data_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os, glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_root = 'resatelliteraindataforicici/'\n",
    "data_rain_root = 'resatelliteraindataforicici_rain/'\n",
    "save_root = 'resatelliteraindataforicici/out/'\n",
    "csv_files = glob.glob(os.path.join(data_root,'*.csv'))\n",
    "csv_files_rain = glob.glob(os.path.join(data_rain_root,'*.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df_from_csv(filepath, save_res = False):\n",
    "    filename = os.path.basename(filepath)\n",
    "    name,ex = os.path.splitext(filename)\n",
    "#     loc = name.split('_')[0]\n",
    "    data = pd.read_csv(filepath, index_col=0, parse_dates=True)\n",
    "    data_max = data.resample('D').max().round(2)\n",
    "    data_max.columns = ['RH_max','T_max']\n",
    "    data_min = data.resample('D').min().round(2)\n",
    "    data_min.columns = ['RH_min','T_min']\n",
    "    data_ = pd.concat((data_min,data_max),axis=1)\n",
    "    data_ = data_.iloc[:,[0,2,1,3]]\n",
    "#     data['date'] = data.index.date\n",
    "    globals()[name] = data_\n",
    "    \n",
    "    if save_res:\n",
    "        filename_out = '_'.join(name.split('_')[:2]) + ex\n",
    "        filepath_out = os.path.join(save_root, filename_out)\n",
    "        if not os.path.exists(save_root):\n",
    "            os.makedirs(save_root)\n",
    "        data_.to_csv(filepath_out, index_label='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for filepath in csv_files:\n",
    "    get_df_from_csv(filepath, save_res=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for filepath in csv_files_rain:\n",
    "    filename = os.path.basename(filepath)\n",
    "    name,ex = os.path.splitext(filename)\n",
    "    data = pd.read_csv(filepath, index_col=0, parse_dates=True).round(3)\n",
    "    globals()[name] = data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "Kamuthi_weather = Kamuthi_weather_revised1.join(Kamuthi)\n",
    "Aruppukottai_weather = Aruppukottai_weather_revised1.join(Aruppukottai)\n",
    "Kovilpatti_weather = Kovilpatti_weather_revised1.join(Kovilpatti)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "Kamuthi_weather.to_csv('resatelliteraindataforicici/Kamuthi_weather.csv', index_label='Date')\n",
    "Aruppukottai_weather.to_csv('resatelliteraindataforicici/Aruppukottai_weather.csv', index_label='Date')\n",
    "Kovilpatti_weather.to_csv('resatelliteraindataforicici/Kovilpatti_weather.csv', index_label='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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