Analysis_for_TBIJ_Report.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "320260bb-8e39-4c0d-b24a-6359d0df25d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.dates as mdates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f111abea-2b88-47be-a9a4-4c090f6134f4",
   "metadata": {},
   "source": [
    "# Prepare data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56be2cf-6a70-44b7-ab26-ffd2fd71bdce",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Read survey and epc data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "af3a799a-36e6-4a62-9439-e50eb3a25d2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read 'SensorID_EPC_Survey_Open_Data' file\n",
    "file_path_ID ='Data/SensorID_EPC_Survey_Open_Data.csv'\n",
    "df_ID_time = pd.read_csv(file_path_ID)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17403108-365e-4f71-9c9d-7fd3cb4f9815",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "ID_list=df_ID_time['ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00a78d9f-cfcc-4ad5-9c19-a4dcbba781cb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert field to datetime type \n",
    "df_ID_time['Battery Charging Start Time'] = pd.to_datetime(df_ID_time['Battery Charging Start Time'], format=\"%d/%m/%Y %H:%M\")  \n",
    "df_ID_time['Battery Charging End Time'] = pd.to_datetime(df_ID_time['Battery Charging End Time'], format=\"%d/%m/%Y %H:%M\")  \n",
    "\n",
    "df_ID_time['Recording Start Time (Participant)'] = pd.to_datetime(df_ID_time['Recording Start Time (Participant) '], format=\"%d/%m/%Y %H:%M\")  \n",
    "df_ID_time['Recording End Time (Participant)'] = pd.to_datetime(df_ID_time['Recording End Time (Participant) '], format=\"%d/%m/%Y %H:%M\")  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36043f1b-ec86-4e9b-8ef0-5e7ad4a3a9fd",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Define function for heat stress index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6045b956-9ff9-43f6-b581-43561a566e31",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define function for heat stress index\n",
    "def calculate_heat_index(temperature, relative_humidity):\n",
    "    c1 = -42.379\n",
    "    c2 = 2.04901523\n",
    "    c3 = 10.14333127\n",
    "    c4 = -0.22475541\n",
    "    c5 = -6.83783e-03\n",
    "    c6 = -5.481717e-02\n",
    "    c7 = 1.22874e-03\n",
    "    c8 = 8.5282e-04\n",
    "    c9 = -1.99e-06\n",
    "\n",
    "    # Convert temperature to Fahrenheit\n",
    "    temp_fahrenheit = (temperature * 1.8) + 32\n",
    "\n",
    "    # Calculate the simple heat index\n",
    "    heat_index_simple = 0.5 * (temp_fahrenheit + 61.0 + (temp_fahrenheit - 68.0) * 1.2 + (relative_humidity * 0.094))\n",
    "\n",
    "    if heat_index_simple > 80:\n",
    "        heat_index = c1 + (c2 * temp_fahrenheit) + (c3 * relative_humidity) + \\\n",
    "                     (c4 * temp_fahrenheit * relative_humidity) + \\\n",
    "                     (c5 * temp_fahrenheit ** 2) + (c6 * relative_humidity ** 2) + \\\n",
    "                     (c7 * temp_fahrenheit ** 2 * relative_humidity) + \\\n",
    "                     (c8 * temp_fahrenheit * relative_humidity ** 2) + \\\n",
    "                     (c9 * temp_fahrenheit ** 2 * relative_humidity ** 2)\n",
    "\n",
    "        if (relative_humidity < 13) and (80 <= temp_fahrenheit <= 112):\n",
    "            adjustment = ((13 - relative_humidity) / 4) * ((17 - abs(temp_fahrenheit - 95)) / 17)\n",
    "            heat_index -= adjustment\n",
    "\n",
    "        if (relative_humidity > 85) and (80 <= temp_fahrenheit <= 87):\n",
    "            adjustment = ((relative_humidity - 85) / 10) * ((87 - temp_fahrenheit) / 5)\n",
    "            heat_index += adjustment\n",
    "\n",
    "        # Convert temperature to Celsius\n",
    "        heat_index_celsius = round((heat_index - 32) / 1.8,2)\n",
    "\n",
    "        return heat_index_celsius\n",
    "    else:\n",
    "        heat_index_simple_celsius = round((heat_index_simple - 32) / 1.8,2)\n",
    "        return heat_index_simple_celsius"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c8941c6-c27e-4d9e-8e10-da7556ae107e",
   "metadata": {},
   "source": [
    "# Generate statistics dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7eb5a2b3-a36b-455d-886f-eb4705139358",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:30: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:31: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      count       mean       std    min     25%    50%    75%    max  \\\n",
      "0   54906.0  24.414297  2.019142  20.52  23.050  23.97  25.39  30.25   \n",
      "1   70281.0  25.562214  1.266366  20.54  24.760  25.46  26.32  29.19   \n",
      "2   56718.0  23.537636  1.763632  20.00  22.160  23.37  24.70  30.70   \n",
      "3   56919.0  25.728024  1.696041  22.02  24.270  25.77  26.90  30.40   \n",
      "4   64961.0  25.067441  1.384199  18.98  24.260  25.23  25.83  28.86   \n",
      "5    1905.0  22.838919  0.794798  21.45  22.160  22.95  23.49  24.27   \n",
      "6   67838.0  22.689428  1.106300  20.57  21.960  22.53  23.38  25.96   \n",
      "7   52662.0  23.812827  2.498582  18.27  21.550  23.90  25.56  31.76   \n",
      "8   55489.0  23.142175  1.767700  18.30  21.840  23.12  24.18  28.12   \n",
      "9   69039.0  23.306204  2.355669  18.47  21.460  23.10  24.81  31.45   \n",
      "10  64624.0  24.214287  1.262814  19.94  23.490  24.32  24.98  27.27   \n",
      "11  50095.0  24.828931  1.462743  21.89  23.510  24.94  25.82  28.07   \n",
      "12  52417.0  23.534161  2.378468  19.20  21.700  23.45  24.77  29.74   \n",
      "13  10265.0  26.724364  1.851601  23.34  24.470  27.51  28.02  30.21   \n",
      "14  74880.0  24.371238  1.781025  20.55  23.020  24.43  25.31  28.77   \n",
      "15  64030.0  21.726997  1.356011  18.93  20.730  21.52  22.27  25.93   \n",
      "16  61895.0  25.059203  1.853706  20.62  23.730  24.82  25.96  30.82   \n",
      "17  68287.0  22.969706  1.332545  20.28  21.960  22.95  23.61  26.62   \n",
      "18  54673.0  23.459063  1.767827  20.27  22.180  23.32  24.29  28.23   \n",
      "19  53645.0  27.098864  1.282466  24.23  26.090  27.11  28.00  30.15   \n",
      "20  74549.0  22.078413  1.746572  18.41  20.720  21.94  23.02  26.95   \n",
      "21  49610.0  24.320605  1.984207  19.98  23.080  24.42  25.51  29.41   \n",
      "22  52128.0  21.814331  1.856223  17.18  20.530  21.65  22.76  27.06   \n",
      "23  63230.0  24.752530  1.552284  19.84  23.720  24.64  25.76  29.16   \n",
      "24  56382.0  23.851260  1.862667  19.40  22.640  23.66  24.96  29.03   \n",
      "25  56260.0  23.701920  0.919710  21.35  23.060  23.57  24.17  26.11   \n",
      "26  61782.0  23.956082  2.220351  19.83  22.350  23.69  25.31  30.60   \n",
      "27  62551.0  25.116307  1.704536  21.72  23.940  25.15  26.06  29.58   \n",
      "28  59512.0  25.874806  1.711160  22.59  24.510  25.44  27.18  30.91   \n",
      "29  54435.0  24.603406  1.708003  21.82  23.120  24.53  25.59  29.38   \n",
      "30  62165.0  24.910916  1.289801  22.48  23.880  25.09  25.85  28.30   \n",
      "31  56226.0  23.757599  1.826940  20.13  22.560  23.39  24.24  29.19   \n",
      "32  55158.0  24.647445  4.368370  16.93  21.760  23.73  26.49  47.40   \n",
      "33  55357.0  23.473933  1.737512  18.40  22.380  23.21  24.33  27.92   \n",
      "34  33276.0  24.074923  1.101605  21.18  23.390  24.20  24.80  26.55   \n",
      "35  57488.0  22.567211  1.634150  19.47  21.285  22.51  23.27  27.93   \n",
      "36  55640.0  22.568058  1.708477  18.32  21.240  22.27  23.73  27.03   \n",
      "37  56528.0  23.726614  2.153434  17.98  22.210  23.53  25.02  29.49   \n",
      "38  54743.0  23.129297  2.078675  19.31  21.550  23.05  24.42  28.71   \n",
      "39  50731.0  21.798360  1.451028  18.82  20.730  21.69  22.52  26.41   \n",
      "\n",
      "          ID  max_HS  ...  days_above_20  hw_days_above_27  hw_days_above_30  \\\n",
      "0   BL007-02   33.49  ...             40                 5                 1   \n",
      "1   BL007-04   31.92  ...             49                 5                 0   \n",
      "2   BL007-07   33.19  ...             41                 5                 1   \n",
      "3   BL007-09   32.50  ...             40                 7                 1   \n",
      "4   BL007-13   31.07  ...             46                 4                 0   \n",
      "5   BL007-14   24.40  ...              2                 0                 0   \n",
      "6   BL007-15   26.41  ...             48                 0                 0   \n",
      "7   BL007-17   34.91  ...             39                 6                 2   \n",
      "8   BL007-18   30.01  ...             39                 4                 0   \n",
      "9   BL007-19   33.74  ...             49                 6                 1   \n",
      "10  BL007-22   28.90  ...             47                 1                 0   \n",
      "11  BL007-23   30.78  ...             36                 2                 0   \n",
      "12  BL007-25   32.12  ...             38                 4                 0   \n",
      "13  BL007-28   35.09  ...              9                 0                 0   \n",
      "14  BL007-31   31.05  ...             53                 5                 0   \n",
      "15  BL007-32   26.29  ...             45                 0                 0   \n",
      "16  BL007-33   34.74  ...             44                 7                 3   \n",
      "17  BL007-34   28.11  ...             48                 0                 0   \n",
      "18  BL007-35   32.31  ...             43                 4                 0   \n",
      "19  BL007-36   33.18  ...             40                 7                 1   \n",
      "20  BL007-39   28.48  ...             53                 0                 0   \n",
      "21  BL007-40   31.41  ...             35                 5                 0   \n",
      "22  BL007-42   28.81  ...             34                 1                 0   \n",
      "23  BL007-43   30.24  ...             45                 6                 0   \n",
      "24  BL007-44   30.96  ...             40                 4                 0   \n",
      "25  BL007-45   26.53  ...             40                 0                 0   \n",
      "26  BL007-46   33.78  ...             44                 7                 1   \n",
      "27  BL007-47   34.88  ...             44                 5                 0   \n",
      "28  BL007-48   35.65  ...             42                 7                 3   \n",
      "29  BL007-49   31.42  ...             39                 5                 0   \n",
      "30  BL007-50   32.07  ...             44                 2                 0   \n",
      "31  BL007-51   32.20  ...             39                 5                 0   \n",
      "32  BL007-52   73.18  ...             40                 5                 5   \n",
      "33  BL007-54   29.70  ...             39                 4                 0   \n",
      "34  BL007-56   27.35  ...             24                 0                 0   \n",
      "35  BL007-57   29.39  ...             42                 1                 0   \n",
      "36  BL007-59   28.67  ...             39                 1                 0   \n",
      "37  BL007-64   32.81  ...             40                 5                 0   \n",
      "38  BL007-66   31.39  ...             39                 3                 0   \n",
      "39  BL007-67   27.55  ...             37                 0                 0   \n",
      "\n",
      "    HS_days_above_27  HS_days_above_325  hw_HS_days_above_27  \\\n",
      "0                  9                  1                    6   \n",
      "1                 20                  0                    6   \n",
      "2                 12                  1                    6   \n",
      "3                 22                  1                    7   \n",
      "4                 11                  0                    5   \n",
      "5                  0                  0                    0   \n",
      "6                  0                  0                    0   \n",
      "7                 11                  1                    7   \n",
      "8                  5                  0                    5   \n",
      "9                 18                  4                    7   \n",
      "10                 6                  0                    4   \n",
      "11                 9                  0                    4   \n",
      "12                 9                  0                    6   \n",
      "13                 8                  3                    0   \n",
      "14                 9                  0                    6   \n",
      "15                 0                  0                    0   \n",
      "16                14                  3                    7   \n",
      "17                 3                  0                    2   \n",
      "18                 8                  0                    4   \n",
      "19                37                  6                    7   \n",
      "20                 2                  0                    2   \n",
      "21                11                  0                    6   \n",
      "22                 2                  0                    2   \n",
      "23                18                  0                    7   \n",
      "24                11                  0                    5   \n",
      "25                 0                  0                    0   \n",
      "26                11                  3                    7   \n",
      "27                20                  2                    7   \n",
      "28                26                  5                    7   \n",
      "29                 7                  0                    6   \n",
      "30                13                  0                    4   \n",
      "31                 7                  0                    5   \n",
      "32                29                 22                    5   \n",
      "33                 6                  0                    5   \n",
      "34                 2                  0                    0   \n",
      "35                 4                  0                    2   \n",
      "36                 2                  0                    2   \n",
      "37                 9                  2                    5   \n",
      "38                 5                  0                    4   \n",
      "39                 1                  0                    1   \n",
      "\n",
      "    hw_HS_days_above_325  pro_time_25  9th_HS_max  10th_HS_max  \n",
      "0                      1     0.298638       32.12        33.49  \n",
      "1                      0     0.669299       31.22        31.82  \n",
      "2                      1     0.201347       29.41        29.57  \n",
      "3                      1     0.646831       32.50        32.47  \n",
      "4                      0     0.555302       30.00        31.07  \n",
      "5                      0     0.000000         NaN          NaN  \n",
      "6                      0     0.044872       26.41        26.07  \n",
      "7                      1     0.327656       32.45        31.90  \n",
      "8                      0     0.132585       29.82        29.69  \n",
      "9                      3     0.228407       33.74        32.44  \n",
      "10                     0     0.244244       28.43        28.90  \n",
      "11                     0     0.483042       30.51        30.78  \n",
      "12                     0     0.230402       32.00        32.12  \n",
      "13                     0     0.689722         NaN          NaN  \n",
      "14                     0     0.322650       30.82        31.05  \n",
      "15                     0     0.034047       25.92        26.28  \n",
      "16                     3     0.449729       32.85        33.43  \n",
      "17                     0     0.097749       27.77        28.11  \n",
      "18                     0     0.177967       31.26        32.31  \n",
      "19                     5     0.960239       32.53        32.51  \n",
      "20                     0     0.078619       28.14        28.48  \n",
      "21                     0     0.320298       31.26        31.41  \n",
      "22                     0     0.075852       27.87        28.81  \n",
      "23                     0     0.426459       30.24        30.06  \n",
      "24                     0     0.242950       30.96        30.58  \n",
      "25                     0     0.109225       26.15        26.53  \n",
      "26                     3     0.281538       33.78        32.85  \n",
      "27                     2     0.543333       34.51        34.88  \n",
      "28                     5     0.590183       32.90        32.96  \n",
      "29                     0     0.371122       30.45        31.42  \n",
      "30                     0     0.514341       31.07        32.07  \n",
      "31                     0     0.182780       31.75        31.69  \n",
      "32                     5     0.366348       69.96        64.71  \n",
      "33                     0     0.200481       29.70        29.53  \n",
      "34                     0     0.174450         NaN          NaN  \n",
      "35                     0     0.111397       27.89        29.00  \n",
      "36                     0     0.097088       28.67        28.63  \n",
      "37                     2     0.251008       32.81        32.59  \n",
      "38                     0     0.172150       30.34        31.39  \n",
      "39                     0     0.030297       27.55        26.52  \n",
      "\n",
      "[40 rows x 29 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ch335s\\AppData\\Local\\Temp\\ipykernel_18080\\3006003413.py:68: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n"
     ]
    }
   ],
   "source": [
    "description_dfs = []\n",
    "\n",
    "\n",
    "# Iterate over each sensor\n",
    "for sensor_id in ID_list:\n",
    "    try:\n",
    "        df = pd.read_csv('Data/Household Indoor Sensor Measurement Data/'+sensor_id+'.csv')\n",
    "\n",
    "        # Convert field to datetime type\n",
    "        df['TIME'] = pd.to_datetime(df['TIME'])\n",
    "        df['TIME'] = df['TIME']\n",
    "\n",
    "        selected_sensor = df_ID_time[df_ID_time['ID'] == sensor_id]\n",
    "            \n",
    "        if pd.notnull(selected_sensor['Battery Charging Start Time']).any():\n",
    "            start_date = selected_sensor['Battery Charging Start Time'] + pd.Timedelta(hours=1)\n",
    "            # Delay 1 hour to allow the sensor settled in the indoor condition and improve the accuracy of indoor temperature measurements\n",
    "            end_date = selected_sensor['Battery Charging End Time']\n",
    "        else:\n",
    "            start_date = selected_sensor['Recording Start Time (Participant) ']\n",
    "            end_date = selected_sensor['Recording End Time (Participant) '] \n",
    "            \n",
    "        start_date_value = start_date.iloc[0]\n",
    "        end_date_value = end_date.iloc[0]\n",
    "\n",
    "        sliced_df = df[(df['TIME'] >= start_date_value) & (df['TIME'] <= end_date_value)]\n",
    "\n",
    "        # Extract the date, time, and hour from the timestamp\n",
    "        sliced_df['Date'] = sliced_df['TIME'].dt.date\n",
    "        sliced_df['Time'] = sliced_df['TIME'].dt.time\n",
    "        sliced_df['Hour'] = sliced_df['TIME'].dt.hour\n",
    "\n",
    "        # Temperature statistics\n",
    "        description = sliced_df['TEMP'].describe()\n",
    "        description_df = pd.DataFrame(description)\n",
    "        description_df = description_df.T\n",
    "        description_df.reset_index(drop=True, inplace=True)\n",
    "        description_df['ID'] = sensor_id\n",
    "\n",
    "        # Add heastress statistics in DataFrame\n",
    "        sliced_df['heatstress'] = sliced_df.apply(lambda row: calculate_heat_index(row['TEMP'], row['HUM']), axis=1)\n",
    "        max_heatstress = sliced_df['heatstress'].max()\n",
    "        min_heatstress = sliced_df['heatstress'].min()\n",
    "        \n",
    "        description_df['max_HS'] = max_heatstress\n",
    "        description_df['min_HS'] = min_heatstress\n",
    "        \n",
    "        # Add heat period days in DataFrame\n",
    "        # Calculate rolling mean over a 3-day window\n",
    "        rolling_mean = sliced_df['TEMP'].rolling(1440, center=True).mean()\n",
    "        # Define a threshold temperature for identifying heat periods\n",
    "        threshold_temp = 25\n",
    "        # Create a mask to identify heat periods where rolling mean is greater than threshold_temp\n",
    "        heat_period_mask = rolling_mean > threshold_temp\n",
    "\n",
    "        heat_periods = []\n",
    "        current_period = None\n",
    "        total_heat_period_duration = 0  \n",
    "\n",
    "        for i, is_heat_period in enumerate(heat_period_mask):\n",
    "            if is_heat_period:\n",
    "                if current_period is None:\n",
    "                    current_period = [i]\n",
    "                else:\n",
    "                    current_period.append(i)\n",
    "            elif current_period is not None:\n",
    "                if len(current_period) > 4380:  # Check if it's more than 3 days\n",
    "                    start_date = df['TIME'].iloc[current_period[0]].strftime('%Y-%m-%d')\n",
    "                    end_date = df['TIME'].iloc[current_period[-1]].strftime('%Y-%m-%d')\n",
    "                    start_date_timestamp = pd.to_datetime(start_date)\n",
    "                    end_date_timestamp = pd.to_datetime(end_date)\n",
    "                    period_duration = (end_date_timestamp - start_date_timestamp).days\n",
    "                    total_heat_period_duration += period_duration  # Accumulate the duration\n",
    "\n",
    "                current_period = None\n",
    "\n",
    "        description_df['heat_period'] = total_heat_period_duration\n",
    "        \n",
    "        # Add heat stress index period days in DataFrame\n",
    "        heatstress_rolling_mean = sliced_df['heatstress'].rolling(1440, center=True).mean()\n",
    "        # Define a threshold temperature for identifying heat periods\n",
    "        threshold_temp = 27\n",
    "        # Create a mask to identify heat periods where rolling mean is greater than threshold_temp\n",
    "        heatstress_period_mask = heatstress_rolling_mean > threshold_temp\n",
    "        \n",
    "        heat_periods = []\n",
    "        current_period = None\n",
    "        total_heat_period_duration = 0  # To accumulate the duration of heat periods\n",
    "\n",
    "        for i, is_heat_period in enumerate(heatstress_period_mask):\n",
    "            if is_heat_period:\n",
    "                if current_period is None:\n",
    "                    current_period = [i]\n",
    "                else:\n",
    "                    current_period.append(i)\n",
    "            elif current_period is not None:\n",
    "                if len(current_period) > 4380:  # Check if it's more than 3 days\n",
    "                    start_date = df['TIME'].iloc[current_period[0]].strftime('%Y-%m-%d')\n",
    "                    end_date = df['TIME'].iloc[current_period[-1]].strftime('%Y-%m-%d')\n",
    "                    start_date_timestamp = pd.to_datetime(start_date)\n",
    "                    end_date_timestamp = pd.to_datetime(end_date)\n",
    "                    period_duration = (end_date_timestamp - start_date_timestamp).days\n",
    "                    total_heat_period_duration += period_duration  # Accumulate the duration\n",
    "                current_period = None\n",
    "\n",
    "        # Heat stress index sustained periods\n",
    "        description_df['heatstress_period'] = total_heat_period_duration\n",
    "\n",
    "        # Add monitoring days for each sensor in DataFrame\n",
    "        periods = sliced_df['Date'].iloc[-1] - sliced_df['Date'].iloc[0]\n",
    "        periods = periods.days\n",
    "        description_df['recored_periods'] = periods\n",
    "        \n",
    "        # Add relative humidity statistics\n",
    "        description_df['max_hum'] = sliced_df['HUM'].max()\n",
    "        \n",
    "        # Proportion of monitoring time when relative humidity exceed 75%\n",
    "        hum_over_75 = len(sliced_df[sliced_df['HUM'] > 75])\n",
    "        per_hum_over_75 = hum_over_75/len(sliced_df)*100\n",
    "        description_df['perc_hum_75'] = round(per_hum_over_75,2)\n",
    "\n",
    "        # Days with maximum temperature over 25 degrees    \n",
    "        daily_max_temp = sliced_df.groupby(sliced_df['Date'])['TEMP'].max()\n",
    "        days_above_25 = (daily_max_temp > 25).sum()\n",
    "        description_df['days_above_25'] = days_above_25\n",
    "        \n",
    "        # Days with maximum temperature over 27 degrees            \n",
    "        daily_max_temp = sliced_df.groupby(sliced_df['Date'])['TEMP'].max()\n",
    "        days_above_27 = (daily_max_temp > 27).sum()\n",
    "        description_df['days_above_27'] = days_above_27\n",
    "        \n",
    "        # Days with maximum temperature over 30 degrees           \n",
    "        days_above_30 = (daily_max_temp > 30).sum()\n",
    "        description_df['days_above_30'] = days_above_30\n",
    "\n",
    "        # Days with maximum temperature over 20 degrees\n",
    "        days_above_20 = (daily_max_temp > 20).sum()-1 # in case the number are more than monitoring days because of the segmentation\n",
    "        description_df['days_above_20'] = days_above_20\n",
    "        \n",
    "        # During heat wave periods, days with temperature exceeding 27 degree\n",
    "        heatwave_start_date = pd.to_datetime('2023-09-04').date()\n",
    "        heatwave_end_date = pd.to_datetime('2023-09-10').date()\n",
    "        df_hw = sliced_df[(sliced_df['Date'] >= heatwave_start_date) & (sliced_df['Date'] <= heatwave_end_date)]\n",
    "        daily_max_temp_hw = df_hw.groupby(df_hw['Date'])['TEMP'].max()\n",
    "        \n",
    "        days_above_27_hw = (daily_max_temp_hw > 27).sum()\n",
    "        description_df['hw_days_above_27'] = days_above_27_hw\n",
    "        \n",
    "        # During heat wave periods, days with heat stress index exceeding 30 degree        \n",
    "        days_above_30_hw = (daily_max_temp_hw > 30).sum()\n",
    "        description_df['hw_days_above_30'] = days_above_30_hw\n",
    "         \n",
    "        # During the whole  monitoring days, days with heat stress index exceeding 27 degree   \n",
    "        daily_max_HS = sliced_df.groupby(sliced_df['Date'])['heatstress'].max()\n",
    "        HS_days_above_27 = (daily_max_HS > 27).sum()\n",
    "        description_df['HS_days_above_27'] = HS_days_above_27\n",
    "        \n",
    "        # During the whole  monitoring days, days with heat stress index exceeding 32.5 degree    \n",
    "        HS_days_above_325 = (daily_max_HS >= 32.5).sum()\n",
    "        description_df['HS_days_above_325'] = HS_days_above_325\n",
    "       \n",
    "        # During heat wave periods, days with heat stress index exceeding 27 degree \n",
    "        daily_max_HS_hw = df_hw.groupby(df_hw['Date'])['heatstress'].max()\n",
    "        \n",
    "        HS_days_above_27_hw = (daily_max_HS_hw > 27).sum()\n",
    "        description_df['hw_HS_days_above_27'] = HS_days_above_27_hw\n",
    "        \n",
    "        # During heat wave periods, days with heat stress index exceeding 32.5 degree \n",
    "        HS_days_above_325_hw = (daily_max_HS_hw >= 32.5).sum()\n",
    "        description_df['hw_HS_days_above_325'] = HS_days_above_325_hw\n",
    "        \n",
    "        # Calculate the proportion of monitoring time with temperature over 25 degrees\n",
    "        time_25=sliced_df[sliced_df['TEMP']>25]['TEMP'].count()\n",
    "        time_all=sliced_df['TEMP'].count()\n",
    "        description_df['pro_time_25']=time_25/time_all\n",
    "        \n",
    "        # Calculate the max heat stress index on 9th Sep and 10th Sep\n",
    "        nineth_date = pd.to_datetime('2023-09-09').date()\n",
    "        tenth_date = pd.to_datetime('2023-09-10').date()\n",
    "        \n",
    "        nineth_max_hs=df_hw[df_hw['Date']==nineth_date]['heatstress'].max()\n",
    "        tenth_max_hs=df_hw[df_hw['Date']==tenth_date]['heatstress'].max()\n",
    "        \n",
    "        description_df['9th_HS_max'] = nineth_max_hs\n",
    "        description_df['10th_HS_max'] = tenth_max_hs\n",
    "    \n",
    "        \n",
    "\n",
    "        # Append the description DataFrame to the list\n",
    "        description_dfs.append(description_df)\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"Error in {str(e)}\")\n",
    "\n",
    "# Concatenate the description DataFrames for all sensors into a single DataFrame\n",
    "description_df = pd.concat(description_dfs, ignore_index=True)\n",
    "\n",
    "\n",
    "print(description_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5b9b3d13-b642-40d1-84a7-2bdba6c06ecb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c1e4da76-e08c-411c-b7ef-75865cb929fe",
   "metadata": {
    "tags": []
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>ID</th>\n",
       "      <th>max_HS</th>\n",
       "      <th>min_HS</th>\n",
       "      <th>heat_period</th>\n",
       "      <th>heatstress_period</th>\n",
       "      <th>recored_periods</th>\n",
       "      <th>max_hum</th>\n",
       "      <th>perc_hum_75</th>\n",
       "      <th>days_above_25</th>\n",
       "      <th>days_above_27</th>\n",
       "      <th>days_above_30</th>\n",
       "      <th>days_above_20</th>\n",
       "      <th>hw_days_above_27</th>\n",
       "      <th>hw_days_above_30</th>\n",
       "      <th>HS_days_above_27</th>\n",
       "      <th>HS_days_above_325</th>\n",
       "      <th>hw_HS_days_above_27</th>\n",
       "      <th>hw_HS_days_above_325</th>\n",
       "      <th>pro_time_25</th>\n",
       "      <th>9th_HS_max</th>\n",
       "      <th>10th_HS_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>54906.0</td>\n",
       "      <td>24.414297</td>\n",
       "      <td>2.019142</td>\n",
       "      <td>20.52</td>\n",
       "      <td>23.05</td>\n",
       "      <td>23.97</td>\n",
       "      <td>25.39</td>\n",
       "      <td>30.25</td>\n",
       "      <td>BL007-02</td>\n",
       "      <td>33.49</td>\n",
       "      <td>20.41</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "      <td>80.33</td>\n",
       "      <td>2.49</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.298638</td>\n",
       "      <td>32.12</td>\n",
       "      <td>33.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70281.0</td>\n",
       "      <td>25.562214</td>\n",
       "      <td>1.266366</td>\n",
       "      <td>20.54</td>\n",
       "      <td>24.76</td>\n",
       "      <td>25.46</td>\n",
       "      <td>26.32</td>\n",
       "      <td>29.19</td>\n",
       "      <td>BL007-04</td>\n",
       "      <td>31.92</td>\n",
       "      <td>20.14</td>\n",
       "      <td>29</td>\n",
       "      <td>8</td>\n",
       "      <td>49</td>\n",
       "      <td>71.65</td>\n",
       "      <td>0.00</td>\n",
       "      <td>42</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.669299</td>\n",
       "      <td>31.22</td>\n",
       "      <td>31.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56718.0</td>\n",
       "      <td>23.537636</td>\n",
       "      <td>1.763632</td>\n",
       "      <td>20.00</td>\n",
       "      <td>22.16</td>\n",
       "      <td>23.37</td>\n",
       "      <td>24.70</td>\n",
       "      <td>30.70</td>\n",
       "      <td>BL007-07</td>\n",
       "      <td>33.19</td>\n",
       "      <td>19.63</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>76.94</td>\n",
       "      <td>0.02</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0.201347</td>\n",
       "      <td>29.41</td>\n",
       "      <td>29.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56919.0</td>\n",
       "      <td>25.728024</td>\n",
       "      <td>1.696041</td>\n",
       "      <td>22.02</td>\n",
       "      <td>24.27</td>\n",
       "      <td>25.77</td>\n",
       "      <td>26.90</td>\n",
       "      <td>30.40</td>\n",
       "      <td>BL007-09</td>\n",
       "      <td>32.50</td>\n",
       "      <td>21.79</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>69.69</td>\n",
       "      <td>0.00</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0.646831</td>\n",
       "      <td>32.50</td>\n",
       "      <td>32.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64961.0</td>\n",
       "      <td>25.067441</td>\n",
       "      <td>1.384199</td>\n",
       "      <td>18.98</td>\n",
       "      <td>24.26</td>\n",
       "      <td>25.23</td>\n",
       "      <td>25.83</td>\n",
       "      <td>28.86</td>\n",
       "      <td>BL007-13</td>\n",
       "      <td>31.07</td>\n",
       "      <td>18.40</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>46</td>\n",
       "      <td>74.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>35</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.555302</td>\n",
       "      <td>30.00</td>\n",
       "      <td>31.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     count       mean       std    min    25%    50%    75%    max        ID  \\\n",
       "0  54906.0  24.414297  2.019142  20.52  23.05  23.97  25.39  30.25  BL007-02   \n",
       "1  70281.0  25.562214  1.266366  20.54  24.76  25.46  26.32  29.19  BL007-04   \n",
       "2  56718.0  23.537636  1.763632  20.00  22.16  23.37  24.70  30.70  BL007-07   \n",
       "3  56919.0  25.728024  1.696041  22.02  24.27  25.77  26.90  30.40  BL007-09   \n",
       "4  64961.0  25.067441  1.384199  18.98  24.26  25.23  25.83  28.86  BL007-13   \n",
       "\n",
       "   max_HS  min_HS  heat_period  heatstress_period  recored_periods  max_hum  \\\n",
       "0   33.49   20.41            8                  6               40    80.33   \n",
       "1   31.92   20.14           29                  8               49    71.65   \n",
       "2   33.19   19.63            7                  4               41    76.94   \n",
       "3   32.50   21.79           26                  9               40    69.69   \n",
       "4   31.07   18.40           18                  7               46    74.35   \n",
       "\n",
       "   perc_hum_75  days_above_25  days_above_27  days_above_30  days_above_20  \\\n",
       "0         2.49             19              7              1             40   \n",
       "1         0.00             42             12              0             49   \n",
       "2         0.02             18              7              1             41   \n",
       "3         0.00             30             18              1             40   \n",
       "4         0.00             35              6              0             46   \n",
       "\n",
       "   hw_days_above_27  hw_days_above_30  HS_days_above_27  HS_days_above_325  \\\n",
       "0                 5                 1                 9                  1   \n",
       "1                 5                 0                20                  0   \n",
       "2                 5                 1                12                  1   \n",
       "3                 7                 1                22                  1   \n",
       "4                 4                 0                11                  0   \n",
       "\n",
       "   hw_HS_days_above_27  hw_HS_days_above_325  pro_time_25  9th_HS_max  \\\n",
       "0                    6                     1     0.298638       32.12   \n",
       "1                    6                     0     0.669299       31.22   \n",
       "2                    6                     1     0.201347       29.41   \n",
       "3                    7                     1     0.646831       32.50   \n",
       "4                    5                     0     0.555302       30.00   \n",
       "\n",
       "   10th_HS_max  \n",
       "0        33.49  \n",
       "1        31.82  \n",
       "2        29.57  \n",
       "3        32.47  \n",
       "4        31.07  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5f8272-095e-4302-8907-eb4af33e6d69",
   "metadata": {},
   "source": [
    "# Data derived evidence for the TBIJ report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f299061-f22d-47e1-b9e9-f69d34248eaf",
   "metadata": {},
   "source": [
    "## 1. All sensors achieved 25C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73c631bf-edd8-474f-a0a5-01fde7becd1a",
   "metadata": {},
   "source": [
    "Every single home monitored went over the 25C limit (the World Health Organization maximum recommended indoor temperature for London)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d8c3600-15d7-4c5e-9587-d880c7065156",
   "metadata": {},
   "source": [
    "All the sensors achieved a temperature of 25°C during the monitoring periods, except for sensor BL007-14, which recorded temperature data for only 2 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "298fb3f8-3886-49a0-b534-cbb6f133088b",
   "metadata": {
    "tags": []
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>ID</th>\n",
       "      <th>max_HS</th>\n",
       "      <th>min_HS</th>\n",
       "      <th>heat_period</th>\n",
       "      <th>heatstress_period</th>\n",
       "      <th>recored_periods</th>\n",
       "      <th>max_hum</th>\n",
       "      <th>perc_hum_75</th>\n",
       "      <th>days_above_25</th>\n",
       "      <th>days_above_27</th>\n",
       "      <th>days_above_30</th>\n",
       "      <th>days_above_20</th>\n",
       "      <th>hw_days_above_27</th>\n",
       "      <th>hw_days_above_30</th>\n",
       "      <th>HS_days_above_27</th>\n",
       "      <th>HS_days_above_325</th>\n",
       "      <th>hw_HS_days_above_27</th>\n",
       "      <th>hw_HS_days_above_325</th>\n",
       "      <th>pro_time_25</th>\n",
       "      <th>9th_HS_max</th>\n",
       "      <th>10th_HS_max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1905.0</td>\n",
       "      <td>22.838919</td>\n",
       "      <td>0.794798</td>\n",
       "      <td>21.45</td>\n",
       "      <td>22.16</td>\n",
       "      <td>22.95</td>\n",
       "      <td>23.49</td>\n",
       "      <td>24.27</td>\n",
       "      <td>BL007-14</td>\n",
       "      <td>24.4</td>\n",
       "      <td>21.34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>65.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count       mean       std    min    25%    50%    75%    max        ID  \\\n",
       "5  1905.0  22.838919  0.794798  21.45  22.16  22.95  23.49  24.27  BL007-14   \n",
       "\n",
       "   max_HS  min_HS  heat_period  heatstress_period  recored_periods  max_hum  \\\n",
       "5    24.4   21.34            0                  0                2    65.52   \n",
       "\n",
       "   perc_hum_75  days_above_25  days_above_27  days_above_30  days_above_20  \\\n",
       "5          0.0              0              0              0              2   \n",
       "\n",
       "   hw_days_above_27  hw_days_above_30  HS_days_above_27  HS_days_above_325  \\\n",
       "5                 0                 0                 0                  0   \n",
       "\n",
       "   hw_HS_days_above_27  hw_HS_days_above_325  pro_time_25  9th_HS_max  \\\n",
       "5                    0                     0          0.0         NaN   \n",
       "\n",
       "   10th_HS_max  \n",
       "5          NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description_df[description_df['max']<25]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57685dfb-f335-4eac-b43c-1fc404e92e13",
   "metadata": {},
   "source": [
    "## 2. Proportion of households over 27C and 30C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a4f0ea-6851-4af7-9a07-70af45bc0bc9",
   "metadata": {},
   "source": [
    "During the mini-heatwave from 4 to 10 September, 85% of participating households recorded temperatures over 27C. Ten of the homes experienced temperatures over 30C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "404d9c55-209a-4fb9-aa32-3cb48d06cadb",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n"
     ]
    }
   ],
   "source": [
    "count_rows_above_27 =(description_df['hw_days_above_27']!=0).sum()\n",
    "print(count_rows_above_27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f7ccb26-0a7b-4ef3-8f84-54e9e3067c33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37\n"
     ]
    }
   ],
   "source": [
    "count_rows_heatwave = description_df['9th_HS_max'].notnull().sum()\n",
    "print(count_rows_heatwave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3eb1215e-2928-47ad-b68c-bb80ff9150cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "83.78% of participating households recorded temperatures over 27C\n"
     ]
    }
   ],
   "source": [
    "proportion = round(count_rows_above_27/count_rows_heatwave*100,2)\n",
    "print(f'{proportion}% of participating households recorded temperatures over 27C')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9157fa4-43e4-4af9-88b7-9d1bd9d66d51",
   "metadata": {},
   "source": [
    "Note that in the report, we have rounded the proportion to 85% to present a more straightforward number for storytelling purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f02d45cc-cf36-466e-960c-cf67688fe712",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10% of homes experienced temperatures over 30C.\n"
     ]
    }
   ],
   "source": [
    "count_rows_above_30 =(description_df['hw_days_above_30']!=0).sum()\n",
    "print(f'{count_rows_above_30}% of homes experienced temperatures over 30C.')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9548ac6e-f4fd-4b62-98ce-81a1f862d3f0",
   "metadata": {},
   "source": [
    "## 3. Longest sustained heat period"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c934d5-972f-48c5-85aa-0a1b7a1a6681",
   "metadata": {},
   "source": [
    "One household saw temperatures remain over the 25C threshold, even at night, for over three weeks. It logged temperatures averaging 27C, despite this not being an especially hot summer. Other homes saw continuous heat periods for 15 and 16 days in a row. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c016d66-a66b-48c2-8d64-15471085e024",
   "metadata": {},
   "source": [
    "_Please refer to the plots of sensor 'BL007-36'._"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228caf39-bbca-426e-99fe-731b2186e452",
   "metadata": {},
   "source": [
    "## 4. Indoor and outdoor temperature gap"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c694ba51-c724-49e2-bd29-f9a12dd91695",
   "metadata": {},
   "source": [
    "Some homes were as much as 8-10C hotter at night over the full monitoring period than the outside air temperature, according to readings at local weather stations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7c76cf4-8bba-4558-a0d3-504458a9df19",
   "metadata": {},
   "source": [
    "_Please refer to the Jupyter notebook titled 'TemperatureDifference.ipnyb' for more information._"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3054d1-a06a-4001-9b37-79bd07d9f3c2",
   "metadata": {},
   "source": [
    "## 5. On 10th Sep, households experienced heat stress index over 27C and 32.5C"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f4f64d-1721-4a36-ac73-71969faf8915",
   "metadata": {},
   "source": [
    "The Heat Stress Index takes into account relative humidity as well as temperature and describes how temperatures are likely to be experienced by human bodies. On 10 September, the hottest day of 2023 in England, 25 of the participating homes experienced conditions that felt like 27C (described as ‘Caution’ in the Heat Stress Index), and eight experienced conditions that felt like 32.5C (described as ‘Extreme Caution’ in the Heat Stress Index, with increased risk of heat exhaustion and heatstroke).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2caa1af5-99ed-45d7-9e98-04b4161ad6a5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25 households experienced conditions that felt like 27C\n"
     ]
    }
   ],
   "source": [
    "tehth_max = description_df[(description_df['10th_HS_max'] >= 27) & (description_df['10th_HS_max'] < 32.5)]\n",
    "number = len(tehth_max)\n",
    "print(f'{number} households experienced conditions that felt like 27C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8289e34-5bd2-48e2-b15a-4c51fe19ff5f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 households experienced conditions that felt like 32.5C\n"
     ]
    }
   ],
   "source": [
    "tehth_max = description_df[description_df['10th_HS_max'] >= 32.5]\n",
    "number = len(tehth_max)\n",
    "print(f'{number} households experienced conditions that felt like 32.5C')"
   ]
  }
 ],
 "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",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}