python/pandas/1_pandas_jeopardy_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Part 3:  Pandas Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This notebook runs through an example using Pandas with the `jeopardy.csv` data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Source:**  200K+Jeopardy questions from [Reddit](https://www.reddit.com/r/datasets/comments/1uyd0t/200000_jeopardy_questions_in_a_json_file/)\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1:  import libraries, check versions, set up preferences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Python 2 & 3 Compatibility\n",
    "from __future__ import print_function, division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# imports a library 'pandas', names it as 'pd'\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "# enables inline plots, without it plots don't show up in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas version: 0.19.2\n",
      "Numpy version: 1.11.3\n"
     ]
    }
   ],
   "source": [
    "# check version of libraries\n",
    "print(\"Pandas version:\",pd.__version__)\n",
    "print(\"Numpy version:\",np.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python Version: 3.5.2 |Anaconda 4.2.0 (x86_64)| (default, Jul  2 2016, 17:52:12) \n",
      "[GCC 4.2.1 Compatible Apple LLVM 4.2 (clang-425.0.28)]\n"
     ]
    }
   ],
   "source": [
    "# confirming which version of Python I am using\n",
    "import sys\n",
    "print(\"Python Version:\", sys.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set various options in pandas\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', 25)\n",
    "pd.set_option('display.precision', 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 3504\r\n",
      "-rw-r--r--  1 reshamashaikh  staff    93171 Feb 18 22:36 1_pandas_jeopardy_example.ipynb\r\n",
      "-rw-r--r--  1 reshamashaikh  staff    10752 Feb 18 20:51 2_pandas_olive_questions.ipynb\r\n",
      "-rw-r--r--@ 1 reshamashaikh  staff  1651525 Feb 18 21:12 jeopardy.csv\r\n",
      "-rw-r-----@ 1 reshamashaikh  staff    28995 May 13  2015 olive.csv\r\n"
     ]
    }
   ],
   "source": [
    "# check size of file; notice this is a bash command -- I can run it in the notebook!\n",
    "!ls -l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2:  read in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read csv data into pandas dataframe\n",
    "df_orig = pd.read_csv('jeopardy.csv', encoding=\"ISO-8859-1\")\n",
    "\n",
    "# Note: I normally don't need to specify encoding.  \n",
    "# But, when I read in this csv file, there was an error.  \n",
    "# I googled it; there are some different unicode types.  I tried a bunch and this worked.  \n",
    "\n",
    "# Data formatting is unpredictable, and one of the skills in data science is to 'google' \n",
    "# and see how to work through data issues"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3:  look at data\n",
    "`df` = dataframe being used.  In our case, it is `df_orig`\n",
    "```python\n",
    "df.shape()\n",
    "df.info()\n",
    "df.head()\n",
    "df.tail()\n",
    "df.columns\n",
    "df.values\n",
    "df.dtype\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10999, 7)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dimensions of dataframe\n",
    "# (rows, colums)\n",
    "df_orig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Show Number</th>\n",
       "      <th>Air Date</th>\n",
       "      <th>Round</th>\n",
       "      <th>Category</th>\n",
       "      <th>Value</th>\n",
       "      <th>Question</th>\n",
       "      <th>Answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4680</td>\n",
       "      <td>12/31/04</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>HISTORY</td>\n",
       "      <td>$200</td>\n",
       "      <td>For the last 8 years of his life, Galileo was ...</td>\n",
       "      <td>Copernicus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4680</td>\n",
       "      <td>12/31/04</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>ESPN's TOP 10 ALL-TIME ATHLETES</td>\n",
       "      <td>$200</td>\n",
       "      <td>No. 2: 1912 Olympian; football star at Carlisl...</td>\n",
       "      <td>Jim Thorpe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4680</td>\n",
       "      <td>12/31/04</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>EVERYBODY TALKS ABOUT IT...</td>\n",
       "      <td>$200</td>\n",
       "      <td>The city of Yuma in this state has a record av...</td>\n",
       "      <td>Arizona</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Show Number  Air Date      Round                         Category Value  \\\n",
       "0         4680  12/31/04  Jeopardy!                          HISTORY  $200   \n",
       "1         4680  12/31/04  Jeopardy!  ESPN's TOP 10 ALL-TIME ATHLETES  $200   \n",
       "2         4680  12/31/04  Jeopardy!      EVERYBODY TALKS ABOUT IT...  $200   \n",
       "\n",
       "                                            Question      Answer  \n",
       "0  For the last 8 years of his life, Galileo was ...  Copernicus  \n",
       "1  No. 2: 1912 Olympian; football star at Carlisl...  Jim Thorpe  \n",
       "2  The city of Yuma in this state has a record av...     Arizona  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4:  summarize data\n",
    "```python\n",
    "df.describe()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Show Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10999.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4308.181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1397.758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>62.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3409.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4541.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5431.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6294.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Show Number\n",
       "count    10999.000\n",
       "mean      4308.181\n",
       "std       1397.758\n",
       "min         62.000\n",
       "25%       3409.000\n",
       "50%       4541.000\n",
       "75%       5431.000\n",
       "max       6294.000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count         10999\n",
       "unique            3\n",
       "top       Jeopardy!\n",
       "freq           5434\n",
       "Name: Round, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig['Round'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Jeopardy!', 'Double Jeopardy!', 'Final Jeopardy!'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the unique values of the column 'Round'\n",
    "df_orig['Round'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Show Number</th>\n",
       "      <th>Air Date</th>\n",
       "      <th>Category</th>\n",
       "      <th>Value</th>\n",
       "      <th>Question</th>\n",
       "      <th>Answer</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Round</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Double Jeopardy!</th>\n",
       "      <td>5382</td>\n",
       "      <td>5382</td>\n",
       "      <td>5382</td>\n",
       "      <td>5382</td>\n",
       "      <td>5382</td>\n",
       "      <td>5382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Final Jeopardy!</th>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jeopardy!</th>\n",
       "      <td>5434</td>\n",
       "      <td>5434</td>\n",
       "      <td>5434</td>\n",
       "      <td>5434</td>\n",
       "      <td>5434</td>\n",
       "      <td>5434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Show Number  Air Date  Category  Value  Question  Answer\n",
       "Round                                                                     \n",
       "Double Jeopardy!         5382      5382      5382   5382      5382    5382\n",
       "Final Jeopardy!           183       183       183    183       183     183\n",
       "Jeopardy!                5434      5434      5434   5434      5434    5434"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig.groupby('Round').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5:  create a new column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_orig['Dollar_Amt'] = df_orig['Value']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 6:  clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Show Number</th>\n",
       "      <th>Air Date</th>\n",
       "      <th>Round</th>\n",
       "      <th>Category</th>\n",
       "      <th>Value</th>\n",
       "      <th>Question</th>\n",
       "      <th>Answer</th>\n",
       "      <th>Dollar_Amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4680</td>\n",
       "      <td>12/31/04</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>HISTORY</td>\n",
       "      <td>$200</td>\n",
       "      <td>For the last 8 years of his life, Galileo was ...</td>\n",
       "      <td>Copernicus</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4680</td>\n",
       "      <td>12/31/04</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>ESPN's TOP 10 ALL-TIME ATHLETES</td>\n",
       "      <td>$200</td>\n",
       "      <td>No. 2: 1912 Olympian; football star at Carlisl...</td>\n",
       "      <td>Jim Thorpe</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4680</td>\n",
       "      <td>12/31/04</td>\n",
       "      <td>Jeopardy!</td>\n",
       "      <td>EVERYBODY TALKS ABOUT IT...</td>\n",
       "      <td>$200</td>\n",
       "      <td>The city of Yuma in this state has a record av...</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Show Number  Air Date      Round                         Category Value  \\\n",
       "0         4680  12/31/04  Jeopardy!                          HISTORY  $200   \n",
       "1         4680  12/31/04  Jeopardy!  ESPN's TOP 10 ALL-TIME ATHLETES  $200   \n",
       "2         4680  12/31/04  Jeopardy!      EVERYBODY TALKS ABOUT IT...  $200   \n",
       "\n",
       "                                            Question      Answer Dollar_Amt  \n",
       "0  For the last 8 years of his life, Galileo was ...  Copernicus        200  \n",
       "1  No. 2: 1912 Olympian; football star at Carlisl...  Jim Thorpe        200  \n",
       "2  The city of Yuma in this state has a record av...     Arizona        200  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove the '$' in the column Dollar_Amt\n",
    "df_orig['Dollar_Amt'].replace(regex=True,inplace=True,to_replace=r'\\$',value=r'')\n",
    "\n",
    "df_orig.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Show Number</th>\n",
       "      <th>Air Date</th>\n",
       "      <th>Round</th>\n",
       "      <th>Category</th>\n",
       "      <th>Value</th>\n",
       "      <th>Question</th>\n",
       "      <th>Answer</th>\n",
       "      <th>Dollar_Amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10994</th>\n",
       "      <td>4362</td>\n",
       "      <td>7/15/03</td>\n",
       "      <td>Double Jeopardy!</td>\n",
       "      <td>BEFORE &amp; AFTER</td>\n",
       "      <td>$1,200</td>\n",
       "      <td>Liberator &amp; king of Scotland who starred in \"D...</td>\n",
       "      <td>Robert the Bruce Willis</td>\n",
       "      <td>1,200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10995</th>\n",
       "      <td>4362</td>\n",
       "      <td>7/15/03</td>\n",
       "      <td>Double Jeopardy!</td>\n",
       "      <td>PEN NAMES</td>\n",
       "      <td>$2,000</td>\n",
       "      <td>This pseudonym means \"2 fathoms deep\"</td>\n",
       "      <td>Mark Twain</td>\n",
       "      <td>2,000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10996</th>\n",
       "      <td>4362</td>\n",
       "      <td>7/15/03</td>\n",
       "      <td>Double Jeopardy!</td>\n",
       "      <td>TELEVISION</td>\n",
       "      <td>$1,600</td>\n",
       "      <td>She's the \"Night Court\" actress seen here</td>\n",
       "      <td>Markie Post</td>\n",
       "      <td>1,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>4362</td>\n",
       "      <td>7/15/03</td>\n",
       "      <td>Double Jeopardy!</td>\n",
       "      <td>INVENTIVE MINDS</td>\n",
       "      <td>$1,600</td>\n",
       "      <td>In 1952 he &amp; Charles Bowman joined forces to d...</td>\n",
       "      <td>Orville Redenbacher</td>\n",
       "      <td>1,600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10998</th>\n",
       "      <td>4362</td>\n",
       "      <td>7/15/03</td>\n",
       "      <td>Double Jeopardy!</td>\n",
       "      <td>REFRESHER COURSE</td>\n",
       "      <td>$1,600</td>\n",
       "      <td>Lemon juice, sugar, gin, club soda &amp; an egg go...</td>\n",
       "      <td>royal gin fizz</td>\n",
       "      <td>1,600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Show Number Air Date             Round          Category   Value  \\\n",
       "10994         4362  7/15/03  Double Jeopardy!    BEFORE & AFTER  $1,200   \n",
       "10995         4362  7/15/03  Double Jeopardy!         PEN NAMES  $2,000   \n",
       "10996         4362  7/15/03  Double Jeopardy!        TELEVISION  $1,600   \n",
       "10997         4362  7/15/03  Double Jeopardy!   INVENTIVE MINDS  $1,600   \n",
       "10998         4362  7/15/03  Double Jeopardy!  REFRESHER COURSE  $1,600   \n",
       "\n",
       "                                                Question  \\\n",
       "10994  Liberator & king of Scotland who starred in \"D...   \n",
       "10995              This pseudonym means \"2 fathoms deep\"   \n",
       "10996          She's the \"Night Court\" actress seen here   \n",
       "10997  In 1952 he & Charles Bowman joined forces to d...   \n",
       "10998  Lemon juice, sugar, gin, club soda & an egg go...   \n",
       "\n",
       "                        Answer Dollar_Amt  \n",
       "10994  Robert the Bruce Willis      1,200  \n",
       "10995               Mark Twain      2,000  \n",
       "10996              Markie Post      1,600  \n",
       "10997      Orville Redenbacher      1,600  \n",
       "10998           royal gin fizz      1,600  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's look at the tail end of the data\n",
    "df_orig.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Show Number</th>\n",
       "      <th>Air Date</th>\n",
       "      <th>Round</th>\n",
       "      <th>Category</th>\n",
       "      <th>Value</th>\n",
       "      <th>Question</th>\n",
       "      <th>Answer</th>\n",
       "      <th>Dollar_Amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10997</th>\n",
       "      <td>4362</td>\n",
       "      <td>7/15/03</td>\n",
       "      <td>Double Jeopardy!</td>\n",
       "      <td>INVENTIVE MINDS</td>\n",
       "      <td>$1,600</td>\n",
       "      <td>In 1952 he &amp; Charles Bowman joined forces to d...</td>\n",
       "      <td>Orville Redenbacher</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10998</th>\n",
       "      <td>4362</td>\n",
       "      <td>7/15/03</td>\n",
       "      <td>Double Jeopardy!</td>\n",
       "      <td>REFRESHER COURSE</td>\n",
       "      <td>$1,600</td>\n",
       "      <td>Lemon juice, sugar, gin, club soda &amp; an egg go...</td>\n",
       "      <td>royal gin fizz</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Show Number Air Date             Round          Category   Value  \\\n",
       "10997         4362  7/15/03  Double Jeopardy!   INVENTIVE MINDS  $1,600   \n",
       "10998         4362  7/15/03  Double Jeopardy!  REFRESHER COURSE  $1,600   \n",
       "\n",
       "                                                Question               Answer  \\\n",
       "10997  In 1952 he & Charles Bowman joined forces to d...  Orville Redenbacher   \n",
       "10998  Lemon juice, sugar, gin, club soda & an egg go...       royal gin fizz   \n",
       "\n",
       "      Dollar_Amt  \n",
       "10997       1600  \n",
       "10998       1600  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we need to do more cleaning.  There is a comma in the dollar amount\n",
    "# remove the commas in the column 'Dollar_Amt'\n",
    "# there are many ways to do it.  here's one:\n",
    "df_orig['Dollar_Amt'] = df_orig['Dollar_Amt'].str.replace(',', '')\n",
    "df_orig.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# create a new column which we want to be numeric\n",
    "df_orig['Dollar_Amt_n'] = df_orig['Dollar_Amt']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Show Number      int64\n",
       "Air Date        object\n",
       "Round           object\n",
       "Category        object\n",
       "Value           object\n",
       "Question        object\n",
       "Answer          object\n",
       "Dollar_Amt      object\n",
       "Dollar_Amt_n    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types\n",
    "df_orig.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     10999\n",
       "unique       52\n",
       "top         400\n",
       "freq       2144\n",
       "Name: Dollar_Amt_n, dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig['Dollar_Amt_n'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['200', '400', '600', '800', '2000', '1000', '1200', '1600', '3200',\n",
       "       'None', '5000', '100', '300', '500', '1500', '4800', '1800', '1100',\n",
       "       '2200', '3400', '3000', '4000', '6800', '1900', '3100', '700',\n",
       "       '1400', '2800', '8000', '6000', '2400', '12000', '3800', '2500',\n",
       "       '6200', '10000', '7000', '1492', '7400', '1300', '7200', '2600',\n",
       "       '3300', '5400', '4500', '2100', '900', '3600', '2127', '367',\n",
       "       '4400', '3500'], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig['Dollar_Amt_n'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 7:  change data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_orig['Dollar_Amt_n'] = pd.to_numeric(df_orig['Dollar_Amt_n'], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   200.,    400.,    600.,    800.,   2000.,   1000.,   1200.,\n",
       "         1600.,   3200.,     nan,   5000.,    100.,    300.,    500.,\n",
       "         1500.,   4800.,   1800.,   1100.,   2200.,   3400.,   3000.,\n",
       "         4000.,   6800.,   1900.,   3100.,    700.,   1400.,   2800.,\n",
       "         8000.,   6000.,   2400.,  12000.,   3800.,   2500.,   6200.,\n",
       "        10000.,   7000.,   1492.,   7400.,   1300.,   7200.,   2600.,\n",
       "         3300.,   5400.,   4500.,   2100.,    900.,   3600.,   2127.,\n",
       "          367.,   4400.,   3500.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_orig['Dollar_Amt_n'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Show Number       int64\n",
       "Air Date         object\n",
       "Round            object\n",
       "Category         object\n",
       "Value            object\n",
       "Question         object\n",
       "Answer           object\n",
       "Dollar_Amt       object\n",
       "Dollar_Amt_n    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check data types\n",
    "df_orig.dtypes\n",
    "\n",
    "# notice Dollar_Amt_n is now type float64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10816.000\n",
       "mean       761.001\n",
       "std        643.154\n",
       "min        100.000\n",
       "25%        400.000\n",
       "50%        600.000\n",
       "75%       1000.000\n",
       "max      12000.000\n",
       "Name: Dollar_Amt_n, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# notice now we see summary statistics (rather than frequency counts for string data)\n",
    "df_orig['Dollar_Amt_n'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 8:  visualize data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1137ef208>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qOiR5Kc3p2GNpgt8a4M3APl1DfzPLQ3wGeG+SpwL7AleXUrpDaGH9U9wTeBPwslmWIUnS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137f0a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do barplot of a categorical variable\n",
    "df_orig['Round'].value_counts().plot('barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1137eadd8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7d3bv3n3IsLtrzrUNceb7ELXWa0opi0nOSld835Dk+FLKKcvOfm/ux6X/d/nTz++b5IET\nbQ7r4osvzvbtXaG9Z8+e7NixI8lTWkwKAAAA91I7d+7Mzp07Dxl2d825tsHf811KeUiSByW5vh90\nVZI70z3F/GCbrUm2JLm8H3R5ktNKKY+dCHVeuqenXzF0zgAAANDSus989+/aPit3v0bsEaWUxyT5\nSv/5hXT3fN/Qt/uVJJ9JclmS1FpvLqW8NcmrSik3JbklyWuSfLzWemXf5upSymVJ3lxK+el0rxp7\nbZLdx+qTzgEAALj3OpLLzh+X7vLx2n/+cz/87UlekOTRSZ6T5LQk16Uruv9drfWOiRi7ktyV5NIk\nJ6S7KfuFy/6fZyd5XbqnnB/o2154BPkCAADAUXUk7/n+SFa/XH3NG6xrrbcleXH/WanNV5NcsN78\nAAAAYGwGv+cbAAAA7u0U3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPEN\nAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAA\nA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8\nAwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAA\nwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU\n3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAA\nADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAM7LijnQBHZmFhIYuLi6u22bRpU7Zs\n2TKjjAAAAFiJ4vsYtLCwkK1bt2Vp6dZV25144knZt2+vAhwAAOAoU3wfgxYXF/vC+5Ik21ZotTdL\nSxdkcXFR8Q0AAHCUKb6PaduSbD/aSQAAALAGD1wDAACAgSm+AQAAYGCKbwAAABiY4hsAAAAGpvgG\nAACAgSm+AQAAYGCKbwAAABiY93yThYWFLC4urtpm06ZN2bJly4wyAgAAmC/rLr5LKU9M8m+S7Ehy\nZpKn11p/b1mblyf5ySSnJfl4kp+utX52YvwJSV6V5EeTnJDksiQvqLV+caLNA5K8LskPJjmQ5D1J\nLqy1fm29ObOyhYWFbN26LUtLt67a7sQTT8q+fXsV4AAAAEfgSC47PznJ/0zygiR1+chSykuTvCjJ\nTyU5O8nXklxWSjl+otmrk/xAkmcmeVKSB6crrie9K8m2JOf1bZ+U5I1HkC+rWFxc7AvvS5JctcLn\nkiwt3brm2XEAAAAOb91nvmutH0zywSQppZTDNLkwyStqre/r2zwnyY1Jnp7k3aWUU5I8N8mzaq0f\n6dv8RJK9pZSza61XllK2JTk/yY5a66f6Ni9O8vullJfUWm9Yb96sZVuS7Uc7CQAAgLnU9IFrpZSH\nJzkjyYcPDqu13pzkiiTn9oMel67on2yzL8nCRJvHJ7npYOHd+1C6M+3ntMwZAAAAhtb6aednpCuQ\nb1w2/MZ+XJJsTnJ7X5Sv1OaMJF+cHFlrvSvJVybaAAAAwDFh7p52vmvXrpx66qlJkv379/dDPxiX\nVAMAAHCkdu/end27dx8y7O6ac22ti+8bkpR0Z7cnz35vTvKpiTbHl1JOWXb2e3M/7mCb0ycDl1Lu\nm+SBE20O6+KLL8727V2hvWfPnuzYsSPJU45oYgAAACBJdu7cmZ07dx4y7O6ac21NLzuvtV6Trjg+\n7+Cw/gFr5yT5RD/oqiR3LmuzNcmWJJf3gy5Pclop5bET4c9LV9hf0TJnAAAAGNqRvOf75CRnpSuE\nk+QRpZTHJPlKrfVv071G7OdLKZ9Ncm2SVyT5fJL3Jt0D2Eopb03yqlLKTUluSfKaJB+vtV7Zt7m6\nlHJZkjeXUn46yfFJXptktyedAwAAcKw5ksvOH5fkj9I9WK0m+c/98LcneW6t9ZWllJPSvZP7tCQf\nTfLUWuvtEzF2JbkryaVJTkh3U/YLl/0/z07yunRPOT/Qt73wCPIFAACAo+pI3vP9kaxxuXqt9aIk\nF60y/rYkL+4/K7X5apIL1psfAAAAjE3rV40BAAAAyyi+AQAAYGCKbwAAABiY4hsAAAAGpvgGAACA\ngSm+AQAAYGCKbwAAABiY4hsAAAAGpvgGAACAgSm+AQAAYGCKbwAAABiY4hsAAAAGpvgGAACAgSm+\nAQAAYGCKbwAAABiY4hsAAAAGpvgGAACAgSm+AQAAYGCKbwAAABiY4hsAAAAGpvgGAACAgSm+AQAA\nYGCKbwAAABiY4hsAAAAGpvgGAACAgSm+AQAAYGCKbwAAABiY4hsAAAAGpvgGAACAgSm+AQAAYGCK\nbwAAABiY4hsAAAAGpvgGAACAgSm+AQAAYGCKbwAAABiY4hsAAAAGpvgGAACAgSm+AQAAYGCKbwAA\nABiY4hsAAAAGpvgGAACAgSm+AQAAYGCKbwAAABiY4hsAAAAGdtzRToD5sbCwkMXFxVXbbNq0KVu2\nbJlRRgAAAOOg+KaJhYWFbN26LUtLt67a7sQTT8q+fXsV4AAAwL2K4psmFhcX+8L7kiTbVmi1N0tL\nF2RxcVHxDQAA3KsovmlsW5LtRzsJAACAUfHANQAAABiY4hsAAAAGpvgGAACAgSm+AQAAYGCKbwAA\nABiYp50zOgsLC1lcXFy1zaZNm7yuDAAAOGYovhmVhYWFbN26rX9n+MpOPPGk7Nu3VwEOAAAcExTf\njMri4mJfeF+S7p3hh7M3S0sXZHFxUfENAAAcExTfjNS2JNuPdhIAAABNeOAaAAAADMyZb+aWB7cB\nAABjofhmLnlwGwAAMCaKb+aSB7cBAABjovhmznlwGwAAcPR54BoAAAAMTPENAAAAA1N8AwAAwMAU\n3wAAADCw5sV3KeUXSikHln0+vazNy0sp15VSbi2l/GEp5axl408opby+lLJYSrmllHJpKeX01rkC\nAADALAx15vsvkmxOckb/+a6DI0opL03yoiQ/leTsJF9Lclkp5fiJv391kh9I8swkT0ry4CTvGShX\nAAAAGNRQrxq7s9b6pRXGXZjkFbXW9yVJKeU5SW5M8vQk7y6lnJLkuUmeVWv9SN/mJ5LsLaWcXWu9\ncqCcAQAAYBBDnfn+llLKF0opnyulXFJK+QdJUkp5eLoz4R8+2LDWenOSK5Kc2w96XLqDApNt9iVZ\nmGgDAAAAx4whiu9PJvnxJOcneX6Shyf5k1LKyekK75ruTPekG/txSXe5+u19Ub5SGwAAADhmNL/s\nvNZ62cSvf1FKuTLJ3yT5kSRXt/7/ltu1a1dOPfXUJMn+/fv7oR9Msn3o/xoAAIA5tXv37uzevfuQ\nYXfXnGsb6p7vv1dr3V9K+UySs5L8cZKS7uz25NnvzUk+1f98Q5LjSymnLDv7vbkft6qLL74427d3\nhfaePXuyY8eOJE/Z6GQAAABwL7Zz587s3LnzkGF315xrG/w936WUb0hXeF9Xa70mXQF93sT4U5Kc\nk+QT/aCrkty5rM3WJFuSXD50vgAAANBa8zPfpZT/mOS/p7vU/JuTvCzJHUl+s2/y6iQ/X0r5bJJr\nk7wiyeeTvDfpHsBWSnlrkleVUm5KckuS1yT5uCedAwAAcCwa4rLzhyR5V5IHJflSko8leXyt9ctJ\nUmt9ZSnlpCRvTHJako8meWqt9faJGLuS3JXk0iQnpLtp+4UD5AoAAACDG+KBazunaHNRkotWGX9b\nkhf3HwAAADimDX7PNwAAANzbKb4BAABgYIpvAAAAGNjg7/mGY93CwkIWFxfXbLdp06Zs2bJlBhkB\nAADHGsU3rGJhYSFbt27L0tKta7Y98cSTsm/f3lUL8GkKeUU8AADMH8U3rGJxcbEvvC9Jsm2Vlnuz\ntHRBFhcXVyycpy3kpyniAQCAY4viG6ayLcn2DUWYrpBfu4hPnEEHAIBjjeIbZm5jhbwz6AAAcOxR\nfMMxpuUZdAAAYDYU33DM2vil8AAAwGx4zzcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAM\nTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPEN\nAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAA\nA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8\nAwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAA\nwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU\n3wAAADAwxTcAAAAMTPENAAAAA1N8AwAAwMAU3wAAADAwxTcAAAAMTPENAAAAAzvuaCcAHD0LCwtZ\nXFxctc2mTZuyZcuWGWUEAADzSfEN91ILCwvZunVblpZuXbXdiSeelH379irAAQBgA+6Fl53vHlmc\nlrHmNU7LWPMaZ/2xFhcX+8L7kiRXTXx+ceLnS7K0dOuaZ8cXFhayZ8+eQz6/9Eu/dI9hCwsL65+q\n3W3mUas4LWPNa5yWseY1TstY8xqnZSxxZhdrXuO0jDWvcVrGmtc4LWOJM7tYLXMadfFdSnlhKeWa\nUsrXSymfLKV8x8ajHvtF070vTstY8xpnI7G2Jdk+8fnkxM/b1vzrg2fQd+zYccjn3/7bf3uPYVu3\nblt3AT7GFefYchpbnJax5jVOy1jzGqdlLHFmF2te47SMNa9xWsaa1zgtY4kzu1gtcxrtZeellB9N\n8p+T/FSSK5PsSnJZKeVba62rn4YDZubQM+iTxfquJBdP/L43S0sXZHFxcdVL2Jffh75///7s2bPn\nkDbuQwcA4Fgz2uI73Z77G2ut70iSUsrzk/xAkucmeeXRTAw4nINn0A86ddnva1vpPvQdO3Yc8vs0\n96G3KuIP91C65bFmGQcAgGPTKIvvUsr9kuxI8ksHh9VaaynlQ0nOPWqJAYM6/Fn09Z9Bb1XEr/ZQ\nuslYs4ozGW+IAwvzdIDCPNp4nCON5WoVADi8URbfSTYluW+SG5cNvzHJ1hX+5sQk2bt3798PuPvn\n9yc5+PPnk7xz4s+uyfK/W27YOMtjzS7OyrHMo/XF2Ugs8+jwsa6ZGHrLsrjTxekK3X+R5Mx+6G8l\n+dGJVtdnaemt+ehHP5pt2w5/b/vh4yyPNbs4SXL99dfnh3/4n+X225cOGb78wMLxx5+Y3/mdS3Pm\nmWfmcIaOszzWrOLMYtqO9Xk0bZyNxFpvnCT50pe+dI8DAp///OfzznfevR7ZtGlTvumbvmnFGLOO\nc6SxxhbncLFmGedwscyjtWOZR+uPc6SxzKO1Y40tzuFiDRVnYp/0xFWDJym11rXazFwp5cwkX0hy\nbq31ionhv5LkSbXWe5z9LqU8O4fu6QMAAMAs/Fit9V2rNRjrme/FJHcl2bxs+OYkN6zwN5cl+bEk\n1ya55+F8AAAAaOvEJA9LV4+uapRnvpOklPLJJFfUWi/sfy9JFpK8ptb6H49qcgAAALAOYz3znSSv\nSvK2UspVuftVYycledvRTAoAAADWa7TFd6313aWUTUlenu5y8/+Z5Pxa65eObmYAAACwPqO97BwA\nAADmxX2OdgIAAAAw7xTfAAAAMDDFNwAAAAxstA9ca6F/YNtzk5yb5Ix+8A1JPpHkbdM+vK1VnNax\nWmg4j45P8vQV4ry31np7y7yZT/Pcj0zbvTefsebE6sb2nbXMp1Wssc2jlsY2j+Z5Xo/N2Ob12PJp\naWx1UZ/ToPN7bh+4Vkr5jnQvOr81yYeS3NiP2pzkvHSvLTu/1vqns4gzQKwNd4yG8+isPs6Dk1yx\nLM45ST6f5Km11s+ulVOraWscZ3QHX8Z40GSjsea5H7Wctsb9cWzTNnf5jDWnVrHGls9ErLMPE+fy\nWuuVU/6+se4xAAAgAElEQVT9qNZHjftQk1hjXK+NbZkdW5w+1uiW2THFmedlv0U+h4l3xOvalnVR\nH2903/9h/485Lr4/meTPkjy/LpvIUkpJ8utJHl1rPXcWcRrn1Gpl3iqfP0zytSTPqbXevGzcKUne\nkeT+tdbzV4vTeNpaxRndwZcxHjRpEWvO+1GTaWvcH8c2bXOZz4hzarHMjiqfPs7pSd6T5AlJFpbF\n2ZLk40meWWv94hpxRrU+apxPq/44qvXaSJfZscUZ4zI7tjjzvOy3/P43vK5tXGON7vtfUa11Lj9J\nvp7k21YZ/21Jvj6rOI1z+sMk/y3JKYcZd0o/7rIZ5nNrkm9fZfyjktw65TxqNW2t4nwyyRvTH6ha\nNq704y6fctqaxGoYp8k8ahVrzvtRk2lr3B/HNm1zmc+Ic2qxzI4qn77tpenOcmw9zLit6XYIf3tW\n31nDed0yn1b9cVTrtZEus2OLM8Zldmxx5nnZb/n9b3hdm7Y11ui+/xVjbOSPx/xJck26oxYrjX9O\nkmtnFadxTq1Wwq3yuS7JD64y/p8muW7KeTS2DdUYD76M8aDJhmPNeT9qMm2N++PYpm0u8xlxTi2W\n2VHl07e7JcljVxm/I8kts/rOGs7rlvm06o+jWq+NdJkdW5wxLrNjizPPy37L73/D69q0rbFG9/2v\n9JnnB679pyRvKqXsSPLh3PMypn+Z5CUzjNMy1leTPCzJX6ww/mF9m1nl85Yk7yilvGKFOD+f5LVT\nxEnaTVurODckOTvJ1SuMPzt3T++sYrWK02oetYo1z/2o1bS17I9jm7Z5zWesObWINbZ8kuS2dGc5\nVvKNfZu1jG191DKfVrHGtl4b4zI7tjhjXGbHFmeel/2W33+LdW3LGmuM3//hbaRyH/snyY+mu5zp\njiQH+s8d/bAfmXWcVrGSvDzJV5LsSvLovkNs7n/eleTLSS6a8Tx6abqjRQeS3NV/DvTDfnYdcZpM\nW8M4L0yylORXk/xQuvtGzul//tV0R9peMOW0NYnVME7LftRqfs9lP2o1bY3749imbW7zGWNOLWKN\nLZ8+zuuTXJvkGZm4/DDdTuIz0p1pee2svrPG09Ykn8bTNpr1Wsv+OLZ51HBej3GZHVWcxt/ZqJb9\nxvOoybo27eqQ0X3/K8bfaIBj4ZPkfknO7D/3O9pxWsRq3TEazqOHp3vK4LlJHn6EMUazoerjjOrg\nS+M4o9uZm9d+1GraGvfHsU3bXOcztpxaxBphPick+bV0Z1zuSndJ89f7n29L8oYkJ8zyOxtgPm04\nn9axxrJeG+MyO7Y4Y1tmxxin5Xc2tmW/4bxuuq5NgzpkjN//4T5z+7Tze4tSysMz8Tj9Wus1RzOf\nllpNW8M490uyqf91sdZ6x5HEaRmrYZxm/WhsfXJs/aiVxv1xbNMmnzWMbZkdYT6npLvncPJ1M1fV\nZU+vnbUx9qUxGeM2bV6NbZkdY5yWxpZTw3k9unXt2Ob1cvfa4ruU8oIkm2qtLx9DnNaxWmg4j56W\n5NRa6zvaZMa90Tz3I9M2O2PLJxlnTqxubN9Zy3xaxRrbPGppbPNonuf12IxtXo8tn5bGVhclbeb3\nvbn4/nC6SwgeMYY4jXNqtTJvlc/VSb6l1nrfjcTpY41qQzXGgy9jPGjSZGU13/2oybQ17o9jm7a5\nzGfEObVYZkeVTx/ncUlOqrX+yQbjjGp91DifVv1xVOu1kS6zY4szxmV2bHHmedlv+f1veF3buMYa\nzfd/ry2+51nLBXFsRrihGuPBlzEeNBlVnxxbP2qlcX8c27TJZw1jW2bHlk8fZ2+Sb53X720ejXGb\nNq9GusyOKk5LY8up8fc/qnXtmOa14hsAuFcopTw43cN8/uZo5wIwr6xrVzb3xXcp5ex0T6mbfBDA\n5bXWK49GnNaxWmg4j85I93qQyThX1FpvaJUr82+e+5Fpu/fmM9acWN3YvrOW+bSKNbZ51NLY5tE8\nz+uxKKXsqLVedbTzWG6ev/ux1UV9ToPN77ktvksppyd5T5InJFnIoS9J35Lk40meWWv94izitI7V\nx9tQx2g4j05O8sYkz0pS071nL0kemKQk2Z3kebXWW6fJq485qg3VGA++jPGgyUZizXM/aj1tjfvj\n2KZtrvIZa04tY40tnz7OcUn+0bI4n65TPj17bOujlvm0itUwzjOTfGA983KNeKNZZscWZyLe6Aq5\nEW2vDyT56yT/Jcnbaq3Xref/b53T2NZFh4l1xOva1nVRH3NU+xCHVRu9s2xsnySXJvlEkq2HGbc1\n3Rf627OK0zink5NckuTOdO/BvLH/3NEP+6/pHnIwq3zekuQzSc5Pct+J4fdN8k+S7Evy5innUatp\naxXn9CQfTfeewGuTXNF/ru2HfTTJ6VNOW5NYDeM0mUetYs15P2oybY3749imbS7zGXFOLZbZUeXT\nx7lPkn+f5Kbc/b7og5+bkrwiyX1m9Z01nNct82nVH1vFOZBkf5I3JTlnmmk4hpbZscVpOY/ul+SV\nST6b5Mokz102fnOSu2aVU8M4B/q+ePBv35fk6ZPzfZZ9stV3P8D3v+F1bdrWWKPah1j1/9jIH4/5\nk+SWJI9dZfyOJLfMKk7jnFqthFvlc1OS71xl/BOS3DTlPBrbhmqMB1/GeNBkw7HmvB81mbbG/XFs\n0zaX+Yw4pxbL7Kjy6du/MskXkzwvycOS3L//PCzJT6XbGfuVWX1nDed1y3xa9cdWcQ4k+f+S7Ol/\n/osk/yrJg6aZnoH649jm0RjXIRelO6v4knRF2FeTvHFi/OYkB2aVU8M4B9Id6D4uyTOT/H664u2G\nJL+S7iFiM+uTjZf9lt//hte1aVtjjWofYtX/YyN/POZPksUk373K+CcnWZxVnMY5tVoJt8pnf5LH\nrTL+O5Lsn3IejW1DNcaDL2M8aLLhWHPej5pMW+P+OLZpm8t8RpxTi2V2VPn07W5Icv4q489PcuOs\nvrOG87plPq36Y6s4B9JfsdOvw97Qz7OlJO9O8o9n2YdGOo/GuA75qyQ/OPH7Wf2w30h3ee60Z77H\ntr3++/44Meyb0x0g+lySu5L8yaz65NjWRRNtN7yuTdsaa1T7EKt97pP59VtJ3l5KeUYp5ZSDA0sp\np5RSnpFu5bB7hnFaxrpPkttXGX9732ZW+bwvyZtKKY9dPqIf9mtJ/vsUcZJ209Yqzm1JTlll/Df2\nbabRKlarOK3mUatY89yPWk1by/44tmmb13zGmlOLWGPLJ+mWgdXu0bw+3eWJaxnb+qhlPq1itcwp\nSVJrvarW+oIkZyb5l0m+KckHSynXTPHnY1xmxxan5Tz65nRXKSRJaq2fTVcsfWe6S3ynfaXT2LbX\n9R4Dav1CrfUVtdZHpjuL+rdTxGmV09jWRQe1WNe2rLHGtg+xso1U7mP+JDmhn0G3pTtK9fX+c1c/\n7A1JTphVnMY5vTPd5Vn3OAOW5LFJ/jTJJTPM5wFJPpDuaOGXk+ztP1/uY70/yWlTzqNW09YqzuvT\n3U/7jCSnTAw/pR92TZLXTjltTWI1jNNkHrWKNef9qMm0Ne6PY5u2ucxnxDm1WGZHlU/f9veTXJZk\n02HGbeq/h/fN6jtrOK9b5tOqP7aKc1dWeVZFujOqvzjj5WNs82iM65C/TnLeYYY/ON0lvn+Q6c58\nj217fY8z30f6GeGy3/L73/C6Nm1rrFHtQ6z2mdunnR/UH0l5XLrLX5LuMomraq03H404LWKVUh6Q\n5F3pLum4Kd09F0l3j8pp6RaGZ9davzqLfCbibEvy+NzzCcxXryNGk2lrGOeEJK9O8tx09/8cPKp2\nfLp7gN6aZFetdc2zja1iNYzTrB81jjV3/Wgi3oamrXF/HNu0zXU+Y8upRayx5dPH+Qfpdo6+Lcmf\n59Cn5z4qyafTXSo71ZmrMa2PWuXTOlaDfn0gyRl1HU80XiHO6JbZscVp3Bffku6tSf/iMOO+Ockf\nJ3lErXXVM+Bj216XUr47ycdrrXeu1m4aY1v2G+fTbF3bog4Z2z7EqrHnvfieZ0N2jKNtLBuqiTij\nOfgyQJzR7cy1MrZ+1Erj/ji2aZPPDHNqtEM3tnzuk24H7B5xkvxBrfXAevNqYYx9aQxKKQ9NslAb\n7ZCaz2trtJw9NMm31VovW2H8g9Pdr//2WeXUMk5LY8up4bwe3bp2bPP6cOa6+C6lHJ/u9QDL34f7\niSTvrbWudm9A8zitY7XQeNqavXuYe6957kem7d6bz1hzYnVj+85a5tMq1tjmUUtjm0fzPK/HppRy\nn8MVj33B+ZBa68KM85nL735sddFEXoPN77ktvkspZ6W7xODB6d6DO3k5xDlJPp/kqbV7QMTgcVrH\n6uNtqGM0nEenJ3lPuicJLiyLsyXd64+euZ5Lysa0oRrjwZexHjTZSKx57kctp631hmpM0zaP+Yw1\np5axxpZPH+eMdNuxyThX1FpvmPLvR7U+arwOaRKrYZyHJFmqtS72vz8xyfP7GH+T5PW11svXmq6J\neKNZZscWZyJey2X2e5N8V7qH5B1Idy/479Va/2qdccayvT4l3Wur/mmSm5O8McnLaq139eM3J7lu\nrcvpW+U0tnXRYWId8bq2dV3UxxzVPsRh1QYPFBjjJ8kfJvlvmXgo0cS4U/pxl80qTuOcTk/y0XQr\nuWvTddgr+p8P9OPWfFhEw3xavnu41bS1inNWuldLfD3d/Uu/1X/+uB/2V0nOmnLamsRqGKfJPGoV\na877Uat3s7fsj2ObtrnMZ8Q5tVhmR5VPH+fkJJekewbCHel2nG7sf74z3VOYT5rVd9ZwXrfMp1V/\nbBXnivSvrErytHQPNXpvkv+Q5HfSPdviB6eIM8ZldmxxWi+zV/Tf1x39v3+a7inXdyZ55ayWj8Zx\nfjXdA+P+WZKf7P/+fUmO78dP9f7yVjm1+u4H+P43vK5N2xprVPsQq/4fG/njMX+S3Jrk21cZ/6gk\nt84qTuOcWq2EW+XT8t3DY9tQjfHgyxgPmmw41pz3o1bvZm/ZH8c2bXOZz4hzarHMjiqfvu1bknwm\n3X2I950Yft90rwjal+TNs/rOGs7rlvm06o+t4vxdkof3P38yyUuXjX9Rkj2z6kMjnUdjXIf8ZpLf\nTbftOSHJa5O8vR/3vene4XzhrHJqGOdvkjx54vdN6Yq4y/rpnOr95a1yGtu6aKL9hte1aVtjjWof\nYtX/YyN/POZPunfPrXikNN3lJNfNKk7jnFqthFvls5jku1cZ/+Qki1POo7FtqMZ48GWMB002HGvO\n+1GTaWvcH8c2bXOZz4hzarHMjiqfvt1NSb5zlfFPSHLTrL6zhvO6ZT6t+mOrOF9N8uj+5xsP/jwx\n/pFJvjarPjTSeTTGdcj+JP9o4veT012lcEr/+wVJrp5VTg3j3Jr+YNDEsG9MV9h9OMnDM33xPbZl\nv+X3v+F1bdrWWKPah1jtM+2L1I9Fb0nyjlLKrlLKo0spm/vPo0spu5K8LcmbZhinZazb0h1pXMk3\n9m1mlc9vJXl7KeUZ/b0ySbr7Zkopz0jyG0l2TxEnaTdtreJ8NcnDVhn/sL7NNFrFahWn1TxqFWue\n+1GraWvZH8c2bfOaz1hzahFrbPkkyX1y9yv4Duf2vs1axrY+aplPq1it4nwkyc7+50+l27md9D1J\nvjBFnDEus2OL03qZrRO/H0h31vO4/vdPZPXtVeucWsVZSLJtckCt9ZZ0Z3Pvn+5s/7TGtuy3/P5b\nrGtb1lhj24dY2UYq97F/krw03VGVA+nuRbmr//m6JD876zitYiV5fbp7GJ6RictP03W6ZyS5Jslr\nZ5jPCUl+LV2nvivdvadf73++Lckbkpwwy2lrGOflSb6SZFeSR6e73Ghz//OuJF9OctGU09YkVsM4\nLfvRhmPNeT9qMm2N++PYpm0u8xlxTi2W2VHl07d/Z5I9OcwZkCSPTXdP6iWz+s4azuuW+bTqj63i\nbEt3tuntSX4+3Rms/5rk/+2HLSX58WN0mR1bnJbz6HfSXep7cpL7Jbk4yV9NjD8nyfWzyqlhnNdk\nhcuT0xVwn8z0Z77Htuy3/P5brWtb1Wqj2odY9f/YyB8fK590l4ic238e0SjOwxvmtK5YQ3SMFtPW\nd/DvTXcEe2e6o9X3uDd1FtPWeGU1qoMvreI0nkctY81lP5qYtu/Z4LS16kOjmrZ5z6dF3x7bMju2\nfPo4D0jygX6Z+HKSvf3ny32s9yc5bcbLbOv144byadUfG8+jR6a7h/jm/rs7kO7M2ceTPH3W87n1\n/B5LnMZ98RFJPpvuAVu3p7sM+fsmxv94kl+eVU4N4zwgE5fTH2b8N2aVy5IHnN9jWxe1XtduqA5p\nvfy3WmYP95nbV42tpJRye5LH1Fr3Hu1cNqq/HOJx6c58Jd3j9K+qtd589LJqo5+2HTn0VQHrnrZW\ncfpYD5+MU2u9Zr0xWsdqEadlP2o5v1to3I9Gtaw17ENj/M5GM6/HNn8mchrNMtt4Pdtqmd2W5PG5\n5+tmrl5vTq2MsS+NTSmlpHtq8X3S3Vd5xxHEMJ/X0HA5Oyndvb0nJPlk7V8Xd5RzGt33P7acGq+z\nN7SuLaWcmeSnc8/X1f23JG+r/Sve1pHPqOb14cxt8V1KedUKoy5M92j8LydJrfX/niLWi5KcneT9\ntdbfLKX88yQ/l27j8DtJ/l2t9c4p87p/uk7xlVrrp5eNOzHJj9Ra3zFNrI0qpWxP9zCEa/rf/3kO\nfbfm62qtvzllrKbvHh6rUsrJSX4k3Sufrkvym7XWLzeK/Q/SvUvyuVO0Pbiy+0StdV8p5dvS9e0T\n0l3m8z9a5DRr96J+VNLd13hWuteyXDbNTmYp5bVJ3l1r/eiwGW5cKeWvk5xf1/mu14b//0OSfLXW\n+nfLht8vybm11j+ZcT73ir49L0rjd083yukH0+2LXFZr/Xjp3q/8kvT7IrXWae+NnEl/LN37kJ9X\na335FG1Ht01rNb9bfm8TMY9oG8LGradfD5zHUd3GtlBKeVySD6W7guLr6dZH70pyfLqnqH86yVNq\nd8/9rHMbbB9inovvA0n+LPd8+NB3p7sP4WtJaq31e9eI8/NJfjbJH6Q7uvfqJP8m3b0tB9LdZ/lr\ntdZfmCKnb+3jbEn3kIqPJXlWrfX6fvzmdE/1u+8UsTa84Syl/FmSf11r/VAp5SfT3efy5nSXjWxN\n937DC2ut/2WNOGelewXDg9O9jmHyhfTnJPl8kqfWWj+7Vk4TMTfU6VvtOJVSPp3ku2qtX+mL4z9J\nd6nNZ9Jt9O5I8viNnAWf+L8ek+51Kqt+/6WUp6R7B+rfJTkp3b0s70jX3++Tro//k2l2VobaATuS\nHYPW/ajhjlOLZe39SXbWWveXUh6Y7nKss9Pd5/igdP3pSbXWL60R50C6dcfnkrw13WtdbphmOqa1\n3g16KeVnVhj1qiSvTDevUmt9zRSxWszrM9MtHzvSzat3JXnBwXXJetazffsNb4Bb9u3+AO7OHOYs\nQa31w9NM0zRa7GA2inFEO5illLNzz350ea31yin//ookr6i1vq+U8rR0B9rfl277+K1JfjDJD9da\n3zdlvAelezbDn/Xbk01J/kW64vK317oar5TyvCSvS7ee/5YkL0x3GeVvpbus8jlJfq7W+qtT5NJ8\nm73C/zPzbdqyuEdcoLaa3w3jNNmGrBK/WSF3JLGGOECxkXzWiDdVv55ov9Flv9k2diJms8JyI+va\nUsrHkvxhrfVl/e8XJHlRrfXxpZQHJPkfSf6k1nrhlNPUYr+/6T7EYdUG166P8ZPk/0m3Q/K9y4bf\nkeQfriPOZ9NtYJPkMeleHP9jE+OfkYkHTKwR63fTbbw3pdsYvK/PcUs/fnOmeIhD/7efS3eU6I/T\nrcR/q//560n+KslZU8S5NclD+5/3JPmXy8Y/O8lfThGn5buHz0xyZbqN0p3pNsDfMDF+2nl0RfrX\nFyR5Wh/vvUn+Q7qdqNuzyusNJuIcSHJ6//Ml6e4/O7X//Rv6aX/XlNP2Q2t8/tWU0/aJJP++//lZ\n6R7A9YsT4385yR/Mqh/1sd4/MV8emO6BJAeSfLGf93uTfNMM+9Hz0i3rf5rudSgXpLuX8M1Jfr3v\n+9O8f7TVsjbZj96Q5C+Tv3+v7UP6PH9tyjjnpTsI+KW+H783XSFwn2nmzUSsn1nhc2eSXzr4+5Q5\n/W26h5lMfg6k24m/Jslfz3Bev73vf49L8n39vP3/kzygH785yYEp4jRZF7Xs2/08ujZdsbTQz+P3\n9dN7Z5J3JzluPf1glf/rMdNOX4sYDfvj6Uk+2s+ba9NtC67ofz7Qjzt9ijhN3j3dtz073YmAA+nW\n1zvSbfs/k24f49Yk29eI8ZdJfrL/+Xv6ZeIFE+N/PMmnZ9wfH73G50em+f7Tbpu24e1Q6/ndME6r\nbUiT5axlrLTbXrfKp0m/7mO1WPabbGP7WC23axte1/bT/4iJ3w8+QX1z//s/TvKFKfNptd/fZB9i\n1f9jI3889k+S70j3kvf/lOR+/bD1Ft+3pi+O+99vz6HvNXxopnj/ZN/2xiSPmvi9pHs4wN+ke3DF\ntIVlqw3nYpIdE7k9Ztn4R2aG753u27bacW6y45RDN3ifS/KPl43/ziQLU07bwQdkHVjlM833vz99\nEdKvqO7IxNMmk3x7unuAZ9KPDjOfjmjHoHE/arXD02pZm5w/Vyf5oWXjz8t0BepknPul2wn4YLqN\n6BeS/GKmP2DSqmj+9XSvB9q2bPh617Wt5vUXkpw98fsJSX6vz/GBmX4922wD3Kpvpysufj13X7X2\n0nS3QyXdWbVrMv3T7je8g9kixgD98dJ0xdzWw4zbmu4A6mGfZLysbZN3T0/07Tene1DTS/rpfPPE\n+P+S5Hen6EPL90W+feL3h60jn1b9cbVt2sHhs9ymNSlQW87vhnFabkNaFXKtltmWByha5NOkX/ex\nWiz7Tbax/d+03K5teF2brlB/wsTvB6/muv/E8vH1KfNptd/fZB9i1f9jI398LHzSnZ18e7pLfr49\n3YpvPTuEf53ufoOk27m5K8n/OTH++5NcM2Wsm5cvPP3w1/UL5BOn+ULTbsP5X5O8pf/53ekusZsc\n/3NJ/tcUca7LKkeTkvzTdJdoTDOPWu04N9lx6lcC3zSR27cvG//QdawYvpDkaauM/z+mnLb9SR45\n8fstOfTI4VQ5tepHE/NpQzsGjftRyx2nVjupB/vRjVn2JNX+O1taz3xeNnxLkovSbcim3SlouUF/\nRrozsS860jgN5/XfJfmWZcOOS3fl0Z/1caZZzpptgFv17XS3S33LxO/H9337Qf3vT8v026MN72C2\niNG6P6ZbH97j1TcT43ckuWWKOO9N/6TmdAe4fmbZ+J9M8pkpc/rKwelKd9DsrmV9a3uSz68R42+T\nPLH/+cH9vP3+ifHfneRvZ9wfF5M8N93663Cf759yWWu1TWtSoLac3w3jtNqGtFzvt1pmW22vW+XT\npF/3sTa87PftNryN7f+m5XZtw+vadFfx/XmSp6Q78PI/kvzRxPjzk3x2ynxa7fc32YdY7XNc5lzt\nrtH/v0opz0p3U/96r9F/Z7oXwL833Yr7lUn+U3/Pxl3p3kl56ZSxrk53tOmQ+ztqrS/qbk3K700Z\n56vpVkZ/scL4h+We97ofzkuTfLyU8pF0R7/+dSnlybn7nu/Hp1vg1/KWdPPoFUk+nEPvHzsv3Tx6\n7RRxkuTUdK+rSJLUWm8rpfxwkt9O8kfpLkeaxkfS3Rf5v9KtVJ7c/3zQ96RbCU3jw6WUO9Odfdua\nQ+f7Q9M/vG8KV6VbGb13hfE13dUQa7k23YGgz/W/n5tupXzQlnT3t62lVT86qPb/PmAit4M+m27H\nYzUt+9GX0303C6WUB6dbcW7J3dP60HQbxbW0nEdvK6Xclm4D/PB0R/sPOmMdce6h1rqQ5KJSysvS\nHc2e5m+eX0p5RpLLSimvrLW+bgP///9u79xj56qKOP6ZyqO0QoDElIfIqygIgoJIkGhE3hIwRUFF\nDUgMCQlqUBOeCmgQKSAYEBREpQEhGAREARMeEpAaoZgQRVCkLQaLWB4CASql4x9zlm63v/3t3b2z\n+7tdvp/kJr/fnr1nz517Zubce86ZucHM/kjcv4OBLwxQTZasHydmXd/Y3+fuy83scMKOVNqjS54t\ngry+/Twxg9JiBtG3W3vhHyJmDqrwLBHPpNs+8R2Bm0dQB5DaH5cRtrob65fv9OIk4J5iP+4FzjKz\n3VnpHz9F7CeswjrEbB7u/pqZvUwM8Fu09u1Oxk3AFWZ2JbFNaR5wvpk5MRY5j4gpU4Ws/rgA2Mzd\nF09UaGYbMlqfBvX9UIsseWfet9o+JNnuZ9WV4q8T25PVryFH97N8LOT6tQxbexrhs24mns/md7TB\niYnAKmSN+7PGEN2p8+S+ph3EsqOPAzP7OGcacArRMU4mFO7ThGNYCvy0an3l/FsmKb+Eakuqv0UY\nohNKB5lVjp3LZ89QfenhhsR+iL8QBmIZ4QivBt7fh5yycg8/BHxigs9bb50WU+1N+g7l/lxJKPeL\nxEz/KeWzV4GjK9RzesdxQEf5ucA1Fa/tQ5RVFF3KZ1IhdyQx4Dt4kvLvUFY0jLAfrQB+Q+yreZaO\nWRUiiE+VZYNZ/ehiYk/VqcQ+oJ8Rg+aDgP1LP7tiVDIi7ET7cURH+Vzgtgr1LKTMcmYdwObEAPxW\nYgDX99v0trqMsHNLiKXw/cw2ZMn6HLosTy925KaKNiTFFmX27dKPfwdsTwy+r6VtGR0xi1Z1G8xv\ngdMmKd+FHv4oo47s/gj8gPBhc2jbwkAMEucUHbqoYl3bAtdQI/d0qeevtMWfAQ6mLKss/+9Bj9lP\nwj9cRswS/YgY1H+d8NkriIFzz73syf1xDvC5Sco3Ao6qUE+WT0vxQ5nyTqwnxYe0fT/T7tfV2RR/\nndielH5dvltb9zvqG9jHlvPT/Bq5tnY6bXvPB+yHWeP+lDHEpL9R52QdU3eQ9JAyhHZtTby13pOy\n9zhv06sAAAohSURBVKLP86t0+qr7UVIGTuN8ZPUj8gcG7f1om6rntZ2fNlAdha6V9k6fwn5Qy6FP\nUN9uRJqgjUbdH4udWG3feEf5lhXqGYoDrmMjiQA389vks4hV98Z+EvhSxbpqDzAz6sjuj8QyykuL\nrr9OvFR+pfy9jHjJve4A7ZlFzNCsPcD1nE5kNulWfhZwfb/1lnNnAOsPcm7d/ti0g2Q/1OU3pteR\n9xDqacV/6NuHZNr9mjqb+mIp+9pq3p+h6D7hY0+gfx+b5teGYWsT5L0t8VJ64HE/SWOIyY6xTTX2\nZsHMtqYtvL8npLyaSsxsLWCGu78wSfnm3mU5UJdzjBi0TgOWunJhrsaw+5FFfvTX3f3VAc79HxEM\ncNJ0HBXrmk4MnvvOGTluujYRZrYbkcJqnrs/1+v7Q2zHlMt6GLYoCzPbjhj4POLuy0f9+6OiTn80\nsw2IbV6zykdPAQu63c+pxMxmEPaxynL4znPT7OO4U8cPNZWM+29muxIr82rb/UwfUsdfD6M9w2DU\nuj+kMfYGxMuA9lRjU2prmz7u18P3GGKRj/pMdz9mhL+5HqF8z7r7wx1l04k30PMSfifl2qZCRk3E\nzHYg9vbf5+6Pmtn2xIzlusBV3mde1Ul+p6e8zex7XYq+QqR5ewbA3b86ivZ0OW8mEcF5NjEbe627\nV9rzPyodyaIfGWVcm5ldBFzn7vfUaHZae8p3W/ox390fGVQ/yuD2udZLBDP7PKvmH73Y3a+tfIHd\nfyfNrlWtK0tG2XVl0CS/lm0fzex4Ig3SLe5+bemTJ1PyKgPfrPJip2l2LcunZepskn0cin/s8GlL\niG10VePY9Kq7Hx9S+7410YeU79eya5n3PlNGTSSpHw3fX9eZNtfRzIOE/Kx9/t47WZnX73Ui6MGm\nbeW1w/JnX9uoZdTEg4guuYww3K+U/58mUmPcQSzV+mjSb/WUd+k/fyKWmLUfK4i8lHcBd46yHwEP\nAxuXv7cg9jA9X9rzLBGoqOdSzVHqSGL/qCqjlGtrO/9vxPLzTQZsd1Z70vSDiJC6b/n7i0R03+8T\nDv0CYm/aMaO6Z1l1Jcsos651iAeKC4itR9eUvw8H1hllP0qUdZp9JPZDvkAEi11S9G0psef25CL3\nM5skoynoQyk6myWjrPvP6j5tEQP4tKx+nXnfaJgPybq2rHufKaOOOt/OBPu1icCAH87oS6OSdaln\n6P5aM99rIGZ2aI+vbAOc7+79RnYftD03EEp2NBHA7ULg3cBH3P0JM5tFpC3p2Z6sa2uajJqImd1H\nGOzTLLIBXELkQD21lJ9N5IHfv0JdteVtZicBxxL5Pu9s+/w1YmnVw93OHUZ7Sj0rCOf0tJldReyP\n/Ji7/9fM3koEKPmPux/Zo540HckiUUYp11ZkvR+R5uizRFTWW4kcqbe4+4qeF5Xbnkz9eJlIN7PY\nzB4s9VzeVn4kcKq779ijnjS7lqSzmTJKqcvMZhOB4DYjgje1R/Leg8j3e5C7P9ajnkb5tWT7+BgR\nQ+GXZrYLEd35KHe/upTPAea6+3Y96mmUXWuozmbZo5T7n+XTSl1ZPiRL9xvlQ7KuLVn3U2RU6tqU\n2CO+GxGV/OdEfvaXSvkaqf9Zuj8p2W8edAz/YPLcqm/kWB1he/4NvKftfyOCMCwmjG8/bwlTrq1p\nMmriQeRWnV3+nkZEBG0P3rQT1SPDZt233YFHifQra5fPBsllmdmPWnlj/wHs11H+QSpEl87UkcT7\nnyWjlGvrkPXaxMzlbcTb6ieJwDSzR9ieTP1YSjj9Vvt26Sjflur54lPsWkZdyTJKqYuY5biRCQLm\nEFF4b6RLwKFh9KNkXcuyjxPlVd6x7f8tqZYPt1F2raE6m9mPat9/knxacr/O0v1G+ZDka8scG9WW\nUTn/SuAPRGyNfYmUxfdTgsAVOfWV7aLOkSjrFN2f7JiGWBNZAhzm7tMmOoBdR9ye9QjFBcCD44j0\nbHcTS3iqknVtTZNRU3EAj7edrxLGq8WLxFvRKqTI293vJ96ivg14wMx2YmXe1n7IvP+t35/O6rlm\nnyxt7UWmjmSRJaP0a3P319z9Onc/kBjsXE68pX90xO3J0o9bgePK33cTUcnbOYLIQdyLzH6dVVeW\njLLq2otIf7ZasJ/y2TeI4FK9aJxfS7SPTxEzeVgE73tL6//CjsRyzV400a41TWfTZJR4/zN8GgzB\nzybZkSb5EEi4tsR7315nHRlBPHB/2d0fcPfbCdu7BLjTzDZu/UydNg5ARj/K0v2u6OF7zWQBoYTd\ncOJN3ah4hHjztWoj3I8nlqT8qo+6sq6taTJqIouA9mWFexL561u8g9UdczfS5O3uL7n7UcDZwO3E\nwLBfMu//HWXp0QbAuzrKtqQEO+lBpo5kkSWjoV6buz/h7mcQyyMPHGF7FpGnHycC+5jZ3cA/ga+Z\n2T1mdln57AzgpAr1ZPbrjLoWkSejrLqeB7aapHyr8p1eNNGvZdnHq4F5ZnY5sUR/LnCemR1nZscC\nPySWH/eiaXZtEc3T2VQZJd3/DJ8Gef16EXn3bfVGTJ0PgcRrS7r33eruV0YQD7JvRJD3iNh+GHHN\ndxHRxkfJInJknaX7XVmrzsliyjiXyIvYjceAvUfUFggn/Rkimf0quPvxZjaNCFRQhaxra5qMmsil\ntBlvd/9zR/lBwJ1UI13eHhF47yWce7/pnLLac2bH/y91/H8IUCVqaKaOZJElo6xrW0wsYZwQd3di\nSfGo2pOmH+7+LzN7H+GwDyEGpB8gAh79HtjL3R+oUFWmnmXUlWlDsur6MfFg+W0iyE77nu99iGBj\nF1Wop4l+rb0Ndezj6URAoj2J2a7vEkGG5hJ5w28mVgj0oml2rYk6OxQZ1bj/WT4N8vp11n1rmg+B\nXBvZqqOO7mfJCOBxYGfg723nLzezw4FfAL/us211SZF1ou53RQHXhBBCCDE2mNmJRHqZTVi57NGI\n5dYXuvvcqWqbEEKMA2Z2DvBedz9ggrK1gOuBQ8oWBNGGHr6FEEIIMXaY2dbEAzhEoJ2FU9keIYQY\nF8oD9oyJ4mu0lW/u7v3Ozo89ehshhBBCiLHD3Re6+/xyLAQwsy3M7CdT3TYhhFiTcffl3R68C5sS\nW1xEB5r5FkIIIcSbgpLX+kEfUe5ZIYR4MyJb2x0FXBNCCCHEWGBmh/b4yjYjaYgQQowxsrWDo5lv\nIYQQQowFZraC3imOXLMxQggxOLK1g6M930IIIYQYF5YAh7n7tIkOYNepbqAQQowBsrUDoodvIYQQ\nQowLC4j8t93oNVMjhBCiN7K1A6I930IIIYQYF84FZk5S/hiw94jaIoQQ44ps7YBoz7cQQgghhBBC\nCDFktOxcCCGEEEIIIYQYMnr4FkIIIYQQQgghhowevoUQQgghhBBCiCGjh28hhBBCCCGEEGLI6OFb\nCCGEEEIIIYQYMnr4FkIIIYQQQgghhowevoUQQgghhBBCiCHzf29tbXNpLDf6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11330b0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do barplot of a numerical variable\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "df_orig['Dollar_Amt_n'].value_counts().plot('bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x113ae9048>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1/yT3SXJtP+ryJJ9PdxXzpTZ7kuxOcmk/6tIk96yqR86UOjvd1dPfP3afAQAAYEgb3vPd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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113ba5ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# do barplot of a categorical variable\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "\n",
    "df_orig['Value'].value_counts().plot('bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}