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berkeley_earth_temp_prophet.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "berkeley_earth_temp_prophet.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"mount_file_id": "1upe2eJmRpQr9Ujx7S-w386Mt8_FIHF0y",
"authorship_tag": "ABX9TyPzthal0jh51mNUtqCb0ba2",
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/adamnovotnycom/8752aa0732576eac32de4e0b9fbda601/berkeley_earth_temp_prophet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e3yh6UQ-iKNF"
},
"source": [
"## Packages"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MXByWp-mQKTp",
"outputId": "3863b689-83ea-4eef-b88a-aa0b055c7684"
},
"source": [
"import datetime\n",
"import numpy as np\n",
"import os\n",
"import sys\n",
"print(sys.version)"
],
"execution_count": 323,
"outputs": [
{
"output_type": "stream",
"text": [
"3.7.10 (default, May 3 2021, 02:48:31) \n",
"[GCC 7.5.0]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gkl01MkHby2x",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "61b8ebb4-a00b-4087-9f0d-a475f85490d6"
},
"source": [
"# Install specific packages\n",
"!pip install pandas==1.1.5 # requires Runtime restart\n",
"!pip install plotly==4.14.3 # requires Runtime restart\n",
"!pip install prophet==1.0.1 # requires Runtime restart\n",
"import pandas as pd\n",
"assert pd.__version__ == \"1.1.5\"\n",
"pd.set_option(\"max_columns\", 100)\n",
"import plotly\n",
"assert plotly.__version__ == \"4.14.3\"\n",
"import plotly.express as px\n",
"import plotly.graph_objs as go\n",
"import prophet\n",
"assert prophet.__version__ == \"1.0\"\n",
"from prophet import Prophet\n",
"from prophet.plot import plot_plotly, plot_components_plotly"
],
"execution_count": 324,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: pandas==1.1.5 in /usr/local/lib/python3.7/dist-packages (1.1.5)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.1.5) (2.8.1)\n",
"Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas==1.1.5) (2018.9)\n",
"Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas==1.1.5) (1.19.5)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas==1.1.5) (1.15.0)\n",
"Requirement already satisfied: plotly==4.14.3 in /usr/local/lib/python3.7/dist-packages (4.14.3)\n",
"Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly==4.14.3) (1.3.3)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from plotly==4.14.3) (1.15.0)\n",
"Requirement already satisfied: prophet==1.0.1 in /usr/local/lib/python3.7/dist-packages (1.0.1)\n",
"Requirement already satisfied: cmdstanpy==0.9.68 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (0.9.68)\n",
"Requirement already satisfied: matplotlib>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (3.2.2)\n",
"Requirement already satisfied: LunarCalendar>=0.0.9 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (0.0.9)\n",
"Requirement already satisfied: Cython>=0.22 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (0.29.23)\n",
"Requirement already satisfied: convertdate>=2.1.2 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (2.3.2)\n",
"Requirement already satisfied: holidays>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (0.10.5.2)\n",
"Requirement already satisfied: pandas>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (1.1.5)\n",
"Requirement already satisfied: setuptools-git>=1.2 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (1.2)\n",
"Requirement already satisfied: python-dateutil>=2.8.0 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (2.8.1)\n",
"Requirement already satisfied: tqdm>=4.36.1 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (4.41.1)\n",
"Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (1.19.5)\n",
"Requirement already satisfied: pystan~=2.19.1.1 in /usr/local/lib/python3.7/dist-packages (from prophet==1.0.1) (2.19.1.1)\n",
"Requirement already satisfied: ujson in /usr/local/lib/python3.7/dist-packages (from cmdstanpy==0.9.68->prophet==1.0.1) (4.0.2)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet==1.0.1) (2.4.7)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet==1.0.1) (0.10.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.0.0->prophet==1.0.1) (1.3.1)\n",
"Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->prophet==1.0.1) (2018.9)\n",
"Requirement already satisfied: ephem>=3.7.5.3 in /usr/local/lib/python3.7/dist-packages (from LunarCalendar>=0.0.9->prophet==1.0.1) (3.7.7.1)\n",
"Requirement already satisfied: pymeeus<=1,>=0.3.13 in /usr/local/lib/python3.7/dist-packages (from convertdate>=2.1.2->prophet==1.0.1) (0.5.11)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet==1.0.1) (1.15.0)\n",
"Requirement already satisfied: hijri-converter in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet==1.0.1) (2.1.1)\n",
"Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from holidays>=0.10.2->prophet==1.0.1) (0.2.1)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g9G5SF9CbNC6"
},
"source": [
"## Temperature Data\n",
"[Berkeley data (Land + Ocean (1850 – Recent))](http://berkeleyearth.org/data/)\n",
"- Temperatures are in Celsius and reported as anomalies relative to the Jan 1951-Dec 1980 average. Uncertainties represent the 95% confidence interval for statistical and spatial undersampling effects as well as ocean biases\n",
"- Estimated Jan 1951-Dec 1980 global mean temperature (C)\n",
" - Using air temperature above sea ice: 14.108 +/- 0.020\n",
" - Using water temperature below sea ice: 14.703 +/- 0.020"
]
},
{
"cell_type": "code",
"metadata": {
"id": "qVrX2rUMaPMK",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 402
},
"outputId": "d76406f0-c4ac-4a56-fd9c-a19e3966cc32"
},
"source": [
"colspecs = [(2, 6), (10, 12), (14, 22), (24, 29)]\n",
"df = pd.read_fwf(\n",
" \"/content/drive/My Drive/Colab Notebooks/berkeley_earth/data/Land_and_Ocean_complete.txt\",\n",
" colspecs=colspecs, \n",
" header=85\n",
")\n",
"df.columns = [\"year\", \"month\", \"anomaly_C\", \"confidence_95_C\"]\n",
"df.head(12)"
],
"execution_count": 325,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>anomaly_C</th>\n",
" <th>confidence_95_C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1850</td>\n",
" <td>1</td>\n",
" <td>-0.725</td>\n",
" <td>0.398</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1850</td>\n",
" <td>2</td>\n",
" <td>-0.179</td>\n",
" <td>0.397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1850</td>\n",
" <td>3</td>\n",
" <td>-0.348</td>\n",
" <td>0.349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1850</td>\n",
" <td>4</td>\n",
" <td>-0.577</td>\n",
" <td>0.352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1850</td>\n",
" <td>5</td>\n",
" <td>-0.600</td>\n",
" <td>0.349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1850</td>\n",
" <td>6</td>\n",
" <td>-0.323</td>\n",
" <td>0.339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1850</td>\n",
" <td>7</td>\n",
" <td>-0.151</td>\n",
" <td>0.298</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1850</td>\n",
" <td>8</td>\n",
" <td>-0.139</td>\n",
" <td>0.325</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>1850</td>\n",
" <td>9</td>\n",
" <td>-0.396</td>\n",
" <td>0.297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>1850</td>\n",
" <td>10</td>\n",
" <td>-0.564</td>\n",
" <td>0.330</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>1850</td>\n",
" <td>11</td>\n",
" <td>-0.551</td>\n",
" <td>0.477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>1850</td>\n",
" <td>12</td>\n",
" <td>-0.463</td>\n",
" <td>0.322</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month anomaly_C confidence_95_C\n",
"0 1850 1 -0.725 0.398\n",
"1 1850 2 -0.179 0.397\n",
"2 1850 3 -0.348 0.349\n",
"3 1850 4 -0.577 0.352\n",
"4 1850 5 -0.600 0.349\n",
"5 1850 6 -0.323 0.339\n",
"6 1850 7 -0.151 0.298\n",
"7 1850 8 -0.139 0.325\n",
"8 1850 9 -0.396 0.297\n",
"9 1850 10 -0.564 0.330\n",
"10 1850 11 -0.551 0.477\n",
"11 1850 12 -0.463 0.322"
]
},
"metadata": {
"tags": []
},
"execution_count": 325
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
},
"id": "y1U9l64tu-oj",
"outputId": "3af30578-9874-4bf5-deb8-971b88c7c2bc"
},
"source": [
"for i, row in df.iterrows():\n",
" if np.isnan(float(row[\"year\"])):\n",
" # start of new dataset\n",
" max_index = i\n",
" break\n",
"df = df.iloc[:max_index, :]\n",
"df.tail(5)"
],
"execution_count": 326,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>anomaly_C</th>\n",
" <th>confidence_95_C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2048</th>\n",
" <td>2020</td>\n",
" <td>9</td>\n",
" <td>1.017</td>\n",
" <td>0.046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2049</th>\n",
" <td>2020</td>\n",
" <td>10</td>\n",
" <td>0.926</td>\n",
" <td>0.048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2050</th>\n",
" <td>2020</td>\n",
" <td>11</td>\n",
" <td>1.064</td>\n",
" <td>0.054</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2051</th>\n",
" <td>2020</td>\n",
" <td>12</td>\n",
" <td>0.855</td>\n",
" <td>0.052</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2052</th>\n",
" <td>2021</td>\n",
" <td>1</td>\n",
" <td>0.883</td>\n",
" <td>0.054</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month anomaly_C confidence_95_C\n",
"2048 2020 9 1.017 0.046\n",
"2049 2020 10 0.926 0.048\n",
"2050 2020 11 1.064 0.054\n",
"2051 2020 12 0.855 0.052\n",
"2052 2021 1 0.883 0.054"
]
},
"metadata": {
"tags": []
},
"execution_count": 326
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qkSahmt0vdZ6",
"outputId": "39178caf-d001-4b69-a8c9-fb9664c99ab5"
},
"source": [
"numeric_cols = [\"year\", \"month\", \"anomaly_C\", \"confidence_95_C\"]\n",
"for col in numeric_cols:\n",
" df[col] = pd.to_numeric(df[col], errors=\"coerce\")\n",
"df = df.dropna(axis=\"rows\")\n",
"df.dtypes"
],
"execution_count": 327,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"year int64\n",
"month int64\n",
"anomaly_C float64\n",
"confidence_95_C float64\n",
"dtype: object"
]
},
"metadata": {
"tags": []
},
"execution_count": 327
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
},
"id": "PaGejueIw6cY",
"outputId": "5bb584e3-9b88-41f7-d951-889459becfd5"
},
"source": [
"df.describe(include=\"all\")"
],
"execution_count": 328,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>anomaly_C</th>\n",
" <th>confidence_95_C</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2053.000000</td>\n",
" <td>2053.000000</td>\n",
" <td>2053.000000</td>\n",
" <td>2053.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1935.041890</td>\n",
" <td>6.497321</td>\n",
" <td>-0.015261</td>\n",
" <td>0.123726</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>49.399081</td>\n",
" <td>3.454186</td>\n",
" <td>0.399470</td>\n",
" <td>0.081411</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1850.000000</td>\n",
" <td>1.000000</td>\n",
" <td>-0.983000</td>\n",
" <td>0.030000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1892.000000</td>\n",
" <td>3.000000</td>\n",
" <td>-0.297000</td>\n",
" <td>0.049000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1935.000000</td>\n",
" <td>6.000000</td>\n",
" <td>-0.095000</td>\n",
" <td>0.117000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1978.000000</td>\n",
" <td>9.000000</td>\n",
" <td>0.185000</td>\n",
" <td>0.164000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2021.000000</td>\n",
" <td>12.000000</td>\n",
" <td>1.328000</td>\n",
" <td>0.599000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month anomaly_C confidence_95_C\n",
"count 2053.000000 2053.000000 2053.000000 2053.000000\n",
"mean 1935.041890 6.497321 -0.015261 0.123726\n",
"std 49.399081 3.454186 0.399470 0.081411\n",
"min 1850.000000 1.000000 -0.983000 0.030000\n",
"25% 1892.000000 3.000000 -0.297000 0.049000\n",
"50% 1935.000000 6.000000 -0.095000 0.117000\n",
"75% 1978.000000 9.000000 0.185000 0.164000\n",
"max 2021.000000 12.000000 1.328000 0.599000"
]
},
"metadata": {
"tags": []
},
"execution_count": 328
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
},
"id": "WyDjv8X8u765",
"outputId": "ad762bd6-3065-4032-b76a-b13522b1adae"
},
"source": [
"def append_date(row):\n",
" row[\"date\"] = datetime.datetime(\n",
" year=int(row[\"year\"]), \n",
" month=int(row[\"month\"]), \n",
" day=1\n",
" )\n",
" return row\n",
"df = df.apply(append_date, axis=1)\n",
"df = df.sort_values(by=\"date\", ascending=True)\n",
"df.head(5)"
],
"execution_count": 329,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>anomaly_C</th>\n",
" <th>confidence_95_C</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1850.0</td>\n",
" <td>1.0</td>\n",
" <td>-0.725</td>\n",
" <td>0.398</td>\n",
" <td>1850-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1850.0</td>\n",
" <td>2.0</td>\n",
" <td>-0.179</td>\n",
" <td>0.397</td>\n",
" <td>1850-02-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1850.0</td>\n",
" <td>3.0</td>\n",
" <td>-0.348</td>\n",
" <td>0.349</td>\n",
" <td>1850-03-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1850.0</td>\n",
" <td>4.0</td>\n",
" <td>-0.577</td>\n",
" <td>0.352</td>\n",
" <td>1850-04-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1850.0</td>\n",
" <td>5.0</td>\n",
" <td>-0.600</td>\n",
" <td>0.349</td>\n",
" <td>1850-05-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month anomaly_C confidence_95_C date\n",
"0 1850.0 1.0 -0.725 0.398 1850-01-01\n",
"1 1850.0 2.0 -0.179 0.397 1850-02-01\n",
"2 1850.0 3.0 -0.348 0.349 1850-03-01\n",
"3 1850.0 4.0 -0.577 0.352 1850-04-01\n",
"4 1850.0 5.0 -0.600 0.349 1850-05-01"
]
},
"metadata": {
"tags": []
},
"execution_count": 329
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 737
},
"id": "Oxw49kkGF-Ed",
"outputId": "e2129875-4811-4012-e668-7873a96b1fc0"
},
"source": [
"fig = px.scatter_matrix(df.loc[:, [\"anomaly_C\", \"confidence_95_C\"]])\n",
"fig.update_layout(\n",
" width=900,\n",
" height=700\n",
")\n",
"fig.update_traces(marker=dict(size=2))\n",
"fig.show()"
],
"execution_count": 330,
"outputs": [
{
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"air_avg_1951_1980 = 14.108\n",
"df[\"air_C\"] = air_avg_1951_1980 + df[\"anomaly_C\"]\n",
"df[\"air_C_high\"] = df[\"air_C\"] + df[\"confidence_95_C\"]\n",
"df[\"air_C_low\"] = df[\"air_C\"] - df[\"confidence_95_C\"]\n",
"df.describe(include=\"all\")"
],
"execution_count": 331,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: FutureWarning:\n",
"\n",
"Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
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"text/plain": [
" year month anomaly_C confidence_95_C \\\n",
"count 2053.000000 2053.000000 2053.000000 2053.000000 \n",
"unique NaN NaN NaN NaN \n",
"top NaN NaN NaN NaN \n",
"freq NaN NaN NaN NaN \n",
"first NaN NaN NaN NaN \n",
"last NaN NaN NaN NaN \n",
"mean 1935.041890 6.497321 -0.015261 0.123726 \n",
"std 49.399081 3.454186 0.399470 0.081411 \n",
"min 1850.000000 1.000000 -0.983000 0.030000 \n",
"25% 1892.000000 3.000000 -0.297000 0.049000 \n",
"50% 1935.000000 6.000000 -0.095000 0.117000 \n",
"75% 1978.000000 9.000000 0.185000 0.164000 \n",
"max 2021.000000 12.000000 1.328000 0.599000 \n",
"\n",
" date air_C air_C_high air_C_low \n",
"count 2053 2053.000000 2053.000000 2053.000000 \n",
"unique 2053 NaN NaN NaN \n",
"top 2004-10-01 00:00:00 NaN NaN NaN \n",
"freq 1 NaN NaN NaN \n",
"first 1850-01-01 00:00:00 NaN NaN NaN \n",
"last 2021-01-01 00:00:00 NaN NaN NaN \n",
"mean NaN 14.092739 14.216465 13.969013 \n",
"std NaN 0.399470 0.357809 0.452085 \n",
"min NaN 13.125000 13.348000 12.602000 \n",
"25% NaN 13.811000 13.969000 13.643000 \n",
"50% NaN 14.013000 14.144000 13.878000 \n",
"75% NaN 14.293000 14.389000 14.215000 \n",
"max NaN 15.436000 15.477000 15.395000 "
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" y=[\"air_C_high\", \"air_C_low\", \"air_C\"]\n",
")\n",
"fig[\"data\"][0][\"line\"] = dict(width=1)\n",
"fig[\"data\"][1][\"line\"] = dict(width=1)\n",
"fig[\"data\"][2][\"line\"] = dict(width=2)\n",
"fig.update_layout(\n",
" title=\"Global Air Temperature\",\n",
" xaxis_title=\"Date\",\n",
" yaxis_title=\"Celsius\",\n",
")\n",
"fig.show()"
],
"execution_count": 332,
"outputs": [
{
"output_type": "display_data",
"data": {
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"## CO2 Data\n",
"- Source: [Our World in Data](https://ourworldindata.org/grapher/annual-co2-emissions-per-country?tab=chart&time=1924..latest&country=~OWID_WRL)\n",
" - Annual CO₂ emissions > Download"
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"co2_df = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/berkeley_earth/data/co2.csv\")\n",
"co2_df = co2_df.rename({\n",
" \"Entity\": \"entity\",\n",
" \"Year\": \"year\",\n",
" \"Annual CO2 emissions\": \"co2_annual_tons\"\n",
"}, axis=1)\n",
"co2_df = co2_df.loc[co2_df[\"entity\"]==\"World\", :]\n",
"co2_df[\"co2_monthly_tons\"] = co2_df[\"co2_annual_tons\"] / 12\n",
"co2_df[\"co2_monthly_bn_tons\"] = co2_df[\"co2_monthly_tons\"] / 1000000000\n",
"co2_df.head(5)"
],
"execution_count": 333,
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"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>entity</th>\n",
" <th>Code</th>\n",
" <th>year</th>\n",
" <th>co2_annual_tons</th>\n",
" <th>co2_monthly_tons</th>\n",
" <th>co2_monthly_bn_tons</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>22643</th>\n",
" <td>World</td>\n",
" <td>OWID_WRL</td>\n",
" <td>1750</td>\n",
" <td>9350528.0</td>\n",
" <td>779210.666667</td>\n",
" <td>0.000779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22644</th>\n",
" <td>World</td>\n",
" <td>OWID_WRL</td>\n",
" <td>1751</td>\n",
" <td>9350528.0</td>\n",
" <td>779210.666667</td>\n",
" <td>0.000779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22645</th>\n",
" <td>World</td>\n",
" <td>OWID_WRL</td>\n",
" <td>1752</td>\n",
" <td>9354192.0</td>\n",
" <td>779516.000000</td>\n",
" <td>0.000780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22646</th>\n",
" <td>World</td>\n",
" <td>OWID_WRL</td>\n",
" <td>1753</td>\n",
" <td>9354192.0</td>\n",
" <td>779516.000000</td>\n",
" <td>0.000780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22647</th>\n",
" <td>World</td>\n",
" <td>OWID_WRL</td>\n",
" <td>1754</td>\n",
" <td>9357856.0</td>\n",
" <td>779821.333333</td>\n",
" <td>0.000780</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" entity Code year co2_annual_tons co2_monthly_tons \\\n",
"22643 World OWID_WRL 1750 9350528.0 779210.666667 \n",
"22644 World OWID_WRL 1751 9350528.0 779210.666667 \n",
"22645 World OWID_WRL 1752 9354192.0 779516.000000 \n",
"22646 World OWID_WRL 1753 9354192.0 779516.000000 \n",
"22647 World OWID_WRL 1754 9357856.0 779821.333333 \n",
"\n",
" co2_monthly_bn_tons \n",
"22643 0.000779 \n",
"22644 0.000779 \n",
"22645 0.000780 \n",
"22646 0.000780 \n",
"22647 0.000780 "
]
},
"metadata": {
"tags": []
},
"execution_count": 333
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 393
},
"id": "HbqeZ_Fkm-1A",
"outputId": "8d5abdeb-8eed-4b17-e6cf-fc19d37acf2e"
},
"source": [
"co2_df.describe(include=\"all\")"
],
"execution_count": 334,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>entity</th>\n",
" <th>Code</th>\n",
" <th>year</th>\n",
" <th>co2_annual_tons</th>\n",
" <th>co2_monthly_tons</th>\n",
" <th>co2_monthly_bn_tons</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>270</td>\n",
" <td>270</td>\n",
" <td>270.000000</td>\n",
" <td>2.700000e+02</td>\n",
" <td>2.700000e+02</td>\n",
" <td>270.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>World</td>\n",
" <td>OWID_WRL</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>270</td>\n",
" <td>270</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1884.500000</td>\n",
" <td>6.121927e+09</td>\n",
" <td>5.101606e+08</td>\n",
" <td>0.510161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>78.086491</td>\n",
" <td>9.743181e+09</td>\n",
" <td>8.119318e+08</td>\n",
" <td>0.811932</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1750.000000</td>\n",
" <td>9.350528e+06</td>\n",
" <td>7.792107e+05</td>\n",
" <td>0.000779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1817.250000</td>\n",
" <td>4.948415e+07</td>\n",
" <td>4.123679e+06</td>\n",
" <td>0.004124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1884.500000</td>\n",
" <td>1.005927e+09</td>\n",
" <td>8.382724e+07</td>\n",
" <td>0.083827</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1951.750000</td>\n",
" <td>6.438569e+09</td>\n",
" <td>5.365474e+08</td>\n",
" <td>0.536547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2019.000000</td>\n",
" <td>3.644139e+10</td>\n",
" <td>3.036782e+09</td>\n",
" <td>3.036782</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" entity Code year co2_annual_tons co2_monthly_tons \\\n",
"count 270 270 270.000000 2.700000e+02 2.700000e+02 \n",
"unique 1 1 NaN NaN NaN \n",
"top World OWID_WRL NaN NaN NaN \n",
"freq 270 270 NaN NaN NaN \n",
"mean NaN NaN 1884.500000 6.121927e+09 5.101606e+08 \n",
"std NaN NaN 78.086491 9.743181e+09 8.119318e+08 \n",
"min NaN NaN 1750.000000 9.350528e+06 7.792107e+05 \n",
"25% NaN NaN 1817.250000 4.948415e+07 4.123679e+06 \n",
"50% NaN NaN 1884.500000 1.005927e+09 8.382724e+07 \n",
"75% NaN NaN 1951.750000 6.438569e+09 5.365474e+08 \n",
"max NaN NaN 2019.000000 3.644139e+10 3.036782e+09 \n",
"\n",
" co2_monthly_bn_tons \n",
"count 270.000000 \n",
"unique NaN \n",
"top NaN \n",
"freq NaN \n",
"mean 0.510161 \n",
"std 0.811932 \n",
"min 0.000779 \n",
"25% 0.004124 \n",
"50% 0.083827 \n",
"75% 0.536547 \n",
"max 3.036782 "
]
},
"metadata": {
"tags": []
},
"execution_count": 334
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Wq4jYxJEn95E",
"outputId": "18b15727-afe3-4799-ad62-0a71de2ff0bd"
},
"source": [
"df = df.join(\n",
" co2_df.loc[:, [\"year\", \"co2_monthly_bn_tons\"]].reset_index().set_index(\"year\", drop=True),\n",
" on=\"year\",\n",
" how=\"left\"\n",
")\n",
"df = df.dropna(axis=\"rows\")\n",
"print(df.head(3))\n",
"print(df.tail(3))"
],
"execution_count": 335,
"outputs": [
{
"output_type": "stream",
"text": [
" year month anomaly_C confidence_95_C date air_C air_C_high \\\n",
"0 1850.0 1.0 -0.725 0.398 1850-01-01 13.383 13.781 \n",
"1 1850.0 2.0 -0.179 0.397 1850-02-01 13.929 14.326 \n",
"2 1850.0 3.0 -0.348 0.349 1850-03-01 13.760 14.109 \n",
"\n",
" air_C_low index co2_monthly_bn_tons \n",
"0 12.985 22743.0 0.016408 \n",
"1 13.532 22743.0 0.016408 \n",
"2 13.411 22743.0 0.016408 \n",
" year month anomaly_C confidence_95_C date air_C \\\n",
"2037 2019.0 10.0 1.105 0.057 2019-10-01 15.213 \n",
"2038 2019.0 11.0 1.002 0.048 2019-11-01 15.110 \n",
"2039 2019.0 12.0 1.148 0.050 2019-12-01 15.256 \n",
"\n",
" air_C_high air_C_low index co2_monthly_bn_tons \n",
"2037 15.270 15.156 22912.0 3.036782 \n",
"2038 15.158 15.062 22912.0 3.036782 \n",
"2039 15.306 15.206 22912.0 3.036782 \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 417
},
"id": "YVrn4Tyn0wes",
"outputId": "d70300ed-c47c-4a57-846b-5979e741aa7e"
},
"source": [
"fig = px.line(\n",
" df, \n",
" x=\"date\", \n",
" y=[\"co2_monthly_bn_tons\"],\n",
" height=400\n",
")\n",
"fig[\"data\"][0][\"line\"] = dict(width=3)\n",
"fig.update_layout(\n",
" title=\"CO2 emissions\",\n",
" xaxis_title=\"Date\",\n",
" yaxis_title=\"bn tons\",\n",
")\n",
"fig.show()"
],
"execution_count": 336,
"outputs": [
{
"output_type": "display_data",
"data": {
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"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DCpHLLwKvGO8"
},
"source": [
"## Forecast"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fAwCuXJib0h5",
"outputId": "4c87b01e-c6ff-456e-83f8-ef5b902c2d06"
},
"source": [
"train_start = datetime.datetime(year=df[\"date\"].min().year, month=df[\"date\"].min().month, day=1)\n",
"train_end = datetime.datetime(year=2019, month=12, day=1)\n",
"forecast_years = 50\n",
"forecast_end = datetime.datetime(year=train_end.year+forecast_years, month=12, day=1)\n",
"print(train_start, train_end, forecast_end)"
],
"execution_count": 337,
"outputs": [
{
"output_type": "stream",
"text": [
"1850-01-01 00:00:00 2019-12-01 00:00:00 2069-12-01 00:00:00\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DR_4akehiQW-",
"outputId": "73676f67-969a-4090-bdd8-95fcb695b215"
},
"source": [
"co2_recent = df.tail(20*12).loc[:, [\"date\", \"co2_monthly_bn_tons\"]].reset_index(drop=True)\n",
"co2_monthly_growth = np.power(\n",
" co2_recent.loc[len(co2_recent)-1, \"co2_monthly_bn_tons\"] /\n",
" co2_recent.loc[0, \"co2_monthly_bn_tons\"],\n",
" (1/(20*12))\n",
")\n",
"# co2_monthly_growth = 0.001 # assumption\n",
"co2_monthly_growth"
],
"execution_count": 338,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1.0015515312722865"
]
},
"metadata": {
"tags": []
},
"execution_count": 338
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RO8uCWYJeAu3"
},
"source": [
"# Generate forecast dataset\n",
"months = 12 * forecast_years\n",
"last_date = train_end\n",
"last_co2 = df.loc[df[\"date\"]==last_date, \"co2_monthly_bn_tons\"].values[0]\n",
"for _ in range(months):\n",
" new_date = (last_date + datetime.timedelta(days=31)).replace(day=1)\n",
" new_co2 = last_co2 * co2_monthly_growth # monthly growth assumption\n",
" if not df[\"date\"].isin([new_date]).any():\n",
" # date not in dataset\n",
" new_row = {\n",
" \"date\": new_date\n",
" }\n",
" df = df.append(new_row, ignore_index=True)\n",
" df.loc[df[\"date\"]==new_date, \"co2_monthly_bn_tons_forecast\"] = new_co2\n",
" last_date = new_date\n",
" last_co2 = new_co2"
],
"execution_count": 339,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 578
},
"id": "SJbM6zas3jbt",
"outputId": "849bb35e-d849-4dec-ecce-cb7003168bb7"
},
"source": [
"df.loc[(\n",
" (df[\"date\"]>train_end - datetime.timedelta(weeks=26))\n",
" & (df[\"date\"]<train_end + datetime.timedelta(weeks=26))\n",
"), :]"
],
"execution_count": 340,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>anomaly_C</th>\n",
" <th>confidence_95_C</th>\n",
" <th>date</th>\n",
" <th>air_C</th>\n",
" <th>air_C_high</th>\n",
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" <th>2034</th>\n",
" <td>2019.0</td>\n",
" <td>7.0</td>\n",
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" <td>0.057</td>\n",
" <td>2019-07-01</td>\n",
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" <th>2035</th>\n",
" <td>2019.0</td>\n",
" <td>8.0</td>\n",
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" <td>0.048</td>\n",
" <td>2019-08-01</td>\n",
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" <td>22912.0</td>\n",
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" <th>2036</th>\n",
" <td>2019.0</td>\n",
" <td>9.0</td>\n",
" <td>0.951</td>\n",
" <td>0.040</td>\n",
" <td>2019-09-01</td>\n",
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" <th>2037</th>\n",
" <td>2019.0</td>\n",
" <td>10.0</td>\n",
" <td>1.105</td>\n",
" <td>0.057</td>\n",
" <td>2019-10-01</td>\n",
" <td>15.213</td>\n",
" <td>15.270</td>\n",
" <td>15.156</td>\n",
" <td>22912.0</td>\n",
" <td>3.036782</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2038</th>\n",
" <td>2019.0</td>\n",
" <td>11.0</td>\n",
" <td>1.002</td>\n",
" <td>0.048</td>\n",
" <td>2019-11-01</td>\n",
" <td>15.110</td>\n",
" <td>15.158</td>\n",
" <td>15.062</td>\n",
" <td>22912.0</td>\n",
" <td>3.036782</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2039</th>\n",
" <td>2019.0</td>\n",
" <td>12.0</td>\n",
" <td>1.148</td>\n",
" <td>0.050</td>\n",
" <td>2019-12-01</td>\n",
" <td>15.256</td>\n",
" <td>15.306</td>\n",
" <td>15.206</td>\n",
" <td>22912.0</td>\n",
" <td>3.036782</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2040</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-01-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.041494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2041</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-02-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.046213</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2042</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-03-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.050939</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2043</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-04-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.055673</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2044</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2020-05-01</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.060414</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year month anomaly_C confidence_95_C date air_C \\\n",
"2034 2019.0 7.0 0.969 0.057 2019-07-01 15.077 \n",
"2035 2019.0 8.0 1.015 0.048 2019-08-01 15.123 \n",
"2036 2019.0 9.0 0.951 0.040 2019-09-01 15.059 \n",
"2037 2019.0 10.0 1.105 0.057 2019-10-01 15.213 \n",
"2038 2019.0 11.0 1.002 0.048 2019-11-01 15.110 \n",
"2039 2019.0 12.0 1.148 0.050 2019-12-01 15.256 \n",
"2040 NaN NaN NaN NaN 2020-01-01 NaN \n",
"2041 NaN NaN NaN NaN 2020-02-01 NaN \n",
"2042 NaN NaN NaN NaN 2020-03-01 NaN \n",
"2043 NaN NaN NaN NaN 2020-04-01 NaN \n",
"2044 NaN NaN NaN NaN 2020-05-01 NaN \n",
"\n",
" air_C_high air_C_low index co2_monthly_bn_tons \\\n",
"2034 15.134 15.020 22912.0 3.036782 \n",
"2035 15.171 15.075 22912.0 3.036782 \n",
"2036 15.099 15.019 22912.0 3.036782 \n",
"2037 15.270 15.156 22912.0 3.036782 \n",
"2038 15.158 15.062 22912.0 3.036782 \n",
"2039 15.306 15.206 22912.0 3.036782 \n",
"2040 NaN NaN NaN NaN \n",
"2041 NaN NaN NaN NaN \n",
"2042 NaN NaN NaN NaN \n",
"2043 NaN NaN NaN NaN \n",
"2044 NaN NaN NaN NaN \n",
"\n",
" co2_monthly_bn_tons_forecast \n",
"2034 NaN \n",
"2035 NaN \n",
"2036 NaN \n",
"2037 NaN \n",
"2038 NaN \n",
"2039 NaN \n",
"2040 3.041494 \n",
"2041 3.046213 \n",
"2042 3.050939 \n",
"2043 3.055673 \n",
"2044 3.060414 "
]
},
"metadata": {
"tags": []
},
"execution_count": 340
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 417
},
"id": "f1axtxqYtI9s",
"outputId": "0a35c99a-6d69-45e7-d91e-8cb53ccf4b38"
},
"source": [
"fig = px.line(\n",
" df.loc[:, [\"date\", \"co2_monthly_bn_tons\", \"co2_monthly_bn_tons_forecast\"]], \n",
" x=\"date\", \n",
" y=[\"co2_monthly_bn_tons\", \"co2_monthly_bn_tons_forecast\"],\n",
" height=400\n",
")\n",
"fig.update_layout(\n",
" title=\"CO2\",\n",
" xaxis_title=\"Date\",\n",
" yaxis_title=\"bn tons\",\n",
")\n",
"fig.show()"
],
"execution_count": 341,
"outputs": [
{
"output_type": "display_data",
"data": {
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{\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"CO2\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Date\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"bn tons\"}}}, {\"responsive\": true} ).then(function(){\n",
" \n",
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"var x = new MutationObserver(function (mutations, observer) {{\n",
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" if (!display || display === 'none') {{\n",
" console.log([gd, 'removed!']);\n",
" Plotly.purge(gd);\n",
" observer.disconnect();\n",
" }}\n",
"}});\n",
"\n",
"// Listen for the removal of the full notebook cells\n",
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"if (outputEl) {{\n",
" x.observe(outputEl, {childList: true});\n",
"}}\n",
"\n",
" }) }; </script> </div>\n",
"</body>\n",
"</html>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 203
},
"id": "41Ozuzc9SDmL",
"outputId": "de166757-873e-47c8-a039-0de6f30480c8"
},
"source": [
"prophet_train_set = df.loc[\n",
" ((df[\"date\"]>=train_start)&(df[\"date\"]<=train_end)), :]\n",
"prophet_train_set = prophet_train_set.loc[:,\n",
" [\"date\", \"air_C\", \"co2_monthly_bn_tons\"]].rename({\n",
" \"date\": \"ds\",\n",
" \"air_C\": \"y\"\n",
" }, axis=1)\n",
"print(prophet_train_set.head(3))\n",
"prophet_train_set.tail(3)"
],
"execution_count": 342,
"outputs": [
{
"output_type": "stream",
"text": [
" ds y co2_monthly_bn_tons\n",
"0 1850-01-01 13.383 0.016408\n",
"1 1850-02-01 13.929 0.016408\n",
"2 1850-03-01 13.760 0.016408\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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" <th>co2_monthly_bn_tons</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2037</th>\n",
" <td>2019-10-01</td>\n",
" <td>15.213</td>\n",
" <td>3.036782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2038</th>\n",
" <td>2019-11-01</td>\n",
" <td>15.110</td>\n",
" <td>3.036782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2039</th>\n",
" <td>2019-12-01</td>\n",
" <td>15.256</td>\n",
" <td>3.036782</td>\n",
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],
"text/plain": [
" ds y co2_monthly_bn_tons\n",
"2037 2019-10-01 15.213 3.036782\n",
"2038 2019-11-01 15.110 3.036782\n",
"2039 2019-12-01 15.256 3.036782"
]
},
"metadata": {
"tags": []
},
"execution_count": 342
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TgKLrmx7pmAz",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a069a9d2-78ea-4e93-99d9-f6c8b1c17eeb"
},
"source": [
"m = Prophet()\n",
"m.add_regressor(\"co2_monthly_bn_tons\", \n",
" prior_scale=0.5, \n",
" mode=\"multiplicative\",\n",
" standardize=True)\n",
"m.fit(prophet_train_set)"
],
"execution_count": 343,
"outputs": [
{
"output_type": "stream",
"text": [
"INFO:prophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n",
"INFO:prophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<prophet.forecaster.Prophet at 0x7fbd82ae0350>"
]
},
"metadata": {
"tags": []
},
"execution_count": 343
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 203
},
"id": "Sd8VgCD4SyvW",
"outputId": "24a8c988-cd2a-44a9-b968-457ffeb4e967"
},
"source": [
"prophet_test_set = df.loc[\n",
" ((df[\"date\"]>train_end)&(df[\"date\"]<=forecast_end)), :]\n",
"prophet_test_set = prophet_test_set.loc[:,\n",
" [\"date\", \"co2_monthly_bn_tons_forecast\"]].rename({\n",
" \"date\": \"ds\",\n",
" \"co2_monthly_bn_tons_forecast\": \"co2_monthly_bn_tons\" # regressor name\n",
" }, axis=1)\n",
"print(prophet_test_set.head(3))\n",
"prophet_test_set.tail(3)"
],
"execution_count": 344,
"outputs": [
{
"output_type": "stream",
"text": [
" ds co2_monthly_bn_tons\n",
"2040 2020-01-01 3.041494\n",
"2041 2020-02-01 3.046213\n",
"2042 2020-03-01 3.050939\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
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" <th></th>\n",
" <th>ds</th>\n",
" <th>co2_monthly_bn_tons</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2637</th>\n",
" <td>2069-10-01</td>\n",
" <td>7.674439</td>\n",
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" <tr>\n",
" <th>2638</th>\n",
" <td>2069-11-01</td>\n",
" <td>7.686346</td>\n",
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" <td>7.698272</td>\n",
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"</table>\n",
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],
"text/plain": [
" ds co2_monthly_bn_tons\n",
"2637 2069-10-01 7.674439\n",
"2638 2069-11-01 7.686346\n",
"2639 2069-12-01 7.698272"
]
},
"metadata": {
"tags": []
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"execution_count": 344
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 299
},
"id": "H4n5nYA1MD0u",
"outputId": "ffab710d-7167-4ff9-afe4-deb72aaa83db"
},
"source": [
"forecast_prophet = m.predict(prophet_test_set)\n",
"forecast_prophet.head(5)"
],
"execution_count": 345,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th></th>\n",
" <th>ds</th>\n",
" <th>trend</th>\n",
" <th>yhat_lower</th>\n",
" <th>yhat_upper</th>\n",
" <th>trend_lower</th>\n",
" <th>trend_upper</th>\n",
" <th>additive_terms</th>\n",
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" <th>additive_terms_upper</th>\n",
" <th>co2_monthly_bn_tons</th>\n",
" <th>co2_monthly_bn_tons_lower</th>\n",
" <th>co2_monthly_bn_tons_upper</th>\n",
" <th>extra_regressors_multiplicative</th>\n",
" <th>extra_regressors_multiplicative_lower</th>\n",
" <th>extra_regressors_multiplicative_upper</th>\n",
" <th>multiplicative_terms</th>\n",
" <th>multiplicative_terms_lower</th>\n",
" <th>multiplicative_terms_upper</th>\n",
" <th>yearly</th>\n",
" <th>yearly_lower</th>\n",
" <th>yearly_upper</th>\n",
" <th>yhat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2020-01-01</td>\n",
" <td>14.999993</td>\n",
" <td>14.822279</td>\n",
" <td>15.244509</td>\n",
" <td>14.999993</td>\n",
" <td>14.999993</td>\n",
" <td>-0.005551</td>\n",
" <td>-0.005551</td>\n",
" <td>-0.005551</td>\n",
" <td>0.002829</td>\n",
" <td>0.002829</td>\n",
" <td>0.002829</td>\n",
" <td>0.002829</td>\n",
" <td>0.002829</td>\n",
" <td>0.002829</td>\n",
" <td>0.002829</td>\n",
" <td>0.002829</td>\n",
" <td>0.002829</td>\n",
" <td>-0.005551</td>\n",
" <td>-0.005551</td>\n",
" <td>-0.005551</td>\n",
" <td>15.036878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2020-02-01</td>\n",
" <td>15.001608</td>\n",
" <td>14.803804</td>\n",
" <td>15.207033</td>\n",
" <td>15.001608</td>\n",
" <td>15.001608</td>\n",
" <td>-0.027245</td>\n",
" <td>-0.027245</td>\n",
" <td>-0.027245</td>\n",
" <td>0.002835</td>\n",
" <td>0.002835</td>\n",
" <td>0.002835</td>\n",
" <td>0.002835</td>\n",
" <td>0.002835</td>\n",
" <td>0.002835</td>\n",
" <td>0.002835</td>\n",
" <td>0.002835</td>\n",
" <td>0.002835</td>\n",
" <td>-0.027245</td>\n",
" <td>-0.027245</td>\n",
" <td>-0.027245</td>\n",
" <td>15.016894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2020-03-01</td>\n",
" <td>15.003119</td>\n",
" <td>14.853414</td>\n",
" <td>15.235101</td>\n",
" <td>15.003119</td>\n",
" <td>15.003119</td>\n",
" <td>-0.003281</td>\n",
" <td>-0.003281</td>\n",
" <td>-0.003281</td>\n",
" <td>0.002841</td>\n",
" <td>0.002841</td>\n",
" <td>0.002841</td>\n",
" <td>0.002841</td>\n",
" <td>0.002841</td>\n",
" <td>0.002841</td>\n",
" <td>0.002841</td>\n",
" <td>0.002841</td>\n",
" <td>0.002841</td>\n",
" <td>-0.003281</td>\n",
" <td>-0.003281</td>\n",
" <td>-0.003281</td>\n",
" <td>15.042462</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2020-04-01</td>\n",
" <td>15.004735</td>\n",
" <td>14.860521</td>\n",
" <td>15.265164</td>\n",
" <td>15.004735</td>\n",
" <td>15.004735</td>\n",
" <td>0.020601</td>\n",
" <td>0.020601</td>\n",
" <td>0.020601</td>\n",
" <td>0.002847</td>\n",
" <td>0.002847</td>\n",
" <td>0.002847</td>\n",
" <td>0.002847</td>\n",
" <td>0.002847</td>\n",
" <td>0.002847</td>\n",
" <td>0.002847</td>\n",
" <td>0.002847</td>\n",
" <td>0.002847</td>\n",
" <td>0.020601</td>\n",
" <td>0.020601</td>\n",
" <td>0.020601</td>\n",
" <td>15.068055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2020-05-01</td>\n",
" <td>15.006298</td>\n",
" <td>14.849527</td>\n",
" <td>15.258638</td>\n",
" <td>15.006298</td>\n",
" <td>15.006298</td>\n",
" <td>0.011566</td>\n",
" <td>0.011566</td>\n",
" <td>0.011566</td>\n",
" <td>0.002853</td>\n",
" <td>0.002853</td>\n",
" <td>0.002853</td>\n",
" <td>0.002853</td>\n",
" <td>0.002853</td>\n",
" <td>0.002853</td>\n",
" <td>0.002853</td>\n",
" <td>0.002853</td>\n",
" <td>0.002853</td>\n",
" <td>0.011566</td>\n",
" <td>0.011566</td>\n",
" <td>0.011566</td>\n",
" <td>15.060678</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ds trend yhat_lower yhat_upper trend_lower trend_upper \\\n",
"0 2020-01-01 14.999993 14.822279 15.244509 14.999993 14.999993 \n",
"1 2020-02-01 15.001608 14.803804 15.207033 15.001608 15.001608 \n",
"2 2020-03-01 15.003119 14.853414 15.235101 15.003119 15.003119 \n",
"3 2020-04-01 15.004735 14.860521 15.265164 15.004735 15.004735 \n",
"4 2020-05-01 15.006298 14.849527 15.258638 15.006298 15.006298 \n",
"\n",
" additive_terms additive_terms_lower additive_terms_upper \\\n",
"0 -0.005551 -0.005551 -0.005551 \n",
"1 -0.027245 -0.027245 -0.027245 \n",
"2 -0.003281 -0.003281 -0.003281 \n",
"3 0.020601 0.020601 0.020601 \n",
"4 0.011566 0.011566 0.011566 \n",
"\n",
" co2_monthly_bn_tons co2_monthly_bn_tons_lower co2_monthly_bn_tons_upper \\\n",
"0 0.002829 0.002829 0.002829 \n",
"1 0.002835 0.002835 0.002835 \n",
"2 0.002841 0.002841 0.002841 \n",
"3 0.002847 0.002847 0.002847 \n",
"4 0.002853 0.002853 0.002853 \n",
"\n",
" extra_regressors_multiplicative extra_regressors_multiplicative_lower \\\n",
"0 0.002829 0.002829 \n",
"1 0.002835 0.002835 \n",
"2 0.002841 0.002841 \n",
"3 0.002847 0.002847 \n",
"4 0.002853 0.002853 \n",
"\n",
" extra_regressors_multiplicative_upper multiplicative_terms \\\n",
"0 0.002829 0.002829 \n",
"1 0.002835 0.002835 \n",
"2 0.002841 0.002841 \n",
"3 0.002847 0.002847 \n",
"4 0.002853 0.002853 \n",
"\n",
" multiplicative_terms_lower multiplicative_terms_upper yearly \\\n",
"0 0.002829 0.002829 -0.005551 \n",
"1 0.002835 0.002835 -0.027245 \n",
"2 0.002841 0.002841 -0.003281 \n",
"3 0.002847 0.002847 0.020601 \n",
"4 0.002853 0.002853 0.011566 \n",
"\n",
" yearly_lower yearly_upper yhat \n",
"0 -0.005551 -0.005551 15.036878 \n",
"1 -0.027245 -0.027245 15.016894 \n",
"2 -0.003281 -0.003281 15.042462 \n",
"3 0.020601 0.020601 15.068055 \n",
"4 0.011566 0.011566 15.060678 "
]
},
"metadata": {
"tags": []
},
"execution_count": 345
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 637
},
"id": "IQY5oMuEyhdh",
"outputId": "8fabc9a3-c701-4c8c-bb0d-57f99bcba6da"
},
"source": [
"plot_plotly(m, forecast_prophet)"
],
"execution_count": 346,
"outputs": [
{
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"data": {
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{
"cell_type": "code",
"metadata": {
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"# combine Prophet forecast with original dataset\n",
"forecast_prophet[\"air_C_forecast\"] = forecast_prophet[\"yhat\"]\n",
"forecast_prophet[\"air_C_high_forecast\"] = forecast_prophet[\"yhat_upper\"]\n",
"forecast_prophet[\"air_C_low_forecast\"] = forecast_prophet[\"yhat_lower\"]\n",
"forecast_prophet[\"date\"] = forecast_prophet[\"ds\"].apply(lambda x: pd.Timestamp(x))\n",
"df = df.join(\n",
" forecast_prophet.loc[: ,[\n",
" \"date\", \"air_C_forecast\", \"air_C_high_forecast\", \"air_C_low_forecast\"\n",
" ]].reset_index().set_index(\"date\", drop=True), \n",
" on=\"date\", \n",
" how=\"left\",\n",
" rsuffix=\"_prophet\"\n",
")\n",
"df.loc[(\n",
" (df[\"date\"]>train_end - datetime.timedelta(weeks=12))\n",
" & (df[\"date\"]<train_end + datetime.timedelta(weeks=26))\n",
"), :]"
],
"execution_count": 347,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th></th>\n",
" <th>year</th>\n",
" <th>month</th>\n",
" <th>anomaly_C</th>\n",
" <th>confidence_95_C</th>\n",
" <th>date</th>\n",
" <th>air_C</th>\n",
" <th>air_C_high</th>\n",
" <th>air_C_low</th>\n",
" <th>index</th>\n",
" <th>co2_monthly_bn_tons</th>\n",
" <th>co2_monthly_bn_tons_forecast</th>\n",
" <th>index_prophet</th>\n",
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" <tbody>\n",
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" <th>2037</th>\n",
" <td>2019.0</td>\n",
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" <td>2019-11-01</td>\n",
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" <td>12.0</td>\n",
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" <td>0.050</td>\n",
" <td>2019-12-01</td>\n",
" <td>15.256</td>\n",
" <td>15.306</td>\n",
" <td>15.206</td>\n",
" <td>22912.0</td>\n",
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" <td>NaN</td>\n",
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" <td>1.0</td>\n",
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" <td>15.207033</td>\n",
" <td>14.803804</td>\n",
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"text/plain": [
" year month anomaly_C confidence_95_C date air_C \\\n",
"2037 2019.0 10.0 1.105 0.057 2019-10-01 15.213 \n",
"2038 2019.0 11.0 1.002 0.048 2019-11-01 15.110 \n",
"2039 2019.0 12.0 1.148 0.050 2019-12-01 15.256 \n",
"2040 NaN NaN NaN NaN 2020-01-01 NaN \n",
"2041 NaN NaN NaN NaN 2020-02-01 NaN \n",
"2042 NaN NaN NaN NaN 2020-03-01 NaN \n",
"2043 NaN NaN NaN NaN 2020-04-01 NaN \n",
"2044 NaN NaN NaN NaN 2020-05-01 NaN \n",
"\n",
" air_C_high air_C_low index co2_monthly_bn_tons \\\n",
"2037 15.270 15.156 22912.0 3.036782 \n",
"2038 15.158 15.062 22912.0 3.036782 \n",
"2039 15.306 15.206 22912.0 3.036782 \n",
"2040 NaN NaN NaN NaN \n",
"2041 NaN NaN NaN NaN \n",
"2042 NaN NaN NaN NaN \n",
"2043 NaN NaN NaN NaN \n",
"2044 NaN NaN NaN NaN \n",
"\n",
" co2_monthly_bn_tons_forecast index_prophet air_C_forecast \\\n",
"2037 NaN NaN NaN \n",
"2038 NaN NaN NaN \n",
"2039 NaN NaN NaN \n",
"2040 3.041494 0.0 15.036878 \n",
"2041 3.046213 1.0 15.016894 \n",
"2042 3.050939 2.0 15.042462 \n",
"2043 3.055673 3.0 15.068055 \n",
"2044 3.060414 4.0 15.060678 \n",
"\n",
" air_C_high_forecast air_C_low_forecast \n",
"2037 NaN NaN \n",
"2038 NaN NaN \n",
"2039 NaN NaN \n",
"2040 15.244509 14.822279 \n",
"2041 15.207033 14.803804 \n",
"2042 15.235101 14.853414 \n",
"2043 15.265164 14.860521 \n",
"2044 15.258638 14.849527 "
]
},
"metadata": {
"tags": []
},
"execution_count": 347
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
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"id": "Kynn3E7ULDRz",
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"source": [
"fig = px.line(\n",
" df, \n",
" x=\"date\", \n",
" y=[\n",
" \"air_C_high\", \n",
" \"air_C_low\", \n",
" \"air_C\", \n",
" \"air_C_high_forecast\",\n",
" \"air_C_low_forecast\",\n",
" \"air_C_forecast\", \n",
" ]\n",
")\n",
"fig[\"data\"][0][\"line\"] = dict(width=1)\n",
"fig[\"data\"][1][\"line\"] = dict(width=1)\n",
"fig[\"data\"][2][\"line\"] = dict(width=2)\n",
"fig[\"data\"][3][\"line\"] = dict(width=1)\n",
"fig[\"data\"][4][\"line\"] = dict(width=1)\n",
"fig[\"data\"][5][\"line\"] = dict(width=2)\n",
"fig.update_layout(\n",
" title=\"Global Air Temperature\",\n",
" xaxis_title=\"Date\",\n",
" yaxis_title=\"Celsius\",\n",
")\n",
"fig.show()"
],
"execution_count": 348,
"outputs": [
{
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