From 00ec688728b8b431fdc75c040b18436c1fbb7ebf Mon Sep 17 00:00:00 2001 From: 269f2cc5a36d63b5f4ed2df8fc9d5af1 <269f2cc5a36d63b5f4ed2df8fc9d5af1@app-learninglab.inria.fr> Date: Wed, 11 Dec 2024 15:20:39 +0000 Subject: [PATCH] Final --- module3/exo3/exercice_fr.ipynb | 1714 +++++++------------------------- 1 file changed, 333 insertions(+), 1381 deletions(-) diff --git a/module3/exo3/exercice_fr.ipynb b/module3/exo3/exercice_fr.ipynb index 5954d79..64d6fa7 100644 --- a/module3/exo3/exercice_fr.ipynb +++ b/module3/exo3/exercice_fr.ipynb @@ -27,8 +27,10 @@ }, { "cell_type": "code", - "execution_count": 270, - "metadata": {}, + "execution_count": 155, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -53,12 +55,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Load data" + "## Load data source\n", + "\n", + "Downloading data from url if .csv file do not exist in folder.\n", + "\n", + "Last download : december 2024" ] }, { "cell_type": "code", - "execution_count": 271, + "execution_count": 154, "metadata": { "scrolled": true }, @@ -1094,7 +1100,7 @@ "[806 rows x 11 columns]" ] }, - "execution_count": 271, + "execution_count": 154, "metadata": {}, "output_type": "execute_result" } @@ -1121,19 +1127,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Prepare data" + "## Prepare data\n", + "\n", + "Preration of data before analyse" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "- Prepare header" + "- Prepare header : we adjust columns's name. We concat it with the first and second row of the data." ] }, { "cell_type": "code", - "execution_count": 272, + "execution_count": 156, "metadata": {}, "outputs": [], "source": [ @@ -1146,12 +1154,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "- String to dateformat\n" + "- String to dateformat : we add a new date column in panda's specific datetime format\n" ] }, { "cell_type": "code", - "execution_count": 273, + "execution_count": 158, "metadata": {}, "outputs": [], "source": [ @@ -1165,12 +1173,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "- String to numeric data" + "- String to numeric data : we convert numeric date from string format to numeric format" ] }, { "cell_type": "code", - "execution_count": 274, + "execution_count": 159, "metadata": {}, "outputs": [], "source": [ @@ -1183,23 +1191,18 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "- Remove unreleavant value" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**strict data** : row with missing value on at least one column are removed. Missing value are denoted as $-99.99$ as said by the csv header.\n", + "- Remove unreleavant value\n", + "\n", + "**strict data** : row with missing value on at least one column are removed. Missing value are denoted as $-99.99$ (as said by the csv header).\n", "\n", "Printed row are those who has been dropped\n", "\n", - "*Note : condition has been simplified due to disposition of the data (2024 december 09), which mean that strict data are not futur proof* " + "*Note : condition has been simplified due to disposition of the data (2024 december), which mean that strict data strategy is not futur proof* " ] }, { "cell_type": "code", - "execution_count": 275, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -1412,7 +1415,7 @@ "805 -99.99 MLO 2024-12-01 " ] }, - "execution_count": 275, + "execution_count": 160, "metadata": {}, "output_type": "execute_result" } @@ -1435,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": 276, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -1558,7 +1561,7 @@ "805 -99.99 MLO 2024-12-01 " ] }, - "execution_count": 276, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1568,9 +1571,18 @@ "raw_data[raw_data[\"fit [ppm]\"] == -99.99]" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Analyse\n", + "\n", + "In the following section we use strict data strategy." + ] + }, { "cell_type": "code", - "execution_count": 277, + "execution_count": 56, "metadata": {}, "outputs": [ { @@ -1605,12 +1617,25 @@ " CO2.1 filled [ppm]\n", " seasonally.2 adjusted filled [ppm]\n", " Sta\n", + " \n", + " \n", " Yr Mn\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " 4\n", + " 1958-03-01\n", " 1958\n", " 03\n", " 21259\n", @@ -1622,10 +1647,9 @@ " 315.71\n", " 314.43\n", " MLO\n", - " 1958-03-01\n", " \n", " \n", - " 5\n", + " 1958-04-01\n", " 1958\n", " 04\n", " 21290\n", @@ -1637,10 +1661,9 @@ " 317.45\n", " 315.15\n", " MLO\n", - " 1958-04-01\n", " \n", " \n", - " 6\n", + " 1958-05-01\n", " 1958\n", " 05\n", " 21320\n", @@ -1652,10 +1675,9 @@ " 317.51\n", " 314.69\n", " MLO\n", - " 1958-05-01\n", " \n", " \n", - " 8\n", + " 1958-07-01\n", " 1958\n", " 07\n", " 21381\n", @@ -1667,10 +1689,9 @@ " 315.87\n", " 315.20\n", " MLO\n", - " 1958-07-01\n", " \n", " \n", - " 9\n", + " 1958-08-01\n", " 1958\n", " 08\n", " 21412\n", @@ -1682,10 +1703,9 @@ " 314.93\n", " 316.22\n", " MLO\n", - " 1958-08-01\n", " \n", " \n", - " 10\n", + " 1958-09-01\n", " 1958\n", " 09\n", " 21443\n", @@ -1697,10 +1717,9 @@ " 313.21\n", " 316.12\n", " MLO\n", - " 1958-09-01\n", " \n", " \n", - " 12\n", + " 1958-11-01\n", " 1958\n", " 11\n", " 21504\n", @@ -1712,10 +1731,9 @@ " 313.33\n", " 315.21\n", " MLO\n", - " 1958-11-01\n", " \n", " \n", - " 13\n", + " 1958-12-01\n", " 1958\n", " 12\n", " 21534\n", @@ -1727,10 +1745,9 @@ " 314.67\n", " 315.44\n", " MLO\n", - " 1958-12-01\n", " \n", " \n", - " 14\n", + " 1959-01-01\n", " 1959\n", " 01\n", " 21565\n", @@ -1742,10 +1759,9 @@ " 315.58\n", " 315.52\n", " MLO\n", - " 1959-01-01\n", " \n", " \n", - " 15\n", + " 1959-02-01\n", " 1959\n", " 02\n", " 21596\n", @@ -1757,10 +1773,9 @@ " 316.49\n", " 315.84\n", " MLO\n", - " 1959-02-01\n", " \n", " \n", - " 16\n", + " 1959-03-01\n", " 1959\n", " 03\n", " 21624\n", @@ -1772,10 +1787,9 @@ " 316.65\n", " 315.37\n", " MLO\n", - " 1959-03-01\n", " \n", " \n", - " 17\n", + " 1959-04-01\n", " 1959\n", " 04\n", " 21655\n", @@ -1787,10 +1801,9 @@ " 317.72\n", " 315.41\n", " MLO\n", - " 1959-04-01\n", " \n", " \n", - " 18\n", + " 1959-05-01\n", " 1959\n", " 05\n", " 21685\n", @@ -1802,10 +1815,9 @@ " 318.29\n", " 315.46\n", " MLO\n", - " 1959-05-01\n", " \n", " \n", - " 19\n", + " 1959-06-01\n", " 1959\n", " 06\n", " 21716\n", @@ -1817,10 +1829,9 @@ " 318.15\n", " 316.00\n", " MLO\n", - " 1959-06-01\n", " \n", " \n", - " 20\n", + " 1959-07-01\n", " 1959\n", " 07\n", " 21746\n", @@ -1832,10 +1843,9 @@ " 316.54\n", " 315.87\n", " MLO\n", - " 1959-07-01\n", " \n", " \n", - " 21\n", + " 1959-08-01\n", " 1959\n", " 08\n", " 21777\n", @@ -1847,10 +1857,9 @@ " 314.79\n", " 316.10\n", " MLO\n", - " 1959-08-01\n", " \n", " \n", - " 22\n", + " 1959-09-01\n", " 1959\n", " 09\n", " 21808\n", @@ -1862,10 +1871,9 @@ " 313.84\n", " 316.76\n", " MLO\n", - " 1959-09-01\n", " \n", " \n", - " 23\n", + " 1959-10-01\n", " 1959\n", " 10\n", " 21838\n", @@ -1877,10 +1885,9 @@ " 313.33\n", " 316.35\n", " MLO\n", - " 1959-10-01\n", " \n", " \n", - " 24\n", + " 1959-11-01\n", " 1959\n", " 11\n", " 21869\n", @@ -1892,10 +1899,9 @@ " 314.81\n", " 316.69\n", " MLO\n", - " 1959-11-01\n", " \n", " \n", - " 25\n", + " 1959-12-01\n", " 1959\n", " 12\n", " 21899\n", @@ -1907,10 +1913,9 @@ " 315.58\n", " 316.35\n", " MLO\n", - " 1959-12-01\n", " \n", " \n", - " 26\n", + " 1960-01-01\n", " 1960\n", " 01\n", " 21930\n", @@ -1922,10 +1927,9 @@ " 316.43\n", " 316.37\n", " MLO\n", - " 1960-01-01\n", " \n", " \n", - " 27\n", + " 1960-02-01\n", " 1960\n", " 02\n", " 21961\n", @@ -1937,10 +1941,9 @@ " 316.98\n", " 316.33\n", " MLO\n", - " 1960-02-01\n", " \n", " \n", - " 28\n", + " 1960-03-01\n", " 1960\n", " 03\n", " 21990\n", @@ -1952,10 +1955,9 @@ " 317.58\n", " 316.27\n", " MLO\n", - " 1960-03-01\n", " \n", " \n", - " 29\n", + " 1960-04-01\n", " 1960\n", " 04\n", " 22021\n", @@ -1967,10 +1969,9 @@ " 319.03\n", " 316.69\n", " MLO\n", - " 1960-04-01\n", " \n", " \n", - " 30\n", + " 1960-05-01\n", " 1960\n", " 05\n", " 22051\n", @@ -1982,10 +1983,9 @@ " 320.03\n", " 317.19\n", " MLO\n", - " 1960-05-01\n", " \n", " \n", - " 31\n", + " 1960-06-01\n", " 1960\n", " 06\n", " 22082\n", @@ -1997,10 +1997,9 @@ " 319.59\n", " 317.45\n", " MLO\n", - " 1960-06-01\n", " \n", " \n", - " 32\n", + " 1960-07-01\n", " 1960\n", " 07\n", " 22112\n", @@ -2012,10 +2011,9 @@ " 318.18\n", " 317.53\n", " MLO\n", - " 1960-07-01\n", " \n", " \n", - " 33\n", + " 1960-08-01\n", " 1960\n", " 08\n", " 22143\n", @@ -2027,10 +2025,9 @@ " 315.90\n", " 317.23\n", " MLO\n", - " 1960-08-01\n", " \n", " \n", - " 34\n", + " 1960-09-01\n", " 1960\n", " 09\n", " 22174\n", @@ -2042,10 +2039,9 @@ " 314.17\n", " 317.11\n", " MLO\n", - " 1960-09-01\n", " \n", " \n", - " 35\n", + " 1960-10-01\n", " 1960\n", " 10\n", " 22204\n", @@ -2057,7 +2053,6 @@ " 313.83\n", " 316.85\n", " MLO\n", - " 1960-10-01\n", " \n", " \n", " ...\n", @@ -2072,10 +2067,9 @@ " ...\n", " ...\n", " ...\n", - " ...\n", " \n", " \n", - " 774\n", + " 2022-05-01\n", " 2022\n", " 05\n", " 44696\n", @@ -2087,10 +2081,9 @@ " 420.78\n", " 417.39\n", " MLO\n", - " 2022-05-01\n", " \n", " \n", - " 775\n", + " 2022-06-01\n", " 2022\n", " 06\n", " 44727\n", @@ -2102,10 +2095,9 @@ " 420.68\n", " 418.10\n", " MLO\n", - " 2022-06-01\n", " \n", " \n", - " 776\n", + " 2022-07-01\n", " 2022\n", " 07\n", " 44757\n", @@ -2117,10 +2109,9 @@ " 418.71\n", " 417.91\n", " MLO\n", - " 2022-07-01\n", " \n", " \n", - " 777\n", + " 2022-08-01\n", " 2022\n", " 08\n", " 44788\n", @@ -2132,10 +2123,9 @@ " 416.75\n", " 418.30\n", " MLO\n", - " 2022-08-01\n", " \n", " \n", - " 778\n", + " 2022-09-01\n", " 2022\n", " 09\n", " 44819\n", @@ -2147,10 +2137,9 @@ " 415.42\n", " 418.91\n", " MLO\n", - " 2022-09-01\n", " \n", " \n", - " 779\n", + " 2022-10-01\n", " 2022\n", " 10\n", " 44849\n", @@ -2162,10 +2151,9 @@ " 415.31\n", " 418.91\n", " MLO\n", - " 2022-10-01\n", " \n", " \n", - " 780\n", + " 2022-11-01\n", " 2022\n", " 11\n", " 44880\n", @@ -2177,10 +2165,9 @@ " 417.03\n", " 419.28\n", " MLO\n", - " 2022-11-01\n", " \n", " \n", - " 781\n", + " 2022-12-01\n", " 2022\n", " 12\n", " 44910\n", @@ -2192,10 +2179,9 @@ " 418.46\n", " 419.38\n", " MKO\n", - " 2022-12-01\n", " \n", " \n", - " 782\n", + " 2023-01-01\n", " 2023\n", " 01\n", " 44941\n", @@ -2207,10 +2193,9 @@ " 419.13\n", " 419.06\n", " MKO\n", - " 2023-01-01\n", " \n", " \n", - " 783\n", + " 2023-02-01\n", " 2023\n", " 02\n", " 44972\n", @@ -2222,10 +2207,9 @@ " 420.33\n", " 419.55\n", " MKO\n", - " 2023-02-01\n", " \n", " \n", - " 784\n", + " 2023-03-01\n", " 2023\n", " 03\n", " 45000\n", @@ -2237,10 +2221,9 @@ " 420.51\n", " 418.97\n", " MLO\n", - " 2023-03-01\n", " \n", " \n", - " 785\n", + " 2023-04-01\n", " 2023\n", " 04\n", " 45031\n", @@ -2252,10 +2235,9 @@ " 422.73\n", " 419.97\n", " MLO\n", - " 2023-04-01\n", " \n", " \n", - " 786\n", + " 2023-05-01\n", " 2023\n", " 05\n", " 45061\n", @@ -2267,10 +2249,9 @@ " 423.78\n", " 420.38\n", " MLO\n", - " 2023-05-01\n", " \n", " \n", - " 787\n", + " 2023-06-01\n", " 2023\n", " 06\n", " 45092\n", @@ -2282,10 +2263,9 @@ " 423.39\n", " 420.81\n", " MLO\n", - " 2023-06-01\n", " \n", " \n", - " 788\n", + " 2023-07-01\n", " 2023\n", " 07\n", " 45122\n", @@ -2297,10 +2277,9 @@ " 421.62\n", " 420.82\n", " MLO\n", - " 2023-07-01\n", " \n", " \n", - " 789\n", + " 2023-08-01\n", " 2023\n", " 08\n", " 45153\n", @@ -2312,10 +2291,9 @@ " 419.56\n", " 421.12\n", " MLO\n", - " 2023-08-01\n", " \n", " \n", - " 790\n", + " 2023-09-01\n", " 2023\n", " 09\n", " 45184\n", @@ -2327,10 +2305,9 @@ " 418.06\n", " 421.56\n", " MLO\n", - " 2023-09-01\n", " \n", " \n", - " 791\n", + " 2023-10-01\n", " 2023\n", " 10\n", " 45214\n", @@ -2342,10 +2319,9 @@ " 418.41\n", " 422.01\n", " MLO\n", - " 2023-10-01\n", " \n", " \n", - " 792\n", + " 2023-11-01\n", " 2023\n", " 11\n", " 45245\n", @@ -2357,10 +2333,9 @@ " 420.11\n", " 422.37\n", " MLO\n", - " 2023-11-01\n", " \n", " \n", - " 793\n", + " 2023-12-01\n", " 2023\n", " 12\n", " 45275\n", @@ -2372,10 +2347,9 @@ " 421.65\n", " 422.57\n", " MLO\n", - " 2023-12-01\n", " \n", " \n", - " 794\n", + " 2024-01-01\n", " 2024\n", " 01\n", " 45306\n", @@ -2387,10 +2361,9 @@ " 422.62\n", " 422.55\n", " MLO\n", - " 2024-01-01\n", " \n", " \n", - " 795\n", + " 2024-02-01\n", " 2024\n", " 02\n", " 45337\n", @@ -2402,10 +2375,9 @@ " 424.34\n", " 423.56\n", " MLO\n", - " 2024-02-01\n", " \n", " \n", - " 796\n", + " 2024-03-01\n", " 2024\n", " 03\n", " 45366\n", @@ -2417,10 +2389,9 @@ " 425.22\n", " 423.65\n", " MLO\n", - " 2024-03-01\n", " \n", " \n", - " 797\n", + " 2024-04-01\n", " 2024\n", " 04\n", " 45397\n", @@ -2432,10 +2403,9 @@ " 426.30\n", " 423.50\n", " MLO\n", - " 2024-04-01\n", " \n", " \n", - " 798\n", + " 2024-05-01\n", " 2024\n", " 05\n", " 45427\n", @@ -2447,10 +2417,9 @@ " 426.70\n", " 423.30\n", " MLO\n", - " 2024-05-01\n", " \n", " \n", - " 799\n", + " 2024-06-01\n", " 2024\n", " 06\n", " 45458\n", @@ -2462,10 +2431,9 @@ " 426.62\n", " 424.06\n", " MLO\n", - " 2024-06-01\n", " \n", " \n", - " 800\n", + " 2024-07-01\n", " 2024\n", " 07\n", " 45488\n", @@ -2477,10 +2445,9 @@ " 425.40\n", " 424.63\n", " MLO\n", - " 2024-07-01\n", " \n", " \n", - " 801\n", + " 2024-08-01\n", " 2024\n", " 08\n", " 45519\n", @@ -2492,10 +2459,9 @@ " 422.70\n", " 424.30\n", " MLO\n", - " 2024-08-01\n", " \n", " \n", - " 802\n", + " 2024-09-01\n", " 2024\n", " 09\n", " 45550\n", @@ -2507,10 +2473,9 @@ " 421.59\n", " 425.11\n", " MLO\n", - " 2024-09-01\n", " \n", " \n", - " 803\n", + " 2024-10-01\n", " 2024\n", " 10\n", " 45580\n", @@ -2522,1201 +2487,76 @@ " 422.05\n", " 425.66\n", " MLO\n", - " 2024-10-01\n", " \n", " \n", "\n", - "

795 rows × 12 columns

\n", + "

795 rows × 11 columns

\n", "" ], "text/plain": [ - " Yr Mn Date Excel Date.1 CO2 [ppm] seasonally adjusted [ppm] \\\n", - "4 1958 03 21259 1958.2027 315.71 314.43 \n", - "5 1958 04 21290 1958.2877 317.45 315.15 \n", - "6 1958 05 21320 1958.3699 317.51 314.69 \n", - "8 1958 07 21381 1958.5370 315.87 315.20 \n", - "9 1958 08 21412 1958.6219 314.93 316.22 \n", - "10 1958 09 21443 1958.7068 313.21 316.12 \n", - "12 1958 11 21504 1958.8740 313.33 315.21 \n", - "13 1958 12 21534 1958.9562 314.67 315.44 \n", - "14 1959 01 21565 1959.0411 315.58 315.52 \n", - "15 1959 02 21596 1959.1260 316.49 315.84 \n", - "16 1959 03 21624 1959.2027 316.65 315.37 \n", - "17 1959 04 21655 1959.2877 317.72 315.41 \n", - "18 1959 05 21685 1959.3699 318.29 315.46 \n", - "19 1959 06 21716 1959.4548 318.15 316.00 \n", - "20 1959 07 21746 1959.5370 316.54 315.87 \n", - "21 1959 08 21777 1959.6219 314.79 316.10 \n", - "22 1959 09 21808 1959.7068 313.84 316.76 \n", - "23 1959 10 21838 1959.7890 313.33 316.35 \n", - "24 1959 11 21869 1959.8740 314.81 316.69 \n", - "25 1959 12 21899 1959.9562 315.58 316.35 \n", - "26 1960 01 21930 1960.0410 316.43 316.37 \n", - "27 1960 02 21961 1960.1257 316.98 316.33 \n", - "28 1960 03 21990 1960.2049 317.58 316.27 \n", - "29 1960 04 22021 1960.2896 319.03 316.69 \n", - "30 1960 05 22051 1960.3716 320.03 317.19 \n", - "31 1960 06 22082 1960.4563 319.59 317.45 \n", - "32 1960 07 22112 1960.5383 318.18 317.53 \n", - "33 1960 08 22143 1960.6230 315.90 317.23 \n", - "34 1960 09 22174 1960.7077 314.17 317.11 \n", - "35 1960 10 22204 1960.7896 313.83 316.85 \n", - ".. ... .. ... ... ... ... \n", - "774 2022 05 44696 2022.3699 420.78 417.39 \n", - "775 2022 06 44727 2022.4548 420.68 418.10 \n", - "776 2022 07 44757 2022.5370 418.71 417.91 \n", - "777 2022 08 44788 2022.6219 416.75 418.30 \n", - "778 2022 09 44819 2022.7068 415.42 418.91 \n", - "779 2022 10 44849 2022.7890 415.31 418.91 \n", - "780 2022 11 44880 2022.8740 417.03 419.28 \n", - "781 2022 12 44910 2022.9562 418.46 419.38 \n", - "782 2023 01 44941 2023.0411 419.13 419.06 \n", - "783 2023 02 44972 2023.1260 420.33 419.55 \n", - "784 2023 03 45000 2023.2027 420.51 418.97 \n", - "785 2023 04 45031 2023.2877 422.73 419.97 \n", - "786 2023 05 45061 2023.3699 423.78 420.38 \n", - "787 2023 06 45092 2023.4548 423.39 420.81 \n", - "788 2023 07 45122 2023.5370 421.62 420.82 \n", - "789 2023 08 45153 2023.6219 419.56 421.12 \n", - "790 2023 09 45184 2023.7068 418.06 421.56 \n", - "791 2023 10 45214 2023.7890 418.41 422.01 \n", - "792 2023 11 45245 2023.8740 420.11 422.37 \n", - "793 2023 12 45275 2023.9562 421.65 422.57 \n", - "794 2024 01 45306 2024.0410 422.62 422.55 \n", - "795 2024 02 45337 2024.1257 424.34 423.56 \n", - "796 2024 03 45366 2024.2049 425.22 423.65 \n", - "797 2024 04 45397 2024.2896 426.30 423.50 \n", - "798 2024 05 45427 2024.3716 426.70 423.30 \n", - "799 2024 06 45458 2024.4563 426.62 424.06 \n", - "800 2024 07 45488 2024.5383 425.40 424.63 \n", - "801 2024 08 45519 2024.6230 422.70 424.30 \n", - "802 2024 09 45550 2024.7077 421.59 425.11 \n", - "803 2024 10 45580 2024.7896 422.05 425.66 \n", - "\n", - " fit [ppm] seasonally.1 adjusted fit [ppm] CO2.1 filled [ppm] \\\n", - "4 316.20 314.91 315.71 \n", - "5 317.31 314.99 317.45 \n", - "6 317.89 315.07 317.51 \n", - "8 315.86 315.22 315.87 \n", - "9 313.96 315.29 314.93 \n", - "10 312.43 315.35 313.21 \n", - "12 313.60 315.46 313.33 \n", - "13 314.77 315.52 314.67 \n", - "14 315.64 315.58 315.58 \n", - "15 316.30 315.64 316.49 \n", - "16 317.00 315.70 316.65 \n", - "17 318.10 315.77 317.72 \n", - "18 318.68 315.85 318.29 \n", - "19 318.08 315.94 318.15 \n", - "20 316.67 316.03 316.54 \n", - "21 314.80 316.13 314.79 \n", - "22 313.29 316.22 313.84 \n", - "23 313.31 316.31 313.33 \n", - "24 314.53 316.40 314.81 \n", - "25 315.72 316.48 315.58 \n", - "26 316.63 316.56 316.43 \n", - "27 317.30 316.64 316.98 \n", - "28 318.04 316.72 317.58 \n", - "29 319.15 316.79 319.03 \n", - "30 319.70 316.86 320.03 \n", - "31 319.05 316.93 319.59 \n", - "32 317.59 316.98 318.18 \n", - "33 315.66 317.02 315.90 \n", - "34 314.10 317.05 314.17 \n", - "35 314.08 317.08 313.83 \n", - ".. ... ... ... \n", - "774 421.23 417.84 420.78 \n", - "775 420.56 418.01 420.68 \n", - "776 418.95 418.18 418.71 \n", - "777 416.77 418.37 416.75 \n", - "778 415.05 418.56 415.42 \n", - "779 415.16 418.75 415.31 \n", - "780 416.72 418.95 417.03 \n", - "781 418.25 419.16 418.46 \n", - "782 419.46 419.38 419.13 \n", - "783 420.40 419.61 420.33 \n", - "784 421.39 419.83 420.51 \n", - "785 422.88 420.10 422.73 \n", - "786 423.76 420.37 423.78 \n", - "787 423.22 420.66 423.39 \n", - "788 421.72 420.95 421.62 \n", - "789 419.66 421.26 419.56 \n", - "790 418.05 421.57 418.06 \n", - "791 418.28 421.87 418.41 \n", - "792 419.94 422.18 420.11 \n", - "793 421.57 422.47 421.65 \n", - "794 422.85 422.77 422.62 \n", - "795 423.85 423.06 424.34 \n", - "796 424.92 423.33 425.22 \n", - "797 426.43 423.61 426.30 \n", - "798 427.29 423.89 426.70 \n", - "799 426.72 424.18 426.62 \n", - "800 425.20 424.46 425.40 \n", - "801 423.13 424.76 422.70 \n", - "802 421.53 425.07 421.59 \n", - "803 421.77 425.37 422.05 \n", - "\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 12 columns]" - ] - }, - "execution_count": 277, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "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": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \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
2023-07-01202307451222023.5370421.62420.82421.72420.95421.62420.82MLO
2023-08-01202308451532023.6219419.56421.12419.66421.26419.56421.12MLO
2023-09-01202309451842023.7068418.06421.56418.05421.57418.06421.56MLO
2023-10-01202310452142023.7890418.41422.01418.28421.87418.41422.01MLO
2023-11-01202311452452023.8740420.11422.37419.94422.18420.11422.37MLO
2023-12-01202312452752023.9562421.65422.57421.57422.47421.65422.57MLO
2024-01-01202401453062024.0410422.62422.55422.85422.77422.62422.55MLO
2024-02-01202402453372024.1257424.34423.56423.85423.06424.34423.56MLO
2024-03-01202403453662024.2049425.22423.65424.92423.33425.22423.65MLO
2024-04-01202404453972024.2896426.30423.50426.43423.61426.30423.50MLO
2024-05-01202405454272024.3716426.70423.30427.29423.89426.70423.30MLO
2024-06-01202406454582024.4563426.62424.06426.72424.18426.62424.06MLO
2024-07-01202407454882024.5383425.40424.63425.20424.46425.40424.63MLO
2024-08-01202408455192024.6230422.70424.30423.13424.76422.70424.30MLO
2024-09-01202409455502024.7077421.59425.11421.53425.07421.59425.11MLO
2024-10-01202410455802024.7896422.05425.66421.77425.37422.05425.66MLO
\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", + " 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", @@ -3913,7 +2753,7 @@ "[795 rows x 11 columns]" ] }, - "execution_count": 278, + "execution_count": 56, "metadata": {}, "output_type": "execute_result" } @@ -3923,18 +2763,25 @@ "sorted_data" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Graph of CO2 concentration in ppm" + ] + }, { "cell_type": "code", - "execution_count": 279, + "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 279, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" }, @@ -3955,18 +2802,25 @@ "sorted_data['CO2 [ppm]'].plot()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Zoom in the last year of the CO2 concentration graph" + ] + }, { "cell_type": "code", - "execution_count": 280, + "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 280, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, @@ -3991,12 +2845,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Caracatérisation de l'oscillation" + "### Caracterization of oscillation\n", + "\n", + "Strategy : to caraterize the oscillation we decide to substract mean of CO2 concentration of a year, for each year. One year start at march." ] }, { "cell_type": "code", - "execution_count": 387, + "execution_count": 70, "metadata": {}, "outputs": [ { @@ -4026,9 +2882,16 @@ "yearly_CO2 = pd.Series(data=yearly_CO2, index=year)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Graph of the oscillation" + ] + }, { "cell_type": "code", - "execution_count": 388, + "execution_count": 71, "metadata": { "hideCode": true, "hideOutput": true, @@ -4038,10 +2901,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 388, + "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, @@ -4068,16 +2931,18 @@ }, { "cell_type": "code", - "execution_count": 389, - "metadata": {}, + "execution_count": 72, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 389, + "execution_count": 72, "metadata": {}, "output_type": "execute_result" }, @@ -4099,17 +2964,26 @@ ] }, { - "cell_type": "code", - "execution_count": 390, + "cell_type": "markdown", "metadata": {}, + "source": [ + "#### Zoom of the last 2 years" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 390, + "execution_count": 73, "metadata": {}, "output_type": "execute_result" }, @@ -4134,32 +3008,43 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Analyse de l'oscillation \n", + "#### Analyse of the 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", + "In the various figures, we can see a regular oscillation, which corresponds to the Earth's respiration. In fact, we see an increase in CO2 concentration at the beginning of October, which coincides with a decrease in sunlight and the loss of leaves by trees - autumn. Trees capture less CO2 and convert it into O2, leading to an increase in the concentration of CO2 in the air. By mid-spring, trees begin to capture CO2 again, causing a drop in 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." + "The peaks in March correspond to the method used to define the oscillation. In fact, we normalize over a year from March onwards. To limit this effect, it would have been interesting to normalize over a sliding window." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Evolution systématique" + "### Systematic evolution" ] }, { - "cell_type": "code", - "execution_count": 404, + "cell_type": "markdown", "metadata": {}, + "source": [ + "Strategy : We use mean data of each year to remove the oscillation of the data.\n", + "\n", + "#### Graph of the evolution" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 404, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" }, @@ -4180,38 +3065,105 @@ "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": "markdown", + "metadata": {}, + "source": [ + "#### Estimate function\n", + "\n", + "Estimate function of the ascend with function parameters. Orange line is real data, Blue line is estimated function" + ] + }, { "cell_type": "code", - "execution_count": 410, + "execution_count": 168, "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" + "name": "stdout", + "output_type": "stream", + "text": [ + "f(x) = 0.01x² + 0.72.x + 314.0\n" ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "yearly_CO2.to_frame(name=\"CO2 mean\")\n", - "yearly_CO2.index = list(range(1, len(yearly_CO2)))" + "yearly_CO2_df = yearly_CO2.to_frame(name=\"CO2 mean\")\n", + "yearly_CO2_df['numeric_index'] = list(range(1, len(yearly_CO2) + 1))\n", + "coef = np.polyfit(yearly_CO2_df['numeric_index'].values, yearly_CO2_df['CO2 mean'].values, 2)\n", + "yearly_CO2_df['fit'] = np.polyval(coef,yearly_CO2_df['numeric_index'].values)\n", + "yearly_CO2_df['fit'].to_timestamp().plot()\n", + "yearly_CO2.to_timestamp().plot()\n", + "\n", + "print(f\"f(x) = {np.round(coef[0], 2)}x² + {np.round(coef[1], 2)}.x + {np.round(coef[2])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Projection for 2030" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimation of the CO2 concentration in the green line." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 165, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 165, + "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_df['fit'].to_timestamp()[-20:].plot()\n", + "yearly_CO2.to_timestamp()[-20:].plot(style='*')\n", + "yearly_CO2_projection = yearly_CO2_df.copy()\n", + "most_recent_date = yearly_CO2_projection.index[-1]\n", + "last_index = yearly_CO2_projection[\"numeric_index\"][-1]\n", + "projection_index = pd.date_range(most_recent_date.start_time, \"2030/03/02\", freq=\"AS-MAR\").normalize()\n", + "numeric_index = range(last_index, last_index + len(projection_index))\n", + "fit_value = np.polyval(coef, list(numeric_index))\n", + "projection_serie = pd.Series(fit_value, index=projection_index)\n", + "projection_serie.plot(style='--')" + ] } ], "metadata": { -- 2.18.1