diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb index 83d18f085017e2bb0c8162472ea4dda7e8988783..5954d79d147ac75c83476f834185c35571cdfd13 100644 --- a/module3/exo3/exercice_fr.ipynb +++ b/module3/exo3/exercice_fr.ipynb @@ -27,16 +27,26 @@ }, { "cell_type": "code", - "execution_count": 265, + "execution_count": 270, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.22.0\n" + ] + } + ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import os\n", "import urllib.request\n", - "import numpy as np" + "import numpy as np\n", + "\n", + "print(pd. __version__) " ] }, { @@ -48,9 +58,9 @@ }, { "cell_type": "code", - "execution_count": 278, + "execution_count": 271, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [ { @@ -1084,7 +1094,7 @@ "[806 rows x 11 columns]" ] }, - "execution_count": 278, + "execution_count": 271, "metadata": {}, "output_type": "execute_result" } @@ -1123,21 +1133,10 @@ }, { "cell_type": "code", - "execution_count": 279, + "execution_count": 272, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Index(['Yr', 'Mn', 'Date', 'Date.1', 'CO2', 'seasonally', 'fit',\n", - " 'seasonally.1', 'CO2.1', 'seasonally.2', 'Sta'],\n", - " dtype='object')\n" - ] - } - ], + "outputs": [], "source": [ - "print(raw_data.columns)\n", "rename = raw_data.columns + [' ' + x if x else '' for x in list(raw_data.iloc[0])] + [' ' + x if x else '' for x in list(raw_data.iloc[1])]\n", "raw_data.columns = rename\n", "data = raw_data.drop(index = raw_data.iloc[0:2].index)" @@ -1152,85 +1151,14 @@ }, { "cell_type": "code", - "execution_count": 288, + "execution_count": 273, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "2 1958-01-01\n", - "3 1958-02-01\n", - "4 1958-03-01\n", - "5 1958-04-01\n", - "6 1958-05-01\n", - "7 1958-06-01\n", - "8 1958-07-01\n", - "9 1958-08-01\n", - "10 1958-09-01\n", - "11 1958-10-01\n", - "12 1958-11-01\n", - "13 1958-12-01\n", - "14 1959-01-01\n", - "15 1959-02-01\n", - "16 1959-03-01\n", - "17 1959-04-01\n", - "18 1959-05-01\n", - "19 1959-06-01\n", - "20 1959-07-01\n", - "21 1959-08-01\n", - "22 1959-09-01\n", - "23 1959-10-01\n", - "24 1959-11-01\n", - "25 1959-12-01\n", - "26 1960-01-01\n", - "27 1960-02-01\n", - "28 1960-03-01\n", - "29 1960-04-01\n", - "30 1960-05-01\n", - "31 1960-06-01\n", - " ... \n", - "776 2022-07-01\n", - "777 2022-08-01\n", - "778 2022-09-01\n", - "779 2022-10-01\n", - "780 2022-11-01\n", - "781 2022-12-01\n", - "782 2023-01-01\n", - "783 2023-02-01\n", - "784 2023-03-01\n", - "785 2023-04-01\n", - "786 2023-05-01\n", - "787 2023-06-01\n", - "788 2023-07-01\n", - "789 2023-08-01\n", - "790 2023-09-01\n", - "791 2023-10-01\n", - "792 2023-11-01\n", - "793 2023-12-01\n", - "794 2024-01-01\n", - "795 2024-02-01\n", - "796 2024-03-01\n", - "797 2024-04-01\n", - "798 2024-05-01\n", - "799 2024-06-01\n", - "800 2024-07-01\n", - "801 2024-08-01\n", - "802 2024-09-01\n", - "803 2024-10-01\n", - "804 2024-11-01\n", - "805 2024-12-01\n", - "Name: Yr Mn, Length: 804, dtype: datetime64[ns]" - ] - }, - "execution_count": 288, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "raw_data = data\n", "raw_data['Yr Mn'] = raw_data['Yr'] + ' ' + raw_data['Mn']\n", - "pd.to_datetime(raw_data['Yr Mn'], format=\"%Y %m\")" + "raw_data['Yr Mn'] = pd.to_datetime(raw_data['Yr Mn'], format=\"%Y %m\")\n", + "data = raw_data" ] }, { @@ -1242,7 +1170,7 @@ }, { "cell_type": "code", - "execution_count": 281, + "execution_count": 274, "metadata": {}, "outputs": [], "source": [ @@ -1271,7 +1199,7 @@ }, { "cell_type": "code", - "execution_count": 282, + "execution_count": 275, "metadata": {}, "outputs": [ { @@ -1306,6 +1234,7 @@ " CO2.1 filled [ppm]\n", " seasonally.2 adjusted filled [ppm]\n", " Sta\n", + " Yr Mn\n", " \n", " \n", " \n", @@ -1322,6 +1251,7 @@ " -99.99\n", " -99.99\n", " MLO\n", + " 1958-01-01\n", " \n", " \n", " 3\n", @@ -1336,6 +1266,7 @@ " -99.99\n", " -99.99\n", " MLO\n", + " 1958-02-01\n", " \n", " \n", " 7\n", @@ -1350,6 +1281,7 @@ " 317.27\n", " 315.15\n", " MLO\n", + " 1958-06-01\n", " \n", " \n", " 11\n", @@ -1364,6 +1296,7 @@ " 312.42\n", " 315.41\n", " MLO\n", + " 1958-10-01\n", " \n", " \n", " 75\n", @@ -1378,6 +1311,7 @@ " 320.04\n", " 319.37\n", " MLO\n", + " 1964-02-01\n", " \n", " \n", " 76\n", @@ -1392,6 +1326,7 @@ " 320.76\n", " 319.42\n", " MLO\n", + " 1964-03-01\n", " \n", " \n", " 77\n", @@ -1406,6 +1341,7 @@ " 321.84\n", " 319.46\n", " MLO\n", + " 1964-04-01\n", " \n", " \n", " 804\n", @@ -1420,6 +1356,7 @@ " -99.99\n", " -99.99\n", " MLO\n", + " 2024-11-01\n", " \n", " \n", " 805\n", @@ -1434,6 +1371,7 @@ " -99.99\n", " -99.99\n", " MLO\n", + " 2024-12-01\n", " \n", " \n", "\n", @@ -1462,19 +1400,19 @@ "804 -99.99 -99.99 -99.99 \n", "805 -99.99 -99.99 -99.99 \n", "\n", - " seasonally.2 adjusted filled [ppm] Sta \n", - "2 -99.99 MLO \n", - "3 -99.99 MLO \n", - "7 315.15 MLO \n", - "11 315.41 MLO \n", - "75 319.37 MLO \n", - "76 319.42 MLO \n", - "77 319.46 MLO \n", - "804 -99.99 MLO \n", - "805 -99.99 MLO " + " seasonally.2 adjusted filled [ppm] Sta Yr Mn \n", + "2 -99.99 MLO 1958-01-01 \n", + "3 -99.99 MLO 1958-02-01 \n", + "7 315.15 MLO 1958-06-01 \n", + "11 315.41 MLO 1958-10-01 \n", + "75 319.37 MLO 1964-02-01 \n", + "76 319.42 MLO 1964-03-01 \n", + "77 319.46 MLO 1964-04-01 \n", + "804 -99.99 MLO 2024-11-01 \n", + "805 -99.99 MLO 2024-12-01 " ] }, - "execution_count": 282, + "execution_count": 275, "metadata": {}, "output_type": "execute_result" } @@ -1497,7 +1435,7 @@ }, { "cell_type": "code", - "execution_count": 262, + "execution_count": 276, "metadata": {}, "outputs": [ { @@ -1532,6 +1470,7 @@ " CO2.1 filled [ppm]\n", " seasonally.2 adjusted filled [ppm]\n", " Sta\n", + " Yr Mn\n", " \n", " \n", " \n", @@ -1548,6 +1487,7 @@ " -99.99\n", " -99.99\n", " MLO\n", + " 1958-01-01\n", " \n", " \n", " 3\n", @@ -1562,6 +1502,7 @@ " -99.99\n", " -99.99\n", " MLO\n", + " 1958-02-01\n", " \n", " \n", " 804\n", @@ -1576,6 +1517,7 @@ " -99.99\n", " -99.99\n", " MLO\n", + " 2024-11-01\n", " \n", " \n", " 805\n", @@ -1590,6 +1532,7 @@ " -99.99\n", " -99.99\n", " MLO\n", + " 2024-12-01\n", " \n", " \n", "\n", @@ -1608,14 +1551,14 @@ "804 -99.99 -99.99 -99.99 \n", "805 -99.99 -99.99 -99.99 \n", "\n", - " seasonally.2 adjusted filled [ppm] Sta \n", - "2 -99.99 MLO \n", - "3 -99.99 MLO \n", - "804 -99.99 MLO \n", - "805 -99.99 MLO " + " seasonally.2 adjusted filled [ppm] Sta Yr Mn \n", + "2 -99.99 MLO 1958-01-01 \n", + "3 -99.99 MLO 1958-02-01 \n", + "804 -99.99 MLO 2024-11-01 \n", + "805 -99.99 MLO 2024-12-01 " ] }, - "execution_count": 262, + "execution_count": 276, "metadata": {}, "output_type": "execute_result" } @@ -1627,7 +1570,7 @@ }, { "cell_type": "code", - "execution_count": 263, + "execution_count": 277, "metadata": {}, "outputs": [ { @@ -1662,6 +1605,7 @@ " CO2.1 filled [ppm]\n", " seasonally.2 adjusted filled [ppm]\n", " Sta\n", + " Yr Mn\n", " \n", " \n", " \n", @@ -1678,6 +1622,7 @@ " 315.71\n", " 314.43\n", " MLO\n", + " 1958-03-01\n", " \n", " \n", " 5\n", @@ -1692,6 +1637,7 @@ " 317.45\n", " 315.15\n", " MLO\n", + " 1958-04-01\n", " \n", " \n", " 6\n", @@ -1706,6 +1652,7 @@ " 317.51\n", " 314.69\n", " MLO\n", + " 1958-05-01\n", " \n", " \n", " 8\n", @@ -1720,6 +1667,7 @@ " 315.87\n", " 315.20\n", " MLO\n", + " 1958-07-01\n", " \n", " \n", " 9\n", @@ -1734,6 +1682,7 @@ " 314.93\n", " 316.22\n", " MLO\n", + " 1958-08-01\n", " \n", " \n", " 10\n", @@ -1748,6 +1697,7 @@ " 313.21\n", " 316.12\n", " MLO\n", + " 1958-09-01\n", " \n", " \n", " 12\n", @@ -1762,6 +1712,7 @@ " 313.33\n", " 315.21\n", " MLO\n", + " 1958-11-01\n", " \n", " \n", " 13\n", @@ -1776,6 +1727,7 @@ " 314.67\n", " 315.44\n", " MLO\n", + " 1958-12-01\n", " \n", " \n", " 14\n", @@ -1790,6 +1742,7 @@ " 315.58\n", " 315.52\n", " MLO\n", + " 1959-01-01\n", " \n", " \n", " 15\n", @@ -1804,6 +1757,7 @@ " 316.49\n", " 315.84\n", " MLO\n", + " 1959-02-01\n", " \n", " \n", " 16\n", @@ -1818,6 +1772,7 @@ " 316.65\n", " 315.37\n", " MLO\n", + " 1959-03-01\n", " \n", " \n", " 17\n", @@ -1832,6 +1787,7 @@ " 317.72\n", " 315.41\n", " MLO\n", + " 1959-04-01\n", " \n", " \n", " 18\n", @@ -1846,6 +1802,7 @@ " 318.29\n", " 315.46\n", " MLO\n", + " 1959-05-01\n", " \n", " \n", " 19\n", @@ -1860,6 +1817,7 @@ " 318.15\n", " 316.00\n", " MLO\n", + " 1959-06-01\n", " \n", " \n", " 20\n", @@ -1874,6 +1832,7 @@ " 316.54\n", " 315.87\n", " MLO\n", + " 1959-07-01\n", " \n", " \n", " 21\n", @@ -1888,6 +1847,7 @@ " 314.79\n", " 316.10\n", " MLO\n", + " 1959-08-01\n", " \n", " \n", " 22\n", @@ -1902,6 +1862,7 @@ " 313.84\n", " 316.76\n", " MLO\n", + " 1959-09-01\n", " \n", " \n", " 23\n", @@ -1916,6 +1877,7 @@ " 313.33\n", " 316.35\n", " MLO\n", + " 1959-10-01\n", " \n", " \n", " 24\n", @@ -1930,6 +1892,7 @@ " 314.81\n", " 316.69\n", " MLO\n", + " 1959-11-01\n", " \n", " \n", " 25\n", @@ -1944,6 +1907,7 @@ " 315.58\n", " 316.35\n", " MLO\n", + " 1959-12-01\n", " \n", " \n", " 26\n", @@ -1958,6 +1922,7 @@ " 316.43\n", " 316.37\n", " MLO\n", + " 1960-01-01\n", " \n", " \n", " 27\n", @@ -1972,6 +1937,7 @@ " 316.98\n", " 316.33\n", " MLO\n", + " 1960-02-01\n", " \n", " \n", " 28\n", @@ -1986,6 +1952,7 @@ " 317.58\n", " 316.27\n", " MLO\n", + " 1960-03-01\n", " \n", " \n", " 29\n", @@ -2000,6 +1967,7 @@ " 319.03\n", " 316.69\n", " MLO\n", + " 1960-04-01\n", " \n", " \n", " 30\n", @@ -2014,6 +1982,7 @@ " 320.03\n", " 317.19\n", " MLO\n", + " 1960-05-01\n", " \n", " \n", " 31\n", @@ -2028,6 +1997,7 @@ " 319.59\n", " 317.45\n", " MLO\n", + " 1960-06-01\n", " \n", " \n", " 32\n", @@ -2042,6 +2012,7 @@ " 318.18\n", " 317.53\n", " MLO\n", + " 1960-07-01\n", " \n", " \n", " 33\n", @@ -2056,6 +2027,7 @@ " 315.90\n", " 317.23\n", " MLO\n", + " 1960-08-01\n", " \n", " \n", " 34\n", @@ -2070,6 +2042,7 @@ " 314.17\n", " 317.11\n", " MLO\n", + " 1960-09-01\n", " \n", " \n", " 35\n", @@ -2084,6 +2057,7 @@ " 313.83\n", " 316.85\n", " MLO\n", + " 1960-10-01\n", " \n", " \n", " ...\n", @@ -2098,6 +2072,7 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 774\n", @@ -2112,6 +2087,7 @@ " 420.78\n", " 417.39\n", " MLO\n", + " 2022-05-01\n", " \n", " \n", " 775\n", @@ -2126,6 +2102,7 @@ " 420.68\n", " 418.10\n", " MLO\n", + " 2022-06-01\n", " \n", " \n", " 776\n", @@ -2140,6 +2117,7 @@ " 418.71\n", " 417.91\n", " MLO\n", + " 2022-07-01\n", " \n", " \n", " 777\n", @@ -2154,6 +2132,7 @@ " 416.75\n", " 418.30\n", " MLO\n", + " 2022-08-01\n", " \n", " \n", " 778\n", @@ -2168,6 +2147,7 @@ " 415.42\n", " 418.91\n", " MLO\n", + " 2022-09-01\n", " \n", " \n", " 779\n", @@ -2182,6 +2162,7 @@ " 415.31\n", " 418.91\n", " MLO\n", + " 2022-10-01\n", " \n", " \n", " 780\n", @@ -2196,6 +2177,7 @@ " 417.03\n", " 419.28\n", " MLO\n", + " 2022-11-01\n", " \n", " \n", " 781\n", @@ -2210,6 +2192,7 @@ " 418.46\n", " 419.38\n", " MKO\n", + " 2022-12-01\n", " \n", " \n", " 782\n", @@ -2224,6 +2207,7 @@ " 419.13\n", " 419.06\n", " MKO\n", + " 2023-01-01\n", " \n", " \n", " 783\n", @@ -2238,6 +2222,7 @@ " 420.33\n", " 419.55\n", " MKO\n", + " 2023-02-01\n", " \n", " \n", " 784\n", @@ -2252,6 +2237,7 @@ " 420.51\n", " 418.97\n", " MLO\n", + " 2023-03-01\n", " \n", " \n", " 785\n", @@ -2266,6 +2252,7 @@ " 422.73\n", " 419.97\n", " MLO\n", + " 2023-04-01\n", " \n", " \n", " 786\n", @@ -2280,6 +2267,7 @@ " 423.78\n", " 420.38\n", " MLO\n", + " 2023-05-01\n", " \n", " \n", " 787\n", @@ -2294,6 +2282,7 @@ " 423.39\n", " 420.81\n", " MLO\n", + " 2023-06-01\n", " \n", " \n", " 788\n", @@ -2308,6 +2297,7 @@ " 421.62\n", " 420.82\n", " MLO\n", + " 2023-07-01\n", " \n", " \n", " 789\n", @@ -2322,6 +2312,7 @@ " 419.56\n", " 421.12\n", " MLO\n", + " 2023-08-01\n", " \n", " \n", " 790\n", @@ -2336,6 +2327,7 @@ " 418.06\n", " 421.56\n", " MLO\n", + " 2023-09-01\n", " \n", " \n", " 791\n", @@ -2350,6 +2342,7 @@ " 418.41\n", " 422.01\n", " MLO\n", + " 2023-10-01\n", " \n", " \n", " 792\n", @@ -2364,6 +2357,7 @@ " 420.11\n", " 422.37\n", " MLO\n", + " 2023-11-01\n", " \n", " \n", " 793\n", @@ -2378,6 +2372,7 @@ " 421.65\n", " 422.57\n", " MLO\n", + " 2023-12-01\n", " \n", " \n", " 794\n", @@ -2392,6 +2387,7 @@ " 422.62\n", " 422.55\n", " MLO\n", + " 2024-01-01\n", " \n", " \n", " 795\n", @@ -2406,6 +2402,7 @@ " 424.34\n", " 423.56\n", " MLO\n", + " 2024-02-01\n", " \n", " \n", " 796\n", @@ -2420,6 +2417,7 @@ " 425.22\n", " 423.65\n", " MLO\n", + " 2024-03-01\n", " \n", " \n", " 797\n", @@ -2434,6 +2432,7 @@ " 426.30\n", " 423.50\n", " MLO\n", + " 2024-04-01\n", " \n", " \n", " 798\n", @@ -2448,6 +2447,7 @@ " 426.70\n", " 423.30\n", " MLO\n", + " 2024-05-01\n", " \n", " \n", " 799\n", @@ -2462,6 +2462,7 @@ " 426.62\n", " 424.06\n", " MLO\n", + " 2024-06-01\n", " \n", " \n", " 800\n", @@ -2476,6 +2477,7 @@ " 425.40\n", " 424.63\n", " MLO\n", + " 2024-07-01\n", " \n", " \n", " 801\n", @@ -2490,6 +2492,7 @@ " 422.70\n", " 424.30\n", " MLO\n", + " 2024-08-01\n", " \n", " \n", " 802\n", @@ -2504,6 +2507,7 @@ " 421.59\n", " 425.11\n", " MLO\n", + " 2024-09-01\n", " \n", " \n", " 803\n", @@ -2518,10 +2522,11 @@ " 422.05\n", " 425.66\n", " MLO\n", + " 2024-10-01\n", " \n", " \n", "\n", - "

795 rows × 11 columns

\n", + "

795 rows × 12 columns

\n", "" ], "text/plain": [ @@ -2651,73 +2656,73 @@ "802 421.53 425.07 421.59 \n", "803 421.77 425.37 422.05 \n", "\n", - " seasonally.2 adjusted filled [ppm] Sta \n", - "4 314.43 MLO \n", - "5 315.15 MLO \n", - "6 314.69 MLO \n", - "8 315.20 MLO \n", - "9 316.22 MLO \n", - "10 316.12 MLO \n", - "12 315.21 MLO \n", - "13 315.44 MLO \n", - "14 315.52 MLO \n", - "15 315.84 MLO \n", - "16 315.37 MLO \n", - "17 315.41 MLO \n", - "18 315.46 MLO \n", - "19 316.00 MLO \n", - "20 315.87 MLO \n", - "21 316.10 MLO \n", - "22 316.76 MLO \n", - "23 316.35 MLO \n", - "24 316.69 MLO \n", - "25 316.35 MLO \n", - "26 316.37 MLO \n", - "27 316.33 MLO \n", - "28 316.27 MLO \n", - "29 316.69 MLO \n", - "30 317.19 MLO \n", - "31 317.45 MLO \n", - "32 317.53 MLO \n", - "33 317.23 MLO \n", - "34 317.11 MLO \n", - "35 316.85 MLO \n", - ".. ... ... \n", - "774 417.39 MLO \n", - "775 418.10 MLO \n", - "776 417.91 MLO \n", - "777 418.30 MLO \n", - "778 418.91 MLO \n", - "779 418.91 MLO \n", - "780 419.28 MLO \n", - "781 419.38 MKO \n", - "782 419.06 MKO \n", - "783 419.55 MKO \n", - "784 418.97 MLO \n", - "785 419.97 MLO \n", - "786 420.38 MLO \n", - "787 420.81 MLO \n", - "788 420.82 MLO \n", - "789 421.12 MLO \n", - "790 421.56 MLO \n", - "791 422.01 MLO \n", - "792 422.37 MLO \n", - "793 422.57 MLO \n", - "794 422.55 MLO \n", - "795 423.56 MLO \n", - "796 423.65 MLO \n", - "797 423.50 MLO \n", - "798 423.30 MLO \n", - "799 424.06 MLO \n", - "800 424.63 MLO \n", - "801 424.30 MLO \n", - "802 425.11 MLO \n", - "803 425.66 MLO \n", + " seasonally.2 adjusted filled [ppm] Sta Yr Mn \n", + "4 314.43 MLO 1958-03-01 \n", + "5 315.15 MLO 1958-04-01 \n", + "6 314.69 MLO 1958-05-01 \n", + "8 315.20 MLO 1958-07-01 \n", + "9 316.22 MLO 1958-08-01 \n", + "10 316.12 MLO 1958-09-01 \n", + "12 315.21 MLO 1958-11-01 \n", + "13 315.44 MLO 1958-12-01 \n", + "14 315.52 MLO 1959-01-01 \n", + "15 315.84 MLO 1959-02-01 \n", + "16 315.37 MLO 1959-03-01 \n", + "17 315.41 MLO 1959-04-01 \n", + "18 315.46 MLO 1959-05-01 \n", + "19 316.00 MLO 1959-06-01 \n", + "20 315.87 MLO 1959-07-01 \n", + "21 316.10 MLO 1959-08-01 \n", + "22 316.76 MLO 1959-09-01 \n", + "23 316.35 MLO 1959-10-01 \n", + "24 316.69 MLO 1959-11-01 \n", + "25 316.35 MLO 1959-12-01 \n", + "26 316.37 MLO 1960-01-01 \n", + "27 316.33 MLO 1960-02-01 \n", + "28 316.27 MLO 1960-03-01 \n", + "29 316.69 MLO 1960-04-01 \n", + "30 317.19 MLO 1960-05-01 \n", + "31 317.45 MLO 1960-06-01 \n", + "32 317.53 MLO 1960-07-01 \n", + "33 317.23 MLO 1960-08-01 \n", + "34 317.11 MLO 1960-09-01 \n", + "35 316.85 MLO 1960-10-01 \n", + ".. ... ... ... \n", + "774 417.39 MLO 2022-05-01 \n", + "775 418.10 MLO 2022-06-01 \n", + "776 417.91 MLO 2022-07-01 \n", + "777 418.30 MLO 2022-08-01 \n", + "778 418.91 MLO 2022-09-01 \n", + "779 418.91 MLO 2022-10-01 \n", + "780 419.28 MLO 2022-11-01 \n", + "781 419.38 MKO 2022-12-01 \n", + "782 419.06 MKO 2023-01-01 \n", + "783 419.55 MKO 2023-02-01 \n", + "784 418.97 MLO 2023-03-01 \n", + "785 419.97 MLO 2023-04-01 \n", + "786 420.38 MLO 2023-05-01 \n", + "787 420.81 MLO 2023-06-01 \n", + "788 420.82 MLO 2023-07-01 \n", + "789 421.12 MLO 2023-08-01 \n", + "790 421.56 MLO 2023-09-01 \n", + "791 422.01 MLO 2023-10-01 \n", + "792 422.37 MLO 2023-11-01 \n", + "793 422.57 MLO 2023-12-01 \n", + "794 422.55 MLO 2024-01-01 \n", + "795 423.56 MLO 2024-02-01 \n", + "796 423.65 MLO 2024-03-01 \n", + "797 423.50 MLO 2024-04-01 \n", + "798 423.30 MLO 2024-05-01 \n", + "799 424.06 MLO 2024-06-01 \n", + "800 424.63 MLO 2024-07-01 \n", + "801 424.30 MLO 2024-08-01 \n", + "802 425.11 MLO 2024-09-01 \n", + "803 425.66 MLO 2024-10-01 \n", "\n", - "[795 rows x 11 columns]" + "[795 rows x 12 columns]" ] }, - "execution_count": 263, + "execution_count": 277, "metadata": {}, "output_type": "execute_result" } @@ -2726,6 +2731,1481 @@ "strict_data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse\n", + "\n", + "Strict_data are used" + ] + }, + { + "cell_type": "code", + "execution_count": 278, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
YrMnDate ExcelDate.1CO2 [ppm]seasonally adjusted [ppm]fit [ppm]seasonally.1 adjusted fit [ppm]CO2.1 filled [ppm]seasonally.2 adjusted filled [ppm]Sta
Yr Mn
1958-03-01195803212591958.2027315.71314.43316.20314.91315.71314.43MLO
1958-04-01195804212901958.2877317.45315.15317.31314.99317.45315.15MLO
1958-05-01195805213201958.3699317.51314.69317.89315.07317.51314.69MLO
1958-07-01195807213811958.5370315.87315.20315.86315.22315.87315.20MLO
1958-08-01195808214121958.6219314.93316.22313.96315.29314.93316.22MLO
1958-09-01195809214431958.7068313.21316.12312.43315.35313.21316.12MLO
1958-11-01195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
1958-12-01195812215341958.9562314.67315.44314.77315.52314.67315.44MLO
1959-01-01195901215651959.0411315.58315.52315.64315.58315.58315.52MLO
1959-02-01195902215961959.1260316.49315.84316.30315.64316.49315.84MLO
1959-03-01195903216241959.2027316.65315.37317.00315.70316.65315.37MLO
1959-04-01195904216551959.2877317.72315.41318.10315.77317.72315.41MLO
1959-05-01195905216851959.3699318.29315.46318.68315.85318.29315.46MLO
1959-06-01195906217161959.4548318.15316.00318.08315.94318.15316.00MLO
1959-07-01195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
1959-08-01195908217771959.6219314.79316.10314.80316.13314.79316.10MLO
1959-09-01195909218081959.7068313.84316.76313.29316.22313.84316.76MLO
1959-10-01195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
1959-11-01195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
1959-12-01195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
1960-01-01196001219301960.0410316.43316.37316.63316.56316.43316.37MLO
1960-02-01196002219611960.1257316.98316.33317.30316.64316.98316.33MLO
1960-03-01196003219901960.2049317.58316.27318.04316.72317.58316.27MLO
1960-04-01196004220211960.2896319.03316.69319.15316.79319.03316.69MLO
1960-05-01196005220511960.3716320.03317.19319.70316.86320.03317.19MLO
1960-06-01196006220821960.4563319.59317.45319.05316.93319.59317.45MLO
1960-07-01196007221121960.5383318.18317.53317.59316.98318.18317.53MLO
1960-08-01196008221431960.6230315.90317.23315.66317.02315.90317.23MLO
1960-09-01196009221741960.7077314.17317.11314.10317.05314.17317.11MLO
1960-10-01196010222041960.7896313.83316.85314.08317.08313.83316.85MLO
....................................
2022-05-01202205446962022.3699420.78417.39421.23417.84420.78417.39MLO
2022-06-01202206447272022.4548420.68418.10420.56418.01420.68418.10MLO
2022-07-01202207447572022.5370418.71417.91418.95418.18418.71417.91MLO
2022-08-01202208447882022.6219416.75418.30416.77418.37416.75418.30MLO
2022-09-01202209448192022.7068415.42418.91415.05418.56415.42418.91MLO
2022-10-01202210448492022.7890415.31418.91415.16418.75415.31418.91MLO
2022-11-01202211448802022.8740417.03419.28416.72418.95417.03419.28MLO
2022-12-01202212449102022.9562418.46419.38418.25419.16418.46419.38MKO
2023-01-01202301449412023.0411419.13419.06419.46419.38419.13419.06MKO
2023-02-01202302449722023.1260420.33419.55420.40419.61420.33419.55MKO
2023-03-01202303450002023.2027420.51418.97421.39419.83420.51418.97MLO
2023-04-01202304450312023.2877422.73419.97422.88420.10422.73419.97MLO
2023-05-01202305450612023.3699423.78420.38423.76420.37423.78420.38MLO
2023-06-01202306450922023.4548423.39420.81423.22420.66423.39420.81MLO
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\n", + "

795 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn Date Excel Date.1 CO2 [ppm] \\\n", + "Yr Mn \n", + "1958-03-01 1958 03 21259 1958.2027 315.71 \n", + "1958-04-01 1958 04 21290 1958.2877 317.45 \n", + "1958-05-01 1958 05 21320 1958.3699 317.51 \n", + "1958-07-01 1958 07 21381 1958.5370 315.87 \n", + "1958-08-01 1958 08 21412 1958.6219 314.93 \n", + "1958-09-01 1958 09 21443 1958.7068 313.21 \n", + "1958-11-01 1958 11 21504 1958.8740 313.33 \n", + "1958-12-01 1958 12 21534 1958.9562 314.67 \n", + "1959-01-01 1959 01 21565 1959.0411 315.58 \n", + "1959-02-01 1959 02 21596 1959.1260 316.49 \n", + "1959-03-01 1959 03 21624 1959.2027 316.65 \n", + "1959-04-01 1959 04 21655 1959.2877 317.72 \n", + "1959-05-01 1959 05 21685 1959.3699 318.29 \n", + "1959-06-01 1959 06 21716 1959.4548 318.15 \n", + "1959-07-01 1959 07 21746 1959.5370 316.54 \n", + "1959-08-01 1959 08 21777 1959.6219 314.79 \n", + "1959-09-01 1959 09 21808 1959.7068 313.84 \n", + "1959-10-01 1959 10 21838 1959.7890 313.33 \n", + "1959-11-01 1959 11 21869 1959.8740 314.81 \n", + "1959-12-01 1959 12 21899 1959.9562 315.58 \n", + "1960-01-01 1960 01 21930 1960.0410 316.43 \n", + "1960-02-01 1960 02 21961 1960.1257 316.98 \n", + "1960-03-01 1960 03 21990 1960.2049 317.58 \n", + "1960-04-01 1960 04 22021 1960.2896 319.03 \n", + "1960-05-01 1960 05 22051 1960.3716 320.03 \n", + "1960-06-01 1960 06 22082 1960.4563 319.59 \n", + "1960-07-01 1960 07 22112 1960.5383 318.18 \n", + "1960-08-01 1960 08 22143 1960.6230 315.90 \n", + "1960-09-01 1960 09 22174 1960.7077 314.17 \n", + "1960-10-01 1960 10 22204 1960.7896 313.83 \n", + "... ... .. ... ... ... \n", + "2022-05-01 2022 05 44696 2022.3699 420.78 \n", + "2022-06-01 2022 06 44727 2022.4548 420.68 \n", + "2022-07-01 2022 07 44757 2022.5370 418.71 \n", + "2022-08-01 2022 08 44788 2022.6219 416.75 \n", + "2022-09-01 2022 09 44819 2022.7068 415.42 \n", + "2022-10-01 2022 10 44849 2022.7890 415.31 \n", + "2022-11-01 2022 11 44880 2022.8740 417.03 \n", + "2022-12-01 2022 12 44910 2022.9562 418.46 \n", + "2023-01-01 2023 01 44941 2023.0411 419.13 \n", + "2023-02-01 2023 02 44972 2023.1260 420.33 \n", + "2023-03-01 2023 03 45000 2023.2027 420.51 \n", + "2023-04-01 2023 04 45031 2023.2877 422.73 \n", + "2023-05-01 2023 05 45061 2023.3699 423.78 \n", + "2023-06-01 2023 06 45092 2023.4548 423.39 \n", + "2023-07-01 2023 07 45122 2023.5370 421.62 \n", + "2023-08-01 2023 08 45153 2023.6219 419.56 \n", + "2023-09-01 2023 09 45184 2023.7068 418.06 \n", + "2023-10-01 2023 10 45214 2023.7890 418.41 \n", + "2023-11-01 2023 11 45245 2023.8740 420.11 \n", + "2023-12-01 2023 12 45275 2023.9562 421.65 \n", + "2024-01-01 2024 01 45306 2024.0410 422.62 \n", + "2024-02-01 2024 02 45337 2024.1257 424.34 \n", + "2024-03-01 2024 03 45366 2024.2049 425.22 \n", + "2024-04-01 2024 04 45397 2024.2896 426.30 \n", + "2024-05-01 2024 05 45427 2024.3716 426.70 \n", + "2024-06-01 2024 06 45458 2024.4563 426.62 \n", + "2024-07-01 2024 07 45488 2024.5383 425.40 \n", + "2024-08-01 2024 08 45519 2024.6230 422.70 \n", + "2024-09-01 2024 09 45550 2024.7077 421.59 \n", + "2024-10-01 2024 10 45580 2024.7896 422.05 \n", + "\n", + " seasonally adjusted [ppm] fit [ppm] \\\n", + "Yr Mn \n", + "1958-03-01 314.43 316.20 \n", + "1958-04-01 315.15 317.31 \n", + "1958-05-01 314.69 317.89 \n", + "1958-07-01 315.20 315.86 \n", + "1958-08-01 316.22 313.96 \n", + "1958-09-01 316.12 312.43 \n", + "1958-11-01 315.21 313.60 \n", + "1958-12-01 315.44 314.77 \n", + "1959-01-01 315.52 315.64 \n", + "1959-02-01 315.84 316.30 \n", + "1959-03-01 315.37 317.00 \n", + "1959-04-01 315.41 318.10 \n", + "1959-05-01 315.46 318.68 \n", + "1959-06-01 316.00 318.08 \n", + "1959-07-01 315.87 316.67 \n", + "1959-08-01 316.10 314.80 \n", + "1959-09-01 316.76 313.29 \n", + "1959-10-01 316.35 313.31 \n", + "1959-11-01 316.69 314.53 \n", + "1959-12-01 316.35 315.72 \n", + "1960-01-01 316.37 316.63 \n", + "1960-02-01 316.33 317.30 \n", + "1960-03-01 316.27 318.04 \n", + "1960-04-01 316.69 319.15 \n", + "1960-05-01 317.19 319.70 \n", + "1960-06-01 317.45 319.05 \n", + "1960-07-01 317.53 317.59 \n", + "1960-08-01 317.23 315.66 \n", + "1960-09-01 317.11 314.10 \n", + "1960-10-01 316.85 314.08 \n", + "... ... ... \n", + "2022-05-01 417.39 421.23 \n", + "2022-06-01 418.10 420.56 \n", + "2022-07-01 417.91 418.95 \n", + "2022-08-01 418.30 416.77 \n", + "2022-09-01 418.91 415.05 \n", + "2022-10-01 418.91 415.16 \n", + "2022-11-01 419.28 416.72 \n", + "2022-12-01 419.38 418.25 \n", + "2023-01-01 419.06 419.46 \n", + "2023-02-01 419.55 420.40 \n", + "2023-03-01 418.97 421.39 \n", + "2023-04-01 419.97 422.88 \n", + "2023-05-01 420.38 423.76 \n", + "2023-06-01 420.81 423.22 \n", + "2023-07-01 420.82 421.72 \n", + "2023-08-01 421.12 419.66 \n", + "2023-09-01 421.56 418.05 \n", + "2023-10-01 422.01 418.28 \n", + "2023-11-01 422.37 419.94 \n", + "2023-12-01 422.57 421.57 \n", + "2024-01-01 422.55 422.85 \n", + "2024-02-01 423.56 423.85 \n", + "2024-03-01 423.65 424.92 \n", + "2024-04-01 423.50 426.43 \n", + "2024-05-01 423.30 427.29 \n", + "2024-06-01 424.06 426.72 \n", + "2024-07-01 424.63 425.20 \n", + "2024-08-01 424.30 423.13 \n", + "2024-09-01 425.11 421.53 \n", + "2024-10-01 425.66 421.77 \n", + "\n", + " seasonally.1 adjusted fit [ppm] CO2.1 filled [ppm] \\\n", + "Yr Mn \n", + "1958-03-01 314.91 315.71 \n", + "1958-04-01 314.99 317.45 \n", + "1958-05-01 315.07 317.51 \n", + "1958-07-01 315.22 315.87 \n", + "1958-08-01 315.29 314.93 \n", + "1958-09-01 315.35 313.21 \n", + "1958-11-01 315.46 313.33 \n", + "1958-12-01 315.52 314.67 \n", + "1959-01-01 315.58 315.58 \n", + "1959-02-01 315.64 316.49 \n", + "1959-03-01 315.70 316.65 \n", + "1959-04-01 315.77 317.72 \n", + "1959-05-01 315.85 318.29 \n", + "1959-06-01 315.94 318.15 \n", + "1959-07-01 316.03 316.54 \n", + "1959-08-01 316.13 314.79 \n", + "1959-09-01 316.22 313.84 \n", + "1959-10-01 316.31 313.33 \n", + "1959-11-01 316.40 314.81 \n", + "1959-12-01 316.48 315.58 \n", + "1960-01-01 316.56 316.43 \n", + "1960-02-01 316.64 316.98 \n", + "1960-03-01 316.72 317.58 \n", + "1960-04-01 316.79 319.03 \n", + "1960-05-01 316.86 320.03 \n", + "1960-06-01 316.93 319.59 \n", + "1960-07-01 316.98 318.18 \n", + "1960-08-01 317.02 315.90 \n", + "1960-09-01 317.05 314.17 \n", + "1960-10-01 317.08 313.83 \n", + "... ... ... \n", + "2022-05-01 417.84 420.78 \n", + "2022-06-01 418.01 420.68 \n", + "2022-07-01 418.18 418.71 \n", + "2022-08-01 418.37 416.75 \n", + "2022-09-01 418.56 415.42 \n", + "2022-10-01 418.75 415.31 \n", + "2022-11-01 418.95 417.03 \n", + "2022-12-01 419.16 418.46 \n", + "2023-01-01 419.38 419.13 \n", + "2023-02-01 419.61 420.33 \n", + "2023-03-01 419.83 420.51 \n", + "2023-04-01 420.10 422.73 \n", + "2023-05-01 420.37 423.78 \n", + "2023-06-01 420.66 423.39 \n", + "2023-07-01 420.95 421.62 \n", + "2023-08-01 421.26 419.56 \n", + "2023-09-01 421.57 418.06 \n", + "2023-10-01 421.87 418.41 \n", + "2023-11-01 422.18 420.11 \n", + "2023-12-01 422.47 421.65 \n", + "2024-01-01 422.77 422.62 \n", + "2024-02-01 423.06 424.34 \n", + "2024-03-01 423.33 425.22 \n", + "2024-04-01 423.61 426.30 \n", + "2024-05-01 423.89 426.70 \n", + "2024-06-01 424.18 426.62 \n", + "2024-07-01 424.46 425.40 \n", + "2024-08-01 424.76 422.70 \n", + "2024-09-01 425.07 421.59 \n", + "2024-10-01 425.37 422.05 \n", + "\n", + " seasonally.2 adjusted filled [ppm] Sta \n", + "Yr Mn \n", + "1958-03-01 314.43 MLO \n", + "1958-04-01 315.15 MLO \n", + "1958-05-01 314.69 MLO \n", + "1958-07-01 315.20 MLO \n", + "1958-08-01 316.22 MLO \n", + "1958-09-01 316.12 MLO \n", + "1958-11-01 315.21 MLO \n", + "1958-12-01 315.44 MLO \n", + "1959-01-01 315.52 MLO \n", + "1959-02-01 315.84 MLO \n", + "1959-03-01 315.37 MLO \n", + "1959-04-01 315.41 MLO \n", + "1959-05-01 315.46 MLO \n", + "1959-06-01 316.00 MLO \n", + "1959-07-01 315.87 MLO \n", + "1959-08-01 316.10 MLO \n", + "1959-09-01 316.76 MLO \n", + "1959-10-01 316.35 MLO \n", + "1959-11-01 316.69 MLO \n", + "1959-12-01 316.35 MLO \n", + "1960-01-01 316.37 MLO \n", + "1960-02-01 316.33 MLO \n", + "1960-03-01 316.27 MLO \n", + "1960-04-01 316.69 MLO \n", + "1960-05-01 317.19 MLO \n", + "1960-06-01 317.45 MLO \n", + "1960-07-01 317.53 MLO \n", + "1960-08-01 317.23 MLO \n", + "1960-09-01 317.11 MLO \n", + "1960-10-01 316.85 MLO \n", + "... ... ... \n", + "2022-05-01 417.39 MLO \n", + "2022-06-01 418.10 MLO \n", + "2022-07-01 417.91 MLO \n", + "2022-08-01 418.30 MLO \n", + "2022-09-01 418.91 MLO \n", + "2022-10-01 418.91 MLO \n", + "2022-11-01 419.28 MLO \n", + "2022-12-01 419.38 MKO \n", + "2023-01-01 419.06 MKO \n", + "2023-02-01 419.55 MKO \n", + "2023-03-01 418.97 MLO \n", + "2023-04-01 419.97 MLO \n", + "2023-05-01 420.38 MLO \n", + "2023-06-01 420.81 MLO \n", + "2023-07-01 420.82 MLO \n", + "2023-08-01 421.12 MLO \n", + "2023-09-01 421.56 MLO \n", + "2023-10-01 422.01 MLO \n", + "2023-11-01 422.37 MLO \n", + "2023-12-01 422.57 MLO \n", + "2024-01-01 422.55 MLO \n", + "2024-02-01 423.56 MLO \n", + "2024-03-01 423.65 MLO \n", + "2024-04-01 423.50 MLO \n", + "2024-05-01 423.30 MLO \n", + "2024-06-01 424.06 MLO \n", + "2024-07-01 424.63 MLO \n", + "2024-08-01 424.30 MLO \n", + "2024-09-01 425.11 MLO \n", + "2024-10-01 425.66 MLO \n", + "\n", + "[795 rows x 11 columns]" + ] + }, + "execution_count": 278, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data = strict_data.set_index('Yr Mn').sort_index().copy()\n", + "sorted_data" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 279, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['CO2 [ppm]'][-200:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Caracatérisation de l'oscillation" + ] + }, + { + "cell_type": "code", + "execution_count": 387, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/!\\ Warning : Incomplete year /!\\ :\n", + "year : 1958 \t number of months of data available : 10\n", + "year : 1963 \t number of months of data available : 11\n", + "year : 1964 \t number of months of data available : 10\n", + "year : 2024 \t number of months of data available : 8\n" + ] + } + ], + "source": [ + "first_march = [pd.Timestamp(y, 3, 1) for y in range(1958, sorted_data.index[-1].year + 2)]\n", + "year = []\n", + "yearly_CO2 = []\n", + "print(\"/!\\ Warning : Incomplete year /!\\ :\")\n", + "for week1, week2 in zip(first_march[:-1], first_march[1:]):\n", + " one_year = sorted_data['CO2 [ppm]'][week1:(week2 - pd.DateOffset(days=1))]\n", + " # assert (13 > len(one_year) > 9), (print(len(one_year), week1, week2, \"\\n\", one_year))\n", + " if len(one_year) != 12:\n", + " print('year :', week1.year, '\\t number of months of data available :', len(one_year))\n", + " yearly_CO2.append(one_year.mean())\n", + " year.append(pd.Period(week1, freq='12M'))\n", + "yearly_CO2 = pd.Series(data=yearly_CO2, index=year)" + ] + }, + { + "cell_type": "code", + "execution_count": 388, + "metadata": { + "hideCode": true, + "hideOutput": true, + "hidePrompt": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 388, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['CO2 norm [ppm]'] = sorted_data['CO2 [ppm]']\n", + "for period, co2_mean_value in yearly_CO2.items():\n", + " selector = (period.start_time <= sorted_data.index) & (sorted_data.index <= period.end_time) # boolean mask of row data datetime in the period of yearly_CO2\n", + " sorted_data['CO2 norm [ppm]'].loc[selector] = sorted_data[selector]['CO2 [ppm]'] - co2_mean_value\n", + "sorted_data['CO2 norm [ppm]'].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 389, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 389, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['CO2 norm [ppm]'][-25:].plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Analyse de l'oscillation \n", + "\n", + "Dans les différentes figures, on peut observer une oscillation régulière, qui correspond à la respiration de la Terre. En effet, on constate une augmentation de la concentration de CO2 au début du mois d'octobre, ce qui coïncide avec la diminution du taux d'ensoleillement et la perte des feuilles par les arbres, c'est l'automne. Ces derniers capturent moins de CO2 pour le transformer en O2, ce qui entraîne une augmentation de la concentration de CO2 dans l'air. Vers le milieu du printemps, les arbres recommencent à capturer le CO2, provoquant ainsi une baisse de sa concentration.\n", + "\n", + "Les pics en mars correspondent à la méthode employée pour définir l'oscillation. En effet, nous normalisons sur une année à partir du mois de mars. Pour limiter cet effet, il aurait été intéressant d'effectuer la normalisation sur une fenêtre glissante." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Evolution systématique" + ] + }, + { + "cell_type": "code", + "execution_count": 404, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 404, + "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_CO2.to_timestamp().plot() # Need to convert to timestamp because pd.version is 0.22 and bug(https://github.com/pandas-dev/pandas/issues/14763) has not been patch" + ] + }, + { + "cell_type": "code", + "execution_count": 410, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Length mismatch: Expected axis has 67 elements, new values have 66 elements", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0myearly_CO2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"CO2 mean\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0myearly_CO2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myearly_CO2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__setattr__\u001b[0;34m(self, name, value)\u001b[0m\n\u001b[1;32m 3625\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3626\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3627\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3628\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3629\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32mpandas/_libs/properties.pyx\u001b[0m in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_set_axis\u001b[0;34m(self, axis, labels, fastpath)\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_index'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_set_subtyp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_all_dates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mset_axis\u001b[0;34m(self, axis, new_labels)\u001b[0m\n\u001b[1;32m 3072\u001b[0m raise ValueError('Length mismatch: Expected axis has %d elements, '\n\u001b[1;32m 3073\u001b[0m \u001b[0;34m'new values have %d elements'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3074\u001b[0;31m (old_len, new_len))\n\u001b[0m\u001b[1;32m 3075\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3076\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Length mismatch: Expected axis has 67 elements, new values have 66 elements" + ] + } + ], + "source": [ + "yearly_CO2.to_frame(name=\"CO2 mean\")\n", + "yearly_CO2.index = list(range(1, len(yearly_CO2)))" + ] + }, { "cell_type": "code", "execution_count": null,