pandas_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We learn how to process csv files using command lines, import to the database, now we willlearn how to manipulate the data in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import psycopg2 as pg\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Connect to an existing database\n",
    "conn = pg.connect(dbname=\"postgres\", host=\"db.dssg.io\", user=\"qhan\", password=\"dssg-2017\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "conn.cursor will return a cursor object, you can use this cursor to perform queries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection success!\n"
     ]
    }
   ],
   "source": [
    "cur = conn.cursor()\n",
    "print \"Connection success!\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can execute the SQL queries and fetch the results "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "script = \"select * from qhan.weather limit 1000;\"\n",
    "cur.execute(script)\n",
    "results = cur.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'N', None), ('US1IAUN0001', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'N', None), ('ASN00073151', datetime.date(2016, 4, 16), 'TMAX', 250, None, None, 'a', None), ('ASN00073151', datetime.date(2016, 4, 16), 'TMIN', 129, None, None, 'a', None), ('ASN00073151', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'a', None), ('US1GALW0015', datetime.date(2016, 4, 16), 'RAIN', 71, None, None, 'N', None), ('US1GAMA0022', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'N', None), ('US1GAMA0022', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'N', None), ('US1IATY0002', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'N', None), ('US1IATY0002', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'N', None), ('US1GAHL0001', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'N', None), ('ASN00072012', datetime.date(2016, 4, 16), 'RAIN', 2, None, None, 'a', None), ('US1GACT0059', datetime.date(2016, 4, 16), 'RAIN', 3, None, None, 'N', None), ('US1DESS0016', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'N', None), ('US1DESS0016', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'N', None), ('US1FLHN0011', datetime.date(2016, 4, 16), 'RAIN', 142, None, None, 'N', None), ('ASN00033268', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'a', None), ('US1FLMT0019', datetime.date(2016, 4, 16), 'RAIN', 99, None, None, 'N', None), ('CA002303093', datetime.date(2016, 4, 16), 'TMAX', -176, None, None, 'C', None), ('CA002303093', datetime.date(2016, 4, 16), 'TMIN', -207, None, None, 'C', None), ('CA002303093', datetime.date(2016, 4, 16), 'TAVG', -192, None, None, 'C', None), ('US1COLR0495', datetime.date(2016, 4, 16), 'RAIN', 48, None, None, 'N', None), ('US1COLR0495', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'N', None), ('US1COLR0495', datetime.date(2016, 4, 16), 'SNWD', 0, None, None, 'N', None), ('US1COLR0495', datetime.date(2016, 4, 16), 'WESD', 0, None, None, 'N', None), ('US1COLR0495', datetime.date(2016, 4, 16), 'WESF', 0, None, None, 'N', None), ('US1COGL0015', datetime.date(2016, 4, 16), 'RAIN', 198, None, None, 'N', None), ('US1COGL0015', datetime.date(2016, 4, 16), 'SNOW', 318, None, None, 'N', None), ('US1COGR0052', datetime.date(2016, 4, 16), 'RAIN', 201, None, None, 'N', None), ('US1COGR0052', datetime.date(2016, 4, 16), 'SNOW', 165, None, None, 'N', None), ('US1COGR0052', datetime.date(2016, 4, 16), 'SNWD', 165, None, None, 'N', None), ('US1COGR0052', datetime.date(2016, 4, 16), 'WESF', 196, None, None, 'N', None), ('USW00053120', datetime.date(2016, 4, 16), 'TMAX', 261, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'TMIN', 56, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'SNWD', 0, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'AWND', 35, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'WDF2', 260, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'WDF5', 270, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'WSF2', 94, None, None, 'W', None), ('USW00053120', datetime.date(2016, 4, 16), 'WSF5', 121, None, None, 'W', None), ('UY000086565', datetime.date(2016, 4, 16), 'TMAX', 252, None, None, 'S', None), ('UY000086565', datetime.date(2016, 4, 16), 'RAIN', 1905, None, None, 'S', None), ('UY000086565', datetime.date(2016, 4, 16), 'TAVG', 207, 'H', None, 'S', None), ('UZM00038149', datetime.date(2016, 4, 16), 'TMAX', 302, None, None, 'S', None), ('UZM00038149', datetime.date(2016, 4, 16), 'TMIN', 160, None, None, 'S', None), ('UZM00038149', datetime.date(2016, 4, 16), 'TAVG', 214, 'H', None, 'S', None), ('US1COEP0180', datetime.date(2016, 4, 16), 'RAIN', 46, None, None, 'N', None), ('US1COEP0180', datetime.date(2016, 4, 16), 'SNOW', 51, None, None, 'N', None), ('US1COEP0180', datetime.date(2016, 4, 16), 'SNWD', 51, None, None, 'N', None), ('USW00014828', datetime.date(2016, 4, 16), 'TMAX', 189, None, None, '7', '0700'), ('USW00014828', datetime.date(2016, 4, 16), 'TMIN', -17, None, None, '7', '0700'), ('USW00014828', datetime.date(2016, 4, 16), 'TOBS', 17, None, None, '7', '0700'), ('USW00014828', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, '7', '0700'), ('USW00014828', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, '7', None), ('USW00014828', datetime.date(2016, 4, 16), 'SNWD', 0, None, None, '7', '0700'), ('USW00014829', datetime.date(2016, 4, 16), 'TMAX', 250, None, None, 'W', '2400'), ('USW00014829', datetime.date(2016, 4, 16), 'TMIN', 56, None, None, 'W', '2400'), ('USW00014829', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'W', '2400'), ('USW00014829', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'W', None), ('USW00014829', datetime.date(2016, 4, 16), 'SNWD', 0, None, None, 'W', None), ('USW00014829', datetime.date(2016, 4, 16), 'AWND', 30, None, None, 'W', None), ('USW00014829', datetime.date(2016, 4, 16), 'WDF2', 110, None, None, 'W', None), ('USW00014829', datetime.date(2016, 4, 16), 'WDF5', 130, None, None, 'W', None), ('USW00014829', datetime.date(2016, 4, 16), 'WSF2', 67, None, None, 'W', None), ('USW00014829', datetime.date(2016, 4, 16), 'WSF5', 81, None, None, 'W', None), ('USW00025506', datetime.date(2016, 4, 16), 'TMAX', 100, None, None, 'W', '2400'), ('USW00025506', datetime.date(2016, 4, 16), 'TMIN', 11, None, None, 'W', '2400'), ('USW00025506', datetime.date(2016, 4, 16), 'RAIN', 0, 'T', None, 'W', '2400'), ('USW00025506', datetime.date(2016, 4, 16), 'AWND', 29, None, None, 'W', None), ('USW00025506', datetime.date(2016, 4, 16), 'PGTM', 1553, None, None, 'W', None), ('USW00025506', datetime.date(2016, 4, 16), 'WDF2', 90, None, None, 'W', None), ('USW00025506', datetime.date(2016, 4, 16), 'WDF5', 100, None, None, 'W', None), ('USW00025506', datetime.date(2016, 4, 16), 'WSF2', 81, None, None, 'W', None), ('USW00025506', datetime.date(2016, 4, 16), 'WSF5', 94, None, None, 'W', None), ('US1COEP0037', datetime.date(2016, 4, 16), 'RAIN', 41, None, None, 'N', None), ('US1COEP0037', datetime.date(2016, 4, 16), 'SNOW', 74, None, None, 'N', None), ('US1COEP0037', datetime.date(2016, 4, 16), 'SNWD', 76, None, None, 'N', None), ('US1COEP0037', datetime.date(2016, 4, 16), 'WESF', 41, None, None, 'N', None), ('ASN00066158', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'a', None), ('USW00013968', datetime.date(2016, 4, 16), 'TMAX', 211, None, None, 'W', '2400'), ('USW00013968', datetime.date(2016, 4, 16), 'TMIN', 161, None, None, 'W', '2400'), ('USW00013968', datetime.date(2016, 4, 16), 'RAIN', 0, 'T', None, 'W', '2400'), ('USW00013968', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'W', None), ('USW00013968', datetime.date(2016, 4, 16), 'SNWD', 0, None, None, 'W', None), ('USW00013968', datetime.date(2016, 4, 16), 'AWND', 69, None, None, 'W', None), ('USW00013968', datetime.date(2016, 4, 16), 'TAVG', 183, 'H', None, 'S', None), ('USW00013968', datetime.date(2016, 4, 16), 'WDF2', 120, None, None, 'W', None), ('USW00013968', datetime.date(2016, 4, 16), 'WDF5', 150, None, None, 'W', None), ('USW00013968', datetime.date(2016, 4, 16), 'WSF2', 103, None, None, 'W', None), ('USW00013968', datetime.date(2016, 4, 16), 'WSF5', 139, None, None, 'W', None), ('USW00003728', datetime.date(2016, 4, 16), 'TMAX', 201, None, None, 'R', None), ('USW00003728', datetime.date(2016, 4, 16), 'TMIN', 94, None, None, 'R', None), ('USW00003728', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'R', None), ('ASN00032031', datetime.date(2016, 4, 16), 'RAIN', 354, None, None, 'a', None), ('USW00012975', datetime.date(2016, 4, 16), 'TMAX', 261, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'TMIN', 189, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'RAIN', 8, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'SNWD', 0, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'AWND', 68, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'WDF2', 110, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'WDF5', 120, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'WSF2', 125, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'WSF5', 179, None, None, 'W', None), ('USW00012975', datetime.date(2016, 4, 16), 'WT01', 1, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'TMAX', 306, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'TMIN', 128, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'RAIN', 0, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'SNOW', 0, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'SNWD', 0, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'AWND', 46, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'WDF2', 90, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'WDF5', 80, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'WSF2', 107, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'WSF5', 152, None, None, 'W', None), ('USW00003179', datetime.date(2016, 4, 16), 'WT08', 1, None, None, 'W', None), ('USS0021B60S', datetime.date(2016, 4, 16), 'TMAX', 140, None, None, 'T', None), ('USS0021B60S', datetime.date(2016, 4, 16), 'TMIN', 30, None, None, 'T', None)]\n"
     ]
    }
   ],
   "source": [
    "print results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes. It's basically a spreadsheet you can program and it's an incredibly useful Python object for data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.read_sql(script, con=conn)\n",
    "type(results_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "      <th>value</th>\n",
       "      <th>measurement_flag</th>\n",
       "      <th>quality_flag</th>\n",
       "      <th>source_flag</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1TXCML112</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US1TXAG0003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US1TXAG0003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>US1TNGN0008</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>US1TNCF0027</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>US1TNCF0027</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ASN00109512</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>US1SDKY0009</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BL000085365</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>305</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>BL000085365</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>275</td>\n",
       "      <td>H</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>US1TXBEE018</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>US1TXBEE018</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ASN00096003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>95</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ASN00096003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>63</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ASN00096003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>CI000085469</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>225</td>\n",
       "      <td>H</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>US1SCOC0060</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>US1SCOC0060</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>CHM00056768</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>262</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>CHM00056768</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>129</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CHM00056768</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>18</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>CHM00056768</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>196</td>\n",
       "      <td>H</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>CHM00053646</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>26</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>CHM00053646</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>100</td>\n",
       "      <td>H</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>CD000064754</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>230</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>CD000064754</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TAVG</td>\n",
       "      <td>354</td>\n",
       "      <td>H</td>\n",
       "      <td>None</td>\n",
       "      <td>S</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ASN00092040</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>USC00251575</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>133</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>7</td>\n",
       "      <td>0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>USW00013968</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WSF2</td>\n",
       "      <td>103</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>USW00013968</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WSF5</td>\n",
       "      <td>139</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>USW00003728</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>201</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>USW00003728</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>94</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>USW00003728</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>R</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>ASN00032031</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>354</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>a</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>976</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>261</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>189</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>8</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>979</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>980</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNWD</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>981</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>AWND</td>\n",
       "      <td>68</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WDF2</td>\n",
       "      <td>110</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WDF5</td>\n",
       "      <td>120</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WSF2</td>\n",
       "      <td>125</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WSF5</td>\n",
       "      <td>179</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WT01</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>987</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>306</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>988</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>128</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>990</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>991</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNWD</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>AWND</td>\n",
       "      <td>46</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>993</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WDF2</td>\n",
       "      <td>90</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WDF5</td>\n",
       "      <td>80</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WSF2</td>\n",
       "      <td>107</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WSF5</td>\n",
       "      <td>152</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WT08</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>USS0021B60S</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>140</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>USS0021B60S</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>30</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         station        date value_type  value measurement_flag quality_flag  \\\n",
       "0    US1TXCML112  2016-04-16       RAIN      5             None         None   \n",
       "1    US1TXBEL028  2016-04-16       RAIN      0             None         None   \n",
       "2    US1TXBEL028  2016-04-16       SNOW      0             None         None   \n",
       "3    US1TXAG0003  2016-04-16       RAIN      0             None         None   \n",
       "4    US1TXAG0003  2016-04-16       SNOW      0             None         None   \n",
       "5    US1TNGN0008  2016-04-16       RAIN      0             None         None   \n",
       "6    US1TNCF0027  2016-04-16       RAIN      0             None         None   \n",
       "7    US1TNCF0027  2016-04-16       SNOW      0             None         None   \n",
       "8    ASN00109512  2016-04-16       RAIN     10             None         None   \n",
       "9    US1SDKY0009  2016-04-16       RAIN      5             None         None   \n",
       "10   BL000085365  2016-04-16       TMAX    305             None         None   \n",
       "11   BL000085365  2016-04-16       TAVG    275                H         None   \n",
       "12   US1TXBEE018  2016-04-16       RAIN      0             None         None   \n",
       "13   US1TXBEE018  2016-04-16       SNOW      0             None         None   \n",
       "14   ASN00096003  2016-04-16       TMAX     95             None         None   \n",
       "15   ASN00096003  2016-04-16       TMIN     63             None         None   \n",
       "16   ASN00096003  2016-04-16       RAIN     10             None         None   \n",
       "17   CI000085469  2016-04-16       TAVG    225                H         None   \n",
       "18   US1SCOC0060  2016-04-16       RAIN      0             None         None   \n",
       "19   US1SCOC0060  2016-04-16       SNOW      0             None         None   \n",
       "20   CHM00056768  2016-04-16       TMAX    262             None         None   \n",
       "21   CHM00056768  2016-04-16       TMIN    129             None         None   \n",
       "22   CHM00056768  2016-04-16       RAIN     18             None         None   \n",
       "23   CHM00056768  2016-04-16       TAVG    196                H         None   \n",
       "24   CHM00053646  2016-04-16       TMIN     26             None         None   \n",
       "25   CHM00053646  2016-04-16       TAVG    100                H         None   \n",
       "26   CD000064754  2016-04-16       TMIN    230             None         None   \n",
       "27   CD000064754  2016-04-16       TAVG    354                H         None   \n",
       "28   ASN00092040  2016-04-16       RAIN      0             None         None   \n",
       "29   USC00251575  2016-04-16       TMAX    133             None         None   \n",
       "..           ...         ...        ...    ...              ...          ...   \n",
       "970  USW00013968  2016-04-16       WSF2    103             None         None   \n",
       "971  USW00013968  2016-04-16       WSF5    139             None         None   \n",
       "972  USW00003728  2016-04-16       TMAX    201             None         None   \n",
       "973  USW00003728  2016-04-16       TMIN     94             None         None   \n",
       "974  USW00003728  2016-04-16       RAIN      0             None         None   \n",
       "975  ASN00032031  2016-04-16       RAIN    354             None         None   \n",
       "976  USW00012975  2016-04-16       TMAX    261             None         None   \n",
       "977  USW00012975  2016-04-16       TMIN    189             None         None   \n",
       "978  USW00012975  2016-04-16       RAIN      8             None         None   \n",
       "979  USW00012975  2016-04-16       SNOW      0             None         None   \n",
       "980  USW00012975  2016-04-16       SNWD      0             None         None   \n",
       "981  USW00012975  2016-04-16       AWND     68             None         None   \n",
       "982  USW00012975  2016-04-16       WDF2    110             None         None   \n",
       "983  USW00012975  2016-04-16       WDF5    120             None         None   \n",
       "984  USW00012975  2016-04-16       WSF2    125             None         None   \n",
       "985  USW00012975  2016-04-16       WSF5    179             None         None   \n",
       "986  USW00012975  2016-04-16       WT01      1             None         None   \n",
       "987  USW00003179  2016-04-16       TMAX    306             None         None   \n",
       "988  USW00003179  2016-04-16       TMIN    128             None         None   \n",
       "989  USW00003179  2016-04-16       RAIN      0             None         None   \n",
       "990  USW00003179  2016-04-16       SNOW      0             None         None   \n",
       "991  USW00003179  2016-04-16       SNWD      0             None         None   \n",
       "992  USW00003179  2016-04-16       AWND     46             None         None   \n",
       "993  USW00003179  2016-04-16       WDF2     90             None         None   \n",
       "994  USW00003179  2016-04-16       WDF5     80             None         None   \n",
       "995  USW00003179  2016-04-16       WSF2    107             None         None   \n",
       "996  USW00003179  2016-04-16       WSF5    152             None         None   \n",
       "997  USW00003179  2016-04-16       WT08      1             None         None   \n",
       "998  USS0021B60S  2016-04-16       TMAX    140             None         None   \n",
       "999  USS0021B60S  2016-04-16       TMIN     30             None         None   \n",
       "\n",
       "    source_flag  time  \n",
       "0             N  None  \n",
       "1             N  None  \n",
       "2             N  None  \n",
       "3             N  None  \n",
       "4             N  None  \n",
       "5             N  None  \n",
       "6             N  None  \n",
       "7             N  None  \n",
       "8             a  None  \n",
       "9             N  None  \n",
       "10            S  None  \n",
       "11            S  None  \n",
       "12            N  None  \n",
       "13            N  None  \n",
       "14            a  None  \n",
       "15            a  None  \n",
       "16            a  None  \n",
       "17            S  None  \n",
       "18            N  None  \n",
       "19            N  None  \n",
       "20            S  None  \n",
       "21            S  None  \n",
       "22            S  None  \n",
       "23            S  None  \n",
       "24            S  None  \n",
       "25            S  None  \n",
       "26            S  None  \n",
       "27            S  None  \n",
       "28            a  None  \n",
       "29            7  0700  \n",
       "..          ...   ...  \n",
       "970           W  None  \n",
       "971           W  None  \n",
       "972           R  None  \n",
       "973           R  None  \n",
       "974           R  None  \n",
       "975           a  None  \n",
       "976           W  None  \n",
       "977           W  None  \n",
       "978           W  None  \n",
       "979           W  None  \n",
       "980           W  None  \n",
       "981           W  None  \n",
       "982           W  None  \n",
       "983           W  None  \n",
       "984           W  None  \n",
       "985           W  None  \n",
       "986           W  None  \n",
       "987           W  None  \n",
       "988           W  None  \n",
       "989           W  None  \n",
       "990           W  None  \n",
       "991           W  None  \n",
       "992           W  None  \n",
       "993           W  None  \n",
       "994           W  None  \n",
       "995           W  None  \n",
       "996           W  None  \n",
       "997           W  None  \n",
       "998           T  None  \n",
       "999           T  None  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Viewing data in pandas\n",
    "\n",
    "There are lots of options for viewing data in pandas. Just like we did in the command line, you can use head and tail to get a quick view of our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "      <th>value</th>\n",
       "      <th>measurement_flag</th>\n",
       "      <th>quality_flag</th>\n",
       "      <th>source_flag</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1TXCML112</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US1TXAG0003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US1TXAG0003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       station        date value_type  value measurement_flag quality_flag  \\\n",
       "0  US1TXCML112  2016-04-16       RAIN      5             None         None   \n",
       "1  US1TXBEL028  2016-04-16       RAIN      0             None         None   \n",
       "2  US1TXBEL028  2016-04-16       SNOW      0             None         None   \n",
       "3  US1TXAG0003  2016-04-16       RAIN      0             None         None   \n",
       "4  US1TXAG0003  2016-04-16       SNOW      0             None         None   \n",
       "\n",
       "  source_flag  time  \n",
       "0           N  None  \n",
       "1           N  None  \n",
       "2           N  None  \n",
       "3           N  None  \n",
       "4           N  None  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "      <th>value</th>\n",
       "      <th>measurement_flag</th>\n",
       "      <th>quality_flag</th>\n",
       "      <th>source_flag</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WSF2</td>\n",
       "      <td>107</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WSF5</td>\n",
       "      <td>152</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>WT08</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>W</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>USS0021B60S</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMAX</td>\n",
       "      <td>140</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>USS0021B60S</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>TMIN</td>\n",
       "      <td>30</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>T</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         station        date value_type  value measurement_flag quality_flag  \\\n",
       "995  USW00003179  2016-04-16       WSF2    107             None         None   \n",
       "996  USW00003179  2016-04-16       WSF5    152             None         None   \n",
       "997  USW00003179  2016-04-16       WT08      1             None         None   \n",
       "998  USS0021B60S  2016-04-16       TMAX    140             None         None   \n",
       "999  USS0021B60S  2016-04-16       TMIN     30             None         None   \n",
       "\n",
       "    source_flag  time  \n",
       "995           W  None  \n",
       "996           W  None  \n",
       "997           W  None  \n",
       "998           T  None  \n",
       "999           T  None  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 8)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can choose the subset of data by selecting both rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "      <th>value</th>\n",
       "      <th>measurement_flag</th>\n",
       "      <th>quality_flag</th>\n",
       "      <th>source_flag</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1TXCML112</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>N</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       station        date value_type  value measurement_flag quality_flag  \\\n",
       "0  US1TXCML112  2016-04-16       RAIN      5             None         None   \n",
       "1  US1TXBEL028  2016-04-16       RAIN      0             None         None   \n",
       "2  US1TXBEL028  2016-04-16       SNOW      0             None         None   \n",
       "\n",
       "  source_flag  time  \n",
       "0           N  None  \n",
       "1           N  None  \n",
       "2           N  None  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at the first 3 rows\n",
    "results_df[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Modifying your dataframe\n",
    "\n",
    "Notice that some columns are completely useless. Let's fix that by learning how to delete columns.\n",
    "\n",
    "This is similar as writing SQL script: select station, date as record_date, value_type, value \n",
    "from qhan.weather\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1TXCML112</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US1TXAG0003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US1TXAG0003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>US1TNGN0008</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>US1TNCF0027</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>US1TNCF0027</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>SNOW</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ASN00109512</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>US1SDKY0009</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       station        date value_type  value\n",
       "0  US1TXCML112  2016-04-16       RAIN      5\n",
       "1  US1TXBEL028  2016-04-16       RAIN      0\n",
       "2  US1TXBEL028  2016-04-16       SNOW      0\n",
       "3  US1TXAG0003  2016-04-16       RAIN      0\n",
       "4  US1TXAG0003  2016-04-16       SNOW      0\n",
       "5  US1TNGN0008  2016-04-16       RAIN      0\n",
       "6  US1TNCF0027  2016-04-16       RAIN      0\n",
       "7  US1TNCF0027  2016-04-16       SNOW      0\n",
       "8  ASN00109512  2016-04-16       RAIN     10\n",
       "9  US1SDKY0009  2016-04-16       RAIN      5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df[['station', 'date', 'value_type', 'value']][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Delete these rows \n",
    "weather = results_df.drop(['measurement_flag', 'quality_flag', 'source_flag', 'time'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "Descriptive Statistics\n",
    "\n",
    "Pandas provides some great functions for descriptive statistics. Some examples:\n",
    "\n",
    "    describe() - \"computes a variety of summary statistics about a Series or the columns of a DataFrame (excluding NAs of course)\" ( See documentation)\n",
    "        includes the count of values, mean, standard deviation, min, 25%, 50%, and 75% values, and the max.\n",
    "\n",
    "    head() and tail(), shown above - \"To view a small sample of a Series or DataFrame object, use the head() and tail() methods. The default number of elements to display is five, but you may pass a custom number.\" ( See documentation.\n",
    "    value_counts() - The value_counts() \"series method and top-level function computes a histogram of a one-dimensional array of values.\" ( See documentation ). This method returns a Series of the counts of the number of times each unique value in the column is present in the column (also known as frequencies), from largest count to least, with the value itself the label for each row.\n",
    "\n",
    "\n",
    "\n",
    "Describing the entire dataframe\n",
    "\n",
    "Now that we have columns, we want to get a better global view of our data. There are several ways"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>351</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>USW00013968</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>11</td>\n",
       "      <td>1000</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            station        date value_type\n",
       "count          1000        1000       1000\n",
       "unique          351           1         26\n",
       "top     USW00013968  2016-04-16       RAIN\n",
       "freq             11        1000        309"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.describe(include=['O']) #categorical variables only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RAIN    309\n",
       "SNOW    146\n",
       "TMAX    125\n",
       "TMIN    121\n",
       "SNWD     83\n",
       "TAVG     60\n",
       "TOBS     47\n",
       "AWND     14\n",
       "WSF2     13\n",
       "WSF5     13\n",
       "WDF5     13\n",
       "WDF2     13\n",
       "WESD     11\n",
       "WESF     10\n",
       "WSFG      5\n",
       "PGTM      4\n",
       "WDFG      4\n",
       "EVAP      1\n",
       "MDPR      1\n",
       "WT03      1\n",
       "SN32      1\n",
       "WT01      1\n",
       "WDMV      1\n",
       "WT08      1\n",
       "DAPR      1\n",
       "SX32      1\n",
       "Name: value_type, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather['value_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fcdeb227dd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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IvpYcMyLvTz77eEH06YHVa0Pj5kG6keUKUq+Rzk6ost2Qwl1nXcs3pOIuzx7P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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcdeb227850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weather['value_type'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subset of dataset similar as select * from weather where value_type == 'RAIN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "      <th>value</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US1TXCML112</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>US1TXBEL028</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US1TXAG0003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>US1TNGN0008</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>US1TNCF0027</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ASN00109512</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>US1SDKY0009</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>5</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>US1TXBEE018</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ASN00096003</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>US1SCOC0060</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>CHM00056768</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>18</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ASN00092040</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>USC00251575</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>USC00257545</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>USC00251200</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>ASN00054023</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>USC00250050</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>43</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>USC00247620</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>NLE00108966</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>NLE00108960</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>34</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>NLE00100496</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>4</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>USC00238740</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>MXM00076593</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>USC00229400</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>381</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>MXM00076040</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>CA1SK000069</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>US1ORLN0045</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>CA1PE000023</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>CA1NS000015</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>168</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>CA1NS000068</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>ASN00074148</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>ASN00074106</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>US1IAUN0001</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>ASN00073151</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>US1GALW0015</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>71</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>US1GAMA0022</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>US1IATY0002</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>US1GAHL0001</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>ASN00072012</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>US1GACT0059</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>3</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>US1DESS0016</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>US1FLHN0011</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>142</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>ASN00033268</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>897</th>\n",
       "      <td>US1FLMT0019</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>99</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>US1COLR0495</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>48</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>906</th>\n",
       "      <td>US1COGL0015</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>198</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>908</th>\n",
       "      <td>US1COGR0052</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>201</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>USW00053120</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>UY000086565</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>1905</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>928</th>\n",
       "      <td>US1COEP0180</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>46</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>USW00014828</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>939</th>\n",
       "      <td>USW00014829</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>USW00025506</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>956</th>\n",
       "      <td>US1COEP0037</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>41</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>ASN00066158</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>USW00013968</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>974</th>\n",
       "      <td>USW00003728</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>ASN00032031</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>354</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>USW00012975</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>8</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>989</th>\n",
       "      <td>USW00003179</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>0</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>309 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         station        date value_type  value   datetime\n",
       "0    US1TXCML112  2016-04-16       RAIN      5 2016-04-16\n",
       "1    US1TXBEL028  2016-04-16       RAIN      0 2016-04-16\n",
       "3    US1TXAG0003  2016-04-16       RAIN      0 2016-04-16\n",
       "5    US1TNGN0008  2016-04-16       RAIN      0 2016-04-16\n",
       "6    US1TNCF0027  2016-04-16       RAIN      0 2016-04-16\n",
       "8    ASN00109512  2016-04-16       RAIN     10 2016-04-16\n",
       "9    US1SDKY0009  2016-04-16       RAIN      5 2016-04-16\n",
       "12   US1TXBEE018  2016-04-16       RAIN      0 2016-04-16\n",
       "16   ASN00096003  2016-04-16       RAIN     10 2016-04-16\n",
       "18   US1SCOC0060  2016-04-16       RAIN      0 2016-04-16\n",
       "22   CHM00056768  2016-04-16       RAIN     18 2016-04-16\n",
       "28   ASN00092040  2016-04-16       RAIN      0 2016-04-16\n",
       "32   USC00251575  2016-04-16       RAIN      0 2016-04-16\n",
       "35   USC00257545  2016-04-16       RAIN      0 2016-04-16\n",
       "41   USC00251200  2016-04-16       RAIN      0 2016-04-16\n",
       "44   ASN00054023  2016-04-16       RAIN      0 2016-04-16\n",
       "48   USC00250050  2016-04-16       RAIN     43 2016-04-16\n",
       "54   USC00247620  2016-04-16       RAIN      0 2016-04-16\n",
       "59   NLE00108966  2016-04-16       RAIN     12 2016-04-16\n",
       "61   NLE00108960  2016-04-16       RAIN     34 2016-04-16\n",
       "65   NLE00100496  2016-04-16       RAIN      4 2016-04-16\n",
       "70   USC00238740  2016-04-16       RAIN      0 2016-04-16\n",
       "75   MXM00076593  2016-04-16       RAIN      0 2016-04-16\n",
       "80   USC00229400  2016-04-16       RAIN    381 2016-04-16\n",
       "88   MXM00076040  2016-04-16       RAIN      0 2016-04-16\n",
       "93   CA1SK000069  2016-04-16       RAIN      0 2016-04-16\n",
       "94   US1ORLN0045  2016-04-16       RAIN      0 2016-04-16\n",
       "96   CA1PE000023  2016-04-16       RAIN      0 2016-04-16\n",
       "98   CA1NS000015  2016-04-16       RAIN    168 2016-04-16\n",
       "99   CA1NS000068  2016-04-16       RAIN      0 2016-04-16\n",
       "..           ...         ...        ...    ...        ...\n",
       "877  ASN00074148  2016-04-16       RAIN      0 2016-04-16\n",
       "879  ASN00074106  2016-04-16       RAIN      0 2016-04-16\n",
       "880  US1IAUN0001  2016-04-16       RAIN      0 2016-04-16\n",
       "884  ASN00073151  2016-04-16       RAIN      0 2016-04-16\n",
       "885  US1GALW0015  2016-04-16       RAIN     71 2016-04-16\n",
       "886  US1GAMA0022  2016-04-16       RAIN      0 2016-04-16\n",
       "888  US1IATY0002  2016-04-16       RAIN      0 2016-04-16\n",
       "890  US1GAHL0001  2016-04-16       RAIN      0 2016-04-16\n",
       "891  ASN00072012  2016-04-16       RAIN      2 2016-04-16\n",
       "892  US1GACT0059  2016-04-16       RAIN      3 2016-04-16\n",
       "893  US1DESS0016  2016-04-16       RAIN      0 2016-04-16\n",
       "895  US1FLHN0011  2016-04-16       RAIN    142 2016-04-16\n",
       "896  ASN00033268  2016-04-16       RAIN      0 2016-04-16\n",
       "897  US1FLMT0019  2016-04-16       RAIN     99 2016-04-16\n",
       "901  US1COLR0495  2016-04-16       RAIN     48 2016-04-16\n",
       "906  US1COGL0015  2016-04-16       RAIN    198 2016-04-16\n",
       "908  US1COGR0052  2016-04-16       RAIN    201 2016-04-16\n",
       "914  USW00053120  2016-04-16       RAIN      0 2016-04-16\n",
       "923  UY000086565  2016-04-16       RAIN   1905 2016-04-16\n",
       "928  US1COEP0180  2016-04-16       RAIN     46 2016-04-16\n",
       "934  USW00014828  2016-04-16       RAIN      0 2016-04-16\n",
       "939  USW00014829  2016-04-16       RAIN      0 2016-04-16\n",
       "949  USW00025506  2016-04-16       RAIN      0 2016-04-16\n",
       "956  US1COEP0037  2016-04-16       RAIN     41 2016-04-16\n",
       "960  ASN00066158  2016-04-16       RAIN      0 2016-04-16\n",
       "963  USW00013968  2016-04-16       RAIN      0 2016-04-16\n",
       "974  USW00003728  2016-04-16       RAIN      0 2016-04-16\n",
       "975  ASN00032031  2016-04-16       RAIN    354 2016-04-16\n",
       "978  USW00012975  2016-04-16       RAIN      8 2016-04-16\n",
       "989  USW00003179  2016-04-16       RAIN      0 2016-04-16\n",
       "\n",
       "[309 rows x 5 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather[weather['value_type'] == \"RAIN\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "select station, date, value_type, \n",
    "value from qhan.weather \n",
    "where value_type = 'RAIN' and value > 300;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "      <th>value</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>USC00229400</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>381</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>US1KSSV0018</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>678</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>US1KSLG0005</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>320</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>US1OKCM0007</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>1270</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>CA1ON000218</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>320</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>840</th>\n",
       "      <td>US1KSST0001</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>305</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>US1KSSV0019</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>815</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>UY000086565</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>1905</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>ASN00032031</td>\n",
       "      <td>2016-04-16</td>\n",
       "      <td>RAIN</td>\n",
       "      <td>354</td>\n",
       "      <td>2016-04-16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         station        date value_type  value   datetime\n",
       "80   USC00229400  2016-04-16       RAIN    381 2016-04-16\n",
       "215  US1KSSV0018  2016-04-16       RAIN    678 2016-04-16\n",
       "216  US1KSLG0005  2016-04-16       RAIN    320 2016-04-16\n",
       "652  US1OKCM0007  2016-04-16       RAIN   1270 2016-04-16\n",
       "676  CA1ON000218  2016-04-16       RAIN    320 2016-04-16\n",
       "840  US1KSST0001  2016-04-16       RAIN    305 2016-04-16\n",
       "841  US1KSSV0019  2016-04-16       RAIN    815 2016-04-16\n",
       "923  UY000086565  2016-04-16       RAIN   1905 2016-04-16\n",
       "975  ASN00032031  2016-04-16       RAIN    354 2016-04-16"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather[(weather['value_type'] == \"RAIN\") & (weather['value'] > 300)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather['datetime'] = pd.to_datetime(weather.date, format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "station               object\n",
       "date                  object\n",
       "value_type            object\n",
       "value                  int64\n",
       "datetime      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "select count(distinct station) as number_stations \n",
    "from weather\n",
    "where date between '2016-01-01' and '2016-02-01' \n",
    "and value_type = 'SNOW'\n",
    "and value > 100; "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>station</th>\n",
       "      <th>date</th>\n",
       "      <th>value_type</th>\n",
       "      <th>value</th>\n",
       "      <th>datetime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [station, date, value_type, value, datetime]\n",
       "Index: []"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather[(weather['value_type'] == \"SNOW\") & (weather['value'] > 300) \n",
    "        & (weather['datetime'] > \"2016-01-01\") & (weather['datetime'] < \"2016-02-01\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Groupby is a powerful method that makes it easy to peform operations on the dataframe by categorial values. Let's try generating a plot of min, max, and average temp over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "value_type\n",
       "AWND      37.000000\n",
       "DAPR       5.000000\n",
       "EVAP      51.000000\n",
       "MDPR     249.000000\n",
       "PGTM    1677.250000\n",
       "RAIN      34.974110\n",
       "SN32      56.000000\n",
       "SNOW       4.849315\n",
       "SNWD     101.481928\n",
       "SX32     117.000000\n",
       "TAVG     134.100000\n",
       "TMAX     188.760000\n",
       "TMIN      68.661157\n",
       "TOBS      89.425532\n",
       "WDF2     118.461538\n",
       "WDF5     120.000000\n",
       "WDFG      67.500000\n",
       "WDMV      51.000000\n",
       "WESD    2126.727273\n",
       "WESF      31.100000\n",
       "WSF2      88.384615\n",
       "WSF5     118.769231\n",
       "WSFG      30.400000\n",
       "WT01       1.000000\n",
       "WT03       1.000000\n",
       "WT08       1.000000\n",
       "Name: value, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.groupby('value_type').value.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prip = weather[weather['value_type'].isin([\"RAIN\", \"SNOW\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RAIN    309\n",
       "SNOW    146\n",
       "Name: value_type, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prip.value_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "prip.value.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "agg(regation) method allows you to run summary statistics after you groupby the records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RAIN</th>\n",
       "      <td>309</td>\n",
       "      <td>0</td>\n",
       "      <td>1905</td>\n",
       "      <td>34.974110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SNOW</th>\n",
       "      <td>146</td>\n",
       "      <td>0</td>\n",
       "      <td>318</td>\n",
       "      <td>4.849315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count  min   max       mean\n",
       "value_type                             \n",
       "RAIN          309    0  1905  34.974110\n",
       "SNOW          146    0   318   4.849315"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prip.groupby('value_type').value.agg(['count','min','max','mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1000, step=1)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you finish the processing, you can export the data.frame to csv (df.to_csv), excel (df.to_excel) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weather.to_csv('weather.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you are using MS SQL Server, we need to set up a generic db connection using sqlalchemy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sqlalchemy\n",
    "\n",
    "TableName = \"data\"\n",
    "\n",
    "DB = {\n",
    "    'drivername': 'mssql+pyodbc',\n",
    "    'servername': 'DAVID-THINK',\n",
    "    #'port': '5432',\n",
    "    #'username': 'lynn',\n",
    "    #'password': '',\n",
    "    'database': 'BizIntel',\n",
    "    'driver': 'SQL Server Native Client 11.0',\n",
    "    'trusted_connection': 'yes',  \n",
    "    'legacy_schema_aliasing': False\n",
    "}\n",
    "\n",
    "# create a db connection\n",
    "engine = create_engine(DB['drivername'] + '://' + DB['servername'] + '/' + DB['database'] + '?' + 'driver=' + DB['driver'] + ';' + 'trusted_connection=' + DB['trusted_connection'], legacy_schema_aliasing=DB['legacy_schema_aliasing'])\n",
    "conn = engine.connect()\n",
    "\n",
    "# Required for querying tables\n",
    "metadata = MetaData(conn)\n",
    "\n",
    "# Table to query\n",
    "tbl = Table(TableName, metadata, autoload=True, schema=\"dbo\")\n",
    "#tbl.create(checkfirst=True)\n",
    "\n",
    "# Select all\n",
    "sql = tbl.select()\n",
    "\n",
    "# run sql code\n",
    "result = conn.execute(sql)\n",
    "\n",
    "# Insert to a dataframe\n",
    "df = pd.DataFrame(data=list(result), columns=result.keys())\n",
    "\n",
    "# Close connection\n",
    "conn.close()\n",
    "\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Refer to http://pandas.pydata.org/pandas-docs/stable/tutorials.html for more tutorials regarding pandas"
   ]
  }
 ],
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