3_select_series_form_df.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_This introduction to pandas is derived from Data School's pandas Q&A with my own notes and code on top of what was provided._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selecting a pandas Series from a DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**What is a series**\n",
    "- It is a m x 1 vector\n",
    "    - m is the number of rows\n",
    "    - 1 is the number of columns\n",
    "- Each column in DataFrame is known as a pandas series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00\n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00\n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00\n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00\n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The csv file is separated by commas\n",
    "url = 'http://bit.ly/uforeports'\n",
    "\n",
    "# method 1: read_table\n",
    "ufo = pd.read_table(url, sep=',')\n",
    "\n",
    "# method 2: read_csv\n",
    "# this is a short-cut here using read_csv because it uses comma as the default separator\n",
    "ufo = pd.read_csv(url)\n",
    "ufo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      Ithaca\n",
       "1                 Willingboro\n",
       "2                     Holyoke\n",
       "3                     Abilene\n",
       "4        New York Worlds Fair\n",
       "5                 Valley City\n",
       "6                 Crater Lake\n",
       "7                        Alma\n",
       "8                     Eklutna\n",
       "9                     Hubbard\n",
       "10                    Fontana\n",
       "11                   Waterloo\n",
       "12                     Belton\n",
       "13                     Keokuk\n",
       "14                  Ludington\n",
       "15                Forest Home\n",
       "16                Los Angeles\n",
       "17                  Hapeville\n",
       "18                     Oneida\n",
       "19                 Bering Sea\n",
       "20                   Nebraska\n",
       "21                        NaN\n",
       "22                        NaN\n",
       "23                  Owensboro\n",
       "24                 Wilderness\n",
       "25                  San Diego\n",
       "26                 Wilderness\n",
       "27                     Clovis\n",
       "28                 Los Alamos\n",
       "29               Ft. Duschene\n",
       "                 ...         \n",
       "18211                 Holyoke\n",
       "18212                  Carson\n",
       "18213                Pasadena\n",
       "18214                  Austin\n",
       "18215                El Campo\n",
       "18216            Garden Grove\n",
       "18217           Berthoud Pass\n",
       "18218              Sisterdale\n",
       "18219            Garden Grove\n",
       "18220             Shasta Lake\n",
       "18221                Franklin\n",
       "18222          Albrightsville\n",
       "18223              Greenville\n",
       "18224                 Eufaula\n",
       "18225             Simi Valley\n",
       "18226           San Francisco\n",
       "18227           San Francisco\n",
       "18228              Kingsville\n",
       "18229                 Chicago\n",
       "18230             Pismo Beach\n",
       "18231             Pismo Beach\n",
       "18232                    Lodi\n",
       "18233               Anchorage\n",
       "18234                Capitola\n",
       "18235          Fountain Hills\n",
       "18236              Grant Park\n",
       "18237             Spirit Lake\n",
       "18238             Eagle River\n",
       "18239             Eagle River\n",
       "18240                    Ybor\n",
       "Name: City, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Method 1: Selecting City series (this will always work)\n",
    "ufo['City']\n",
    "\n",
    "# Method 2: Selecting City series\n",
    "ufo.City\n",
    "\n",
    "# 'City' is case-sensitive, you cannot use 'city'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# confirm type\n",
    "type(ufo['City'])\n",
    "type(ufo.City)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How do you select a column name with spacing between words?**\n",
    "- You cannot use method 2 (ufo.category_name)\n",
    "- You have to use method 1 (ufo['category name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1           NaN\n",
       "2           NaN\n",
       "3           NaN\n",
       "4           NaN\n",
       "5           NaN\n",
       "6           NaN\n",
       "7           NaN\n",
       "8           NaN\n",
       "9           NaN\n",
       "10          NaN\n",
       "11          NaN\n",
       "12          RED\n",
       "13          NaN\n",
       "14          NaN\n",
       "15          NaN\n",
       "16          NaN\n",
       "17          NaN\n",
       "18          NaN\n",
       "19          RED\n",
       "20          NaN\n",
       "21          NaN\n",
       "22          NaN\n",
       "23          NaN\n",
       "24          NaN\n",
       "25          NaN\n",
       "26          NaN\n",
       "27          NaN\n",
       "28          NaN\n",
       "29          NaN\n",
       "          ...  \n",
       "18211       NaN\n",
       "18212       NaN\n",
       "18213     GREEN\n",
       "18214       NaN\n",
       "18215       NaN\n",
       "18216    ORANGE\n",
       "18217       NaN\n",
       "18218       NaN\n",
       "18219       NaN\n",
       "18220      BLUE\n",
       "18221       NaN\n",
       "18222       NaN\n",
       "18223       NaN\n",
       "18224       NaN\n",
       "18225       NaN\n",
       "18226       NaN\n",
       "18227       NaN\n",
       "18228       NaN\n",
       "18229       NaN\n",
       "18230       NaN\n",
       "18231       NaN\n",
       "18232       NaN\n",
       "18233       RED\n",
       "18234       NaN\n",
       "18235       NaN\n",
       "18236       NaN\n",
       "18237       NaN\n",
       "18238       NaN\n",
       "18239       RED\n",
       "18240       NaN\n",
       "Name: Colors Reported, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ufo['Colors Reported']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**How do I create a new panda Series in a DataFrame?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcd'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of concatenating strings\n",
    "'ab' + 'cd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Colors Reported</th>\n",
       "      <th>Shape Reported</th>\n",
       "      <th>State</th>\n",
       "      <th>Time</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ithaca</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TRIANGLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>6/1/1930 22:00</td>\n",
       "      <td>Ithaca, NY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Willingboro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NJ</td>\n",
       "      <td>6/30/1930 20:00</td>\n",
       "      <td>Willingboro, NJ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OVAL</td>\n",
       "      <td>CO</td>\n",
       "      <td>2/15/1931 14:00</td>\n",
       "      <td>Holyoke, CO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abilene</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DISK</td>\n",
       "      <td>KS</td>\n",
       "      <td>6/1/1931 13:00</td>\n",
       "      <td>Abilene, KS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York Worlds Fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LIGHT</td>\n",
       "      <td>NY</td>\n",
       "      <td>4/18/1933 19:00</td>\n",
       "      <td>New York Worlds Fair, NY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   City Colors Reported Shape Reported State             Time  \\\n",
       "0                Ithaca             NaN       TRIANGLE    NY   6/1/1930 22:00   \n",
       "1           Willingboro             NaN          OTHER    NJ  6/30/1930 20:00   \n",
       "2               Holyoke             NaN           OVAL    CO  2/15/1931 14:00   \n",
       "3               Abilene             NaN           DISK    KS   6/1/1931 13:00   \n",
       "4  New York Worlds Fair             NaN          LIGHT    NY  4/18/1933 19:00   \n",
       "\n",
       "                   Location  \n",
       "0                Ithaca, NY  \n",
       "1           Willingboro, NJ  \n",
       "2               Holyoke, CO  \n",
       "3               Abilene, KS  \n",
       "4  New York Worlds Fair, NY  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# created a new column called \"Location\" with a concatenation of \"City\" and \"State\"\n",
    "ufo['Location'] = ufo.City + ', ' + ufo.State\n",
    "ufo.head()"
   ]
  }
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