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remram44/Resample.ipynb

Created June 16, 2022 16:59
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Resample.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 25,
"id": "23b443fa",
"metadata": {},
"outputs": [],
"source": [
"import numpy\n",
"import pandas"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "13087362",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" 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>TIMESTAMP</th>\n",
" <th>TEMP-F</th>\n",
" <th>RH-PC</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-11-21 17:30:00</td>\n",
" <td>72.8</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-11-21 18:00:00</td>\n",
" <td>71.7</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-11-21 18:30:00</td>\n",
" <td>71.4</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-11-21 19:00:00</td>\n",
" <td>71.0</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-11-21 19:30:00</td>\n",
" <td>70.8</td>\n",
" <td>24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TIMESTAMP TEMP-F RH-PC\n",
"0 2017-11-21 17:30:00 72.8 25\n",
"1 2017-11-21 18:00:00 71.7 24\n",
"2 2017-11-21 18:30:00 71.4 24\n",
"3 2017-11-21 19:00:00 71.0 24\n",
"4 2017-11-21 19:30:00 70.8 24"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = pandas.DataFrame({\n",
" 'TIMESTAMP': pandas.to_datetime([\n",
" '2017-11-21 17:30',\n",
" '2017-11-21 18:00',\n",
" '2017-11-21 18:30',\n",
" '2017-11-21 19:00',\n",
" '2017-11-21 19:30',\n",
" ]),\n",
" 'TEMP-F': [\n",
" 72.8,\n",
" 71.7,\n",
" 71.4,\n",
" 71.0,\n",
" 70.8,\n",
" ],\n",
" 'RH-PC': [\n",
" 25,\n",
" 24,\n",
" 24,\n",
" 24,\n",
" 24,\n",
" ],\n",
"})\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "884da7fb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" 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>TIMESTAMP</th>\n",
" <th>TEMP-F</th>\n",
" <th>RH-PC</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-11-21 17:00:00</td>\n",
" <td>72.8</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-11-21 18:00:00</td>\n",
" <td>71.7</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-11-21 18:00:00</td>\n",
" <td>71.4</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>2017-11-21 19:00:00</td>\n",
" <td>71.0</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>2017-11-21 19:00:00</td>\n",
" <td>70.8</td>\n",
" <td>24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TIMESTAMP TEMP-F RH-PC\n",
"0 2017-11-21 17:00:00 72.8 25\n",
"1 2017-11-21 18:00:00 71.7 24\n",
"2 2017-11-21 18:00:00 71.4 24\n",
"3 2017-11-21 19:00:00 71.0 24\n",
"4 2017-11-21 19:00:00 70.8 24"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Map to hourly timestamps\n",
"def map_hourly(row):\n",
" t = row['TIMESTAMP']\n",
" row['TIMESTAMP'] = pandas.Timestamp(\n",
" tz=t.tz,\n",
" year=t.year,\n",
" month=t.month,\n",
" day=t.day,\n",
" hour=t.hour,\n",
" )\n",
" return row\n",
"mapped_data = data.copy().apply(map_hourly, axis=1)\n",
"mapped_data"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "009ca1e0",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" .dataframe tbody tr th {\n",
" 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>TIMESTAMP</th>\n",
" <th>TEMP-F</th>\n",
" <th>RH-PC</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2017-11-21 17:00:00</td>\n",
" <td>72.80</td>\n",
" <td>25.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2017-11-21 18:00:00</td>\n",
" <td>71.55</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2017-11-21 19:00:00</td>\n",
" <td>70.90</td>\n",
" <td>24.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TIMESTAMP TEMP-F RH-PC\n",
"0 2017-11-21 17:00:00 72.80 25.0\n",
"1 2017-11-21 18:00:00 71.55 24.0\n",
"2 2017-11-21 19:00:00 70.90 24.0"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Aggregate\n",
"aggregated_data = mapped_data.groupby(by='TIMESTAMP').agg(numpy.mean).reset_index()\n",
"aggregated_data"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4d77df09",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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"nbformat_minor": 5
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