{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "pd.options.mode.chained_assignment = None # default='warn'\n", "import isoweek\n", "import os\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous téléchargeons les données hebdomadaires de CO2 dans l'atmosphère de l'observatoire du Mauna Loa Oberservatory. Le lien suggeré dans MOOC ne marchait pas (le 11 janvier 2024), donc cet site est utilisé. Dans les deux cas, ce sont les données du même observatoire." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "if os.path.exists(\"co2_weekly_mlo.csv\"):\n", " data_path = \"co2_weekly_mlo.csv\"\n", "else:\n", " data_path = \"https://gml.noaa.gov/webdata/ccgg/trends/co2/co2_weekly_mlo.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous omitterons les premières lignes car ils sont des commentaires. L'unité de la mésure sont des $\\mu$mol de $CO_2$ par mol de l'air, aussi appelé \"parts per million\" (ppm)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
019745191974.3795333.375-999.99-999.9950.39
119745261974.3986332.956-999.99-999.9950.05
21974621974.4178332.355-999.99-999.9949.59
31974691974.4370332.207-999.99-999.9949.64
419746161974.4562332.377-999.99-999.9950.06
519746231974.4753331.735-999.99-999.9949.72
619746301974.4945331.696-999.99-999.9950.03
71974771974.5137331.466-999.99-999.9950.20
819747141974.5329330.835-999.99-999.9950.01
919747211974.5521330.767-999.99-999.9950.41
1019747281974.5712329.814-999.99-999.9949.97
111974841974.5904329.855-999.99-999.9950.54
1219748111974.6096329.155-999.99-999.9950.37
1319748181974.6288329.066-999.99-999.9950.80
1419748251974.6479328.337-999.99-999.9950.54
151974911974.6671328.065-999.99-999.9950.68
161974981974.6863327.564-999.99-999.9950.53
1719749151974.7055326.726-999.99-999.9949.95
1819749221974.7247326.995-999.99-999.9950.38
1919749291974.7438327.315-999.99-999.9950.76
2019741061974.7630327.076-999.99-999.9950.49
21197410131974.7822327.235-999.99-999.9950.53
22197410201974.8014327.405-999.99-999.9950.51
23197410271974.8205327.647-999.99-999.9950.50
2419741131974.8397327.807-999.99-999.9950.39
25197411101974.8589328.506-999.99-999.9950.79
26197411171974.8781328.616-999.99-999.9950.59
27197411241974.8973328.465-999.99-999.9950.14
2819741211974.9164328.805-999.99-999.9950.20
2919741281974.9356329.397-999.99-999.9950.53
..............................
256020236112023.4425424.136421.00398.78141.49
256120236182023.4616423.497420.84398.39141.28
256220236252023.4808422.197420.32398.78140.46
25632023722023.5000422.624419.91398.34141.41
25642023792023.5192422.355419.18397.93141.66
256520237162023.5384421.345418.36396.93141.19
256620237232023.5575421.284418.03397.30141.67
256720237302023.5767420.836418.10396.80141.75
25682023862023.5959420.026417.36395.65141.46
256920238132023.6151418.984417.25395.24140.92
257020238202023.6342419.312416.64395.22141.71
257120238272023.6534419.275416.42394.45142.09
25722023932023.6726418.644416.27393.92141.81
257320239102023.6918418.522416.15393.52141.96
257420239172023.7110418.335415.65393.79141.94
257520239242023.7301418.297415.34393.46141.97
257620231012023.7493418.316415.30393.52141.96
257720231082023.7685418.535415.39393.58142.05
2578202310152023.7877419.467415.82393.98142.77
2579202310222023.8068418.916416.19394.23141.93
2580202310292023.8260419.057416.41394.47141.75
258120231152023.8452419.285417.00394.80141.64
2582202311122023.8644421.226417.31395.64143.24
2583202311192023.8836421.215418.38395.26142.88
2584202311262023.9027420.312417.81396.21141.66
258520231232023.9219421.022419.23396.43142.07
2586202312102023.9411422.207418.81396.39142.96
2587202312172023.9603422.245419.05397.72142.74
2588202312242023.9795421.865419.41397.53142.12
2589202312312023.9986422.524419.34397.73142.55
\n", "

2590 rows × 9 columns

\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "0 1974 5 19 1974.3795 333.37 5 -999.99 -999.99 \n", "1 1974 5 26 1974.3986 332.95 6 -999.99 -999.99 \n", "2 1974 6 2 1974.4178 332.35 5 -999.99 -999.99 \n", "3 1974 6 9 1974.4370 332.20 7 -999.99 -999.99 \n", "4 1974 6 16 1974.4562 332.37 7 -999.99 -999.99 \n", "5 1974 6 23 1974.4753 331.73 5 -999.99 -999.99 \n", "6 1974 6 30 1974.4945 331.69 6 -999.99 -999.99 \n", "7 1974 7 7 1974.5137 331.46 6 -999.99 -999.99 \n", "8 1974 7 14 1974.5329 330.83 5 -999.99 -999.99 \n", "9 1974 7 21 1974.5521 330.76 7 -999.99 -999.99 \n", "10 1974 7 28 1974.5712 329.81 4 -999.99 -999.99 \n", "11 1974 8 4 1974.5904 329.85 5 -999.99 -999.99 \n", "12 1974 8 11 1974.6096 329.15 5 -999.99 -999.99 \n", "13 1974 8 18 1974.6288 329.06 6 -999.99 -999.99 \n", "14 1974 8 25 1974.6479 328.33 7 -999.99 -999.99 \n", "15 1974 9 1 1974.6671 328.06 5 -999.99 -999.99 \n", "16 1974 9 8 1974.6863 327.56 4 -999.99 -999.99 \n", "17 1974 9 15 1974.7055 326.72 6 -999.99 -999.99 \n", "18 1974 9 22 1974.7247 326.99 5 -999.99 -999.99 \n", "19 1974 9 29 1974.7438 327.31 5 -999.99 -999.99 \n", "20 1974 10 6 1974.7630 327.07 6 -999.99 -999.99 \n", "21 1974 10 13 1974.7822 327.23 5 -999.99 -999.99 \n", "22 1974 10 20 1974.8014 327.40 5 -999.99 -999.99 \n", "23 1974 10 27 1974.8205 327.64 7 -999.99 -999.99 \n", "24 1974 11 3 1974.8397 327.80 7 -999.99 -999.99 \n", "25 1974 11 10 1974.8589 328.50 6 -999.99 -999.99 \n", "26 1974 11 17 1974.8781 328.61 6 -999.99 -999.99 \n", "27 1974 11 24 1974.8973 328.46 5 -999.99 -999.99 \n", "28 1974 12 1 1974.9164 328.80 5 -999.99 -999.99 \n", "29 1974 12 8 1974.9356 329.39 7 -999.99 -999.99 \n", "... ... ... ... ... ... ... ... ... \n", "2560 2023 6 11 2023.4425 424.13 6 421.00 398.78 \n", "2561 2023 6 18 2023.4616 423.49 7 420.84 398.39 \n", "2562 2023 6 25 2023.4808 422.19 7 420.32 398.78 \n", "2563 2023 7 2 2023.5000 422.62 4 419.91 398.34 \n", "2564 2023 7 9 2023.5192 422.35 5 419.18 397.93 \n", "2565 2023 7 16 2023.5384 421.34 5 418.36 396.93 \n", "2566 2023 7 23 2023.5575 421.28 4 418.03 397.30 \n", "2567 2023 7 30 2023.5767 420.83 6 418.10 396.80 \n", "2568 2023 8 6 2023.5959 420.02 6 417.36 395.65 \n", "2569 2023 8 13 2023.6151 418.98 4 417.25 395.24 \n", "2570 2023 8 20 2023.6342 419.31 2 416.64 395.22 \n", "2571 2023 8 27 2023.6534 419.27 5 416.42 394.45 \n", "2572 2023 9 3 2023.6726 418.64 4 416.27 393.92 \n", "2573 2023 9 10 2023.6918 418.52 2 416.15 393.52 \n", "2574 2023 9 17 2023.7110 418.33 5 415.65 393.79 \n", "2575 2023 9 24 2023.7301 418.29 7 415.34 393.46 \n", "2576 2023 10 1 2023.7493 418.31 6 415.30 393.52 \n", "2577 2023 10 8 2023.7685 418.53 5 415.39 393.58 \n", "2578 2023 10 15 2023.7877 419.46 7 415.82 393.98 \n", "2579 2023 10 22 2023.8068 418.91 6 416.19 394.23 \n", "2580 2023 10 29 2023.8260 419.05 7 416.41 394.47 \n", "2581 2023 11 5 2023.8452 419.28 5 417.00 394.80 \n", "2582 2023 11 12 2023.8644 421.22 6 417.31 395.64 \n", "2583 2023 11 19 2023.8836 421.21 5 418.38 395.26 \n", "2584 2023 11 26 2023.9027 420.31 2 417.81 396.21 \n", "2585 2023 12 3 2023.9219 421.02 2 419.23 396.43 \n", "2586 2023 12 10 2023.9411 422.20 7 418.81 396.39 \n", "2587 2023 12 17 2023.9603 422.24 5 419.05 397.72 \n", "2588 2023 12 24 2023.9795 421.86 5 419.41 397.53 \n", "2589 2023 12 31 2023.9986 422.52 4 419.34 397.73 \n", "\n", " increase since 1800 \n", "0 50.39 \n", "1 50.05 \n", "2 49.59 \n", "3 49.64 \n", "4 50.06 \n", "5 49.72 \n", "6 50.03 \n", "7 50.20 \n", "8 50.01 \n", "9 50.41 \n", "10 49.97 \n", "11 50.54 \n", "12 50.37 \n", "13 50.80 \n", "14 50.54 \n", "15 50.68 \n", "16 50.53 \n", "17 49.95 \n", "18 50.38 \n", "19 50.76 \n", "20 50.49 \n", "21 50.53 \n", "22 50.51 \n", "23 50.50 \n", "24 50.39 \n", "25 50.79 \n", "26 50.59 \n", "27 50.14 \n", "28 50.20 \n", "29 50.53 \n", "... ... \n", "2560 141.49 \n", "2561 141.28 \n", "2562 140.46 \n", "2563 141.41 \n", "2564 141.66 \n", "2565 141.19 \n", "2566 141.67 \n", "2567 141.75 \n", "2568 141.46 \n", "2569 140.92 \n", "2570 141.71 \n", "2571 142.09 \n", "2572 141.81 \n", "2573 141.96 \n", "2574 141.94 \n", "2575 141.97 \n", "2576 141.96 \n", "2577 142.05 \n", "2578 142.77 \n", "2579 141.93 \n", "2580 141.75 \n", "2581 141.64 \n", "2582 143.24 \n", "2583 142.88 \n", "2584 141.66 \n", "2585 142.07 \n", "2586 142.96 \n", "2587 142.74 \n", "2588 142.12 \n", "2589 142.55 \n", "\n", "[2590 rows x 9 columns]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_data = pd.read_csv(data_path, skiprows=35)\n", "co2_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'y a pas des semaines sans données." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [year, month, day, decimal, average, ndays, 1 year ago, 10 years ago, increase since 1800]\n", "Index: []" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_data[co2_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous regardons s'il y a des semaines avec des données impossibles, comme des concentrations négatives. Après nous créeons un nouveau DataFrame sans ces semaines." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
7219751051975.7603-999.990326.98-999.99-999.99
8119751271975.9329-999.990329.32-999.99-999.99
82197512141975.9521-999.990329.68-999.99-999.99
83197512211975.9712-999.990329.96-999.99-999.99
84197512281975.9904-999.990330.27-999.99-999.99
11019766271976.4877-999.990333.05-999.99-999.99
40919823211982.2178-999.990342.37-999.99-999.99
41219824111982.2753-999.990342.85-999.99-999.99
41319824181982.2945-999.990342.66-999.99-999.99
4811983871983.5986-999.990340.84-999.99-999.99
5151984411984.2500-999.990344.80-999.99-999.99
5161984481984.2691-999.990345.23-999.99-999.99
51719844151984.2883-999.990345.67-999.99-999.99
51819844221984.3074-999.990345.86-999.99-999.99
1639200510162005.7904-999.990374.67358.21-999.99
178020086292008.4932-999.990385.53368.22-999.99
17812008762008.5123-999.990385.38368.88-999.99
178220087132008.5314-999.990384.45367.73-999.99
\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "72 1975 10 5 1975.7603 -999.99 0 326.98 -999.99 \n", "81 1975 12 7 1975.9329 -999.99 0 329.32 -999.99 \n", "82 1975 12 14 1975.9521 -999.99 0 329.68 -999.99 \n", "83 1975 12 21 1975.9712 -999.99 0 329.96 -999.99 \n", "84 1975 12 28 1975.9904 -999.99 0 330.27 -999.99 \n", "110 1976 6 27 1976.4877 -999.99 0 333.05 -999.99 \n", "409 1982 3 21 1982.2178 -999.99 0 342.37 -999.99 \n", "412 1982 4 11 1982.2753 -999.99 0 342.85 -999.99 \n", "413 1982 4 18 1982.2945 -999.99 0 342.66 -999.99 \n", "481 1983 8 7 1983.5986 -999.99 0 340.84 -999.99 \n", "515 1984 4 1 1984.2500 -999.99 0 344.80 -999.99 \n", "516 1984 4 8 1984.2691 -999.99 0 345.23 -999.99 \n", "517 1984 4 15 1984.2883 -999.99 0 345.67 -999.99 \n", "518 1984 4 22 1984.3074 -999.99 0 345.86 -999.99 \n", "1639 2005 10 16 2005.7904 -999.99 0 374.67 358.21 \n", "1780 2008 6 29 2008.4932 -999.99 0 385.53 368.22 \n", "1781 2008 7 6 2008.5123 -999.99 0 385.38 368.88 \n", "1782 2008 7 13 2008.5314 -999.99 0 384.45 367.73 \n", "\n", " increase since 1800 \n", "72 -999.99 \n", "81 -999.99 \n", "82 -999.99 \n", "83 -999.99 \n", "84 -999.99 \n", "110 -999.99 \n", "409 -999.99 \n", "412 -999.99 \n", "413 -999.99 \n", "481 -999.99 \n", "515 -999.99 \n", "516 -999.99 \n", "517 -999.99 \n", "518 -999.99 \n", "1639 -999.99 \n", "1780 -999.99 \n", "1781 -999.99 \n", "1782 -999.99 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_data.loc[co2_data[\"average\"] < 0]" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
019745191974.3795333.375-999.99-999.9950.39
119745261974.3986332.956-999.99-999.9950.05
21974621974.4178332.355-999.99-999.9949.59
31974691974.4370332.207-999.99-999.9949.64
419746161974.4562332.377-999.99-999.9950.06
519746231974.4753331.735-999.99-999.9949.72
619746301974.4945331.696-999.99-999.9950.03
71974771974.5137331.466-999.99-999.9950.20
819747141974.5329330.835-999.99-999.9950.01
919747211974.5521330.767-999.99-999.9950.41
1019747281974.5712329.814-999.99-999.9949.97
111974841974.5904329.855-999.99-999.9950.54
1219748111974.6096329.155-999.99-999.9950.37
1319748181974.6288329.066-999.99-999.9950.80
1419748251974.6479328.337-999.99-999.9950.54
151974911974.6671328.065-999.99-999.9950.68
161974981974.6863327.564-999.99-999.9950.53
1719749151974.7055326.726-999.99-999.9949.95
1819749221974.7247326.995-999.99-999.9950.38
1919749291974.7438327.315-999.99-999.9950.76
2019741061974.7630327.076-999.99-999.9950.49
21197410131974.7822327.235-999.99-999.9950.53
22197410201974.8014327.405-999.99-999.9950.51
23197410271974.8205327.647-999.99-999.9950.50
2419741131974.8397327.807-999.99-999.9950.39
25197411101974.8589328.506-999.99-999.9950.79
26197411171974.8781328.616-999.99-999.9950.59
27197411241974.8973328.465-999.99-999.9950.14
2819741211974.9164328.805-999.99-999.9950.20
2919741281974.9356329.397-999.99-999.9950.53
..............................
256020236112023.4425424.136421.00398.78141.49
256120236182023.4616423.497420.84398.39141.28
256220236252023.4808422.197420.32398.78140.46
25632023722023.5000422.624419.91398.34141.41
25642023792023.5192422.355419.18397.93141.66
256520237162023.5384421.345418.36396.93141.19
256620237232023.5575421.284418.03397.30141.67
256720237302023.5767420.836418.10396.80141.75
25682023862023.5959420.026417.36395.65141.46
256920238132023.6151418.984417.25395.24140.92
257020238202023.6342419.312416.64395.22141.71
257120238272023.6534419.275416.42394.45142.09
25722023932023.6726418.644416.27393.92141.81
257320239102023.6918418.522416.15393.52141.96
257420239172023.7110418.335415.65393.79141.94
257520239242023.7301418.297415.34393.46141.97
257620231012023.7493418.316415.30393.52141.96
257720231082023.7685418.535415.39393.58142.05
2578202310152023.7877419.467415.82393.98142.77
2579202310222023.8068418.916416.19394.23141.93
2580202310292023.8260419.057416.41394.47141.75
258120231152023.8452419.285417.00394.80141.64
2582202311122023.8644421.226417.31395.64143.24
2583202311192023.8836421.215418.38395.26142.88
2584202311262023.9027420.312417.81396.21141.66
258520231232023.9219421.022419.23396.43142.07
2586202312102023.9411422.207418.81396.39142.96
2587202312172023.9603422.245419.05397.72142.74
2588202312242023.9795421.865419.41397.53142.12
2589202312312023.9986422.524419.34397.73142.55
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2572 rows × 9 columns

\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "0 1974 5 19 1974.3795 333.37 5 -999.99 -999.99 \n", "1 1974 5 26 1974.3986 332.95 6 -999.99 -999.99 \n", "2 1974 6 2 1974.4178 332.35 5 -999.99 -999.99 \n", "3 1974 6 9 1974.4370 332.20 7 -999.99 -999.99 \n", "4 1974 6 16 1974.4562 332.37 7 -999.99 -999.99 \n", "5 1974 6 23 1974.4753 331.73 5 -999.99 -999.99 \n", "6 1974 6 30 1974.4945 331.69 6 -999.99 -999.99 \n", "7 1974 7 7 1974.5137 331.46 6 -999.99 -999.99 \n", "8 1974 7 14 1974.5329 330.83 5 -999.99 -999.99 \n", "9 1974 7 21 1974.5521 330.76 7 -999.99 -999.99 \n", "10 1974 7 28 1974.5712 329.81 4 -999.99 -999.99 \n", "11 1974 8 4 1974.5904 329.85 5 -999.99 -999.99 \n", "12 1974 8 11 1974.6096 329.15 5 -999.99 -999.99 \n", "13 1974 8 18 1974.6288 329.06 6 -999.99 -999.99 \n", "14 1974 8 25 1974.6479 328.33 7 -999.99 -999.99 \n", "15 1974 9 1 1974.6671 328.06 5 -999.99 -999.99 \n", "16 1974 9 8 1974.6863 327.56 4 -999.99 -999.99 \n", "17 1974 9 15 1974.7055 326.72 6 -999.99 -999.99 \n", "18 1974 9 22 1974.7247 326.99 5 -999.99 -999.99 \n", "19 1974 9 29 1974.7438 327.31 5 -999.99 -999.99 \n", "20 1974 10 6 1974.7630 327.07 6 -999.99 -999.99 \n", "21 1974 10 13 1974.7822 327.23 5 -999.99 -999.99 \n", "22 1974 10 20 1974.8014 327.40 5 -999.99 -999.99 \n", "23 1974 10 27 1974.8205 327.64 7 -999.99 -999.99 \n", "24 1974 11 3 1974.8397 327.80 7 -999.99 -999.99 \n", "25 1974 11 10 1974.8589 328.50 6 -999.99 -999.99 \n", "26 1974 11 17 1974.8781 328.61 6 -999.99 -999.99 \n", "27 1974 11 24 1974.8973 328.46 5 -999.99 -999.99 \n", "28 1974 12 1 1974.9164 328.80 5 -999.99 -999.99 \n", "29 1974 12 8 1974.9356 329.39 7 -999.99 -999.99 \n", "... ... ... ... ... ... ... ... ... \n", "2560 2023 6 11 2023.4425 424.13 6 421.00 398.78 \n", "2561 2023 6 18 2023.4616 423.49 7 420.84 398.39 \n", "2562 2023 6 25 2023.4808 422.19 7 420.32 398.78 \n", "2563 2023 7 2 2023.5000 422.62 4 419.91 398.34 \n", "2564 2023 7 9 2023.5192 422.35 5 419.18 397.93 \n", "2565 2023 7 16 2023.5384 421.34 5 418.36 396.93 \n", "2566 2023 7 23 2023.5575 421.28 4 418.03 397.30 \n", "2567 2023 7 30 2023.5767 420.83 6 418.10 396.80 \n", "2568 2023 8 6 2023.5959 420.02 6 417.36 395.65 \n", "2569 2023 8 13 2023.6151 418.98 4 417.25 395.24 \n", "2570 2023 8 20 2023.6342 419.31 2 416.64 395.22 \n", "2571 2023 8 27 2023.6534 419.27 5 416.42 394.45 \n", "2572 2023 9 3 2023.6726 418.64 4 416.27 393.92 \n", "2573 2023 9 10 2023.6918 418.52 2 416.15 393.52 \n", "2574 2023 9 17 2023.7110 418.33 5 415.65 393.79 \n", "2575 2023 9 24 2023.7301 418.29 7 415.34 393.46 \n", "2576 2023 10 1 2023.7493 418.31 6 415.30 393.52 \n", "2577 2023 10 8 2023.7685 418.53 5 415.39 393.58 \n", "2578 2023 10 15 2023.7877 419.46 7 415.82 393.98 \n", "2579 2023 10 22 2023.8068 418.91 6 416.19 394.23 \n", "2580 2023 10 29 2023.8260 419.05 7 416.41 394.47 \n", "2581 2023 11 5 2023.8452 419.28 5 417.00 394.80 \n", "2582 2023 11 12 2023.8644 421.22 6 417.31 395.64 \n", "2583 2023 11 19 2023.8836 421.21 5 418.38 395.26 \n", "2584 2023 11 26 2023.9027 420.31 2 417.81 396.21 \n", "2585 2023 12 3 2023.9219 421.02 2 419.23 396.43 \n", "2586 2023 12 10 2023.9411 422.20 7 418.81 396.39 \n", "2587 2023 12 17 2023.9603 422.24 5 419.05 397.72 \n", "2588 2023 12 24 2023.9795 421.86 5 419.41 397.53 \n", "2589 2023 12 31 2023.9986 422.52 4 419.34 397.73 \n", "\n", " increase since 1800 \n", "0 50.39 \n", "1 50.05 \n", "2 49.59 \n", "3 49.64 \n", "4 50.06 \n", "5 49.72 \n", "6 50.03 \n", "7 50.20 \n", "8 50.01 \n", "9 50.41 \n", "10 49.97 \n", "11 50.54 \n", "12 50.37 \n", "13 50.80 \n", "14 50.54 \n", "15 50.68 \n", "16 50.53 \n", "17 49.95 \n", "18 50.38 \n", "19 50.76 \n", "20 50.49 \n", "21 50.53 \n", "22 50.51 \n", "23 50.50 \n", "24 50.39 \n", "25 50.79 \n", "26 50.59 \n", "27 50.14 \n", "28 50.20 \n", "29 50.53 \n", "... ... \n", "2560 141.49 \n", "2561 141.28 \n", "2562 140.46 \n", "2563 141.41 \n", "2564 141.66 \n", "2565 141.19 \n", "2566 141.67 \n", "2567 141.75 \n", "2568 141.46 \n", "2569 140.92 \n", "2570 141.71 \n", "2571 142.09 \n", "2572 141.81 \n", "2573 141.96 \n", "2574 141.94 \n", "2575 141.97 \n", "2576 141.96 \n", "2577 142.05 \n", "2578 142.77 \n", "2579 141.93 \n", "2580 141.75 \n", "2581 141.64 \n", "2582 143.24 \n", "2583 142.88 \n", "2584 141.66 \n", "2585 142.07 \n", "2586 142.96 \n", "2587 142.74 \n", "2588 142.12 \n", "2589 142.55 \n", "\n", "[2572 rows x 9 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_clean = co2_data.loc[co2_data[\"average\"]>0]\n", "co2_clean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous convertirons les premières colonnes en \"datetime\" de pandas, donc un format de temps." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/plain": [ "0 1974-05-19\n", "1 1974-05-26\n", "2 1974-06-02\n", "3 1974-06-09\n", "4 1974-06-16\n", "5 1974-06-23\n", "6 1974-06-30\n", "7 1974-07-07\n", "8 1974-07-14\n", "9 1974-07-21\n", "10 1974-07-28\n", "11 1974-08-04\n", "12 1974-08-11\n", "13 1974-08-18\n", "14 1974-08-25\n", "15 1974-09-01\n", "16 1974-09-08\n", "17 1974-09-15\n", "18 1974-09-22\n", "19 1974-09-29\n", "20 1974-10-06\n", "21 1974-10-13\n", "22 1974-10-20\n", "23 1974-10-27\n", "24 1974-11-03\n", "25 1974-11-10\n", "26 1974-11-17\n", "27 1974-11-24\n", "28 1974-12-01\n", "29 1974-12-08\n", " ... \n", "2560 2023-06-11\n", "2561 2023-06-18\n", "2562 2023-06-25\n", "2563 2023-07-02\n", "2564 2023-07-09\n", "2565 2023-07-16\n", "2566 2023-07-23\n", "2567 2023-07-30\n", "2568 2023-08-06\n", "2569 2023-08-13\n", "2570 2023-08-20\n", "2571 2023-08-27\n", "2572 2023-09-03\n", "2573 2023-09-10\n", "2574 2023-09-17\n", "2575 2023-09-24\n", "2576 2023-10-01\n", "2577 2023-10-08\n", "2578 2023-10-15\n", "2579 2023-10-22\n", "2580 2023-10-29\n", "2581 2023-11-05\n", "2582 2023-11-12\n", "2583 2023-11-19\n", "2584 2023-11-26\n", "2585 2023-12-03\n", "2586 2023-12-10\n", "2587 2023-12-17\n", "2588 2023-12-24\n", "2589 2023-12-31\n", "Length: 2572, dtype: datetime64[ns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weeks = pd.to_datetime(co2_clean[[\"year\", \"month\", \"day\"]])\n", "weeks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec ceci, il est facile de tester s'ils y a des semaines omises. Ce sont les semaines ou il n'y avait pas de moyen sensible mesuré." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1975-09-28 00:00:00 1975-10-12 00:00:00\n", "1975-11-30 00:00:00 1976-01-04 00:00:00\n", "1976-06-20 00:00:00 1976-07-04 00:00:00\n", "1982-03-14 00:00:00 1982-03-28 00:00:00\n", "1982-04-04 00:00:00 1982-04-25 00:00:00\n", "1983-07-31 00:00:00 1983-08-14 00:00:00\n", "1984-03-25 00:00:00 1984-04-29 00:00:00\n", "2005-10-09 00:00:00 2005-10-23 00:00:00\n", "2008-06-22 00:00:00 2008-07-20 00:00:00\n" ] } ], "source": [ "for p1, p2 in zip(weeks[:-1], weeks[1:]):\n", " delta = p2 - p1\n", " if delta > pd.Timedelta('7 days'):\n", " print(p1, p2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maintenant, nous pouvons tracer le développement de la concentration de $CO_2$ pendant le temps." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,\"$\\\\mu$ mol/mol de CO2 dans l'atmosphère\")" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "co2_clean.plot(x=\"decimal\", y=\"average\", title=\"Changement de la concentration de CO2 dans l'air\",\n", " legend=None)\n", "plt.xlabel(\"year\")\n", "plt.ylabel(r\"$\\mu$ mol/mol de CO2 dans l'atmosphère\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour séparer les oscillations et la croissance, nous faisons une separation des données en années." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800yeartime
019745191974.3795333.375-999.99-999.9950.390.3795
119745261974.3986332.956-999.99-999.9950.050.3986
21974621974.4178332.355-999.99-999.9949.590.4178
31974691974.4370332.207-999.99-999.9949.640.4370
419746161974.4562332.377-999.99-999.9950.060.4562
519746231974.4753331.735-999.99-999.9949.720.4753
619746301974.4945331.696-999.99-999.9950.030.4945
71974771974.5137331.466-999.99-999.9950.200.5137
819747141974.5329330.835-999.99-999.9950.010.5329
919747211974.5521330.767-999.99-999.9950.410.5521
1019747281974.5712329.814-999.99-999.9949.970.5712
111974841974.5904329.855-999.99-999.9950.540.5904
1219748111974.6096329.155-999.99-999.9950.370.6096
1319748181974.6288329.066-999.99-999.9950.800.6288
1419748251974.6479328.337-999.99-999.9950.540.6479
151974911974.6671328.065-999.99-999.9950.680.6671
161974981974.6863327.564-999.99-999.9950.530.6863
1719749151974.7055326.726-999.99-999.9949.950.7055
1819749221974.7247326.995-999.99-999.9950.380.7247
1919749291974.7438327.315-999.99-999.9950.760.7438
2019741061974.7630327.076-999.99-999.9950.490.7630
21197410131974.7822327.235-999.99-999.9950.530.7822
22197410201974.8014327.405-999.99-999.9950.510.8014
23197410271974.8205327.647-999.99-999.9950.500.8205
2419741131974.8397327.807-999.99-999.9950.390.8397
25197411101974.8589328.506-999.99-999.9950.790.8589
26197411171974.8781328.616-999.99-999.9950.590.8781
27197411241974.8973328.465-999.99-999.9950.140.8973
2819741211974.9164328.805-999.99-999.9950.200.9164
2919741281974.9356329.397-999.99-999.9950.530.9356
.................................
256020236112023.4425424.136421.00398.78141.490.4425
256120236182023.4616423.497420.84398.39141.280.4616
256220236252023.4808422.197420.32398.78140.460.4808
25632023722023.5000422.624419.91398.34141.410.5000
25642023792023.5192422.355419.18397.93141.660.5192
256520237162023.5384421.345418.36396.93141.190.5384
256620237232023.5575421.284418.03397.30141.670.5575
256720237302023.5767420.836418.10396.80141.750.5767
25682023862023.5959420.026417.36395.65141.460.5959
256920238132023.6151418.984417.25395.24140.920.6151
257020238202023.6342419.312416.64395.22141.710.6342
257120238272023.6534419.275416.42394.45142.090.6534
25722023932023.6726418.644416.27393.92141.810.6726
257320239102023.6918418.522416.15393.52141.960.6918
257420239172023.7110418.335415.65393.79141.940.7110
257520239242023.7301418.297415.34393.46141.970.7301
257620231012023.7493418.316415.30393.52141.960.7493
257720231082023.7685418.535415.39393.58142.050.7685
2578202310152023.7877419.467415.82393.98142.770.7877
2579202310222023.8068418.916416.19394.23141.930.8068
2580202310292023.8260419.057416.41394.47141.750.8260
258120231152023.8452419.285417.00394.80141.640.8452
2582202311122023.8644421.226417.31395.64143.240.8644
2583202311192023.8836421.215418.38395.26142.880.8836
2584202311262023.9027420.312417.81396.21141.660.9027
258520231232023.9219421.022419.23396.43142.070.9219
2586202312102023.9411422.207418.81396.39142.960.9411
2587202312172023.9603422.245419.05397.72142.740.9603
2588202312242023.9795421.865419.41397.53142.120.9795
2589202312312023.9986422.524419.34397.73142.550.9986
\n", "

2572 rows × 10 columns

\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "0 1974 5 19 1974.3795 333.37 5 -999.99 -999.99 \n", "1 1974 5 26 1974.3986 332.95 6 -999.99 -999.99 \n", "2 1974 6 2 1974.4178 332.35 5 -999.99 -999.99 \n", "3 1974 6 9 1974.4370 332.20 7 -999.99 -999.99 \n", "4 1974 6 16 1974.4562 332.37 7 -999.99 -999.99 \n", "5 1974 6 23 1974.4753 331.73 5 -999.99 -999.99 \n", "6 1974 6 30 1974.4945 331.69 6 -999.99 -999.99 \n", "7 1974 7 7 1974.5137 331.46 6 -999.99 -999.99 \n", "8 1974 7 14 1974.5329 330.83 5 -999.99 -999.99 \n", "9 1974 7 21 1974.5521 330.76 7 -999.99 -999.99 \n", "10 1974 7 28 1974.5712 329.81 4 -999.99 -999.99 \n", "11 1974 8 4 1974.5904 329.85 5 -999.99 -999.99 \n", "12 1974 8 11 1974.6096 329.15 5 -999.99 -999.99 \n", "13 1974 8 18 1974.6288 329.06 6 -999.99 -999.99 \n", "14 1974 8 25 1974.6479 328.33 7 -999.99 -999.99 \n", "15 1974 9 1 1974.6671 328.06 5 -999.99 -999.99 \n", "16 1974 9 8 1974.6863 327.56 4 -999.99 -999.99 \n", "17 1974 9 15 1974.7055 326.72 6 -999.99 -999.99 \n", "18 1974 9 22 1974.7247 326.99 5 -999.99 -999.99 \n", "19 1974 9 29 1974.7438 327.31 5 -999.99 -999.99 \n", "20 1974 10 6 1974.7630 327.07 6 -999.99 -999.99 \n", "21 1974 10 13 1974.7822 327.23 5 -999.99 -999.99 \n", "22 1974 10 20 1974.8014 327.40 5 -999.99 -999.99 \n", "23 1974 10 27 1974.8205 327.64 7 -999.99 -999.99 \n", "24 1974 11 3 1974.8397 327.80 7 -999.99 -999.99 \n", "25 1974 11 10 1974.8589 328.50 6 -999.99 -999.99 \n", "26 1974 11 17 1974.8781 328.61 6 -999.99 -999.99 \n", "27 1974 11 24 1974.8973 328.46 5 -999.99 -999.99 \n", "28 1974 12 1 1974.9164 328.80 5 -999.99 -999.99 \n", "29 1974 12 8 1974.9356 329.39 7 -999.99 -999.99 \n", "... ... ... ... ... ... ... ... ... \n", "2560 2023 6 11 2023.4425 424.13 6 421.00 398.78 \n", "2561 2023 6 18 2023.4616 423.49 7 420.84 398.39 \n", "2562 2023 6 25 2023.4808 422.19 7 420.32 398.78 \n", "2563 2023 7 2 2023.5000 422.62 4 419.91 398.34 \n", "2564 2023 7 9 2023.5192 422.35 5 419.18 397.93 \n", "2565 2023 7 16 2023.5384 421.34 5 418.36 396.93 \n", "2566 2023 7 23 2023.5575 421.28 4 418.03 397.30 \n", "2567 2023 7 30 2023.5767 420.83 6 418.10 396.80 \n", "2568 2023 8 6 2023.5959 420.02 6 417.36 395.65 \n", "2569 2023 8 13 2023.6151 418.98 4 417.25 395.24 \n", "2570 2023 8 20 2023.6342 419.31 2 416.64 395.22 \n", "2571 2023 8 27 2023.6534 419.27 5 416.42 394.45 \n", "2572 2023 9 3 2023.6726 418.64 4 416.27 393.92 \n", "2573 2023 9 10 2023.6918 418.52 2 416.15 393.52 \n", "2574 2023 9 17 2023.7110 418.33 5 415.65 393.79 \n", "2575 2023 9 24 2023.7301 418.29 7 415.34 393.46 \n", "2576 2023 10 1 2023.7493 418.31 6 415.30 393.52 \n", "2577 2023 10 8 2023.7685 418.53 5 415.39 393.58 \n", "2578 2023 10 15 2023.7877 419.46 7 415.82 393.98 \n", "2579 2023 10 22 2023.8068 418.91 6 416.19 394.23 \n", "2580 2023 10 29 2023.8260 419.05 7 416.41 394.47 \n", "2581 2023 11 5 2023.8452 419.28 5 417.00 394.80 \n", "2582 2023 11 12 2023.8644 421.22 6 417.31 395.64 \n", "2583 2023 11 19 2023.8836 421.21 5 418.38 395.26 \n", "2584 2023 11 26 2023.9027 420.31 2 417.81 396.21 \n", "2585 2023 12 3 2023.9219 421.02 2 419.23 396.43 \n", "2586 2023 12 10 2023.9411 422.20 7 418.81 396.39 \n", "2587 2023 12 17 2023.9603 422.24 5 419.05 397.72 \n", "2588 2023 12 24 2023.9795 421.86 5 419.41 397.53 \n", "2589 2023 12 31 2023.9986 422.52 4 419.34 397.73 \n", "\n", " increase since 1800 yeartime \n", "0 50.39 0.3795 \n", "1 50.05 0.3986 \n", "2 49.59 0.4178 \n", "3 49.64 0.4370 \n", "4 50.06 0.4562 \n", "5 49.72 0.4753 \n", "6 50.03 0.4945 \n", "7 50.20 0.5137 \n", "8 50.01 0.5329 \n", "9 50.41 0.5521 \n", "10 49.97 0.5712 \n", "11 50.54 0.5904 \n", "12 50.37 0.6096 \n", "13 50.80 0.6288 \n", "14 50.54 0.6479 \n", "15 50.68 0.6671 \n", "16 50.53 0.6863 \n", "17 49.95 0.7055 \n", "18 50.38 0.7247 \n", "19 50.76 0.7438 \n", "20 50.49 0.7630 \n", "21 50.53 0.7822 \n", "22 50.51 0.8014 \n", "23 50.50 0.8205 \n", "24 50.39 0.8397 \n", "25 50.79 0.8589 \n", "26 50.59 0.8781 \n", "27 50.14 0.8973 \n", "28 50.20 0.9164 \n", "29 50.53 0.9356 \n", "... ... ... \n", "2560 141.49 0.4425 \n", "2561 141.28 0.4616 \n", "2562 140.46 0.4808 \n", "2563 141.41 0.5000 \n", "2564 141.66 0.5192 \n", "2565 141.19 0.5384 \n", "2566 141.67 0.5575 \n", "2567 141.75 0.5767 \n", "2568 141.46 0.5959 \n", "2569 140.92 0.6151 \n", "2570 141.71 0.6342 \n", "2571 142.09 0.6534 \n", "2572 141.81 0.6726 \n", "2573 141.96 0.6918 \n", "2574 141.94 0.7110 \n", "2575 141.97 0.7301 \n", "2576 141.96 0.7493 \n", "2577 142.05 0.7685 \n", "2578 142.77 0.7877 \n", "2579 141.93 0.8068 \n", "2580 141.75 0.8260 \n", "2581 141.64 0.8452 \n", "2582 143.24 0.8644 \n", "2583 142.88 0.8836 \n", "2584 141.66 0.9027 \n", "2585 142.07 0.9219 \n", "2586 142.96 0.9411 \n", "2587 142.74 0.9603 \n", "2588 142.12 0.9795 \n", "2589 142.55 0.9986 \n", "\n", "[2572 rows x 10 columns]" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year_min = co2_clean[\"year\"].iloc[0]\n", "year_max = co2_clean[\"year\"].iloc[-1]\n", "co2_clean[\"yeartime\"]=co2_clean[\"decimal\"]-co2_clean[\"year\"]\n", "co2_clean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans cette boucle, nous calculons la moyenne de chaque année séparement." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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yearyearly average
01974.0329.502121
11975.0331.217447
21976.0332.031765
31977.0333.844423
41978.0335.420755
51979.0336.880192
61980.0338.762308
71981.0340.130577
81982.0341.347551
91983.0343.165882
101984.0344.680000
111985.0346.342885
121986.0347.616154
131987.0349.335000
141988.0351.698269
151989.0353.215849
161990.0354.453846
171991.0355.741346
181992.0356.547115
191993.0357.228077
201994.0358.965192
211995.0360.979245
221996.0362.756346
231997.0363.887500
241998.0366.865385
251999.0368.521538
262000.0369.732830
272001.0371.341154
282002.0373.474808
292003.0375.987500
302004.0377.720385
312005.0380.056078
322006.0382.083585
332007.0384.083462
342008.0385.793469
352009.0387.663077
362010.0390.085192
372011.0391.859808
382012.0394.068868
392013.0396.773269
402014.0398.857500
412015.0401.020769
422016.0404.434423
432017.0406.774906
442018.0408.746346
452019.0411.681346
462020.0414.226154
472021.0416.404808
482022.0418.514615
\n", "
" ], "text/plain": [ " year yearly average\n", "0 1974.0 329.502121\n", "1 1975.0 331.217447\n", "2 1976.0 332.031765\n", "3 1977.0 333.844423\n", "4 1978.0 335.420755\n", "5 1979.0 336.880192\n", "6 1980.0 338.762308\n", "7 1981.0 340.130577\n", "8 1982.0 341.347551\n", "9 1983.0 343.165882\n", "10 1984.0 344.680000\n", "11 1985.0 346.342885\n", "12 1986.0 347.616154\n", "13 1987.0 349.335000\n", "14 1988.0 351.698269\n", "15 1989.0 353.215849\n", "16 1990.0 354.453846\n", "17 1991.0 355.741346\n", "18 1992.0 356.547115\n", "19 1993.0 357.228077\n", "20 1994.0 358.965192\n", "21 1995.0 360.979245\n", "22 1996.0 362.756346\n", "23 1997.0 363.887500\n", "24 1998.0 366.865385\n", "25 1999.0 368.521538\n", "26 2000.0 369.732830\n", "27 2001.0 371.341154\n", "28 2002.0 373.474808\n", "29 2003.0 375.987500\n", "30 2004.0 377.720385\n", "31 2005.0 380.056078\n", "32 2006.0 382.083585\n", "33 2007.0 384.083462\n", "34 2008.0 385.793469\n", "35 2009.0 387.663077\n", "36 2010.0 390.085192\n", "37 2011.0 391.859808\n", "38 2012.0 394.068868\n", "39 2013.0 396.773269\n", "40 2014.0 398.857500\n", "41 2015.0 401.020769\n", "42 2016.0 404.434423\n", "43 2017.0 406.774906\n", "44 2018.0 408.746346\n", "45 2019.0 411.681346\n", "46 2020.0 414.226154\n", "47 2021.0 416.404808\n", "48 2022.0 418.514615" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_data = np.zeros([year_max-year_min, 2])\n", "for i in range(year_min, year_max):\n", " year = co2_clean.loc[co2_clean[\"year\"]==i]\n", " y_avg = year[[\"average\"]].mean()\n", " avg_data[i-year_min, 0] = i\n", " avg_data[i-year_min, 1] = y_avg.values[0]\n", " \n", "yearly_avg = pd.DataFrame(avg_data, columns=[\"year\", \"yearly average\"])\n", "yearly_avg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous convertissons le temps dans une année en semaines calendriers car c'est plus intuitive. Après, nous joindrons les données originaux avec la moyenne annuelle." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [ { "data": { "text/html": [ "
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800yeartimeweekyearly average
019745191974.3795333.375-999.99-999.9950.390.379519.7340329.502121
119745261974.3986332.956-999.99-999.9950.050.398620.7272329.502121
21974621974.4178332.355-999.99-999.9949.590.417821.7256329.502121
31974691974.4370332.207-999.99-999.9949.640.437022.7240329.502121
419746161974.4562332.377-999.99-999.9950.060.456223.7224329.502121
519746231974.4753331.735-999.99-999.9949.720.475324.7156329.502121
619746301974.4945331.696-999.99-999.9950.030.494525.7140329.502121
71974771974.5137331.466-999.99-999.9950.200.513726.7124329.502121
819747141974.5329330.835-999.99-999.9950.010.532927.7108329.502121
919747211974.5521330.767-999.99-999.9950.410.552128.7092329.502121
1019747281974.5712329.814-999.99-999.9949.970.571229.7024329.502121
111974841974.5904329.855-999.99-999.9950.540.590430.7008329.502121
1219748111974.6096329.155-999.99-999.9950.370.609631.6992329.502121
1319748181974.6288329.066-999.99-999.9950.800.628832.6976329.502121
1419748251974.6479328.337-999.99-999.9950.540.647933.6908329.502121
151974911974.6671328.065-999.99-999.9950.680.667134.6892329.502121
161974981974.6863327.564-999.99-999.9950.530.686335.6876329.502121
1719749151974.7055326.726-999.99-999.9949.950.705536.6860329.502121
1819749221974.7247326.995-999.99-999.9950.380.724737.6844329.502121
1919749291974.7438327.315-999.99-999.9950.760.743838.6776329.502121
2019741061974.7630327.076-999.99-999.9950.490.763039.6760329.502121
21197410131974.7822327.235-999.99-999.9950.530.782240.6744329.502121
22197410201974.8014327.405-999.99-999.9950.510.801441.6728329.502121
23197410271974.8205327.647-999.99-999.9950.500.820542.6660329.502121
2419741131974.8397327.807-999.99-999.9950.390.839743.6644329.502121
25197411101974.8589328.506-999.99-999.9950.790.858944.6628329.502121
26197411171974.8781328.616-999.99-999.9950.590.878145.6612329.502121
27197411241974.8973328.465-999.99-999.9950.140.897346.6596329.502121
2819741211974.9164328.805-999.99-999.9950.200.916447.6528329.502121
2919741281974.9356329.397-999.99-999.9950.530.935648.6512329.502121
.......................................
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254320236182023.4616423.497420.84398.39141.280.461624.0032NaN
254420236252023.4808422.197420.32398.78140.460.480825.0016NaN
25452023722023.5000422.624419.91398.34141.410.500026.0000NaN
25462023792023.5192422.355419.18397.93141.660.519226.9984NaN
254720237162023.5384421.345418.36396.93141.190.538427.9968NaN
254820237232023.5575421.284418.03397.30141.670.557528.9900NaN
254920237302023.5767420.836418.10396.80141.750.576729.9884NaN
25502023862023.5959420.026417.36395.65141.460.595930.9868NaN
255120238132023.6151418.984417.25395.24140.920.615131.9852NaN
255220238202023.6342419.312416.64395.22141.710.634232.9784NaN
255320238272023.6534419.275416.42394.45142.090.653433.9768NaN
25542023932023.6726418.644416.27393.92141.810.672634.9752NaN
255520239102023.6918418.522416.15393.52141.960.691835.9736NaN
255620239172023.7110418.335415.65393.79141.940.711036.9720NaN
255720239242023.7301418.297415.34393.46141.970.730137.9652NaN
255820231012023.7493418.316415.30393.52141.960.749338.9636NaN
255920231082023.7685418.535415.39393.58142.050.768539.9620NaN
2560202310152023.7877419.467415.82393.98142.770.787740.9604NaN
2561202310222023.8068418.916416.19394.23141.930.806841.9536NaN
2562202310292023.8260419.057416.41394.47141.750.826042.9520NaN
256320231152023.8452419.285417.00394.80141.640.845243.9504NaN
2564202311122023.8644421.226417.31395.64143.240.864444.9488NaN
2565202311192023.8836421.215418.38395.26142.880.883645.9472NaN
2566202311262023.9027420.312417.81396.21141.660.902746.9404NaN
256720231232023.9219421.022419.23396.43142.070.921947.9388NaN
2568202312102023.9411422.207418.81396.39142.960.941148.9372NaN
2569202312172023.9603422.245419.05397.72142.740.960349.9356NaN
2570202312242023.9795421.865419.41397.53142.120.979550.9340NaN
2571202312312023.9986422.524419.34397.73142.550.998651.9272NaN
\n", "

2572 rows × 12 columns

\n", "
" ], "text/plain": [ " year month day decimal average ndays 1 year ago 10 years ago \\\n", "0 1974 5 19 1974.3795 333.37 5 -999.99 -999.99 \n", "1 1974 5 26 1974.3986 332.95 6 -999.99 -999.99 \n", "2 1974 6 2 1974.4178 332.35 5 -999.99 -999.99 \n", "3 1974 6 9 1974.4370 332.20 7 -999.99 -999.99 \n", "4 1974 6 16 1974.4562 332.37 7 -999.99 -999.99 \n", "5 1974 6 23 1974.4753 331.73 5 -999.99 -999.99 \n", "6 1974 6 30 1974.4945 331.69 6 -999.99 -999.99 \n", "7 1974 7 7 1974.5137 331.46 6 -999.99 -999.99 \n", "8 1974 7 14 1974.5329 330.83 5 -999.99 -999.99 \n", "9 1974 7 21 1974.5521 330.76 7 -999.99 -999.99 \n", "10 1974 7 28 1974.5712 329.81 4 -999.99 -999.99 \n", "11 1974 8 4 1974.5904 329.85 5 -999.99 -999.99 \n", "12 1974 8 11 1974.6096 329.15 5 -999.99 -999.99 \n", "13 1974 8 18 1974.6288 329.06 6 -999.99 -999.99 \n", "14 1974 8 25 1974.6479 328.33 7 -999.99 -999.99 \n", "15 1974 9 1 1974.6671 328.06 5 -999.99 -999.99 \n", "16 1974 9 8 1974.6863 327.56 4 -999.99 -999.99 \n", "17 1974 9 15 1974.7055 326.72 6 -999.99 -999.99 \n", "18 1974 9 22 1974.7247 326.99 5 -999.99 -999.99 \n", "19 1974 9 29 1974.7438 327.31 5 -999.99 -999.99 \n", "20 1974 10 6 1974.7630 327.07 6 -999.99 -999.99 \n", "21 1974 10 13 1974.7822 327.23 5 -999.99 -999.99 \n", "22 1974 10 20 1974.8014 327.40 5 -999.99 -999.99 \n", "23 1974 10 27 1974.8205 327.64 7 -999.99 -999.99 \n", "24 1974 11 3 1974.8397 327.80 7 -999.99 -999.99 \n", "25 1974 11 10 1974.8589 328.50 6 -999.99 -999.99 \n", "26 1974 11 17 1974.8781 328.61 6 -999.99 -999.99 \n", "27 1974 11 24 1974.8973 328.46 5 -999.99 -999.99 \n", "28 1974 12 1 1974.9164 328.80 5 -999.99 -999.99 \n", "29 1974 12 8 1974.9356 329.39 7 -999.99 -999.99 \n", "... ... ... ... ... ... ... ... ... \n", "2542 2023 6 11 2023.4425 424.13 6 421.00 398.78 \n", "2543 2023 6 18 2023.4616 423.49 7 420.84 398.39 \n", "2544 2023 6 25 2023.4808 422.19 7 420.32 398.78 \n", "2545 2023 7 2 2023.5000 422.62 4 419.91 398.34 \n", "2546 2023 7 9 2023.5192 422.35 5 419.18 397.93 \n", "2547 2023 7 16 2023.5384 421.34 5 418.36 396.93 \n", "2548 2023 7 23 2023.5575 421.28 4 418.03 397.30 \n", "2549 2023 7 30 2023.5767 420.83 6 418.10 396.80 \n", "2550 2023 8 6 2023.5959 420.02 6 417.36 395.65 \n", "2551 2023 8 13 2023.6151 418.98 4 417.25 395.24 \n", "2552 2023 8 20 2023.6342 419.31 2 416.64 395.22 \n", "2553 2023 8 27 2023.6534 419.27 5 416.42 394.45 \n", "2554 2023 9 3 2023.6726 418.64 4 416.27 393.92 \n", "2555 2023 9 10 2023.6918 418.52 2 416.15 393.52 \n", "2556 2023 9 17 2023.7110 418.33 5 415.65 393.79 \n", "2557 2023 9 24 2023.7301 418.29 7 415.34 393.46 \n", "2558 2023 10 1 2023.7493 418.31 6 415.30 393.52 \n", "2559 2023 10 8 2023.7685 418.53 5 415.39 393.58 \n", "2560 2023 10 15 2023.7877 419.46 7 415.82 393.98 \n", "2561 2023 10 22 2023.8068 418.91 6 416.19 394.23 \n", "2562 2023 10 29 2023.8260 419.05 7 416.41 394.47 \n", "2563 2023 11 5 2023.8452 419.28 5 417.00 394.80 \n", "2564 2023 11 12 2023.8644 421.22 6 417.31 395.64 \n", "2565 2023 11 19 2023.8836 421.21 5 418.38 395.26 \n", "2566 2023 11 26 2023.9027 420.31 2 417.81 396.21 \n", "2567 2023 12 3 2023.9219 421.02 2 419.23 396.43 \n", "2568 2023 12 10 2023.9411 422.20 7 418.81 396.39 \n", "2569 2023 12 17 2023.9603 422.24 5 419.05 397.72 \n", "2570 2023 12 24 2023.9795 421.86 5 419.41 397.53 \n", "2571 2023 12 31 2023.9986 422.52 4 419.34 397.73 \n", "\n", " increase since 1800 yeartime week yearly average \n", "0 50.39 0.3795 19.7340 329.502121 \n", "1 50.05 0.3986 20.7272 329.502121 \n", "2 49.59 0.4178 21.7256 329.502121 \n", "3 49.64 0.4370 22.7240 329.502121 \n", "4 50.06 0.4562 23.7224 329.502121 \n", "5 49.72 0.4753 24.7156 329.502121 \n", "6 50.03 0.4945 25.7140 329.502121 \n", "7 50.20 0.5137 26.7124 329.502121 \n", "8 50.01 0.5329 27.7108 329.502121 \n", "9 50.41 0.5521 28.7092 329.502121 \n", "10 49.97 0.5712 29.7024 329.502121 \n", "11 50.54 0.5904 30.7008 329.502121 \n", "12 50.37 0.6096 31.6992 329.502121 \n", "13 50.80 0.6288 32.6976 329.502121 \n", "14 50.54 0.6479 33.6908 329.502121 \n", "15 50.68 0.6671 34.6892 329.502121 \n", "16 50.53 0.6863 35.6876 329.502121 \n", "17 49.95 0.7055 36.6860 329.502121 \n", "18 50.38 0.7247 37.6844 329.502121 \n", "19 50.76 0.7438 38.6776 329.502121 \n", "20 50.49 0.7630 39.6760 329.502121 \n", "21 50.53 0.7822 40.6744 329.502121 \n", "22 50.51 0.8014 41.6728 329.502121 \n", "23 50.50 0.8205 42.6660 329.502121 \n", "24 50.39 0.8397 43.6644 329.502121 \n", "25 50.79 0.8589 44.6628 329.502121 \n", "26 50.59 0.8781 45.6612 329.502121 \n", "27 50.14 0.8973 46.6596 329.502121 \n", "28 50.20 0.9164 47.6528 329.502121 \n", "29 50.53 0.9356 48.6512 329.502121 \n", "... ... ... ... ... \n", "2542 141.49 0.4425 23.0100 NaN \n", "2543 141.28 0.4616 24.0032 NaN \n", "2544 140.46 0.4808 25.0016 NaN \n", "2545 141.41 0.5000 26.0000 NaN \n", "2546 141.66 0.5192 26.9984 NaN \n", "2547 141.19 0.5384 27.9968 NaN \n", "2548 141.67 0.5575 28.9900 NaN \n", "2549 141.75 0.5767 29.9884 NaN \n", "2550 141.46 0.5959 30.9868 NaN \n", "2551 140.92 0.6151 31.9852 NaN \n", "2552 141.71 0.6342 32.9784 NaN \n", "2553 142.09 0.6534 33.9768 NaN \n", "2554 141.81 0.6726 34.9752 NaN \n", "2555 141.96 0.6918 35.9736 NaN \n", "2556 141.94 0.7110 36.9720 NaN \n", "2557 141.97 0.7301 37.9652 NaN \n", "2558 141.96 0.7493 38.9636 NaN \n", "2559 142.05 0.7685 39.9620 NaN \n", "2560 142.77 0.7877 40.9604 NaN \n", "2561 141.93 0.8068 41.9536 NaN \n", "2562 141.75 0.8260 42.9520 NaN \n", "2563 141.64 0.8452 43.9504 NaN \n", "2564 143.24 0.8644 44.9488 NaN \n", "2565 142.88 0.8836 45.9472 NaN \n", "2566 141.66 0.9027 46.9404 NaN \n", "2567 142.07 0.9219 47.9388 NaN \n", "2568 142.96 0.9411 48.9372 NaN \n", "2569 142.74 0.9603 49.9356 NaN \n", "2570 142.12 0.9795 50.9340 NaN \n", "2571 142.55 0.9986 51.9272 NaN \n", "\n", "[2572 rows x 12 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "co2_clean[\"week\"]=52*co2_clean[\"yeartime\"]\n", "co2_merge = pd.merge(co2_clean, yearly_avg, how=\"outer\")\n", "co2_merge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec cela, nous pouvons tracer l'oscillation de $CO_2$ en chaque année qui est liée aux différents saisons de l'année." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,\"Oscillation pendant l'année\")" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "co2_merge[\"oscillation\"]=co2_merge[\"average\"]-co2_merge[\"yearly average\"]\n", "\n", "co2_merge.plot(x=[\"week\"], y=[\"oscillation\"], kind=\"scatter\", s=5)\n", "plt.xlabel(\"Numéro de semaine\")\n", "plt.ylabel(r\"$\\mu$ mol/mol de CO2 dans l'atmosphère\")\n", "plt.title(\"Oscillation pendant l'année\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La moyenne annuelle supprime cette oscillation. Donc, en tracant seulement la moyenne annuelle, on voit clairement l'augmentation de $CO_2$ dans l'atmosphère à cause du changement climatique par nous humains." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5,1,'La moyenne annuelle de la concentration de CO2')" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_avg.plot(x=[\"year\"], y=[\"yearly average\"], legend=None)\n", "plt.xlabel(\"year\")\n", "plt.ylabel(r\"$\\mu$ mol/mol de CO2 dans l'atmosphère\")\n", "plt.title(\"La moyenne annuelle de la concentration de CO2\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cette courbe montre très bien comment le $CO_2$ monte dans notre atmosphère chaque année depuis les années 1970. Ceci implique l'effet de serre qui augmente la température globale. Pour que nous humains peuvent toujours habiter sur la terre, il est nécessaire que le niveau de $CO_2$ finisse d'augmenter." ] } ], "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 }