonallyfitseasonally.1CO2.1seasonally.2
0NaNNaNNaNNaNNaNadjustedNaNadjusted fitfilledadjusted filled
1NaNNaNExcelNaN[ppm][ppm][ppm][ppm][ppm][ppm]
21958.01.0212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99
31958.02.0212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99
41958.03.0212591958.2027315.70314.44316.18314.90315.70314.44
51958.04.0212901958.2877317.46315.16317.29314.98317.46315.16
61958.05.0213201958.3699317.51314.71317.86315.06317.51314.71
71958.06.0213511958.4548-99.99-99.99317.24315.14317.24315.14
81958.07.0213811958.5370315.86315.19315.86315.21315.86315.19
91958.08.0214121958.6219314.93316.19313.99315.28314.93316.19
101958.09.0214431958.7068313.21316.08312.45315.35313.21316.08
111958.010.0214731958.7890-99.99-99.99312.43315.40312.43315.40
121958.011.0215041958.8740313.33315.20313.61315.46313.33315.20
131958.012.0215341958.9562314.67315.43314.76315.51314.67315.43
141959.01.0215651959.0411315.58315.54315.62315.57315.58315.54
151959.02.0215961959.1260316.49315.86316.27315.63316.49315.86
161959.03.0216241959.2027316.65315.38316.98315.69316.65315.38
171959.04.0216551959.2877317.72315.42318.09315.77317.72315.42
181959.05.0216851959.3699318.29315.49318.65315.85318.29315.49
191959.06.0217161959.4548318.15316.03318.04315.94318.15316.03
201959.07.0217461959.5370316.54315.86316.67316.03316.54315.86
211959.08.0217771959.6219314.80316.06314.82316.12314.80316.06
221959.09.0218081959.7068313.84316.73313.31316.22313.84316.73
231959.010.0218381959.7890313.33316.33313.32316.30313.33316.33
241959.011.0218691959.8740314.81316.68314.54316.39314.81316.68
251959.012.0218991959.9562315.58316.35315.72316.47315.58316.35
261960.01.0219301960.0410316.43316.39316.61316.56316.43316.39
271960.02.0219611960.1257316.98316.35317.27316.64316.98316.35
281960.03.0219901960.2049317.58316.28318.03316.71317.58316.28
291960.04.0220211960.2896319.03316.70319.14316.79319.03316.70
.................................
7282018.07.0432962018.5370408.90408.08409.44408.65408.90408.08
7292018.08.0433272018.6219407.10408.63407.34408.91407.10408.63
7302018.09.0433582018.7068405.59409.08405.67409.19405.59409.08
7312018.010.0433882018.7890405.99409.61405.85409.45405.99409.61
7322018.011.0434192018.8740408.12410.38407.49409.73408.12410.38
7332018.012.0434492018.9562409.23410.15409.08409.99409.23410.15
7342019.01.0434802019.0411410.92410.87410.31410.25410.92410.87
7352019.02.0435112019.1260411.66410.90411.26410.49411.66410.90
7362019.03.0435392019.2027412.00410.46412.26410.70412.00410.46
7372019.04.0435702019.2877413.52410.72413.75410.93413.52410.72
7382019.05.0436002019.3699414.83411.42414.55411.15414.83411.42
7392019.06.0436312019.4548413.96411.38413.92411.37413.96411.38
7402019.07.0436612019.5370411.85411.03412.37411.58411.85411.03
7412019.08.0436922019.6219410.08411.62410.23411.80410.08411.62
7422019.09.0437232019.7068408.55412.06408.50412.03408.55412.06
7432019.010.0437532019.7890408.43412.06408.63412.24408.43412.06
7442019.011.0437842019.8740410.29412.56410.22412.47410.29412.56
7452019.012.0438142019.9562411.85412.78411.77412.68411.85412.78
7462020.01.0438452020.0410413.37413.32412.96412.89413.37413.32
7472020.02.0438762020.1257414.09413.33413.87413.10414.09413.33
7482020.03.0439052020.2049414.51412.94414.88413.29414.51412.94
7492020.04.0439362020.2896416.18413.35-99.99-99.99416.18413.35
7502020.05.0439662020.3716-99.99-99.99-99.99-99.99-99.99-99.99
7512020.06.0439972020.4563-99.99-99.99-99.99-99.99-99.99-99.99
7522020.07.0440272020.5383-99.99-99.99-99.99-99.99-99.99-99.99
7532020.08.0440582020.6230-99.99-99.99-99.99-99.99-99.99-99.99
7542020.09.0440892020.7077-99.99-99.99-99.99-99.99-99.99-99.99
7552020.010.0441192020.7896-99.99-99.99-99.99-99.99-99.99-99.99
7562020.011.0441502020.8743-99.99-99.99-99.99-99.99-99.99-99.99
7572020.012.0441802020.9563-99.99-99.99-99.99-99.99-99.99-99.99
\n", "

758 rows × 10 columns

\n", "
" ], "text/plain": [ " Yr Mn Date Date.1 CO2 seasonally fit seasonally.1 \\\n", "0 NaN NaN NaN NaN NaN adjusted NaN adjusted fit \n", "1 NaN NaN Excel NaN [ppm] [ppm] [ppm] [ppm] \n", "2 1958.0 1.0 21200 1958.0411 -99.99 -99.99 -99.99 -99.99 \n", "3 1958.0 2.0 21231 1958.1260 -99.99 -99.99 -99.99 -99.99 \n", "4 1958.0 3.0 21259 1958.2027 315.70 314.44 316.18 314.90 \n", "5 1958.0 4.0 21290 1958.2877 317.46 315.16 317.29 314.98 \n", "6 1958.0 5.0 21320 1958.3699 317.51 314.71 317.86 315.06 \n", "7 1958.0 6.0 21351 1958.4548 -99.99 -99.99 317.24 315.14 \n", "8 1958.0 7.0 21381 1958.5370 315.86 315.19 315.86 315.21 \n", "9 1958.0 8.0 21412 1958.6219 314.93 316.19 313.99 315.28 \n", "10 1958.0 9.0 21443 1958.7068 313.21 316.08 312.45 315.35 \n", "11 1958.0 10.0 21473 1958.7890 -99.99 -99.99 312.43 315.40 \n", "12 1958.0 11.0 21504 1958.8740 313.33 315.20 313.61 315.46 \n", "13 1958.0 12.0 21534 1958.9562 314.67 315.43 314.76 315.51 \n", "14 1959.0 1.0 21565 1959.0411 315.58 315.54 315.62 315.57 \n", "15 1959.0 2.0 21596 1959.1260 316.49 315.86 316.27 315.63 \n", "16 1959.0 3.0 21624 1959.2027 316.65 315.38 316.98 315.69 \n", "17 1959.0 4.0 21655 1959.2877 317.72 315.42 318.09 315.77 \n", "18 1959.0 5.0 21685 1959.3699 318.29 315.49 318.65 315.85 \n", "19 1959.0 6.0 21716 1959.4548 318.15 316.03 318.04 315.94 \n", "20 1959.0 7.0 21746 1959.5370 316.54 315.86 316.67 316.03 \n", "21 1959.0 8.0 21777 1959.6219 314.80 316.06 314.82 316.12 \n", "22 1959.0 9.0 21808 1959.7068 313.84 316.73 313.31 316.22 \n", "23 1959.0 10.0 21838 1959.7890 313.33 316.33 313.32 316.30 \n", "24 1959.0 11.0 21869 1959.8740 314.81 316.68 314.54 316.39 \n", "25 1959.0 12.0 21899 1959.9562 315.58 316.35 315.72 316.47 \n", "26 1960.0 1.0 21930 1960.0410 316.43 316.39 316.61 316.56 \n", "27 1960.0 2.0 21961 1960.1257 316.98 316.35 317.27 316.64 \n", "28 1960.0 3.0 21990 1960.2049 317.58 316.28 318.03 316.71 \n", "29 1960.0 4.0 22021 1960.2896 319.03 316.70 319.14 316.79 \n", ".. ... ... ... ... ... ... ... ... \n", "728 2018.0 7.0 43296 2018.5370 408.90 408.08 409.44 408.65 \n", "729 2018.0 8.0 43327 2018.6219 407.10 408.63 407.34 408.91 \n", "730 2018.0 9.0 43358 2018.7068 405.59 409.08 405.67 409.19 \n", "731 2018.0 10.0 43388 2018.7890 405.99 409.61 405.85 409.45 \n", "732 2018.0 11.0 43419 2018.8740 408.12 410.38 407.49 409.73 \n", "733 2018.0 12.0 43449 2018.9562 409.23 410.15 409.08 409.99 \n", "734 2019.0 1.0 43480 2019.0411 410.92 410.87 410.31 410.25 \n", "735 2019.0 2.0 43511 2019.1260 411.66 410.90 411.26 410.49 \n", "736 2019.0 3.0 43539 2019.2027 412.00 410.46 412.26 410.70 \n", "737 2019.0 4.0 43570 2019.2877 413.52 410.72 413.75 410.93 \n", "738 2019.0 5.0 43600 2019.3699 414.83 411.42 414.55 411.15 \n", "739 2019.0 6.0 43631 2019.4548 413.96 411.38 413.92 411.37 \n", "740 2019.0 7.0 43661 2019.5370 411.85 411.03 412.37 411.58 \n", "741 2019.0 8.0 43692 2019.6219 410.08 411.62 410.23 411.80 \n", "742 2019.0 9.0 43723 2019.7068 408.55 412.06 408.50 412.03 \n", "743 2019.0 10.0 43753 2019.7890 408.43 412.06 408.63 412.24 \n", "744 2019.0 11.0 43784 2019.8740 410.29 412.56 410.22 412.47 \n", "745 2019.0 12.0 43814 2019.9562 411.85 412.78 411.77 412.68 \n", "746 2020.0 1.0 43845 2020.0410 413.37 413.32 412.96 412.89 \n", "747 2020.0 2.0 43876 2020.1257 414.09 413.33 413.87 413.10 \n", "748 2020.0 3.0 43905 2020.2049 414.51 412.94 414.88 413.29 \n", "749 2020.0 4.0 43936 2020.2896 416.18 413.35 -99.99 -99.99 \n", "750 2020.0 5.0 43966 2020.3716 -99.99 -99.99 -99.99 -99.99 \n", "751 2020.0 6.0 43997 2020.4563 -99.99 -99.99 -99.99 -99.99 \n", "752 2020.0 7.0 44027 2020.5383 -99.99 -99.99 -99.99 -99.99 \n", "753 2020.0 8.0 44058 2020.6230 -99.99 -99.99 -99.99 -99.99 \n", "754 2020.0 9.0 44089 2020.7077 -99.99 -99.99 -99.99 -99.99 \n", "755 2020.0 10.0 44119 2020.7896 -99.99 -99.99 -99.99 -99.99 \n", "756 2020.0 11.0 44150 2020.8743 -99.99 -99.99 -99.99 -99.99 \n", "757 2020.0 12.0 44180 2020.9563 -99.99 -99.99 -99.99 -99.99 \n", "\n", " CO2.1 seasonally.2 \n", "0 filled adjusted filled \n", "1 [ppm] [ppm] \n", "2 -99.99 -99.99 \n", "3 -99.99 -99.99 \n", "4 315.70 314.44 \n", "5 317.46 315.16 \n", "6 317.51 314.71 \n", "7 317.24 315.14 \n", "8 315.86 315.19 \n", "9 314.93 316.19 \n", "10 313.21 316.08 \n", "11 312.43 315.40 \n", "12 313.33 315.20 \n", "13 314.67 315.43 \n", "14 315.58 315.54 \n", "15 316.49 315.86 \n", "16 316.65 315.38 \n", "17 317.72 315.42 \n", "18 318.29 315.49 \n", "19 318.15 316.03 \n", "20 316.54 315.86 \n", "21 314.80 316.06 \n", "22 313.84 316.73 \n", "23 313.33 316.33 \n", "24 314.81 316.68 \n", "25 315.58 316.35 \n", "26 316.43 316.39 \n", "27 316.98 316.35 \n", "28 317.58 316.28 \n", "29 319.03 316.70 \n", ".. ... ... \n", "728 408.90 408.08 \n", "729 407.10 408.63 \n", "730 405.59 409.08 \n", "731 405.99 409.61 \n", "732 408.12 410.38 \n", "733 409.23 410.15 \n", "734 410.92 410.87 \n", "735 411.66 410.90 \n", "736 412.00 410.46 \n", "737 413.52 410.72 \n", "738 414.83 411.42 \n", "739 413.96 411.38 \n", "740 411.85 411.03 \n", "741 410.08 411.62 \n", "742 408.55 412.06 \n", "743 408.43 412.06 \n", "744 410.29 412.56 \n", "745 411.85 412.78 \n", "746 413.37 413.32 \n", "747 414.09 413.33 \n", "748 414.51 412.94 \n", "749 416.18 413.35 \n", "750 -99.99 -99.99 \n", "751 -99.99 -99.99 \n", "752 -99.99 -99.99 \n", "753 -99.99 -99.99 \n", "754 -99.99 -99.99 \n", "755 -99.99 -99.99 \n", "756 -99.99 -99.99 \n", "757 -99.99 -99.99 \n", "\n", "[758 rows x 10 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les deux premières lignes contiennent des unités et non des valeurs, on les retire du tableau pour l'instant." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "data = raw_data.iloc[2:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour ce jeu de données, les 4 premières colonnes sont des dates, et seule la colonne 5 contient des mesures brutes. Nous allons conserver uniquement les informations sur l'année, le mois, et la valeur brute de la mesure." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "useful_data = data.iloc[0:len(data.index), [0,1,4]]\n", "#useful_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vérifie que les données ont un type approprié." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 1958.0\n", " 2.0\n", " -99.99\n" ] } ], "source": [ "print(type(useful_data['Yr'][3]), useful_data['Yr'][3])\n", "print(type(useful_data['Mn'][3]), useful_data['Mn'][3])\n", "print(type(useful_data['CO2'][3]), useful_data['CO2'][3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On voit que la troisième colonne n'est pas bien interprétée, peut être à cause du signe '-'. On essaye de convertir les données." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "useful_data['CO2'] = useful_data['CO2'].astype(float)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les explications jointes au fichier indiquent que les valeurs manquantes sont remplacées par la valeur -99.99. On souhaite donc supprimer chaque ligne comportant cette valeur." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 3, 7, 11, 75, 76, 77, 750, 751, 752, 753, 754, 755, 756, 757]\n" ] } ], "source": [ "liste = []\n", "for i in range(len(useful_data.index)):\n", " try:\n", " if(useful_data['CO2'][useful_data.index[i]] == -99.99):\n", " liste.append(useful_data.index[i])\n", " except:\n", " print(i, ' ', end='')\n", "print(liste)\n", "useful_data.drop(liste, inplace=True)" ] }, { "cell_type": "code", "execution_count": 9, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
YrMnCO2
41958.03.0315.70
51958.04.0317.46
61958.05.0317.51
81958.07.0315.86
91958.08.0314.93
101958.09.0313.21
121958.011.0313.33
131958.012.0314.67
141959.01.0315.58
151959.02.0316.49
161959.03.0316.65
171959.04.0317.72
181959.05.0318.29
191959.06.0318.15
201959.07.0316.54
211959.08.0314.80
221959.09.0313.84
231959.010.0313.33
241959.011.0314.81
251959.012.0315.58
261960.01.0316.43
271960.02.0316.98
281960.03.0317.58
291960.04.0319.03
301960.05.0320.04
311960.06.0319.58
321960.07.0318.18
331960.08.0315.90
341960.09.0314.17
351960.010.0313.83
............
7202017.011.0405.17
7212017.012.0406.75
7222018.01.0408.05
7232018.02.0408.34
7242018.03.0409.25
7252018.04.0410.30
7262018.05.0411.30
7272018.06.0410.88
7282018.07.0408.90
7292018.08.0407.10
7302018.09.0405.59
7312018.010.0405.99
7322018.011.0408.12
7332018.012.0409.23
7342019.01.0410.92
7352019.02.0411.66
7362019.03.0412.00
7372019.04.0413.52
7382019.05.0414.83
7392019.06.0413.96
7402019.07.0411.85
7412019.08.0410.08
7422019.09.0408.55
7432019.010.0408.43
7442019.011.0410.29
7452019.012.0411.85
7462020.01.0413.37
7472020.02.0414.09
7482020.03.0414.51
7492020.04.0416.18
\n", "

741 rows × 3 columns

\n", "
" ], "text/plain": [ " Yr Mn CO2\n", "4 1958.0 3.0 315.70\n", "5 1958.0 4.0 317.46\n", "6 1958.0 5.0 317.51\n", "8 1958.0 7.0 315.86\n", "9 1958.0 8.0 314.93\n", "10 1958.0 9.0 313.21\n", "12 1958.0 11.0 313.33\n", "13 1958.0 12.0 314.67\n", "14 1959.0 1.0 315.58\n", "15 1959.0 2.0 316.49\n", "16 1959.0 3.0 316.65\n", "17 1959.0 4.0 317.72\n", "18 1959.0 5.0 318.29\n", "19 1959.0 6.0 318.15\n", "20 1959.0 7.0 316.54\n", "21 1959.0 8.0 314.80\n", "22 1959.0 9.0 313.84\n", "23 1959.0 10.0 313.33\n", "24 1959.0 11.0 314.81\n", "25 1959.0 12.0 315.58\n", "26 1960.0 1.0 316.43\n", "27 1960.0 2.0 316.98\n", "28 1960.0 3.0 317.58\n", "29 1960.0 4.0 319.03\n", "30 1960.0 5.0 320.04\n", "31 1960.0 6.0 319.58\n", "32 1960.0 7.0 318.18\n", "33 1960.0 8.0 315.90\n", "34 1960.0 9.0 314.17\n", "35 1960.0 10.0 313.83\n", ".. ... ... ...\n", "720 2017.0 11.0 405.17\n", "721 2017.0 12.0 406.75\n", "722 2018.0 1.0 408.05\n", "723 2018.0 2.0 408.34\n", "724 2018.0 3.0 409.25\n", "725 2018.0 4.0 410.30\n", "726 2018.0 5.0 411.30\n", "727 2018.0 6.0 410.88\n", "728 2018.0 7.0 408.90\n", "729 2018.0 8.0 407.10\n", "730 2018.0 9.0 405.59\n", "731 2018.0 10.0 405.99\n", "732 2018.0 11.0 408.12\n", "733 2018.0 12.0 409.23\n", "734 2019.0 1.0 410.92\n", "735 2019.0 2.0 411.66\n", "736 2019.0 3.0 412.00\n", "737 2019.0 4.0 413.52\n", "738 2019.0 5.0 414.83\n", "739 2019.0 6.0 413.96\n", "740 2019.0 7.0 411.85\n", "741 2019.0 8.0 410.08\n", "742 2019.0 9.0 408.55\n", "743 2019.0 10.0 408.43\n", "744 2019.0 11.0 410.29\n", "745 2019.0 12.0 411.85\n", "746 2020.0 1.0 413.37\n", "747 2020.0 2.0 414.09\n", "748 2020.0 3.0 414.51\n", "749 2020.0 4.0 416.18\n", "\n", "[741 rows x 3 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "useful_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On souhaite maintenant convertir l'année et le mois en un format plus adapté à Pandas, et à l'utiliser comme index. Un méthode possible est présentée ici, en rassemblant les deux informations puis en appliquant une fonction pour une mise au format Pandas." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "useful_data['period'] = useful_data['Yr']*100 + useful_data['Mn']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "useful_data['period'] = useful_data['period'].astype(int)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "useful_data = useful_data.iloc[0:len(useful_data.index), [2,3]]" ] }, { "cell_type": "code", "execution_count": 13, "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", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
CO2
period
1958-03315.70
1958-04317.46
1958-05317.51
1958-07315.86
1958-08314.93
1958-09313.21
1958-11313.33
1958-12314.67
1959-01315.58
1959-02316.49
1959-03316.65
1959-04317.72
1959-05318.29
1959-06318.15
1959-07316.54
1959-08314.80
1959-09313.84
1959-10313.33
1959-11314.81
1959-12315.58
1960-01316.43
1960-02316.98
1960-03317.58
1960-04319.03
1960-05320.04
1960-06319.58
1960-07318.18
1960-08315.90
1960-09314.17
1960-10313.83
......
2017-11405.17
2017-12406.75
2018-01408.05
2018-02408.34
2018-03409.25
2018-04410.30
2018-05411.30
2018-06410.88
2018-07408.90
2018-08407.10
2018-09405.59
2018-10405.99
2018-11408.12
2018-12409.23
2019-01410.92
2019-02411.66
2019-03412.00
2019-04413.52
2019-05414.83
2019-06413.96
2019-07411.85
2019-08410.08
2019-09408.55
2019-10408.43
2019-11410.29
2019-12411.85
2020-01413.37
2020-02414.09
2020-03414.51
2020-04416.18
\n", "

741 rows × 1 columns

\n", "
" ], "text/plain": [ " CO2\n", "period \n", "1958-03 315.70\n", "1958-04 317.46\n", "1958-05 317.51\n", "1958-07 315.86\n", "1958-08 314.93\n", "1958-09 313.21\n", "1958-11 313.33\n", "1958-12 314.67\n", "1959-01 315.58\n", "1959-02 316.49\n", "1959-03 316.65\n", "1959-04 317.72\n", "1959-05 318.29\n", "1959-06 318.15\n", "1959-07 316.54\n", "1959-08 314.80\n", "1959-09 313.84\n", "1959-10 313.33\n", "1959-11 314.81\n", "1959-12 315.58\n", "1960-01 316.43\n", "1960-02 316.98\n", "1960-03 317.58\n", "1960-04 319.03\n", "1960-05 320.04\n", "1960-06 319.58\n", "1960-07 318.18\n", "1960-08 315.90\n", "1960-09 314.17\n", "1960-10 313.83\n", "... ...\n", "2017-11 405.17\n", "2017-12 406.75\n", "2018-01 408.05\n", "2018-02 408.34\n", "2018-03 409.25\n", "2018-04 410.30\n", "2018-05 411.30\n", "2018-06 410.88\n", "2018-07 408.90\n", "2018-08 407.10\n", "2018-09 405.59\n", "2018-10 405.99\n", "2018-11 408.12\n", "2018-12 409.23\n", "2019-01 410.92\n", "2019-02 411.66\n", "2019-03 412.00\n", "2019-04 413.52\n", "2019-05 414.83\n", "2019-06 413.96\n", "2019-07 411.85\n", "2019-08 410.08\n", "2019-09 408.55\n", "2019-10 408.43\n", "2019-11 410.29\n", "2019-12 411.85\n", "2020-01 413.37\n", "2020-02 414.09\n", "2020-03 414.51\n", "2020-04 416.18\n", "\n", "[741 rows x 1 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def convertIntoPeriod(anneeEtMois):\n", " y = (int)(anneeEtMois/100)\n", " m = (int)(anneeEtMois%100)\n", " return pd.Period(pd.Timestamp(y,m,1), 'M')\n", "useful_data['period'] = [convertIntoPeriod(date) for date in useful_data['period']]\n", "useful_data.set_index('period')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "useful_data['CO2'].plot()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "useful_data['CO2'][-60:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On voit de prime abord une augmentation globale, et des oscillations assez régulières avec des minima locaux les mois de Septembre / Octobre et des maxima locaux les mois de Mai et Juin." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour caractériser la croissance globale de la concentration de CO2 dans l'atmosphère, on va tenter de joindre au graphe des courbes de tendance linéaire et exponentielle, et voir quelle est la plus appropriée." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a1, b1 = np.polyfit([x for x in range(len(useful_data.index))], useful_data['CO2'], 1)\n", "a2, b2 = np.polyfit([x for x in range(len(useful_data.index))], [np.log(y) for y in useful_data['CO2']], 1)\n", "fit_data = [x*a1 + b1 for x in range(len(useful_data.index))]\n", "fit_dataExp = [np.exp(b2)*np.exp(a2*x) for x in range(len(useful_data.index))]\n", "useful_data['CO2'].plot()\n", "plt.plot([x for x in range(len(useful_data.index))], fit_data)\n", "plt.plot([x for x in range(len(useful_data.index))], fit_dataExp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ces courbes de tendance ne sont pas satisfaisantes, elles ne semblent pas adaptées aux données. On tente une courbe de tendance polynomiale de degré 2." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "a3, b3, c3 = np.polyfit([x for x in range(len(useful_data.index))], useful_data['CO2'], 2)\n", "fit_dataCarre = [x*x*a3 + b3*x + c3 for x in range(len(useful_data.index))]\n", "useful_data['CO2'].plot()\n", "plt.plot([x for x in range(len(useful_data.index))], fit_dataCarre)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cette courbe de tendance a l'air plus à même de nous fournir des données moyennes correctes. On souhaite maintenant faire une extrapolation jusqu'en 2025. Plutôt que de donner des valeurs par mois, il est plus pertinent ici de donner des valeurs moyennées par années.\n", "Pour ça, il suffit d'intégrer la fonction fit_dataCarre entre les bornes qui nous intéressent. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "749 2020-04\n", "Name: period, dtype: object\n" ] } ], "source": [ "#Valeur moyenne 2020\n", "borne1 = useful_data['period'][-1:]\n", "print(borne1)" ] }, { "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 }