Ready_to_use_Weather.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34m1-1-16_5-31-17_Weather\u001b[m\u001b[m/\r\n",
      "Untitled.ipynb\r\n",
      "air_store_info_with_nearest_active_station.csv\r\n",
      "weather_ready_to_use.csv\r\n",
      "weather_stations.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "air = pd.read_csv('air_store_info_with_nearest_active_station.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "air_info = pd.read_csv('../air_store_info.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dict1 = air.set_index('air_store_id')['station_id'].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "air_88ca84051ba95339 tokyo__haneda-kana__haneda.csv\n",
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      "air_fef9ccb3ba0da2f7 hyogo__himeji-kana__gimpe.csv\n",
      "air_bdd32aa407c16335 fukuoka__fukuoka-kana__fukuoka.csv\n",
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      "air_9aa92007e3628dbc osaka__osaka-kana__osaka.csv\n",
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      "air_43d577e0c9460e64 hyogo__nishinomiya-kana__nishinomiya.csv\n",
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      "air_b45b8e456f53942a hokkaido_ishikari__sapporo-katakana__satporo.csv\n",
      "air_0a74a5408a0b8642 hokkaido_ishikari__sapporo-katakana__satporo.csv\n",
      "air_890d7e28e8eaaa11 tokyo__tokyo-kana__tonokyo.csv\n",
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      "air_f4936b91c9addbf0 tokyo__tokyo-kana__tonokyo.csv\n",
      "air_47beaffd3806c979 tokyo__tokyo-kana__tonokyo.csv\n",
      "air_edd5e3d696a5811b tokyo__tokyo-kana__tonokyo.csv\n",
      "air_6b65745d432fd77f tokyo__tokyo-kana__tonokyo.csv\n",
      "air_9bf595ef095572fb tokyo__tokyo-kana__tonokyo.csv\n",
      "air_764f71040a413d4d tokyo__tokyo-kana__tonokyo.csv\n",
      "air_10bbe8acd943d8f6 tokyo__tokyo-kana__tonokyo.csv\n",
      "air_7514d90009613cd6 hokkaido_ishikari__sapporo-katakana__satporo.csv\n",
      "air_c8fe396d6c46275d hokkaido_ishikari__sapporo-katakana__satporo.csv\n"
     ]
    }
   ],
   "source": [
    "calendar_date = []\n",
    "low_temperature =[]\n",
    "avg_temperature = []\n",
    "high_temperature = []\n",
    "precipitation = []\n",
    "air_id = []\n",
    "for i in air_info.air_store_id.values:\n",
    "    print i,dict1[i] + '.csv'\n",
    "    k = pd.read_csv('1-1-16_5-31-17_Weather/'+dict1[i] + '.csv')\n",
    "    for enu,j in enumerate(k.calendar_date.values):\n",
    "        calendar_date.append(j)\n",
    "        low_temperature.append(k.low_temperature.values[enu])\n",
    "        avg_temperature.append(k.avg_temperature.values[enu])\n",
    "        high_temperature.append(k.high_temperature.values[enu])\n",
    "        precipitation.append(k.precipitation[enu])\n",
    "        air_id.append(i)\n",
    "        \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame({'air_store_id':air_id,'precipitation':precipitation,'visit_date':calendar_date,'low_temperature':low_temperature,'avg_temperature':avg_temperature,'high_temperature':high_temperature})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.to_csv('weather_ready_to_use.csv',index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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