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berkeley_earth_temp_data.ipynb
{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "berkeley_earth_temp_data.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"mount_file_id": "1-bmdUC2LEV4cqFDmULSr5MDj2lKdimiC",
"authorship_tag": "ABX9TyOY77XfcFepQSk4dbXSAPFD",
"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/e844fbfdbcc563123cbbfcd96604bb7b/berkeley_earth_temp_data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MXByWp-mQKTp",
"outputId": "6848a334-b5d7-4dbd-a6af-8e72c0a9ae33"
},
"source": [
"import datetime\n",
"import numpy as np\n",
"import os\n",
"import pandas as pd\n",
"import sys\n",
"print(sys.version)\n",
"print(pd.__version__)"
],
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"text": [
"3.7.10 (default, May 3 2021, 02:48:31) \n",
"[GCC 7.5.0]\n",
"1.1.5\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gkl01MkHby2x",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2a87805e-65a8-4d19-dde7-e1fa30bd7a27"
},
"source": [
"if pd.__version__[0] == \"0\":\n",
" !pip install pandas==1.1.5\n",
" import pandas as pd\n",
"assert pd.__version__[0] == \"1\"\n",
"print(pd.__version__) "
],
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"text": [
"1.1.5\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "KKubro-R4shY",
"outputId": "35f770be-fe8e-4039-abd6-477c59ba5307"
},
"source": [
"import plotly\n",
"if plotly.__version__ != \"4.14.3\":\n",
" !pip install plotly==4.14.3\n",
"import plotly.express as px\n",
"import plotly.graph_objs as go\n",
"assert plotly.__version__ == \"4.14.3\"\n",
"print(plotly.__version__) "
],
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": [
"4.14.3\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "g9G5SF9CbNC6"
},
"source": [
"## 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": "ed488439-d9c9-4da2-82c2-7eacf2fd2fb6"
},
"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": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" .dataframe thead th {\n",
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" }\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": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
},
"id": "9jMj-gIkwFno",
"outputId": "3fba45c6-89c4-48cc-f58c-b6b6df3087af"
},
"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": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
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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": 28
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qkSahmt0vdZ6",
"outputId": "2edfc7ce-9ea6-45e5-b401-1dc19deb8339"
},
"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": 29,
"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": 29
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 284
},
"id": "PaGejueIw6cY",
"outputId": "abc4b027-2a60-46cf-9935-c50e03d470d6"
},
"source": [
"df.describe(include=\"all\")"
],
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
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" 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": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 195
},
"id": "WyDjv8X8u765",
"outputId": "090bd0ec-c604-437d-ec90-aab7dbff24c1"
},
"source": [
"def append_date(row):\n",
" row[\"date\"] = datetime.date(year=int(row[\"year\"]), month=int(row[\"month\"]), day=1)\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": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"</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",
" <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": 31
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 737
},
"id": "Oxw49kkGF-Ed",
"outputId": "de7952b0-973c-4573-e4c0-66e21960bdd0"
},
"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": 32,
"outputs": [
{
"output_type": "display_data",
"data": {
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},
{
"cell_type": "code",
"metadata": {
"colab": {
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"height": 410
},
"id": "ewmTsuZ57Iwq",
"outputId": "b4f805a9-e356-4a89-8c4e-d216de70b875"
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"source": [
"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": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>year</th>\n",
" <th>month</th>\n",
" <th>anomaly_C</th>\n",
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" <th>date</th>\n",
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" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
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" <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",
" <td>NaN</td>\n",
" <td>14.092739</td>\n",
" <td>14.216465</td>\n",
" <td>13.969013</td>\n",
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" <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",
" <td>NaN</td>\n",
" <td>0.399470</td>\n",
" <td>0.357809</td>\n",
" <td>0.452085</td>\n",
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" <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",
" <td>NaN</td>\n",
" <td>13.125000</td>\n",
" <td>13.348000</td>\n",
" <td>12.602000</td>\n",
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" <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",
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" <td>13.811000</td>\n",
" <td>13.969000</td>\n",
" <td>13.643000</td>\n",
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" <tr>\n",
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" <td>1935.000000</td>\n",
" <td>6.000000</td>\n",
" <td>-0.095000</td>\n",
" <td>0.117000</td>\n",
" <td>NaN</td>\n",
" <td>14.013000</td>\n",
" <td>14.144000</td>\n",
" <td>13.878000</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",
" <td>NaN</td>\n",
" <td>14.293000</td>\n",
" <td>14.389000</td>\n",
" <td>14.215000</td>\n",
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" <td>15.436000</td>\n",
" <td>15.477000</td>\n",
" <td>15.395000</td>\n",
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"text/plain": [
" year month ... air_C_high air_C_low\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",
"mean 1935.041890 6.497321 ... 14.216465 13.969013\n",
"std 49.399081 3.454186 ... 0.357809 0.452085\n",
"min 1850.000000 1.000000 ... 13.348000 12.602000\n",
"25% 1892.000000 3.000000 ... 13.969000 13.643000\n",
"50% 1935.000000 6.000000 ... 14.144000 13.878000\n",
"75% 1978.000000 9.000000 ... 14.389000 14.215000\n",
"max 2021.000000 12.000000 ... 15.477000 15.395000\n",
"\n",
"[11 rows x 8 columns]"
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},
"metadata": {
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"execution_count": 33
}
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"source": [
"fig = px.line(\n",
" df, \n",
" x=\"date\", \n",
" 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": 38,
"outputs": [
{
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},
"metadata": {
"tags": []
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}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TgKLrmx7pmAz"
},
"source": [
""
],
"execution_count": 34,
"outputs": []
}
]
}
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