diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index d403457c8be78496b85694918d3d28e6f611a0fb..a7e6a86bccd04bfe9adf9904e56b5ff14389aa6e 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -41,9 +41,976 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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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 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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" + "raw_data" ] }, { @@ -66,7 +1033,7 @@ "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, la date, et la valeur brute de la mesure." + "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." ] }, { @@ -75,7 +1042,7 @@ "metadata": {}, "outputs": [], "source": [ - "useful_data = data.iloc[0:758, [0,1,4]]\n", + "useful_data = data.iloc[0:len(data.index), [0,1,4]]\n", "#useful_data" ] }, @@ -634,6 +1601,13 @@ "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, @@ -696,123 +1670,123 @@ " \n", " \n", " \n", - " 1958-01-13/1958-01-19\n", + " 1958-03\n", " 315.70\n", " \n", " \n", - " 1958-01-20/1958-01-26\n", + " 1958-04\n", " 317.46\n", " \n", " \n", - " 1958-01-27/1958-02-02\n", + " 1958-05\n", " 317.51\n", " \n", " \n", - " 1958-02-10/1958-02-16\n", + " 1958-07\n", " 315.86\n", " \n", " \n", - " 1958-02-17/1958-02-23\n", + " 1958-08\n", " 314.93\n", " \n", " \n", - " 1958-02-24/1958-03-02\n", + " 1958-09\n", " 313.21\n", " \n", " \n", - " 1958-03-10/1958-03-16\n", + " 1958-11\n", " 313.33\n", " \n", " \n", - " 1958-03-17/1958-03-23\n", + " 1958-12\n", " 314.67\n", " \n", " \n", - " 1958-12-29/1959-01-04\n", + " 1959-01\n", " 315.58\n", " \n", " \n", - " 1959-01-05/1959-01-11\n", + " 1959-02\n", " 316.49\n", " \n", " \n", - " 1959-01-12/1959-01-18\n", + " 1959-03\n", " 316.65\n", " \n", " \n", - " 1959-01-19/1959-01-25\n", + " 1959-04\n", " 317.72\n", " \n", " \n", - " 1959-01-26/1959-02-01\n", + " 1959-05\n", " 318.29\n", " \n", " \n", - " 1959-02-02/1959-02-08\n", + " 1959-06\n", " 318.15\n", " \n", " \n", - " 1959-02-09/1959-02-15\n", + " 1959-07\n", " 316.54\n", " \n", " \n", - " 1959-02-16/1959-02-22\n", + " 1959-08\n", " 314.80\n", " \n", " \n", - " 1959-02-23/1959-03-01\n", + " 1959-09\n", " 313.84\n", " \n", " \n", - " 1959-03-02/1959-03-08\n", + " 1959-10\n", " 313.33\n", " \n", " \n", - " 1959-03-09/1959-03-15\n", + " 1959-11\n", " 314.81\n", " \n", " \n", - " 1959-03-16/1959-03-22\n", + " 1959-12\n", " 315.58\n", " \n", " \n", - " 1960-01-04/1960-01-10\n", + " 1960-01\n", " 316.43\n", " \n", " \n", - " 1960-01-11/1960-01-17\n", + " 1960-02\n", " 316.98\n", " \n", " \n", - " 1960-01-18/1960-01-24\n", + " 1960-03\n", " 317.58\n", " \n", " \n", - " 1960-01-25/1960-01-31\n", + " 1960-04\n", " 319.03\n", " \n", " \n", - " 1960-02-01/1960-02-07\n", + " 1960-05\n", " 320.04\n", " \n", " \n", - " 1960-02-08/1960-02-14\n", + " 1960-06\n", " 319.58\n", " \n", " \n", - " 1960-02-15/1960-02-21\n", + " 1960-07\n", " 318.18\n", " \n", " \n", - " 1960-02-22/1960-02-28\n", + " 1960-08\n", " 315.90\n", " \n", " \n", - " 1960-02-29/1960-03-06\n", + " 1960-09\n", " 314.17\n", " \n", " \n", - " 1960-03-07/1960-03-13\n", + " 1960-10\n", " 313.83\n", " \n", " \n", @@ -820,123 +1794,123 @@ " ...\n", " \n", " \n", - " 2017-03-13/2017-03-19\n", + " 2017-11\n", " 405.17\n", " \n", " \n", - " 2017-03-20/2017-03-26\n", + " 2017-12\n", " 406.75\n", " \n", " \n", - " 2018-01-01/2018-01-07\n", + " 2018-01\n", " 408.05\n", " \n", " \n", - " 2018-01-08/2018-01-14\n", + " 2018-02\n", " 408.34\n", " \n", " \n", - " 2018-01-15/2018-01-21\n", + " 2018-03\n", " 409.25\n", " \n", " \n", - " 2018-01-22/2018-01-28\n", + " 2018-04\n", " 410.30\n", " \n", " \n", - " 2018-01-29/2018-02-04\n", + " 2018-05\n", " 411.30\n", " \n", " \n", - " 2018-02-05/2018-02-11\n", + " 2018-06\n", " 410.88\n", " \n", " \n", - " 2018-02-12/2018-02-18\n", + " 2018-07\n", " 408.90\n", " \n", " \n", - " 2018-02-19/2018-02-25\n", + " 2018-08\n", " 407.10\n", " \n", " \n", - " 2018-02-26/2018-03-04\n", + " 2018-09\n", " 405.59\n", " \n", " \n", - " 2018-03-05/2018-03-11\n", + " 2018-10\n", " 405.99\n", " \n", " \n", - " 2018-03-12/2018-03-18\n", + " 2018-11\n", " 408.12\n", " \n", " \n", - " 2018-03-19/2018-03-25\n", + " 2018-12\n", " 409.23\n", " \n", " \n", - " 2018-12-31/2019-01-06\n", + " 2019-01\n", " 410.92\n", " \n", " \n", - " 2019-01-07/2019-01-13\n", + " 2019-02\n", " 411.66\n", " \n", " \n", - " 2019-01-14/2019-01-20\n", + " 2019-03\n", " 412.00\n", " \n", " \n", - " 2019-01-21/2019-01-27\n", + " 2019-04\n", " 413.52\n", " \n", " \n", - " 2019-01-28/2019-02-03\n", + " 2019-05\n", " 414.83\n", " \n", " \n", - " 2019-02-04/2019-02-10\n", + " 2019-06\n", " 413.96\n", " \n", " \n", - " 2019-02-11/2019-02-17\n", + " 2019-07\n", " 411.85\n", " \n", " \n", - " 2019-02-18/2019-02-24\n", + " 2019-08\n", " 410.08\n", " \n", " \n", - " 2019-02-25/2019-03-03\n", + " 2019-09\n", " 408.55\n", " \n", " \n", - " 2019-03-04/2019-03-10\n", + " 2019-10\n", " 408.43\n", " \n", " \n", - " 2019-03-11/2019-03-17\n", + " 2019-11\n", " 410.29\n", " \n", " \n", - " 2019-03-18/2019-03-24\n", + " 2019-12\n", " 411.85\n", " \n", " \n", - " 2019-12-30/2020-01-05\n", + " 2020-01\n", " 413.37\n", " \n", " \n", - " 2020-01-06/2020-01-12\n", + " 2020-02\n", " 414.09\n", " \n", " \n", - " 2020-01-13/2020-01-19\n", + " 2020-03\n", " 414.51\n", " \n", " \n", - " 2020-01-20/2020-01-26\n", + " 2020-04\n", " 416.18\n", " \n", " \n", @@ -945,69 +1919,69 @@ "" ], "text/plain": [ - " CO2\n", - "period \n", - "1958-01-13/1958-01-19 315.70\n", - "1958-01-20/1958-01-26 317.46\n", - "1958-01-27/1958-02-02 317.51\n", - "1958-02-10/1958-02-16 315.86\n", - "1958-02-17/1958-02-23 314.93\n", - "1958-02-24/1958-03-02 313.21\n", - "1958-03-10/1958-03-16 313.33\n", - "1958-03-17/1958-03-23 314.67\n", - "1958-12-29/1959-01-04 315.58\n", - "1959-01-05/1959-01-11 316.49\n", - "1959-01-12/1959-01-18 316.65\n", - "1959-01-19/1959-01-25 317.72\n", - "1959-01-26/1959-02-01 318.29\n", - "1959-02-02/1959-02-08 318.15\n", - "1959-02-09/1959-02-15 316.54\n", - "1959-02-16/1959-02-22 314.80\n", - "1959-02-23/1959-03-01 313.84\n", - "1959-03-02/1959-03-08 313.33\n", - "1959-03-09/1959-03-15 314.81\n", - "1959-03-16/1959-03-22 315.58\n", - "1960-01-04/1960-01-10 316.43\n", - "1960-01-11/1960-01-17 316.98\n", - "1960-01-18/1960-01-24 317.58\n", - "1960-01-25/1960-01-31 319.03\n", - "1960-02-01/1960-02-07 320.04\n", - "1960-02-08/1960-02-14 319.58\n", - "1960-02-15/1960-02-21 318.18\n", - "1960-02-22/1960-02-28 315.90\n", - "1960-02-29/1960-03-06 314.17\n", - "1960-03-07/1960-03-13 313.83\n", - "... ...\n", - "2017-03-13/2017-03-19 405.17\n", - "2017-03-20/2017-03-26 406.75\n", - "2018-01-01/2018-01-07 408.05\n", - "2018-01-08/2018-01-14 408.34\n", - "2018-01-15/2018-01-21 409.25\n", - "2018-01-22/2018-01-28 410.30\n", - "2018-01-29/2018-02-04 411.30\n", - "2018-02-05/2018-02-11 410.88\n", - "2018-02-12/2018-02-18 408.90\n", - "2018-02-19/2018-02-25 407.10\n", - "2018-02-26/2018-03-04 405.59\n", - "2018-03-05/2018-03-11 405.99\n", - "2018-03-12/2018-03-18 408.12\n", - "2018-03-19/2018-03-25 409.23\n", - "2018-12-31/2019-01-06 410.92\n", - "2019-01-07/2019-01-13 411.66\n", - "2019-01-14/2019-01-20 412.00\n", - "2019-01-21/2019-01-27 413.52\n", - "2019-01-28/2019-02-03 414.83\n", - "2019-02-04/2019-02-10 413.96\n", - "2019-02-11/2019-02-17 411.85\n", - "2019-02-18/2019-02-24 410.08\n", - "2019-02-25/2019-03-03 408.55\n", - "2019-03-04/2019-03-10 408.43\n", - "2019-03-11/2019-03-17 410.29\n", - "2019-03-18/2019-03-24 411.85\n", - "2019-12-30/2020-01-05 413.37\n", - "2020-01-06/2020-01-12 414.09\n", - "2020-01-13/2020-01-19 414.51\n", - "2020-01-20/2020-01-26 416.18\n", + " 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]" ] @@ -1018,11 +1992,10 @@ } ], "source": [ - "def convertIntoPeriod(anneeEtSemaine):\n", - " y = (int)(anneeEtSemaine/100)\n", - " w = (int)(anneeEtSemaine%100)\n", - " per = isoweek.Week(y,w)\n", - " return pd.Period(per.day(0), 'W')\n", + "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')" ] @@ -1035,7 +2008,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -1059,6 +2032,45 @@ "useful_data['CO2'].plot()" ] }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "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": [ + "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": "code", "execution_count": null,