diff --git a/module3/exo3/carbon_dioxyde_concentration.ipynb b/module3/exo3/carbon_dioxyde_concentration.ipynb index 94365a59e4a4d5e01adf6e715332b234b346e7df..bfc095993fa425d05d1d074c8003b8357e79299f 100644 --- a/module3/exo3/carbon_dioxyde_concentration.ipynb +++ b/module3/exo3/carbon_dioxyde_concentration.ipynb @@ -60,12 +60,2250 @@ " urllib.request.urlretrieve(data_url, \"monthly_in_situ_co2_mlo.csv\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comme expliqué dans le fichier `csv`, les données sont organisées en 10 colonnes :\n", + "\n", + "Numéro de colonne | Libellé | Contenu | Unité ou format (si applicable)\n", + ":---------------- | :------ | :------ | :--------------------\n", + "1 | Yr | Année du relevé |\n", + "2 | Mn | Mois du relevé |\n", + "3 | Date | Date du relevé | format Excel\n", + "4 | Date | Date du relevé | date décimale\n", + "5 | CO2 | Relevé de la concentration en C02 (2012 SIO manometric mole fraction scale), ajusté aux 24h du 15ème de chaque mois | ppm\n", + "6 | seasonally adjusted | Relevés orignaux (CO2) ajustés pour retirer le cycle saisonnier régulier | ppm\n", + "7 | fit | Relevés originaux (C02) lissés | ppm\n", + "8 | seasonally adjusted fit | Relevés lissés ajustés saisonalement | ppm\n", + "9 | CO2 filled | Relevés originaux (CO2) complétés par les résultats lissés (fit) | ppm\n", + "10 | seasonally adjusted fit filled | Relevés ajustés saisonalement (seasonally sdjusted) complétés par les résultats lissés et ajustés (seasonally adjusted fit) | ppm\n", + "\n", + "Les 55 premières lignes contiennent les explications ci-dessus, donc nous ne les traiterons pas avec les autres données. Nous renommons également les colonnes pour plus de concision et de clarté." + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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418.70 416.12 418.58 416.02 418.70 \n", + "762 2021 7 44392 2021.5370 416.65 415.84 416.97 416.20 416.65 \n", + "763 2021 8 44423 2021.6219 414.34 415.89 414.79 416.39 414.34 \n", + "764 2021 9 44454 2021.7068 412.90 416.41 413.05 416.58 412.90 \n", + "765 2021 10 44484 2021.7890 413.55 417.17 413.15 416.76 413.55 \n", + "766 2021 11 44515 2021.8740 414.82 417.09 414.70 416.94 414.82 \n", + "767 2021 12 44545 2021.9562 416.43 417.36 416.20 417.11 416.43 \n", + "768 2022 1 44576 2022.0411 418.01 417.94 417.35 417.26 418.01 \n", + "769 2022 2 44607 2022.1260 418.99 418.20 418.20 417.40 418.99 \n", + "770 2022 3 44635 2022.2027 418.45 416.90 419.08 417.51 418.45 \n", + "771 2022 4 44666 2022.2877 420.02 417.23 420.44 417.63 420.02 \n", + "772 2022 5 44696 2022.3699 420.78 417.36 421.16 417.75 420.78 \n", + "773 2022 6 44727 2022.4548 420.68 418.09 420.43 417.86 420.68 \n", + "774 2022 7 44757 2022.5370 418.66 417.85 418.75 417.98 418.66 \n", + "775 2022 8 44788 2022.6219 -99.99 -99.99 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"756 415.08 \n", + "757 415.69 \n", + "758 415.62 \n", + "759 415.46 \n", + "760 415.55 \n", + "761 416.12 \n", + "762 415.84 \n", + "763 415.89 \n", + "764 416.41 \n", + "765 417.17 \n", + "766 417.09 \n", + "767 417.36 \n", + "768 417.94 \n", + "769 418.20 \n", + "770 416.90 \n", + "771 417.23 \n", + "772 417.36 \n", + "773 418.09 \n", + "774 417.85 \n", + "775 -99.99 \n", + "776 -99.99 \n", + "777 -99.99 \n", + "778 -99.99 \n", + "779 -99.99 \n", + "\n", + "[780 rows x 10 columns]" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raw_data = pd.read_csv(\"monthly_in_situ_co2_mlo.csv\", skiprows=56)\n", + "c_names = raw_data.columns.values\n", + "raw_data = raw_data.rename(columns = {c_names[0]:\"Yr\", c_names[1]:\"Mn\", c_names[2]:\"EDate\", c_names[3]:\"DDate\", c_names[4]:\"C02\", c_names[5]:\"SA\", c_names[6]:\"Fit\", c_names[7]:\"SAFit\", c_names[8]:\"Filled\", c_names[9]:\"SAFitFilled\"})\n", + "raw_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On notera que les valeurs manquantes sont notées `-99.99` dans le fichier, et que même les colonnes `Filled` contiennent ces valeurs manquantes pour les données de janvier et février 1958 et les mois les plus récents :" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Yr Mn EDate DDate C02 SA Fit SAFit Filled \\\n", + "0 1958 1 21200 1958.0411 -99.99 -99.99 -99.99 -99.99 -99.99 \n", + "1 1958 2 21231 1958.1260 -99.99 -99.99 -99.99 -99.99 -99.99 \n", + "775 2022 8 44788 2022.6219 -99.99 -99.99 -99.99 -99.99 -99.99 \n", + "776 2022 9 44819 2022.7068 -99.99 -99.99 -99.99 -99.99 -99.99 \n", + "777 2022 10 44849 2022.7890 -99.99 -99.99 -99.99 -99.99 -99.99 \n", + "778 2022 11 44880 2022.8740 -99.99 -99.99 -99.99 -99.99 -99.99 \n", + "779 2022 12 44910 2022.9562 -99.99 -99.99 -99.99 -99.99 -99.99 \n", + "\n", + " SAFitFilled \n", + "0 -99.99 \n", + "1 -99.99 \n", + "775 -99.99 \n", + "776 -99.99 \n", + "777 -99.99 \n", + "778 -99.99 \n", + "779 -99.99 " + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "missing_data = raw_data.iloc[lambda df: [(raw_data.at[row,'SAFitFilled'] == -99.99) for row in df.index]]\n", + "missing_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This missing data is removed from the dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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.................................
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77420227447572022.5370418.66417.85418.75417.98418.66417.85
\n", + "

773 rows × 10 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn EDate DDate C02 SA Fit SAFit Filled \\\n", + "2 1958 3 21259 1958.2027 315.71 314.44 316.20 314.91 315.71 \n", + "3 1958 4 21290 1958.2877 317.45 315.16 317.30 314.99 317.45 \n", + "4 1958 5 21320 1958.3699 317.51 314.70 317.88 315.07 317.51 \n", + "5 1958 6 21351 1958.4548 -99.99 -99.99 317.26 315.15 317.26 \n", + "6 1958 7 21381 1958.5370 315.87 315.20 315.86 315.22 315.87 \n", + "7 1958 8 21412 1958.6219 314.93 316.21 313.98 315.29 314.93 \n", + "8 1958 9 21443 1958.7068 313.21 316.10 312.45 315.36 313.21 \n", + "9 1958 10 21473 1958.7890 -99.99 -99.99 312.43 315.41 312.43 \n", + "10 1958 11 21504 1958.8740 313.33 315.21 313.61 315.46 313.33 \n", + "11 1958 12 21534 1958.9562 314.67 315.43 314.77 315.52 314.67 \n", + "12 1959 1 21565 1959.0411 315.58 315.52 315.64 315.58 315.58 \n", + "13 1959 2 21596 1959.1260 316.49 315.84 316.29 315.64 316.49 \n", + "14 1959 3 21624 1959.2027 316.65 315.37 316.99 315.70 316.65 \n", + "15 1959 4 21655 1959.2877 317.72 315.42 318.09 315.77 317.72 \n", + "16 1959 5 21685 1959.3699 318.29 315.48 318.67 315.85 318.29 \n", + "17 1959 6 21716 1959.4548 318.15 316.01 318.06 315.94 318.15 \n", + "18 1959 7 21746 1959.5370 316.54 315.87 316.67 316.03 316.54 \n", + "19 1959 8 21777 1959.6219 314.80 316.08 314.81 316.13 314.80 \n", + "20 1959 9 21808 1959.7068 313.84 316.74 313.30 316.22 313.84 \n", + "21 1959 10 21838 1959.7890 313.33 316.33 313.32 316.31 313.33 \n", + "22 1959 11 21869 1959.8740 314.81 316.69 314.54 316.40 314.81 \n", + "23 1959 12 21899 1959.9562 315.58 316.35 315.73 316.48 315.58 \n", + "24 1960 1 21930 1960.0410 316.43 316.37 316.63 316.56 316.43 \n", + "25 1960 2 21961 1960.1257 316.98 316.33 317.30 316.64 316.98 \n", + "26 1960 3 21990 1960.2049 317.58 316.27 318.04 316.72 317.58 \n", + "27 1960 4 22021 1960.2896 319.03 316.70 319.14 316.80 319.03 \n", + "28 1960 5 22051 1960.3716 320.03 317.21 319.69 316.87 320.03 \n", + "29 1960 6 22082 1960.4563 319.59 317.46 319.03 316.93 319.59 \n", + "30 1960 7 22112 1960.5383 318.18 317.53 317.60 316.98 318.18 \n", + "31 1960 8 22143 1960.6230 315.90 317.22 315.67 317.02 315.90 \n", + ".. ... .. ... ... ... ... ... ... ... \n", + "745 2020 2 43876 2020.1257 414.05 413.27 413.83 413.04 414.05 \n", + "746 2020 3 43905 2020.2049 414.45 412.88 414.82 413.23 414.45 \n", + "747 2020 4 43936 2020.2896 416.11 413.31 416.25 413.43 416.11 \n", + "748 2020 5 43966 2020.3716 417.15 413.76 417.02 413.63 417.15 \n", + "749 2020 6 43997 2020.4563 416.29 413.74 416.36 413.83 416.29 \n", + "750 2020 7 44027 2020.5383 414.42 413.64 414.77 414.03 414.42 \n", + "751 2020 8 44058 2020.6230 412.52 414.10 412.61 414.23 412.52 \n", + "752 2020 9 44089 2020.7077 411.18 414.69 410.90 414.43 411.18 \n", + "753 2020 10 44119 2020.7896 411.12 414.73 411.02 414.62 411.12 \n", + "754 2020 11 44150 2020.8743 412.88 415.15 412.57 414.81 412.88 \n", + "755 2020 12 44180 2020.9563 413.89 414.81 414.08 414.99 413.89 \n", + "756 2021 1 44211 2021.0411 415.15 415.08 415.25 415.16 415.15 \n", + "757 2021 2 44242 2021.1260 416.47 415.69 416.13 415.34 416.47 \n", + "758 2021 3 44270 2021.2027 417.16 415.62 417.06 415.50 417.16 \n", + "759 2021 4 44301 2021.2877 418.24 415.46 418.47 415.67 418.24 \n", + "760 2021 5 44331 2021.3699 418.95 415.55 419.24 415.84 418.95 \n", + "761 2021 6 44362 2021.4548 418.70 416.12 418.58 416.02 418.70 \n", + "762 2021 7 44392 2021.5370 416.65 415.84 416.97 416.20 416.65 \n", + "763 2021 8 44423 2021.6219 414.34 415.89 414.79 416.39 414.34 \n", + "764 2021 9 44454 2021.7068 412.90 416.41 413.05 416.58 412.90 \n", + "765 2021 10 44484 2021.7890 413.55 417.17 413.15 416.76 413.55 \n", + "766 2021 11 44515 2021.8740 414.82 417.09 414.70 416.94 414.82 \n", + "767 2021 12 44545 2021.9562 416.43 417.36 416.20 417.11 416.43 \n", + "768 2022 1 44576 2022.0411 418.01 417.94 417.35 417.26 418.01 \n", + "769 2022 2 44607 2022.1260 418.99 418.20 418.20 417.40 418.99 \n", + "770 2022 3 44635 2022.2027 418.45 416.90 419.08 417.51 418.45 \n", + "771 2022 4 44666 2022.2877 420.02 417.23 420.44 417.63 420.02 \n", + "772 2022 5 44696 2022.3699 420.78 417.36 421.16 417.75 420.78 \n", + "773 2022 6 44727 2022.4548 420.68 418.09 420.43 417.86 420.68 \n", + "774 2022 7 44757 2022.5370 418.66 417.85 418.75 417.98 418.66 \n", + "\n", + " SAFitFilled \n", + "2 314.44 \n", + "3 315.16 \n", + "4 314.70 \n", + "5 315.15 \n", + "6 315.20 \n", + "7 316.21 \n", + "8 316.10 \n", + "9 315.41 \n", + "10 315.21 \n", + "11 315.43 \n", + "12 315.52 \n", + "13 315.84 \n", + "14 315.37 \n", + "15 315.42 \n", + "16 315.48 \n", + "17 316.01 \n", + "18 315.87 \n", + "19 316.08 \n", + "20 316.74 \n", + "21 316.33 \n", + "22 316.69 \n", + "23 316.35 \n", + "24 316.37 \n", + "25 316.33 \n", + "26 316.27 \n", + "27 316.70 \n", + "28 317.21 \n", + "29 317.46 \n", + "30 317.53 \n", + "31 317.22 \n", + ".. ... \n", + "745 413.27 \n", + "746 412.88 \n", + "747 413.31 \n", + "748 413.76 \n", + "749 413.74 \n", + "750 413.64 \n", + "751 414.10 \n", + "752 414.69 \n", + "753 414.73 \n", + "754 415.15 \n", + "755 414.81 \n", + "756 415.08 \n", + "757 415.69 \n", + "758 415.62 \n", + "759 415.46 \n", + "760 415.55 \n", + "761 416.12 \n", + "762 415.84 \n", + "763 415.89 \n", + "764 416.41 \n", + "765 417.17 \n", + "766 417.09 \n", + "767 417.36 \n", + "768 417.94 \n", + "769 418.20 \n", + "770 416.90 \n", + "771 417.23 \n", + "772 417.36 \n", + "773 418.09 \n", + "774 417.85 \n", + "\n", + "[773 rows x 10 columns]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = raw_data.drop(missing_data.index).copy()\n", + "data" + ] + }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quelques graphiques\n", + "\n", + "Nous utilisons les données complétées pour la suite de cette analyse.\n", + "\n", + "Voici tout d'abord une représentation de l'évolution de C02 atmosphérique depuis 1958 :" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data[\"Filled\"].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let us zoom at the scale of a few years to observe the oscillation." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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