From d4899fc73f84b0bf85c11a602bba52df08453a2f Mon Sep 17 00:00:00 2001 From: e962457f401959b28b04eb460a3018de Date: Sat, 26 Apr 2025 03:14:56 +0000 Subject: [PATCH] =?UTF-8?q?reproduction=20des=20r=C3=A9sultats=20de=20d97f?= =?UTF-8?q?0d3124c1cacaa2bf55ee45ff1548?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ...de d97f0d3124c1cacaa2bf55ee45ff1548.ipynb" | 4487 +++++++++++++++++ module3/exo3/exercice_en.ipynb | 25 - 2 files changed, 4487 insertions(+), 25 deletions(-) create mode 100644 "module3/exo3/Reproduction des r\303\251sultats de d97f0d3124c1cacaa2bf55ee45ff1548.ipynb" delete mode 100644 module3/exo3/exercice_en.ipynb diff --git "a/module3/exo3/Reproduction des r\303\251sultats de d97f0d3124c1cacaa2bf55ee45ff1548.ipynb" "b/module3/exo3/Reproduction des r\303\251sultats de d97f0d3124c1cacaa2bf55ee45ff1548.ipynb" new file mode 100644 index 0000000..dc914e7 --- /dev/null +++ "b/module3/exo3/Reproduction des r\303\251sultats de d97f0d3124c1cacaa2bf55ee45ff1548.ipynb" @@ -0,0 +1,4487 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + " %matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "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", + "
YrMnDateDateCO2seasonallyfitseasonallyCO2seasonallySta
0adjustedadjusted fitfilledadjusted filledNaN
1Excel[ppm][ppm][ppm][ppm][ppm][ppm]NaN
2195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99MLO
3195802212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99MLO
4195803212591958.2027315.71314.43316.19314.90315.71314.43MLO
5195804212901958.2877317.45315.15317.30314.98317.45315.15MLO
6195805213201958.3699317.51314.68317.89315.06317.51314.68MLO
7195806213511958.4548-99.99-99.99317.27315.14317.27315.14MLO
8195807213811958.5370315.87315.20315.85315.21315.87315.20MLO
9195808214121958.6219314.93316.23313.95315.28314.93316.23MLO
10195809214431958.7068313.21316.12312.41315.35313.21316.12MLO
11195810214731958.7890-99.99-99.99312.41315.40312.41315.40MLO
12195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
13195812215341958.9562314.67315.43314.76315.51314.67315.43MLO
14195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
15195902215961959.1260316.49315.83316.29315.63316.49315.83MLO
16195903216241959.2027316.65315.37316.99315.69316.65315.37MLO
17195904216551959.2877317.72315.41318.09315.76317.72315.41MLO
18195905216851959.3699318.29315.46318.68315.84318.29315.46MLO
19195906217161959.4548318.15315.99318.07315.93318.15315.99MLO
20195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
21195908217771959.6219314.79316.10314.79316.12314.79316.10MLO
22195909218081959.7068313.84316.76313.27316.22313.84316.76MLO
23195910218381959.7890313.33316.35313.30316.30313.33316.35MLO
24195911218691959.8740314.81316.69314.52316.39314.81316.69MLO
25195912218991959.9562315.58316.35315.72316.47315.58316.35MLO
26196001219301960.0410316.43316.37316.62316.55316.43316.37MLO
27196002219611960.1257316.98316.33317.29316.63316.98316.33MLO
28196003219901960.2049317.58316.27318.03316.71317.58316.27MLO
29196004220211960.2896319.03316.69319.14316.79319.03316.69MLO
....................................
788202307451222023.5370421.62420.82421.71420.94421.62420.82MLO
789202308451532023.6219419.56421.11419.66421.25419.56421.11MLO
790202309451842023.7068418.06421.56418.05421.57418.06421.56MLO
791202310452142023.7890418.41422.01418.28421.87418.41422.01MLO
792202311452452023.8740420.11422.37419.95422.18420.11422.37MLO
793202312452752023.9562421.65422.57421.58422.48421.65422.57MLO
794202401453062024.0410422.62422.55422.86422.78422.62422.55MLO
795202402453372024.1257424.34423.56423.87423.07424.34423.56MLO
796202403453662024.2049425.22423.66424.92423.34425.22423.66MLO
797202404453972024.2896426.30423.51426.44423.63426.30423.51MLO
798202405454272024.3716426.70423.30427.29423.90426.70423.30MLO
799202406454582024.4563426.62424.07426.71424.18426.62424.07MLO
800202407454882024.5383425.40424.63425.19424.45425.40424.63MLO
801202408455192024.6230422.70424.29423.10424.73422.70424.29MLO
802202409455502024.7077421.60425.11421.48425.01421.60425.11MLO
803202410455802024.7896422.05425.65421.68425.27422.05425.65MLO
804202411456112024.8743423.61425.86423.30425.53423.61425.86MLO
805202412456412024.9563425.01425.93424.87425.77425.01425.93MLO
806202501456722025.0411426.42426.35426.10426.01426.42426.35MLO
807202502457032025.1260427.00426.21427.04426.25427.00426.21MLO
808202503457312025.2027427.73426.19428.01426.45427.73426.19MLO
809202504457622025.2877-99.99-99.99-99.99-99.99-99.99-99.99MLO
810202505457922025.3699-99.99-99.99-99.99-99.99-99.99-99.99MLO
811202506458232025.4548-99.99-99.99-99.99-99.99-99.99-99.99MLO
812202507458532025.5370-99.99-99.99-99.99-99.99-99.99-99.99MLO
813202508458842025.6219-99.99-99.99-99.99-99.99-99.99-99.99MLO
814202509459152025.7068-99.99-99.99-99.99-99.99-99.99-99.99MLO
815202510459452025.7890-99.99-99.99-99.99-99.99-99.99-99.99MLO
816202511459762025.8740-99.99-99.99-99.99-99.99-99.99-99.99MLO
817202512460062025.9562-99.99-99.99-99.99-99.99-99.99-99.99MLO
\n", + "

818 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn Date Date CO2 seasonally fit \\\n", + "0 adjusted \n", + "1 Excel [ppm] [ppm] [ppm] \n", + "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", + "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", + "4 1958 03 21259 1958.2027 315.71 314.43 316.19 \n", + "5 1958 04 21290 1958.2877 317.45 315.15 317.30 \n", + "6 1958 05 21320 1958.3699 317.51 314.68 317.89 \n", + "7 1958 06 21351 1958.4548 -99.99 -99.99 317.27 \n", + "8 1958 07 21381 1958.5370 315.87 315.20 315.85 \n", + "9 1958 08 21412 1958.6219 314.93 316.23 313.95 \n", + "10 1958 09 21443 1958.7068 313.21 316.12 312.41 \n", + "11 1958 10 21473 1958.7890 -99.99 -99.99 312.41 \n", + "12 1958 11 21504 1958.8740 313.33 315.21 313.60 \n", + "13 1958 12 21534 1958.9562 314.67 315.43 314.76 \n", + "14 1959 01 21565 1959.0411 315.58 315.52 315.64 \n", + "15 1959 02 21596 1959.1260 316.49 315.83 316.29 \n", + "16 1959 03 21624 1959.2027 316.65 315.37 316.99 \n", + "17 1959 04 21655 1959.2877 317.72 315.41 318.09 \n", + "18 1959 05 21685 1959.3699 318.29 315.46 318.68 \n", + "19 1959 06 21716 1959.4548 318.15 315.99 318.07 \n", + "20 1959 07 21746 1959.5370 316.54 315.87 316.67 \n", + "21 1959 08 21777 1959.6219 314.79 316.10 314.79 \n", + "22 1959 09 21808 1959.7068 313.84 316.76 313.27 \n", + "23 1959 10 21838 1959.7890 313.33 316.35 313.30 \n", + "24 1959 11 21869 1959.8740 314.81 316.69 314.52 \n", + "25 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", + "26 1960 01 21930 1960.0410 316.43 316.37 316.62 \n", + "27 1960 02 21961 1960.1257 316.98 316.33 317.29 \n", + "28 1960 03 21990 1960.2049 317.58 316.27 318.03 \n", + "29 1960 04 22021 1960.2896 319.03 316.69 319.14 \n", + ".. ... ... ... ... ... ... ... \n", + "788 2023 07 45122 2023.5370 421.62 420.82 421.71 \n", + "789 2023 08 45153 2023.6219 419.56 421.11 419.66 \n", + "790 2023 09 45184 2023.7068 418.06 421.56 418.05 \n", + "791 2023 10 45214 2023.7890 418.41 422.01 418.28 \n", + "792 2023 11 45245 2023.8740 420.11 422.37 419.95 \n", + "793 2023 12 45275 2023.9562 421.65 422.57 421.58 \n", + "794 2024 01 45306 2024.0410 422.62 422.55 422.86 \n", + "795 2024 02 45337 2024.1257 424.34 423.56 423.87 \n", + "796 2024 03 45366 2024.2049 425.22 423.66 424.92 \n", + "797 2024 04 45397 2024.2896 426.30 423.51 426.44 \n", + "798 2024 05 45427 2024.3716 426.70 423.30 427.29 \n", + "799 2024 06 45458 2024.4563 426.62 424.07 426.71 \n", + "800 2024 07 45488 2024.5383 425.40 424.63 425.19 \n", + "801 2024 08 45519 2024.6230 422.70 424.29 423.10 \n", + "802 2024 09 45550 2024.7077 421.60 425.11 421.48 \n", + "803 2024 10 45580 2024.7896 422.05 425.65 421.68 \n", + "804 2024 11 45611 2024.8743 423.61 425.86 423.30 \n", + "805 2024 12 45641 2024.9563 425.01 425.93 424.87 \n", + "806 2025 01 45672 2025.0411 426.42 426.35 426.10 \n", + "807 2025 02 45703 2025.1260 427.00 426.21 427.04 \n", + "808 2025 03 45731 2025.2027 427.73 426.19 428.01 \n", + "809 2025 04 45762 2025.2877 -99.99 -99.99 -99.99 \n", + "810 2025 05 45792 2025.3699 -99.99 -99.99 -99.99 \n", + "811 2025 06 45823 2025.4548 -99.99 -99.99 -99.99 \n", + "812 2025 07 45853 2025.5370 -99.99 -99.99 -99.99 \n", + "813 2025 08 45884 2025.6219 -99.99 -99.99 -99.99 \n", + "814 2025 09 45915 2025.7068 -99.99 -99.99 -99.99 \n", + "815 2025 10 45945 2025.7890 -99.99 -99.99 -99.99 \n", + "816 2025 11 45976 2025.8740 -99.99 -99.99 -99.99 \n", + "817 2025 12 46006 2025.9562 -99.99 -99.99 -99.99 \n", + "\n", + " seasonally CO2 seasonally Sta \n", + "0 adjusted fit filled adjusted filled NaN \n", + "1 [ppm] [ppm] [ppm] NaN \n", + "2 -99.99 -99.99 -99.99 MLO \n", + "3 -99.99 -99.99 -99.99 MLO \n", + "4 314.90 315.71 314.43 MLO \n", + "5 314.98 317.45 315.15 MLO \n", + "6 315.06 317.51 314.68 MLO \n", + "7 315.14 317.27 315.14 MLO \n", + "8 315.21 315.87 315.20 MLO \n", + "9 315.28 314.93 316.23 MLO \n", + "10 315.35 313.21 316.12 MLO \n", + "11 315.40 312.41 315.40 MLO \n", + "12 315.46 313.33 315.21 MLO \n", + "13 315.51 314.67 315.43 MLO \n", + "14 315.57 315.58 315.52 MLO \n", + "15 315.63 316.49 315.83 MLO \n", + "16 315.69 316.65 315.37 MLO \n", + "17 315.76 317.72 315.41 MLO \n", + "18 315.84 318.29 315.46 MLO \n", + "19 315.93 318.15 315.99 MLO \n", + "20 316.03 316.54 315.87 MLO \n", + "21 316.12 314.79 316.10 MLO \n", + "22 316.22 313.84 316.76 MLO \n", + "23 316.30 313.33 316.35 MLO \n", + "24 316.39 314.81 316.69 MLO \n", + "25 316.47 315.58 316.35 MLO \n", + "26 316.55 316.43 316.37 MLO \n", + "27 316.63 316.98 316.33 MLO \n", + "28 316.71 317.58 316.27 MLO \n", + "29 316.79 319.03 316.69 MLO \n", + ".. ... ... ... ... \n", + "788 420.94 421.62 420.82 MLO \n", + "789 421.25 419.56 421.11 MLO \n", + "790 421.57 418.06 421.56 MLO \n", + "791 421.87 418.41 422.01 MLO \n", + "792 422.18 420.11 422.37 MLO \n", + "793 422.48 421.65 422.57 MLO \n", + "794 422.78 422.62 422.55 MLO \n", + "795 423.07 424.34 423.56 MLO \n", + "796 423.34 425.22 423.66 MLO \n", + "797 423.63 426.30 423.51 MLO \n", + "798 423.90 426.70 423.30 MLO \n", + "799 424.18 426.62 424.07 MLO \n", + "800 424.45 425.40 424.63 MLO \n", + "801 424.73 422.70 424.29 MLO \n", + "802 425.01 421.60 425.11 MLO \n", + "803 425.27 422.05 425.65 MLO \n", + "804 425.53 423.61 425.86 MLO \n", + "805 425.77 425.01 425.93 MLO \n", + "806 426.01 426.42 426.35 MLO \n", + "807 426.25 427.00 426.21 MLO \n", + "808 426.45 427.73 426.19 MLO \n", + "809 -99.99 -99.99 -99.99 MLO \n", + "810 -99.99 -99.99 -99.99 MLO \n", + "811 -99.99 -99.99 -99.99 MLO \n", + "812 -99.99 -99.99 -99.99 MLO \n", + "813 -99.99 -99.99 -99.99 MLO \n", + "814 -99.99 -99.99 -99.99 MLO \n", + "815 -99.99 -99.99 -99.99 MLO \n", + "816 -99.99 -99.99 -99.99 MLO \n", + "817 -99.99 -99.99 -99.99 MLO \n", + "\n", + "[818 rows x 11 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_file = 'https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv'\n", + "data = pd.read_csv(data_file, skiprows=61)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "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", + "
YrMnDate_excelDateCO2seasonally_adjustedfitseasonally_adjusted_fitCO2_filledseasonally_adjusted_filledSta
0195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99MLO
1195802212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99MLO
2195803212591958.2027315.71314.43316.19314.90315.71314.43MLO
3195804212901958.2877317.45315.15317.30314.98317.45315.15MLO
4195805213201958.3699317.51314.68317.89315.06317.51314.68MLO
5195806213511958.4548-99.99-99.99317.27315.14317.27315.14MLO
6195807213811958.5370315.87315.20315.85315.21315.87315.20MLO
7195808214121958.6219314.93316.23313.95315.28314.93316.23MLO
8195809214431958.7068313.21316.12312.41315.35313.21316.12MLO
9195810214731958.7890-99.99-99.99312.41315.40312.41315.40MLO
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
11195812215341958.9562314.67315.43314.76315.51314.67315.43MLO
12195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
13195902215961959.1260316.49315.83316.29315.63316.49315.83MLO
14195903216241959.2027316.65315.37316.99315.69316.65315.37MLO
15195904216551959.2877317.72315.41318.09315.76317.72315.41MLO
16195905216851959.3699318.29315.46318.68315.84318.29315.46MLO
17195906217161959.4548318.15315.99318.07315.93318.15315.99MLO
18195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
19195908217771959.6219314.79316.10314.79316.12314.79316.10MLO
20195909218081959.7068313.84316.76313.27316.22313.84316.76MLO
21195910218381959.7890313.33316.35313.30316.30313.33316.35MLO
22195911218691959.8740314.81316.69314.52316.39314.81316.69MLO
23195912218991959.9562315.58316.35315.72316.47315.58316.35MLO
24196001219301960.0410316.43316.37316.62316.55316.43316.37MLO
25196002219611960.1257316.98316.33317.29316.63316.98316.33MLO
26196003219901960.2049317.58316.27318.03316.71317.58316.27MLO
27196004220211960.2896319.03316.69319.14316.79319.03316.69MLO
28196005220511960.3716320.03317.19319.70316.86320.03317.19MLO
29196006220821960.4563319.59317.44319.04316.92319.59317.44MLO
....................................
786202307451222023.5370421.62420.82421.71420.94421.62420.82MLO
787202308451532023.6219419.56421.11419.66421.25419.56421.11MLO
788202309451842023.7068418.06421.56418.05421.57418.06421.56MLO
789202310452142023.7890418.41422.01418.28421.87418.41422.01MLO
790202311452452023.8740420.11422.37419.95422.18420.11422.37MLO
791202312452752023.9562421.65422.57421.58422.48421.65422.57MLO
792202401453062024.0410422.62422.55422.86422.78422.62422.55MLO
793202402453372024.1257424.34423.56423.87423.07424.34423.56MLO
794202403453662024.2049425.22423.66424.92423.34425.22423.66MLO
795202404453972024.2896426.30423.51426.44423.63426.30423.51MLO
796202405454272024.3716426.70423.30427.29423.90426.70423.30MLO
797202406454582024.4563426.62424.07426.71424.18426.62424.07MLO
798202407454882024.5383425.40424.63425.19424.45425.40424.63MLO
799202408455192024.6230422.70424.29423.10424.73422.70424.29MLO
800202409455502024.7077421.60425.11421.48425.01421.60425.11MLO
801202410455802024.7896422.05425.65421.68425.27422.05425.65MLO
802202411456112024.8743423.61425.86423.30425.53423.61425.86MLO
803202412456412024.9563425.01425.93424.87425.77425.01425.93MLO
804202501456722025.0411426.42426.35426.10426.01426.42426.35MLO
805202502457032025.1260427.00426.21427.04426.25427.00426.21MLO
806202503457312025.2027427.73426.19428.01426.45427.73426.19MLO
807202504457622025.2877-99.99-99.99-99.99-99.99-99.99-99.99MLO
808202505457922025.3699-99.99-99.99-99.99-99.99-99.99-99.99MLO
809202506458232025.4548-99.99-99.99-99.99-99.99-99.99-99.99MLO
810202507458532025.5370-99.99-99.99-99.99-99.99-99.99-99.99MLO
811202508458842025.6219-99.99-99.99-99.99-99.99-99.99-99.99MLO
812202509459152025.7068-99.99-99.99-99.99-99.99-99.99-99.99MLO
813202510459452025.7890-99.99-99.99-99.99-99.99-99.99-99.99MLO
814202511459762025.8740-99.99-99.99-99.99-99.99-99.99-99.99MLO
815202512460062025.9562-99.99-99.99-99.99-99.99-99.99-99.99MLO
\n", + "

816 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn Date_excel Date CO2 seasonally_adjusted \\\n", + "0 1958 01 21200 1958.0411 -99.99 -99.99 \n", + "1 1958 02 21231 1958.1260 -99.99 -99.99 \n", + "2 1958 03 21259 1958.2027 315.71 314.43 \n", + "3 1958 04 21290 1958.2877 317.45 315.15 \n", + "4 1958 05 21320 1958.3699 317.51 314.68 \n", + "5 1958 06 21351 1958.4548 -99.99 -99.99 \n", + "6 1958 07 21381 1958.5370 315.87 315.20 \n", + "7 1958 08 21412 1958.6219 314.93 316.23 \n", + "8 1958 09 21443 1958.7068 313.21 316.12 \n", + "9 1958 10 21473 1958.7890 -99.99 -99.99 \n", + "10 1958 11 21504 1958.8740 313.33 315.21 \n", + "11 1958 12 21534 1958.9562 314.67 315.43 \n", + "12 1959 01 21565 1959.0411 315.58 315.52 \n", + "13 1959 02 21596 1959.1260 316.49 315.83 \n", + "14 1959 03 21624 1959.2027 316.65 315.37 \n", + "15 1959 04 21655 1959.2877 317.72 315.41 \n", + "16 1959 05 21685 1959.3699 318.29 315.46 \n", + "17 1959 06 21716 1959.4548 318.15 315.99 \n", + "18 1959 07 21746 1959.5370 316.54 315.87 \n", + "19 1959 08 21777 1959.6219 314.79 316.10 \n", + "20 1959 09 21808 1959.7068 313.84 316.76 \n", + "21 1959 10 21838 1959.7890 313.33 316.35 \n", + "22 1959 11 21869 1959.8740 314.81 316.69 \n", + "23 1959 12 21899 1959.9562 315.58 316.35 \n", + "24 1960 01 21930 1960.0410 316.43 316.37 \n", + "25 1960 02 21961 1960.1257 316.98 316.33 \n", + "26 1960 03 21990 1960.2049 317.58 316.27 \n", + "27 1960 04 22021 1960.2896 319.03 316.69 \n", + "28 1960 05 22051 1960.3716 320.03 317.19 \n", + "29 1960 06 22082 1960.4563 319.59 317.44 \n", + ".. ... ... ... ... ... ... \n", + "786 2023 07 45122 2023.5370 421.62 420.82 \n", + "787 2023 08 45153 2023.6219 419.56 421.11 \n", + "788 2023 09 45184 2023.7068 418.06 421.56 \n", + "789 2023 10 45214 2023.7890 418.41 422.01 \n", + "790 2023 11 45245 2023.8740 420.11 422.37 \n", + "791 2023 12 45275 2023.9562 421.65 422.57 \n", + "792 2024 01 45306 2024.0410 422.62 422.55 \n", + "793 2024 02 45337 2024.1257 424.34 423.56 \n", + "794 2024 03 45366 2024.2049 425.22 423.66 \n", + "795 2024 04 45397 2024.2896 426.30 423.51 \n", + "796 2024 05 45427 2024.3716 426.70 423.30 \n", + "797 2024 06 45458 2024.4563 426.62 424.07 \n", + "798 2024 07 45488 2024.5383 425.40 424.63 \n", + "799 2024 08 45519 2024.6230 422.70 424.29 \n", + "800 2024 09 45550 2024.7077 421.60 425.11 \n", + "801 2024 10 45580 2024.7896 422.05 425.65 \n", + "802 2024 11 45611 2024.8743 423.61 425.86 \n", + "803 2024 12 45641 2024.9563 425.01 425.93 \n", + "804 2025 01 45672 2025.0411 426.42 426.35 \n", + "805 2025 02 45703 2025.1260 427.00 426.21 \n", + "806 2025 03 45731 2025.2027 427.73 426.19 \n", + "807 2025 04 45762 2025.2877 -99.99 -99.99 \n", + "808 2025 05 45792 2025.3699 -99.99 -99.99 \n", + "809 2025 06 45823 2025.4548 -99.99 -99.99 \n", + "810 2025 07 45853 2025.5370 -99.99 -99.99 \n", + "811 2025 08 45884 2025.6219 -99.99 -99.99 \n", + "812 2025 09 45915 2025.7068 -99.99 -99.99 \n", + "813 2025 10 45945 2025.7890 -99.99 -99.99 \n", + "814 2025 11 45976 2025.8740 -99.99 -99.99 \n", + "815 2025 12 46006 2025.9562 -99.99 -99.99 \n", + "\n", + " fit seasonally_adjusted_fit CO2_filled \\\n", + "0 -99.99 -99.99 -99.99 \n", + "1 -99.99 -99.99 -99.99 \n", + "2 316.19 314.90 315.71 \n", + "3 317.30 314.98 317.45 \n", + "4 317.89 315.06 317.51 \n", + "5 317.27 315.14 317.27 \n", + "6 315.85 315.21 315.87 \n", + "7 313.95 315.28 314.93 \n", + "8 312.41 315.35 313.21 \n", + "9 312.41 315.40 312.41 \n", + "10 313.60 315.46 313.33 \n", + "11 314.76 315.51 314.67 \n", + "12 315.64 315.57 315.58 \n", + "13 316.29 315.63 316.49 \n", + "14 316.99 315.69 316.65 \n", + "15 318.09 315.76 317.72 \n", + "16 318.68 315.84 318.29 \n", + "17 318.07 315.93 318.15 \n", + "18 316.67 316.03 316.54 \n", + "19 314.79 316.12 314.79 \n", + "20 313.27 316.22 313.84 \n", + "21 313.30 316.30 313.33 \n", + "22 314.52 316.39 314.81 \n", + "23 315.72 316.47 315.58 \n", + "24 316.62 316.55 316.43 \n", + "25 317.29 316.63 316.98 \n", + "26 318.03 316.71 317.58 \n", + "27 319.14 316.79 319.03 \n", + "28 319.70 316.86 320.03 \n", + "29 319.04 316.92 319.59 \n", + ".. ... ... ... \n", + "786 421.71 420.94 421.62 \n", + "787 419.66 421.25 419.56 \n", + "788 418.05 421.57 418.06 \n", + "789 418.28 421.87 418.41 \n", + "790 419.95 422.18 420.11 \n", + "791 421.58 422.48 421.65 \n", + "792 422.86 422.78 422.62 \n", + "793 423.87 423.07 424.34 \n", + "794 424.92 423.34 425.22 \n", + "795 426.44 423.63 426.30 \n", + "796 427.29 423.90 426.70 \n", + "797 426.71 424.18 426.62 \n", + "798 425.19 424.45 425.40 \n", + "799 423.10 424.73 422.70 \n", + "800 421.48 425.01 421.60 \n", + "801 421.68 425.27 422.05 \n", + "802 423.30 425.53 423.61 \n", + "803 424.87 425.77 425.01 \n", + "804 426.10 426.01 426.42 \n", + "805 427.04 426.25 427.00 \n", + "806 428.01 426.45 427.73 \n", + "807 -99.99 -99.99 -99.99 \n", + "808 -99.99 -99.99 -99.99 \n", + "809 -99.99 -99.99 -99.99 \n", + "810 -99.99 -99.99 -99.99 \n", + "811 -99.99 -99.99 -99.99 \n", + "812 -99.99 -99.99 -99.99 \n", + "813 -99.99 -99.99 -99.99 \n", + "814 -99.99 -99.99 -99.99 \n", + "815 -99.99 -99.99 -99.99 \n", + "\n", + " seasonally_adjusted_filled Sta \n", + "0 -99.99 MLO \n", + "1 -99.99 MLO \n", + "2 314.43 MLO \n", + "3 315.15 MLO \n", + "4 314.68 MLO \n", + "5 315.14 MLO \n", + "6 315.20 MLO \n", + "7 316.23 MLO \n", + "8 316.12 MLO \n", + "9 315.40 MLO \n", + "10 315.21 MLO \n", + "11 315.43 MLO \n", + "12 315.52 MLO \n", + "13 315.83 MLO \n", + "14 315.37 MLO \n", + "15 315.41 MLO \n", + "16 315.46 MLO \n", + "17 315.99 MLO \n", + "18 315.87 MLO \n", + "19 316.10 MLO \n", + "20 316.76 MLO \n", + "21 316.35 MLO \n", + "22 316.69 MLO \n", + "23 316.35 MLO \n", + "24 316.37 MLO \n", + "25 316.33 MLO \n", + "26 316.27 MLO \n", + "27 316.69 MLO \n", + "28 317.19 MLO \n", + "29 317.44 MLO \n", + ".. ... ... \n", + "786 420.82 MLO \n", + "787 421.11 MLO \n", + "788 421.56 MLO \n", + "789 422.01 MLO \n", + "790 422.37 MLO \n", + "791 422.57 MLO \n", + "792 422.55 MLO \n", + "793 423.56 MLO \n", + "794 423.66 MLO \n", + "795 423.51 MLO \n", + "796 423.30 MLO \n", + "797 424.07 MLO \n", + "798 424.63 MLO \n", + "799 424.29 MLO \n", + "800 425.11 MLO \n", + "801 425.65 MLO \n", + "802 425.86 MLO \n", + "803 425.93 MLO \n", + "804 426.35 MLO \n", + "805 426.21 MLO \n", + "806 426.19 MLO \n", + "807 -99.99 MLO \n", + "808 -99.99 MLO \n", + "809 -99.99 MLO \n", + "810 -99.99 MLO \n", + "811 -99.99 MLO \n", + "812 -99.99 MLO \n", + "813 -99.99 MLO \n", + "814 -99.99 MLO \n", + "815 -99.99 MLO \n", + "\n", + "[816 rows x 11 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "columns_label = ['Yr', 'Mn', 'Date_excel', 'Date', 'CO2', 'seasonally_adjusted',\n", + " 'fit', 'seasonally_adjusted_fit', 'CO2_filled', 'seasonally_adjusted_filled', 'Sta']\n", + "\n", + "data.columns = columns_label\n", + "data = data.drop([0, 1]).reset_index().drop('index',axis=1)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "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", + "
YrMnDate_excelDateCO2seasonally_adjustedfitseasonally_adjusted_fitCO2_filledseasonally_adjusted_filledSta
2195803212591958.2027315.71314.43316.19314.90315.71314.43MLO
3195804212901958.2877317.45315.15317.30314.98317.45315.15MLO
4195805213201958.3699317.51314.68317.89315.06317.51314.68MLO
6195807213811958.5370315.87315.20315.85315.21315.87315.20MLO
7195808214121958.6219314.93316.23313.95315.28314.93316.23MLO
8195809214431958.7068313.21316.12312.41315.35313.21316.12MLO
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
11195812215341958.9562314.67315.43314.76315.51314.67315.43MLO
12195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
13195902215961959.1260316.49315.83316.29315.63316.49315.83MLO
14195903216241959.2027316.65315.37316.99315.69316.65315.37MLO
15195904216551959.2877317.72315.41318.09315.76317.72315.41MLO
16195905216851959.3699318.29315.46318.68315.84318.29315.46MLO
17195906217161959.4548318.15315.99318.07315.93318.15315.99MLO
18195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
19195908217771959.6219314.79316.10314.79316.12314.79316.10MLO
20195909218081959.7068313.84316.76313.27316.22313.84316.76MLO
21195910218381959.7890313.33316.35313.30316.30313.33316.35MLO
22195911218691959.8740314.81316.69314.52316.39314.81316.69MLO
23195912218991959.9562315.58316.35315.72316.47315.58316.35MLO
24196001219301960.0410316.43316.37316.62316.55316.43316.37MLO
25196002219611960.1257316.98316.33317.29316.63316.98316.33MLO
26196003219901960.2049317.58316.27318.03316.71317.58316.27MLO
27196004220211960.2896319.03316.69319.14316.79319.03316.69MLO
28196005220511960.3716320.03317.19319.70316.86320.03317.19MLO
29196006220821960.4563319.59317.44319.04316.92319.59317.44MLO
30196007221121960.5383318.18317.53317.59316.97318.18317.53MLO
31196008221431960.6230315.90317.24315.65317.01315.90317.24MLO
32196009221741960.7077314.17317.11314.08317.04314.17317.11MLO
33196010222041960.7896313.83316.85314.06317.07313.83316.85MLO
....................................
777202210448492022.7890415.31418.90415.15418.73415.31418.90MLO
778202211448802022.8740417.03419.27416.71418.94417.03419.27MLO
779202212449102022.9562418.46419.38418.24419.14418.46419.38MKO
780202301449412023.0411419.13419.06419.44419.36419.13419.06MKO
781202302449722023.1260420.33419.56420.38419.59420.33419.56MKO
782202303450002023.2027420.51418.98421.37419.82420.51418.98MLO
783202304450312023.2877422.73419.98422.86420.08422.73419.98MLO
784202305450612023.3699423.78420.39423.74420.36423.78420.39MLO
785202306450922023.4548423.39420.82423.20420.65423.39420.82MLO
786202307451222023.5370421.62420.82421.71420.94421.62420.82MLO
787202308451532023.6219419.56421.11419.66421.25419.56421.11MLO
788202309451842023.7068418.06421.56418.05421.57418.06421.56MLO
789202310452142023.7890418.41422.01418.28421.87418.41422.01MLO
790202311452452023.8740420.11422.37419.95422.18420.11422.37MLO
791202312452752023.9562421.65422.57421.58422.48421.65422.57MLO
792202401453062024.0410422.62422.55422.86422.78422.62422.55MLO
793202402453372024.1257424.34423.56423.87423.07424.34423.56MLO
794202403453662024.2049425.22423.66424.92423.34425.22423.66MLO
795202404453972024.2896426.30423.51426.44423.63426.30423.51MLO
796202405454272024.3716426.70423.30427.29423.90426.70423.30MLO
797202406454582024.4563426.62424.07426.71424.18426.62424.07MLO
798202407454882024.5383425.40424.63425.19424.45425.40424.63MLO
799202408455192024.6230422.70424.29423.10424.73422.70424.29MLO
800202409455502024.7077421.60425.11421.48425.01421.60425.11MLO
801202410455802024.7896422.05425.65421.68425.27422.05425.65MLO
802202411456112024.8743423.61425.86423.30425.53423.61425.86MLO
803202412456412024.9563425.01425.93424.87425.77425.01425.93MLO
804202501456722025.0411426.42426.35426.10426.01426.42426.35MLO
805202502457032025.1260427.00426.21427.04426.25427.00426.21MLO
806202503457312025.2027427.73426.19428.01426.45427.73426.19MLO
\n", + "

800 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn Date_excel Date CO2 seasonally_adjusted \\\n", + "2 1958 03 21259 1958.2027 315.71 314.43 \n", + "3 1958 04 21290 1958.2877 317.45 315.15 \n", + "4 1958 05 21320 1958.3699 317.51 314.68 \n", + "6 1958 07 21381 1958.5370 315.87 315.20 \n", + "7 1958 08 21412 1958.6219 314.93 316.23 \n", + "8 1958 09 21443 1958.7068 313.21 316.12 \n", + "10 1958 11 21504 1958.8740 313.33 315.21 \n", + "11 1958 12 21534 1958.9562 314.67 315.43 \n", + "12 1959 01 21565 1959.0411 315.58 315.52 \n", + "13 1959 02 21596 1959.1260 316.49 315.83 \n", + "14 1959 03 21624 1959.2027 316.65 315.37 \n", + "15 1959 04 21655 1959.2877 317.72 315.41 \n", + "16 1959 05 21685 1959.3699 318.29 315.46 \n", + "17 1959 06 21716 1959.4548 318.15 315.99 \n", + "18 1959 07 21746 1959.5370 316.54 315.87 \n", + "19 1959 08 21777 1959.6219 314.79 316.10 \n", + "20 1959 09 21808 1959.7068 313.84 316.76 \n", + "21 1959 10 21838 1959.7890 313.33 316.35 \n", + "22 1959 11 21869 1959.8740 314.81 316.69 \n", + "23 1959 12 21899 1959.9562 315.58 316.35 \n", + "24 1960 01 21930 1960.0410 316.43 316.37 \n", + "25 1960 02 21961 1960.1257 316.98 316.33 \n", + "26 1960 03 21990 1960.2049 317.58 316.27 \n", + "27 1960 04 22021 1960.2896 319.03 316.69 \n", + "28 1960 05 22051 1960.3716 320.03 317.19 \n", + "29 1960 06 22082 1960.4563 319.59 317.44 \n", + "30 1960 07 22112 1960.5383 318.18 317.53 \n", + "31 1960 08 22143 1960.6230 315.90 317.24 \n", + "32 1960 09 22174 1960.7077 314.17 317.11 \n", + "33 1960 10 22204 1960.7896 313.83 316.85 \n", + ".. ... ... ... ... ... ... \n", + "777 2022 10 44849 2022.7890 415.31 418.90 \n", + "778 2022 11 44880 2022.8740 417.03 419.27 \n", + "779 2022 12 44910 2022.9562 418.46 419.38 \n", + "780 2023 01 44941 2023.0411 419.13 419.06 \n", + "781 2023 02 44972 2023.1260 420.33 419.56 \n", + "782 2023 03 45000 2023.2027 420.51 418.98 \n", + "783 2023 04 45031 2023.2877 422.73 419.98 \n", + "784 2023 05 45061 2023.3699 423.78 420.39 \n", + "785 2023 06 45092 2023.4548 423.39 420.82 \n", + "786 2023 07 45122 2023.5370 421.62 420.82 \n", + "787 2023 08 45153 2023.6219 419.56 421.11 \n", + "788 2023 09 45184 2023.7068 418.06 421.56 \n", + "789 2023 10 45214 2023.7890 418.41 422.01 \n", + "790 2023 11 45245 2023.8740 420.11 422.37 \n", + "791 2023 12 45275 2023.9562 421.65 422.57 \n", + "792 2024 01 45306 2024.0410 422.62 422.55 \n", + "793 2024 02 45337 2024.1257 424.34 423.56 \n", + "794 2024 03 45366 2024.2049 425.22 423.66 \n", + "795 2024 04 45397 2024.2896 426.30 423.51 \n", + "796 2024 05 45427 2024.3716 426.70 423.30 \n", + "797 2024 06 45458 2024.4563 426.62 424.07 \n", + "798 2024 07 45488 2024.5383 425.40 424.63 \n", + "799 2024 08 45519 2024.6230 422.70 424.29 \n", + "800 2024 09 45550 2024.7077 421.60 425.11 \n", + "801 2024 10 45580 2024.7896 422.05 425.65 \n", + "802 2024 11 45611 2024.8743 423.61 425.86 \n", + "803 2024 12 45641 2024.9563 425.01 425.93 \n", + "804 2025 01 45672 2025.0411 426.42 426.35 \n", + "805 2025 02 45703 2025.1260 427.00 426.21 \n", + "806 2025 03 45731 2025.2027 427.73 426.19 \n", + "\n", + " fit seasonally_adjusted_fit CO2_filled \\\n", + "2 316.19 314.90 315.71 \n", + "3 317.30 314.98 317.45 \n", + "4 317.89 315.06 317.51 \n", + "6 315.85 315.21 315.87 \n", + "7 313.95 315.28 314.93 \n", + "8 312.41 315.35 313.21 \n", + "10 313.60 315.46 313.33 \n", + "11 314.76 315.51 314.67 \n", + "12 315.64 315.57 315.58 \n", + "13 316.29 315.63 316.49 \n", + "14 316.99 315.69 316.65 \n", + "15 318.09 315.76 317.72 \n", + "16 318.68 315.84 318.29 \n", + "17 318.07 315.93 318.15 \n", + "18 316.67 316.03 316.54 \n", + "19 314.79 316.12 314.79 \n", + "20 313.27 316.22 313.84 \n", + "21 313.30 316.30 313.33 \n", + "22 314.52 316.39 314.81 \n", + "23 315.72 316.47 315.58 \n", + "24 316.62 316.55 316.43 \n", + "25 317.29 316.63 316.98 \n", + "26 318.03 316.71 317.58 \n", + "27 319.14 316.79 319.03 \n", + "28 319.70 316.86 320.03 \n", + "29 319.04 316.92 319.59 \n", + "30 317.59 316.97 318.18 \n", + "31 315.65 317.01 315.90 \n", + "32 314.08 317.04 314.17 \n", + "33 314.06 317.07 313.83 \n", + ".. ... ... ... \n", + "777 415.15 418.73 415.31 \n", + "778 416.71 418.94 417.03 \n", + "779 418.24 419.14 418.46 \n", + "780 419.44 419.36 419.13 \n", + "781 420.38 419.59 420.33 \n", + "782 421.37 419.82 420.51 \n", + "783 422.86 420.08 422.73 \n", + "784 423.74 420.36 423.78 \n", + "785 423.20 420.65 423.39 \n", + "786 421.71 420.94 421.62 \n", + "787 419.66 421.25 419.56 \n", + "788 418.05 421.57 418.06 \n", + "789 418.28 421.87 418.41 \n", + "790 419.95 422.18 420.11 \n", + "791 421.58 422.48 421.65 \n", + "792 422.86 422.78 422.62 \n", + "793 423.87 423.07 424.34 \n", + "794 424.92 423.34 425.22 \n", + "795 426.44 423.63 426.30 \n", + "796 427.29 423.90 426.70 \n", + "797 426.71 424.18 426.62 \n", + "798 425.19 424.45 425.40 \n", + "799 423.10 424.73 422.70 \n", + "800 421.48 425.01 421.60 \n", + "801 421.68 425.27 422.05 \n", + "802 423.30 425.53 423.61 \n", + "803 424.87 425.77 425.01 \n", + "804 426.10 426.01 426.42 \n", + "805 427.04 426.25 427.00 \n", + "806 428.01 426.45 427.73 \n", + "\n", + " seasonally_adjusted_filled Sta \n", + "2 314.43 MLO \n", + "3 315.15 MLO \n", + "4 314.68 MLO \n", + "6 315.20 MLO \n", + "7 316.23 MLO \n", + "8 316.12 MLO \n", + "10 315.21 MLO \n", + "11 315.43 MLO \n", + "12 315.52 MLO \n", + "13 315.83 MLO \n", + "14 315.37 MLO \n", + "15 315.41 MLO \n", + "16 315.46 MLO \n", + "17 315.99 MLO \n", + "18 315.87 MLO \n", + "19 316.10 MLO \n", + "20 316.76 MLO \n", + "21 316.35 MLO \n", + "22 316.69 MLO \n", + "23 316.35 MLO \n", + "24 316.37 MLO \n", + "25 316.33 MLO \n", + "26 316.27 MLO \n", + "27 316.69 MLO \n", + "28 317.19 MLO \n", + "29 317.44 MLO \n", + "30 317.53 MLO \n", + "31 317.24 MLO \n", + "32 317.11 MLO \n", + "33 316.85 MLO \n", + ".. ... ... \n", + "777 418.90 MLO \n", + "778 419.27 MLO \n", + "779 419.38 MKO \n", + "780 419.06 MKO \n", + "781 419.56 MKO \n", + "782 418.98 MLO \n", + "783 419.98 MLO \n", + "784 420.39 MLO \n", + "785 420.82 MLO \n", + "786 420.82 MLO \n", + "787 421.11 MLO \n", + "788 421.56 MLO \n", + "789 422.01 MLO \n", + "790 422.37 MLO \n", + "791 422.57 MLO \n", + "792 422.55 MLO \n", + "793 423.56 MLO \n", + "794 423.66 MLO \n", + "795 423.51 MLO \n", + "796 423.30 MLO \n", + "797 424.07 MLO \n", + "798 424.63 MLO \n", + "799 424.29 MLO \n", + "800 425.11 MLO \n", + "801 425.65 MLO \n", + "802 425.86 MLO \n", + "803 425.93 MLO \n", + "804 426.35 MLO \n", + "805 426.21 MLO \n", + "806 426.19 MLO \n", + "\n", + "[800 rows x 11 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " data = data[data['CO2'].astype(float)>0]\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def convert_to_month_start(year, month):\n", + " return pd.Timestamp(year=year, month=month, day=1)\n", + "\n", + "data.loc[:, 'period'] = [convert_to_month_start(y, m) for y, m in zip(data['Yr'].astype(int), data['Mn'].astype(int))]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "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_excelDateCO2seasonally_adjustedfitseasonally_adjusted_fitCO2_filledseasonally_adjusted_filledSta
period
1958-03-01195803212591958.2027315.71314.43316.19314.90315.71314.43MLO
1958-04-01195804212901958.2877317.45315.15317.30314.98317.45315.15MLO
1958-05-01195805213201958.3699317.51314.68317.89315.06317.51314.68MLO
1958-07-01195807213811958.5370315.87315.20315.85315.21315.87315.20MLO
1958-08-01195808214121958.6219314.93316.23313.95315.28314.93316.23MLO
1958-09-01195809214431958.7068313.21316.12312.41315.35313.21316.12MLO
1958-11-01195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
1958-12-01195812215341958.9562314.67315.43314.76315.51314.67315.43MLO
1959-01-01195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
1959-02-01195902215961959.1260316.49315.83316.29315.63316.49315.83MLO
1959-03-01195903216241959.2027316.65315.37316.99315.69316.65315.37MLO
1959-04-01195904216551959.2877317.72315.41318.09315.76317.72315.41MLO
1959-05-01195905216851959.3699318.29315.46318.68315.84318.29315.46MLO
1959-06-01195906217161959.4548318.15315.99318.07315.93318.15315.99MLO
1959-07-01195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
1959-08-01195908217771959.6219314.79316.10314.79316.12314.79316.10MLO
1959-09-01195909218081959.7068313.84316.76313.27316.22313.84316.76MLO
1959-10-01195910218381959.7890313.33316.35313.30316.30313.33316.35MLO
1959-11-01195911218691959.8740314.81316.69314.52316.39314.81316.69MLO
1959-12-01195912218991959.9562315.58316.35315.72316.47315.58316.35MLO
1960-01-01196001219301960.0410316.43316.37316.62316.55316.43316.37MLO
1960-02-01196002219611960.1257316.98316.33317.29316.63316.98316.33MLO
1960-03-01196003219901960.2049317.58316.27318.03316.71317.58316.27MLO
1960-04-01196004220211960.2896319.03316.69319.14316.79319.03316.69MLO
1960-05-01196005220511960.3716320.03317.19319.70316.86320.03317.19MLO
1960-06-01196006220821960.4563319.59317.44319.04316.92319.59317.44MLO
1960-07-01196007221121960.5383318.18317.53317.59316.97318.18317.53MLO
1960-08-01196008221431960.6230315.90317.24315.65317.01315.90317.24MLO
1960-09-01196009221741960.7077314.17317.11314.08317.04314.17317.11MLO
1960-10-01196010222041960.7896313.83316.85314.06317.07313.83316.85MLO
....................................
2022-10-01202210448492022.7890415.31418.90415.15418.73415.31418.90MLO
2022-11-01202211448802022.8740417.03419.27416.71418.94417.03419.27MLO
2022-12-01202212449102022.9562418.46419.38418.24419.14418.46419.38MKO
2023-01-01202301449412023.0411419.13419.06419.44419.36419.13419.06MKO
2023-02-01202302449722023.1260420.33419.56420.38419.59420.33419.56MKO
2023-03-01202303450002023.2027420.51418.98421.37419.82420.51418.98MLO
2023-04-01202304450312023.2877422.73419.98422.86420.08422.73419.98MLO
2023-05-01202305450612023.3699423.78420.39423.74420.36423.78420.39MLO
2023-06-01202306450922023.4548423.39420.82423.20420.65423.39420.82MLO
2023-07-01202307451222023.5370421.62420.82421.71420.94421.62420.82MLO
2023-08-01202308451532023.6219419.56421.11419.66421.25419.56421.11MLO
2023-09-01202309451842023.7068418.06421.56418.05421.57418.06421.56MLO
2023-10-01202310452142023.7890418.41422.01418.28421.87418.41422.01MLO
2023-11-01202311452452023.8740420.11422.37419.95422.18420.11422.37MLO
2023-12-01202312452752023.9562421.65422.57421.58422.48421.65422.57MLO
2024-01-01202401453062024.0410422.62422.55422.86422.78422.62422.55MLO
2024-02-01202402453372024.1257424.34423.56423.87423.07424.34423.56MLO
2024-03-01202403453662024.2049425.22423.66424.92423.34425.22423.66MLO
2024-04-01202404453972024.2896426.30423.51426.44423.63426.30423.51MLO
2024-05-01202405454272024.3716426.70423.30427.29423.90426.70423.30MLO
2024-06-01202406454582024.4563426.62424.07426.71424.18426.62424.07MLO
2024-07-01202407454882024.5383425.40424.63425.19424.45425.40424.63MLO
2024-08-01202408455192024.6230422.70424.29423.10424.73422.70424.29MLO
2024-09-01202409455502024.7077421.60425.11421.48425.01421.60425.11MLO
2024-10-01202410455802024.7896422.05425.65421.68425.27422.05425.65MLO
2024-11-01202411456112024.8743423.61425.86423.30425.53423.61425.86MLO
2024-12-01202412456412024.9563425.01425.93424.87425.77425.01425.93MLO
2025-01-01202501456722025.0411426.42426.35426.10426.01426.42426.35MLO
2025-02-01202502457032025.1260427.00426.21427.04426.25427.00426.21MLO
2025-03-01202503457312025.2027427.73426.19428.01426.45427.73426.19MLO
\n", + "

800 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " Yr Mn Date_excel Date CO2 seasonally_adjusted \\\n", + "period \n", + "1958-03-01 1958 03 21259 1958.2027 315.71 314.43 \n", + "1958-04-01 1958 04 21290 1958.2877 317.45 315.15 \n", + "1958-05-01 1958 05 21320 1958.3699 317.51 314.68 \n", + "1958-07-01 1958 07 21381 1958.5370 315.87 315.20 \n", + "1958-08-01 1958 08 21412 1958.6219 314.93 316.23 \n", + "1958-09-01 1958 09 21443 1958.7068 313.21 316.12 \n", + "1958-11-01 1958 11 21504 1958.8740 313.33 315.21 \n", + "1958-12-01 1958 12 21534 1958.9562 314.67 315.43 \n", + "1959-01-01 1959 01 21565 1959.0411 315.58 315.52 \n", + "1959-02-01 1959 02 21596 1959.1260 316.49 315.83 \n", + "1959-03-01 1959 03 21624 1959.2027 316.65 315.37 \n", + "1959-04-01 1959 04 21655 1959.2877 317.72 315.41 \n", + "1959-05-01 1959 05 21685 1959.3699 318.29 315.46 \n", + "1959-06-01 1959 06 21716 1959.4548 318.15 315.99 \n", + "1959-07-01 1959 07 21746 1959.5370 316.54 315.87 \n", + "1959-08-01 1959 08 21777 1959.6219 314.79 316.10 \n", + "1959-09-01 1959 09 21808 1959.7068 313.84 316.76 \n", + "1959-10-01 1959 10 21838 1959.7890 313.33 316.35 \n", + "1959-11-01 1959 11 21869 1959.8740 314.81 316.69 \n", + "1959-12-01 1959 12 21899 1959.9562 315.58 316.35 \n", + "1960-01-01 1960 01 21930 1960.0410 316.43 316.37 \n", + "1960-02-01 1960 02 21961 1960.1257 316.98 316.33 \n", + "1960-03-01 1960 03 21990 1960.2049 317.58 316.27 \n", + "1960-04-01 1960 04 22021 1960.2896 319.03 316.69 \n", + "1960-05-01 1960 05 22051 1960.3716 320.03 317.19 \n", + "1960-06-01 1960 06 22082 1960.4563 319.59 317.44 \n", + "1960-07-01 1960 07 22112 1960.5383 318.18 317.53 \n", + "1960-08-01 1960 08 22143 1960.6230 315.90 317.24 \n", + "1960-09-01 1960 09 22174 1960.7077 314.17 317.11 \n", + "1960-10-01 1960 10 22204 1960.7896 313.83 316.85 \n", + "... ... ... ... ... ... ... \n", + "2022-10-01 2022 10 44849 2022.7890 415.31 418.90 \n", + "2022-11-01 2022 11 44880 2022.8740 417.03 419.27 \n", + "2022-12-01 2022 12 44910 2022.9562 418.46 419.38 \n", + "2023-01-01 2023 01 44941 2023.0411 419.13 419.06 \n", + "2023-02-01 2023 02 44972 2023.1260 420.33 419.56 \n", + "2023-03-01 2023 03 45000 2023.2027 420.51 418.98 \n", + "2023-04-01 2023 04 45031 2023.2877 422.73 419.98 \n", + "2023-05-01 2023 05 45061 2023.3699 423.78 420.39 \n", + "2023-06-01 2023 06 45092 2023.4548 423.39 420.82 \n", + "2023-07-01 2023 07 45122 2023.5370 421.62 420.82 \n", + "2023-08-01 2023 08 45153 2023.6219 419.56 421.11 \n", + "2023-09-01 2023 09 45184 2023.7068 418.06 421.56 \n", + "2023-10-01 2023 10 45214 2023.7890 418.41 422.01 \n", + "2023-11-01 2023 11 45245 2023.8740 420.11 422.37 \n", + "2023-12-01 2023 12 45275 2023.9562 421.65 422.57 \n", + "2024-01-01 2024 01 45306 2024.0410 422.62 422.55 \n", + "2024-02-01 2024 02 45337 2024.1257 424.34 423.56 \n", + "2024-03-01 2024 03 45366 2024.2049 425.22 423.66 \n", + "2024-04-01 2024 04 45397 2024.2896 426.30 423.51 \n", + "2024-05-01 2024 05 45427 2024.3716 426.70 423.30 \n", + "2024-06-01 2024 06 45458 2024.4563 426.62 424.07 \n", + "2024-07-01 2024 07 45488 2024.5383 425.40 424.63 \n", + "2024-08-01 2024 08 45519 2024.6230 422.70 424.29 \n", + "2024-09-01 2024 09 45550 2024.7077 421.60 425.11 \n", + "2024-10-01 2024 10 45580 2024.7896 422.05 425.65 \n", + "2024-11-01 2024 11 45611 2024.8743 423.61 425.86 \n", + "2024-12-01 2024 12 45641 2024.9563 425.01 425.93 \n", + "2025-01-01 2025 01 45672 2025.0411 426.42 426.35 \n", + "2025-02-01 2025 02 45703 2025.1260 427.00 426.21 \n", + "2025-03-01 2025 03 45731 2025.2027 427.73 426.19 \n", + "\n", + " fit seasonally_adjusted_fit CO2_filled \\\n", + "period \n", + "1958-03-01 316.19 314.90 315.71 \n", + "1958-04-01 317.30 314.98 317.45 \n", + "1958-05-01 317.89 315.06 317.51 \n", + "1958-07-01 315.85 315.21 315.87 \n", + "1958-08-01 313.95 315.28 314.93 \n", + "1958-09-01 312.41 315.35 313.21 \n", + "1958-11-01 313.60 315.46 313.33 \n", + "1958-12-01 314.76 315.51 314.67 \n", + "1959-01-01 315.64 315.57 315.58 \n", + "1959-02-01 316.29 315.63 316.49 \n", + "1959-03-01 316.99 315.69 316.65 \n", + "1959-04-01 318.09 315.76 317.72 \n", + "1959-05-01 318.68 315.84 318.29 \n", + "1959-06-01 318.07 315.93 318.15 \n", + "1959-07-01 316.67 316.03 316.54 \n", + "1959-08-01 314.79 316.12 314.79 \n", + "1959-09-01 313.27 316.22 313.84 \n", + "1959-10-01 313.30 316.30 313.33 \n", + "1959-11-01 314.52 316.39 314.81 \n", + "1959-12-01 315.72 316.47 315.58 \n", + "1960-01-01 316.62 316.55 316.43 \n", + "1960-02-01 317.29 316.63 316.98 \n", + "1960-03-01 318.03 316.71 317.58 \n", + "1960-04-01 319.14 316.79 319.03 \n", + "1960-05-01 319.70 316.86 320.03 \n", + "1960-06-01 319.04 316.92 319.59 \n", + "1960-07-01 317.59 316.97 318.18 \n", + "1960-08-01 315.65 317.01 315.90 \n", + "1960-09-01 314.08 317.04 314.17 \n", + "1960-10-01 314.06 317.07 313.83 \n", + "... ... ... ... \n", + "2022-10-01 415.15 418.73 415.31 \n", + "2022-11-01 416.71 418.94 417.03 \n", + "2022-12-01 418.24 419.14 418.46 \n", + "2023-01-01 419.44 419.36 419.13 \n", + "2023-02-01 420.38 419.59 420.33 \n", + "2023-03-01 421.37 419.82 420.51 \n", + "2023-04-01 422.86 420.08 422.73 \n", + "2023-05-01 423.74 420.36 423.78 \n", + "2023-06-01 423.20 420.65 423.39 \n", + "2023-07-01 421.71 420.94 421.62 \n", + "2023-08-01 419.66 421.25 419.56 \n", + "2023-09-01 418.05 421.57 418.06 \n", + "2023-10-01 418.28 421.87 418.41 \n", + "2023-11-01 419.95 422.18 420.11 \n", + "2023-12-01 421.58 422.48 421.65 \n", + "2024-01-01 422.86 422.78 422.62 \n", + "2024-02-01 423.87 423.07 424.34 \n", + "2024-03-01 424.92 423.34 425.22 \n", + "2024-04-01 426.44 423.63 426.30 \n", + "2024-05-01 427.29 423.90 426.70 \n", + "2024-06-01 426.71 424.18 426.62 \n", + "2024-07-01 425.19 424.45 425.40 \n", + "2024-08-01 423.10 424.73 422.70 \n", + "2024-09-01 421.48 425.01 421.60 \n", + "2024-10-01 421.68 425.27 422.05 \n", + "2024-11-01 423.30 425.53 423.61 \n", + "2024-12-01 424.87 425.77 425.01 \n", + "2025-01-01 426.10 426.01 426.42 \n", + "2025-02-01 427.04 426.25 427.00 \n", + "2025-03-01 428.01 426.45 427.73 \n", + "\n", + " seasonally_adjusted_filled Sta \n", + "period \n", + "1958-03-01 314.43 MLO \n", + "1958-04-01 315.15 MLO \n", + "1958-05-01 314.68 MLO \n", + "1958-07-01 315.20 MLO \n", + "1958-08-01 316.23 MLO \n", + "1958-09-01 316.12 MLO \n", + "1958-11-01 315.21 MLO \n", + "1958-12-01 315.43 MLO \n", + "1959-01-01 315.52 MLO \n", + "1959-02-01 315.83 MLO \n", + "1959-03-01 315.37 MLO \n", + "1959-04-01 315.41 MLO \n", + "1959-05-01 315.46 MLO \n", + "1959-06-01 315.99 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", + "1960-06-01 317.44 MLO \n", + "1960-07-01 317.53 MLO \n", + "1960-08-01 317.24 MLO \n", + "1960-09-01 317.11 MLO \n", + "1960-10-01 316.85 MLO \n", + "... ... ... \n", + "2022-10-01 418.90 MLO \n", + "2022-11-01 419.27 MLO \n", + "2022-12-01 419.38 MKO \n", + "2023-01-01 419.06 MKO \n", + "2023-02-01 419.56 MKO \n", + "2023-03-01 418.98 MLO \n", + "2023-04-01 419.98 MLO \n", + "2023-05-01 420.39 MLO \n", + "2023-06-01 420.82 MLO \n", + "2023-07-01 420.82 MLO \n", + "2023-08-01 421.11 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", + "2024-03-01 423.66 MLO \n", + "2024-04-01 423.51 MLO \n", + "2024-05-01 423.30 MLO \n", + "2024-06-01 424.07 MLO \n", + "2024-07-01 424.63 MLO \n", + "2024-08-01 424.29 MLO \n", + "2024-09-01 425.11 MLO \n", + "2024-10-01 425.65 MLO \n", + "2024-11-01 425.86 MLO \n", + "2024-12-01 425.93 MLO \n", + "2025-01-01 426.35 MLO \n", + "2025-02-01 426.21 MLO \n", + "2025-03-01 426.19 MLO \n", + "\n", + "[800 rows x 11 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted_data = data.set_index('period').sort_index()\n", + "sorted_data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sorted_data['CO2'] = sorted_data['CO2'].astype(float)\n", + "sorted_data['CO2'].plot(color='blue')\n", + "plt.ylabel(r'Concentration en $CO_2$ [ppm]')\n", + "plt.xlabel('Année')\n", + "plt.grid(linestyle=':')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parametres fittés: a = 55.09, b = 0.0166, c = 258.48\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "from scipy.optimize import curve_fit\n", + "\n", + "def exponential_func(t, a, b, c):\n", + " return a * np.exp(b * t) + c\n", + "\n", + "start_year = sorted_data.index.min().year\n", + "sorted_data[\"period_fractional\"] = (sorted_data.index.year - start_year) + (sorted_data.index.month - 1) / 12\n", + "initial_guess = [1, 0.03, 300]\n", + "popt, pcov = curve_fit(exponential_func, sorted_data[\"period_fractional\"], sorted_data[\"CO2\"], p0=initial_guess)\n", + "fitted_values = exponential_func(sorted_data[\"period_fractional\"], *popt)\n", + "plt.plot(sorted_data.index, sorted_data['CO2'], color='blue', label='Concentration mésurée')\n", + "plt.plot(sorted_data.index, fitted_values, color='red', label='Augumentation lente')\n", + "plt.legend()\n", + "plt.ylabel(r\"Concentration en $CO_2$ [ppm]\")\n", + "plt.xlabel(\"Année\")\n", + "plt.show()\n", + "print(f\"Parametres fittés: a = {popt[0]:.2f}, b = {popt[1]:.4f}, c = {popt[2]:.2f}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module3/exo3/exercice_en.ipynb b/module3/exo3/exercice_en.ipynb deleted file mode 100644 index 0bbbe37..0000000 --- a/module3/exo3/exercice_en.ipynb +++ /dev/null @@ -1,25 +0,0 @@ -{ - "cells": [], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} - -- 2.18.1