diff --git a/module3/exo3/Exercice_evaluation_pairs_sujet_1_CO2.ipynb b/module3/exo3/Exercice_evaluation_pairs_sujet_1_CO2.ipynb index b740164b07bda61196b9b7b15bc924c3dd6df4f1..b956d7ee0e3837dd8c1b3671ffd254ae50fca1df 100644 --- a/module3/exo3/Exercice_evaluation_pairs_sujet_1_CO2.ipynb +++ b/module3/exo3/Exercice_evaluation_pairs_sujet_1_CO2.ipynb @@ -13,17 +13,2424 @@ "import os" ] }, + { + "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 marcherait pas (le 11 kanvier 2024), donc cet site est utilisé." + ] + }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "#change when website works again\n", - "if os.path.exists(\"incidence-PAY-3.csv\"):\n", - " data_path = \"incidence-PAY-3.csv\"\n", + "if os.path.exists(\"co2_weekly_mlo.csv\"):\n", + " data_path = \"co2_weekly_mlo.csv\"\n", "else:\n", - " data_path = \"https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html\"" + " 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." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "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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2590 rows × 9 columns

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" + ], + "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 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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
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yearmonthdaydecimalaveragendays1 year ago10 years agoincrease since 1800
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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
..............................
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256120236182023.4616423.497420.84398.39141.28
256220236252023.4808422.197420.32398.78140.46
25632023722023.5000422.624419.91398.34141.41
25642023792023.5192422.355419.18397.93141.66
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256620237232023.5575421.284418.03397.30141.67
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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
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2578202310152023.7877419.467415.82393.98142.77
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258120231152023.8452419.285417.00394.80141.64
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2589202312312023.9986422.524419.34397.73142.55
\n", + "

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": 52, + "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": 53, + "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": 53, + "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": 55, + "metadata": { + "collapsed": true + }, + "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": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py:1716: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", + " series.name = label\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 64, + "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_clean.plot(x=[\"decimal\"], y=[\"average\"])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Separation des données en années." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "year_min = co2_clean[\"year\"].iloc[0]\n", + "year_max = co2_clean[\"year\"].iloc[-1]" ] }, {