diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 4b1d4edf58f04ca2c7307a04d67ca52bd18279f5..eba60735e69bceb8ce75db174fa550606c2adeb5 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -1126,6 +1126,456 @@
"cell_type": "code",
"execution_count": 9,
"metadata": {},
+ "outputs": [],
+ "source": [
+ "useful_data\n",
+ "useful_data_copie = pd.DataFrame.copy(useful_data, deep = True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "On souhaite maintenant convertir l'année et le mois en un format plus adapté à Pandas, et à l'utiliser comme index. Un méthode possible est présentée ici, en rassemblant les deux informations puis en appliquant une fonction pour une mise au format Pandas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "useful_data['period'] = useful_data['Yr']*100 + useful_data['Mn']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "useful_data['period'] = useful_data['period'].astype(int)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "useful_data = useful_data.iloc[0:len(useful_data.index), [2,3]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
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+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " CO2 | \n",
+ "
\n",
+ " \n",
+ " period | \n",
+ " | \n",
+ "
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+ " \n",
+ " \n",
+ " \n",
+ " 1958-03 | \n",
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+ " \n",
+ " 1958-04 | \n",
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\n",
+ " \n",
+ " 1958-05 | \n",
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\n",
+ " \n",
+ " 1958-07 | \n",
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+ "
\n",
+ " \n",
+ " 1958-08 | \n",
+ " 314.93 | \n",
+ "
\n",
+ " \n",
+ " 1958-09 | \n",
+ " 313.21 | \n",
+ "
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+ " \n",
+ " 1958-11 | \n",
+ " 313.33 | \n",
+ "
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+ " \n",
+ " 1958-12 | \n",
+ " 314.67 | \n",
+ "
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+ " \n",
+ " 1959-01 | \n",
+ " 315.58 | \n",
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+ " \n",
+ " 1959-02 | \n",
+ " 316.49 | \n",
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+ " \n",
+ " 1959-03 | \n",
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+ " \n",
+ " 1959-04 | \n",
+ " 317.72 | \n",
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+ " \n",
+ " 1959-05 | \n",
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+ " \n",
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+ "
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+ " \n",
+ " 1959-07 | \n",
+ " 316.54 | \n",
+ "
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+ " \n",
+ " 1959-08 | \n",
+ " 314.80 | \n",
+ "
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+ " \n",
+ " 1959-09 | \n",
+ " 313.84 | \n",
+ "
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+ " \n",
+ " 1959-10 | \n",
+ " 313.33 | \n",
+ "
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+ " \n",
+ " 1959-11 | \n",
+ " 314.81 | \n",
+ "
\n",
+ " \n",
+ " 1959-12 | \n",
+ " 315.58 | \n",
+ "
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+ " \n",
+ " 1960-01 | \n",
+ " 316.43 | \n",
+ "
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+ " \n",
+ " 1960-02 | \n",
+ " 316.98 | \n",
+ "
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+ " \n",
+ " 1960-03 | \n",
+ " 317.58 | \n",
+ "
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+ " \n",
+ " 1960-04 | \n",
+ " 319.03 | \n",
+ "
\n",
+ " \n",
+ " 1960-05 | \n",
+ " 320.04 | \n",
+ "
\n",
+ " \n",
+ " 1960-06 | \n",
+ " 319.58 | \n",
+ "
\n",
+ " \n",
+ " 1960-07 | \n",
+ " 318.18 | \n",
+ "
\n",
+ " \n",
+ " 1960-08 | \n",
+ " 315.90 | \n",
+ "
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+ " \n",
+ " 1960-09 | \n",
+ " 314.17 | \n",
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+ " \n",
+ " 1960-10 | \n",
+ " 313.83 | \n",
+ "
\n",
+ " \n",
+ " ... | \n",
+ " ... | \n",
+ "
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+ " \n",
+ " 2017-11 | \n",
+ " 405.17 | \n",
+ "
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+ " \n",
+ " 2017-12 | \n",
+ " 406.75 | \n",
+ "
\n",
+ " \n",
+ " 2018-01 | \n",
+ " 408.05 | \n",
+ "
\n",
+ " \n",
+ " 2018-02 | \n",
+ " 408.34 | \n",
+ "
\n",
+ " \n",
+ " 2018-03 | \n",
+ " 409.25 | \n",
+ "
\n",
+ " \n",
+ " 2018-04 | \n",
+ " 410.30 | \n",
+ "
\n",
+ " \n",
+ " 2018-05 | \n",
+ " 411.30 | \n",
+ "
\n",
+ " \n",
+ " 2018-06 | \n",
+ " 410.88 | \n",
+ "
\n",
+ " \n",
+ " 2018-07 | \n",
+ " 408.90 | \n",
+ "
\n",
+ " \n",
+ " 2018-08 | \n",
+ " 407.10 | \n",
+ "
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+ " \n",
+ " 2018-09 | \n",
+ " 405.59 | \n",
+ "
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+ " \n",
+ " 2018-10 | \n",
+ " 405.99 | \n",
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+ " \n",
+ " 2018-11 | \n",
+ " 408.12 | \n",
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+ " \n",
+ " 2018-12 | \n",
+ " 409.23 | \n",
+ "
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+ " \n",
+ " 2019-01 | \n",
+ " 410.92 | \n",
+ "
\n",
+ " \n",
+ " 2019-02 | \n",
+ " 411.66 | \n",
+ "
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+ " \n",
+ " 2019-03 | \n",
+ " 412.00 | \n",
+ "
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+ " \n",
+ " 2019-04 | \n",
+ " 413.52 | \n",
+ "
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+ " \n",
+ " 2019-05 | \n",
+ " 414.83 | \n",
+ "
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+ " \n",
+ " 2019-06 | \n",
+ " 413.96 | \n",
+ "
\n",
+ " \n",
+ " 2019-07 | \n",
+ " 411.85 | \n",
+ "
\n",
+ " \n",
+ " 2019-08 | \n",
+ " 410.08 | \n",
+ "
\n",
+ " \n",
+ " 2019-09 | \n",
+ " 408.55 | \n",
+ "
\n",
+ " \n",
+ " 2019-10 | \n",
+ " 408.43 | \n",
+ "
\n",
+ " \n",
+ " 2019-11 | \n",
+ " 410.29 | \n",
+ "
\n",
+ " \n",
+ " 2019-12 | \n",
+ " 411.85 | \n",
+ "
\n",
+ " \n",
+ " 2020-01 | \n",
+ " 413.37 | \n",
+ "
\n",
+ " \n",
+ " 2020-02 | \n",
+ " 414.09 | \n",
+ "
\n",
+ " \n",
+ " 2020-03 | \n",
+ " 414.51 | \n",
+ "
\n",
+ " \n",
+ " 2020-04 | \n",
+ " 416.18 | \n",
+ "
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+ " \n",
+ "
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+ "
741 rows × 1 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " CO2\n",
+ "period \n",
+ "1958-03 315.70\n",
+ "1958-04 317.46\n",
+ "1958-05 317.51\n",
+ "1958-07 315.86\n",
+ "1958-08 314.93\n",
+ "1958-09 313.21\n",
+ "1958-11 313.33\n",
+ "1958-12 314.67\n",
+ "1959-01 315.58\n",
+ "1959-02 316.49\n",
+ "1959-03 316.65\n",
+ "1959-04 317.72\n",
+ "1959-05 318.29\n",
+ "1959-06 318.15\n",
+ "1959-07 316.54\n",
+ "1959-08 314.80\n",
+ "1959-09 313.84\n",
+ "1959-10 313.33\n",
+ "1959-11 314.81\n",
+ "1959-12 315.58\n",
+ "1960-01 316.43\n",
+ "1960-02 316.98\n",
+ "1960-03 317.58\n",
+ "1960-04 319.03\n",
+ "1960-05 320.04\n",
+ "1960-06 319.58\n",
+ "1960-07 318.18\n",
+ "1960-08 315.90\n",
+ "1960-09 314.17\n",
+ "1960-10 313.83\n",
+ "... ...\n",
+ "2017-11 405.17\n",
+ "2017-12 406.75\n",
+ "2018-01 408.05\n",
+ "2018-02 408.34\n",
+ "2018-03 409.25\n",
+ "2018-04 410.30\n",
+ "2018-05 411.30\n",
+ "2018-06 410.88\n",
+ "2018-07 408.90\n",
+ "2018-08 407.10\n",
+ "2018-09 405.59\n",
+ "2018-10 405.99\n",
+ "2018-11 408.12\n",
+ "2018-12 409.23\n",
+ "2019-01 410.92\n",
+ "2019-02 411.66\n",
+ "2019-03 412.00\n",
+ "2019-04 413.52\n",
+ "2019-05 414.83\n",
+ "2019-06 413.96\n",
+ "2019-07 411.85\n",
+ "2019-08 410.08\n",
+ "2019-09 408.55\n",
+ "2019-10 408.43\n",
+ "2019-11 410.29\n",
+ "2019-12 411.85\n",
+ "2020-01 413.37\n",
+ "2020-02 414.09\n",
+ "2020-03 414.51\n",
+ "2020-04 416.18\n",
+ "\n",
+ "[741 rows x 1 columns]"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def convertIntoPeriod(anneeEtMois):\n",
+ " y = (int)(anneeEtMois/100)\n",
+ " m = (int)(anneeEtMois%100)\n",
+ " return pd.Period(pd.Timestamp(y,m,1), 'M')\n",
+ "useful_data['period'] = [convertIntoPeriod(date) for date in useful_data['period']]\n",
+ "useful_data.set_index('period')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "useful_data['CO2'].plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "On a un premier aperçu de nos données, mais l'échelle ne correspond pas à ce que nous voulons. De plus, il va être difficile avec des données manquantes de travailler proprement avec ces indices. On va donc repartir d'une copie de useful_data, et renseigner la date sous la forme du nombre de mois en partant de l'an 1958. Janvier 1959 sera donc référencé par \"13\", etc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
"outputs": [
{
"data": {
@@ -1151,6 +1601,7 @@
" Yr | \n",
" Mn | \n",
" CO2 | \n",
+ " IndexMois | \n",
" \n",
" \n",
" \n",
@@ -1159,485 +1610,549 @@
" 1958.0 | \n",
" 3.0 | \n",
" 315.70 | \n",
+ " 3 | \n",
" \n",
" \n",
" 5 | \n",
" 1958.0 | \n",
" 4.0 | \n",
" 317.46 | \n",
+ " 4 | \n",
"
\n",
" \n",
" 6 | \n",
" 1958.0 | \n",
" 5.0 | \n",
" 317.51 | \n",
+ " 5 | \n",
"
\n",
" \n",
" 8 | \n",
" 1958.0 | \n",
" 7.0 | \n",
" 315.86 | \n",
+ " 7 | \n",
"
\n",
" \n",
" 9 | \n",
" 1958.0 | \n",
" 8.0 | \n",
" 314.93 | \n",
+ " 8 | \n",
"
\n",
" \n",
" 10 | \n",
" 1958.0 | \n",
" 9.0 | \n",
" 313.21 | \n",
+ " 9 | \n",
"
\n",
" \n",
" 12 | \n",
" 1958.0 | \n",
" 11.0 | \n",
" 313.33 | \n",
+ " 11 | \n",
"
\n",
" \n",
" 13 | \n",
" 1958.0 | \n",
" 12.0 | \n",
" 314.67 | \n",
+ " 12 | \n",
"
\n",
" \n",
" 14 | \n",
" 1959.0 | \n",
" 1.0 | \n",
" 315.58 | \n",
+ " 13 | \n",
"
\n",
" \n",
" 15 | \n",
" 1959.0 | \n",
" 2.0 | \n",
" 316.49 | \n",
+ " 14 | \n",
"
\n",
" \n",
" 16 | \n",
" 1959.0 | \n",
" 3.0 | \n",
" 316.65 | \n",
+ " 15 | \n",
"
\n",
" \n",
" 17 | \n",
" 1959.0 | \n",
" 4.0 | \n",
" 317.72 | \n",
+ " 16 | \n",
"
\n",
" \n",
" 18 | \n",
" 1959.0 | \n",
" 5.0 | \n",
" 318.29 | \n",
+ " 17 | \n",
"
\n",
" \n",
" 19 | \n",
" 1959.0 | \n",
" 6.0 | \n",
" 318.15 | \n",
+ " 18 | \n",
"
\n",
" \n",
" 20 | \n",
" 1959.0 | \n",
" 7.0 | \n",
" 316.54 | \n",
+ " 19 | \n",
"
\n",
" \n",
" 21 | \n",
" 1959.0 | \n",
" 8.0 | \n",
" 314.80 | \n",
+ " 20 | \n",
"
\n",
" \n",
" 22 | \n",
" 1959.0 | \n",
" 9.0 | \n",
" 313.84 | \n",
+ " 21 | \n",
"
\n",
" \n",
" 23 | \n",
" 1959.0 | \n",
" 10.0 | \n",
" 313.33 | \n",
+ " 22 | \n",
"
\n",
" \n",
" 24 | \n",
" 1959.0 | \n",
" 11.0 | \n",
" 314.81 | \n",
+ " 23 | \n",
"
\n",
" \n",
" 25 | \n",
" 1959.0 | \n",
" 12.0 | \n",
" 315.58 | \n",
+ " 24 | \n",
"
\n",
" \n",
" 26 | \n",
" 1960.0 | \n",
" 1.0 | \n",
" 316.43 | \n",
+ " 25 | \n",
"
\n",
" \n",
" 27 | \n",
" 1960.0 | \n",
" 2.0 | \n",
" 316.98 | \n",
+ " 26 | \n",
"
\n",
" \n",
" 28 | \n",
" 1960.0 | \n",
" 3.0 | \n",
" 317.58 | \n",
+ " 27 | \n",
"
\n",
" \n",
" 29 | \n",
" 1960.0 | \n",
" 4.0 | \n",
" 319.03 | \n",
+ " 28 | \n",
"
\n",
" \n",
" 30 | \n",
" 1960.0 | \n",
" 5.0 | \n",
" 320.04 | \n",
+ " 29 | \n",
"
\n",
" \n",
" 31 | \n",
" 1960.0 | \n",
" 6.0 | \n",
" 319.58 | \n",
+ " 30 | \n",
"
\n",
" \n",
" 32 | \n",
" 1960.0 | \n",
" 7.0 | \n",
" 318.18 | \n",
+ " 31 | \n",
"
\n",
" \n",
" 33 | \n",
" 1960.0 | \n",
" 8.0 | \n",
" 315.90 | \n",
+ " 32 | \n",
"
\n",
" \n",
" 34 | \n",
" 1960.0 | \n",
" 9.0 | \n",
" 314.17 | \n",
+ " 33 | \n",
"
\n",
" \n",
" 35 | \n",
" 1960.0 | \n",
" 10.0 | \n",
" 313.83 | \n",
+ " 34 | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
" ... | \n",
" ... | \n",
+ " ... | \n",
"
\n",
" \n",
" 720 | \n",
" 2017.0 | \n",
" 11.0 | \n",
" 405.17 | \n",
+ " 719 | \n",
"
\n",
" \n",
" 721 | \n",
" 2017.0 | \n",
" 12.0 | \n",
" 406.75 | \n",
+ " 720 | \n",
"
\n",
" \n",
" 722 | \n",
" 2018.0 | \n",
" 1.0 | \n",
" 408.05 | \n",
+ " 721 | \n",
"
\n",
" \n",
" 723 | \n",
" 2018.0 | \n",
" 2.0 | \n",
" 408.34 | \n",
+ " 722 | \n",
"
\n",
" \n",
" 724 | \n",
" 2018.0 | \n",
" 3.0 | \n",
" 409.25 | \n",
+ " 723 | \n",
"
\n",
" \n",
" 725 | \n",
" 2018.0 | \n",
" 4.0 | \n",
" 410.30 | \n",
+ " 724 | \n",
"
\n",
" \n",
" 726 | \n",
" 2018.0 | \n",
" 5.0 | \n",
" 411.30 | \n",
+ " 725 | \n",
"
\n",
" \n",
" 727 | \n",
" 2018.0 | \n",
" 6.0 | \n",
" 410.88 | \n",
+ " 726 | \n",
"
\n",
" \n",
" 728 | \n",
" 2018.0 | \n",
" 7.0 | \n",
" 408.90 | \n",
+ " 727 | \n",
"
\n",
" \n",
" 729 | \n",
" 2018.0 | \n",
" 8.0 | \n",
" 407.10 | \n",
+ " 728 | \n",
"
\n",
" \n",
" 730 | \n",
" 2018.0 | \n",
" 9.0 | \n",
" 405.59 | \n",
+ " 729 | \n",
"
\n",
" \n",
" 731 | \n",
" 2018.0 | \n",
" 10.0 | \n",
" 405.99 | \n",
+ " 730 | \n",
"
\n",
" \n",
" 732 | \n",
" 2018.0 | \n",
" 11.0 | \n",
" 408.12 | \n",
+ " 731 | \n",
"
\n",
" \n",
" 733 | \n",
" 2018.0 | \n",
" 12.0 | \n",
" 409.23 | \n",
+ " 732 | \n",
"
\n",
" \n",
" 734 | \n",
" 2019.0 | \n",
" 1.0 | \n",
" 410.92 | \n",
+ " 733 | \n",
"
\n",
" \n",
" 735 | \n",
" 2019.0 | \n",
" 2.0 | \n",
" 411.66 | \n",
+ " 734 | \n",
"
\n",
" \n",
" 736 | \n",
" 2019.0 | \n",
" 3.0 | \n",
" 412.00 | \n",
+ " 735 | \n",
"
\n",
" \n",
" 737 | \n",
" 2019.0 | \n",
" 4.0 | \n",
" 413.52 | \n",
+ " 736 | \n",
"
\n",
" \n",
" 738 | \n",
" 2019.0 | \n",
" 5.0 | \n",
" 414.83 | \n",
+ " 737 | \n",
"
\n",
" \n",
" 739 | \n",
" 2019.0 | \n",
" 6.0 | \n",
" 413.96 | \n",
+ " 738 | \n",
"
\n",
" \n",
" 740 | \n",
" 2019.0 | \n",
" 7.0 | \n",
" 411.85 | \n",
+ " 739 | \n",
"
\n",
" \n",
" 741 | \n",
" 2019.0 | \n",
" 8.0 | \n",
" 410.08 | \n",
+ " 740 | \n",
"
\n",
" \n",
" 742 | \n",
" 2019.0 | \n",
" 9.0 | \n",
" 408.55 | \n",
+ " 741 | \n",
"
\n",
" \n",
" 743 | \n",
" 2019.0 | \n",
" 10.0 | \n",
" 408.43 | \n",
+ " 742 | \n",
"
\n",
" \n",
" 744 | \n",
" 2019.0 | \n",
" 11.0 | \n",
" 410.29 | \n",
+ " 743 | \n",
"
\n",
" \n",
" 745 | \n",
" 2019.0 | \n",
" 12.0 | \n",
" 411.85 | \n",
+ " 744 | \n",
"
\n",
" \n",
" 746 | \n",
" 2020.0 | \n",
" 1.0 | \n",
" 413.37 | \n",
+ " 745 | \n",
"
\n",
" \n",
" 747 | \n",
" 2020.0 | \n",
" 2.0 | \n",
" 414.09 | \n",
+ " 746 | \n",
"
\n",
" \n",
" 748 | \n",
" 2020.0 | \n",
" 3.0 | \n",
" 414.51 | \n",
+ " 747 | \n",
"
\n",
" \n",
" 749 | \n",
" 2020.0 | \n",
" 4.0 | \n",
" 416.18 | \n",
+ " 748 | \n",
"
\n",
" \n",
"\n",
- "741 rows × 3 columns
\n",
+ "741 rows × 4 columns
\n",
""
],
"text/plain": [
- " Yr Mn CO2\n",
- "4 1958.0 3.0 315.70\n",
- "5 1958.0 4.0 317.46\n",
- "6 1958.0 5.0 317.51\n",
- "8 1958.0 7.0 315.86\n",
- "9 1958.0 8.0 314.93\n",
- "10 1958.0 9.0 313.21\n",
- "12 1958.0 11.0 313.33\n",
- "13 1958.0 12.0 314.67\n",
- "14 1959.0 1.0 315.58\n",
- "15 1959.0 2.0 316.49\n",
- "16 1959.0 3.0 316.65\n",
- "17 1959.0 4.0 317.72\n",
- "18 1959.0 5.0 318.29\n",
- "19 1959.0 6.0 318.15\n",
- "20 1959.0 7.0 316.54\n",
- "21 1959.0 8.0 314.80\n",
- "22 1959.0 9.0 313.84\n",
- "23 1959.0 10.0 313.33\n",
- "24 1959.0 11.0 314.81\n",
- "25 1959.0 12.0 315.58\n",
- "26 1960.0 1.0 316.43\n",
- "27 1960.0 2.0 316.98\n",
- "28 1960.0 3.0 317.58\n",
- "29 1960.0 4.0 319.03\n",
- "30 1960.0 5.0 320.04\n",
- "31 1960.0 6.0 319.58\n",
- "32 1960.0 7.0 318.18\n",
- "33 1960.0 8.0 315.90\n",
- "34 1960.0 9.0 314.17\n",
- "35 1960.0 10.0 313.83\n",
- ".. ... ... ...\n",
- "720 2017.0 11.0 405.17\n",
- "721 2017.0 12.0 406.75\n",
- "722 2018.0 1.0 408.05\n",
- "723 2018.0 2.0 408.34\n",
- "724 2018.0 3.0 409.25\n",
- "725 2018.0 4.0 410.30\n",
- "726 2018.0 5.0 411.30\n",
- "727 2018.0 6.0 410.88\n",
- "728 2018.0 7.0 408.90\n",
- "729 2018.0 8.0 407.10\n",
- "730 2018.0 9.0 405.59\n",
- "731 2018.0 10.0 405.99\n",
- "732 2018.0 11.0 408.12\n",
- "733 2018.0 12.0 409.23\n",
- "734 2019.0 1.0 410.92\n",
- "735 2019.0 2.0 411.66\n",
- "736 2019.0 3.0 412.00\n",
- "737 2019.0 4.0 413.52\n",
- "738 2019.0 5.0 414.83\n",
- "739 2019.0 6.0 413.96\n",
- "740 2019.0 7.0 411.85\n",
- "741 2019.0 8.0 410.08\n",
- "742 2019.0 9.0 408.55\n",
- "743 2019.0 10.0 408.43\n",
- "744 2019.0 11.0 410.29\n",
- "745 2019.0 12.0 411.85\n",
- "746 2020.0 1.0 413.37\n",
- "747 2020.0 2.0 414.09\n",
- "748 2020.0 3.0 414.51\n",
- "749 2020.0 4.0 416.18\n",
+ " Yr Mn CO2 IndexMois\n",
+ "4 1958.0 3.0 315.70 3\n",
+ "5 1958.0 4.0 317.46 4\n",
+ "6 1958.0 5.0 317.51 5\n",
+ "8 1958.0 7.0 315.86 7\n",
+ "9 1958.0 8.0 314.93 8\n",
+ "10 1958.0 9.0 313.21 9\n",
+ "12 1958.0 11.0 313.33 11\n",
+ "13 1958.0 12.0 314.67 12\n",
+ "14 1959.0 1.0 315.58 13\n",
+ "15 1959.0 2.0 316.49 14\n",
+ "16 1959.0 3.0 316.65 15\n",
+ "17 1959.0 4.0 317.72 16\n",
+ "18 1959.0 5.0 318.29 17\n",
+ "19 1959.0 6.0 318.15 18\n",
+ "20 1959.0 7.0 316.54 19\n",
+ "21 1959.0 8.0 314.80 20\n",
+ "22 1959.0 9.0 313.84 21\n",
+ "23 1959.0 10.0 313.33 22\n",
+ "24 1959.0 11.0 314.81 23\n",
+ "25 1959.0 12.0 315.58 24\n",
+ "26 1960.0 1.0 316.43 25\n",
+ "27 1960.0 2.0 316.98 26\n",
+ "28 1960.0 3.0 317.58 27\n",
+ "29 1960.0 4.0 319.03 28\n",
+ "30 1960.0 5.0 320.04 29\n",
+ "31 1960.0 6.0 319.58 30\n",
+ "32 1960.0 7.0 318.18 31\n",
+ "33 1960.0 8.0 315.90 32\n",
+ "34 1960.0 9.0 314.17 33\n",
+ "35 1960.0 10.0 313.83 34\n",
+ ".. ... ... ... ...\n",
+ "720 2017.0 11.0 405.17 719\n",
+ "721 2017.0 12.0 406.75 720\n",
+ "722 2018.0 1.0 408.05 721\n",
+ "723 2018.0 2.0 408.34 722\n",
+ "724 2018.0 3.0 409.25 723\n",
+ "725 2018.0 4.0 410.30 724\n",
+ "726 2018.0 5.0 411.30 725\n",
+ "727 2018.0 6.0 410.88 726\n",
+ "728 2018.0 7.0 408.90 727\n",
+ "729 2018.0 8.0 407.10 728\n",
+ "730 2018.0 9.0 405.59 729\n",
+ "731 2018.0 10.0 405.99 730\n",
+ "732 2018.0 11.0 408.12 731\n",
+ "733 2018.0 12.0 409.23 732\n",
+ "734 2019.0 1.0 410.92 733\n",
+ "735 2019.0 2.0 411.66 734\n",
+ "736 2019.0 3.0 412.00 735\n",
+ "737 2019.0 4.0 413.52 736\n",
+ "738 2019.0 5.0 414.83 737\n",
+ "739 2019.0 6.0 413.96 738\n",
+ "740 2019.0 7.0 411.85 739\n",
+ "741 2019.0 8.0 410.08 740\n",
+ "742 2019.0 9.0 408.55 741\n",
+ "743 2019.0 10.0 408.43 742\n",
+ "744 2019.0 11.0 410.29 743\n",
+ "745 2019.0 12.0 411.85 744\n",
+ "746 2020.0 1.0 413.37 745\n",
+ "747 2020.0 2.0 414.09 746\n",
+ "748 2020.0 3.0 414.51 747\n",
+ "749 2020.0 4.0 416.18 748\n",
"\n",
- "[741 rows x 3 columns]"
+ "[741 rows x 4 columns]"
]
},
- "execution_count": 9,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "useful_data"
+ "udc = useful_data_copie\n",
+ "udc['IndexMois'] = [(int)(udc['Mn'][x] + (udc['Yr'][x] - 1958)*12) for x in udc.index]\n",
+ "udc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "On souhaite maintenant convertir l'année et le mois en un format plus adapté à Pandas, et à l'utiliser comme index. Un méthode possible est présentée ici, en rassemblant les deux informations puis en appliquant une fonction pour une mise au format Pandas."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "useful_data['period'] = useful_data['Yr']*100 + useful_data['Mn']"
+ "On vérifie à l'aide de la dernière valeur que tout est correct :"
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 16,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "748"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "useful_data['period'] = useful_data['period'].astype(int)"
+ "testIndex = (2020 - 1958)*12 + 4\n",
+ "testIndex"
]
},
{
- "cell_type": "code",
- "execution_count": 12,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "useful_data = useful_data.iloc[0:len(useful_data.index), [2,3]]"
+ "On utilise notre nouvelle colonne comme index et on supprime les autres."
]
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -1662,362 +2177,425 @@
" \n",
" | \n",
" CO2 | \n",
- "
\n",
- " \n",
- " period | \n",
- " | \n",
+ " IndexMois | \n",
"
\n",
" \n",
" \n",
" \n",
- " 1958-03 | \n",
+ " 4 | \n",
" 315.70 | \n",
+ " 3 | \n",
"
\n",
" \n",
- " 1958-04 | \n",
+ " 5 | \n",
" 317.46 | \n",
+ " 4 | \n",
"
\n",
" \n",
- " 1958-05 | \n",
+ " 6 | \n",
" 317.51 | \n",
+ " 5 | \n",
"
\n",
" \n",
- " 1958-07 | \n",
+ " 8 | \n",
" 315.86 | \n",
+ " 7 | \n",
"
\n",
" \n",
- " 1958-08 | \n",
+ " 9 | \n",
" 314.93 | \n",
+ " 8 | \n",
"
\n",
" \n",
- " 1958-09 | \n",
+ " 10 | \n",
" 313.21 | \n",
+ " 9 | \n",
"
\n",
" \n",
- " 1958-11 | \n",
+ " 12 | \n",
" 313.33 | \n",
+ " 11 | \n",
"
\n",
" \n",
- " 1958-12 | \n",
+ " 13 | \n",
" 314.67 | \n",
+ " 12 | \n",
"
\n",
" \n",
- " 1959-01 | \n",
+ " 14 | \n",
" 315.58 | \n",
+ " 13 | \n",
"
\n",
" \n",
- " 1959-02 | \n",
+ " 15 | \n",
" 316.49 | \n",
+ " 14 | \n",
"
\n",
" \n",
- " 1959-03 | \n",
+ " 16 | \n",
" 316.65 | \n",
+ " 15 | \n",
"
\n",
" \n",
- " 1959-04 | \n",
+ " 17 | \n",
" 317.72 | \n",
+ " 16 | \n",
"
\n",
" \n",
- " 1959-05 | \n",
+ " 18 | \n",
" 318.29 | \n",
+ " 17 | \n",
"
\n",
" \n",
- " 1959-06 | \n",
+ " 19 | \n",
" 318.15 | \n",
+ " 18 | \n",
"
\n",
" \n",
- " 1959-07 | \n",
+ " 20 | \n",
" 316.54 | \n",
+ " 19 | \n",
"
\n",
" \n",
- " 1959-08 | \n",
+ " 21 | \n",
" 314.80 | \n",
+ " 20 | \n",
"
\n",
" \n",
- " 1959-09 | \n",
+ " 22 | \n",
" 313.84 | \n",
+ " 21 | \n",
"
\n",
" \n",
- " 1959-10 | \n",
+ " 23 | \n",
" 313.33 | \n",
+ " 22 | \n",
"
\n",
" \n",
- " 1959-11 | \n",
+ " 24 | \n",
" 314.81 | \n",
+ " 23 | \n",
"
\n",
" \n",
- " 1959-12 | \n",
+ " 25 | \n",
" 315.58 | \n",
+ " 24 | \n",
"
\n",
" \n",
- " 1960-01 | \n",
+ " 26 | \n",
" 316.43 | \n",
+ " 25 | \n",
"
\n",
" \n",
- " 1960-02 | \n",
+ " 27 | \n",
" 316.98 | \n",
+ " 26 | \n",
"
\n",
" \n",
- " 1960-03 | \n",
+ " 28 | \n",
" 317.58 | \n",
+ " 27 | \n",
"
\n",
" \n",
- " 1960-04 | \n",
+ " 29 | \n",
" 319.03 | \n",
+ " 28 | \n",
"
\n",
" \n",
- " 1960-05 | \n",
+ " 30 | \n",
" 320.04 | \n",
+ " 29 | \n",
"
\n",
" \n",
- " 1960-06 | \n",
+ " 31 | \n",
" 319.58 | \n",
+ " 30 | \n",
"
\n",
" \n",
- " 1960-07 | \n",
+ " 32 | \n",
" 318.18 | \n",
+ " 31 | \n",
"
\n",
" \n",
- " 1960-08 | \n",
+ " 33 | \n",
" 315.90 | \n",
+ " 32 | \n",
"
\n",
" \n",
- " 1960-09 | \n",
+ " 34 | \n",
" 314.17 | \n",
+ " 33 | \n",
"
\n",
" \n",
- " 1960-10 | \n",
+ " 35 | \n",
" 313.83 | \n",
+ " 34 | \n",
"
\n",
" \n",
" ... | \n",
" ... | \n",
+ " ... | \n",
"
\n",
" \n",
- " 2017-11 | \n",
+ " 720 | \n",
" 405.17 | \n",
+ " 719 | \n",
"
\n",
" \n",
- " 2017-12 | \n",
+ " 721 | \n",
" 406.75 | \n",
+ " 720 | \n",
"
\n",
" \n",
- " 2018-01 | \n",
+ " 722 | \n",
" 408.05 | \n",
+ " 721 | \n",
"
\n",
" \n",
- " 2018-02 | \n",
+ " 723 | \n",
" 408.34 | \n",
+ " 722 | \n",
"
\n",
" \n",
- " 2018-03 | \n",
+ " 724 | \n",
" 409.25 | \n",
+ " 723 | \n",
"
\n",
" \n",
- " 2018-04 | \n",
+ " 725 | \n",
" 410.30 | \n",
+ " 724 | \n",
"
\n",
" \n",
- " 2018-05 | \n",
+ " 726 | \n",
" 411.30 | \n",
+ " 725 | \n",
"
\n",
" \n",
- " 2018-06 | \n",
+ " 727 | \n",
" 410.88 | \n",
+ " 726 | \n",
"
\n",
" \n",
- " 2018-07 | \n",
+ " 728 | \n",
" 408.90 | \n",
+ " 727 | \n",
"
\n",
" \n",
- " 2018-08 | \n",
+ " 729 | \n",
" 407.10 | \n",
+ " 728 | \n",
"
\n",
" \n",
- " 2018-09 | \n",
+ " 730 | \n",
" 405.59 | \n",
+ " 729 | \n",
"
\n",
" \n",
- " 2018-10 | \n",
+ " 731 | \n",
" 405.99 | \n",
+ " 730 | \n",
"
\n",
" \n",
- " 2018-11 | \n",
+ " 732 | \n",
" 408.12 | \n",
+ " 731 | \n",
"
\n",
" \n",
- " 2018-12 | \n",
+ " 733 | \n",
" 409.23 | \n",
+ " 732 | \n",
"
\n",
" \n",
- " 2019-01 | \n",
+ " 734 | \n",
" 410.92 | \n",
+ " 733 | \n",
"
\n",
" \n",
- " 2019-02 | \n",
+ " 735 | \n",
" 411.66 | \n",
+ " 734 | \n",
"
\n",
" \n",
- " 2019-03 | \n",
+ " 736 | \n",
" 412.00 | \n",
+ " 735 | \n",
"
\n",
" \n",
- " 2019-04 | \n",
+ " 737 | \n",
" 413.52 | \n",
+ " 736 | \n",
"
\n",
" \n",
- " 2019-05 | \n",
+ " 738 | \n",
" 414.83 | \n",
+ " 737 | \n",
"
\n",
" \n",
- " 2019-06 | \n",
+ " 739 | \n",
" 413.96 | \n",
+ " 738 | \n",
"
\n",
" \n",
- " 2019-07 | \n",
+ " 740 | \n",
" 411.85 | \n",
+ " 739 | \n",
"
\n",
" \n",
- " 2019-08 | \n",
+ " 741 | \n",
" 410.08 | \n",
+ " 740 | \n",
"
\n",
" \n",
- " 2019-09 | \n",
+ " 742 | \n",
" 408.55 | \n",
+ " 741 | \n",
"
\n",
" \n",
- " 2019-10 | \n",
+ " 743 | \n",
" 408.43 | \n",
+ " 742 | \n",
"
\n",
" \n",
- " 2019-11 | \n",
+ " 744 | \n",
" 410.29 | \n",
+ " 743 | \n",
"
\n",
" \n",
- " 2019-12 | \n",
+ " 745 | \n",
" 411.85 | \n",
+ " 744 | \n",
"
\n",
" \n",
- " 2020-01 | \n",
+ " 746 | \n",
" 413.37 | \n",
+ " 745 | \n",
"
\n",
" \n",
- " 2020-02 | \n",
+ " 747 | \n",
" 414.09 | \n",
+ " 746 | \n",
"
\n",
" \n",
- " 2020-03 | \n",
+ " 748 | \n",
" 414.51 | \n",
+ " 747 | \n",
"
\n",
" \n",
- " 2020-04 | \n",
+ " 749 | \n",
" 416.18 | \n",
+ " 748 | \n",
"
\n",
" \n",
"\n",
- "741 rows × 1 columns
\n",
+ "741 rows × 2 columns
\n",
""
],
"text/plain": [
- " CO2\n",
- "period \n",
- "1958-03 315.70\n",
- "1958-04 317.46\n",
- "1958-05 317.51\n",
- "1958-07 315.86\n",
- "1958-08 314.93\n",
- "1958-09 313.21\n",
- "1958-11 313.33\n",
- "1958-12 314.67\n",
- "1959-01 315.58\n",
- "1959-02 316.49\n",
- "1959-03 316.65\n",
- "1959-04 317.72\n",
- "1959-05 318.29\n",
- "1959-06 318.15\n",
- "1959-07 316.54\n",
- "1959-08 314.80\n",
- "1959-09 313.84\n",
- "1959-10 313.33\n",
- "1959-11 314.81\n",
- "1959-12 315.58\n",
- "1960-01 316.43\n",
- "1960-02 316.98\n",
- "1960-03 317.58\n",
- "1960-04 319.03\n",
- "1960-05 320.04\n",
- "1960-06 319.58\n",
- "1960-07 318.18\n",
- "1960-08 315.90\n",
- "1960-09 314.17\n",
- "1960-10 313.83\n",
- "... ...\n",
- "2017-11 405.17\n",
- "2017-12 406.75\n",
- "2018-01 408.05\n",
- "2018-02 408.34\n",
- "2018-03 409.25\n",
- "2018-04 410.30\n",
- "2018-05 411.30\n",
- "2018-06 410.88\n",
- "2018-07 408.90\n",
- "2018-08 407.10\n",
- "2018-09 405.59\n",
- "2018-10 405.99\n",
- "2018-11 408.12\n",
- "2018-12 409.23\n",
- "2019-01 410.92\n",
- "2019-02 411.66\n",
- "2019-03 412.00\n",
- "2019-04 413.52\n",
- "2019-05 414.83\n",
- "2019-06 413.96\n",
- "2019-07 411.85\n",
- "2019-08 410.08\n",
- "2019-09 408.55\n",
- "2019-10 408.43\n",
- "2019-11 410.29\n",
- "2019-12 411.85\n",
- "2020-01 413.37\n",
- "2020-02 414.09\n",
- "2020-03 414.51\n",
- "2020-04 416.18\n",
+ " CO2 IndexMois\n",
+ "4 315.70 3\n",
+ "5 317.46 4\n",
+ "6 317.51 5\n",
+ "8 315.86 7\n",
+ "9 314.93 8\n",
+ "10 313.21 9\n",
+ "12 313.33 11\n",
+ "13 314.67 12\n",
+ "14 315.58 13\n",
+ "15 316.49 14\n",
+ "16 316.65 15\n",
+ "17 317.72 16\n",
+ "18 318.29 17\n",
+ "19 318.15 18\n",
+ "20 316.54 19\n",
+ "21 314.80 20\n",
+ "22 313.84 21\n",
+ "23 313.33 22\n",
+ "24 314.81 23\n",
+ "25 315.58 24\n",
+ "26 316.43 25\n",
+ "27 316.98 26\n",
+ "28 317.58 27\n",
+ "29 319.03 28\n",
+ "30 320.04 29\n",
+ "31 319.58 30\n",
+ "32 318.18 31\n",
+ "33 315.90 32\n",
+ "34 314.17 33\n",
+ "35 313.83 34\n",
+ ".. ... ...\n",
+ "720 405.17 719\n",
+ "721 406.75 720\n",
+ "722 408.05 721\n",
+ "723 408.34 722\n",
+ "724 409.25 723\n",
+ "725 410.30 724\n",
+ "726 411.30 725\n",
+ "727 410.88 726\n",
+ "728 408.90 727\n",
+ "729 407.10 728\n",
+ "730 405.59 729\n",
+ "731 405.99 730\n",
+ "732 408.12 731\n",
+ "733 409.23 732\n",
+ "734 410.92 733\n",
+ "735 411.66 734\n",
+ "736 412.00 735\n",
+ "737 413.52 736\n",
+ "738 414.83 737\n",
+ "739 413.96 738\n",
+ "740 411.85 739\n",
+ "741 410.08 740\n",
+ "742 408.55 741\n",
+ "743 408.43 742\n",
+ "744 410.29 743\n",
+ "745 411.85 744\n",
+ "746 413.37 745\n",
+ "747 414.09 746\n",
+ "748 414.51 747\n",
+ "749 416.18 748\n",
"\n",
- "[741 rows x 1 columns]"
+ "[741 rows x 2 columns]"
]
},
- "execution_count": 13,
+ "execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "def convertIntoPeriod(anneeEtMois):\n",
- " y = (int)(anneeEtMois/100)\n",
- " m = (int)(anneeEtMois%100)\n",
- " return pd.Period(pd.Timestamp(y,m,1), 'M')\n",
- "useful_data['period'] = [convertIntoPeriod(date) for date in useful_data['period']]\n",
- "useful_data.set_index('period')"
+ "del udc['Yr']\n",
+ "del udc['Mn']\n",
+ "udc.reset_index()\n",
+ "udc.set_index('IndexMois')\n",
+ "udc"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pour une raison quelconque Pandas refuse d'indexer correctement le tableau ... Tant pis. On utilisera la colonne IndexMois en guise d'abscisses pour les plots."
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ "[]"
]
},
- "execution_count": 14,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -2029,27 +2607,34 @@
}
],
"source": [
- "useful_data['CO2'].plot()"
+ "plt.plot(udc['IndexMois'], udc['CO2'])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "On vérifie la façon dont les données manquantes sont gérées."
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- ""
+ "[]"
]
},
- "execution_count": 15,
+ "execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
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\n",
"text/plain": [
""
]
@@ -2061,41 +2646,78 @@
}
],
"source": [
- "useful_data['CO2'][-60:].plot()"
+ "plt.plot(udc['IndexMois'][0:10], udc['CO2'][0:10])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "4 3\n",
+ "5 4\n",
+ "6 5\n",
+ "8 7\n",
+ "9 8\n",
+ "10 9\n",
+ "12 11\n",
+ "13 12\n",
+ "14 13\n",
+ "15 14\n",
+ "Name: IndexMois, dtype: int64 4 315.70\n",
+ "5 317.46\n",
+ "6 317.51\n",
+ "8 315.86\n",
+ "9 314.93\n",
+ "10 313.21\n",
+ "12 313.33\n",
+ "13 314.67\n",
+ "14 315.58\n",
+ "15 316.49\n",
+ "Name: CO2, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(udc['IndexMois'][0:10], udc['CO2'][0:10])"
]
},
{
"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."
+ "On voit que la valeur 6 en abscisse n'a pas d'ordonnée, et que la droite est tracée entre les points 5 et 7. Il n'y a pas de problème. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Pour caractériser la croissance globale de la concentration de CO2 dans l'atmosphère, on va tenter de joindre au graphe des courbes de tendance linéaire et exponentielle, et voir quelle est la plus appropriée."
+ "On peut s'intéresser maintenant aux résultats. On voit une croissance globale, et des oscillations locales. On peut zoomer sur trois années pour voir ce qu'il se passe localement par exemple."
]
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[]"
+ "[]"
]
},
- "execution_count": 26,
+ "execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -2107,40 +2729,98 @@
}
],
"source": [
- "a1, b1 = np.polyfit([x for x in range(len(useful_data.index))], useful_data['CO2'], 1)\n",
- "a2, b2 = np.polyfit([x for x in range(len(useful_data.index))], [np.log(y) for y in useful_data['CO2']], 1)\n",
- "fit_data = [x*a1 + b1 for x in range(len(useful_data.index))]\n",
- "fit_dataExp = [np.exp(b2)*np.exp(a2*x) for x in range(len(useful_data.index))]\n",
- "useful_data['CO2'].plot()\n",
- "plt.plot([x for x in range(len(useful_data.index))], fit_data)\n",
- "plt.plot([x for x in range(len(useful_data.index))], fit_dataExp)"
+ "plt.plot(udc['IndexMois'][24:60], udc['CO2'][24:60])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "29 5 / 30 6 / 31 7 / 32 8 / 33 9 / 34 10 / 35 11 / 36 0 / 37 1 / 38 2 / 39 3 / 40 4 / 41 5 / 42 6 / 43 7 / 44 8 / 45 9 / 46 10 / 47 11 / 48 0 / 49 1 / 50 2 / 51 3 / 52 4 / 53 5 / 54 6 / 55 7 / 56 8 / 57 9 / 58 10 / 59 11 / 60 0 / "
+ ]
+ }
+ ],
+ "source": [
+ "for i in range(29,61):\n",
+ " print(i, i%12, end=' / ')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Ces courbes de tendance ne sont pas satisfaisantes, elles ne semblent pas adaptées aux données. On tente une courbe de tendance polynomiale de degré 2."
+ "On voit des minima locaux aux abscisses 34, 45, 58 qui correspondent aux mois Octobre, Septembre, Octobre. Il semble donc que la concentration en CO2 soit périodiquement minimale à cette période de l'année. De même, on voit des maxima locaux aux abscisses 41 et 54, soit en mai et juin. On peut faire une autre vérification par précaution."
]
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "400 4 / 401 5 / 402 6 / 403 7 / 404 8 / 405 9 / 406 10 / 407 11 / 408 0 / 409 1 / 410 2 / 411 3 / 412 4 / 413 5 / 414 6 / 415 7 / 416 8 / 417 9 / 418 10 / 419 11 / 420 0 / 421 1 / 422 2 / 423 3 / 424 4 / 425 5 / 426 6 / 427 7 / 428 8 / 429 9 / 430 10 / 431 11 / 432 0 / 433 1 / 434 2 / 435 3 / 436 4 / "
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.plot(udc['IndexMois'][400:436], udc['CO2'][400:436])\n",
+ "for i in range(400,437):\n",
+ " print(i, i%12, end=' / ')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "On voit les maxima en 413 (mai), 425 (mai), 437(mai) et des les minima en 417 (septembre), 429 (septembre), 441 (septembre). L'hypothèse se tient."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Pour caractériser la croissance et faire des prévisions pour les années à venir, on souhaite joindre une courbe de tendance et son équation à ces données. On s'intéressera juste aux moyennes annuelles ici. \n",
+ "On présente ici 3 \"fit\", respectivement linéaire, polynomial de degré 2 et exponentiel. On choisira par la suite celui qui nous semble le plus en adéquation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "[]"
+ ""
]
},
- "execution_count": 28,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
""
]
@@ -2152,38 +2832,101 @@
}
],
"source": [
- "a3, b3, c3 = np.polyfit([x for x in range(len(useful_data.index))], useful_data['CO2'], 2)\n",
- "fit_dataCarre = [x*x*a3 + b3*x + c3 for x in range(len(useful_data.index))]\n",
- "useful_data['CO2'].plot()\n",
- "plt.plot([x for x in range(len(useful_data.index))], fit_dataCarre)"
+ "#Fit lineaire avec numpy\n",
+ "a, b = np.polyfit(udc['IndexMois'], udc['CO2'], 1)\n",
+ "yLin = a*udc['IndexMois'] + b\n",
+ "\n",
+ "#Fit de degré 2\n",
+ "a2, b2, c2 = np.polyfit(udc['IndexMois'], udc['CO2'], 2)\n",
+ "yCarre = a2*udc['IndexMois']**2 + b2*udc['IndexMois'] + c2\n",
+ "\n",
+ "#Fit exponentiel\n",
+ "aExp, bExp = np.polyfit(udc['IndexMois'], [np.log(y) for y in udc['CO2']], 1)\n",
+ "yExp = np.exp(bExp)*np.exp(aExp*udc['IndexMois'])\n",
+ "\n",
+ "plt.plot(udc['IndexMois'], udc['CO2'], label='data')\n",
+ "plt.plot(udc['IndexMois'], yLin, label='lin')\n",
+ "plt.plot(udc['IndexMois'], yCarre, label='deg2')\n",
+ "plt.plot(udc['IndexMois'], yExp, label='exp')\n",
+ "plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Cette courbe de tendance a l'air plus à même de nous fournir des données moyennes correctes. On souhaite maintenant faire une extrapolation jusqu'en 2025. Plutôt que de donner des valeurs par mois, il est plus pertinent ici de donner des valeurs moyennées par années.\n",
- "Pour ça, il suffit d'intégrer la fonction fit_dataCarre entre les bornes qui nous intéressent. "
+ "Le polynôme de degré 2 est plus adapté à nos données ici. Pour faire des extrapolations sur les années à suivre, il suffit de tracer la courbe en étendant sur la plage de valeurs des abscisses qui nous intéresse. Pour obtenir des valeurs annuelles moyennes, il suffit d'intégrer la fonction sur 12 mois."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#on va afficher les valeurs de 2010 à 2025, sachant que nos données s'arrêtent à Avril 2020 (748)\n",
+ "borneJanvier2010 = (2010-1958)*12 + 1\n",
+ "borneDecembre2025 = (2025-1958)*12 +12\n",
+ "x1 = [x for x in range(borneJanvier2010, 749)]\n",
+ "x2 = [x for x in range(borneJanvier2010, borneDecembre2025+1)]\n",
+ "\n",
+ "y1 = udc['CO2'][-(749-borneJanvier2010):]\n",
+ "y2 = a2*np.asarray(x2)**2 + b2*np.asarray(x2) + c2\n",
+ "\n",
+ "plt.plot(x1, y1)\n",
+ "plt.plot(x2, y2)"
]
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "749 2020-04\n",
- "Name: period, dtype: object\n"
+ "valeur moyenne pour l'annee 2020 : [CO2] = 412.89 ppm\n",
+ "valeur moyenne pour l'annee 2021 : [CO2] = 415.29 ppm\n",
+ "valeur moyenne pour l'annee 2022 : [CO2] = 417.72 ppm\n",
+ "valeur moyenne pour l'annee 2023 : [CO2] = 420.18 ppm\n",
+ "valeur moyenne pour l'annee 2024 : [CO2] = 422.66 ppm\n",
+ "valeur moyenne pour l'annee 2025 : [CO2] = 425.17 ppm\n"
]
}
],
"source": [
- "#Valeur moyenne 2020\n",
- "borne1 = useful_data['period'][-1:]\n",
- "print(borne1)"
+ "#Valeurs moyennes : on peut se contenter d'une intégration manuelle ici\n",
+ "for x in range(2020, 2026):\n",
+ " borneInf = (x-1958)*12+1\n",
+ " borneSup = borneInf + 12\n",
+ " Y2 = (a2*borneSup**3)/3 + (b2*borneSup**2)/2 + c2*borneSup\n",
+ " Y1 = (a2*borneInf**3)/3 + (b2*borneInf**2)/2 + c2*borneInf\n",
+ " meanValue = (Y2-Y1)/(borneSup-borneInf)\n",
+ " print(\"valeur moyenne pour l'annee \", x, \" : [CO2] = \", format(meanValue, '0.2f'), \" ppm\")"
]
},
{