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Wintus/weather_and_abs_humidity_in_a_week.ipynb

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weather_and_abs_humidity_in_a_week.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"collapsed_sections": [],
"name": "weather and abs humidity in a week",
"authorship_tag": "ABX9TyM1exx8VvwJwo3Ke5QYxq2r",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/Wintus/bf5ea2529bdb091b9e1262b4b98acc40/.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"# Weather\n",
"\n",
"Using Open-Meteo\n",
"\n",
"https://open-meteo.com/en"
],
"metadata": {
"id": "KLY9NWV8gnmg"
}
},
{
"cell_type": "markdown",
"source": [
"## 地点設定\n",
"\n",
"そして通信"
],
"metadata": {
"id": "qjN70Bb5oQEE"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8m-9Naaegink",
"outputId": "bcfd47bc-49c9-4412-c86b-663e7282e7fa"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<Response [200]>"
]
},
"metadata": {},
"execution_count": 1
}
],
"source": [
"import requests\n",
"\n",
"\n",
"# input\n",
"latitude = 35.6918211 #@param {type:\"number\"}\n",
"longitude = 139.7425546 #@param {type:\"number\"}\n",
"\n",
"# fetch\n",
"URL = 'https://api.open-meteo.com/v1/forecast'\n",
"params = {\n",
" 'latitude': latitude,\n",
" 'longitude': longitude,\n",
" 'hourly': [\n",
" 'temperature_2m',\n",
" 'relativehumidity_2m',\n",
" 'surface_pressure'\n",
" ],\n",
" 'windspeed_unit': 'ms',\n",
" 'timezone': 'Asia/Tokyo',\n",
"}\n",
"\n",
"r = requests.get(URL, params=params)\n",
"weather = r.json()\n",
"r"
]
},
{
"cell_type": "code",
"source": [
"import math\n",
"\n",
"\n",
"def vapor_pressure(temp):\n",
" \"\"\"saturated one of water\n",
" temp: ℃\"\"\"\n",
" return 6.1094*math.exp(17.625*temp / (temp+243.04))"
],
"metadata": {
"id": "XvQsDYJ2nRPx"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import data_table\n",
"\n",
"\n",
"# data_table.num_rows_per_page = 24\n",
"data_table.enable_dataframe_formatter()"
],
"metadata": {
"id": "0ZeNvV6vlmFU"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"\n",
"\n",
"# pd.set_option('styler.format.precision', 3)\n",
"# pd.set_option(\"display.precision\", 3)\n",
"\n",
"pd.__version__"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"id": "Dglr3f9B6QMP",
"outputId": "ae1a3b26-0e4a-4207-902c-027dc08fadc2"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'1.3.5'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"source": [
"df = pd.DataFrame(weather['hourly'])\n",
"df = df.astype({\n",
" 'temperature_2m': 'float',\n",
" 'relativehumidity_2m': 'float',\n",
" 'surface_pressure': 'float',\n",
"})\n",
"\n",
"# df.style.format(precision=3)\n",
"\n",
"df"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 636
},
"id": "PtoXT_gVk9FC",
"outputId": "68355340-007c-4f54-eb58-72ab2087833a"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" time temperature_2m relativehumidity_2m surface_pressure\n",
"0 2022-10-05T00:00 23.3 80.0 1003.2\n",
"1 2022-10-05T01:00 23.0 82.0 1003.2\n",
"2 2022-10-05T02:00 23.0 81.0 1003.1\n",
"3 2022-10-05T03:00 23.1 80.0 1002.8\n",
"4 2022-10-05T04:00 22.8 80.0 1003.4\n",
".. ... ... ... ...\n",
"163 2022-10-11T19:00 22.1 62.0 1008.7\n",
"164 2022-10-11T20:00 21.2 70.0 1009.4\n",
"165 2022-10-11T21:00 20.2 78.0 1010.2\n",
"166 2022-10-11T22:00 19.5 81.0 1010.6\n",
"167 2022-10-11T23:00 19.0 83.0 1010.8\n",
"\n",
"[168 rows x 4 columns]"
],
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"\n",
" <div id=\"df-83c5ede7-f93c-487c-8912-0d48889f0d44\">\n",
" <div class=\"colab-df-container\">\n",
" <div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
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" <th></th>\n",
" <th>time</th>\n",
" <th>temperature_2m</th>\n",
" <th>relativehumidity_2m</th>\n",
" <th>surface_pressure</th>\n",
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" <th>2</th>\n",
" <td>2022-10-05T02:00</td>\n",
" <td>23.0</td>\n",
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" <td>1003.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2022-10-05T03:00</td>\n",
" <td>23.1</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>165</th>\n",
" <td>2022-10-11T21:00</td>\n",
" <td>20.2</td>\n",
" <td>78.0</td>\n",
" <td>1010.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>166</th>\n",
" <td>2022-10-11T22:00</td>\n",
" <td>19.5</td>\n",
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" <tr>\n",
" <th>167</th>\n",
" <td>2022-10-11T23:00</td>\n",
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"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a8bd4d5e58f96183/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"2022-10-05T00:00\",\n{\n 'v': 23.3,\n 'f': \"23.3\",\n },\n{\n 'v': 80.0,\n 'f': \"80.0\",\n },\n{\n 'v': 1003.2,\n 'f': \"1003.2\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"2022-10-05T01:00\",\n{\n 'v': 23.0,\n 'f': \"23.0\",\n },\n{\n 'v': 82.0,\n 'f': \"82.0\",\n },\n{\n 'v': 1003.2,\n 'f': \"1003.2\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"2022-10-05T02:00\",\n{\n 'v': 23.0,\n 'f': \"23.0\",\n },\n{\n 'v': 81.0,\n 'f': \"81.0\",\n },\n{\n 'v': 1003.1,\n 'f': \"1003.1\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"2022-10-05T03:00\",\n{\n 'v': 23.1,\n 'f': \"23.1\",\n },\n{\n 'v': 80.0,\n 'f': \"80.0\",\n },\n{\n 'v': 1002.8,\n 'f': \"1002.8\",\n }],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"2022-10-05T04:00\",\n{\n 'v': 22.8,\n 'f': \"22.8\",\n },\n{\n 'v': 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},
"metadata": {},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"source": [
"## 絶対湿度\n",
"\n",
"計算および表示"
],
"metadata": {
"id": "4fznVE8woiUT"
}
},
{
"cell_type": "code",
"source": [
"e = df['vapor_pressure'] = df['relativehumidity_2m']/100 * df['temperature_2m'].map(vapor_pressure)\n",
"# HR‰\n",
"df['humidity_ratio_permil'] = 0.622 * e / (df['surface_pressure'] - e) * 1000\n",
"# VH\n",
"df['volumetric_humidity'] = 216.7 * e / (df['temperature_2m'] + 273.15)\n",
"\n",
"# df = df.round({\n",
"# 'vapor_pressure': 1,\n",
"# 'humidity_ratio_permil': 1,\n",
"# 'volumetric_humidity': 1,\n",
"# })\n",
"df = df.round(1)\n",
"\n",
"df"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 636
},
"id": "fZBw8wjXoFRm",
"outputId": "dc1abc10-8eec-425d-becd-eba042cd5f28"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" time temperature_2m relativehumidity_2m surface_pressure \\\n",
"0 2022-10-05T00:00 23.3 80.0 1003.2 \n",
"1 2022-10-05T01:00 23.0 82.0 1003.2 \n",
"2 2022-10-05T02:00 23.0 81.0 1003.1 \n",
"3 2022-10-05T03:00 23.1 80.0 1002.8 \n",
"4 2022-10-05T04:00 22.8 80.0 1003.4 \n",
".. ... ... ... ... \n",
"163 2022-10-11T19:00 22.1 62.0 1008.7 \n",
"164 2022-10-11T20:00 21.2 70.0 1009.4 \n",
"165 2022-10-11T21:00 20.2 78.0 1010.2 \n",
"166 2022-10-11T22:00 19.5 81.0 1010.6 \n",
"167 2022-10-11T23:00 19.0 83.0 1010.8 \n",
"\n",
" vapor_pressure humidity_ratio_permil volumetric_humidity \n",
"0 22.8 14.5 16.7 \n",
"1 23.0 14.6 16.8 \n",
"2 22.7 14.4 16.6 \n",
"3 22.6 14.3 16.5 \n",
"4 22.2 14.0 16.2 \n",
".. ... ... ... \n",
"163 16.5 10.3 12.1 \n",
"164 17.6 11.0 12.9 \n",
"165 18.4 11.6 13.6 \n",
"166 18.3 11.5 13.6 \n",
"167 18.2 11.4 13.5 \n",
"\n",
"[168 rows x 7 columns]"
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"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a8bd4d5e58f96183/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n\"2022-10-05T00:00\",\n{\n 'v': 23.3,\n 'f': \"23.3\",\n },\n{\n 'v': 80.0,\n 'f': \"80.0\",\n },\n{\n 'v': 1003.2,\n 'f': \"1003.2\",\n },\n{\n 'v': 22.8,\n 'f': \"22.8\",\n },\n{\n 'v': 14.5,\n 'f': \"14.5\",\n },\n{\n 'v': 16.7,\n 'f': \"16.7\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n\"2022-10-05T01:00\",\n{\n 'v': 23.0,\n 'f': \"23.0\",\n },\n{\n 'v': 82.0,\n 'f': \"82.0\",\n },\n{\n 'v': 1003.2,\n 'f': \"1003.2\",\n },\n{\n 'v': 23.0,\n 'f': \"23.0\",\n },\n{\n 'v': 14.6,\n 'f': \"14.6\",\n },\n{\n 'v': 16.8,\n 'f': \"16.8\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n\"2022-10-05T02:00\",\n{\n 'v': 23.0,\n 'f': \"23.0\",\n },\n{\n 'v': 81.0,\n 'f': \"81.0\",\n },\n{\n 'v': 1003.1,\n 'f': \"1003.1\",\n },\n{\n 'v': 22.7,\n 'f': \"22.7\",\n },\n{\n 'v': 14.4,\n 'f': \"14.4\",\n },\n{\n 'v': 16.6,\n 'f': \"16.6\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n\"2022-10-05T03:00\",\n{\n 'v': 23.1,\n 'f': \"23.1\",\n },\n{\n 'v': 80.0,\n 'f': \"80.0\",\n },\n{\n 'v': 1002.8,\n 'f': \"1002.8\",\n },\n{\n 'v': 22.6,\n 'f': \"22.6\",\n },\n{\n 'v': 14.3,\n 'f': \"14.3\",\n },\n{\n 'v': 16.5,\n 'f': \"16.5\",\n }],\n [{\n 'v': 4,\n 'f': \"4\",\n },\n\"2022-10-05T04:00\",\n{\n 'v': 22.8,\n 'f': \"22.8\",\n },\n{\n 'v': 80.0,\n 'f': \"80.0\",\n },\n{\n 'v': 1003.4,\n 'f': \"1003.4\",\n },\n{\n 'v': 22.2,\n 'f': \"22.2\",\n },\n{\n 'v': 14.0,\n 'f': \"14.0\",\n },\n{\n 'v': 16.2,\n 'f': \"16.2\",\n }],\n [{\n 'v': 5,\n 'f': \"5\",\n },\n\"2022-10-05T05:00\",\n{\n 'v': 22.4,\n 'f': \"22.4\",\n },\n{\n 'v': 79.0,\n 'f': \"79.0\",\n },\n{\n 'v': 1004.3,\n 'f': \"1004.3\",\n },\n{\n 'v': 21.4,\n 'f': \"21.4\",\n },\n{\n 'v': 13.5,\n 'f': \"13.5\",\n },\n{\n 'v': 15.7,\n 'f': \"15.7\",\n }],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n\"2022-10-05T06:00\",\n{\n 'v': 22.1,\n 'f': \"22.1\",\n },\n{\n 'v': 80.0,\n 'f': \"80.0\",\n },\n{\n 'v': 1005.3,\n 'f': \"1005.3\",\n },\n{\n 'v': 21.2,\n 'f': \"21.2\",\n },\n{\n 'v': 13.4,\n 'f': \"13.4\",\n },\n{\n 'v': 15.6,\n 'f': \"15.6\",\n }],\n [{\n 'v': 7,\n 'f': \"7\",\n },\n\"2022-10-05T07:00\",\n{\n 'v': 22.1,\n 'f': \"22.1\",\n },\n{\n 'v': 83.0,\n 'f': \"83.0\",\n },\n{\n 'v': 1006.1,\n 'f': \"1006.1\",\n },\n{\n 'v': 22.0,\n 'f': \"22.0\",\n },\n{\n 'v': 13.9,\n 'f': \"13.9\",\n },\n{\n 'v': 16.2,\n 'f': \"16.2\",\n }],\n [{\n 'v': 8,\n 'f': \"8\",\n },\n\"2022-10-05T08:00\",\n{\n 'v': 22.7,\n 'f': \"22.7\",\n },\n{\n 'v': 76.0,\n 'f': \"76.0\",\n },\n{\n 'v': 1007.1,\n 'f': \"1007.1\",\n },\n{\n 'v': 20.9,\n 'f': \"20.9\",\n },\n{\n 'v': 13.2,\n 'f': \"13.2\",\n },\n{\n 'v': 15.3,\n 'f': \"15.3\",\n }],\n [{\n 'v': 9,\n 'f': \"9\",\n },\n\"2022-10-05T09:00\",\n{\n 'v': 23.8,\n 'f': \"23.8\",\n },\n{\n 'v': 56.0,\n 'f': \"56.0\",\n },\n{\n 'v': 1007.3,\n 'f': \"1007.3\",\n },\n{\n 'v': 16.5,\n 'f': \"16.5\",\n },\n{\n 'v': 10.3,\n 'f': \"10.3\",\n },\n{\n 'v': 12.0,\n 'f': \"12.0\",\n }],\n [{\n 'v': 10,\n 'f': \"10\",\n },\n\"2022-10-05T10:00\",\n{\n 'v': 23.8,\n 'f': \"23.8\",\n },\n{\n 'v': 64.0,\n 'f': \"64.0\",\n },\n{\n 'v': 1008.4,\n 'f': \"1008.4\",\n },\n{\n 'v': 18.8,\n 'f': \"18.8\",\n },\n{\n 'v': 11.8,\n 'f': \"11.8\",\n },\n{\n 'v': 13.7,\n 'f': \"13.7\",\n }],\n [{\n 'v': 11,\n 'f': \"11\",\n },\n\"2022-10-05T11:00\",\n{\n 'v': 23.7,\n 'f': \"23.7\",\n },\n{\n 'v': 70.0,\n 'f': \"70.0\",\n },\n{\n 'v': 1008.5,\n 'f': \"1008.5\",\n },\n{\n 'v': 20.5,\n 'f': \"20.5\",\n },\n{\n 'v': 12.9,\n 'f': \"12.9\",\n },\n{\n 'v': 14.9,\n 'f': \"14.9\",\n }],\n [{\n 'v': 12,\n 'f': \"12\",\n },\n\"2022-10-05T12:00\",\n{\n 'v': 22.1,\n 'f': \"22.1\",\n },\n{\n 'v': 80.0,\n 'f': \"80.0\",\n },\n{\n 'v': 1008.9,\n 'f': \"1008.9\",\n },\n{\n 'v': 21.2,\n 'f': \"21.2\",\n },\n{\n 'v': 13.4,\n 'f': \"13.4\",\n },\n{\n 'v': 15.6,\n 'f': \"15.6\",\n }],\n [{\n 'v': 13,\n 'f': \"13\",\n },\n\"2022-10-05T13:00\",\n{\n 'v': 20.5,\n 'f': \"20.5\",\n },\n{\n 'v': 87.0,\n 'f': \"87.0\",\n },\n{\n 'v': 1008.5,\n 'f': \"1008.5\",\n },\n{\n 'v': 20.9,\n 'f': \"20.9\",\n },\n{\n 'v': 13.2,\n 'f': \"13.2\",\n },\n{\n 'v': 15.5,\n 'f': \"15.5\",\n }],\n [{\n 'v': 14,\n 'f': \"14\",\n },\n\"2022-10-05T14:00\",\n{\n 'v': 19.8,\n 'f': \"19.8\",\n },\n{\n 'v': 88.0,\n 'f': \"88.0\",\n },\n{\n 'v': 1009.4,\n 'f': \"1009.4\",\n },\n{\n 'v': 20.3,\n 'f': \"20.3\",\n },\n{\n 'v': 12.8,\n 'f': \"12.8\",\n },\n{\n 'v': 15.0,\n 'f': \"15.0\",\n }],\n [{\n 'v': 15,\n 'f': \"15\",\n },\n\"2022-10-05T15:00\",\n{\n 'v': 19.7,\n 'f': \"19.7\",\n },\n{\n 'v': 82.0,\n 'f': \"82.0\",\n },\n{\n 'v': 1009.8,\n 'f': \"1009.8\",\n },\n{\n 'v': 18.8,\n 'f': \"18.8\",\n },\n{\n 'v': 11.8,\n 'f': \"11.8\",\n },\n{\n 'v': 13.9,\n 'f': \"13.9\",\n }],\n [{\n 'v': 16,\n 'f': \"16\",\n },\n\"2022-10-05T16:00\",\n{\n 'v': 18.6,\n 'f': \"18.6\",\n },\n{\n 'v': 78.0,\n 'f': \"78.0\",\n },\n{\n 'v': 1010.7,\n 'f': \"1010.7\",\n },\n{\n 'v': 16.7,\n 'f': \"16.7\",\n },\n{\n 'v': 10.4,\n 'f': \"10.4\",\n },\n{\n 'v': 12.4,\n 'f': \"12.4\",\n }],\n [{\n 'v': 17,\n 'f': \"17\",\n },\n\"2022-10-05T17:00\",\n{\n 'v': 18.0,\n 'f': \"18.0\",\n },\n{\n 'v': 85.0,\n 'f': \"85.0\",\n },\n{\n 'v': 1011.2,\n 'f': \"1011.2\",\n },\n{\n 'v': 17.5,\n 'f': \"17.5\",\n },\n{\n 'v': 11.0,\n 'f': \"11.0\",\n },\n{\n 'v': 13.0,\n 'f': \"13.0\",\n }],\n [{\n 'v': 18,\n 'f': \"18\",\n },\n\"2022-10-05T18:00\",\n{\n 'v': 17.8,\n 'f': \"17.8\",\n },\n{\n 'v': 85.0,\n 'f': \"85.0\",\n },\n{\n 'v': 1011.8,\n 'f': \"1011.8\",\n },\n{\n 'v': 17.3,\n 'f': \"17.3\",\n },\n{\n 'v': 10.8,\n 'f': \"10.8\",\n },\n{\n 'v': 12.9,\n 'f': \"12.9\",\n }],\n [{\n 'v': 19,\n 'f': \"19\",\n },\n\"2022-10-05T19:00\",\n{\n 'v': 17.8,\n 'f': \"17.8\",\n },\n{\n 'v': 83.0,\n 'f': \"83.0\",\n },\n{\n 'v': 1012.6,\n 'f': \"1012.6\",\n },\n{\n 'v': 16.9,\n 'f': \"16.9\",\n },\n{\n 'v': 10.5,\n 'f': \"10.5\",\n },\n{\n 'v': 12.6,\n 'f': \"12.6\",\n }],\n [{\n 'v': 20,\n 'f': \"20\",\n },\n\"2022-10-05T20:00\",\n{\n 'v': 17.7,\n 'f': \"17.7\",\n },\n{\n 'v': 82.0,\n 'f': \"82.0\",\n },\n{\n 'v': 1012.9,\n 'f': \"1012.9\",\n },\n{\n 'v': 16.6,\n 'f': \"16.6\",\n },\n{\n 'v': 10.3,\n 'f': \"10.3\",\n },\n{\n 'v': 12.3,\n 'f': \"12.3\",\n }],\n [{\n 'v': 21,\n 'f': \"21\",\n },\n\"2022-10-05T21:00\",\n{\n 'v': 17.4,\n 'f': \"17.4\",\n },\n{\n 'v': 84.0,\n 'f': \"84.0\",\n },\n{\n 'v': 1013.3,\n 'f': \"1013.3\",\n },\n{\n 'v': 16.7,\n 'f': \"16.7\",\n },\n{\n 'v': 10.4,\n 'f': \"10.4\",\n },\n{\n 'v': 12.4,\n 'f': \"12.4\",\n }],\n [{\n 'v': 22,\n 'f': \"22\",\n },\n\"2022-10-05T22:00\",\n{\n 'v': 17.3,\n 'f': \"17.3\",\n },\n{\n 'v': 83.0,\n 'f': \"83.0\",\n },\n{\n 'v': 1013.3,\n 'f': \"1013.3\",\n },\n{\n 'v': 16.4,\n 'f': \"16.4\",\n },\n{\n 'v': 10.2,\n 'f': \"10.2\",\n },\n{\n 'v': 12.2,\n 'f': \"12.2\",\n }],\n [{\n 'v': 23,\n 'f': \"23\",\n },\n\"2022-10-05T23:00\",\n{\n 'v': 17.1,\n 'f': \"17.1\",\n },\n{\n 'v': 82.0,\n 'f': \"82.0\",\n },\n{\n 'v': 1013.7,\n 'f': \"1013.7\",\n },\n{\n 'v': 16.0,\n 'f': \"16.0\",\n },\n{\n 'v': 9.9,\n 'f': \"9.9\",\n },\n{\n 'v': 11.9,\n 'f': \"11.9\",\n }],\n [{\n 'v': 24,\n 'f': \"24\",\n },\n\"2022-10-06T00:00\",\n{\n 'v': 16.8,\n 'f': \"16.8\",\n },\n{\n 'v': 83.0,\n 'f': \"83.0\",\n },\n{\n 'v': 1014.1,\n 'f': \"1014.1\",\n },\n{\n 'v': 15.8,\n 'f': \"15.8\",\n },\n{\n 'v': 9.9,\n 'f': \"9.9\",\n },\n{\n 'v': 11.8,\n 'f': \"11.8\",\n }],\n [{\n 'v': 25,\n 'f': \"25\",\n },\n\"2022-10-06T01:00\",\n{\n 'v': 16.4,\n 'f': \"16.4\",\n },\n{\n 'v': 84.0,\n 'f': \"84.0\",\n },\n{\n 'v': 1014.7,\n 'f': \"1014.7\",\n },\n{\n 'v': 15.6,\n 'f': \"15.6\",\n },\n{\n 'v': 9.7,\n 'f': \"9.7\",\n },\n{\n 'v': 11.7,\n 'f': \"11.7\",\n }],\n [{\n 'v': 26,\n 'f': \"26\",\n },\n\"2022-10-06T02:00\",\n{\n 'v': 16.2,\n 'f': \"16.2\",\n },\n{\n 'v': 83.0,\n 'f': \"83.0\",\n },\n{\n 'v': 1014.5,\n 'f': \"1014.5\",\n },\n{\n 'v': 15.3,\n 'f': \"15.3\",\n },\n{\n 'v': 9.5,\n 'f': \"9.5\",\n },\n{\n 'v': 11.4,\n 'f': \"11.4\",\n }],\n [{\n 'v': 27,\n 'f': \"27\",\n },\n\"2022-10-06T03:00\",\n{\n 'v': 16.0,\n 'f': \"16.0\",\n },\n{\n 'v': 83.0,\n 'f': \"83.0\",\n },\n{\n 'v': 1013.9,\n 'f': \"1013.9\",\n },\n{\n 'v': 15.1,\n 'f': \"15.1\",\n },\n{\n 'v': 9.4,\n 'f': \"9.4\",\n },\n{\n 'v': 11.3,\n 'f': \"11.3\",\n }],\n [{\n 'v': 28,\n 'f': \"28\",\n },\n\"2022-10-06T04:00\",\n{\n 'v': 15.6,\n 'f': \"15.6\",\n },\n{\n 'v': 79.0,\n 'f': \"79.0\",\n },\n{\n 'v': 1013.2,\n 'f': \"1013.2\",\n },\n{\n 'v': 14.0,\n 'f': \"14.0\",\n },\n{\n 'v': 8.7,\n 'f': \"8.7\",\n },\n{\n 'v': 10.5,\n 'f': \"10.5\",\n }],\n [{\n 'v': 29,\n 'f': \"29\",\n },\n\"2022-10-06T05:00\",\n{\n 'v': 15.6,\n 'f': \"15.6\",\n },\n{\n 'v': 77.0,\n 'f': \"77.0\",\n },\n{\n 'v': 1014.2,\n 'f': \"1014.2\",\n },\n{\n 'v': 13.6,\n 'f': \"13.6\",\n },\n{\n 'v': 8.5,\n 'f': \"8.5\",\n },\n{\n 'v': 10.2,\n 'f': \"10.2\",\n }],\n [{\n 'v': 30,\n 'f': \"30\",\n },\n\"2022-10-06T06:00\",\n{\n 'v': 15.3,\n 'f': \"15.3\",\n },\n{\n 'v': 79.0,\n 'f': \"79.0\",\n },\n{\n 'v': 1014.8,\n 'f': \"1014.8\",\n },\n{\n 'v': 13.7,\n 'f': \"13.7\",\n },\n{\n 'v': 8.5,\n 'f': \"8.5\",\n },\n{\n 'v': 10.3,\n 'f': \"10.3\",\n }],\n [{\n 'v': 31,\n 'f': \"31\",\n },\n\"2022-10-06T07:00\",\n{\n 'v': 15.3,\n 'f': \"15.3\",\n },\n{\n 'v': 78.0,\n 'f': \"78.0\",\n },\n{\n 'v': 1015.7,\n 'f': \"1015.7\",\n },\n{\n 'v': 13.5,\n 'f': \"13.5\",\n },\n{\n 'v': 8.4,\n 'f': \"8.4\",\n },\n{\n 'v': 10.2,\n 'f': \"10.2\",\n }],\n [{\n 'v': 32,\n 'f': \"32\",\n },\n\"2022-10-06T08:00\",\n{\n 'v': 15.4,\n 'f': \"15.4\",\n },\n{\n 'v': 75.0,\n 'f': \"75.0\",\n },\n{\n 'v': 1016.2,\n 'f': \"1016.2\",\n },\n{\n 'v': 13.1,\n 'f': \"13.1\",\n },\n{\n 'v': 8.1,\n 'f': \"8.1\",\n },\n{\n 'v': 9.8,\n 'f': \"9.8\",\n }],\n [{\n 'v': 33,\n 'f': \"33\",\n },\n\"2022-10-06T09:00\",\n{\n 'v': 15.5,\n 'f': \"15.5\",\n },\n{\n 'v': 72.0,\n 'f': \"72.0\",\n },\n{\n 'v': 1016.0,\n 'f': \"1016.0\",\n },\n{\n 'v': 12.7,\n 'f': \"12.7\",\n },\n{\n 'v': 7.8,\n 'f': \"7.8\",\n },\n{\n 'v': 9.5,\n 'f': \"9.5\",\n }],\n [{\n 'v': 34,\n 'f': \"34\",\n },\n\"2022-10-06T10:00\",\n{\n 'v': 15.4,\n 'f': \"15.4\",\n },\n{\n 'v': 71.0,\n 'f': \"71.0\",\n },\n{\n 'v': 1016.4,\n 'f': \"1016.4\",\n },\n{\n 'v': 12.4,\n 'f': \"12.4\",\n },\n{\n 'v': 7.7,\n 'f': \"7.7\",\n },\n{\n 'v': 9.3,\n 'f': \"9.3\",\n }],\n [{\n 'v': 35,\n 'f': \"35\",\n },\n\"2022-10-06T11:00\",\n{\n 'v': 15.4,\n 'f': \"15.4\",\n },\n{\n 'v': 70.0,\n 'f': \"70.0\",\n },\n{\n 'v': 1015.9,\n 'f': \"1015.9\",\n },\n{\n 'v': 12.2,\n 'f': \"12.2\",\n },\n{\n 'v': 7.6,\n 'f': \"7.6\",\n },\n{\n 'v': 9.2,\n 'f': \"9.2\",\n }],\n [{\n 'v': 36,\n 'f': \"36\",\n },\n\"2022-10-06T12:00\",\n{\n 'v': 15.0,\n 'f': \"15.0\",\n },\n{\n 'v': 74.0,\n 'f': \"74.0\",\n },\n{\n 'v': 1015.5,\n 'f': \"1015.5\",\n },\n{\n 'v': 12.6,\n 'f': \"12.6\",\n },\n{\n 'v': 7.8,\n 'f': \"7.8\",\n },\n{\n 'v': 9.5,\n 'f': \"9.5\",\n }],\n [{\n 'v': 37,\n 'f': \"37\",\n },\n\"2022-10-06T13:00\",\n{\n 'v': 14.6,\n 'f': \"14.6\",\n },\n{\n 'v': 81.0,\n 'f': \"81.0\",\n },\n{\n 'v': 1015.3,\n 'f': \"1015.3\",\n },\n{\n 'v': 13.4,\n 'f': \"13.4\",\n },\n{\n 'v': 8.3,\n 'f': \"8.3\",\n },\n{\n 'v': 10.1,\n 'f': \"10.1\",\n }],\n [{\n 'v': 38,\n 'f': \"38\",\n },\n\"2022-10-06T14:00\",\n{\n 'v': 14.2,\n 'f': \"14.2\",\n },\n{\n 'v': 81.0,\n 'f': \"81.0\",\n },\n{\n 'v': 1015.0,\n 'f': \"1015.0\",\n },\n{\n 'v': 13.1,\n 'f': \"13.1\",\n },\n{\n 'v': 8.1,\n 'f': \"8.1\",\n },\n{\n 'v': 9.9,\n 'f': \"9.9\",\n }],\n [{\n 'v': 39,\n 'f': \"39\",\n },\n\"2022-10-06T15:00\",\n{\n 'v': 13.9,\n 'f': \"13.9\",\n },\n{\n 'v': 80.0,\n 'f': \"80.0\",\n },\n{\n 'v': 1015.3,\n 'f': \"1015.3\",\n },\n{\n 'v': 12.7,\n 'f': \"12.7\",\n },\n{\n 'v': 7.9,\n 'f': \"7.9\",\n },\n{\n 'v': 9.6,\n 'f': \"9.6\",\n }],\n [{\n 'v': 40,\n 'f': \"40\",\n },\n\"2022-10-06T16:00\",\n{\n 'v': 13.6,\n 'f': \"13.6\",\n },\n{\n 'v': 82.0,\n 'f': \"82.0\",\n },\n{\n 'v': 1015.6,\n 'f': \"1015.6\",\n },\n{\n 'v': 12.7,\n 'f': \"12.7\",\n },\n{\n 'v': 7.9,\n 'f': \"7.9\",\n },\n{\n 'v': 9.6,\n 'f': \"9.6\",\n }],\n [{\n 'v': 41,\n 'f': \"41\",\n },\n\"2022-10-06T17:00\",\n{\n 'v': 13.6,\n 'f': \"13.6\",\n },\n{\n 'v': 83.0,\n 'f': \"83.0\",\n },\n{\n 'v': 1016.0,\n 'f': \"1016.0\",\n },\n{\n 'v': 12.9,\n 'f': \"12.9\",\n },\n{\n 'v': 8.0,\n 'f': \"8.0\",\n },\n{\n 'v': 9.8,\n 'f': \"9.8\",\n }],\n [{\n 'v': 42,\n 'f': \"42\",\n },\n\"2022-10-06T18:00\",\n{\n 'v': 13.7,\n 'f': \"13.7\",\n },\n{\n 'v': 85.0,\n 'f': \"85.0\",\n },\n{\n 'v': 1015.9,\n 'f': \"1015.9\",\n },\n{\n 'v': 13.3,\n 'f': \"13.3\",\n },\n{\n 'v': 8.3,\n 'f': \"8.3\",\n },\n{\n 'v': 10.0,\n 'f': \"10.0\",\n }],\n [{\n 'v': 43,\n 'f': \"43\",\n },\n\"2022-10-06T19:00\",\n{\n 'v': 13.8,\n 'f': \"13.8\",\n },\n{\n 'v': 84.0,\n 'f': \"84.0\",\n },\n{\n 'v': 1016.8,\n 'f': \"1016.8\",\n },\n{\n 'v': 13.2,\n 'f': \"13.2\",\n },\n{\n 'v': 8.2,\n 'f': \"8.2\",\n },\n{\n 'v': 10.0,\n 'f': \"10.0\",\n }],\n 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'v': 37.0,\n 'f': \"37.0\",\n },\n{\n 'v': 1006.6,\n 'f': \"1006.6\",\n },\n{\n 'v': 12.1,\n 'f': \"12.1\",\n },\n{\n 'v': 7.6,\n 'f': \"7.6\",\n },\n{\n 'v': 8.8,\n 'f': \"8.8\",\n }],\n [{\n 'v': 161,\n 'f': \"161\",\n },\n\"2022-10-11T17:00\",\n{\n 'v': 24.5,\n 'f': \"24.5\",\n },\n{\n 'v': 44.0,\n 'f': \"44.0\",\n },\n{\n 'v': 1007.2,\n 'f': \"1007.2\",\n },\n{\n 'v': 13.5,\n 'f': \"13.5\",\n },\n{\n 'v': 8.5,\n 'f': \"8.5\",\n },\n{\n 'v': 9.8,\n 'f': \"9.8\",\n }],\n [{\n 'v': 162,\n 'f': \"162\",\n },\n\"2022-10-11T18:00\",\n{\n 'v': 23.1,\n 'f': \"23.1\",\n },\n{\n 'v': 54.0,\n 'f': \"54.0\",\n },\n{\n 'v': 1008.1,\n 'f': \"1008.1\",\n },\n{\n 'v': 15.2,\n 'f': \"15.2\",\n },\n{\n 'v': 9.5,\n 'f': \"9.5\",\n },\n{\n 'v': 11.1,\n 'f': \"11.1\",\n }],\n [{\n 'v': 163,\n 'f': \"163\",\n },\n\"2022-10-11T19:00\",\n{\n 'v': 22.1,\n 'f': \"22.1\",\n },\n{\n 'v': 62.0,\n 'f': \"62.0\",\n },\n{\n 'v': 1008.7,\n 'f': \"1008.7\",\n },\n{\n 'v': 16.5,\n 'f': \"16.5\",\n },\n{\n 'v': 10.3,\n 'f': \"10.3\",\n },\n{\n 'v': 12.1,\n 'f': \"12.1\",\n }],\n [{\n 'v': 164,\n 'f': \"164\",\n },\n\"2022-10-11T20:00\",\n{\n 'v': 21.2,\n 'f': \"21.2\",\n },\n{\n 'v': 70.0,\n 'f': \"70.0\",\n },\n{\n 'v': 1009.4,\n 'f': \"1009.4\",\n },\n{\n 'v': 17.6,\n 'f': \"17.6\",\n },\n{\n 'v': 11.0,\n 'f': \"11.0\",\n },\n{\n 'v': 12.9,\n 'f': \"12.9\",\n }],\n [{\n 'v': 165,\n 'f': \"165\",\n },\n\"2022-10-11T21:00\",\n{\n 'v': 20.2,\n 'f': \"20.2\",\n },\n{\n 'v': 78.0,\n 'f': \"78.0\",\n },\n{\n 'v': 1010.2,\n 'f': \"1010.2\",\n },\n{\n 'v': 18.4,\n 'f': \"18.4\",\n },\n{\n 'v': 11.6,\n 'f': \"11.6\",\n },\n{\n 'v': 13.6,\n 'f': \"13.6\",\n }],\n [{\n 'v': 166,\n 'f': \"166\",\n },\n\"2022-10-11T22:00\",\n{\n 'v': 19.5,\n 'f': \"19.5\",\n },\n{\n 'v': 81.0,\n 'f': \"81.0\",\n },\n{\n 'v': 1010.6,\n 'f': \"1010.6\",\n },\n{\n 'v': 18.3,\n 'f': \"18.3\",\n },\n{\n 'v': 11.5,\n 'f': \"11.5\",\n },\n{\n 'v': 13.6,\n 'f': \"13.6\",\n }],\n [{\n 'v': 167,\n 'f': \"167\",\n },\n\"2022-10-11T23:00\",\n{\n 'v': 19.0,\n 'f': \"19.0\",\n },\n{\n 'v': 83.0,\n 'f': \"83.0\",\n },\n{\n 'v': 1010.8,\n 'f': \"1010.8\",\n },\n{\n 'v': 18.2,\n 'f': \"18.2\",\n },\n{\n 'v': 11.4,\n 'f': \"11.4\",\n },\n{\n 'v': 13.5,\n 'f': \"13.5\",\n }]],\n columns: [[\"number\", \"index\"], [\"string\", \"time\"], [\"number\", \"temperature_2m\"], [\"number\", \"relativehumidity_2m\"], [\"number\", \"surface_pressure\"], [\"number\", \"vapor_pressure\"], [\"number\", \"humidity_ratio_permil\"], [\"number\", \"volumetric_humidity\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n "
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "markdown",
"source": [
"# 追加検証"
],
"metadata": {
"id": "N7JDQgTzoJ75"
}
},
{
"cell_type": "code",
"source": [
"df.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 261
},
"id": "1TxRbKRBAwtW",
"outputId": "dcca9e95-227e-483f-dff7-5bf62ac55abd"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" temperature_2m relativehumidity_2m surface_pressure vapor_pressure \\\n",
"count 168.000000 168.000000 168.000000 168.000000 \n",
"mean 18.312500 74.744048 1011.816071 15.597619 \n",
"std 3.842797 12.940211 5.069237 2.953856 \n",
"min 13.100000 24.000000 1002.400000 8.300000 \n",
"25% 14.925000 69.000000 1008.325000 13.400000 \n",
"50% 17.700000 79.000000 1012.850000 15.300000 \n",
"75% 21.250000 84.000000 1015.625000 16.725000 \n",
"max 27.600000 93.000000 1020.900000 23.000000 \n",
"\n",
" humidity_ratio_permil volumetric_humidity \n",
"count 168.000000 168.000000 \n",
"mean 9.751786 11.581548 \n",
"std 1.906779 2.093435 \n",
"min 5.200000 6.000000 \n",
"25% 8.400000 10.100000 \n",
"50% 9.500000 11.400000 \n",
"75% 10.425000 12.425000 \n",
"max 14.600000 16.800000 "
],
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" <th>relativehumidity_2m</th>\n",
" <th>surface_pressure</th>\n",
" <th>vapor_pressure</th>\n",
" <th>humidity_ratio_permil</th>\n",
" <th>volumetric_humidity</th>\n",
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" <th>count</th>\n",
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" <th>mean</th>\n",
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" <td>1011.816071</td>\n",
" <td>15.597619</td>\n",
" <td>9.751786</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>3.842797</td>\n",
" <td>12.940211</td>\n",
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" <th>min</th>\n",
" <td>13.100000</td>\n",
" <td>24.000000</td>\n",
" <td>1002.400000</td>\n",
" <td>8.300000</td>\n",
" <td>5.200000</td>\n",
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" <th>25%</th>\n",
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" <td>16.725000</td>\n",
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"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/a8bd4d5e58f96183/data_table.js\";\n\n window.createDataTable({\n data: [[\"count\",\n{\n 'v': 168.0,\n 'f': \"168.0\",\n },\n{\n 'v': 168.0,\n 'f': \"168.0\",\n },\n{\n 'v': 168.0,\n 'f': \"168.0\",\n },\n{\n 'v': 168.0,\n 'f': \"168.0\",\n },\n{\n 'v': 168.0,\n 'f': \"168.0\",\n },\n{\n 'v': 168.0,\n 'f': \"168.0\",\n }],\n [\"mean\",\n{\n 'v': 18.3125,\n 'f': \"18.3125\",\n },\n{\n 'v': 74.74404761904762,\n 'f': \"74.74404761904762\",\n },\n{\n 'v': 1011.8160714285715,\n 'f': \"1011.8160714285715\",\n },\n{\n 'v': 15.597619047619048,\n 'f': \"15.597619047619048\",\n },\n{\n 'v': 9.751785714285713,\n 'f': \"9.751785714285713\",\n },\n{\n 'v': 11.58154761904762,\n 'f': \"11.58154761904762\",\n }],\n [\"std\",\n{\n 'v': 3.8427969401665365,\n 'f': \"3.8427969401665365\",\n },\n{\n 'v': 12.940211209768082,\n 'f': \"12.940211209768082\",\n },\n{\n 'v': 5.0692371528892455,\n 'f': \"5.0692371528892455\",\n },\n{\n 'v': 2.9538557443107964,\n 'f': \"2.9538557443107964\",\n },\n{\n 'v': 1.9067787431591423,\n 'f': \"1.9067787431591423\",\n },\n{\n 'v': 2.093435416254876,\n 'f': \"2.093435416254876\",\n }],\n [\"min\",\n{\n 'v': 13.1,\n 'f': \"13.1\",\n },\n{\n 'v': 24.0,\n 'f': \"24.0\",\n },\n{\n 'v': 1002.4,\n 'f': \"1002.4\",\n },\n{\n 'v': 8.3,\n 'f': \"8.3\",\n },\n{\n 'v': 5.2,\n 'f': \"5.2\",\n },\n{\n 'v': 6.0,\n 'f': \"6.0\",\n }],\n [\"25%\",\n{\n 'v': 14.925,\n 'f': \"14.925\",\n },\n{\n 'v': 69.0,\n 'f': \"69.0\",\n },\n{\n 'v': 1008.325,\n 'f': \"1008.325\",\n },\n{\n 'v': 13.4,\n 'f': \"13.4\",\n },\n{\n 'v': 8.4,\n 'f': \"8.4\",\n },\n{\n 'v': 10.1,\n 'f': \"10.1\",\n }],\n [\"50%\",\n{\n 'v': 17.7,\n 'f': \"17.7\",\n },\n{\n 'v': 79.0,\n 'f': \"79.0\",\n },\n{\n 'v': 1012.8499999999999,\n 'f': \"1012.8499999999999\",\n },\n{\n 'v': 15.3,\n 'f': \"15.3\",\n },\n{\n 'v': 9.5,\n 'f': \"9.5\",\n },\n{\n 'v': 11.4,\n 'f': \"11.4\",\n }],\n [\"75%\",\n{\n 'v': 21.25,\n 'f': \"21.25\",\n },\n{\n 'v': 84.0,\n 'f': \"84.0\",\n },\n{\n 'v': 1015.625,\n 'f': \"1015.625\",\n },\n{\n 'v': 16.725,\n 'f': \"16.725\",\n },\n{\n 'v': 10.425,\n 'f': \"10.425\",\n },\n{\n 'v': 12.425,\n 'f': \"12.425\",\n }],\n [\"max\",\n{\n 'v': 27.6,\n 'f': \"27.6\",\n },\n{\n 'v': 93.0,\n 'f': \"93.0\",\n },\n{\n 'v': 1020.9,\n 'f': \"1020.9\",\n },\n{\n 'v': 23.0,\n 'f': \"23.0\",\n },\n{\n 'v': 14.6,\n 'f': \"14.6\",\n },\n{\n 'v': 16.8,\n 'f': \"16.8\",\n }]],\n columns: [[\"string\", \"index\"], [\"number\", \"temperature_2m\"], [\"number\", \"relativehumidity_2m\"], [\"number\", \"surface_pressure\"], [\"number\", \"vapor_pressure\"], [\"number\", \"humidity_ratio_permil\"], [\"number\", \"volumetric_humidity\"]],\n columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n rowsPerPage: 25,\n helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n suppressOutputScrolling: true,\n minimumWidth: undefined,\n });\n "
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"# density of dry air\n",
"d = df['volumetric_humidity'] / (df['humidity_ratio_permil'] / 1000)\n",
"d.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lnkFIW512dwf",
"outputId": "62e7168e-9551-4b7f-e885-62c975c0a78f"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 168.000000\n",
"mean 1190.557913\n",
"std 22.518562\n",
"min 1135.714286\n",
"25% 1172.648903\n",
"50% 1195.977360\n",
"75% 1207.920792\n",
"max 1225.000000\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"# density of dry air\n",
"d = df['surface_pressure'] / (287.050 * (df['temperature_2m'] + 273.15))\n",
"d.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ijoR5ix7Be9D",
"outputId": "ae3e4df6-8a53-4dce-cbeb-fcc8b201b88e"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"count 168.000000\n",
"mean 0.012096\n",
"std 0.000199\n",
"min 0.011611\n",
"25% 0.011955\n",
"50% 0.012156\n",
"75% 0.012266\n",
"max 0.012344\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "ke3cyQpqIddY"
},
"execution_count": null,
"outputs": []
}
]
}
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