PandasCheatSheet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"top\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1>Python <a href=\"http://pandas.pydata.org/pandas-docs/stable/\">Pandas</a> Cheat Sheet</h1></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a data analyst, these are common tasks I perform using Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Pandas display output options](#options)\n",
    "- [Quick summary statistics, # of rows, columns, etc](#quick_summary)\n",
    "- [Sorting More Than One Column](#sorting)\n",
    "- [Removing duplicates](#removing_duplicates)\n",
    "- [Creating a column based on values from another column](#map)\n",
    "- [Removing a column](#remove_column)\n",
    "- [Replacing values in a series](#replacing_values)\n",
    "- [Reading CSV or Excel files](http://pandas.pydata.org/pandas-docs/stable/io.html)\n",
    "- [Renaming axis](#renaming_axis)\n",
    "- [Renaming indexes or columns](#renaming_indexes_columns)\n",
    "- [Binning data into intervals](#binning)\n",
    "- [Grouping data](#group)\n",
    "- [Sub-selecting or Slicing a Data Frame](#slicing)\n",
    "- [Boolean indexing](#boolean)\n",
    "- [Obtaining columns with partial column labels](#partial_column_labels)\n",
    "- [Getting value counts](#value_counts)\n",
    "- [Getting cumulative sum](#cum_sum)\n",
    "- [Pivot table](#pivot_table)\n",
    "- [Creating a new column based on a grouping using transform() method](#transform)\n",
    "- [Pivot table Percent of Rows](#perc_rows)\n",
    "- [Pivot table Percent of Columns](#perc_columns)\n",
    "- [Transpose a data frame](#transpose)\n",
    "- [Converting index to a column](#index2column)\n",
    "- [Converting column to index](#column2index)\n",
    "- [How to add or fill in missing dates](#missing_dates)\n",
    "- [How to connect to an ODBC data source (Windows)](#database)\n",
    "- [How to convert data in wide format to long format using melt()](#melt)\n",
    "- [How to convert data in long format data to wide format using pivot()](#pivot)\n",
    "- [Using category data type to control sort order](#category)\n",
    "- [Merging 2 data frames using merge()](#merge)\n",
    "- [Finding rows containing data with missing values](#missing)\n",
    "- [Converting a data type of a column in a data frame](#convert_type)\n",
    "- [Plotting data frames using MATPLOTLIB ver 1.5+](#matplotlib)\n",
    "- [Plotting data frame directly and creating sub-plots](#subplots)\n",
    "- [Creating a Bokeh chart from a Data Frame](#bokeh)\n",
    "- [Making plotly charts directly from data frame](#plotly)\n",
    "- [Method chaining](#chaining)\n",
    "- [Sending Pandas data frame to R using rpy2 IPython notebook extension](#rpy2)\n",
    "- [Python clone of R's dplyr](http://nbviewer.jupyter.org/github/pybokeh/jupyter_notebooks/blob/master/dplython/dplython_example.ipynb)\n",
    "- [BONUS #1: A HUGE list of python and pandas snippets by Chris Albon](http://chrisalbon.com/)\n",
    "- [BONUS #2: More goodies from a major pandas contributor, Tom Augspurger](http://tomaugspurger.github.io/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='options'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting display output [options](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.set_option.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option(\"display.max_rows\",1000)    # or pd.options.display.max_rows=1000\n",
    "pd.set_option(\"display.max_columns\",20)   # or pd.options.display.max_columns=20 \n",
    "pd.set_option('precision',7)\n",
    "pd.set_option('large_repr', 'truncate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"quick_summary\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick summary statistics using df.describe() and data types using df.info().  Also check out [pandas-summary](https://github.com/mouradmourafiq/pandas-summary) - an extension for data frame's describe() method."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top]](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>group</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  ounces\n",
       "0     a     4.0\n",
       "1     a     3.0\n",
       "2     a    12.0\n",
       "3     b     6.0\n",
       "4     b     7.5\n",
       "5     b     8.0\n",
       "6     c     3.0\n",
       "7     c     5.0\n",
       "8     c     6.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame({'group': ['a', 'a', 'a', 'b','b', 'b', 'c', 'c','c'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.055556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.855307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ounces\n",
       "count   9.000000\n",
       "mean    6.055556\n",
       "std     2.855307\n",
       "min     3.000000\n",
       "25%     4.000000\n",
       "50%     6.000000\n",
       "75%     7.500000\n",
       "max    12.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### df.info() shows data types, number of rows and columns, and memory usage of your data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 9 entries, 0 to 8\n",
      "Data columns (total 2 columns):\n",
      "group     9 non-null object\n",
      "ounces    9 non-null float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 216.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='sorting'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sorting More Than One Column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top]](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Just pass a list of columns you want to sort and pass corresponding list of True/False to the ascending parameter**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>group</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  ounces\n",
       "6     c     3.0\n",
       "7     c     5.0\n",
       "8     c     6.0\n",
       "3     b     6.0\n",
       "4     b     7.5\n",
       "5     b     8.0\n",
       "1     a     3.0\n",
       "0     a     4.0\n",
       "2     a    12.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=['group','ounces'], ascending=[False, True], inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='removing_duplicates'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame({'k1': ['one'] * 3 + ['two'] * 4, 'k2': [3, 2, 1, 3, 3, 4, 4]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   3\n",
       "1  one   2\n",
       "2  one   1\n",
       "3  two   3\n",
       "4  two   3\n",
       "5  two   4\n",
       "6  two   4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='k2')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>one</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   3\n",
       "1  one   2\n",
       "2  one   1\n",
       "3  two   3\n",
       "5  two   4"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates()  # by default, duplicate is defined by all columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define duplicates by column name(s):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>k1</th>\n",
       "      <th>k2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>two</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    k1  k2\n",
       "0  one   3\n",
       "3  two   3"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop_duplicates(subset='k1')  # duplicate in column k1 only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"map\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a new column based on values from another column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>food</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bacon</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pastrami</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacon</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces\n",
       "0        bacon     4.0\n",
       "1  pulled pork     3.0\n",
       "2        bacon    12.0\n",
       "3     Pastrami     6.0\n",
       "4  corned beef     7.5\n",
       "5        Bacon     8.0\n",
       "6     pastrami     3.0\n",
       "7    honey ham     5.0\n",
       "8     nova lox     6.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame({'food': ['bacon', 'pulled pork', 'bacon', 'Pastrami','corned beef', 'Bacon', 'pastrami', 'honey ham','nova lox'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Suppose you wanted to add a column indicating the type of animal that each food came from. Let’s write down a mapping of each distinct meat type to the kind of animal using a dictionary and a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "meat_to_animal = {\n",
    "'bacon': 'pig',\n",
    "'pulled pork': 'pig',\n",
    "'pastrami': 'cow',\n",
    "'corned beef': 'cow',\n",
    "'honey ham': 'pig',\n",
    "'nova lox': 'salmon'\n",
    "}\n",
    "\n",
    "def meat2animal(series):\n",
    "    if series[\"food\"]=='bacon':\n",
    "        return 'pig'\n",
    "    elif series[\"food\"]=='pulled pork':\n",
    "        return 'pig'\n",
    "    elif series[\"food\"]=='pastrami':\n",
    "        return 'cow'\n",
    "    elif series[\"food\"]=='corned beef':\n",
    "        return 'cow'\n",
    "    elif series[\"food\"]=='honey ham':\n",
    "        return 'pig'\n",
    "    else:\n",
    "        return 'salmon'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>food</th>\n",
       "      <th>ounces</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bacon</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pastrami</td>\n",
       "      <td>6.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacon</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "      <td>salmon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces  animal\n",
       "0        bacon     4.0     pig\n",
       "1  pulled pork     3.0     pig\n",
       "2        bacon    12.0     pig\n",
       "3     Pastrami     6.0     cow\n",
       "4  corned beef     7.5     cow\n",
       "5        Bacon     8.0     pig\n",
       "6     pastrami     3.0     cow\n",
       "7    honey ham     5.0     pig\n",
       "8     nova lox     6.0  salmon"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['animal'] = data['food'].map(str.lower).map(meat_to_animal)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Or we could use apply() and apply the meat2animal() function to the new column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>food</th>\n",
       "      <th>ounces</th>\n",
       "      <th>animal</th>\n",
       "      <th>animal2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bacon</td>\n",
       "      <td>4.0</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>pulled pork</td>\n",
       "      <td>3.0</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bacon</td>\n",
       "      <td>12.0</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pastrami</td>\n",
       "      <td>6.0</td>\n",
       "      <td>cow</td>\n",
       "      <td>salmon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>corned beef</td>\n",
       "      <td>7.5</td>\n",
       "      <td>cow</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bacon</td>\n",
       "      <td>8.0</td>\n",
       "      <td>pig</td>\n",
       "      <td>salmon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>pastrami</td>\n",
       "      <td>3.0</td>\n",
       "      <td>cow</td>\n",
       "      <td>cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>honey ham</td>\n",
       "      <td>5.0</td>\n",
       "      <td>pig</td>\n",
       "      <td>pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nova lox</td>\n",
       "      <td>6.0</td>\n",
       "      <td>salmon</td>\n",
       "      <td>salmon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces  animal animal2\n",
       "0        bacon     4.0     pig     pig\n",
       "1  pulled pork     3.0     pig     pig\n",
       "2        bacon    12.0     pig     pig\n",
       "3     Pastrami     6.0     cow  salmon\n",
       "4  corned beef     7.5     cow     cow\n",
       "5        Bacon     8.0     pig  salmon\n",
       "6     pastrami     3.0     cow     cow\n",
       "7    honey ham     5.0     pig     pig\n",
       "8     nova lox     6.0  salmon  salmon"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# axis=1 means to apply the function for each row, but I prefer to use axis='columns' instead\n",
    "data['animal2'] = data.apply(meat2animal,axis='columns')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can also use [dataframe.assign()](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro-chained-assignment) function which was added in pandas version 0.16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.246394</td>\n",
       "      <td>-0.496658</td>\n",
       "      <td>-0.496105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.690199</td>\n",
       "      <td>1.245658</td>\n",
       "      <td>1.356873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.132093</td>\n",
       "      <td>0.834331</td>\n",
       "      <td>-0.158322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.046662</td>\n",
       "      <td>0.717323</td>\n",
       "      <td>-0.065050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.355658</td>\n",
       "      <td>0.439520</td>\n",
       "      <td>-0.809196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2     ratio\n",
       "0  0.246394 -0.496658 -0.496105\n",
       "1  1.690199  1.245658  1.356873\n",
       "2 -0.132093  0.834331 -0.158322\n",
       "3 -0.046662  0.717323 -0.065050\n",
       "4 -0.355658  0.439520 -0.809196"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame({'data1' : np.random.randn(5),\n",
    "                   'data2' : np.random.randn(5)})\n",
    "\n",
    "df.assign(ratio = df['data1'] / df['data2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='remove_column'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing or dropping a column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back top top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>food</th>\n",
       "      <th>ounces</th>\n",
       "      <th>animal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>       bacon</td>\n",
       "      <td>  4.0</td>\n",
       "      <td>    pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> pulled pork</td>\n",
       "      <td>  3.0</td>\n",
       "      <td>    pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>       bacon</td>\n",
       "      <td> 12.0</td>\n",
       "      <td>    pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>    Pastrami</td>\n",
       "      <td>  6.0</td>\n",
       "      <td>    cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> corned beef</td>\n",
       "      <td>  7.5</td>\n",
       "      <td>    cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>       Bacon</td>\n",
       "      <td>  8.0</td>\n",
       "      <td>    pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>    pastrami</td>\n",
       "      <td>  3.0</td>\n",
       "      <td>    cow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>   honey ham</td>\n",
       "      <td>  5.0</td>\n",
       "      <td>    pig</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>    nova lox</td>\n",
       "      <td>  6.0</td>\n",
       "      <td> salmon</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          food  ounces  animal\n",
       "0        bacon     4.0     pig\n",
       "1  pulled pork     3.0     pig\n",
       "2        bacon    12.0     pig\n",
       "3     Pastrami     6.0     cow\n",
       "4  corned beef     7.5     cow\n",
       "5        Bacon     8.0     pig\n",
       "6     pastrami     3.0     cow\n",
       "7    honey ham     5.0     pig\n",
       "8     nova lox     6.0  salmon"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop('animal2', axis='columns', inplace=True)  # dropping/removing a column\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"replacing_values\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing Values in a Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1    -999\n",
       "2       2\n",
       "3    -999\n",
       "4   -1000\n",
       "5       3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.Series([1., -999., 2., -999., -1000., 3.])\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you want to replace -999 with NaN:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1     NaN\n",
       "2       2\n",
       "3     NaN\n",
       "4   -1000\n",
       "5       3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace(-999, np.nan, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you want to replace multiple values at once, you instead pass a list then the substitute value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       1\n",
       "1    -999\n",
       "2       2\n",
       "3    -999\n",
       "4   -1000\n",
       "5       3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.Series([1., -999., 2., -999., -1000., 3.])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1   NaN\n",
       "2     2\n",
       "3   NaN\n",
       "4   NaN\n",
       "5     3\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.replace([-999, -1000], np.nan, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"renaming_axis\"></a>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming Axis Indexes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td> 4</td>\n",
       "      <td> 5</td>\n",
       "      <td>  6</td>\n",
       "      <td>  7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td> 8</td>\n",
       "      <td> 9</td>\n",
       "      <td> 10</td>\n",
       "      <td> 11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "New York    8    9     10    11\n",
       "\n",
       "[3 rows x 4 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame(np.arange(12).reshape((3, 4)),index=['Ohio', 'Colorado', 'New York'],columns=['one', 'two', 'three', 'four'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['OHIO', 'COLORADO', 'NEW YORK'], dtype=object)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.map(str.upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OHIO</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>COLORADO</th>\n",
       "      <td> 4</td>\n",
       "      <td> 5</td>\n",
       "      <td>  6</td>\n",
       "      <td>  7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEW YORK</th>\n",
       "      <td> 8</td>\n",
       "      <td> 9</td>\n",
       "      <td> 10</td>\n",
       "      <td> 11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "OHIO        0    1      2     3\n",
       "COLORADO    4    5      6     7\n",
       "NEW YORK    8    9     10    11\n",
       "\n",
       "[3 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index = data.index.map(str.upper)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you want to create a transformed version of a data set without modifying the original, a useful method is rename:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ONE</th>\n",
       "      <th>TWO</th>\n",
       "      <th>THREE</th>\n",
       "      <th>FOUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td> 4</td>\n",
       "      <td> 5</td>\n",
       "      <td>  6</td>\n",
       "      <td>  7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td> 8</td>\n",
       "      <td> 9</td>\n",
       "      <td> 10</td>\n",
       "      <td> 11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ONE  TWO  THREE  FOUR\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "New York    8    9     10    11\n",
       "\n",
       "[3 rows x 4 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index=str.title, columns=str.upper, inplace=True)  # str.title means to make the 1st letter capitalized only\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"renaming_indexes_columns\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Renaming Indexes or Columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### <i>rename()</i> can be used in conjunction with a dict-like object providing new values for a subset of the axis labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td> 4</td>\n",
       "      <td> 5</td>\n",
       "      <td>  6</td>\n",
       "      <td>  7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td> 8</td>\n",
       "      <td> 9</td>\n",
       "      <td> 10</td>\n",
       "      <td> 11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  three  four\n",
       "Ohio        0    1      2     3\n",
       "Colorado    4    5      6     7\n",
       "New York    8    9     10    11"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame(np.arange(12).reshape((3, 4)),index=['Ohio', 'Colorado', 'New York'],columns=['one', 'two', 'three', 'four'])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>peekaboo</th>\n",
       "      <th>four</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>INDIANA</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td> 4</td>\n",
       "      <td> 5</td>\n",
       "      <td>  6</td>\n",
       "      <td>  7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td> 8</td>\n",
       "      <td> 9</td>\n",
       "      <td> 10</td>\n",
       "      <td> 11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          one  two  peekaboo  four\n",
       "INDIANA     0    1         2     3\n",
       "Colorado    4    5         6     7\n",
       "New York    8    9        10    11"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index={'Ohio': 'INDIANA'},columns={'three': 'peekaboo'},inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also apply str functions to modify the index or column labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ONE</th>\n",
       "      <th>TWO</th>\n",
       "      <th>PEEKABOO</th>\n",
       "      <th>FOUR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td> 0</td>\n",
       "      <td> 1</td>\n",
       "      <td>  2</td>\n",
       "      <td>  3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td> 4</td>\n",
       "      <td> 5</td>\n",
       "      <td>  6</td>\n",
       "      <td>  7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td> 8</td>\n",
       "      <td> 9</td>\n",
       "      <td> 10</td>\n",
       "      <td> 11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ONE  TWO  PEEKABOO  FOUR\n",
       "Indiana     0    1         2     3\n",
       "Colorado    4    5         6     7\n",
       "New York    8    9        10    11"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.rename(index=str.title, columns=str.upper, inplace=True)  # str.title means to make the 1st letter capitalized only\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"binning\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Binning Data Into Intervals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let’s divide these into bins of 18 to 25, 26 to 35, 35 to 60, and finally 60 and older. To do so, you have to use <i>cut()</i> function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "bins = [18, 25, 35, 60, 100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  (18, 25]\n",
       "  (18, 25]\n",
       "  (18, 25]\n",
       "  (25, 35]\n",
       "  (18, 25]\n",
       "  (18, 25]\n",
       "  (35, 60]\n",
       "  (25, 35]\n",
       " (60, 100]\n",
       "  (35, 60]\n",
       "  (35, 60]\n",
       "  (25, 35]\n",
       "Levels (4): Index(['(18, 25]', '(25, 35]', '(35, 60]', '(60, 100]'], dtype=object)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats = pd.cut(ages, bins)\n",
    "cats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 0, 0, 2, 1, 3, 2, 2, 1], dtype=int32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['(18, 25]', '(25, 35]', '(35, 60]', '(60, 100]'], dtype='object')"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cats.levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 25]     5\n",
       "(35, 60]     3\n",
       "(25, 35]     3\n",
       "(60, 100]    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.value_counts(cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If you want the right value to be exclusive in the intervals:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  [18, 26)\n",
       "  [18, 26)\n",
       "  [18, 26)\n",
       "  [26, 36)\n",
       "  [18, 26)\n",
       "  [18, 26)\n",
       "  [36, 61)\n",
       "  [26, 36)\n",
       " [61, 100)\n",
       "  [36, 61)\n",
       "  [36, 61)\n",
       "  [26, 36)\n",
       "Levels (4): Index(['[18, 26)', '[26, 36)', '[36, 61)', '[61, 100)'], dtype=object)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(ages, [18, 26, 36, 61, 100], right=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can also pass your own bin names by passing a list or array to the labels option:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Youth         5\n",
       "YoungAdult    3\n",
       "MiddleAged    3\n",
       "Senior        1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_names = ['Youth', 'YoungAdult', 'MiddleAged', 'Senior']\n",
    "pd.cut(ages, bins, labels=group_names)\n",
    "pd.value_counts(pd.cut(ages, bins, labels=group_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"group\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping Data ( see also [value_count()](#value_counts) and [pivot_table()](#pivot_table) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.206295</td>\n",
       "      <td> 0.277025</td>\n",
       "      <td> a</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.210670</td>\n",
       "      <td> 1.700554</td>\n",
       "      <td> a</td>\n",
       "      <td> two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 1.090656</td>\n",
       "      <td> 0.141390</td>\n",
       "      <td> b</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.755093</td>\n",
       "      <td> 1.402054</td>\n",
       "      <td> b</td>\n",
       "      <td> two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 0.251278</td>\n",
       "      <td> 0.767201</td>\n",
       "      <td> a</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -1.206295  0.277025    a  one\n",
       "1 -0.210670  1.700554    a  two\n",
       "2  1.090656  0.141390    b  one\n",
       "3 -0.755093  1.402054    b  two\n",
       "4  0.251278  0.767201    a  one\n",
       "\n",
       "[5 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],\n",
    "                   'key2' : ['one', 'two', 'one', 'two', 'one'],\n",
    "                   'data1' : np.random.randn(5),\n",
    "                   'data2' : np.random.randn(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a      -0.388562\n",
       "b       0.167782\n",
       "dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df['data1'].groupby(df['key1'])\n",
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='slicing'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sub-selecting or Slicing a Data Frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering by label name: [[loc](#loc)]<br>\n",
    "Filtering by index row and/or column: [[iloc](#iloc)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='df'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.618255</td>\n",
       "      <td>0.699026</td>\n",
       "      <td>-0.857985</td>\n",
       "      <td>-0.167018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.193699</td>\n",
       "      <td>0.304388</td>\n",
       "      <td>0.036477</td>\n",
       "      <td>0.073046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.409751</td>\n",
       "      <td>0.599982</td>\n",
       "      <td>1.359995</td>\n",
       "      <td>0.901001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.165882</td>\n",
       "      <td>-0.156601</td>\n",
       "      <td>-0.764553</td>\n",
       "      <td>0.665824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.190956</td>\n",
       "      <td>-0.515755</td>\n",
       "      <td>-0.866012</td>\n",
       "      <td>-0.908422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.849617</td>\n",
       "      <td>-1.473412</td>\n",
       "      <td>0.013182</td>\n",
       "      <td>0.443061</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  0.618255  0.699026 -0.857985 -0.167018\n",
       "2013-01-02  1.193699  0.304388  0.036477  0.073046\n",
       "2013-01-03 -1.409751  0.599982  1.359995  0.901001\n",
       "2013-01-04  0.165882 -0.156601 -0.764553  0.665824\n",
       "2013-01-05  0.190956 -0.515755 -0.866012 -0.908422\n",
       "2013-01-06  0.849617 -1.473412  0.013182  0.443061"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "dates = pd.date_range('20130101',periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting first n rows of data frame using index slicing syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td> 1.495158</td>\n",
       "      <td> 2.136794</td>\n",
       "      <td> 0.034707</td>\n",
       "      <td>-0.128909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td> 0.562039</td>\n",
       "      <td>-1.150613</td>\n",
       "      <td> 0.265513</td>\n",
       "      <td> 0.093981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 0.973170</td>\n",
       "      <td> 0.877070</td>\n",
       "      <td> 0.682884</td>\n",
       "      <td>-0.026271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01  1.495158  2.136794  0.034707 -0.128909\n",
       "2013-01-02  0.562039 -1.150613  0.265513  0.093981\n",
       "2013-01-03  0.973170  0.877070  0.682884 -0.026271"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]  # get first 3 rows of the data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing based on data frame's index range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td> 0.562039</td>\n",
       "      <td>-1.150613</td>\n",
       "      <td> 0.265513</td>\n",
       "      <td> 0.093981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 0.973170</td>\n",
       "      <td> 0.877070</td>\n",
       "      <td> 0.682884</td>\n",
       "      <td>-0.026271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.146283</td>\n",
       "      <td> 1.350325</td>\n",
       "      <td>-0.040049</td>\n",
       "      <td> 1.069154</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  0.562039 -1.150613  0.265513  0.093981\n",
       "2013-01-03  0.973170  0.877070  0.682884 -0.026271\n",
       "2013-01-04 -1.146283  1.350325 -0.040049  1.069154"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['20130102':'20130104'] # get rows by index range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='loc'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing based on column labels/names using loc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[view df](#slicing)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td> 1.495158</td>\n",
       "      <td> 2.136794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td> 0.562039</td>\n",
       "      <td>-1.150613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 0.973170</td>\n",
       "      <td> 0.877070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.146283</td>\n",
       "      <td> 1.350325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td> 0.919800</td>\n",
       "      <td>-0.058590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td> 1.286808</td>\n",
       "      <td>-0.357197</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-01  1.495158  2.136794\n",
       "2013-01-02  0.562039 -1.150613\n",
       "2013-01-03  0.973170  0.877070\n",
       "2013-01-04 -1.146283  1.350325\n",
       "2013-01-05  0.919800 -0.058590\n",
       "2013-01-06  1.286808 -0.357197"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['A','B']]  # syntax is: df.loc[rows_index, cols_index]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing based on row index label and column label combined using loc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td> 0.562039</td>\n",
       "      <td>-1.150613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 0.973170</td>\n",
       "      <td> 0.877070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.146283</td>\n",
       "      <td> 1.350325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-02  0.562039 -1.150613\n",
       "2013-01-03  0.973170  0.877070\n",
       "2013-01-04 -1.146283  1.350325"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130102':'20130104',['A','B']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='iloc'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slicing based on index position of the row or column using iloc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[view df](#slicing)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A   -1.146283\n",
       "B    1.350325\n",
       "C   -0.040049\n",
       "D    1.069154\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]  # returns 4th row (index=3) of the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-1.146283</td>\n",
       "      <td> 1.350325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td> 0.919800</td>\n",
       "      <td>-0.058590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B\n",
       "2013-01-04 -1.146283  1.350325\n",
       "2013-01-05  0.919800 -0.058590"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5,0:2]  # returns specific range of rows and columns of the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td> 0.562039</td>\n",
       "      <td> 0.265513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td> 1.286808</td>\n",
       "      <td>-0.205763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         C\n",
       "2013-01-02  0.562039  0.265513\n",
       "2013-01-06  1.286808 -0.205763"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,5],[0,2]] # returns specific rows and columns using lists containing columns or row indexes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td> 0.562039</td>\n",
       "      <td>-1.150613</td>\n",
       "      <td> 0.265513</td>\n",
       "      <td> 0.093981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 0.973170</td>\n",
       "      <td> 0.877070</td>\n",
       "      <td> 0.682884</td>\n",
       "      <td>-0.026271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-02  0.562039 -1.150613  0.265513  0.093981\n",
       "2013-01-03  0.973170  0.877070  0.682884 -0.026271"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3,:]  # returning specific rows and returning all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td> 2.136794</td>\n",
       "      <td> 0.034707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-1.150613</td>\n",
       "      <td> 0.265513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 0.877070</td>\n",
       "      <td> 0.682884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td> 1.350325</td>\n",
       "      <td>-0.040049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.058590</td>\n",
       "      <td> 0.083294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.357197</td>\n",
       "      <td>-0.205763</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   B         C\n",
       "2013-01-01  2.136794  0.034707\n",
       "2013-01-02 -1.150613  0.265513\n",
       "2013-01-03  0.877070  0.682884\n",
       "2013-01-04  1.350325 -0.040049\n",
       "2013-01-05 -0.058590  0.083294\n",
       "2013-01-06 -0.357197 -0.205763"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:,1:3]  # returning all rows and specific columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.1506133975623973"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,1]  # getting secific scalar/single value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='boolean'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [Boolean Indexing](http://pandas.pydata.org/pandas-docs/stable/indexing.html#boolean-indexing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[view df](#df)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boolean operators are: **|** for or, <strong>&</strong> for and, and **~** for not. These **must** be grouped by using parentheses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 2.351035</td>\n",
       "      <td> 0.085318</td>\n",
       "      <td>-2.265766</td>\n",
       "      <td> 0.432268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td> 0.447390</td>\n",
       "      <td> 0.205224</td>\n",
       "      <td>-0.614702</td>\n",
       "      <td> 0.212125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.455682</td>\n",
       "      <td>-2.765288</td>\n",
       "      <td> 0.475253</td>\n",
       "      <td> 0.432910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td> 1.000494</td>\n",
       "      <td>-0.462591</td>\n",
       "      <td>-0.638053</td>\n",
       "      <td> 0.481087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-03  2.351035  0.085318 -2.265766  0.432268\n",
       "2013-01-04  0.447390  0.205224 -0.614702  0.212125\n",
       "2013-01-05 -0.455682 -2.765288  0.475253  0.432910\n",
       "2013-01-06  1.000494 -0.462591 -0.638053  0.481087"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.A > -0.5]  # or df[df[\"A\"] > -0.5], this syntax works when there is a space in the column name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or I've seen some people do boolean filtering by passing \"criteria\" variable to the data frame like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 2.351035</td>\n",
       "      <td> 0.085318</td>\n",
       "      <td>-2.265766</td>\n",
       "      <td> 0.432268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td> 0.447390</td>\n",
       "      <td> 0.205224</td>\n",
       "      <td>-0.614702</td>\n",
       "      <td> 0.212125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.455682</td>\n",
       "      <td>-2.765288</td>\n",
       "      <td> 0.475253</td>\n",
       "      <td> 0.432910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td> 1.000494</td>\n",
       "      <td>-0.462591</td>\n",
       "      <td>-0.638053</td>\n",
       "      <td> 0.481087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-03  2.351035  0.085318 -2.265766  0.432268\n",
       "2013-01-04  0.447390  0.205224 -0.614702  0.212125\n",
       "2013-01-05 -0.455682 -2.765288  0.475253  0.432910\n",
       "2013-01-06  1.000494 -0.462591 -0.638053  0.481087"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "criteria = df.A > -0.5\n",
    "df[criteria]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.618255</td>\n",
       "      <td>0.699026</td>\n",
       "      <td>-0.857985</td>\n",
       "      <td>-0.167018</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>1.193699</td>\n",
       "      <td>0.304388</td>\n",
       "      <td>0.036477</td>\n",
       "      <td>0.073046</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.409751</td>\n",
       "      <td>0.599982</td>\n",
       "      <td>1.359995</td>\n",
       "      <td>0.901001</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>0.165882</td>\n",
       "      <td>-0.156601</td>\n",
       "      <td>-0.764553</td>\n",
       "      <td>0.665824</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.190956</td>\n",
       "      <td>-0.515755</td>\n",
       "      <td>-0.866012</td>\n",
       "      <td>-0.908422</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.849617</td>\n",
       "      <td>-1.473412</td>\n",
       "      <td>0.013182</td>\n",
       "      <td>0.443061</td>\n",
       "      <td>three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2013-01-01  0.618255  0.699026 -0.857985 -0.167018    one\n",
       "2013-01-02  1.193699  0.304388  0.036477  0.073046    one\n",
       "2013-01-03 -1.409751  0.599982  1.359995  0.901001    two\n",
       "2013-01-04  0.165882 -0.156601 -0.764553  0.665824  three\n",
       "2013-01-05  0.190956 -0.515755 -0.866012 -0.908422   four\n",
       "2013-01-06  0.849617 -1.473412  0.013182  0.443061  three"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.copy()\n",
    "df2['E']=['one', 'one','two','three','four','three']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td> 2.351035</td>\n",
       "      <td> 0.085318</td>\n",
       "      <td>-2.265766</td>\n",
       "      <td> 0.432268</td>\n",
       "      <td>  two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.455682</td>\n",
       "      <td>-2.765288</td>\n",
       "      <td> 0.475253</td>\n",
       "      <td> 0.432910</td>\n",
       "      <td> four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2013-01-03  2.351035  0.085318 -2.265766  0.432268   two\n",
       "2013-01-05 -0.455682 -2.765288  0.475253  0.432910  four"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2['E'].isin(['two','four'])]  # read as \"return rows where column E contains two or four\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can also do partial string matching.  So let's say you don't know the exact spelling a word is you want to match, you can do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-1.409751</td>\n",
       "      <td>0.599982</td>\n",
       "      <td>1.359995</td>\n",
       "      <td>0.901001</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>0.190956</td>\n",
       "      <td>-0.515755</td>\n",
       "      <td>-0.866012</td>\n",
       "      <td>-0.908422</td>\n",
       "      <td>four</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D     E\n",
       "2013-01-03 -1.409751  0.599982  1.359995  0.901001   two\n",
       "2013-01-05  0.190956 -0.515755 -0.866012 -0.908422  four"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.E.str.contains(\"tw|ou\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using ~ to do a \"NOT\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.564503</td>\n",
       "      <td>-1.195000</td>\n",
       "      <td> 0.441140</td>\n",
       "      <td>-0.291384</td>\n",
       "      <td>   one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.893038</td>\n",
       "      <td>-0.372430</td>\n",
       "      <td>-0.436048</td>\n",
       "      <td>-0.545141</td>\n",
       "      <td>   one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td> 0.447390</td>\n",
       "      <td> 0.205224</td>\n",
       "      <td>-0.614702</td>\n",
       "      <td> 0.212125</td>\n",
       "      <td> three</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td> 1.000494</td>\n",
       "      <td>-0.462591</td>\n",
       "      <td>-0.638053</td>\n",
       "      <td> 0.481087</td>\n",
       "      <td> three</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D      E\n",
       "2013-01-01 -0.564503 -1.195000  0.441140 -0.291384    one\n",
       "2013-01-02 -0.893038 -0.372430 -0.436048 -0.545141    one\n",
       "2013-01-04  0.447390  0.205224 -0.614702  0.212125  three\n",
       "2013-01-06  1.000494 -0.462591 -0.638053  0.481087  three"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[~df2['E'].isin(['two','four'])]  # column E containing values not in two or four"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtering using query() method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.683630</td>\n",
       "      <td>-0.961407</td>\n",
       "      <td> 1.052018</td>\n",
       "      <td> 0.512382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>-0.755844</td>\n",
       "      <td> 1.672273</td>\n",
       "      <td> 0.365384</td>\n",
       "      <td> 1.030149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>-0.532466</td>\n",
       "      <td> 0.616725</td>\n",
       "      <td> 1.066731</td>\n",
       "      <td>-1.056756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td> 0.153878</td>\n",
       "      <td>-1.270198</td>\n",
       "      <td> 0.346587</td>\n",
       "      <td> 0.150570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td> 1.645820</td>\n",
       "      <td> 0.817894</td>\n",
       "      <td> 0.168950</td>\n",
       "      <td>-0.833291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.768034</td>\n",
       "      <td> 1.145290</td>\n",
       "      <td>-0.612168</td>\n",
       "      <td> 0.325120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.683630 -0.961407  1.052018  0.512382\n",
       "2013-01-02 -0.755844  1.672273  0.365384  1.030149\n",
       "2013-01-03 -0.532466  0.616725  1.066731 -1.056756\n",
       "2013-01-04  0.153878 -1.270198  0.346587  0.150570\n",
       "2013-01-05  1.645820  0.817894  0.168950 -0.833291\n",
       "2013-01-06 -0.768034  1.145290 -0.612168  0.325120"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "dates = pd.date_range('20130101',periods=6)\n",
    "df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td> 1.64582</td>\n",
       "      <td> 0.817894</td>\n",
       "      <td> 0.16895</td>\n",
       "      <td>-0.833291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  A         B        C         D\n",
       "2013-01-05  1.64582  0.817894  0.16895 -0.833291"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('A > C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td> 0.153878</td>\n",
       "      <td>-1.270198</td>\n",
       "      <td> 0.346587</td>\n",
       "      <td> 0.150570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td> 1.645820</td>\n",
       "      <td> 0.817894</td>\n",
       "      <td> 0.168950</td>\n",
       "      <td>-0.833291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-04  0.153878 -1.270198  0.346587  0.150570\n",
       "2013-01-05  1.645820  0.817894  0.168950 -0.833291"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('A > 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td> 0.153878</td>\n",
       "      <td>-1.270198</td>\n",
       "      <td> 0.346587</td>\n",
       "      <td> 0.15057</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C        D\n",
       "2013-01-04  0.153878 -1.270198  0.346587  0.15057"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('A > 0 & A < 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>-0.683630</td>\n",
       "      <td>-0.961407</td>\n",
       "      <td> 1.052018</td>\n",
       "      <td> 0.512382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td> 0.153878</td>\n",
       "      <td>-1.270198</td>\n",
       "      <td> 0.346587</td>\n",
       "      <td> 0.150570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td> 1.645820</td>\n",
       "      <td> 0.817894</td>\n",
       "      <td> 0.168950</td>\n",
       "      <td>-0.833291</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2013-01-01 -0.683630 -0.961407  1.052018  0.512382\n",
       "2013-01-04  0.153878 -1.270198  0.346587  0.150570\n",
       "2013-01-05  1.645820  0.817894  0.168950 -0.833291"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('A > B | A > C')  # where A is greater than B or A is greater than C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='partial_column_labels'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Obtaining columns with partial column labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0.740132</td>\n",
       "      <td> 1.220225</td>\n",
       "      <td> a</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1.223369</td>\n",
       "      <td> 0.830145</td>\n",
       "      <td> a</td>\n",
       "      <td> two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.046775</td>\n",
       "      <td>-1.149754</td>\n",
       "      <td> b</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.068403</td>\n",
       "      <td> 0.675124</td>\n",
       "      <td> b</td>\n",
       "      <td> two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.467039</td>\n",
       "      <td> 0.640412</td>\n",
       "      <td> a</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0  0.740132  1.220225    a  one\n",
       "1  1.223369  0.830145    a  two\n",
       "2 -2.046775 -1.149754    b  one\n",
       "3 -3.068403  0.675124    b  two\n",
       "4 -0.467039  0.640412    a  one"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],\n",
    "                   'key2' : ['one', 'two', 'one', 'two', 'one'],\n",
    "                   'data1' : np.random.randn(5),\n",
    "                   'data2' : np.random.randn(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0.740132</td>\n",
       "      <td> 1.220225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1.223369</td>\n",
       "      <td> 0.830145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.046775</td>\n",
       "      <td>-1.149754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-3.068403</td>\n",
       "      <td> 0.675124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.467039</td>\n",
       "      <td> 0.640412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2\n",
       "0  0.740132  1.220225\n",
       "1  1.223369  0.830145\n",
       "2 -2.046775 -1.149754\n",
       "3 -3.068403  0.675124\n",
       "4 -0.467039  0.640412"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.filter(like='data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='value_counts'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Value Counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is so tempting to use the groupby() function or pivot_table, but most of the time, value_counts() function is all we need."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>group</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>c</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>c</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>c</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  ounces\n",
       "0     a     4.0\n",
       "1     a     3.0\n",
       "2     a    12.0\n",
       "3     b     6.0\n",
       "4     b     7.5\n",
       "5     b     8.0\n",
       "6     c     3.0\n",
       "7     c     5.0\n",
       "8     c     6.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame({'group': ['a', 'a', 'a', 'b','b', 'b', 'c', 'c','c'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "b    3\n",
       "c    3\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.group.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='cum_sum'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting Cumulative Sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    3\n",
       "b    6\n",
       "c    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.group.value_counts().cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pivot_table'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot table example - see also this excellent [article](http://pbpython.com/pandas-pivot-table-explained.html) on pivot tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> a</td>\n",
       "      <td>  4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> a</td>\n",
       "      <td>  3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> a</td>\n",
       "      <td> 12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> b</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> b</td>\n",
       "      <td>  7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> b</td>\n",
       "      <td>  8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> c</td>\n",
       "      <td>  3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> c</td>\n",
       "      <td>  5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> c</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  ounces\n",
       "0     a     4.0\n",
       "1     a     3.0\n",
       "2     a    12.0\n",
       "3     b     6.0\n",
       "4     b     7.5\n",
       "5     b     8.0\n",
       "6     c     3.0\n",
       "7     c     5.0\n",
       "8     c     6.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame({'group': ['a', 'a', 'a', 'b','b', 'b', 'c', 'c','c'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating the means of each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "a        6.333333\n",
       "b        7.166667\n",
       "c        4.666667\n",
       "Name: ounces, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(values='ounces',index='group',aggfunc=np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting counts by group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "a        3\n",
       "b        3\n",
       "c        3\n",
       "Name: ounces, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(values='ounces',index='group',aggfunc='count')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting cumulative sum or running total of the group counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group\n",
       "a        3\n",
       "b        6\n",
       "c        9\n",
       "Name: ounces, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pivot_table(values='ounces',index='group',aggfunc='count').cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# sample data can be dowloaded here: http://pbpython.com/extras/sales-funnel.xlsx\n",
    "df = pd.read_csv('/home/pybokeh/Downloads/sales-funnel.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Account</th>\n",
       "      <th>Name</th>\n",
       "      <th>Rep</th>\n",
       "      <th>Manager</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 714466</td>\n",
       "      <td>              Trantow-Barrows</td>\n",
       "      <td>  Craig Booker</td>\n",
       "      <td> Debra Henley</td>\n",
       "      <td>         CPU</td>\n",
       "      <td> 1</td>\n",
       "      <td> 30000</td>\n",
       "      <td> presented</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 714466</td>\n",
       "      <td>              Trantow-Barrows</td>\n",
       "      <td>  Craig Booker</td>\n",
       "      <td> Debra Henley</td>\n",
       "      <td>    Software</td>\n",
       "      <td> 1</td>\n",
       "      <td> 10000</td>\n",
       "      <td> presented</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 714466</td>\n",
       "      <td>              Trantow-Barrows</td>\n",
       "      <td>  Craig Booker</td>\n",
       "      <td> Debra Henley</td>\n",
       "      <td> Maintenance</td>\n",
       "      <td> 2</td>\n",
       "      <td>  5000</td>\n",
       "      <td>   pending</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 737550</td>\n",
       "      <td> Fritsch, Russel and Anderson</td>\n",
       "      <td>  Craig Booker</td>\n",
       "      <td> Debra Henley</td>\n",
       "      <td>         CPU</td>\n",
       "      <td> 1</td>\n",
       "      <td> 35000</td>\n",
       "      <td>  declined</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 146832</td>\n",
       "      <td>                 Kiehn-Spinka</td>\n",
       "      <td> Daniel Hilton</td>\n",
       "      <td> Debra Henley</td>\n",
       "      <td>         CPU</td>\n",
       "      <td> 2</td>\n",
       "      <td> 65000</td>\n",
       "      <td>       won</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Account                          Name            Rep       Manager  \\\n",
       "0   714466               Trantow-Barrows   Craig Booker  Debra Henley   \n",
       "1   714466               Trantow-Barrows   Craig Booker  Debra Henley   \n",
       "2   714466               Trantow-Barrows   Craig Booker  Debra Henley   \n",
       "3   737550  Fritsch, Russel and Anderson   Craig Booker  Debra Henley   \n",
       "4   146832                  Kiehn-Spinka  Daniel Hilton  Debra Henley   \n",
       "\n",
       "       Product  Quantity  Price     Status  \n",
       "0          CPU         1  30000  presented  \n",
       "1     Software         1  10000  presented  \n",
       "2  Maintenance         2   5000    pending  \n",
       "3          CPU         1  35000   declined  \n",
       "4          CPU         2  65000        won  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Product</th>\n",
       "      <th>CPU</th>\n",
       "      <th>Maintenance</th>\n",
       "      <th>Monitor</th>\n",
       "      <th>Software</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rep</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cedric Moss</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Craig Booker</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Hilton</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Smith</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wendy Yule</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Product        CPU  Maintenance  Monitor  Software\n",
       "Rep                                               \n",
       "Cedric Moss      2            1        0         1\n",
       "Craig Booker     2            1        0         1\n",
       "Daniel Hilton    2            0        0         1\n",
       "John Smith       1            1        0         0\n",
       "Wendy Yule       2            1        1         0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_rep = pd.pivot_table(data=df, index='Rep', columns='Product', values='Quantity', aggfunc='count', fill_value=0)\n",
    "by_rep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"transform\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a new column based on a grouping using transform() method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get sample data from dplython library which is a clone of dplyr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from dplython import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat      cut color clarity  depth  table  price     x     y  \\\n",
       "0           1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1           2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2           3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3           4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4           5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "\n",
       "      z  \n",
       "0  2.43  \n",
       "1  2.31  \n",
       "2  2.31  \n",
       "3  2.63  \n",
       "4  2.75  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Goal:** Create a column that contains the difference between a diamond's carat and the average of the carats for *that diamond's* color.<br><br>\n",
    "Key take-away is to use the transform method of your specific column of the groupby object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>carat_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>31597</th>\n",
       "      <td>31598</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.77</td>\n",
       "      <td>2.33</td>\n",
       "      <td>-0.457795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31600</th>\n",
       "      <td>31601</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.68</td>\n",
       "      <td>2.31</td>\n",
       "      <td>-0.457795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31601</th>\n",
       "      <td>31602</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.7</td>\n",
       "      <td>60.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.77</td>\n",
       "      <td>3.72</td>\n",
       "      <td>2.31</td>\n",
       "      <td>-0.457795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38276</th>\n",
       "      <td>38277</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>386</td>\n",
       "      <td>3.82</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.34</td>\n",
       "      <td>-0.447795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38277</th>\n",
       "      <td>38278</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.6</td>\n",
       "      <td>60.0</td>\n",
       "      <td>386</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2.32</td>\n",
       "      <td>-0.447795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38278</th>\n",
       "      <td>38279</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>386</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.29</td>\n",
       "      <td>-0.447795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38279</th>\n",
       "      <td>38280</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>58.3</td>\n",
       "      <td>59.0</td>\n",
       "      <td>386</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.93</td>\n",
       "      <td>2.30</td>\n",
       "      <td>-0.447795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>55</td>\n",
       "      <td>0.22</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.3</td>\n",
       "      <td>62.0</td>\n",
       "      <td>404</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.88</td>\n",
       "      <td>2.31</td>\n",
       "      <td>-0.437795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.5</td>\n",
       "      <td>61.0</td>\n",
       "      <td>357</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.97</td>\n",
       "      <td>2.40</td>\n",
       "      <td>-0.427795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>VS1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>402</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.44</td>\n",
       "      <td>-0.427795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  carat        cut color clarity  depth  table  price     x  \\\n",
       "31597       31598   0.20      Ideal     D     VS2   61.5   57.0    367  3.81   \n",
       "31600       31601   0.20    Premium     D     VS2   62.3   60.0    367  3.73   \n",
       "31601       31602   0.20    Premium     D     VS2   61.7   60.0    367  3.77   \n",
       "38276       38277   0.21    Premium     D     VS2   61.6   59.0    386  3.82   \n",
       "38277       38278   0.21    Premium     D     VS2   60.6   60.0    386  3.85   \n",
       "38278       38279   0.21    Premium     D     VS2   59.1   62.0    386  3.89   \n",
       "38279       38280   0.21    Premium     D     VS2   58.3   59.0    386  3.96   \n",
       "54             55   0.22    Premium     D     VS2   59.3   62.0    404  3.91   \n",
       "28             29   0.23  Very Good     D     VS2   60.5   61.0    357  3.96   \n",
       "34             35   0.23  Very Good     D     VS1   61.9   58.0    402  3.92   \n",
       "\n",
       "          y     z  carat_diff  \n",
       "31597  3.77  2.33   -0.457795  \n",
       "31600  3.68  2.31   -0.457795  \n",
       "31601  3.72  2.31   -0.457795  \n",
       "38276  3.78  2.34   -0.447795  \n",
       "38277  3.81  2.32   -0.447795  \n",
       "38278  3.86  2.29   -0.447795  \n",
       "38279  3.93  2.30   -0.447795  \n",
       "54     3.88  2.31   -0.437795  \n",
       "28     3.97  2.40   -0.427795  \n",
       "34     3.96  2.44   -0.427795  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(diamonds.assign(carat_diff=diamonds\n",
    "                 .groupby('color')['carat']\n",
    "                 .transform(lambda x:x-x.mean())\n",
    "                )\n",
    "                .sort_values(by=['color','carat_diff'])\n",
    "                .head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For those curious how to do this using dplyr / dplython:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pybokeh/envs/jupyter/lib/python3.5/site-packages/dplython/dplython.py:379: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  df[key] = val.applyFcns(df)\n",
      "/home/pybokeh/envs/jupyter/lib/python3.5/site-packages/dplython/dplython.py:429: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  return lambda df: DplyFrame(df.sort(names))\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>carat_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25981</th>\n",
       "      <td>31598</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.81</td>\n",
       "      <td>3.77</td>\n",
       "      <td>2.33</td>\n",
       "      <td>-0.457795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25982</th>\n",
       "      <td>31601</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.68</td>\n",
       "      <td>2.31</td>\n",
       "      <td>-0.457795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25983</th>\n",
       "      <td>31602</td>\n",
       "      <td>0.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.7</td>\n",
       "      <td>60.0</td>\n",
       "      <td>367</td>\n",
       "      <td>3.77</td>\n",
       "      <td>3.72</td>\n",
       "      <td>2.31</td>\n",
       "      <td>-0.457795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26960</th>\n",
       "      <td>38277</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.6</td>\n",
       "      <td>59.0</td>\n",
       "      <td>386</td>\n",
       "      <td>3.82</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.34</td>\n",
       "      <td>-0.447795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26961</th>\n",
       "      <td>38278</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.6</td>\n",
       "      <td>60.0</td>\n",
       "      <td>386</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2.32</td>\n",
       "      <td>-0.447795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26962</th>\n",
       "      <td>38279</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.1</td>\n",
       "      <td>62.0</td>\n",
       "      <td>386</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.86</td>\n",
       "      <td>2.29</td>\n",
       "      <td>-0.447795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26963</th>\n",
       "      <td>38280</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>58.3</td>\n",
       "      <td>59.0</td>\n",
       "      <td>386</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.93</td>\n",
       "      <td>2.30</td>\n",
       "      <td>-0.447795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22409</th>\n",
       "      <td>55</td>\n",
       "      <td>0.22</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>59.3</td>\n",
       "      <td>62.0</td>\n",
       "      <td>404</td>\n",
       "      <td>3.91</td>\n",
       "      <td>3.88</td>\n",
       "      <td>2.31</td>\n",
       "      <td>-0.437795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22404</th>\n",
       "      <td>29</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>VS2</td>\n",
       "      <td>60.5</td>\n",
       "      <td>61.0</td>\n",
       "      <td>357</td>\n",
       "      <td>3.96</td>\n",
       "      <td>3.97</td>\n",
       "      <td>2.40</td>\n",
       "      <td>-0.427795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22405</th>\n",
       "      <td>35</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>VS1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>58.0</td>\n",
       "      <td>402</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.44</td>\n",
       "      <td>-0.427795</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  carat        cut color clarity  depth  table  price     x  \\\n",
       "25981       31598   0.20      Ideal     D     VS2   61.5   57.0    367  3.81   \n",
       "25982       31601   0.20    Premium     D     VS2   62.3   60.0    367  3.73   \n",
       "25983       31602   0.20    Premium     D     VS2   61.7   60.0    367  3.77   \n",
       "26960       38277   0.21    Premium     D     VS2   61.6   59.0    386  3.82   \n",
       "26961       38278   0.21    Premium     D     VS2   60.6   60.0    386  3.85   \n",
       "26962       38279   0.21    Premium     D     VS2   59.1   62.0    386  3.89   \n",
       "26963       38280   0.21    Premium     D     VS2   58.3   59.0    386  3.96   \n",
       "22409          55   0.22    Premium     D     VS2   59.3   62.0    404  3.91   \n",
       "22404          29   0.23  Very Good     D     VS2   60.5   61.0    357  3.96   \n",
       "22405          35   0.23  Very Good     D     VS1   61.9   58.0    402  3.92   \n",
       "\n",
       "          y     z  carat_diff  \n",
       "25981  3.77  2.33   -0.457795  \n",
       "25982  3.68  2.31   -0.457795  \n",
       "25983  3.72  2.31   -0.457795  \n",
       "26960  3.78  2.34   -0.447795  \n",
       "26961  3.81  2.32   -0.447795  \n",
       "26962  3.86  2.29   -0.447795  \n",
       "26963  3.93  2.30   -0.447795  \n",
       "22409  3.88  2.31   -0.437795  \n",
       "22404  3.97  2.40   -0.427795  \n",
       "22405  3.96  2.44   -0.427795  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using dplyr/dplython syntax\n",
    "(diamonds >>\n",
    "  group_by(X.color) >>\n",
    "  mutate(carat_diff = X.carat - X.carat.mean()) >>\n",
    "  ungroup() >>\n",
    "  arrange(X.color, X.carat_diff) >>\n",
    "  head(10)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here's a [comparison](http://nbviewer.jupyter.org/github/pybokeh/jupyter_notebooks/blob/master/dplython/dplython_example.ipynb) I made between dplython and pandas.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='perc_rows'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Percent of rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Product</th>\n",
       "      <th>CPU</th>\n",
       "      <th>Maintenance</th>\n",
       "      <th>Monitor</th>\n",
       "      <th>Software</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rep</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cedric Moss</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Craig Booker</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Hilton</th>\n",
       "      <td>66.666667</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Smith</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wendy Yule</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Product              CPU  Maintenance  Monitor   Software\n",
       "Rep                                                      \n",
       "Cedric Moss    50.000000           25        0  25.000000\n",
       "Craig Booker   50.000000           25        0  25.000000\n",
       "Daniel Hilton  66.666667            0        0  33.333333\n",
       "John Smith     50.000000           50        0   0.000000\n",
       "Wendy Yule     50.000000           25       25   0.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perc_of_rows = by_rep.apply(lambda x : x / x.sum() * 100, axis='columns') # or axis=1\n",
    "perc_of_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='perc_columns'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Percent of columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Product</th>\n",
       "      <th>CPU</th>\n",
       "      <th>Maintenance</th>\n",
       "      <th>Monitor</th>\n",
       "      <th>Software</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rep</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Cedric Moss</th>\n",
       "      <td>22.222222</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Craig Booker</th>\n",
       "      <td>22.222222</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daniel Hilton</th>\n",
       "      <td>22.222222</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Smith</th>\n",
       "      <td>11.111111</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wendy Yule</th>\n",
       "      <td>22.222222</td>\n",
       "      <td>25</td>\n",
       "      <td>100</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Product              CPU  Maintenance  Monitor   Software\n",
       "Rep                                                      \n",
       "Cedric Moss    22.222222           25        0  33.333333\n",
       "Craig Booker   22.222222           25        0  33.333333\n",
       "Daniel Hilton  22.222222            0        0  33.333333\n",
       "John Smith     11.111111           25        0   0.000000\n",
       "Wendy Yule     22.222222           25      100   0.000000"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perc_of_columns = by_rep.apply(lambda x : x / x.sum() * 100, axis='index') # or axis=0\n",
    "perc_of_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='transpose'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transpose a data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say you have a data frame with several columns and having a hard time scrolling to see what the data looks like.  Transpose it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.548420</td>\n",
       "      <td> 0.381045</td>\n",
       "      <td> a</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.327573</td>\n",
       "      <td> 0.273854</td>\n",
       "      <td> a</td>\n",
       "      <td> two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.356345</td>\n",
       "      <td>-0.045686</td>\n",
       "      <td> b</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.476385</td>\n",
       "      <td> 1.075987</td>\n",
       "      <td> b</td>\n",
       "      <td> two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.923575</td>\n",
       "      <td>-0.973773</td>\n",
       "      <td> a</td>\n",
       "      <td> one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -0.548420  0.381045    a  one\n",
       "1 -0.327573  0.273854    a  two\n",
       "2 -1.356345 -0.045686    b  one\n",
       "3 -0.476385  1.075987    b  two\n",
       "4 -0.923575 -0.973773    a  one"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({'key1' : ['a', 'a', 'b', 'b', 'a'],\n",
    "                   'key2' : ['one', 'two', 'one', 'two', 'one'],\n",
    "                   'data1' : np.random.randn(5),\n",
    "                   'data2' : np.random.randn(5)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>data1</th>\n",
       "      <td>-0.5484204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data2</th>\n",
       "      <td> 0.3810447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <td>         a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key2</th>\n",
       "      <td>       one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0\n",
       "data1 -0.5484204\n",
       "data2  0.3810447\n",
       "key1           a\n",
       "key2         one"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1).transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also do df.dtypes to accomplish similar output, but it's nice to see actual data along with the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data1    float64\n",
       "data2    float64\n",
       "key1      object\n",
       "key2      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"index2column\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Converting a data frame index to a column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> a</td>\n",
       "      <td>  4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> a</td>\n",
       "      <td>  3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> a</td>\n",
       "      <td> 12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> b</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> b</td>\n",
       "      <td>  7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> b</td>\n",
       "      <td>  8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> c</td>\n",
       "      <td>  3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> c</td>\n",
       "      <td>  5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> c</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  ounces\n",
       "0     a     4.0\n",
       "1     a     3.0\n",
       "2     a    12.0\n",
       "3     b     6.0\n",
       "4     b     7.5\n",
       "5     b     8.0\n",
       "6     c     3.0\n",
       "7     c     5.0\n",
       "8     c     6.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame({'group': ['a', 'a', 'a', 'b','b', 'b', 'c', 'c','c'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>group</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0</td>\n",
       "      <td> a</td>\n",
       "      <td>  4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 1</td>\n",
       "      <td> a</td>\n",
       "      <td>  3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 2</td>\n",
       "      <td> a</td>\n",
       "      <td> 12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 3</td>\n",
       "      <td> b</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 4</td>\n",
       "      <td> b</td>\n",
       "      <td>  7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> 5</td>\n",
       "      <td> b</td>\n",
       "      <td>  8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> 6</td>\n",
       "      <td> c</td>\n",
       "      <td>  3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> 7</td>\n",
       "      <td> c</td>\n",
       "      <td>  5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> 8</td>\n",
       "      <td> c</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index group  ounces\n",
       "0      0     a     4.0\n",
       "1      1     a     3.0\n",
       "2      2     a    12.0\n",
       "3      3     b     6.0\n",
       "4      4     b     7.5\n",
       "5      5     b     8.0\n",
       "6      6     c     3.0\n",
       "7      7     c     5.0\n",
       "8      8     c     6.0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.reset_index(level=0, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"column2index\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to set the data frame's index to be one of the columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top]](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b   c\n",
       "0   1   2   3\n",
       "1  10  11  12\n",
       "2  20  21  22"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "col = ['a','b','c']\n",
    "data = pd.DataFrame([[1,2,3],[10,11,12],[20,21,22]],columns=col)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     b   c\n",
       "a         \n",
       "1    2   3\n",
       "10  11  12\n",
       "20  21  22"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.set_index('a')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To remove the index name (\"a\"), do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     b   c\n",
       "1    2   3\n",
       "10  11  12\n",
       "20  21  22"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index.name = None\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='missing_dates'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to add or fill in missing dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top]](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "09-02-2013     2\n",
       "09-03-2013    10\n",
       "09-06-2013     5\n",
       "09-07-2013     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "idx = pd.date_range('09-01-2013', '09-30-2013')\n",
    "\n",
    "s = pd.Series({'09-02-2013': 2,\n",
    "               '09-03-2013': 10,\n",
    "               '09-06-2013': 5,\n",
    "               '09-07-2013': 1})\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From above, we have holes in our data.  Let's fill in those missing holes using the idx date_range we specified above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-09-01     0\n",
       "2013-09-02     2\n",
       "2013-09-03    10\n",
       "2013-09-04     0\n",
       "2013-09-05     0\n",
       "2013-09-06     5\n",
       "2013-09-07     1\n",
       "2013-09-08     0\n",
       "2013-09-09     0\n",
       "2013-09-10     0\n",
       "2013-09-11     0\n",
       "2013-09-12     0\n",
       "2013-09-13     0\n",
       "2013-09-14     0\n",
       "2013-09-15     0\n",
       "2013-09-16     0\n",
       "2013-09-17     0\n",
       "2013-09-18     0\n",
       "2013-09-19     0\n",
       "2013-09-20     0\n",
       "2013-09-21     0\n",
       "2013-09-22     0\n",
       "2013-09-23     0\n",
       "2013-09-24     0\n",
       "2013-09-25     0\n",
       "2013-09-26     0\n",
       "2013-09-27     0\n",
       "2013-09-28     0\n",
       "2013-09-29     0\n",
       "2013-09-30     0\n",
       "Freq: D, dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index = pd.DatetimeIndex(s.index)\n",
    "\n",
    "s = s.reindex(idx, fill_value=0)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='database'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to connect and query against a database registered as an ODBC data source (Windows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top]](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import pyodbc           # If using PostgreSQL, MySQL, SQLite, Redshift, MS SQL Server, or Oracle, use db.py instead\n",
    "import pandas as pd\n",
    "from getpass import getpass    # Module that will create a text input widget AND mask your password\n",
    "\n",
    "userid = 'your_userid'\n",
    "pw = getpass(prompt='Enter your password: ')\n",
    "\n",
    "cnxn_string = 'DSN=your_dsn;UID=' + userid + ';PWD=' + pw\n",
    "\n",
    "cnxn = pyodbc.connect(cnxn_string)\n",
    "cursor = cnxn.cursor()\n",
    "\n",
    "sql = \"\"\"\n",
    "SELECT * from your_table...\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(sql, cnxn, index_col=None, parse_dates={'some_column':\"%Y-%m-%d\"})\n",
    "\n",
    "# Close connections\n",
    "cursor.close()\n",
    "cnxn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also check out Yhat's [db.py](https://github.com/yhat/db.py).  Currently, doesn't support IBM DB2.  So I'm stuck with pyodbc at work for now."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='melt'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to convert data in wide format to long format using pd.melt()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using [ggplot](https://github.com/yhat/ggplot) to plot data where you have multiple data series you want to plot, you need to convert data in wide format to long format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from pandas_datareader import data as web\n",
    "from datetime import datetime\n",
    "import pandas as pd\n",
    "\n",
    "stocks = {'tech':['GOOGL', 'MSFT', 'LNKD', 'YHOO', 'FB','HPQ','AMZN'],\n",
    "          'auto':['TM','F','GM','HMC','NSANY','HYMTF'],\n",
    "          'housing':['HD','WMT','LOW']\n",
    "          }\n",
    "\n",
    "start_date = datetime(2014,1,1)\n",
    "end_date = datetime(2014, 11, 30)\n",
    "\n",
    "def get_px(stock, start, end):\n",
    "    return web.get_data_yahoo(stock, start, end)['Adj Close']\n",
    "\n",
    "df = pd.DataFrame({n: get_px(n, start_date, end_date) for n in stocks['tech']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Below is what data looks like in 'wide' format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>AMZN</th>\n",
       "      <th>FB</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>LNKD</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>YHOO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2014-01-02</th>\n",
       "      <td>397.970001</td>\n",
       "      <td>54.709999</td>\n",
       "      <td>557.117134</td>\n",
       "      <td>26.726783</td>\n",
       "      <td>207.639999</td>\n",
       "      <td>35.448341</td>\n",
       "      <td>39.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>396.440002</td>\n",
       "      <td>54.560001</td>\n",
       "      <td>553.053047</td>\n",
       "      <td>27.383841</td>\n",
       "      <td>207.419998</td>\n",
       "      <td>35.209856</td>\n",
       "      <td>40.119999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>393.630005</td>\n",
       "      <td>57.200001</td>\n",
       "      <td>559.219245</td>\n",
       "      <td>27.335529</td>\n",
       "      <td>203.919998</td>\n",
       "      <td>34.465786</td>\n",
       "      <td>39.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>398.029999</td>\n",
       "      <td>57.919998</td>\n",
       "      <td>570.000007</td>\n",
       "      <td>27.229239</td>\n",
       "      <td>209.639999</td>\n",
       "      <td>34.732887</td>\n",
       "      <td>40.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>401.920013</td>\n",
       "      <td>58.230000</td>\n",
       "      <td>571.186226</td>\n",
       "      <td>26.523869</td>\n",
       "      <td>209.059998</td>\n",
       "      <td>34.112826</td>\n",
       "      <td>41.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>401.010010</td>\n",
       "      <td>57.220001</td>\n",
       "      <td>565.685676</td>\n",
       "      <td>26.678471</td>\n",
       "      <td>215.250000</td>\n",
       "      <td>33.893420</td>\n",
       "      <td>40.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>397.660004</td>\n",
       "      <td>57.939999</td>\n",
       "      <td>565.655647</td>\n",
       "      <td>26.765435</td>\n",
       "      <td>218.750000</td>\n",
       "      <td>34.379931</td>\n",
       "      <td>41.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-13</th>\n",
       "      <td>390.980011</td>\n",
       "      <td>55.910000</td>\n",
       "      <td>562.052070</td>\n",
       "      <td>27.171264</td>\n",
       "      <td>213.539993</td>\n",
       "      <td>33.368755</td>\n",
       "      <td>39.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-14</th>\n",
       "      <td>397.540009</td>\n",
       "      <td>57.740002</td>\n",
       "      <td>575.275276</td>\n",
       "      <td>27.876635</td>\n",
       "      <td>216.220001</td>\n",
       "      <td>34.131905</td>\n",
       "      <td>41.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-15</th>\n",
       "      <td>395.869995</td>\n",
       "      <td>57.599998</td>\n",
       "      <td>574.884895</td>\n",
       "      <td>27.866972</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>35.066764</td>\n",
       "      <td>41.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AMZN         FB       GOOGL        HPQ        LNKD  \\\n",
       "Date                                                                   \n",
       "2014-01-02  397.970001  54.709999  557.117134  26.726783  207.639999   \n",
       "2014-01-03  396.440002  54.560001  553.053047  27.383841  207.419998   \n",
       "2014-01-06  393.630005  57.200001  559.219245  27.335529  203.919998   \n",
       "2014-01-07  398.029999  57.919998  570.000007  27.229239  209.639999   \n",
       "2014-01-08  401.920013  58.230000  571.186226  26.523869  209.059998   \n",
       "2014-01-09  401.010010  57.220001  565.685676  26.678471  215.250000   \n",
       "2014-01-10  397.660004  57.939999  565.655647  26.765435  218.750000   \n",
       "2014-01-13  390.980011  55.910000  562.052070  27.171264  213.539993   \n",
       "2014-01-14  397.540009  57.740002  575.275276  27.876635  216.220001   \n",
       "2014-01-15  395.869995  57.599998  574.884895  27.866972  216.000000   \n",
       "\n",
       "                 MSFT       YHOO  \n",
       "Date                              \n",
       "2014-01-02  35.448341  39.590000  \n",
       "2014-01-03  35.209856  40.119999  \n",
       "2014-01-06  34.465786  39.930000  \n",
       "2014-01-07  34.732887  40.919998  \n",
       "2014-01-08  34.112826  41.020000  \n",
       "2014-01-09  33.893420  40.919998  \n",
       "2014-01-10  34.379931  41.230000  \n",
       "2014-01-13  33.368755  39.990002  \n",
       "2014-01-14  34.131905  41.139999  \n",
       "2014-01-15  35.066764  41.070000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>Date</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>FB</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>LNKD</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>YHOO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>397.970001</td>\n",
       "      <td>54.709999</td>\n",
       "      <td>557.117134</td>\n",
       "      <td>26.726783</td>\n",
       "      <td>207.639999</td>\n",
       "      <td>35.448341</td>\n",
       "      <td>39.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>396.440002</td>\n",
       "      <td>54.560001</td>\n",
       "      <td>553.053047</td>\n",
       "      <td>27.383841</td>\n",
       "      <td>207.419998</td>\n",
       "      <td>35.209856</td>\n",
       "      <td>40.119999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>393.630005</td>\n",
       "      <td>57.200001</td>\n",
       "      <td>559.219245</td>\n",
       "      <td>27.335529</td>\n",
       "      <td>203.919998</td>\n",
       "      <td>34.465786</td>\n",
       "      <td>39.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>398.029999</td>\n",
       "      <td>57.919998</td>\n",
       "      <td>570.000007</td>\n",
       "      <td>27.229239</td>\n",
       "      <td>209.639999</td>\n",
       "      <td>34.732887</td>\n",
       "      <td>40.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>401.920013</td>\n",
       "      <td>58.230000</td>\n",
       "      <td>571.186226</td>\n",
       "      <td>26.523869</td>\n",
       "      <td>209.059998</td>\n",
       "      <td>34.112826</td>\n",
       "      <td>41.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>401.010010</td>\n",
       "      <td>57.220001</td>\n",
       "      <td>565.685676</td>\n",
       "      <td>26.678471</td>\n",
       "      <td>215.250000</td>\n",
       "      <td>33.893420</td>\n",
       "      <td>40.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>397.660004</td>\n",
       "      <td>57.939999</td>\n",
       "      <td>565.655647</td>\n",
       "      <td>26.765435</td>\n",
       "      <td>218.750000</td>\n",
       "      <td>34.379931</td>\n",
       "      <td>41.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-13</td>\n",
       "      <td>390.980011</td>\n",
       "      <td>55.910000</td>\n",
       "      <td>562.052070</td>\n",
       "      <td>27.171264</td>\n",
       "      <td>213.539993</td>\n",
       "      <td>33.368755</td>\n",
       "      <td>39.990002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>397.540009</td>\n",
       "      <td>57.740002</td>\n",
       "      <td>575.275276</td>\n",
       "      <td>27.876635</td>\n",
       "      <td>216.220001</td>\n",
       "      <td>34.131905</td>\n",
       "      <td>41.139999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>395.869995</td>\n",
       "      <td>57.599998</td>\n",
       "      <td>574.884895</td>\n",
       "      <td>27.866972</td>\n",
       "      <td>216.000000</td>\n",
       "      <td>35.066764</td>\n",
       "      <td>41.070000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date        AMZN         FB       GOOGL        HPQ        LNKD  \\\n",
       "0 2014-01-02  397.970001  54.709999  557.117134  26.726783  207.639999   \n",
       "1 2014-01-03  396.440002  54.560001  553.053047  27.383841  207.419998   \n",
       "2 2014-01-06  393.630005  57.200001  559.219245  27.335529  203.919998   \n",
       "3 2014-01-07  398.029999  57.919998  570.000007  27.229239  209.639999   \n",
       "4 2014-01-08  401.920013  58.230000  571.186226  26.523869  209.059998   \n",
       "5 2014-01-09  401.010010  57.220001  565.685676  26.678471  215.250000   \n",
       "6 2014-01-10  397.660004  57.939999  565.655647  26.765435  218.750000   \n",
       "7 2014-01-13  390.980011  55.910000  562.052070  27.171264  213.539993   \n",
       "8 2014-01-14  397.540009  57.740002  575.275276  27.876635  216.220001   \n",
       "9 2014-01-15  395.869995  57.599998  574.884895  27.866972  216.000000   \n",
       "\n",
       "        MSFT       YHOO  \n",
       "0  35.448341  39.590000  \n",
       "1  35.209856  40.119999  \n",
       "2  34.465786  39.930000  \n",
       "3  34.732887  40.919998  \n",
       "4  34.112826  41.020000  \n",
       "5  33.893420  40.919998  \n",
       "6  34.379931  41.230000  \n",
       "7  33.368755  39.990002  \n",
       "8  34.131905  41.139999  \n",
       "9  35.066764  41.070000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column from the index values\n",
    "df.reset_index(level=0, inplace=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is what data looks like in 'long' format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>Date</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>397.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>396.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>393.630005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>398.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>401.920013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date variable       value\n",
       "0 2014-01-02     AMZN  397.970001\n",
       "1 2014-01-03     AMZN  396.440002\n",
       "2 2014-01-06     AMZN  393.630005\n",
       "3 2014-01-07     AMZN  398.029999\n",
       "4 2014-01-08     AMZN  401.920013"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_long = pd.melt(df, id_vars=['Date']).dropna()\n",
    "df_long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>Date</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1605</th>\n",
       "      <td>2014-11-21</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>51.040001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1606</th>\n",
       "      <td>2014-11-24</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>51.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1607</th>\n",
       "      <td>2014-11-25</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>51.720001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1608</th>\n",
       "      <td>2014-11-26</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>51.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>2014-11-28</td>\n",
       "      <td>YHOO</td>\n",
       "      <td>51.740002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date variable      value\n",
       "1605 2014-11-21     YHOO  51.040001\n",
       "1606 2014-11-24     YHOO  51.830002\n",
       "1607 2014-11-25     YHOO  51.720001\n",
       "1608 2014-11-26     YHOO  51.930000\n",
       "1609 2014-11-28     YHOO  51.740002"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_long.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>Date</th>\n",
       "      <th>Stock</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-02</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>397.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-03</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>396.440002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-06</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>393.630005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-07</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>398.029999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-08</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>401.920013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-01-09</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>401.010010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-01-10</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>397.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-01-13</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>390.980011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-01-14</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>397.540009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-01-15</td>\n",
       "      <td>AMZN</td>\n",
       "      <td>395.869995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Stock       Price\n",
       "0 2014-01-02  AMZN  397.970001\n",
       "1 2014-01-03  AMZN  396.440002\n",
       "2 2014-01-06  AMZN  393.630005\n",
       "3 2014-01-07  AMZN  398.029999\n",
       "4 2014-01-08  AMZN  401.920013\n",
       "5 2014-01-09  AMZN  401.010010\n",
       "6 2014-01-10  AMZN  397.660004\n",
       "7 2014-01-13  AMZN  390.980011\n",
       "8 2014-01-14  AMZN  397.540009\n",
       "9 2014-01-15  AMZN  395.869995"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_long.rename(columns={'variable': 'Stock', 'value':'Price'},inplace=True)\n",
    "df_long.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now we can plot the stock prices.  Since the data is in long format, we can take advantage of ggplot's color= parameter, which will magically make line charts with different colors for each stock.  If the data was in wide format, you would have had to manually specify all the different stocks to plot or use a loop construct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)] [[back to section](#melt)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnIAAAH9CAYAAACeIJEFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8FVXe+PHPzNyW3riBJEBIKAFC70R6U1EBESkKIoos\ngm1X/e3zgD4u292+umvDBq4KAgICAhZ6VTqht1BSIAkJKSS3zJzfH5ELd9NDSEg479eL125mzpw5\ncz2ZfO+pihBCIEmSJEmSJNU5am0XQJIkSZIkSaoaGchJkiRJkiTVUTKQkyRJkiRJqqNkICdJkiRJ\nklRHyUBOkiRJkiSpjpKBnCRJkiRJUh0lAzlJkmrMxx9/jNlsru1ilCopKQlVVdm2bVuJP9+M2/3Z\nJUmqm2QgJ0m3WHJyMiaTiaioKHRdr1IeQ4YMYcqUKdVSnoKCAl599VVatWqFr68vYWFh9OjRgzff\nfNOTZurUqQwcOLBa7lebFi5ciKqqjB07tkrXN23alLS0NHr06FFmOrfbzZtvvkmPHj0IDAwkKCiI\nLl268Pvf/57s7Owq3VuSJKkiTLVdAEmq7z744APi4uJITU1lxYoVjBo1qlbL8/TTT7NhwwbeeOMN\nOnbsSE5ODnv27OH8+fO1Wq5b4d1336VXr14sX76c9PR07HZ7pa5XVZXw8PAy07hcLu6//3527NjB\na6+9Rv/+/bHb7Rw6dIi3334bf39/nnvuuZt5DEmSpNIJSZJuGV3XRXR0tHj77bfFM888I+69995i\naaKjo8Vvf/tbr2NPPvmkGDBggBBCiMmTJwtFUbz+bdy4UQghxNGjR8Xw4cOFv7+/8Pf3Fw888IA4\nefJkmWUKDg4W//73v0s9/9prrxW737x584QQQqSkpIhx48aJ4OBg4ePjIwYMGCB27drldf3JkyfF\nQw89JEJDQ4Wvr6/o0KGDWLlypRBCiI8++kiYTCZP2oKCAvHggw+K9u3bi5SUFCGEEHPnzhWtW7cW\nNptNhIaGin79+okLFy6U+UwlOX78uFBVVRw+fFi0a9dOvP7668XSLFy4UDRv3lzYbDaRkJAgli9f\nLhRFEVu3bhVCCHHmzBmvn0vyl7/8RaiqKnbs2FHi+aysrBKfXQghVq1aJbp06SKsVqsIDw8XM2bM\nEPn5+Z7ziYmJYtiwYSI4OFj4+fmJNm3aiE8++cRzPjc3Vzz33HMiKipK+Pr6is6dO4svv/yy4h+S\nJEl1nuxalaRbaPXq1Vy+fJmJEycybdo0vvnmG86ePeuVRlEUFEUpdu21Y2+88QZ9+/Zl3LhxpKWl\nkZaWRu/evSkoKGDYsGE4nU42bdrExo0bycvL45577sHlcpVapoiICFavXk1WVlaJ519++WUeeeQR\nEhISPPcbO3YsQghGjRrF8ePHWbVqFT/88AMNGzZk6NChZGZmApCWlkZCQgI5OTmsWLGCQ4cO8fvf\n/x5N04rdJysriyFDhpCVlcWWLVuIiIhg9+7dPP3008yePZvjx4+zceNGJk+e7HWdqqrMmTOn7A8e\neO+99+jbty9t2rThZz/7GXPnzvU6v3fvXh555BHGjRvHgQMHeOmll3j++efLzfe/ffLJJwwePJie\nPXuWeD44OLjE4wcOHGDEiBEMGDCAAwcOMG/ePFauXMn06dM9aSZMmIDdbmf79u0kJibyt7/9jZCQ\nEACEEDzwwAMcPHiQL774gkOHDvH0008zfvx41q1bV+nnkCSpjqrtSFKS6rMRI0aIn/3sZ56f77rr\nLvHKK694pWnWrJn43e9+53XsxhY5IYQYMmSImDJlilea999/X/j6+orMzEzPsYsXLwofHx8xf/78\nUsu0detWER0dLTRNEx06dBDTpk0Ty5YtK/P+Qgjx3XffCUVRxJEjRzzHHA6HiIiIEL/+9a+FEEK8\n8sorIiIiQly9erXEe19rlTp//rxo27ateOihh4TD4fCc//LLL0VQUJDIyckptfytW7cus0XxWrnC\nw8PF559/LoQQ4sqVK8LPz0989913njSPPvqo6NOnj9d1//rXvyrdIufr6yuef/75MssjRPEWuYkT\nJ4qePXt6pVm+fLlQVVWcO3dOCCFEUFCQ+Pjjj0vMb/369cJms4krV654HZ8yZYoYNWpUueWRJKl+\nkC1yknSLJCcn8/XXX3u1sEybNo0PP/ywypMebnTo0CHi4+MJDQ31HAsPDycuLo7Dhw+Xel1CQgKn\nTp1i8+bNTJ48mYsXLzJmzBhGjBhR7v3CwsJo3bq155jFYqFnz56e++3evZuEhAR8fHxKzccwDHr3\n7k2HDh1YvHgxFovFc27YsGHExsYSExPDhAkTmDt3rqe175ojR44wY8aMMsu6dOlSAB566CEAAgMD\nGTt2LO+++65XPgkJCV7X3XXXXWXmWxIhRKWvATh8+DD9+vXzOtavXz+EEJ7P86WXXvJMPJkzZw57\n9+71pP3xxx9xOp1ERUUREBDg+ffpp59y8uTJKpVJkqS6RwZyknSLfPDBB+i6Tvfu3TGbzZjNZp58\n8knS0tJYsWKFJ52qqsWCgbK6Rm9UUhBRkcBC0zR69+7NL37xC5YtW8bHH3/MypUr2bx5c4XuW9b9\nyru/qqo88MADrFu3jsTERK9zfn5+7Nq1i6VLl9KqVSveeecdWrRowZ49eypVpnfffZeMjAx8fX09\nn/38+fNZvnw5GRkZFS5rRcTFxXHo0KEqXVve/V955RWOHz/O2LFjSUxMpFevXrz66qtAUUAcFBTE\n/v37vf4dOXKE1atXV6k8kiTVPTKQk6RbwDAMPvjgA2bPnu31R3bfvn2MHz+e9957z5M2PDyc5ORk\nr+v37t3rNW7OYrHgdru90rRr147Dhw97tVhdvHiR48eP065du0qV91or26VLlzz3++9Ww/j4eDIz\nMzly5IjnmMPhYOfOnZ77devWjW3btnH16tUy7/fWW28xbtw4Bg4cyP79+73OqapK3759mTNnDrt3\n7yYiIoLPPvusws9y4sQJNm7cyNKlS4sFOdHR0Xz44YcAtG3bttj6cFu3bq3wfa6ZOHEi69atY8eO\nHSWeL235kfj4eDZt2uR1bOPGjSiKQnx8vOdYTEwMTz/9NIsWLWLOnDm8/fbbQNFnnZ2dTUFBAbGx\nsV7/GjduXOnnkCSpjqq1Tl1JqsdWrlwpVFUV58+fL3bum2++EZqmiaSkJCFE0biy0NBQ8c0334ij\nR4+KF154QQQFBXmNUZs5c6Zo27atOHXqlEhPTxcul0sUFBSI6OhoMXjwYLFnzx6xa9cuMWDAANGy\nZUvhcrlKLVu/fv3EO++8I3788UeRlJQkvvvuO9GjRw8RGhrqGW/35z//WdjtdnHo0CGRnp7uGcfW\ns2dP0alTJ7F161Zx8OBBMXbsWK/rUlNTRXh4uBgyZIjYunWrOH36tFixYoVYvXq1EKL4OLEXX3xR\nhIaGema+Llu2TPz9738Xu3btEmfPnhVffvml8Pf3Fx9++KHnmri4OPGvf/2r1Od76aWXRMuWLUs8\nN2vWLNGiRQshhBC7d+8WqqqK2bNni2PHjokvv/xSxMbGVnqMnMvlEkOHDhWBgYHiL3/5i+dzXb16\ntRg5cqR44403Snz2AwcOCJPJJH7+85+LI0eOiNWrV4smTZqIxx57TAhRNCN1xowZYt26deL06dNi\nz549on///qJfv36ePIYOHSpatWolli1bJk6dOiV27dol3njjDTF37txSyytJUv0iAzlJugVGjhwp\nEhISSjzncrmE3W4Xr776qhCi6A/2pEmTREhIiAgPDxdz5swRU6dOFQMHDvRcc/r0adGvXz/h7+8v\nVFX1LD9y7NixYsuPnDp1qsyy/fGPfxR9+/YV4eHhwmaziaZNm4pJkyZ5TWK4fPmyGD58uAgKCvJa\nfiQ1NVWMHz/ea/mR3bt3e+V//Phx8eCDD4qgoCDh6+srOnXq5BXImc1mr/T/+7//K4KDg8XOnTvF\npk2bxKBBg4Tdbhc2m020atWq2LIhiqKIOXPmlPhs1yY5zJo1q8Tz+/fvF6qqiu+//14IIcSCBQtE\n8+bNhdVqFb169fJMNqhMICeEEG63W/zzn/8U3bp1E35+fiIwMFB07txZvPLKK+Ly5culPvvXX38t\nunbtKqxWq7Db7WLGjBmeiSKFhYXikUceETExMcJms4nw8HAxfvx4r6VYCgoKxP/8z/+ImJgYYbFY\nRKNGjcS9994r1q9fX2Z5JUmqPxQhqmGQSAVkZGSwePFiz89ZWVkMHDjQM+A5Ozub4OBgHn74Yc9A\n6c2bN3u6mO69915atGhRE0WVJEkC4NixY7Rp04Z9+/bRoUOH2i6OJElSMTU2Rq5BgwZMnz6d6dOn\nM23aNMxmM23atGHLli3Exsby3HPPERsby5YtW4CisTqJiYnMnDmTiRMnsmrVKgzDqKniSpJ0h8vI\nyGDRokX4+fnRvHnz2i6OJElSiWplssPp06cJDQ0lKCiIY8eO0alTJwA6duzI0aNHgaJvwu3bt0fT\nNEJCQggNDS02IFySJOlWGTt2LO+//z7vvPMOfn5+tV0cSZKkEtXKXquJiYmeWW75+fn4+/sD4O/v\nT35+PgC5ubleM68CAwPJzc2t+cJKknRHkrsjSJJUF9R4IOd2uzl+/DhDhw4tdq6kbYr+W05ODnl5\neV7H/P39CQwMrLYySpIkSZIk1QU1HsidPHmSiIgIT1eFn58fubm5BAQEkJub6zkeEBDAlStXPNfl\n5OQQGBjI7t272bhxo1ee/fv3Z+DAgTX3EJIkSZIkSbeBGg/kDh48SPv27T0/x8XFsX//fvr06cO+\nffs8C5PGxcWxZMkSevfuTW5uLpcvXyYqKorAwEDi4uK88nQ6naSnp9foc9wMk8lESEgIWVlZxRZ5\nvdNYrVYcDkdtF6NWyfpwnawPJbuT64isExVTF+qI3W6v7SLUSzUayDmdTk6fPu21p2OfPn1YtGgR\ne/bs8Sw/AkWr3cfHx/Pvf/8bVVW57777UBSFwMDAYt2oKSkpFd7S6HbidrvrZLmrk8lkuuM/g2tk\nfZD1oTx3Yh2RdaJy7sQ6cqersXXkbqWUlJTaLkKlmM1m7HY76enpd/wvnI+PDwUFBbVdjFol68N1\nsj6U7E6uI7JOVExdqCORkZG1XYR6Se61KkmSJEmSVEfJQE6SJEmSJKmOkoGcJEmSJEl3vKSkJFRV\nRVXrVmhUt0orSZIkSVK9l56ezuOPP05kZCQ2m42IiAgGDRrEzp07PWmuBV3nzp2r1ntXZE3b24kM\n5CRJkiRJuq1MnTqV+fPn07RpU6ZOnUpCQgKHDx/m1KlTXukURaEezNm8KTKQkyRJkiTptrJ+/XoU\nRWHt2rX861//YsmSJaSmpvLAAw8AeLo/hRDExMSgqiqbNm0C4L333qN9+/b4+/vTsmVLXn31Va+1\nCHfs2MGwYcOw2+0EBATQu3fvEmdGX9vAwGQy8Z///KcGnrpqZCAnSZIkSdJtJTIyEiEE3bt3Z+bM\nmSxYsIDs7GwCAgIAeP755z1pn3jiCV544QWioqJ46623mD59OsnJyYwfPx63283vfvc7T/rExEQG\nDBjAd999R3x8PBMmTCA7O7vYki0XLlxg8ODBZGRk8PHHHzNx4sSae/hKkuvI1YK6sN5PTZFrRMn6\ncCNZH0p2J9cRWScqpi7UkcqsI7dx40YmTJhAWlqa51hgYCCffvop9913H1DUKqcoCmfOnKFp06YA\ntG3blqNHjzJv3jwmTZrEgQMH6NSpE5qmkZeXx4svvshbb73FyJEjWbp0KQCGYaAoCmfPniU2NhaA\nli1bcurUqds+iAPZIidJkiRJ0m2mf//+XLhwgU2bNvGb3/yGmJgYcnJymDVrVpnXJSUloSgKbdq0\nAfBs6WkYBufPn+fMmTMA9OrVy3PNtYDwRidOnCA6Opr777+/Oh/rlpCBnCRJkiRJt5UNGzagqip9\n+vRh9uzZ/PWvfwUgLy/Pk+Za8KXruudYTEwMQgiOHDkCwLFjx4CiYK1JkyaeFrcdO3Z4rjEMo9j9\nJ0+ezJkzZ7j//vtv+xZhGchJkiRJknRbGTNmDLGxsYwdO5bp06fz3HPPATB06FBPmqZNmyKEYObM\nmfz85z/n6tWrzJw5EygaQ/fkk08ycuRIAJ588kmsVivTp0/HarWyfPlyBg4cyFNPPUW7du3Iycnx\n5KsoCu+//z5Dhgxh27ZtjBkzBrfbXYNPXzkykJMkSZIk6bbywgsv0LBhQ9avX8/HH3+Mpmk899xz\nnpY5gNdff53GjRuzdu1a3njjDQoLC5kxYwZvvfUWUVFRLFiwAJPJxKxZs/jnP/8JQHx8PBs2bGDI\nkCEkJiby+eefExwcjMVi8bq/pmksWbKE9u3bs2bNGp544okaff7KkJMdakFdGJRaU+RAZlkfbiTr\nQ8nu5Doi60TF1IU6UpnJDlLFyRY5SZIkSZKkOkoGcpIkSZIkSXWUDOQkSZIkSZLqKBnISZIkSZIk\n1VEykJMkSZIkSaqjZCAnSZIkSZJUR8lATpIkSZIkqY6SgZwkSZIkSVIdJQM5SZIkSZKkOkoGcpIk\nSZIkSXWUDOQkSZIkSZLqKBnISZIkSZIk1VGKEELUdiFuVmZmJqpad2JSRVGwWCw4nU7qwcd/U1RV\nxTCM2i5GrZL14TpZH0p2J9cRWScqpi7UkZCQkNouQr1kqu0CVAeHw1HbRagUs9lMcHAw+fn5uFyu\n2i5OrfLx8aGgoKC2i1GrZH24TtaHkt3JdUTWiYqpC3VEBnK3Rt1pxpIkSZIkSaohAwYMIDQ0FKfT\nWdtFKZMM5CRJkiRJkm6QlJTEDz/8QHh4OF999VVtF6dM9aJrVZIkSZKk+iPz9CncGenVlp+pgZ2w\n2OYVTj9//nyGDBlCz549mTdvHmPGjAHg8ccfx9fXl6SkJDZv3kynTp1YtGgRf/jDH5g/fz6NGjXi\n888/p1OnTixcuJCpU6d68nQ6nSQkJLB+/Xoef/xx/Pz8OHv2LJs2baJt27Z89tlnxMbGVv7ZKn2F\nJEmSJEnSLeTOSCf3rX9UW34BM16ASgZyc+bMoUePHsyZM4f09HTsdjsAixYt4ptvvqFt27YMHz6c\nXr168dvf/pZ//OMf/N///R+/+MUvWLduHePGjWPcuHEA5Obm0rNnTx555BHPPRYuXMiaNWvo3Lkz\nkydPZvbs2Xz++eeVfjbZtSpJkiRJkvSTLVu2kJyczIgRI2jZsiVt27bl008/9ZwfPXo0nTt3xmq1\n8uCDD+Ln58fEiRNRFIWxY8eyd+9er/wMw2DChAkMHDiQp556CiiaZTx69Gi6deuGpmk8+uij7Nu3\nr0rllYGcJEmSJEnST+bNm8ewYcMICAgA4OGHH2bevHme8+Hh4Z7/b7PZvH728fEhLy/PK7/Zs2eT\nn5/PG2+84XW8YcOGZV5XUbJrVZIkSZIkCSgoKOCLL77AMAwiIiKAoiXOrly5woEDB1AUpVL5LViw\ngIULF/Ljjz+iadqtKLIM5CRJkiRJkgCWLVuGyWRi//79WCwWAIQQjB07lvnz51cqr7179/Lss8/y\n3XffERYW5nWuOhdtloGcJEmSJEm3FVMDe9EEhWrMryLmz5/PE088QePGjb2OP/PMMzz33HMMHTrU\nq1VOUZRirXTXfl6+fDnZ2dn06dPHc65fv36sWrWqzOsqq15s0ZWSklLbRagUs9mM3W4nPT39tl2B\nu6bIVdtlfbiRrA8lu5PriKwTFVMX6khkZGRtF6FekpMdJEmSJEmS6igZyEmSJEmSJNVRMpCTJEmS\nJEmqo2QgJ0mSJEmSVEfJQE6SJEmSJKmOkoGcJEmSJElSHSUDOUmSJEmSpDpKBnKSJEmSJEl1lAzk\nJEmSJEmS6ii5RZdEnnGFAvKw4Ye/ElShbUKEEFXeTkSSJEmSpOohA7k7XLJ+muWO9wlUQrgqclEV\njQHmB2lh6lBi+nQjmR2utZzXT9Jca88Qy1g0RavhUkuSJEmSBDKQu6M5RSHfOD/nbssjNDe1QwhB\nqpHEWudnnDOO09/8oFeQlujewVbn1/Qy300/8yjWOxfzpeMdWmodaKg2xa5GYlLMtfhEkiRJknRn\nqdFArqCggK+++or09HQARo0aRWhoKIsXLyY7O5vg4GAefvhhfHx8ANi8eTN79+5FURTuvfdeWrRo\nUZPFrbPcws1pPRFfJYDGWvMS06ToSWx0LaWx2oLmpnYAKIpCpBbDI7YXWe34hGWO97jPOhmb4osu\n3GxzruYh23QaqEUbHz9gfYKj+m5S9bMccv9AlrhEqNqQIZaxhKuNK1TWg44dRIgY/JTA6nl4SZLu\nSLpwA4rsIZDuODUayK1Zs4aWLVsybtw4dF3H5XKxadMmYmNj6dOnD1u2bGHLli0MHTqUS5cukZiY\nyMyZM8nJyWH+/Pk8++yzqKqcn1GaTCONQ+6dHHHvJkS1c0VkMsn2S2yKjyfNFeMyW10rSTGSuMs8\nnNZal2L5WBUbI6xPstm1goWF/2SEdSoXjfOEqY08QRyAppiIN/Uk3tQTAJdwss+9me3ONYy0TeW8\nfoIGagQ+in+J5TWEzrf5C4nXetLPMrKaPw1Jqh2ZRhqrHf9huHUSoWpDz3EhBMf0PWx1fU0brSu9\nzfegKPJ9Vl22ub7mgnGK0dansSq22i6OJNWYGnuLFBYWcvbsWbp0KQocNE3DZrNx7NgxOnXqBEDH\njh05evQoAMeOHaN9+/ZomkZISAihoaEkJyfXVHHrDLdwcsj9AwsL3+BLxztomBhne5axtmeJ0dqy\n2bmc/a6tbHauYJ1zCZ8X/o1QtSGTbb+kjalbqX9IVEWlv2UkXcwDWFT4Jjtda+louqvMspgVC51N\nfUkzznHCvZ9ljvdIdO8sNX2acR6L4sNh9484heOmPgdJul1kGmk4KGBx4b+5oJ/0HD+s/8hO17cM\ntozhvHGS9a4va7GU9YsQguP6fnwJYLljLi75PpGqUVJSEqqqYhhGbRelRDXWIpeVlYWfnx/Lli0j\nLS2NyMhI7rnnHvLz8/H3L2qx8ff3Jz8/H4Dc3FwaN77ePRcYGEhubi45OTnk5eV55e10OvHz86up\nR7lpJpPJ63+r6rhrH+sKv6SBFkFP21BiTfFe3QoDtFF8nv8PdNyEa1EYGEzxmYW/GlThe3Qx96OB\nOYLdjvXE2TqhltNtYcZMJ6MPqxzzaW3uzDnjOAnme0pMm6yfJN7ajUzXRY6LPXS29KtwueqT6qoP\n9YGmaZjNdXucZYGRSytzR5qb27Hy6scMsD1Ia3MXfij4huG+j9HY1Jxoayvm5v6azmo/wrWocvOs\nz3XEKRzsdW5iv2MLD/s9Q4hm9zpfkTpxUT+Ppmg85P8zVhd8ykrXPEb7TrujxuzW5zpSk5o1a8al\nS5fQtKK/dYqisHbt2louVdlq7L+4YRikpqYyfPhwoqKiWL16NVu2bPFKU5HlLHbv3s3GjRu9jvXv\n35+BAwdWa3lrQkhISJWvzddzWXv2c6ZEvkyMT5tSUtn5Jf+o8j2u52KnKwkVTj9MH02D3HB6Bg7i\n12emExDmh031LZYu+cIpBgePxkfz5YOUP5KqneHh8On4aiV3xdZ3N1MfpNuHO8NJQy2KbiF9aOJs\nxocpr3OcPYTbougc0cuTbrDlQX4s+JZHw54ny51Btjudy650IixNifZpVWLe9amOFBoFbMtey+Yr\nX9PCJ54INZp02zlaBbetdF57MtfTIbAX4Q0aMlE8z+cX32St/imTGv0cTSn6M+c0HFhUa3U/xm2n\nPtWR2qAoCitXrmTQoEGeY0lJSbVXoAqosUAuMDCQwMBAoqKKvn22bduWLVu24O/vT25uLgEBAeTm\n5npa1gICArhy5Yrn+pycHAIDA2ncuDFxcXFeeTudTs8EirrAZDIREhJCVlYWbre7SnnsdmwgRmuL\nf14D0vNuv2dvTXeuZOYSoTVjd9o2Wpk7ep13ikKSC5No4I7EuArT/Ofw9dX5bEhdSXfr4Foqde2o\njvpQX1itVhyOut0tln41lUBTA9Ld6ahYmeDzAmsKPiPBOszrPdVCdGJzwdf83+knCFBDCFRC8FeD\nWOv+gqkB/4dFuR501GQdydTTMCtWAtVbFxAku0+z7Opcok1xjPV5hgZaBEf03RzK3k0rV1evtOXV\nCSEE+/K2cbfPBM/nO0Qdx7Kr7zP/3D+4z+cxcsRl5uW+zszA33sCu/qmLrxH7HZ7+Yl+kplxCrez\n+v62mSx2whqUPPmvoj744AN+9atfIYTgxRdf5MUXX6ym0t2cGqvRAQEBBAYGkpGRQYMGDTh9+jR2\nux273c7+/fvp06cP+/bto3Xr1gDExcWxZMkSevfuTW5uLpcvXyYqKgpFUQgM9J7hmJKSgsvlqqlH\nqTZut7vS5T7s/pEoNZb9jq0MsIy+7Z87WonjlDORGLy/Zf/gWkdjtTmqYcLhKkBBpYPWh42OpXRS\n78wu1qrUh/rGZDLV+c8gR8/CR/XzPIcFH0ZYngRBsWebZP0lKorXWNVVxnx2F2ygudYeHTcN1AjP\nuVtdR1zCyaLCt3AJJ30s99HO1Kv8iypJCINvC7+gn3kkrU1dwQCX4SKSWL5xL6DQWeg1RKS8OnHY\n/SOq0LAbUV7phpsf45PCP3HecZLzxkkcFJDmvEC4Wn5Xdl1WX94jbmc6uek336N0TYD9BaBigZwQ\nosTjGzZs4OTJk5w6dYpBgwbRqVMnBg+u/YaHGv1qMnz4cL788kt0XSckJIRRo0ZhGAaLFi1iz549\nnuVHAMLDw4mPj+ff//43qqpy33333fE7CThEIeudS1DR8FH8aKze3LeLmtBMa8MexwaEMDx/rM7o\nhzng3soE6wteaRursTgoJN1Ixl7PX7ZS7St6WYsqzxy9ZFxgv2srbU3didJiPcfzxBX8leAK5VHS\nUhm9zMNYWPgG211raKq2YqRtapXKVxW73etppDalt/keljveR0El3tSjWu9xRN+NppiI+68Z876K\nP0FKGBeNc/grQQQoIeW+8/NFDlucKxhlm1bsv6NJMdPR1Id97i2kG8kEK3bSjfofyEk3RwjBqFGj\nPGMNBw4nAZQrAAAgAElEQVQcyN///ncAXnvtNXx8fGjXrh1Tpkzh888/v/MCuUaNGjFt2rRixydP\nnlxi+n79+tGv353ZOlOSs/pRItVY+ltG4hAFdSKwDVXDMWPlonGeRlo0WUY63zgW8IB1Cv6q9x87\nRVFprXXlsHsX/S3yZStVP7dwIzBw4+Qrx4dcNi7SSI0mQosmUo2hodq0QktXpOnn+MrxPm1M3Vnr\n/IwYrS0DzKMAhXyRg/9NrIsYpjbiHsujWBUfvnF+XuV8KivDSGWfazOP2H5BoBrKSOtUFhf+mxPu\n/aiKSoTajCZqC8LVxuVOeirNVZHHVtcq7rM8XuL7q6nWitXOT8gXucRpnRliGVdmfpudK2hj6l7q\nupVtTd3ZUbAGm+JHJ1MfLhkXiKdnlcou3RkURWH58uUljpFr0qSJ51jTpk05ePBgTRevRPVzsMBt\nKttIZ5trDSPNT1Tp+jP6YWK1eK+1qeqCFlp7TuoHCVUbssLxIb3N9xCpxZSYtr2pN58V/o1Opj4E\nqWE1XFKpvtvg+pIj7l1YsNHO1Iv7rY+Tqp8l1Uhih2st6UYyQUoYgy1jidCiS8wjVU/iK8eHDLGM\npbmpHd3FEL5yvM9G1zJ6mIdhxoJJsdxUOWNN8RhCJ09cwS2cmLm1sy/zRQ7LHe8zwPIggWooAKFq\nQx62PUOWkY4bFynGab51LiRXZBOlxtLbck+FF/6GopaO75wLaa11JVJrVmKaeFNPApRgWpu6sNbx\nORtdS7mPSSWmTdXPct44wWTb/5Z6T5viS7ypJz6KP+FqY4679le4vJL0386dO+cZo3/u3DnPmP/a\nJgO5GpRhpHJc30uOkYWdsgd9rnZ8goFBM60NzbTW+OBPkn6E3qUs5XE7a661Z63zU7JEOpFaDB3M\npc+ADVRD6GoewHrnEkZan6oTrY5S3eASDk649zPR9hJu3Nh/Wty6hak9LWgPFO0OsN21hmP6nhID\nuRT9DCscHzHMOp4YrWjcp03x4QHrE3xU8Ftaa13xVyq+vE9ZVEUjWGnAZZFOFLdueSWXcLDc8T7t\nTL2KxqzdIEQNJ0QNB6AVRet9XhW5nHQfZGnhe9xvfdyrW7kkhtBJdO/kkF60puR9lpJ7YKCoBT/0\np/sNtY5nXsEfGGg8iIJ3C6AQgk2u5SSYh3tNCilJP3PRYuNOCskwUjGEjo4bAwMVFaVolCLZIoNC\ncbXc55HuXL/97W957733OH36NB9//DGffvppbRcJkIFcjcoV2QCccO2nOSUvLwBF347P6EfobxlF\nkn6ETc7l+CmB+CmBnm/LdUlDtQkuXFwVudxrKfnb9Y26mPpzxL2Lk/oBWpo6lptekirihH6ASDXG\nE5iURFNMRGtxbHOtLnYuWT/NSsdH3G19lGZaa69zPoofIWo4J/QD+FVTIAcQqjbispFGFM2qLc8b\nGcJgtfM/NFAi6GEaUqFrfJUAOpgTCFLDWOX4mMd9ZpcaTCXrp1nvXIKP4k+CeThN1BYV7pb1VfyJ\n1eI56NhBB7wXI08XyeSJK7TVupWbz7Uvg1Z88FcC2eH6hj3uDSioCAwEBgaCQCWEqyKPaT6/wvzT\n87iEgzTjHJeMC55/vkoAD9ueqdAzSFVnsth/mqBQffndDEVR6N+/Py1atMAwDF5++WWGDKnY78yt\nJgO5GnStS+K4ax/38HCp6c7oh4nW4og39SDe1ANd6KQaZzDd4u6VW0VRFO61TCREtWOqwNR/TTEx\nyDKGNc5PidbisMjtdqQKOOz+kUT3DuK0LqQaSeSLHIKUUALVMIKUMA66t9PF1L/cfMLVJqQbKehC\n90xGEEKw3rmEwZaHiwVx1zRRW3JE30WMWtq6jpUXqoRz2bhUbfm5hZMr4jIKKiGKnU2u5biEk/us\nkyvd+h2txdFYa8F+9xa6m70HfF8VeWxxruCccZy+5hG00jpVqXW9gymBtYWfEWft6jV28ah7N220\nrpWeqGJXG3PAvY3xthe8ZgNfs6Twbc7pJ2huaocQgmWOubhxEaFGE621pot5AF8UvolbuO6oxYZr\nQ9FSITU/oe/MmTPFjjVr1gxd1wGYOrXmJh9VlAzkqsAQOukihWClAdYb9jEtT67Ipq2pB5ucy8l1\nZ5ea7rR+mJZaB8/PmqLRWGtxU2WubZXtrmisNaep2pLtrjX0t4y6RaWS6pNz+jFCFDsXjJNEqbEE\nq3ZyjMtcEZmcNA5gwUaMFl9uPlbFRqASQqZIRRgCH8WfqyIXF06aa+1Kva6p1opd7nXV2iIXpjbi\nuL632vLb4lrFCX0/KiqgYMbCWNtzVV5brad5GIsL36Kj6S4sig1DGCS6d7DdtYY2pq48ZvvlTX0R\na6RGE22O48OC3xBn6kJH012EKHaOuvcwxjajCuUdSm/z3aWOM47R2nDGOExz2pFsnCJf5PCY7X9Q\nbwgYg5UGZIo0/AnimHsvXczlfzmQpFtJBnKVYAiD/e4t7HCtxab4omFitHV6hbe8yhPZBCsNiDXH\nk5j/Ay3oXCyNW7i4oJ9kaDmzte4EfSwP8J+CP9HG1K1Sg6qlO9MlI5l7rBO9l5eo2uRKGqpNSdZP\ns9u9His+2NVI4k09y2wBilSboWGq1BZ45QlVw8l0XSx2PF/kst65mKGWCZXaID7VSGK45TEi1Rgu\nGucJVEOxVeLL6H8LUxvRXGvHasd/GGB5kDXOT1FQeMg2nQY/jUG8GYqiMNx/It2VwRx072BJ4dv4\nKgEEqMFVmvRVUivcjWK0tuxxbEQIwQ+u7+hmHuQVxAGEq41JN5JJE+fY6fqGzqZ+ciyvVKuqtoDS\nHSjDSGGh4w1O6gcYZ3uOKT6zaW3qylfODyqcR67Ixl8JppW5IwfzfigxzXnjBHY1Eh+l7uwde6v4\nKv4kWIbzvXMRie4dXNBP1XaRpNuUSzjJEZcJU6pnRncjtSk7XGsJVxsTrjbhmL6Xtlr3Mq8xKRaa\naa0JraYyAAQr4eSIy+ji+kr9TuFguWMu5/TjnNePVzgvt3CTaVwkXC1aWL2R1hRf5ea3wxtoeQhF\nUZlf+EditLY8bJ1ZLUHcjfzVYHpb7uFJn1fpbh7smcBQ3ULUcEyYWep4lxxxmTYljMGzq5GkG8mc\nM47joIAccfmWlEWSKkq2yJXgoHs7CgrtTL1wCxc7Xd+S6N5OgmU47bTr38q7mgay0/UNbuEud+yX\nIXSuilz8lSCCTCGszfuMq6Y8zHgPEj6tH/LMhpMgXutBupHMCfcB3Dh5WJODjKXiMowUQpWG1bb9\nUkO1CQ4KSTDfS7DSgFZGpwq1tD1grdrSQqUxKSbC1Eacc5+gERHoQmeV42PsShStzV1J0o/QwtTB\n65oUPQkDN75KAL5KAFZ8UBSFDCOFYCXMM5C/umiKxnDLY2SL9HJbvG7+XibiTMV7MqpTD/NQCkQ+\n7U29S6xPdjWK4679ZBmXaKg25aJxXi6VJNUqGcj9l3QjhW3Or9EUE3niCsfcewhTI5jo8zJ+/7XI\np6ZoBCjB5IjMcr+F54scfBQ/NEXDrJhp6dOBk+6DtFGuf+MTQnBGP8xo69O35NnqIkVRGWh5iAKR\nz0cFv/PaIUKSrrlkJFfriv0N1caMtT7jaVmK0apvAkNldTAlsMe5ge6iL2sLPkNFY7BlDNkigz3u\nom7Aa117mUYayx1zCVMbcVXkcVXk4sZFgvlezFhpqDYp525VY1JMNFBubRBXU9qaym55tauRpBln\nCVMaEaO14ZJxwbM0iyTVBhnI/eSYey+H3Du5IjLpY3mAcLUx3zu/4C7z/bQwtS/1uiClAdkig1DK\nDuSudate096/J9szv6ON5Xogly6SMWHxrKMkXeej+GFVfLgiMglWbm4auVRxp9yJBKsNCFMb1XZR\nSuQUDty4SDcuYK/GcZSKopa6aHVNa611YZtrNV9cepvLxiVGW3+GqmiEEI6KSoZIwW24aKRGs9+9\nlU6mvvS2XF9vMttIZ0HhP4nUYmhWjTNq71Q2xY8AJYSmWhwN1SbscW2s7SJJdzgZyP3khL4fuxpF\nO7UXLbWOKIrCeFv5a9iEqA3INjLKHVSdK7IJuCGQa+3XmcUX36PQXOAZbHxaP0Ss7FYtlV2N4pKR\nTLAqA7macMK9n2+cCwhR7UywvnDbtYQWiqt86XiHK0YmGibiTfVz6yWTYqGj5S5OFO5nnO9zmPWi\nrlFFUWimteaLwjdR0WipdeSEvp9Jtv/ndX2waqeF1p5EfSc9zcNq4xHqnRZaB1pqHQhSw7hkXPBq\nFZWkmnZ7vZlvkRT9DOlGSplpLhkXaGfqSStT5dY7utYiV568/wrkbKoPTU0tOaMfAiDbyCDRvZMW\nWofSsrjjhatRXDIuVDi9Ltzsdq3HEMYtLFX9dFY/xjrnEh62zUTDxEH3DgpEHonuHWxwLsUpHJXO\n0xAGBSK/Wsp3LYiLUmN5yPY0jbXmnp0a6qME6z280OQP+KrekxO6mgYxwjqVKT6vcMm4QFMtrsSx\nfN3NQwlRwutN92dt628ZSaQWg58SiFmxyAkPUq2q9y1yywrnki6S8cGPR20vlRikFYqrFIp8gpUG\nlc4/WLVzxnW43HS5IptAJcTrWEtzR4459xGuNmap4116mIbeNt05t6NwpTH79S0VTr/FtZK97k20\n0jp7BdFS2VL0M6xx/If7rVMIVxsz0DKaJYVvs8W1kig1FrNiYanjXUZap2JTfCuUZ6qexDrnEnLE\nZUZap95UPS8UBSx1vEuUGks/88iiJSqsj1U5v7pAVTQsqg3I9ToepIYSRNFuL2Ntz2AgSrw+SA3l\nMdsvZavRLWBXoor26JUTHqRaUq8DuRzjMpeM80z1eY3PCv9OknGkxBmh6UYydjWqSl1HwRVskcsV\nWUSq3n+8Wpjb833BIpbob9PPMqLYPoeSt/CfulbL68bY6fqWdONCUTesYidf5BBA6YGc7Ba5Lt1I\nYaXjI4ZZH/Es4hyuNuZp39950ghhsMn1FYsL32K07Wf4KgGl5lcg8tnqXMUZ4zB9zQ9gVXxZ4fiQ\nOFMX2pl6VnqZiqIg7h0i1GaeIE4qYlIsZZ6Xn9WtEaY2IlNcpLaWbBdCcMC9jTitY7l7eEv1U73u\nWk02ThOpxaIqGt3Ng9np+oZz+nHyDO9dFS4ZF7BXccZboBJKvrjitc5TSS4bF4stYGlTfGlvSmCw\n5WEZxFXAtRXz80Tpu2LkGdnsdW2khdaBMdYZhPwUyJWmUOTzQeGvEbL7lSwjnWWO9+hvebDMWZqK\notLPPJLmWjsWFf6LXCOrWBqXcLLftZVPCl7HpJiYZPslrU1didHaMNb2LCbMrHLMR4iSW5BKcslI\nZknhW0SozehvHiUDE+m2EPbTfri15ZK4wDbX13yc90dWZvyHAqN6hi9IdUf9DuT000SpRa0KLbUO\nhCjh/OD6jk8K/8xax2dk/vTLd8m4UOWdAzRFw18J5koZYySKFivNIkQpPhu1r+UBmptK3/ZHuk5R\nFBprLThrHCs1zQXjFI215rQ2dSVQDcVPCSgzkDunnyBPXCGvjDTXFIg83MJZpbLf7nKNbJY63qWX\n+e4KrdOlKAq9LffQ3tSbLxzewVy2kcGHBb/hrHGUUbZpDLCM9to9IEQN5y7zfbhwclkU37WgJEfd\nu1lW+B4dzAkyiJNuK6FqQ8/fktpw3L2PDqa7mOI/C6dRyAd5v/E0Wpx0H6y1ckk1p153rSYbp+lg\nTgCKxpjcbX0EKBoTd8C9jSWFb9NQbUK6SKa7eUiV7xOs2Mk20ostG1IoCrBgIdNI/Wmx0iruFyR5\nxGhtOKUn0s7Uq8Tz5/WTNFavd3L4KYHlBHJFK+PniMtldr8CbHAuJVKNpaP5riqU/Pa2zfU1cVoX\n2pt6V+q6LuYBZIl0jui76aEW/Q4l6UeI1eIZah1f6nWKotBCa89J/UC5S5sUinw2Ob9ilO0puVWb\ndNsJVcLJFhkYQket4Xe8EILj+j5GWJ/AXw1itH0q7YwEthSs4px+nHQjhXA1ikA1tEbLJdWsetsi\nd1XkkS9yaKAUH4NjU3zpYR7CEz6zaaa1oYESSWgJrWUVFaO1YZd7XbHZkV875pPo3kmGSL3lK57f\nKZpprTmvn0AXblL0pGJB2gXjJE20lp6f/ZSgUgM5IQRnjWPYlagKzTrLMFK5IjJv7gFuQ7rQOaMf\npoMpoUrXx6htOa+f8PycYiQR+dP4urK00Dpwwn0Al3BQKK6Wmm67aw3NTe1lECfdlsyKFV8loMxe\nmVslzTiHCbPX37kQzc491kd52PYMLbT2nNITy83nR9f3nHYfupVFlW6hehvIJelHiFSbFdvw+EYm\nxUJH812Msj11U9+kOpruQkFhr/v6wpC60EkxznBSP0CGkVKvl0aoSb5KACFqOOuci1np+JB5BX9k\nWeF7HHXv4bJxCadweO2yUVaLXLbIwECnmdam3ABNF26yxCVy6mEgl2KcJkgNI0Ct2szeKK05acY5\nT7dzqnGGSLVZuddFqjFcFbnMLZjD/ILXS+ye0oWbw+5d9DbfXaWySVJNCFMa1Vj3aq6RzSl3Imf1\nY6x1fkZ7U+9Shxo0r0Agd9C9nUT3Dr51LuCifv5WFFmqoF/96ldMmjSp0tfVi0AuVT/r+f9CCPa5\nNrPFuYJu5sE1cn9FURlmmcAu1zoyjFSgaCasvxJImnGOC/qpat9E+k4Wo7XhpH6QMbaZPOXzGnGm\nLhx2/8inhX+msdbC66XmpwSQT8mB3Dn9ONFqHEFqKDklDNi/UbbIQCDKTVcXndITidWqPk7TqtgI\nUxuRYiSRa2ThFu4K7b6hKipjbDOY4jOLvpYHWOp4l8v6Ja80acY5QlR7mTNjJam2haoNKzze82b9\n6P6eLa6VbHQuI8F8L13M/UtNG6214pJxodT1GxPdO9juXMMo6zSGWMax3DGXU+7yW/DuBAsWLKBn\nz574+/vTsGFDevXqxdtvv31L71nVsb/1YozcKuc8Jtn+Hxoq3zsXk24kM9b2HMFq5deFq6ogNYy7\nLPex1vEZ423Pk2KcpqnWinyRyyn9oAzkqlEXU39aa109/33bmLrRxtSNfJGDgvcvQlktcpeMCzRS\nowlUQjkqdpd5zwwjlUg1plYHNd8KQghO6YmMtE69qXyaqi05r5+gQM0nUmtW4RfStZncbUzdcOPi\n85x/MsYywzOm57xxgiZqy7KykKRaF6Y24pxe+iSs6iKEIEk/wgjrExX6m2JSLDTRWnJSP+A1/tUQ\nOptcX3FWP8oY20xCVDshqh0fxY+1zs84pO+kl/nuO3Y4w1//+lf+/Oc/89Zbb3H33Xfj5+fHvn37\n+Mtf/sKTTz6JxVL2Uj9VVZlZ/DeqF4FctBrHOudisoxLBKt2xtmew6xYa7wc8VpPTrkT2en6lkyR\nSkutEwKDVD0JX8W//AykCrEoNiyKrdhxPyWw2DFfJYACkYchjGLd7FniEq3VLgQoIeSIslvaMo00\nGqvNuWhcwCEKsZZw/7rovHECMxbClJvbSzVaa80qx8f4K8EVmvVakvam3igmWFLwNg9bZ+KvBnNe\nP0H3GmpZl6SqilCbsdm5givGZYKqYWJBppFGqBJebG3TLJGOgU5YJXbo6GLqz2rnJ8RpnbEoNgpF\nAV875gMwzva816LekVoMk2wvk+jeydLC9xhufYwmWu2skHc28xRZ7vRqyy/EZCc6rHm56a5cucJr\nr73GJ598woMPPug53qlTJ/7zn/940jz77LOsWbMGX19fnnrqKWbNmoWiKAgh+N3vfsf7779PQUEB\n99xzD2+++SaBgUV/n+bPn8+rr75Kfn4+L7zwAu+//z4ffvghgwYNqvKz1YtArq/lARYU/oP2pgS6\nmPrX2tIEiqIwxDqWTwv+ggsnA8yj8VF88bXKbqHaoikmrPhQQB5+eAd6WUY6IUo4Poof+eJKmbPO\nMkUacVpnApUQcsVlrCVMoqmL9rg20MV8878zUVos91kf55x+jJZaxyrn091nEAWuqyxxvMNw6yQu\nGReIVMufOCFJtSlEtdPNPJDVzk942PrMTa1Q4BZuPiv8G0MsY2lj6uZ1Lkk/QjO1daV+X6O0WJqo\nrdjk+ormWjs2OZcTrbWmn3lEie87k2Khk7kvYWojvnbM50Hbzwiv4jqrNyPLnc5nuf+otvweCXiB\naMoP5LZv347D4WDkyJGlpnn22WfJzc3lzJkzZGRkMGzYMCIiInjiiSf46KOPmDdvHhs2bMBut/PY\nY4/xzDPPMH/+fA4fPszMmTNZu3Yt3bt3Z9asWaSklL19aEXUizFyNsWXx31m0dU8oNbXl/JTAhlg\nGU2AEkKAGoxJsRCtxdVqme50JXWvFop8dFz4KYFoiglfJYBccaXUPDKNNMLURgQq5Y+nqysyjTQu\nGcm01qpnMeooLZbelntveqmD7ubBdDL1ZVHhv7CrUVhqoXVdkiqri6k/PvixzfX1TeVzybiAGQvb\nXKtxC5fnuEs4OKMfplkZi3WXpq/lfnKMy+xxbaSbeRADLA+WO8GvidaSQZaHWO6YyxWj/k3yKk1G\nRgYNGjRAVa+HRwkJCYSEhODr68umTZtYuHAhf/jDH/Dz8yM6OpoXX3yRTz75BIBPP/2UF198kWbN\nmuHn58cf/vAHFixYgK7rLF68mBEjRpCQkIDZbObXv/51tcQs9aJFzmq1en3ota0jvWkvepT6i6Io\nClevXsVsNmMy1Yv/BFWmqio+Pj7lJ7wJAa4QXGYHPpbr98l0pRLqbISvb1G3QrCzAQ5LHj7m4t88\nncJB3tVsInybEirCKdByq7XMtVUfjuT/SGefvgT4Fu+Sri3X6kMvnyG01jvhFIX4mG5t/agL7uR3\nRk28I6rLCNsUPrrye5prbWluacdVI5cMPZWm5lYVziO94Dzxth7k6JdZ5noPHZ0regYOUYhdi6SV\nXwesavHPo6w64oMPj/r+vNLP04FeuAsdLCt4j0lBL+GnVuxdka1nsPHqchqbWxBv6UGy+xRJrmOM\n4ebG4taEsLAwMjIyMAzDE1ds27YNgCZNmpCWlobL5SI6OtpzTdOmTUlOTgYgNTW12Dm3283FixdJ\nTU2lcePr4w59fHwIC7v5PXrrxRvB4XDUdhEqxWw2ExwcTH5+Pi6Xq/wL6jEfHx8KCgpu7T0MP7Ic\n6RTo1++T5j5PMGGee/uLYNIL0wh3Ny12/TrnEmK1djgLnfgZgWToFymg+spcW/XhlCORe62Tbvnn\nXxk31gcrfljxo8B1+5SvttzJ74yaeEdUFwWNu82PsjJ3Ho/Yfs73zsVcEheYavu/Cu/lfdZxnNZa\nV7qaBnFeP0GAEkKQORRf/FEUFcNBie+fW1VH2tCDbPUyC7Lf5CHbDCxY2e/eQlOtlWeykks4uGQk\nc9E4z0XjHGf143Q03cXhgh/5Pn8xjdRommp1Y9JS7969sVqtLFu2jNGjRxc736BBA8xmM0lJSbRp\nU9Q6eu7cOU+AFhkZSVJSkif9uXPnMJlMNGrUiIiICI4duz4ppqCggMzMm2/trBeBnCSVxU8JJNNI\nQwjhacbOMi4RcsNOHGFqIy4Z54mnh9e1x937SNKP8IjtRaBob90UI6nUex1176aRGl2jM6ar4opx\nGYcowF5PxvpJ0u0iSoulk7kPCx1vYsGKBRupxjkitWblXiuEQYp+hkHmMfgq/lWeOFTdepnvxkEB\nXxS+QROtBSf0Axxwb2OE9UnWOReTYiQRpjaiodqEplocCeb7CFJDEUJgYNSpXY2Cg4N57bXXmDFj\nBkIIhg0bhp+fHwcOHCA/Px9N0xg7diyzZ89m/vz5ZGZm8ve//52XX34ZgAkTJvD6669z77330qBB\nA2bNmsX48eNRVZWHHnqI3r17s337drp27cqvfvWrKs9UvZEM5KR6L87UhVWOeWQZ6QywjCJYtZMl\n0olTr78ko7XWrHR85BXsnXDvZ4PzS0bZpnn2Cg1UQrlsXMQpCovNnHWIQr53Lqa9qRf9LKUPlL0d\nnNWP0lRrVeFWAkmSKq6baTC54grtTb045T7ISf1AhQK5y+ISVsUHfzXo1heyEhRFob95FAeUrRzV\n9/Co7UU2O1cyv/B1epiHMNI6FU0pHk4oioJG1YK4EJOdRwJeuNmie+VXUS+//DJRUVH86U9/4rHH\nHsPPz4/Y2Fj+9Kc/kZCQQMeOHXn22WeJjY3FZrMxbdo0pkyZAsATTzxBSkoK/fr1o7Cw0DNrFSA+\nPp4333yT8ePHe2athoeHY7UWjQNWFKVKY+YUUR3hYC2rjlkfNclsNmO320lPT7/jukn+W011m+jC\nzV73Jna51tPB1Jvj+n7us0727LghhOCDwt8w2jqdUDWcI+5dbHauYJRtmteMLbdwsdb5GWf1Y7TQ\n2hNv6kmkGoOiKOx3beGgezsOCnnC9kqFfyGrqz7kixzcwl2h5Q9WOD6ihda+2Ky42laXutFq0p38\nzqjrdSLdSGaF4yOm2GaX+074wfUtV4zMMvcpLk1N1xG3cJJupBKhRZef+CeRkbIH4EZ5eXmEhIRw\n8uRJr3F1lSW/jkt3BE0x0c08iIm2F8kWmeSKy4Qo17s/FUWhmdaaJP0IB93b2epaxUO2p4tNuzcp\nZu6zTmayz/8SpjbiO+cXzCv8Iz+6vmefe0vRbDBU0kVyjT6fQxSwpPBtvnd+UW7ai/p5zusnaCpn\nU0vSLddAiURF47xxosx0V0Uee1yb6Gau+npiNcmkWCoVxElFVqxYwdWrV8nPz+ell16iQ4cONxXE\ngQzkpDuMvxrMcOsknvL5NSbFe3XuGLUtu1zf84PrO8ZYZxCmlr5Irp8SQFfzQB6z/ZJhlglcMTII\nVhoQpTb3bAZfUZl6Gj/mbKjqIyGEYI3jUyLUZlw0zpNnlL6Myh7XBpY73meQ5SH85LZXknTLKYpC\nP8sIvnN+gVMUlppup+sbWps6e43dleqfr776iqioKKKiojh16hQLFiy46TxlICfdka6NebtRE60l\nkYCacmwAACAASURBVFoMD1tnEqxWbDyFoihEas0YYh3HSNtUFEUhztSZw/oPZQZUbuEkRT+DW7j4\n6uqHLEv/kMt61fZqTNR3cJVcBlnG0EJrzzF9T4npkvXT7HKtZ4Lt57Q2Vc/acZIklS9Wi6eJ2oLt\nrrUlns8zrnDUvZse5mE1XDKpps2dO5esrCyys7P59ttvadny5mfzykBOkn5iUazcb51y0wvahquN\n6Wj6/+zdd3iUVdr48e9TpqSSQighQAgloVcpAlIEpAiyYgcRsa4iq+u6677u713L7usWe113F0Ww\nC7qooCuCIFhQqtTQCSSUECAJSaY85ffHYCCSBkwymeT+XBcX5Kn3hJPMPec55z6D+Mg3C7/tK/eY\nneYm3vM+z+uev5CgNmVEwiSWexZw0jrBHnMLq/yL+cQ7m9X+pZXeq9A6zje+RYx0XoemaGTofdhq\nrD7ruBK7iM98bzDSdS0xatwFvT4hxLnr7hhc4Xqs640VdNR7y1KO4rxIIidEDbhIvxQ3UewwN5S7\nP9c6QD99FAMdY7ks8noGNRrDMeswb3qeYp1/BT7bQ1O1ZblJ2U9s22aJbx49HINprAbWXkxR0/Dj\nI9vcXea4xb53aK91p43WKbgvVAhRLYlKU/LtvDKrNQD4bA+bjFX01IeEKDIR7qT8iBA1QFEU2mgd\nOWjtpRMXnbX/iJVNL8dQ2mgdcSgOHKqT6dEP4fcbpTPbTNtglX8xPttb7jJVW83VnLTz6aOfXlRe\nUVT6OIazyr+YK7U7ANhgrOSknc8450019GqFEFXRFJ04pTF59iGaKi1Lt3/tX0iqlkEj9cIr/IuG\nSXrkhKghzdVUcsw9Z223bZsj1oGzZsQqilqmPIGm6CSqzci1zp4BW2QXsML3MaNc151VbLOTdhHH\n7SNkm7s5Yh1glf9zxjqnllvnSQhRe5LUFmV+njca35Jl7mCY8+wVBISoLvnNLkQNSVJbUGAfw2OX\nlJlcUWgfR1ccRClVr1vYVE3hsLWfFlpa6Tbbtlnqm0cXvT9N1JSzztEUnUGOy/nIOwsLixHOa+r8\nShNCNARJajJHrUDd0wPmTr71fcrV7ntwlTP5SojqkkROiBqiKRpN1JYcsvaSqnUs3X7EOkCS0qKS\nM09rqrZkv1m2/tRuczPHrVzGuG+s8Lx0vSfttK7k23ml6yEKIUKrsZrMLv8m8q08FnnncplrMvHV\nnCEvREXk0aoQNShZTeWgubfMtlwru9yetPI0UVty2DpQZtt6YwX9HKPQFUel52qKLkmcEHVIkppM\nrpXDR95ZXOS4lNZSlFsEgfTICVGDkrU2LPa+w0ErCxUVVVHJtbIZ4phYrfMTlaYU2ifw2iW4lAiO\nW7kctQ7SVutaw5ELIYItQonGqbhprramhz441OGIekISOSFqUGs1nTGuGzExsbGwbAtbs0nVMqp1\nvqpopGoZ/Gh8w0WOS9lkfEcnvQ+6TFwQIixd6bqDRkrieS2OLkR55N1AiBqkKhottQur3D3IMY53\nPM8RrTRis/E917pnBik6IURtk+EOIthkjJwQdVycmkQnvQ9f+RYw3nWzDI4WQghRSnrkhAgDAx2X\n09cxArcSFepQhBBC1CHSIydEGNAUTZI4IYSoYampqSxZsqTMttmzZzN4cN2dnCKJnBBCCCEEgeUV\nw20iijxaFUIIIUSdsivvILlGcdCul6RH0jax+QVfJzU1lTvvvJO5c+dy8OBBJk6cyMsvv4zLFVgP\n++9//ztPP/00qqry6KOPcuutt7Jz507S0tKquPL5k0ROCCGEEHVKrlHMM4Vrg3a9e2N60baax9q2\nXen+t956i88//5zIyEjGjx/Pn/70Jx577DE+++wznnzySZYuXUpqaiq33nprrfTu1Woi9/TTT+Ny\nuVBVFVVVuf322ykuLmbevHmcOHGCuLg4rr76aiIiAuvOrVixgnXr1qEoCmPGjKFdu3a1Ga4QQggh\nGhDbtpk4cSK6fjo98vl89O7dGwg8ep0xYwYtWgSWWXzooYe45557eOyxx3jvvfeYPn06nTp1AuCR\nRx7hnXfeqfGYa3WMnKIoTJs2jTvvvJPbb78dgJUrV5KWlsbMmTNJS0tj5cqVABw5coRNmzZx9913\nM2XKFBYuXIhlWbUZrhBCCCEaEEVRWLBgAcePHy/989JLL5XppWvZsmXpv1u1akVOTg4ABw8ePGtf\nbQj5ZIfMzEx69OgBQPfu3dm2bVvp9q5du6JpGvHx8SQkJJCdnR3KUIUQQgjRwPz8UWtWVlaZf//U\nO9e8efOz9tWGWk/k5syZwyuvvMKaNWsAKCoqIjo6GoDo6GiKiooAKCwsJDY2tvS82NhYCgsLaztc\nIYQQQgggkNS99NJLZGdnc+zYMf785z9z7bXXAnDNNdcwe/Zstm7dSnFxMY888kitxFSrY+RuueUW\nYmJiKCoqYs6cOTRu3LjM/uoMCiwoKODkyZNltvl8PqKiwqfG1k/P3s98Bt9QaZqGw+EIdRghJe3h\nNGkP5WvIbUTaRPXUtzaSpEdyb0yvoF7vfJ1ZkkRRFG644QZGjRpFTk4OEydO5A9/+AMAo0eP5t57\n72X48OFomsZjjz3GW2+9FZT4K43Prmp6Rg1ZtmwZTqeTNWvWMG3aNGJiYigsLGT27Nncc889rFix\nAqC0CN/cuXMZNmwYO3bsYPny5WWuNWTIEIYNG1brr0EIIYQQDUebNm2YNWsWw4cPr9bxqqrWn/Ij\nPp8P27ZxuVz4fD527drFkCFDSE9PZ8OGDQwaNIj169eTkZEBQHp6OvPnz2fAgAEUFhZy7NgxWrRo\nQWxsLOnp6WddOzc3t7ZeygXTdZ34+HiOHz+OYRihDiekXC4XXq831GGElLSH06Q9lK8htxFpE9UT\nDm0kKUnWia4JtZbIFRUVlU7DtSyLbt260a5dO5KTk3n//fdZu3ZtafkRgCZNmtC5c2defPFFVFVl\n3LhxKIpCbGxsmbFzADk5Ofj9/tp6KUFjGEZYxh1Muq43+O/BT6Q9SHuoSkNsI9Imzk1DbCN1WW3U\nkQvZo9Vg+mnqb7hwOBwkJSWRm5vb4H/gIiIiKCkpCXUYISXt4TRpD+VryG1E2kT1hEMbSU5ODnUI\n9VLIy48IIYQQQojzI4mcEEIIIUSYkkROCCGEECJMSSInhBBCCBGmJJETQgghhAhTksgJIYQQQoQp\nSeSEEEIIIcKUJHJCCCGEEGFKEjkhhBBCiDAliZwQQgghRJiSRE4IIYQQIkxJIieEEEIIcZ6mTZvG\n//t//69ax86ePZvBgwcH9f6SyAkhhBBCAKmpqSxZsuSczlEUBUVRaiiiqkkiJ4QQQgjB+Sdltm3X\nQDTVo4fszkIIIYQQ5diVk0duoRG06yXF6LRNTjyvc5ctW8aUKVP49a9/zV//+lc0TeP//u//mDZt\n2lnHFhYWMmHCBLp3784zzzxDXl4eN998M8uXLycjI4NRo0Zd4Cs5myRyQgghhKhTcgsNnllRGLTr\n3Ts4hrYXcP7hw4cpKCggJyeHzz//nKuuuopf/OIXNGrUCAj05OXl5TFmzBhGjx7No48+CsDdd99N\nZGQkhw4dYvfu3Vx22WWkpaUF4RWdJo9WhRBCCCEq4XA4+N///V80TWPMmDFER0eTmZlZuj87O5uh\nQ4dy7bXXliZxpmnywQcf8OijjxIREUHnzp256aabgv4YVhI5IYQQQohKJCYmoqqnU6bIyEhOnjwJ\nBMbHLVy4EI/Hwx133FF6TG5uLoZh0LJly9JtrVq1CnpsksgJIYQQQpwnRVG47bbbuOyyyxg7dizF\nxcUAJCUloes6WVlZpcee+e9gkUROCCGEEOIUn8+Hx+Mp/WMYlU+6+OlR6QsvvEB6ejrjx4/H4/Gg\naRpXXnklDz/8MCUlJWzZsoXXX3896KVKZLKDEEIIIeqUpBidewfHBPV61TV27NgyXw8cOLDS5OvM\nkiX//Oc/mTZtGhMnTuSjjz7ihRde4Oabb6ZZs2Z07NiR6dOns2zZsvN6DRXe3w5l8ZMgycnJCXUI\n58ThcJCUlERubi5+vz/U4YRUREQEJSUloQ4jpKQ9nCbtoXwNuY1Im6iecGgjycnJoQ6hXpJHq0II\nIYQQYUoSOSGEEEKIMCWJnBBCCCFEmKoXY+Ty8vLK1Hep6xRFwel04vP5Qro+W12gqiqWZYU6jJCS\n9nCatIfyNeQ2Im2iesKhjcTHx4c6hHqpXsxa9Xq9oQ7hnDgcDuLi4igqKqqzg1JriwxklvZwJmkP\n5WvIbUTaRPWEQxuRRK5mhE83lhBCCCGEKEMSOSGEEEKIMCWJnBBCCCFEmJJETgghhBAiTEkiJ4QQ\nQggRpiSRE0IIIYQIU5LICSGEEEKEKUnkhBBCCCHClCRyQgghhBBhShI5IYQQQoggePnll2natCmx\nsbEcO3asVu4piZwQQgghBJCamorL5SIvL6/M9p49e6KqKllZWRWe6/f7uf/++1myZAkFBQX8+OOP\ntGzZsqZDlkROCCGEEAJAURTS0tJ4++23S7dt3LiRkpISFEWp9NxDhw7h8Xjo2LFjTYdZhl6rdxNC\nCCGEqML+XXnk5xpBu16jJJ2WbROrdeyUKVOYM2cOM2bMAOD1119n6tSp/OEPfwBg0aJFPPDAA+zf\nv5/Y2Fjuu+8+JkyYQI8ePQCIi4vjoosu4rvvvsPr9RITE4OiKGzfvp1mzZoF7TX9RBI5IYQQQtQp\n+bkGHz1TGLTrTbg3hpZtq3ds//79mTt3Ltu2baN9+/a8++67fP311/zhD3/Atm1uueUW5s2bx8CB\nA8nPz2f37t20b9+eLVu20KZNG/Lz81FVleXLlzNlyhT2798ftNdRHnm0KoQQQghxhhtvvJE5c+aw\nePFiOnXqRIsWLUr3OZ1ONm/eTEFBAY0aNaJnz54A2LZd5ho//7qmSCInhBBCCHGKoijceOONvPnm\nm6WPVX9KyhRFYf78+SxatIjU1FSGDh3Kd999F9J4JZETQgghhDhDq1atSEtL49NPP+XKK68ss69P\nnz785z//ITc3l4kTJ3LNNdeUe42qJkcEiyRyQgghhBA/M2vWLJYuXUpERETpNq/Xy5tvvkl+fj6a\nphETE4OmaeWe37RpU/Ly8igoKKjROGWygxBCCCHqlEZJOhPujQnq9c5VWlpama9/6mF74403uOee\nezBNk4yMDN58882zjgHIyMjg+uuvJy0tDcuy2LJlS43MWlXs2hqNV4NycnJCHcI5cTgcJCUlkZub\ni9/vD3U4IRUREUFJSUmowwgpaQ+nSXsoX0NuI9Imqicc2khycnKoQ6iX5NGqEEIIIUSYkkROCCGE\nECJM1eoYOcuy+Oc//0lsbCw33HADxcXFzJs3jxMnThAXF8fVV19dOqhwxYoVrFu3DkVRGDNmDO3a\ntavNUCtkrFuN8d1KnNdMRk1MCnU4QgghhGjAarVH7rvvviMp6XTys3LlStLS0pg5cyZpaWmsXLkS\ngCNHjrBp0ybuvvtupkyZwsKFC7EsqzZDPYvt8eB97w38y5agts/A+++XsfJyQxqTEEIIIRq2ShO5\n3NxcnnzySYYPH05iYiK6rpOYmMill17KE088QW5u9ROZ/Px8duzYQa9evUq3ZWZmlq5N1r17d7Zt\n21a6vWvXrmiaRnx8PAkJCWRnZ5/P6wsKM2svnuefQHG6cN99H84Ro9EHDML30QfVvoaxZRNWQX4N\nRimEEEKIhqbCRO7BBx+kV69eZGZmcuutt7J48WK2bt3K4sWLmT59Otu3b6dXr148+OCD1brRf//7\nX0aNGlVmam5RURHR0dEAREdHU1RUBEBhYSGxsbGlx8XGxlJYeP5rrvmXfUHJ80/iX7EM+xx69mzL\nwv/lYrxzX8UxdgLOiVejOJ0A6Bdfgn0sD3NHZrWuZSz7AmvvnvOKXwghhBCiPBWOkUtJSWHnzp24\nXK6z9vXq1YvJkyfj8Xj497//XeVNMjMziYqKonnz5uzZU34yU90KyAUFBZw8ebLMNp/PR1RUVLnH\nm9kHML7+ioirrsf75WIsvw/3ZeOqvI/t81H02isAxNz7W9S4+LIHOBwoY6/Au/hT3J26VHm9kmN5\naIYPh8OBrge+7T/93ZBpmobD4Qh1GCEl7eE0aQ/la8htRNpE9TTkNtLQVfg/PmPGjCpPdrvd1Tpu\n//79ZGZmsmPHDgzDwOv18sEHHxAVFUVhYSExMTEUFhaWJmMxMTHk559+DFlQUFDaQ7dmzRqWL19e\n5voTot20mnEvqttdZrttmux//kmSrptC7OChGD17s/+R/yGiUxeie/Upc6zv0EFsnw9Xq9YA5L75\nOhGJiTS9cyaKWn7HpT10OLvfe4OEyEi0ChJJALOoiPziIiI1jfgzxgjGx8dXeI5oeKQ9iKpIGxFV\nkTbS8Jxz6r5p0yY2b95M586d6dKl6p4ogBEjRjBixAgA9u7dyzfffMOVV17J559/zoYNGxg0aBDr\n168nIyMDgPT0dObPn8+AAQMoLCzk2LFjtGjRAoDevXuTnp5e5vol78xlz29/hbPvABy9+6I1DiRL\n3q+/wnQ48KR3wntqPJ97ys0cfvUV8nOycfa7uPQaRa/9E2P3Tpx9+qFEx+Bb9Q3R9/+eo3l5lb42\nNTmFI+vXonfIqPAY80AWACePHsXIzUXXdeLj4zl+/DiGYVTre1hfuVwuvF5vqMMIKWkPp0l7KF9D\nbiPSJqonHNrImZMdRfBUmsjdeeedjBw5kkmTJgGBdcd++ctf0rNnT7Zv384f//hH7r333vO++aBB\ng3j//fdZu3ZtafkRgCZNmtC5c2defPFFVFVl3LhxpY9eY2Njy4yfA8i57kasnGyMNd/jfeEp1IRE\ntG498S/7Avctd5Zt1M1b4Lp9Bp7X/4U/9zCOkWOxC/Ixsvbhvvd3GKtXQWEhzuunYjqcmFVUyFZa\ntMS3bw92m7Zn7TPWr0FNaYV1+DAAZnFRmYrbhmHU2QrctUXX9Qb/PfiJtAdpD1VpiG1E2sS5aYht\npKGrdImu5s2bs3nzZhISEoDAuLmHH36YW2+9ld27dzN06FCysrJqLdiKnLlEl22aWDszMdatQU1q\nguPSy8o9xy46ifeN11BiG6HEJ4DPi3PCpHO+t/HjeswNa3HdOL3s9S0Lz18fRe87ADQN/8rlaBmd\ncF11fVgspVJbZPmd8Fhap7ZIeyhfQ24j0iaqJxzaiCzRVTMq7JGbNm0ax48f5/777wfg2LFjHD58\nmOXLl/P1118DcOLECaZPn86rr75aO9FWg6JpaOmd0NI7VX5cVDSu6Xfim/8OxldLcc+4/7zup7Zs\nhW/hh9i2XWbChnUgC7u4CHP3TpTExqgtUsAjv4yEEEIIETwVJnKzZ89m1apVTJ48mREjRvDggw9y\n4sQJ5s6dC4BpmixevLhOJXHnSnE4cF4zGWvAINTkFud3jbh4MC3sgnyURnGl280tG9H7XYyxehWq\nYaCmtcXaH/reSyGEEELUH5WOkfvTn/7ExIkTcblcGIbBwoULS/fNnz+/tJhvOFNUFa11m/M/X1FQ\nW7bG//EHqGntUZs2Q23aHHPzJlzXTcHan4W1fx/6oKGYO7YHMXIhhBBCNHSVJnKTJk1i6NCh7Nq1\niw4dOhAXd7rHqXPnzrz00ks1HmA4cF4xCXPrJuzDh/FvXI91+BBKZCRKckqgJy57P2rTZvJoVQgh\nhAhTDz/8MLt27Sp9MllXVFl+JDExkcTExDLbjh8/TufOnWssqHCjxsWjDhhc+rVt22CaKIqCltYe\nc/NGlMhIbI8nhFEKIYQQojJTpkzB6XSWGTa2fPlyJk2axFVXXRXCyCpW6Vqrr7/+Op999lnp16tX\nryYlJYXExETS09PJzKze8lQNjaIoKKeqa6vtOuCadju4I8DroZJJwkIIIYQIoeeee45PP/2UL774\nAgCPx8Ntt93GU089RfPmzUMcXfkqTeSeeOIJmjVrVvr1bbfdxsiRI/nxxx8ZMWIEDzzwQI0HGO4U\nRUFNSAwkdooCRt2cFi6EEELUFXm7jnD4u/1B+5O360i17puQkMDzzz/P7bffTnFxMY888gjt27dn\n6tSp2LaNz+fjpptuIjY2li5durBmzZrSc7du3crQoUOJj4+nS5cufPzxx6X78vPzmTp1Kk2aNCE1\nNZU///nPQevYqfTR6v79++nWrRsAWVlZbNy4kS+++ILExET+8pe/0Lbt2UVwRSVcbvB4ILLi5bwa\nGs83K7DbdkD5WZFnIYQQDZeR66Xwmd1Bu17MvWlQzZTlqquu4p133uG6667jm2++YcOGDaX7Pvro\nIz788ENmz57NQw89xIwZM/j222/x+/2MHz+eW2+9lS+++IIVK1ZwxRVXsHr1ajp06MA999xDYWEh\ne/bs4ejRo4waNYrmzZszffr0SiKpnkp75BwOR+nSKN9++y0ZGRml4+WkSOO5U9xuGSd3BnPbZorm\nvY1/+RehDkUIIYQo9dJLL/Hll1/yxz/+sXSJUIDBgwczevRoFEVhypQppUned999R1FREQ8++CC6\nrjNs2DAuv/xy3n77bUzT5N133+Xxxx8nKiqK1q1bc//99wdt0kSlidwll1zCQw89xI8//sjzzz/P\n+PHjS/dlZmbW2efFdVUgkWvYya9tGFiHcjA2rMP34ftE3zgdY/0a7JMnQx2aEEIIAQSWCm3cuPFZ\nEzubNm1a+u/IyEg8Hg+WZZGTk0PLli3LHNu6dWtycnLIy8vD7/fTunXr0n2tWrUiOzs7KLFWmsg9\n++yzrFu3joEDBxIREcHvfve70n1z5sxh9OjRQQmiwXBFBB6tNjC2YeBbuICSp/9CyaP/g/etOZib\n1uO4bByunn3QunTH/+2KUIcphBBCVOjM1Zt+Ljk5mf3795cZ97Zv3z5atGhB48aNcTgc7N27t3Rf\nVlYWKSkpQYmr0jFyKSkpfPnll+Xu++tf/xqUABqS+vBo1co/gfeV50HTUNxuiIpGbZaM2iIFNTkF\nJSGxtLHbto19MAffogUoLjeu66aiJDUpndH7E8eQS/G89Ax63wGoZ6yOIYQQQtQVlU1O6NevH5GR\nkfztb3/j17/+NV9//TWffPIJDz/8MKqqcs011/DQQw8xZ84c8vLyePrpp4M2YbTSRG7JkiVnZaAO\nh4PWrVvTqlWroATQoLjd4A3vRM7cuhm1ZWscI8dge0qwCwuwc7Ix16/Bv+gjbK8Xvf9A9K7d8X34\nPnbRSfQ+/dCHXIqilt8BrCYkovcfiH/hAlw33FTLr0gIIURdoye5AhMUgni9C6Uoylk50U9fO51O\nPv74Y+666y4ef/xxUlJSmDt3Lh06dADg+eef55577iEtLQ23283tt9/OzTfffMExASh2JSlmamrq\nWUH7/X6OHDnCRRddxHvvvVdmEGCo5OTkhDqEavF98iFKXAKRw0aQlJREbm4ufn94lSPxvPYKep/+\n6F27l7vfOnEc/ycfYu7IxDH2CvS+Ayrtjv5p0ozt9+F5+q84r5+K1rJ1hcfXRw6HI2zbQ7DJJKry\nNeQ2Im2iesKhjSQnJ4c6hHqp0h65M5/nnqm4uJgHH3yQ++67j/fee68m4qqf3BFhPdnB9niw9u1F\nq6TXTI2Lxzn55sDKFnqVC4eUUhxOtB69MTf/2OASOSGEEOJ8VTrZoSKRkZE8/vjjfPXVV8GOp15T\nwvzRqrkjE7V1KorLXelxZ65scS60Tl0wt2w+3/CEEEKIBue8EjkAXdcxTTOYsdR74TzZwbZtjB++\nRe/crcbuoSangM+DdeRwjd1DCCGEqE/OvdvklKeffprevXsHM5b6z+WGaj5aNXfvxD6aC7oOmg66\nhn3sGGbmVlzXTK6xlRCsE8fxvfcmdkE+WnontI6dUVPTMLdsxC4sQOvdt0buC6CoKlpGF8ytm1Cb\nNK36BCHCiF1Sgu31oMbFhzoUIUQ9UmkiN3jw4LO2+f1+srKycLvdLFy4sMYCq48Ud0S1euRsrwfv\nG6+hdekGpgmGAaYRWNrL58Xcuwu9W8+gxWX7fRhff4W5fRvW4UM4LhmG1iEDc9sW/IsXYeXmAuCa\ndhuKpgXtvuXRevTC985ctO695A1PhC3bU4J19Ch2XuCPdeQQ5vZtoKq4bpyO1rpNqEMUQtQTlSZy\nt9xyy9kn6DqtWrWif//+OJ3OGgusPlJiG2HnHsb2+So9ztywDq1tO1xXXnvWPv+Xi7Gy90O3ntgF\nBVjHjqJERp13D5bt8+J57gnU5BQcw0ehJDUpreWmNm+BY9hI7MJCrGNH0Vqlntc9zoWWmoY+cAje\n2f/Cfcc9KBERNX5PIYLJ3LUD79xZKIlJqImNURIbo7XrgHPcFViHDuKd+yqK04napi2uq28IdbhC\niDBXaSI3bdq0WgqjYVCbNkNtnYZ3yX9hasUL5Ro/fItj5Njyr5HSCv+yL7ANg5Lnn0BNbIx94hhE\nxaD36I3WvSdqbKNqx2Ru3ICS1LTS+m1KTAxaTEy1r3mh9EFDsE8cx/vGq7huvuO8Jk4IURts00TR\nNKz8E9iHD6F1yMD4+iucl09E79P/rOO1mFjcd87Eyt6P8Y2sZiKEuHAVTnZ49tln8Xq9lZ7s8Xh4\n9tlngx5UfeYYdwW+Vd/gyz5Q7n4r5wB2URFquw7l7ldbpGDlHMDatR01sTHuO2fi/u3/4hw7AevI\nITzP/A3PrJcx1nyPXcX/H4Dxw3fofc9+wwklRVFwjLsCJTIS37y3sS0LM3ML/lXfhDo0IUr5v11J\nycMP4vnnC3ieewLvu29grPkeM2svWrdeFZ6nNk5CTU7BLi6uxWiFEPVVhV0dhw4dom3btowbN44h\nQ4aQnp5OTEwMhYWFZGZmsnz5chYtWsTUqVNrM96wp8Y2wjVyNEfmzMJ5yy/P2m9sXI/WvVeFqyAo\nkVEoUdH4ly1BOzWDVFFVtLbt0dq2x55wJea2rRjffxOYGFFJT5t1+BD2iWNoHToG58UFkaKqqWmx\npQAAIABJREFUOK+ZjHfWP/C9+wbm9q0osXE4+l0c6tCECHywWLYY94z7sY4dRW2WjH00F+/sf6Jf\nfAlKFcNOlMhI7OKiWopWCFGfVdgj9/jjj7Nu3TratWvHrFmzGDNmDF26dGHs2LG89tprZGRksH79\nev785z/XZrz1gnPAYGyvF/+a78/aZ27ehNa5a6Xnqy1aYu3bU+5xisOJ3rU7rsk3Y+7cjlWQX+41\nzAP78b47F73fwBqfwHC+FIcT19RbsE8W4rpuKnbBCXnzEyFnHcrBO+8dXDdMQ23aDL1jF9T4BLT2\n6TivuxHH4GFVXyQiErwebMuq+YCFEPVapYOPkpKSeOCBB4K2sGtNcblcqBX0YNVFiqLgvG4KeS8+\nQ1zX7qhR0QAYhw/iMfxEtetQ6bJWtGmL93geUS1SKj4mIgKrVx+UtT9glRRjGwZRV98Afj/Fn32C\n74fviLpiEs7efSu/Vw1TVZWIyiY0REQQOfM3AFjffIV+6CDOKhLdcKMoCsXFxTgcDvQGPh6wyvYQ\nYlZBPvlzXyX6ymtwZXQ6+4C+A6p9LY/bjRsbtRqvtyG3kbreJuqKhtxGGrp68b9d1Vi+usbhcJCU\n0YmC7j0p+PB91CbNMHfvCMw+zeiMp4oSJXa3Hjhat6ly/UGldz9KXngSNTUNfD7y33oda/dO1JRW\nuGf+Bis6psp71bRzWUfRTmlFyc7tmGntajiq2uVwOIiLi6OoqKjOrpFYW+ryupq234f3Xy+h9boI\nq2OXC48zIpKSY3moatU94g25jdTlNlGXhEMbiY+XklI1oV4kcuHKfdk4Cv/+J+yjuWjdemB89SXO\n626s8jwlMgolMqrK49TmyTivm4qW0RFKPHjnv41j3BXoHbsEI/xap7Vug3/5klCHIRoo/+JPURIS\n0YePCsr1lMhI7KIiaByUy4UN3+eLMDf9CLqGY9hItM7dKhwTLISomiRyIaS4I3DPfADcESiqimPA\n2QWYL5TetXvgHw4n7pvvCPr1a5PaqjVW9v7Skg9C1CYrax+Oy8YFbyhCZFS9mLlq+32gO6r1fbHy\nT2Cs+gb3bXdh5+fj/+JT/MuX4Bw5BrVDx5AO8xAiXEkiF2LV6VkTAYo7ArVZMuaWjehde5R7jO33\n4Xnmb6gtWuK49DLUps1qOUpRX1l5R1ETk4J2PSUyCsJ48o5tmhjLvsC/chl6/0E4Lxt39jGGgbV7\nB+auHej9BmL+uA69SzfUZsnQLBm1Qwbm5o34Fn2MsnwprptuRXG5Q/BqhAhf1e7PPnr0KHPmzOFv\nf/sbANnZ2ezfv7/GAhOiPI7ho/B/8VmFs/3MTT+ixCegJCTi++zjWo5O1Fe2xwN+HwSxMLYS5j1y\n5paNGFs34Zp+J+aP6zBWr8K27cCSf1s24X3/LUoe/yP+pYuxi4rwvvkaxtof0HpdVHoNRVHQu3TD\n/asHwLax9u4O4SsSIjxVK5Fbvnw5GRkZvPXWWzz22GMA7Nixg7vuuqtGgxPi59T26YE6ep8vxNy2\nBbuwsMx+Y/Uq9P4DcQy9FGvv7moVRRaiKvaxoygJiUF99BfuteTMjRtw9BuI1rI1rinT8a9cjufv\nf6Lk8Ycxvvnq1KSqB3DfORPnpOtQk5qCbaOWs9SfoqqordtgHZDOgfrKyjsa6hDqrWo9Wv3Vr37F\nO++8w4gRI0pnnfTv359Vq1bVaHBC/JyiKDgnTMK/4kv8367A2p8VWEKsTVuUps2xjhxGy+iMouuo\nLVtj7tiG3qV7SGI11q1GTU1DjU8Iyf1F5fwrlmEdyELv3Re1XYdKB9xbR4+iJgZ5VkJkFJw4Htxr\n1hLb58PckYnziklAYGKV+1cPYB86iBITixIdXeZ4RVFwTroO+2RhhcmwmtISY+0POGo8elHbrKNH\n8M76Bzz7j1CHUi9VK5Hbt28fI0aMKLPN4XBgmmaNBCVEZdRmzUsXG7ctC/tQDuaeXVg7t+MYcmnp\n2qxap66YWzeHLJHzL12MY8RoSeTKYZ04jp17BK19epXH2rYd9EHw1onj+Jd9gWPopfgXL8L+8D20\nnn0CSV05CZt97ChKkBM5JTISqw49WrX9fuy8o5j79mDt2YXtKcF1zeRyx/GamVtRW7ZCiTqdsCmK\ngtI8ucLrKw4HSiU/C2qLllgff1Aj/9+idlh5uRirv8c6fBCtZWv03v2wPSV4X/0HjpFjQh1evVWt\nRK5jx4589tlnjB49unTbkiVL6Nq1fhVmFeFHUVWU5BTU5BQYOKTMPq1jZ/yLPw3JLFe7pAQ7Lxe7\n+GSt3jcc2IWFgU/npoH7gT9U+qZtWxaeF5/Gdc3koE1csU0T/2efBB7BDx6GY/AwrIM5GGu/x/OP\n51CbNMV5xVWoTZqWnmPlHUUr55HghQiMkav9R6u2bWNu3ICVnYV9LA/7xAmsE8fBU4ISl4DaOhWt\nXQesgzl458zCNf0OFKcLAOtYXmBs3PffVm8Fi3OgxMWDZWMX5KM0igvqtUXNsf0+zM0bMX74DuvI\nYfQevdF79MHas5OS5/4OqopzzHj0nn1CHWq9Va1E7qmnnuLyyy9n7NixeDwebr/9dj7++GMWLFhQ\n0/EJcd7URnEoMbFYB3PQUlqWe4xtmpjr16D37hvUe1vZgbE+dlH4joGqCbZt431rNlq3Hphrf8DO\nPYJyRsJ01vEHs7EPZmOsX1PurMhz5f/mK/yffYLarDnOX1xTul1tnoxz3EQcl12O/7OPMVZ9jXP8\nlafjyDuKEuQ3olCNkbP278O/aAF6/4GoXVuhxMejNoqH6Ogyj5dty8L/0Xw8T/0FrXNXzD27sAsL\n0Tp2xjl2AmqQ12hWFAW1RUrgZ6e4KNB753RhHsvDdkdU2UtnlxSDaZ31WFfUHCv7AN43X0NJaoLe\nfyBaxy6lT0To1gN90FDs/BNo9ayIe11TrUSuf//+bNiwgTfeeIPo6GhatWrFDz/8QEpKJUtECVEH\naG3aYu3ZVWEiZ6xehX/BPLSMTmUeE50Lu6AA3+cLsXZm4hh+GVqffoE3I4cT6tCjs9pmHT4Eulam\nZIe5dRO2z4vj0suwCwswd2SW6fn6OXPbFtS09pgb1mKPGntBj9xs28b4diWu2+5Ga9m63GMUXUfv\n2Qfve28F7r93N4rbHUjkamKMXAjah7n2h1MTgkZUepyiqjgnXo2VvR9z2xacEyahtkqt0eK9akor\nfAvmoaBg+7ygqpRYFnrfi3GMvrzS/3/fxx9i7dqB66bbUJNb1FiMIsDM2ot37qs4J0w6Xa/0Z9TE\nxhDsnxtxlmolch6Ph6SkJH73u9+VbvP5fHg8Htxuqfkj6i61TRrmhrUweOhZ+2yvF2Pp5yixsViH\nDqK1bX9O17YNA+Pr5fhXLEPv3RfndVPxffIhtteDdWA/alq7sJ6VeCFs08T72ivYtoXicKK1z0Dt\nkI7/809xjr4cRVXR2mdgrFmFY+AlZc+1rEB9tahozMwtOEaNC7xJZ+1Fa92m4nvaNkCFb/Z2TnZg\n1mRKq0pjV5q3wD5ZiHXiOL55bwd6egwDJSb2HL8LlfupR642x4TZfh/GxvWBQuTVpLZoidqi/A9C\nwaZ37YHicqP3HwiWCV4f7uho8v/xHP4F83BMmFSaSNoFBSixgf8Tu+gk5rbNOMeMx/PaK7iuvgGt\nQ0bpdW3Lwty6Cb1zt1p5HeHOtm3sYxXXTbQNA9/8d3GOv7LCJE7Unmp9tBo5ciRr164ts23NmjVl\nxswJURepqWmYe3eXW3fOWPU1apu2aBldsA4frPY1bdvG2LIRzzN/xcraGyivMGY8Wmoarmum4F++\nFDNrD1qH9AabyJlbNqEkJBLx4MO4Jt+MEh+P8c0K1IRE1PTAIzmtXXusvXswM7fi/+YrvB+8S/4z\nf6PksYco+fuf8b0zF+toLmpqGnr3Xhhff1WarNl+P8aGtXhmvYz3w/ewLQvfh+/heeFJrIPZ5cZk\nbFyH1rVHlUmToqpobdvj//RjlIhI3LfejWPoiKD3RCkOJ6gq+HxBvW5lzK2bA4lZHR2DpjZthmPw\n0MDECJcbJTYWNSoK1y13Yh3Nxffem4HhEPv3UfLXRzA2/wiAseZ7tI5d0C8agGvyNLzz3sZY833p\ndc013+N7czZW9oEQvbLwYu3fh+fJx/G+9+ZZJZ4CY0w/Rm3cGE2SuDqhWj1yGzdupG/fsmOI+vbt\ny/r162skKCGCRY1tFBhUfuQQSrOyM+rMrZtxDBuJdSwPK6d6v+DNrL2BCRSFBTivuOqsWZdqk6bo\nnbtibFyP2jIVY/X3FVypfjO+XYHef1DpTEa1eTKOS4aXOUaJiETr0g3/ymWoiUmoySlE9B+IPy4e\ndB3fgvmBdTh1HX3gJXhnvYz/4w9AVTHWr0VNboHepz/GN1/hnfUydnERjosH45n1D7S0dqht24Np\ngGGAERgL6Zp6W7XiV9u2x79gXqD+2an4a8JPEx4Ul6tGrv9zxtofwnLQueJy47rpVrxvz8H75mvY\neUfRBw3Bt2A+nHpk7rx+KgBaahru2+7G+/q/sPKOove7GP8Xn6F16Y5/1de4rrw2xK+m7rP27kbr\n3RclKoqS5/6OY8Ro9Iv6Yx85jHfe2yguF67rbpTZxXVEtRK5uLg4Dh8+TPPmzUu3HTlyhGgZVCrC\ngNqmbWCs1RmJnO0pwTqYg9omDRxOzHU/VHkd3+eLAhMjhlyK3qdfhTNhHSPHoLZuExh03QB75Kyj\nR7DzjqJ1rnpWu+uq68t87YiIwCgpCew7VWIGCLxxTLsN3/x3UZo2w33XvagJiQBoae3wznsL1+Sb\nURsnoXXtibH2B6ycAyiaDnrgjz5sVKXlMc6ktU/HSExC61b+UnDBojRthu/9t3AMHREodn3GG6Nt\nGGD4UdwRQbmXVZCPlbUP7YabgnK92qY4nLgm34zvg3dRklNwjpkADifG8qU4ho9CPWPco5rUBPcd\nM/F9NB/Pk/+H1rUHztHjKXn6L5jdemJu34oSn4DaLBm1WXOUiEhs04SSkjKTJay8XOyjuRARiXLq\nDxERKJoWKH104nhpO6xPrKy9aF17onfvid6jN74F8zFWfY1dUIBz9Di03v0kiatDqpXITZo0icmT\nJ/Pss8/Stm1bdu7cya9//Wuuvvrqmo5PiAvm6D8Q7xuvYR8/hmPsBBSXG3PnDtTWqSgOJ2qzZliH\nD2NbVoWPz2zTxPj+W9x331dlXTglOga910XYPh92UcMrP2JlZwcS2SCXfFEio3DdOP3s7TExuG++\n4/TXbjeOiwdf0L3UhETcv36wxt+sXDfegvnjOnyLPgJNw3HJMLQu3cHw43393yjuCFxTbwnKvcz1\na9A6dy0tJRKOFE0rk+A7R4yGEeUP8VFiYnBNnobt9YCmo+g6WkZHfPPeRuvTD/vwIfwb1mEdOogS\nEYHtKQFFIeLX/4MSHY3t9eL998soSU3A68EuLgn0nsbE4L7nN4GJUl98RsSDf6z18kY1ybZtzH17\ncVz+CwDUZsm4brsbc8sm1GbNUBs3CXGE4ueqlcj96U9/4je/+Q39+vUrneAwffp0Hn/88ZqOT4gL\npian4J75G3wLF+B57gmcV16LtWNb6WPRwCftCOwTx1Eq+HRt7dqBmph4TsV9FacTCFTB/+nfDYF9\n9AhK4+AtLh8qtdHjoGgaes8+aN17YW3fin/5Unz/mYfidKKmtQssQ2cY4Liw9Q7skhKMNT/gnHhV\nkCIPH4rr9IQ85y+uBUU5XSIDSnvWFLcb/+LP8H+1BOfYK/B/uRi1TVtc10wucz3Pv1/CXLca4+vl\nAFi7tqMFuRRLKNnHjgZWxomLL92mqCp6F5koUldVK5GLiIjgxRdf5Pnnn+fo0aM0btwYtQanoAsR\nbIo7Atek6zC3bcH3/lvYJcW477rv9P6mzbAOHazwMYmxcT1a157nft+o6MCn+AaUyFm5R9DS688b\nW21QVBUtozNaRufATNb8EyhNm+N9+VmsrL1wDt9P27ax809g5RzAyj6Alb0/8KisU1fUSmb9NgRK\nOQmxoqqlH+D0YSPwPPs3ME2MDeuIKGd2r2P4KLxz/o2a1BS930CMDevqVSJn7dtb7nq4ou6qMJHb\nu3cvqampAOzevbvMvpMnTz8uSktLq5nIhKgBWkYn3DN/g7FxQ5lCtGpKK6xd26FTlzLHm3t3Yx3Y\nj7llI44KHuFUKjIKiorgjE+39Z119Aj6oCFVHyjKpURGlS6LpbZPx9y5vdqJnO0pwfPCU9g+H2qL\nwIonet8BaBUstSXKUmMb4Rg1DrsgH/dd95aWNzmTltYOrW0HtN590Vq2omTJZ0Hpda9saEdtMnfv\nRG2dGuowxDmoMJHr2rUrhaemHbdrV35VZkVRZL1VEXaUyCgc/S4us03vOwDPs3/DMWwUSnQ01sEc\nfP/9JLAeaEZnnGOvOK+SDUpUaJZhqgm2xwO6Xuax1FnHWBZ2bq6MowkSrV0H/J99Uu6+M9/4rbxc\n1MQkjO++Rm3VGufVk2Uw+nn6+e+G8pw5VlNtnYbxw7c4Bp7/hxfbsvA89Th6v4uDvvTZubBOHMfc\nuhnnZZeHLAZx7ir8jVx4Ru0Yq5waXELUJ2psI/SuPfAtXgSGgbl9G45hI9CnTK80camKEhkVFhMe\n7IICrBPHApXYI6POSgJsvw/PP56FkhL0QUPR+w4ot2SGXZAPbheKFAoPCrVVKlbuYayThZB0etyh\nsX4Nvg/eDRS9tcHM3II+8BKMtatx33qXJHG1yDluAp5/PB94dH0OY2jPZB/KAcvCWP095sYNoIDr\n5juCNmO5yvt7vYCNf+nngZ/tmJhaua8IjirfoQzDID09nS1btuCqpVpHQoSCfslwPK88h96nPxH3\n/09QkpELXU+ztqr++xbMwzp8ELu4GGwbJTERNaFx4O/klMBSWs0CteD8y5dQ8tVSHP0Hog8YVOaR\nnX1UeuOCSdF1tC7d8X3/LbRJw7Zt/Ev+i7Hme1y3z8DatwdME8eEK/HOfRWtTVvUps1CHXaDojZu\ngmPwUHxvvY7zuikVroZQGXP7NrSOXXCMHI11YD++j+ZjH8tDSa6dZTC978zF2pkJLjcR9/++Vu4p\ngqfKRE7XdVRVpaSkRBI5Ua+pCYlE/v6R4F40Mgq7qPqJnH2yEHPXTsxd2wMzZVu3OWvWXLDZJcWY\nu3cS8bv/DawrWlyEdSwPOy8PO+8oxtrVUFKM66bbUCIicF0/FSv3CP6vllLy5P+h9+mPY8hwlMgo\nrNzDqEmSyAWT4+LBeOfMwrpiEiVvz8HMPYL7l79CiYlFO2O5MfedM0GenoSEPngYaBqel5/DMXgo\n+qChpSVJqvNhzNy+DceQS1HcEWjtOqAkJGIXFtRG6IH6gvv2EPHQY6AqYV2epqGq1jOj++67j2uv\nvZbf//73tGzZskyjlMkOQlRMiYrCPnKk0mOM3TvZ//Iz+AsKsE4WBnpV2rZH79QV30fzazxGc9OP\naO3TS3sglcgotMgoOJUklFf4Qk1qgmvSdViXXoaxbAklp8YXWnt2y4y3IFObt0BtnETW/9yPktIK\n1213BZb3+pkLGQIgLoyiqjgGDUXr1BXfgnkYP67DOW5ioDB11j5c026rcEZ8oDh5Nmpa29PXi4nF\nLqidRM5cvwatSzcZDhHGqvWTP2PGDAAWL15cZnt1Jzv4/X5mz56NYRiYpklGRgYjRoyguLiYefPm\nceLECeLi4rj66quJiAiMCVixYgXr1q1DURTGjBlT4YQLIeoyJTIaq3hPhfttTwkl78wladK1FMcn\nYMbGlfkkb3s82CcLUaKDP2bFNk3sk4UY61aj/2zh+upS4+JxTrwKrfdFGCuXYx07ij54aHADFbgv\nuxx37iH8ffpjyASzOktNSMQ17XbM9WvwvjsXrUNH9H4D8P7zeVy33lVm2IFtGFg7MzFWf4+amlYm\nOVdiYs+pR87cthmrsIDjThee/BOYHg9ax85oZ6x2UR7btgP1BX8hxf3DWbUSuQud7OBwOLjppptw\nOp2Ypsmrr77Kvn37yMzMJC0tjUGDBrFy5UpWrlzJyJEjOXLkCJs2beLuu++moKCAOXPmcM8990jt\nOhF2lKioSn8h+z75D470jsQOGoI3NxfL7z99rqKgtkjByt6Plt6p0vvYnhJ8CxegxifiGD6yyrhs\n28Y7dxbW/iyU2NgLroOltWyNdmqtSxF8eps04vv2Izc3FySRq9MURUHv2afMmraK04V3zixct96F\nlb0fc9OPmNs2ozZphta1O3r3XmWvEROLfeRQte5nHczGO/9dnN16YMTGgt8AwDtnFu5b7iyzNOGZ\n7MJCfAveR3G7Gnx9wXBXaWZUVFTE73//eyZMmMAf//hHvF7ved/IearGjmma2LZNREQEmZmZ9OgR\nWMuwe/fubNu2DYDMzEy6du2KpmnEx8eTkJBAdnb2ed9biFBRk1tgFxbgX7oY27bL7DO2bMLauxv3\nqaVwyj2/RUusA/srvYddXITnhafAMPB/uyKwZmQVzNXfQdFJIv7nESJ+9dtyC6UKIYJDv6g/Wnon\nPH97DGPlctSUlrh/9Vvcd9yD4+JLUKLKrlt+Lo9Wje+/xdF/IBFXXkvS9VNxjxmPc9RYnON/gXfO\nLOwzPhyWnrNxAyXPP4GS1ATXbTNklnOYq7RHbsaMGaxevZrRo0czf/588vLyeOGFF87rRpZl8cor\nr3D8+HH69OlDkyZNKCoqIvrUAsXR0dEUnRoUXlhYSErK6dk6sbGxpeVQCgoKyhQkBvD5fERFhU+x\nS/3UWBZdxrSgaRqO+pxENIpDv+s+iv71IorPg/vyX6AoCtbJQkoWzCPyxuk4TrXdcttD61R8a76v\n8Htk2zbFH76Ho0s3IsZfyckXn0bZvRPHzwobn8nKz6fkv4uI+uVMtDo2Lqbet4fz1JB/Z9SXNuGY\neBX2uAnljm/8OSUhAfNkYZWv2/Z6KflxPdH3//6sNuLo3RdrzQ+wbTOOXhcBYBUX4fnwfcwDWUTd\ndBt6qvTE1QeV/lb49NNPWbt2LcnJycycOZPBgwefdyKnqiq//OUv8Xg8zJ07lz17yo4bqu4ngjVr\n1rB8+fIy24YMGcKwYaEroni+4uMbTrX/Bi0pCfMPj5Lz9F/hkw9pPO02Dr0zh0aDhtC4b//Sw8pr\nD/7uPTnw8YckJZVf0uDEF//FW1REyn2/Q9F1nEOHU7JpA0lDKv55OPzBu8QNH0Fi1+4X/tpErZLf\nGQ2DX4HsopMV/tz/JH/pYsyMTjRt175025ltJGL0WE58tpCky8Zieb1kv/AUEW3b0fiXM1GlCkW9\nUWkiV1RURHJy4Pl6y5Ytyc/Pv+Abut1uOnToQE5ODlFRURQWFhITE0NhYWFpr1pMTEyZexUUFBB7\naqmU3r17k56eXuaaPp8vMHYkTOi6Tnx8PMePH8cwjFCHE1Iul+uCHtmHE9fNd1A0+5+c/H+/BcNE\nm3Q9ubm5lbYH27ax/H4Obd2C9rOF6M2cAxR9+D5Rd9/H0ePHA8entefku29ycM0PKLpO8Vuvg+5A\njU9AjU9AcbvxbdlEzAN/qJM/Mw2pPZyLhvw7oyG2CdvwY+Sf4MjhwxUu22Ubfgo/+oDIydMq/D1i\nt2iF98hhsv+7CGPzRkhIhNHjyaulGbE/V1ViKs5PpYmcaZosXboUODW7xTBKv/7J8OHDq7xJUVER\nqqoSERGB3+9n165dDB06lPT0dDZs2MCgQYNYv349GRkZAKSnpzN//nwGDBhAYWEhx44do0WLFkDg\nMWvsz9a/y8nJwV/OOIC6zjCMsIw7mHRdbzjfA1XFOfVW/J9+jN6nLwbAGa+9ovagXdSfkv8uLFNP\nzvZ58bzxGo6xE7Di4k9PktAduK6eTNGrrwDgHDMeJakJ9vHj2CeOYZ04jvOq6zFUtcy964oG1R7O\nQ0P8ndFg24TLjT8/HyW67Pg5c0cmVtZe0DSUZs2xW7Qs8/35eRtxjBqLd9U3KHHxOCdManAfBBqC\nShO5Jk2acMstt5R+nZiYWOZr4KxHpOU5efIkH374YaCcgm3TvXt30tLSaNasGe+//z5r164tLT/y\n0307d+7Miy++iKqqjBs3TgZjinpB0XWc4yue3FAex5BL8Tz9OOb+faXlBHyf/Ac1pVWZmXE/0TI6\nBdaC9PnQ2nUIbKyiDIEQom5RYmKwC8smcrZt41u0ACUqOjBR6q77qrzOz2fQivqn0kRu7969QblJ\n06ZNufPOO8/aHhkZyU033VTuOZdccgmXXHJ+ta2EqE8UlwvHyLH4P/kP6p0zMTf9iLVnN+4ZFf8S\n16QorxBhrbSWXPMWpdus7dtAUXDd8kvweFAiamctVlG3SWE2IcKA1rMPtmlifLsC3ycf4Lz6ehRX\n3ZpxKoQIHiW2Efapag0AVv4J/F8uxjF4GIqiSBInSjW8uexChCFFVXGOm4j3Xy+gDxoqPW5C1HNK\nTAzWsTyMDesw1n6PdSALvVsvtG49Qx2aqGMkkRMiTGht0nDecNMFr8IghKj7lEbx+D/+ALVdB/Re\nF6FNublaNehEwyOJnBBhRO8itd+EaAj03hehd+qK8rMqDUL8nCRyQgghRB2jOJwgPXCiGmSygxBC\nCCFEmJJETgghhBAiTEkiJ4QQQggRpiSRE0IIIYQIU5LICSGEEEKEKUnkhBBCCCHClCRyQgghhBBh\nShI5IYQQQogwJYmcEEIIIUSYkkROCCGEECJMSSInhBBCCBGmJJETQgghhAhTksgJIYQQQoQpSeSE\nEEIIIcKUJHJCCCGEEGFKEjkhhBBCiDAliZwQQgghRJiSRE4IIYQQIkxJIieEEEIIEaYU27btUAdx\nofLy8lDV8MlJFUXB6XTi8/moB9/+C6KqKpZlhTqMkJL2cJq0h/I15DYibaJ6wqGNxMfHhzqEekkP\ndQDB4PV6Qx3COXE4HMTFxVFUVITf7w91OCEVERFBSUlJqMMIKWkPp0l7KF9DbiPSJqqyDYMuAAAg\nAElEQVQnHNqIJHI1I3y6sYQQQgghRBmSyAkhhBBChClJ5IQQQgghwpQkckIIIYQQYUoSOSGEEKKe\n8fhtPtjsC3UYohZIIieEEELUM19nGazcZ1Dsr5ulSETwSCInhBBC1CM+0+arPX6iHHCs+PwSOa8h\nCWC4kEROCCGEqCdK/DYLtvhpHa/RJkEjr/jciymvyzF4ZGkJR4ukEHM4kESuHCV+G498GhFCiBpn\nWjbHii32nTCxbBvLtlmdbeAz5XfwufCbNgs3nuCxJYX4LZurOjtJiFA4XlL193HTYYOFmT7+u8PP\nkZMW/9nqo1szjbnrfRiW/D/UdfViZYdg+3S7H4cG4zOcoQ5FCCHqnc2HDZbtMThWYlPgsYlxKegq\nxLoVohwK23JN8oodXNbeEepQw8KabINF20tIS4pkxsVRJEUEetISIhRyK3m0WuK3eX+Tj5wCi94t\ndPI9Nk+s9NA3RWdSZwevrvHx6Xa/vBfWcZLIlSO7wMKUHmUhhAgq27b5YpfBN1kGv+jkIDlGJS5C\nQVcVLNvmy90GecU29w9y8/y3Hi5K0WgREeqo6zbDtHlno4+ZA6Pol96c3Nxc/P5TiVykQuZRs9zz\nDhVavLbGS4f/z96dh8l51Qe+/57zbrV19d6tltTaZUnWbtmWZMsbTgBjSAhglmBCSAIkEAiBZCbL\nneeZyZ3cm0y2yVwIGUhCIAuDbWIbwmLAxMa7sfbd2rde1XtXV73bOfePt9RSW93a1a3uPp/naVWr\nupZTVafO+3t/Z6uz+OymFK4lAHjzYoeMnezd+r5VLn/5fInFtTFL661xe03G5TGB3BtorWkdUEQq\nGezp2WKii2QYhjFheoqK/7Mj4I45NitnWEiRtIlKa3pLmpr02RE6u9sjeoqarCvIOIKMK8g4kHEE\nQsAPDoS8fjrmt+9IkU+NbFulENy/8GwG7u75Dl/Z7PNr61NUmsTcmPoDTYUrWFBz/uG8Ji3pHqVr\ndWdbxCO7At6x1OX22SPvl/fOfi45V/CB1S7/si3gs5tSI/52MVprXj4R89LxCCFACvjT91zGCzMu\nmQnk3qC3pHEsaKqQHO1RLBnnsxCtNUGMCSANw7ghvHYqRgh4+lDEDw+GvGWxw4pGi3/bHfLKyYi5\nlZKNc2z6fM2LxyKW1lsMhYqhQFMINUMBw0tgzMpLfmN9iqx78fbtZxbaZBz482f7+K2NHnVZM6R7\nNP0lPWaAVZNJxsgVAs0/b/O5d77DkZ6YV0/GfPRWjzlVFz++La61WN9s8a/bfT52mzccyF+I0ppH\ndwYc61W8a7mLY4E2Q+2uGxPIvUHrgKapQjKnUnK4Jx73QG5HW8yLxyN+Y31qXJ/XMAzjjbTWbD4V\n8f5VLnOrJLs7Yp46EPLE3pCMA//1TWkOdMW8ejJi0Nd8coNHdfraBFxCCO6c6zAY2fzkaMi7lptx\nWqPp9/V52c0zUrbAkfDskRA/gkd2BVSnBL99Z4qKy8iuvXmRwxdf9XlkZ8B7VrjYcuz7Rkrzf3YE\nDAaa37ojZZIS48AEcm/QOqCYWSFZWGPxvQMhS+tj+kuafl/TV74sRTAjJ9g4x75mjdYZuztiTvQp\ntNaISzjzMQzDuBJKa7QG6wIH5ZP9mljD3CqJEIIVjTbLGyz2dSpmV0qyrmBNk82aput3KLl3YYo/\n+kGRtyx2LimTN91cKCMHUJ2W/ORoxK/d6rGwRl7RccWSgo/e6vHP2wK++IrPu8tZtsFAM69KooGD\nXYqtrRG72mIW1Fj8yjpveNydcX1Nu0Cus6AYCjVzx0gpt/QrltZbzKuWhLHmyb0hlZ4gnxJUeoKG\nnMS14PXTin/cEvDpjd4FG8LLobRmX2eM0tBT1NRkzJfAMIxrb2dbxJN7Q/p9TU06adcasoLmSsmq\nGRZCCHqKiu+/HnDLTGvEwV8IwbKG8eupqEpLVjRavHg84mcXmcFyb9TvXziQq80I/FhccRB3hmcL\nPrLO5cVjEV98tYQlBJ4NloBCqKn0BGtn2nx2k3PNExzGhU2rQK4Uaf7upz5DoebWWTYPLHHOO2No\nHVDct8DBswW/e9fY06VWz9B86ac+j+4KSNuCW2bZNFdeXeU91a/JOILmSsGpfkVNxnwZDMO4tn56\nMuL7B0Let9JlXrXkdEHTWVB0FDQ/OhSypSUm7cCu9pj1zTb3zZ/44OneBQ5/+2qJ++bb2CbLM0K/\nr5lXPfaxYkGNZEm9dU16eKQQbJrnsHGOjRSggUNdispUcjJgTIxpFcj92+6ABbWSty9xeXxPwF88\nX+IDq1yaKyVf3xGwpyMmVtCYu3iFPzM1+7v7Q2IN/7zV53N3pS6YSvYjzdbWGIXCa+2hb6BEHCvW\nz7bJp5K1k5bWW7gWtAxoVs64lq/eMIzp7mhPzLf2BXxyfYoZFcmBd2ZeMDOf/H7ffJsfHAyRAn7/\nnvQN05XZVCGZlZdsbkmCS+Osvot0rd4179oH4md6oQSwuM4sSzLRps03om1A8fppxR/cmwRbD6/x\n2NEW8ZUtPpWeIOsKfv+eNBpwLvGMryol+cXVHgD/uMXnRwdD3rZk9AG5Uaz5h80+AmjK2+SICSLo\nLij+7jWf961yeeVExHtXugyFmi0to6/9c71orTncozjRq7h3wcSfgRuGcW0orWkf1Oxsi3nuWMj7\nV3rDQdwb2ZYYsw2baPfOd3h8T8Bts61Lmjk5XVysa9WY+qZNIPfCsYgNzdaIjNmqGTYLqi1eORlx\n1zz7qgZmvutmlz97vsiaJnv47PaxXQErGy2W1Fs8sTckZQs+fIuL57rU19fR2dlJEAR8fUfA/3yx\nxDuXOSyulXQWNP/eH171a75USmu+uiWgo6DoL2lWzrCoNd26hjHpHeyK+ebugFjBvGrJb9+ZGrHu\n22SyuFZiS9jXqbipVvLIroD7Fzo0lrv0VHl9i9GCPK01bYPJigR9JcX/etFnTpVkeaPFsnrrhsk8\nXomBC8xaNaaHKR/I9ZYUthRsaY343bvOX9Ij541chPJK5VOCt93k8siuZAKEH8ErJ5P9AhfWSLa0\nRPzePenzGhkhBO9d6fKWxXo4eKrLQiHQDIXJmLnr7cm9IaUoWU39GzsCXj+t2Dhncjb2hmHAoK/5\n9r6AA12Kd97ssLLx2oyRmkhCCO6Z7/DMkZDOgsWJPsXfvFzittk2bQOKwz2KnCt4cIkzPGHjjINd\nii++6vOf7kqxqz1mfo1kca3FjrYk0J1ZIXngJodFtWe7CbXWfGNnwKCvWTPTZkWDRWoc2uPLESnN\nUAi5GzOJaoyTKRHIxUqPOnN0wNf8ybMlYgUrGi2qUtc3OFnfbLH5VMQLxyI8SzCrQrK3M+b1LsWM\nCjlm+tuWgtpzZqhKIVg1w+KPnymypC45Y1xWb5G7Dunz3pJi86mIP7w3jS0Fi+ss9nbEbJwzdtVo\n6Ve8fCJibpVk3awpUYUMY0pQWvPKiZjvvR6wbpbNf7o7RWoKreO1tsniu/tDnjoQ8umNKfp9zeHu\nmNtm27xvlUVLv+Jb+wKePRrxc0sd5lUngdmPDoXMzgt+fDjkSI/i4TUuc6ss1jfbhLFmd0fM17b6\nfGitx+JyMLe1NeZYr+L+hQ7bW2P+bXfA4lqLO+bY476+6FgGfE3OFaareZobt6NwX18fjz/+OIVC\nAYB169axYcMGhoaGeOyxx+jt7aWqqoqHHnqIdDqZLfrcc8+xdetWhBA88MADLFq0aNTH/s9PFck4\nsHamzT3z7OHZns8fC7l1ls3blzpcoxVCLkgKwUMrXT7/UonqtOBNCx1+dDDku/sDbrnMgOcDqz36\nSoq9nYpd7TGP7wlY22Tz0Mpre+p1sEuxsMYiXT7TXFwr+fbeAKX1qI3Dke6Yf9jis7HZ5gcHQg53\nK96+1Bm+v2EYE+NoT8w3dgZkHMHHb08xKz/1suqWFLx5scPpQnJyPKMCbjpnsP2SeovP1aV47VTM\n17YGzK6UzK1KZuZ+5s4U/88zRarTgjnnrDDgWMlaeDlX8NUtPv/57mSs9BN7An6tvPvBrbNshsJk\nnOG/bPf5+O0ptE7W/XzL4okbU9zvaypNt+q0N26BnJSSt7zlLTQ1NeH7Pl/60pdYuHAhW7duZcGC\nBWzatInnn3+e559/np/92Z+lo6ODXbt28clPfpL+/n6+9rWv8alPfQopz2+c/sdb0/QUNS8ej/ir\nF0osrkvOml46HvGbG8b3jLQxJ7lrnsOPD4fc3GDROqD44cGIDzde/hlcZUqyoVmyodnGjzT/4ycl\nDncniy1eK4e6YhbVnn1Pq9PJIp8t/ZrZlSPfN60139oX8vPLXG6dZXPfgqT75v99tsjPLnK4Y459\nVWvqDYWadHmzZsMwLm7QT7bBciR8ZbPPLyx3WT1j8nejXsiGi8xalUJw+2ybNU0Wr52M2H9a8eBS\nhwpP8DOLHKpSYtT3Z1GtxdqZyazdvpLm9tn2iC2sMo5gfXOy7MY/bfUpRhqlYGm9HHNd0svxnf0B\n98x3yF1kvN7+zphipFnTZF90MWBjehi3U7aKigqampoA8DyPuro6+vv72b9/P2vWrAFg9erV7Nu3\nD4D9+/ezcuVKLMuiurqampoaTp06NepjSyGozUjesdTlD+9LM7dK8vXtAfOrrQlZ2+b+hTaf2pjM\njl09w+amOkn9Ve4T6NmCty1xeGJPMDyo91o42K1GjAsBWN5o8c/bklm4nQU1fP3W1pgw1twyM7l9\n2hG8d6XHr9+eYnd7zJ89V2J3e4zWGq01r5+Oh/dYvJgDXTF/9OMiezvVxW9sGNOc1pqjPTF/8UKJ\nL7xc4s+fL/GmhQ5rmuwpHcRdDtcS3DHX4SPrPG6ZmQR/9y90Ljgc5M2LHDafiugoqDEzbbfOslhU\na/HOZS4PLHF46sDVT0xr6Vc8fShi86kIgFKoidX5bafSmn/bE/DEnpBIJTsNXc5WW8bUNCEDnHp6\nemhra2P27NkUCgVyuRwAuVxuuOt1YGCA2bNnD98nn88zMDBw0cdO2cmA2Lvm2YzyPRgXlhTMyidf\nrpl5ya/ffm32Tb1lpsXTh0KO9qhrkpXrKSpKkT5v3bwHlzgsq7fY3hrz+ZdK5FOCukyy9+wv33L+\npskz85KP3+6xt1Px7X0B39kPeU9wpEexYY7NL9x84e7gY70x/7TVZ2WjxdaWiJvHcdV4w5gMjvfG\nfGVLC6cHfPpLigFfk3HgoZUuy+otWgf0cJtjXLmcJ/jAapfajBxzGSohBO9ZkbRpkdI8fSjieG98\nSRvQj+W1UxELqiWvnoxYOcPiL54rEWuYXSlZUmdxU52kuVKyoy0m4wgqU/DaqZiXT0TcfR3WiTMm\nl3EP5Hzf55FHHuGtb30rnueN+NulnEn29/czODg44rogCMhms9e0nNeTbdsjLi/HssaYo72wpPHq\nv7yHWwMW19l47vmB1rIZyc97teZQV8yJvphfvCV7wVm0q2fBypkeh7tjOgYVH7zF5k+fGeQtSywq\nx5hoopE8sjPkPSvTLKqz+eMfD4C0L3ktv6ngaurDVGNZFo5jDkxvtP90QMqx+MDaLBk7ycKc+x2Z\n713gzpPceNeJtbMv/bkcYONcxbY2zcL6se+3vzOiPitH3a0nVprNLUU+fWeWL75c4B82B9y/2OPu\n+R6HuyP2dUQ8sjPp7pUCPrg2GUP+5VeHWDPTYeO8FEII045MY+P6icdxzCOPPMKqVatYtmwZANls\nloGBASoqKhgYGBgOyCoqKujr6xu+b39/P/l8ns2bN/Pss8+OeNx77rmH++67b/xeyDVSXV192fdZ\nt2CQp/b0UV9ff1XPvbtliG/vHeQ3722kvj5zwds2NlzeY597+7tvkjx9RPMrd9aNGpw9sa2bxsoU\nb1nThBCCebUhLX6WW+dOnsD8WrmS+mBMD4P7OljW5LF2YeVEF8V4g/vtgD/+3ik+dl/dqBPE9rcX\n+ermVhRwz+IKHr69DikFSmn2d5T4yesDzKh0WblgBm/q7eblI4O8f8NsbEswZybcW36c7kLE0S6f\ntc0ZNNAV9vDgiirS7sjg0LQj08+4BXJaa5588knq6+vZuHHj8PVLlixh+/btbNq0iW3btrF06dLh\n67/5zW+yceNGBgYG6O7uZtasWeTzeZYsWTLisYMgoLOzc7xeylWzbZvq6mp6enqIouiy7ltnK15v\nK9LW3nHRiQVaa145EXLLrJF7ym45FfDojhIfuTVDk1egs7NwRa/jUtw1W/G1LUU+8a+HeesSj03z\n3OHGzo80394xwOfuznL69GkAVjTAd7d30pwuTJsp9VdTH6Yaz/PwfX+ii3HDae0pctu87LSsIzd6\nnXABz9L89PU2FtSMPKQOhZq/fmaAD6xOs6DW4u9/OsiffK9AhSvZ0RaS9wSrmhweXu3S2dnJplma\n22ak6Ok+PepzNafh9OkhAO5phsG+Ls70T02GduRqExDG6MYtkDt+/Dg7duygsbGRv/3bvwXg/vvv\nZ9OmTTz66KNs2bJlePkRgIaGBpYvX84XvvAFpJQ8+OCDCCHI5/Pk8/kRj93S0kIYjt9OCNdKFEWX\nXW5XQG1GcPi0P7xG0li2t0Z8fVvAjpaAD9+SBFDPHQ358eGIj9/uMSuvr/v7lrbg47e5tA4oHtsV\n8NMTAe9b6dKYk7x8LGRRnU2VqwjDZJLDuibBaycUj+8c4ueWTa9VLq+kPkw1tm1P+/dgNF2FmPqc\nQxQNTbv3ZzLUiVUzLH5yqERKOtSkBZYURLHm717zWd5gsbQOIObX1rl87/WQCk/zqQ0edcOT4GLC\nMNmW0QGu5uWadmT6EVpfwymQE6SlpWWii3BZHMehvr6ezs7OK/rCPb4nIG3DWxY7Y44rDGLNn/6k\nxEMrXJ4+FKKB2rTgWK/iY7d5o47VuN6U1rx0POL7B0I2zXXY1hrx/rUVzK0YefZYCDSff7nE+tn2\ntNj39Wrrw1SSTqcpFosTXYwbitaa33uqyJc/tICB3q5pV0cmQ53oKSoe3RXQMaiH13azJTRk5fBJ\n9PU2GdqRmTNnTnQRpiQzKnISumWmxVe3BLx2KuYTG7zz9k481a94Yk/AvCrJ0nqLBTWSvR0xJ/oU\nv7kxddF1iq4XKQR3znW4ucHim7sDLAFL6m1KpZGBXNYVfOw2j8+/5JPzBLeOsVxAT1GNuSaUYUwV\nA0Gy/FDKkVx83r4xEarTko/dlqxOEClN95Cmt6SZXy2nzRARY+KYQG4Smltl8V/uS/HtfSHPHI54\n1/KkC7KvpPje6yF7O2LevNgZXjjTtQSrm2xWN01kqc+qTkt+dZ2H0mPPVK5OSz56m8ffvFIi5wqW\n1o/cA/HpQ0lm7+YGi/etdCf1pteGcSE9RUX1JN3ofjqypaAhJ2jITXRJjOnCtA6TlBCCe+c7bG6J\n6C4qnjoQ8mfPJUHP792T5s65zlXtsnC9CSEuWr4ZFZKPrPP4l+0+bQPJGDqlNY/sCtjZHvP796So\nzQj+4vkSB7uS8SVBrHliT0DxEhciNowbVRBrekuKnqIesRezYRjGuUxGbhLLp5KdI/7k2RIrGy0+\ne2dqQsa+XU/zqy3edpPLv24P+I31Hk/uDegsaD6x3sOzBT+/zOWm2ph/2pbs/9o6oDnUHWNJeMfS\n6TVZwphanj4Usq9TsXqGRfUU+14bhnHtmNZhkntwicOnN6b40NqJmcAwHjY0W2Rd+KMfF4kUfPS2\nJIg7Y1mDxefuTHO0N9mp4rObUrxyIqJryGz3ZUxOWmu2tcS0Dyr2dMTnjYM1DMM4w2TkJrmsK6b8\n+DAhBB9c49FZUMwfY8mVfErw67en0FojhOCueQ4/PBjy/lVTeMl7Y8o61a+TBWTn2/zwYMTPLDaB\nnGEYozOtgzEp5FwxZhB3rjOTJ+6YY7OjLTZj5YxJaWtrxJomi9tnJ+fapmvVMIyxmNbBmJIqvGSm\n62unbswVzg1jLLvaI149EXHLTJvajOSDq12aKkxTbRjG6EzXqjFlbZxj8/jugE1z7eu21pzWmqEQ\nOguKYgTNlXLC1ukzJo+dbRGP7AporpRIoBAm2zkNBZqUI/jldd5w8LZuln1Dz0A3DGNimUDOmLIW\n1UiKEZwe0tRnRz8QFkPNV7b4/Oq6kRMoxhLEmmePRLQNKE4PaU4XFBqoz0o8G072KfKeYE2TxVtv\nMrNmjdH95GjEA4sdKjyBFJBxBBlXkHEEWReziKxhGJfMBHLGlCWEYEG15EiPoj47etfUttaYg12K\nLS0xG+dc/Ovw7b0hnUOKdTNt6rOCuowk654dm6e0pnVA87UtPs2VEcsbzVfsRqS0Zk9HjECwrGF8\nV99vG1B0FBTrmz2TaTMM46qZo4wxpc2rlhzpiYcHjb/Ra6ci7pxr88KxkA3N1phdsLHS/PRUzJ7O\nmN/ZlCLtjH47KQSz8oJ3r3D5xo6ARbXWJWX6jOuvpV9xekhxsk+xsz3GtZLP5Ym9mk1zbW6fbY/5\nuV5LLx6P2NBsuksNw7g2TCBnTGnzqy1eODb6hIeuoSQz8hu3e/zZczFHexTza86fGdtdVPz1iyXq\nMpJfXute0sH+pjqLpgrBrvaYdWPsFWtcW/2+Jow1tW+Y4dldVDyxJ+Rkn2JWXtJUIXjPCpcF1cnt\njvUqnjsa8YODRW6ZaXPXXJuG3PWZXOBHmi0tEZ/blLouj28YxvRjjjDGlNZUIej3NYVAn7fe3paW\nmDVNNrYluHeBw3f2h3xygzwvK/ejgyG3z7Z5cMnljXmbV21xql+xbtZVv4wpbXtrxJN7Q+ZWSRY3\nwKxczKy8xJaCUqT50qs+d8+3WdM0srmKleZglyLjCmZWCP7hNZ8+X/PZO1NUeIJIaZ45HPHskZC7\n5zv80hoX2zo/CJ9XbTGv2qK3pHjxWMTnXy7xruXuec93LWxpiVlQY5m9Uw3DuGZMIGdMaZYUzKmS\nHO2Jzxuvtq8z5s2LHADWN1u8fCLip6dGdsN2DSl2tsX83j3py37uWZWSZw6HV/cCprgo1nx7X8g7\nljooDScGYl46GnB6SPOOJQ4n+hVZV/CtvUlG7e75Dm0Dim2tETvbkx0PeoqKuVUWKTvJhP79az7L\nGiy2tkTUZQWfuTN1XpZuNFUpyduWuNRkJLva42seyGmtefF4yNsv84TAMAzjQkwgZ0x5i2stnjkS\nMb/GIlPuFi2GmpZ+xfya5AAvRdLd9oWXS3x7bzB831DBfQucK9o9Y3ZecrJfDe82YZzvuWMRM3KC\ntTOTpmhTOk2xWKSzoPiHzT5hDL9zV4pSpPnBgZD//h9Fmiokq5ssPnOHQ21G0j6oeGJPwPtWuVSm\nBDk3ohBqfm6Zy80NF19E+o0W1kieOhBe88/tQJeiFMHiOpONMwzj2jGBnDHl3TvfZsDX/NULJX6l\nvD7Xwa6YudVyeMA7JGvA/df700Rv2KI161zZ81Z4AkcKeoqamszkD+SeORwyKy9ZXHf5wdG5fnQw\n5GhPTHdRU4rg47efv41afVbymTtS+BGkbEHKFrx3pce7l+vzJgk05iQfv/3smLO751/hB1ZWlxFo\nDd1FTe01+twKgeYbOwLevcI1S4sYhnFNmUDOmPIsKXjnzS6zKyP+5pUS717ucrBLsWSUgCR1jWeY\nzsoLTvYrai7QtRfGmvb+8IbeZmVrS8QzRyI0ms9tSpP3rux9Ggw0Pz4c8ourXXJu0u09VmDj2QLv\nDS3UeMz0FEKwsEZyqEtdUpfsWJTWtA1oDnbHbD4Vs2qGdUUZQsMwjAsxgZwxbdw6y2ZGTvKVLT6D\nvua37rj+MwdnV0pO9StWzRj7Ni8eC9jR3s6nNp6fmZoosdLsbI957mhET1Hjx5pPrE+xoy3ia1t9\nPrTGpTJ1+UHO9taIZfUWK27w9fUW1EgOdsc0VggasnLUmcqdBUVLv+LmBgvnnMxu15Di3/eFHOyO\nSdmCRbWSe+bbrJ5hgjjDMK69G7s1NYxrbHal5LfvTPHy8Yimiuuf3ZmVl7x0/ML7vW5rCTnRp1B6\n4gfBlyLNKycinjsaUZkS3DPfprlS4khBzhPMyDk8dSDkz58rcf9Ch7vmXd56aFtaYt604Oq6PsfD\nolqLb+4OOdilqEoLPrHew5aCWGl2tce8dDyiZUDRkJM8vidk3SyL5Q0WOVfw5dd8bptl83PLHDM7\n1TCM684Ecsa0k3MFP7NofIKJxbUW39obsr01YvUosyD7S5pT/TE5z6ZjUFE7AcuLtQ0ohkLN7o6Y\nV05ELK61+NBal7lV52eQLCl42xKXW2fZPL4n4JWTEe9enmzq3j6oONarmJ2X1GUF3389ZCDQeFbS\nRepago6CYmn9jR/cNOYk/+3+NFkXvrI54B83++RTgt0dMQ1ZycY5NqsaLWxL0Dag2FpeQqWjoLh/\ngTNu9cswDMMEcoZxHaUdwYfXunzpNZ+mCnneQrM72iJWNDoI2+VkX0xtanwHwr98IuK7+wPqspI5\n5WzlpYwLa8hJPnabx872mK9vDyhGmoaspLlS8urJgJ6iTroTm2z8KJnU4EeaD62ZPNtSVZTHAf7i\napcXjkWkHbhnvkPjGz7DGRWSBypcHrgJM0PZMIxxZwI5w7jOmqssHljs8NWtPr91RyrJTA0qntgb\ncKJP8aFbMvSEHif7Bll9hWPHRlvw+GL2d8Z8d3/Ab25IXdFOBkIIVs2wWdFoIRi532wQQWoctrsa\nD2nn0jO4JogzDGO83fh9HIYxBWycY9NUIXlsV4DWmu/sD2mqkHxifYrljQ7z6jxO9MYj7nO6oGgf\nVGM8YpL9OdgV86WflvgvPypyqDse87aQLL777JGQ546GnOpX/Mt2nw/f4l31dlRSiBEBjBRiygRx\nhmEYNzqTkTOMcSCE4KEVLn/9Yokn94Yc7Y354Jr08Dp282o9TvYpYqXZ0xHz4vGIo72KhmzS3Xku\npTW722OePhxRDDT3LnBYMwO+tTfkt+44fzkPrZMZqN/eFzIjJwlizRN7kiVAFsOt2AUAACAASURB\nVI6yt6xhGIYxeUyJQM7zPKScPMlFIQRDQ0M4joNtT4mP4IpJKUmnL3/7q8koDXx8o8ef/Ecv77g5\nQ2Uued1CCFwCbAn//ZkStRmLuxdk+PWZLn/wvR58PKrOmf342PYC+zpj3rYsw5qZyQKzSmteOtnH\n1nbJpvkp9rQFHO+Naa6yeGp/kUKg+eAtOZY1umit6RxUNFTceEHcdKoPl2M6txmmTlya6VxHpjuh\ntdYTXYir1dLSMtFFuCyO41BfX09nZydhOL334kyXt2SaTrqGFFUpMTzo/0x9+I+dLeQcxaz82aDt\na1t9FtdabJyTNMx7O2Me3RnwuU2p88bEnepXfPmnfrKYbbdiRaPFsd6YjXNs1s++vGVCJsp0rA+X\nYjq3GaZOXJrJUEdmzpw50UWYkkzYbhjjbKxZoStmOOc1wMsbLLa1xmxottjSEvPk3oAPrfVGndgw\nKy/5zB0e3z8Q8skNHvXZyZOlNgzDMK6MCeQM4wa2rMHisd0Bf/VCiVjDR29L0Vw5doBWlZa8f9WN\ns0OEYRiGcX2ZQM4wbmAZR/D2JQ41GcmSurH3JTUMwzCmJxPIGcYN7s65ZpcAwzAMY3RmEI1hGIZh\nGMYkZQI5wzAMwzCMScoEcoZhGIZhGJOUCeQMwzAMwzAmKRPIGYZhGIZhTFImkDMMwzAMw5ikTCBn\nGIZhGIYxSZlAzjAMwzAMY5IygZxhGIZhGMYkZQI5wzAMwzCMScoEcoZhGIZhGJOUCeQMwzAMwzAm\nKRPIGYZhGIZhTFImkDMMwzAMw5ikTCBnGIZhGIYxSZlAzjAMwzAMY5IygZxhGIZhGMYkZQI5wzAM\nwzCMScoeryd64oknOHDgANlslk984hMADA0N8dhjj9Hb20tVVRUPPfQQ6XQagOeee46tW7cihOCB\nBx5g0aJF41VUwzAMwzCMSWHcMnJr167l4YcfHnHd888/z4IFC/j0pz/NggULeP755wHo6Ohg165d\nfPKTn+Thhx/mO9/5Dkqp8SqqYRiGYRjGpDBugdzcuXNJpVIjrtu/fz9r1qwBYPXq1ezbt2/4+pUr\nV2JZFtXV1dTU1HDq1KnxKqphGIZhGMakMG5dq6MpFArkcjkAcrkchUIBgIGBAWbPnj18u3w+z8DA\nAAD9/f0MDg6OeJwgCMhms+NU6qtn2/aIy+nMsiwcx5noYkwoUx/OMvVhdNO5jpg6cWmmcx2Z7m6Y\nT1wIcUm327x5M88+++yI6+655x7uu+++61Gs66q6unqii2DcQEx9MC7G1BHjYkwdmX4mNJDLZrMM\nDAxQUVHBwMDAcFatoqKCvr6+4dv19/eTz+cBWLduHUuWLBnxOEEQ0NnZOX4Fv0q2bVNdXU1PTw9R\nFE10cSaU53n4vj/RxZhQpj6cZerD6KZzHTF14tJMhjpSX18/0UWYkiY0kFuyZAnbt29n06ZNbNu2\njaVLlw5f/81vfpONGzcyMDBAd3c3s2bNApJu1jNB3RktLS2EYTju5b9aURRNynJfS7ZtT/v34AxT\nH0x9uJjpWEdMnbg807GOTHfjFsg99thjHD16lKGhIf7yL/+S++67j02bNvHoo4+yZcuW4eVHABoa\nGli+fDlf+MIXkFLy4IMPXnLXq2EYhmEYxnQxboHce97znlGv//CHPzzq9XfffTd333339SySYRiG\nYRjGpGZ2djAMwzAMw5ikTCBnGIZhGIYxSZlAzjAMwzAMY5IygZxhGIZhGMYkZQI5wzAMwzCMScoE\ncoZhGIZhGJPUDbNFl2EYhmFMd1r7REPPEAf70aoXhIcQHggPhIWOOrG81bi5t090UY0bhAnkDMMw\njHGntQYCtBpEq0HQJaQzHyFctA5QUSvoAOXOA5yLPp6K2tFqAGHVIWQeISRaa+JgByo4gCbCTt2B\njruIis+gtQ86AiK0joEYIbJ4lb+KtGeUyxiDDgGV/GgFwkHIdLn8CiGsq34vVNRJWPguCIEKjyOd\n+TjZBxBWLWi/XFYfrUOEzOP3fRk7vR5p3dhbXmmt0HEXEJd/Zk5wiaYmE8gZhmEYly0JcgoImR/j\n7z4qakNHrai4HWk3Ie35aF0gKj5H7O8CBEJmETIL2Ki4C2nPQIXHEHY9Apfe/jaENQM7dRuWtwYh\nU2itUdExdNSGilqIw0PlstSg4i7QfhIEIUAI7NTtCB0R9H8FRAY393aErAFhAxaifBkHuyn1fRHL\nWYyKWtFxJwiLZBSSBCFBh1jeanTUgYpbcLJvw07diRCXNlJJxb3oqAWlutBxNzruIg6P4GTuQ8hK\n7NQdWO6iCz6Gk76LsPAUXv7hi3xG6pLLda1pNYjf/8/ouA1EmuQ9vG1CyjLVmUDOMAxjnKmonXDo\nh0kmCoEQDpR/pFWPndqQZKQQWO7Ca/rcWhXRqhthNSKEjdY+Ou5Bx92ouJ04PABIpDUDYVUicNAo\n0CW0LoEqofUQcfA6EGK5K7DTGxGymtjfhgqPoeJWdNyLsBuQ1kykXU/s7yYs/ACEh51ai5t7N0Jm\n3vC+tKHi01j5jyBkCoBUymWwbwtx6acEhW9huTeXs3jdSHs+0p6Bm3oP0p47HLRoVULHp9F6COks\nGr7eTt9Tfr9H3/LRTt2KkFVo1YOTvgdhNyKE+4b3b4Co+BIifRPSnkEw8A101IGTe9dFt5JUUTul\n3s8j7dlIqxZh1SCdeTi5X0Ba1Zf8Gdrpuyn1/DnBwGNI9yZUcIDYyTNgLyQKPLSuIiq+QDj0Q6Sz\nIHlNcScq7gQUQuYQsgIhcgiZgzP/lzmEyKFVFypqR4h0EmTL8u0AITyE1TTqa9VaASFxsI9w8HEs\n71acyo9ek6ylMTahk/zwpNbS0jLRRbgsjuNQX19PZ2fntN/cOJ1OUywWJ7oYE8rUh7OmQ33QOqTU\n8z+xvBVYzjzQoAmTLjwdEodHiP2tCKsOdAnLW0cqdyvV1TV0de4n9NtQcSda9SKdeVjOAhAuKjyC\nVn0k3ZDh2e445SNkDmnVEQe7kwO0VVW+rQ06QFjVCFmDtGqR7iJAoKN2tOpPyoZEiBRCpECkEDKN\ndOYhRAVR6Xkifzs67sLyVmM5NyHtJoRVf00O4OfWCa0GifytgMRObbghAgStipR6/xeWs6icBVQk\nH2qMilrQqhdh1SHtZqLSyzjpu7HTG6/J8waFb6Hjbix3KVIEOFYfQ0OnUFEH0p6LW/EQKjoJqoiw\nGpB2PSDLgfAAWg8O/44aHO7mFlYl0pqJ1iW0HgRVKJ90JIEswkXIinKXeAGtg3O6oG2E3YCb+4Wk\nbp5j5kzTtXo9mEBuAtwoB26tBgBZ7ta4NCruJSr+GGk3Y7nLETKD1iEqPEwc7CEODmC5S3Gyb0PH\npxFWzXlntOeaDgfui7lR6sONYKrXB6014eC/oVU/bv6Xx8zgaO0DLuhBgsEn0XEbti1R1ICoQ5bH\ngcXhQVR0KhlfZs8pB39hMo5LeEnQJVy06kPF7VjusnKGykKrISAGkbtoJmkiTYY6oeIeouJPAE3S\nhShAWEirASFr0Oo0KjyOkBXYmTdfl/f73HYkCILr9plqrVDRseQEQOZAZMoBvkPSTT3285pA7vow\nXavTQOzvRas+LG8VKm4jDvYRB/vQ8WkApDMfy1mEtGcjrIbyQOFR0uZqEL/vf2M584n9XQSDjyPt\nmajoFNKaieUuw614iGjoxxRP/18gPYSowKv8CNKqHflYWqFVH2HpBFrVXFYwaRiTgdYxWvWi465k\n9iEQ+7vQqh+v8qMXPOAJ4ZV/qcDLPzxmsG95N19x+d7YrWlcOWlV4+Z+/gK3WACp28etPNczMBdC\nYjnzr9vjG5fPBHKTmIq7SWZapYZnXXHOpYrbifwt6Pg00mokGPwmwp6RBFzZdyKduUlXTrAXFR4m\nDHajog4gRFj1SFmLsGqRVi0aRTT0NFbqVtzs24AksEtmWM0dEYjJ/K+gVR9CVhKVnqfU85dY3kqE\nSKPj06j4NDruQsgMkV1LFLZiuctwMm9CiAoQcng8hmFcKq0GUXFnMsanPD7qbGbKAa2IwwNIWY2V\nuv2KB4FrHZUH2Z9Aq0Gks4Bk5mOA5d5M7G8mLPyg/B3IJ1lpmYx/EnYTbuaXyoPrDcMwrp5pTSap\nOHgdv/+rCJFBa798ULLKs7CS36VVje2uwErdVh7UHJ8/pkRY2Km1kFo7fJVWheFgS8ddxNExUD5u\n/kMjzsSEzI2aERBCIKwqIJldZXtriUo/BXQyrd6qQ1i1COGRTqcZKnQTlV7G7/tyebBsjOUswk7f\ngXQWT9isK+P6UNEpVHwaIWuwnOaL3z7uRsiqUeuBViW06kGFRwkK30Na1WhVKC9BkQUdnjN+RyOd\nBUSlVwmLzyPtxvKMyXz5p2L49zd2N2pVIiw+gwr2oqK25ATHno2QOcLCt8qBoiYYeBQhM7j5DyPt\nmTfEGC7DMKY2E8jdgLTWxP4WtC4hZGX5AFOJjtsJi88hcIjDQ3j5j1x0mvq5LvWgImQWS2bBmXul\nL+ENj5fDydx3gb+ncTL3Dd9GqxKxv5Wg8O+gSlipdeUMXhVu7sFrUibj0iRDaDUQJrMQZQYhK674\n8eLgAH7/P2E581HRCaTdXB7XFYBw0UGGKBJJYKRKxOHrqOgUQuax3OWgfZTqTWZZql7QcTJQ36rF\nq/zoJQWGWitUeDAZyK8KaNWPijrK/+9PhiG4y3Bz7yYY/CYqOoVWhWTsZ+7nkfass12fb6CiFoSs\nGZ5xaRiGcb2ZQG4cqbiL2N8Nbj1+8Sa0kmgNKnydODxUPkBkiUqvouJWLLsZpfahVR9a9YPI4GTu\nBWzszN1TdpyCkCns9Eas1AZUdJzY34J05hANPU3srbrgwVrrmKj4Alr1YrlLsNwlF3wurTUqPIR0\nFl71uBKtioRDTyGtRqzU+kmZSVRxLyo6kfyEySW6CFjlmY6F5HeZBpxkIotwkwH1KHTURrK8QT55\nT2W6nBEroXVA7O/By/8SlrsIrQOi4stACFQml1onz6FDEKlkTS1vFSo8jAqPglWNLZciZRXCqi4P\ntL68z00IieXeNObftQ7w+/6RYvf/jeWuxMt/uLwsSO2Y9zlD2mYwt2EY48vMWh0nYfEFwsL3sbzl\nCD2EoJvQ7xiexm15K9FRK1on08Td3DsvONtzqricGWlR8WWi0stYqXWgFba3FmT6nNXZQ8LBJ9C6\nhOUsJiz+hFTVryPtWWM/ZmkzwcC/4OTejZO+87LLr1U/cXgUFR4j9rci3SXJ50iM5SzATt+HLHcz\nj3p/rSDaiWu1EqiZxFE8IjMkrRnYmfuvy+BlHfcn2SZdIA72oMLDaB0jnWakPQfLbkY6zSMWfNVa\nJ8tW6AB0gKZ8qQNAIK1GEHayJll4KFmJvry9kBAewp55wUD8RpmhqHWICl5HujffEDM6p/PM5hul\nTtzoJkMdMbNWrw+TkbtMWivQQ5c1GF/rmLDwfVJVv4m0G4e/cB0d7QR+1/B2MsaFWanbiaOj5a17\nYopD30+COOEANkLYSPcmvFwymFxYdfh9XyFV/elRV5/XqkhY+DZuxQcJBp8EnQRfIxY/1X6yllLc\nj4qOIkQW6TSjohZUeAyti0hnLpY9Dzf/S1jOvOGuu9jfhd//96SqPjVqUB6HxwkGH0MKi0ztHRR6\nt6C1GB6nJe0ZhMWfoHUJJ/vgeQFFsgzAUWJ/O3GwNwmuZAWWMx87tQlpN5RvF5UnmXSg447hoCoY\n/PfyOC6vvGTM2xGy+iKzKc+Of7zwh1VzWd3+NxohHCxv+UQXwzAM46KmbCCnog5if0eSaYhPJ+O+\nvFXoqK2cLVmE5S5GWE0ARMXnkq4bmUZadUhnAdKeWx4/cwwVHkdFx5OuJkBaTVipddjemosunaHC\n15PHtBtHXC+EvGC2xhhJCIlX8f5zrvnABW9vp9ai49OUev8WL/9L5WvjJLBRvcksXHc5dmodwqol\nKr5AVHq5vMp+6px1uFIgMziZNyUzI6OTWO4SnMyby4uejgzCz3TdSWcxeuDr+L3/G8tbhbSbkc4s\nwCEa+iFh8UXc3DtI5TZQ3dBAJO8470zacpfh932ZUs+fY6fWI515SZ0MDxD720FksL3VePlfBpFB\nq17iYB+l3v8vGcumA7TqR1jVyZpWVj1ad6PjTrzKX0kWpDUMwzAmrSkTyGmt0XErkb+D2N8BegjL\nXYmTfQvSakKpLmJ/J5a3EoSNCg7i97+EVsXyZII0dupOtC6i445k2xXVC9hIpxnLnouTuQ9pzwGR\nQgX7ifzXKBa+j5f/RSx32fllUkMgXKLSNixv7fmFNq47O/MzaCL8vr8rz+i1k0kfIo2dfhOWtwoA\ny5l3GUHNpe0XKITArXgvsb8FFZ4g9LcmMx5lCmHVka7+HMIafc2+4ceQObyqz6DC14n8rUmgKauw\nnHl4lb9x3skBVvI3J30vWnUl2TdZZZa7MAzDmKKmROs+dPoPSVaUdrC8VbgVD43Ydw/AsvIjJwd4\na4BkRW4dtyGdJSNur7UGPTjmqueWdzOWdzNxeAS//x/L+xJWI2QVUlYni90W/yOZbaoGcMprrxnj\nSwiBm30Asg9M0PPb2KnbhxcD1TpGx52XtX2REOKSJm6MuI9MIeTYYwMNwzCMqWFKBHLpmj9A66Cc\nebi8gcnSqoZRNisWQoC4+DILljOfdPXvoqKT6LgHpXqJw8MApKp/JxlLFZ1AWpWXVS5jahLCQtgz\nJroYhmEYY9KxRh0pogdidCFGD8YwFCOqHawlGeTM85ff0ZFGD0TovghKChwJvkIHChyBcCSYuQ7X\nxZQI5ITMIpi4LZ6EzGG5S0f9m7RqwVs5ziUyDMMwjEun2nyiV/oBiHcOIjIWotZBZC3IJr/r0yH+\n37Ugqm3QoNoDRMZC+wqKMaLCRuQtSFkQKvAkwpXoSEGo4d6JfY1T1ZRYfqSrqwspJ8+sTyEErusS\nBAFT4O2/KlJKlFITXYwJZerDWaY+jG461xFTJy7NxeqIGoyIDhaI233inhDVG6K6Q/RQjKyyidt9\nvLtrKZXAr0vDrAzCAilBSIGUkKuReClBsKMfmZZYs9LoYozwZBLEyQv3iFVXn9/7ZVy9KZGR831/\nootwWRzHoaqqikKhcMOu9zNezBpRpj6cy9SH0U3nOnKpdULFmtAHJwXyIgHFVGTbNvlMBf3tvZR6\nAoJjPupAEd0RIDoDCBX9nkfBdShaFiXhUJIpAiFwu2KG0pUUXkrG7VbUKPRrgyiVbN+ttSaOoNCj\ncTxB1QyBP6QIiv00LpRU1EhsDxxPnL10wB/SBEOglEZruP+9JpC7HqZEIGcYhmFMHkFJ09eu8Icg\nGNLJAb+YHPj9YnLw94eSrFK+NsL2FE4KnJTATUFQgr52he0KpAXFfk3L6zFaQ+SD5YCbEnhZqJoh\nUQoK3Zo5Ky1ytYJCTxJYCAECkn8E2A5UNkgylQIhodCriYORZbdcqG6SnNob035EMWeFxcwl8poH\nj4M9iu5TCssWSBssFLIjQL/ci+yLKKUcUq5GDkRYAyGW1vQgiIEYQcF26El7lLJZnJU11K92sV1B\npYCqcqZNWkmmTVigFUgL8vVizLHmWmsGezS9bRovk3webQdjiv2aUgEiXxEGEPlJUO1mBKns2Yye\ncX2YQM4wDOMaCEqa3jZFaVAT+STZjPKPUrp8mdw2Vy3IVgniCGxX4GXAyyTZjOu5k4TWSdmKA5ru\nFsVQnyYKIQqSgMUf0vS0aoSEdF7gD2pUDNIGIc90s5UDoDOBgAWWnQRQXadU8pqcJJiyHYF1zu9a\nw2C3YqBLU9kg8LICLyNw08l7kKkSVM+UuOnk/dAa4h7BUG9IEElKg5rBboHjaGbMl0ShRilBXbPk\ntp93yFZJtNKEAYQlTWkQeloVsvx6jmyL6TqlyFULzrzNZ7YT1iSv4cjWkOJA8rqzVQL7DWt5hyXo\nblHUNktmLbXY8t2AnU8L1v+CS6Yy+Qwt++Kfo4o1Yalcb9o1rQdiMhUCYUHbvgh5ZIjmbEDKj3CD\nEDdWDNk2fU1ZWJHHLUZ0D0BcZVO/zqNytsesOfUMFrtROqIWmHON648QgooaQUXN2euqZ5gIbaJN\n+UBOK50MsgSwQPfHIEBU2SO+aDpU6IFyX3/20paFMAxjetNaU+zXbP1+yJGtMZUNknQ+6VqS1tmA\n50xG4swKR20HFUN9SbYlDM5moOIIvEySyfDSgnSFoKJWUNmgqGsY4HS7T7EQEwwlGY90BWSrJakc\n9J/WlAbKbd2ZTFP596AIA12KrhNJoJXKCaqbJLlqgeUmQZabT7JRN20UoGGoX5PKCSwb4qgclGqG\nA1KtkmBHKVBREuzd8qCD7UIcJj9RmASKyf+TADFT6VDdJLDspIRaadQJH3WkiJyXQs5JJTMfizHR\n5gHil/uosQUEGgIF8pwITIPI28hmDztTic5nEDLJ2rkpQbYKamefDTSaFl9e266j5LhAUaFLCl2M\nQYNs9hBpC0LNinU2e7dqfvwVn7CUfC5akwSAOnlfamZKamYm5Ti+O2aoL3nvHC/JalXUCJpusrCO\nFKg5OcDcIR+rOYV9cxbZ6CAaXESNQ4UlaByjrI5jk6uyKYYCNb1636e9KTHZ4fj39qNaAlAaLIGw\nBVgC1eoT7xg8e8NII/I2KI32FbLeRTS6iLQk2jaIsAW6qBDVNnJuClllQzqZdYMnEV5yiUqmZpOx\nsFZkkVXOZZV3MuyJNxrVHaJ7I7AF+ArZ6Cbv5xXSWuP5Nr4TIayr3LC+nGkoFTR+IUnzCwn5OkFF\n7Y11xhiFesQZ+2SqD6XBJOuESLphpJWMR8rWJIGH1preVk3rwZih/uRAm2RkRDlLk1x6WUEmL0jl\noLdN09ueBBhC2/jFkDhKgoc41MMZHkTSpRb5uhwkJJkkaSXZm1IheT4vK+jr0ARFXc72JH93UqBi\niILk0rKTx9RngpG4nDUrZ6DctMBNJQFZFGgGujRBKXmOM61mHCb1bNFtNmsfcHBTV1eP4+hsF2Mw\npBnqT5630COQ2gUrwPYUbirJ/BQHNIXeJICrqJWkK895/nIZtQY3lQxUr50tyFSO7/dBaw2RBjvp\nstORRneHqBMl4r1DxPuHEDkLa36K+GAR3RMmJ9MZC9nskX/PbHwnGvlYgHCS7JvuiVCHioTP9cJg\njLUsg7w5i7U4k7TZgOoKUcdLWIvTxAeKhD/qhjg5XmAl3ajaTwJFUe+ALdB9cTK+LGMlx4G0hLSV\ntP/HS0lg6STvt5zh4ry1Fjk7CfDiSBMFSd2IfE13S5IBVRE0r7DI1yWZPiHEcCAbvdCLOlzEeUcd\n1qLMZScUJkM7YvZavT6mRCB3+I9eQ87yEE7SSBBpiDWiysZeV4HInR9s6GKM6gjR7QG6P8JanUPW\nu8mX6ngJ1RKge0MoKXSgk/VwfJWcEWqQc1PowZh4TwF7XR57fR7R4CDspOHQWqP2FJDNqfOCncv5\nwmmlk7PBoRjVHqAOFREpCa5Ed4WQkoiMTBrt4Z9yP0HaQs50wZGICgtRbY899qGkiA8OofYPofuT\nRpNUOXhNSfRATLxzENngoiONcCWq1Uc2ecnj5qyzP/UussFNGjm33HgXY1RbgGrx0a0BqtVHtQZo\nmTRk/uwMx4I0IUl3zaCwKcaSsHw2b7uQrki6XWpmSqIAju+KKPRo/EISwEkrOYinssnYGK2gt02R\nq5E03SSHu1KEKHerCBg4rejr0MxdaTFjsYXjMfy3M7fvP63pOBLTeSzpF8tVS+QF2lgvC6msIA5J\nsi3FZKxNoUcz2KMIS5QzEwLHg2yVRWNzFjvtY7kKyz4b/LjppBsuKMFQX5L9OVO+M2f/YSkJXNsO\nxQz1JlmU2uZknI9fSL7ew11i5cxQHGk6jiZl8bLlA4rWZ7uZzrk8U5ZCryYsaaqbygfHOKmfcQSD\nPRrbOZOJEMxeZpGtSt7nKNTDGZozB7hSQTPUpykNavL1yWdqu+ClHTTR2ffATgKpODobkNjlgdR2\nOZOkFPgFjZdNPrDSoCbfkNSDYEjjlwOjsKSx7KSrT1pJFklzZqxQ+b0pjx2KIwiKSeCmVVKOirqk\nC/BsPUqCQecqg7dLMV4HaV1S6O4wCboqLHSg0b3ltcEkyQnX8A8QafTpED0Yo4vljNWQStYeG4qh\nEKOHyqk8JwmGdH+MqLKRTS5yaQZraRZZ8/+3d6+xcVz33ce/58zsldzlXbIoSrJUS7RFKraUprHr\n2nLiwI7sBPITJG1i2EETt6gAGynSAH0aBA/avqiLvmpRII3tAI7bGgGcoLEVRJYSp7FcI3YUV9aN\nkixbsq7UhaR42SX3MjtzzvNilivJokzZMTlL8f8BCC6Xt/8uh3t+8z9nZsKd4cmgpmIXwubVHuxg\nrcUOVggOTBAcnMCcKKM749hcEO64dyUwR0uoZpf4/+kIT5MREO7YBzZ8rXMVdqgSNgUyLvq6eLgj\n/36/11iC3+bwXxvDDHiQ0OiF4WugWhBHt7mQdLCFIHwuR33saOXC7ZyPaovhrs3grm9GJT/cjFA9\nBrnjQZ6+YBgNaKX42rJboy7pmnRNBLnTp09H9rvtuE/lF8ME7xSx5ythN29hHFs0mAEP3RIj8dhi\nlKupFAzjxyo035Bm8fULp/yHM8MVgj3j+Dtz2GEfyqYa1hxUq4uzMo0tG0zJYJtjtRdPW51Iqe6r\nhu8nAtSQh/UsasJHeQarFcpalKXWwcRREFiCjgS5thT5ikNgIKEtMW2J67ADczLWQFk7pDLh1EvM\nNTQVPVwvwPEMTjlAlwPcMQ8376NMWI11wt8ZNMXxmmKUG+MU0zFOnXcYGXFoTvl0FIt06VK4IDaw\nOLlK2C1pi0NHnKAzSb4pyfCIZvi0QVnL8lWQadXhVFQqDBt21MecLGEnDCrroHsb6D+pGO63Fw7J\nrwYUa8M1MJk2zdFdPsOnDZXyhc9D+L6xRbHges2ibAV3uEJ5wlwIzReeQmpZqQAAH5tJREFUdKyC\nIOGQT8Qp+E64LiheneKprolqbNUkG8KuUOGsj58LMKcq6HOG8rkJqJjauiqsxTOaYd+lP9NIoqX6\n3HMh1MSSilgiDHwLlmsybZpi3jJ43NTCjVIXQpe14W2loWOZJtFwIeyp6spvRTXE6vC2X50WSzep\ncG3RFIu6rbEUx8PvSzZSC+92IgBT3daC6hKG8QDy4UlGbd4PP3YUzso06Ztb8RL+7/Q/OR1rbTi4\nj4bpUCXDTku4g3Tlhd4faQ1lE3Z1JjtCrgo73a5CxRXENcFbE9jxAOfGBuILUnS0tTNw5AzeYAk7\nEoYnvTyFXpwId15j4f+xnQiAcK0VWoEG4uEf0571MMPVHdSSweZ87PkK9nwFU329Ua2x8OfkfWxc\nETQ50ORgbBh4Jt8qQUBJBQy1BkxkDJUUVJLgpaGYNpRTUExbSilLJW5JFKCh5JBsSZJ0HTQKF42D\nCm8rTYdKklExJqxPUjlkVJy2VAZT8sKvfc/fpmwDztsSo7ZMAoeUcomhSSgHtwixUxWc5hiqPYbS\nKjw5raM+9AxA3nqUbECHTk39dzUWO+ZjBzzMuUr4frgSPq9pJ1zSU3uLhe+b3PDv/zu62iBnraVM\nQMH6FPAp24CkcknjklIOCRxy1uOsLVC0ASUCytanaAPKBARYsipOs4oTx2HUliniU7GGCgYfQ8Ua\nxm2F07bAJ50FaKUw1vLI9bf9zo9TXO6aCHLf++YRUhlFKqsuea90uKfesujCHn9uKOwONLapS6ZB\nKmXLqQMB/YcCvEK4TqX5Oo0TD6eOatNIF00naSf8vrOHDX7Fkm6EjPZpqPi42pJb2ED2VwPYUkDg\nQ3rCw9eauAkoaxcv7WKzLqmiRzxfCTuKQHFRmuH2RsZdlwqaILjQIRgfDo8aMv5kxyLsYE21EDm8\nL+xOpTKKZCzsVFYC8H3wK4rAM+BZir4iltI0L9RkOxSxuMIrVo8gK4LxLW1LNG5cUcyHXaFKOeyG\nmWDykmbheB1LhN2QZCMEE4biiGG8GLa43HjYsXHjkO3Q9N7RiFcpXfY3tdbCeLWLd8YjeLuAebcY\nDlZ+2CGlwbn8BbnBwVmWRGUczGCF4OAE7iey4Z5u8+UdSXOmTPBOAXddFtU49Z6wteEet7flPM6N\n6YuSDhcWIinCwW8swJwohR3QjINemsT5vVRYa8oJp2cA7/nBcEoprXE6k2S62yilKgSOuXAonQKK\nhuDgBPZ8hcQjnZBxao/BBjYMQePVwBRTYVchpcPBYarAZS141cG+EGDGfOx5PwwGxclFUIThy1ow\noBocVNYJO7ExDWkddjKqne+wQ6sgqcOAUDbYsx7B4WI4PRTujofBsNodZrJ7m3GwDZpcuUz8bQ/3\nSBkyDs6qNM7iJGgwZ71w7Wp7DGdFKvzeaY4QtNZCPsAMeNihCmYwDCs272MGw0FONYfPkS2F3SRK\nJpxuS2n0siRuT2PYRSma8Lk67VWDafi82MDWbru/nyH2ufb37d6UbcCIVyK9s4TzizGCFk1Fhc8h\nvkX5FuWDLlucEgwutxSzls7DmthEuM2ZTBisKi0ak1Skj1TQQ0EYAn0LGmyDxgLqogCtKhYbUwQZ\nTXmBxktakokYOutSadGU2zTFVkW50eIpQ0q55KzHi95xsiqOj8FFE1OaOA5xNNfpNMt1ljadxEGF\ng7g1YZeTsN7JzRjC22UbMGiLlK3BYPCxGCyBtVQwDNgCE9anQbmUbEDeVpjAx7MBPmFH3EXjotFA\nGUObStCiEpQJKNqACgbPBnjVULFEN9Kk4gRYlukMTSqOjyWBJl4NLkkVhpec9bhJt9CkEwTWcNKM\nc8zkOWHynDDjFK2PRrHGbaORGAV8PuN20awvv9LBBxVYiyb89xuxZQLC57JSDUYVzCW3fQwOijHr\nccZMYJQinohTLpcx1WE9sJa89fAIACjagAI+MRRpFSONS1xpSjagiE/B+vgYUrh06gbSyq09P0lc\nkspBochZjzFbpoyhWcVJ4RJTmhjVt+p2cqPTTPKi6zzL1OrMuCaCXN8bpyjmLcV8uKakmAtv2wDi\naRjut4wNhGtwJo9CGh+2OLFw3Ug8BUMnDAuu1yzpCbsepfHwSKLAr16upLp25sJb9WguB677PYd4\nCor5cIFwMR9O46QyisYGS0eugG53af39FOnrYmjroIeS9O8coHiqxITrMmzi5M8GmJRDtkPT2BpO\n4zgutemgWDKc1ptcj3Qt+CDnDbM23NtVsWpYucJg7lvDeVtCo2gbd/G3DePvnwCvui5yQRyVcQgO\nFyHvo5enCA4XcW9pRC2Kh0Fn/MLUkDleggaHxFcXoRfGp/ydl9QZWOygh80HmHeLBMdL4XRTqTrN\nVDa4tzYR+1wbKqan3ZO21lLZeh5/+2g4MFfDIMVqmG10UA1h0KIUhg5bMOFZ2Vvc8PEUqmGvYMAh\nXPeT1uFC8bYYpWZFLu2HL8baIa41MeXgaI0Z9ynlPMpuGPrdog0D9GQXyVFYLyAoGbyExU9CpVmT\nW+1iUuFQrpkcykGjyKoYDSrGqC2z2TtGmQCLxTeWtlOw9IjLwgGXWKAZvi4gUdF0DLhcd0ITK0Il\nAV7KUkpa/KTCSTm4KQenonCGfJJDFhuDUrvGa9dU2sOwcq7RY7zNEmuKVU/TYNEX1ecGECsqFr4D\nre9YKo1QSlvKCcvodZZCBgJt8R1LoC2BCoPs723xWXhMc2a5oRQ3Ybgl7EzqaqCJj8PifpcziwNe\n+3SJ0SXQrpMkcHEJv85FE0eTJUZGxzHAOVPgnClS1IbABmgLTvW5nLBhR0UrhTbh79H6wu/U1Wdd\nB4q4B35KkVQuKRzO2gJlG1QH4DCcTQ7GBetjsGyML6dTR3fVHLj0NSKwFr8aYgyWBmLo9+mgejbg\nmMlTsGGX96jJMWF9XBQeASVrKBNQtgGNKkaDcnkrGEGh8DF0qBTLnSxLdSNLdYZ2laRMwEuVkzho\nLJYd/jnWOG10O8206STNKkEDl+40BtawOxjiYDBKyfo4SoWBuBp4hm2Jg8Fobb+wodpZVKhaMJoM\nSW71tovCVL92sW4g5cbJZpvI5cYI/KC2/WVVnHgYrUlVO2+uuvIOh2/DgDgTnWkJcjPjmghyVzu1\nagKLdianpsJD0/PnwymoBcsdEunZCUf1uJYhKqlUiolCgbz1iCmHEVtizHrEcYihiavwxeytYISz\npkCDitGukzQSC/e8CfCqe6ieDThtJ3gnGKt1ESrW0KFTNCgXtwCZIUVmENI5xfj1mpGlioLj4w4E\nLD/k0j7o4MUthbRhPG0YTwUUOjXJBUkMliaVoFOlcZS+JABcfNtRisBaxm2FtHJpUnEK+ExYn4Kt\nkLcVjpgxTpkJKtbQrBMsSjWhvAvT0ZPiyqFFJQiwVKwhFoRTlmUCcilDSYfTHgXrM2CLtWmPpX4D\n143EcIcNE/GAXMpnJOVzPuVhY4pmnaBZxcO9blx+UTnJdTpN2QYUCShZn1J1Lx6gUcXIqnitqxLO\nLNuwcYcljkOqutceu+i5gYuXb9pqs88yZj0mbIUULnfEFvEJZwFKqdqgbasdGu+i7krB+ozbCk4A\ncU+RKEKspCgXPYYLRYqFCr5jocOl3K7xUmFQm9wOABboFDE0eVup1WmqXaGwARneztsKY9YjoRxS\nF3UkYurCVKBTDV8OigWkWDjkMnF8gpivSMZcjLUEWAJjCLA0NCaJLU7W1oN9EPP5NWO2TxLtW0OJ\nABd1STfpSkZMmT3BEEdMjlFTZrjaTWuudgo1ihMmzyLdwO+7HaRxCbCXbNsNyqXXacNBYbFX9Xvf\nay5sIxLkZsa8CnIfVGBtbfABarcmP46jP9Rey0f1D+dbw4St4GNre3Yx9GV7qKOmTAGfNpWsvlBc\nGFQvHozd6h5fpbq360wOVtXBbtiWMVhiaIoETNgKXnWPPo5DQoV79eH7qWu5WN567FIjvFo8Rcn6\n+FhaVBgwJgfxig33vlfoLMucDBPWZ8gUmcAPOwjV6ZHJbkK7SrLaaSVVfSEcM2UGbak2JRLOWoYv\nluO2goHa2hAPQ856xNCkcEmocADP2wqDpoiDYtiWOWcKBNUB31wUTmq3rUUrRSMxJqiQsx5pwr39\n8C3GMp3hep0hrhwmdEAlk2BgbBg/uHR9WMkGDNtybQ88wBJTYX3J2rSQSxKHBTpFHM2wLXPKjDNu\nfRJKk8YlXe02pJVLYC2j1mPUljlh8gyYIhviy1j8ns7LZJhyp/k7fpTkyg5TmwuD9EyZi9tEyfqM\nWo9hW8K3liW6kZaPYPr1/cyFbUSC3My4Js4jZ6xlwBbxMdVOQNgNmFxM26TiUwauyYFq3FY4bMYY\nsWU8azhixhgwRcoE6NoKj0mqdlujWKwbWOO00qaS4ZruakQqEzBkSpQJwsCFTwxNVsVpN2mWjXu4\nQZlG6+BVF5FmVZyR6iB82hQwWNzqY4hV1zH0mwlK1e5OGBDCNvnFa0Imp2fiyql1UBpUjGFbCtfO\nVB/FxVMvoAiq4SlG2KkIw4rBVL+uWcVx0XgEpAhDQVI5VGz4feXq4/Bs2CmrYFio0nTpBioYSrWF\nswElfEo2YF1iIQ/FV7FUN85IK79JJ2jid38BvcmZuUvLtLkxOho7GCwmPpIX4A6VuuJibAAUNJMA\nMtxC+5W/rDr9I4T4YJLK5Trlch3pqEsR88A1EeT+X/G3NFQXZZqLOiWGMMS4StGhUrUQEa6NCN9r\nNGnlsEJn6VApEsrh/tiy2pTT+4WLCVvhuMmzzx/mHTsWrlOphsgYDu06STMJHK1oqE4Fjtkyg6bI\nsfwxzhbHGDFlEirsXuWsR5OKs0Q30qkbiKHDaaFqZ6pRxbg7tpg0Lknl0qqSlx3FZWpTUmGgCjC0\nq9RVd1SstZc9ZmvDTtMH7coE1nDKTHDOFmodu+RF01QNyqUp3Tjn9raFEEKIenFNBLn/m1pLVk29\nCN1ay6AtMmK92tE3iWqoSKBx3mfR53QaVIzVTiurndbpv/giM9kC10qFjw/n4jbiVZsquCqlPsyP\nwlGaZU6GZWQ+xHcLIYQQYjrXRJC7UoiDMIQsUGkWSItbCCGEENeY+rp2kRBCCCGEuGoS5IQQQggh\n5igJckIIIYQQc5QEOSGEEEKIOUqCnBBCCCHEHCVBTgghhBBijpIgJ4QQQggxR0mQE0IIIYSYo+r6\nhMDvvPMO27Ztw1rLunXr+KM/+qOoSxJCCCGEqBt125EzxvDiiy/y0EMP8eijj7Jv3z4GBwejLksI\nIYQQom7UbZDr7++ntbWVlpYWHMeht7eXt956K+qyhBBCCCHqRt0GuVwuR1NTU+3jbDZLPp+PsCIh\nhBBCiPpSt2vklFJT3p/L5RgfH7/kPs/zaGhomI2yPhKu617yfj5zHIdYLBZ1GZGS7eEC2R6mNp+3\nEdkmrs583kbmu7r9i2cyGcbGxmof53I5stksO3fu5JVXXrnka9evX8+nPvWp2S7xQ8vlcrz88st8\n/OMfp6WlJepyRMRkexDTkW1ETEe2kfmrboNcZ2cnw8PDjIyMkMlk6Ovr44tf/CKJRILu7u5Lvrax\nsTGiKj+c8fFxXnnlFbq7u8lms1GXIyIm24OYjmwjYjqyjcxfdRvkHMfhvvvu49lnn8UYw7p16+jo\n6ACQjVQIIYQQgjoOcgArV65k5cqVUZchhBBCCFGX6vaoVSGEEEII8f4kyEWgsbGR9evXz7m1fWJm\nyPYgpiPbiJiObCPzl7LW2qiLEEIIIYQQH5x05IQQok4888wz/O///i+FQiHqUoQQc4R05GbQ448/\nfsnHlUqldmLLSqXC3/7t30ZRlojIe7eHi3mex9/93d/NXjGiLh0+fJi33nqLPXv2cP3117NmzRq6\nu7tJJBJRlybqxJVeR4IgIAgCeR2ZhyTIzaInnniCTZs2XXZbzE/nzp2jr6+Pd955hwULFvCFL3wh\n6pJEnfjnf/5nNm7cyP79+zl8+DBdXV309vZy0003RV2aqDPlcpk33niDnTt3cuONN3LvvfdGXZKY\nZXV9+hEhrjW5XI5du3Zx6NAhmpub6e3t5c4775RLEIlLKKVYsWIFK1asYHR0lBdffJHnnntOui2i\nplgs8pvf/IY9e/awZs0a/vzP/5x0Oh11WSICEuRmkVw2RRw8eJDf/OY33HPPPaxZs0auiyimZIxh\nx44d7N+/n0KhQE9PD/fcc0/UZYk6MD4+zuuvv87+/ftZu3YtmzZtIplMRl2WiJBMrc4w3/c5f/48\nAO3t7TiOA8Do6CjNzc1RliYiMjAwwL59+2pTqmvWrGHFihW1bUPMXzt27ODAgQOMjo7S09NDb28v\nnZ2dUZcl6sg//MM/kE6nWbt2LfF4/JLPWWu5/fbbI6pMREWC3Aw6e/YsP/rRj0in0wwODtLR0cF9\n990nL8zz1LFjx5j8d1NKAXDq1Cn6+vrI5XL89V//dZTliTqwZcsWent7WbZsWdSliDq1ffv29/38\nXXfdNSt1iPohQW4G/eAHP+Azn/kMS5Ys4YknnuCrX/0qzz33HF/72teiLk1E4Ic//OH7fv7BBx+c\npUpEPRsbG2N0dBRjTO2+5cuXR1iREKKeyQKdGVQqlViyZEnt43Q6jed5EVYkoiRBTUznpZdeYv/+\n/XR0dNS6tiBBTlwwOjrK1q1bOXnyJABdXV1s2LBB1mDPYxLkZpAxhiAIcBwHYwx9fX1yVNE8dvLk\nSX7961+TSCT49Kc/TTKZZHBwkK6urqhLE3Xi4MGDPPbYY3IQjLiizZs3c8stt/DHf/zHKKXYt28f\nmzdv5k//9E+jLk1ERK7sMINuu+222oEOmUyGI0eO8MADD0RclYjKCy+8wMc+9jE6OzvZunUrsViM\nbdu2RV2WqCMtLS1UKpWoyxB1rFAocPPNN+M4Dlprbr75ZorFYtRliQjJbt8MWrduXe32ww8/HGEl\noh7EYjFWr14NwM6dO9Fa4/t+xFWJehKPx3nyySdZsWJFrStnreX++++PuDJRLxoaGti1axcf+9jH\nANi7d6/M9MxzEuRm0DPPPIO1trbW5b235aCH+WXVqlW8/PLLrF27FoAjR47IiYDFJbq7u+nu7o66\nDFHHNm7cyLZt23jppZcAWLp0qcz0zHNy1OoM2rZtG77v09PTA0BfXx+u63LzzTcDyGlI5pl/+Zd/\nqd12XZeOjg7uvvtu2tvbI6xK1JtKpcL58+ex1tLe3i5hX0yrVCrJSYHnMQlyM2iq66nKNVaFEO/1\n+uuv8/GPf5yjR4+yZcuW2hGIIyMjfO5zn2PVqlURVyjqTblc5u2336avr4+zZ8/yzW9+M+qSRERk\nanUGGWM4fvx47eSex48fv+TcUGJ+2L17N7fccssl93mex4EDB9i1a5dMsQv27NnDbbfdxksvvcTX\nv/712lVfRkdHefbZZyXIiZoDBw7Q19fH6dOnueGGG7j11lvl9DTznAS5GbRx40Z++tOfUiqVAEgk\nEmzcuDHiqsRs+9WvfsWKFSvIZrOcPn2aN998k3fffZdVq1axYcOGqMsTdSAIAiA8IObiS/c1NzfL\n1Kq4xI9//GPWrFnDX/zFX5BKpaIuR9QBmVqdBeVyGWutrGGYpw4cOMB///d/o5SiVCqxYcMGbrzx\nRrm2qqj5yU9+QiKRwFpLuVymp6endo6wdDrNfffdF3WJok4cP36cvr4+3n77bRYtWkRvby+rVq26\n7LqrYv6QICfELDl8+DC7d++mv7+f1atXs3btWjnQQQDhAQ47d+6kv7+fcrl82eflqiDivay1HD16\nlL6+Pg4fPsxf/dVfRV2SiIgEOSFmWalUYu/evezevRvHcXjkkUeiLkkIMYcZY9Bazu8/X0mQEyJC\n586dY+HChVGXIYQQYo6SIDfDBgYGOHToEPl8Hggv1dXd3c2CBQsirkwIIYQQc50EuRn0P//zP+zf\nv5/e3l6ampoAyOVy7Nu3j97eXu64446IKxRCCCHEXCanH5lBu3bt4tFHH61dM3HSbbfdxne/+10J\nckKIy0gXX7wf3/d58803p9xG1q1bd9l4I6598hefQVpr8vl87Sztk3K5XO2aq2J+kUFavJ+Lu/hd\nXV1A+HrxX//1X9LFF0B4qpp0Os1dd911yUzP7t27ef755/nSl74UcYVitkmQm0EbNmzgP/7jP2ht\nbSWbzQLhP9zw8DD3339/xNWJ2SaDtJiOdPHFdM6ePcs3vvGNS+7LZrN0dXXxr//6rxFVJaIkQW4G\n3XDDDXzjG9+gv7+fXC4HhB2Yrq4u6cjNQzJIi+lIF19MJ5VKsW/fPlavXl07qXgQBBw8eFCu9DBP\nSZCbYUqpWvdFzG8ySIvpSBdfTOeLX/wiv/zlL9m6dSuJRAIIrx60fPlymVadp+So1Rn0i1/8gnvu\nueey+0+dOsWhQ4e4++67I6hKROXw4cNs2bLlioP0DTfcEHGFoh5Ya6WLL65KoVAAIJ1OR1yJiJJ0\n5GZQX1/flEGuqamJPXv2SJCbZ2SqXVwN6eKLqyUBToAEuRmVz+d5/PHHoy5D1BEZpMX7kS6+mM7T\nTz/N17/+9cvuf/fdd/n1r3/Nww8/HEFVIkoS5GZQJpORCxmLGhmkxXSkiy+mMzQ0xNGjR1FKcfHK\nqEqlQn9/f4SViahIkJtB69ati7oEUUdkkBbTkS6+mI7nebz++utTfm7p0qWzXI2oBxLkZtBdd90V\ndQmijsggLaYjXXwxnXQ6zYMPPhh1GaKOSJATYpbIIC2mI118MZ2NGzdGXYKoM3L6ESFmyfbt26VL\nK4QQ4iMlQU4IIYQQYo7SURdwLZu8MPp7TUxMMDg4OMvVCCGEEOJaI0FuBn3/+9+f8v7R0VE2b948\ny9UIIYQQ4lojBzvMoHK5zMsvv3zZWfuDIODMmTMRVSWEqFf5fJ5MJnPZ/RMTExQKBTo6OiKoStST\ngwcPctNNN112/+DgIAMDA/T09ERQlYiSdORmkFKKRCJBPB6/5C2VSsk5w+YhmWoX05EuvpjO1q1b\np7xfKcX27dtntxhRF6QjN4Pi8Th/+Id/GHUZok58//vfn/L0I6Ojo2zdupU/+7M/i6AqUU+kiy+m\nUygUeOaZZ6b83NDQ0OwWI+qCBLkZ9Oijj0ZdgqgjMkiL6Ux28aciXXwBkEgkprxCjJi/JMjNoMkX\n5F/+8pcsWbKE7u7uiCsSUZJBWkxHuvhiOosWLaKzszPqMkQdkSA3C/bu3Uu5XGbPnj3ce++9NDU1\nRV2SiIAM0mI60sUX03nooYeiLkHUGTnYYRZorbn//vu54447eOGFF3jttdcwxkRdlphlMkiL6Vzc\nxT906FDE1Yh69vTTT/Pzn/+ccrkcdSkiYhLkZtCxY8c4duwYvu9z9OhRyuUyd955J2fOnOGpp56K\nujwxy2SQFldr7969HD58mB/96EeMjY1FXY6oQ7lcjq6uLv7zP/+TgwcPRl2OiJBMrc6g1157DQgX\nub/++uuXfC6bzUZRkqgDMtUupjPZxT9z5gwvvPACK1eu5NZbb0Vr2fcWF/T09LBy5Uq2b9/O3r17\nuffee2lubo66LDHLJMjNoAcffBCAn/zkJ3zhC1+IuBpRL2SQFldy7NgxgFoXXynFnXfeyZtvvslT\nTz3Fpk2boi1QRG7yXHGlUql2FHw8HkdrzXe/+12+853vRFugmHUS5GaBhDgBMkiL6UkXX0wnHo8D\nlx8Fv3jxYhYvXhxVWSJCEuSEmCUySIvpSBdfTGfyyHfXdfmDP/iDiKsR9UBZa23URQgxn8ggLYQQ\n4qMiQU4IIYQQYo6S1dVCCCGEEHOUBDkhhBBCiDlKgpwQQgghxBwlQU4IIYQQYo6SICeEmFHXX389\n6XSabDZLS0sLt99+O08++SRXc5zVsWPH0FrLtYmFEOIKJMgJIWaUUoqf/exn5HI5Tpw4wd/8zd/w\nT//0TzzyyCNX/TPk4HohhJiaBDkhxKzJZDJ8/vOf57nnnuPf//3f2b9/P1u2bGHt2rU0NTWxdOlS\n/v7v/7729XfeeScAzc3NZDIZduzYAcDTTz/N6tWraW1t5bOf/SwnTpyI5PEIIUTUJMgJIWbdJz7x\nCbq6unj11VdpbGzk2WefZWxsjC1btvC9732PzZs3A/Dqq68CMDY2Rj6f55Of/CSbN2/mH//xH3n+\n+ecZGhrijjvu4Ctf+UqUD0cIISIjQU4IEYnOzk5GRkZYv349PT09AKxZs4Yvf/nLvPLKK8DUU6pP\nPPEE3/72t+nu7kZrzbe//W12797NyZMnZ7V+IYSoBxLkhBCR6O/vp7W1lR07dvCpT32KBQsW0Nzc\nzJNPPsn58+ev+H3Hjx/nL//yL2lpaaGlpYW2trbazxNCiPlGgpwQYta98cYb9Pf3c/vtt/Pggw/y\nwAMPcOrUKUZHR9m0aVPtKFWl1GXfu3TpUp566ilGRkZqbxMTE9x6662z/TCEECJyEuSEEDNucoo0\nl8vxs5/9jK985Ss8/PDD9Pb2Mj4+TktLC/F4nN/+9rf88Ic/rAW4jo4OtNYcOXKk9rM2bdrE448/\nzoEDB4Bw/dyPf/zj2X9QQghRB9yoCxBCXPs+//nP47ouWmt6enr41re+xaZNmwD4t3/7N771rW/x\n2GOPsX79ev7kT/6E0dFRANLpNN/5zne4/fbbqVQq/PznP+eBBx5gfHycL3/5yxw/fpympibuuece\nvvSlL0X5EIUQIhLKygmahBBCCCHmJJlaFUIIIYSYoyTICSGEEELMURLkhBBCCCHmKAlyQgghhBBz\nlAQ5IYQQQog5SoKcEEIIIcQcJUFOCCGEEGKOkiAnhBBCCDFHSZATQgghhJij/j8gqZ9p5j6yOgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f81ad614588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from ggplot import *\n",
    "\n",
    "stock_plot = ggplot(df_long, aes(x='Date', y='Price', color='Stock')) + \\\n",
    "        geom_line() + \\\n",
    "        ylab('Price (US$)') + \\\n",
    "        ggtitle('Auto Stocks: Adj Close') + \\\n",
    "        scale_x_date(labels='%b %d %y',breaks=date_breaks('3 months') )\n",
    "    \n",
    "# Have to use MATPLOTLIB api to rotate x tick labels.  Hopefully some day, ggplot will implement a rotation option.\n",
    "fig = stock_plot.draw()\n",
    "ax = fig.gca()\n",
    "labels = ax.get_xticklabels() \n",
    "for label in labels: \n",
    "    label.set_rotation(270)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### [ggplot](http://blog.yhathq.com/posts/ggplot-for-python.html) is AWESOME!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='pivot'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to convert data in long format to wide format using pd.pivot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Stock</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>FB</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>LNKD</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>YHOO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></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>2014-01-02</th>\n",
       "      <td> 397.97</td>\n",
       "      <td> 54.71</td>\n",
       "      <td> 557.12</td>\n",
       "      <td> 27.17</td>\n",
       "      <td> 207.64</td>\n",
       "      <td> 35.91</td>\n",
       "      <td> 39.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td> 396.44</td>\n",
       "      <td> 54.56</td>\n",
       "      <td> 553.05</td>\n",
       "      <td> 27.84</td>\n",
       "      <td> 207.42</td>\n",
       "      <td> 35.67</td>\n",
       "      <td> 40.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td> 393.63</td>\n",
       "      <td> 57.20</td>\n",
       "      <td> 559.22</td>\n",
       "      <td> 27.79</td>\n",
       "      <td> 203.92</td>\n",
       "      <td> 34.92</td>\n",
       "      <td> 39.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td> 398.03</td>\n",
       "      <td> 57.92</td>\n",
       "      <td> 570.00</td>\n",
       "      <td> 27.68</td>\n",
       "      <td> 209.64</td>\n",
       "      <td> 35.19</td>\n",
       "      <td> 40.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td> 401.92</td>\n",
       "      <td> 58.23</td>\n",
       "      <td> 571.19</td>\n",
       "      <td> 26.96</td>\n",
       "      <td> 209.06</td>\n",
       "      <td> 34.56</td>\n",
       "      <td> 41.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Stock         AMZN     FB   GOOGL    HPQ    LNKD   MSFT   YHOO\n",
       "Date                                                          \n",
       "2014-01-02  397.97  54.71  557.12  27.17  207.64  35.91  39.59\n",
       "2014-01-03  396.44  54.56  553.05  27.84  207.42  35.67  40.12\n",
       "2014-01-06  393.63  57.20  559.22  27.79  203.92  34.92  39.93\n",
       "2014-01-07  398.03  57.92  570.00  27.68  209.64  35.19  40.92\n",
       "2014-01-08  401.92  58.23  571.19  26.96  209.06  34.56  41.02"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted = df_long.pivot(index='Date', columns='Stock', values='Price')\n",
    "pivoted.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='category'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using category data type to control sort order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>medal</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bronze</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>silver</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>silver</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gold</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bronze</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bronze</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gold</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gold</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gold</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    medal  ounces\n",
       "0  bronze     4.0\n",
       "1  silver     3.0\n",
       "2  silver    12.0\n",
       "3    gold     6.0\n",
       "4  bronze     7.5\n",
       "5  bronze     8.0\n",
       "6    gold     3.0\n",
       "7    gold     5.0\n",
       "8    gold     6.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.DataFrame({'medal': ['bronze', 'silver', 'silver', 'gold','bronze', 'bronze', 'gold', 'gold','gold'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medal     category\n",
       "ounces     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"medal\"] = data[\"medal\"].astype(\"category\")\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>medal</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bronze</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bronze</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bronze</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gold</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gold</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gold</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gold</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>silver</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>silver</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    medal  ounces\n",
       "0  bronze     4.0\n",
       "4  bronze     7.5\n",
       "5  bronze     8.0\n",
       "3    gold     6.0\n",
       "6    gold     3.0\n",
       "7    gold     5.0\n",
       "8    gold     6.0\n",
       "1  silver     3.0\n",
       "2  silver    12.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=[\"medal\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now define the order of the cetgorical data using set_categories()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)] [[back to section](#category)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "data[\"medal\"].cat.set_categories([ \"gold\",\"silver\",\"bronze\"],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": 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>medal</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gold</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gold</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gold</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gold</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>silver</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>silver</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>bronze</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bronze</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bronze</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    medal  ounces\n",
       "3    gold     6.0\n",
       "6    gold     3.0\n",
       "7    gold     5.0\n",
       "8    gold     6.0\n",
       "1  silver     3.0\n",
       "2  silver    12.0\n",
       "0  bronze     4.0\n",
       "4  bronze     7.5\n",
       "5  bronze     8.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by=[\"medal\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='merge'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging 2 data frames using merge()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> a</td>\n",
       "      <td>  4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> a</td>\n",
       "      <td>  3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> a</td>\n",
       "      <td> 12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> b</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> b</td>\n",
       "      <td>  7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> b</td>\n",
       "      <td>  8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> c</td>\n",
       "      <td>  3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> c</td>\n",
       "      <td>  5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> c</td>\n",
       "      <td>  6.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  ounces\n",
       "0     a     4.0\n",
       "1     a     3.0\n",
       "2     a    12.0\n",
       "3     b     6.0\n",
       "4     b     7.5\n",
       "5     b     8.0\n",
       "6     c     3.0\n",
       "7     c     5.0\n",
       "8     c     6.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "left = pd.DataFrame({'group': ['a', 'a', 'a', 'b','b', 'b', 'c', 'c','c'],\n",
    "                 'ounces': [4, 3, 12, 6, 7.5, 8, 3, 5, 6]})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> a</td>\n",
       "      <td>   alpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> b</td>\n",
       "      <td>    beta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> c</td>\n",
       "      <td> charlie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  label    value\n",
       "0     a    alpha\n",
       "1     b     beta\n",
       "2     c  charlie"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'label': ['a','b','c'],\n",
    "                     'value': ['alpha','beta','charlie']})\n",
    "right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By default, merge() does an inner join, but you can specify 'left', 'right', or 'outer' joins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>group</th>\n",
       "      <th>ounces</th>\n",
       "      <th>label</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> a</td>\n",
       "      <td>  4.0</td>\n",
       "      <td> a</td>\n",
       "      <td>   alpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> a</td>\n",
       "      <td>  3.0</td>\n",
       "      <td> a</td>\n",
       "      <td>   alpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> a</td>\n",
       "      <td> 12.0</td>\n",
       "      <td> a</td>\n",
       "      <td>   alpha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> b</td>\n",
       "      <td>  6.0</td>\n",
       "      <td> b</td>\n",
       "      <td>    beta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> b</td>\n",
       "      <td>  7.5</td>\n",
       "      <td> b</td>\n",
       "      <td>    beta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td> b</td>\n",
       "      <td>  8.0</td>\n",
       "      <td> b</td>\n",
       "      <td>    beta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td> c</td>\n",
       "      <td>  3.0</td>\n",
       "      <td> c</td>\n",
       "      <td> charlie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td> c</td>\n",
       "      <td>  5.0</td>\n",
       "      <td> c</td>\n",
       "      <td> charlie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td> c</td>\n",
       "      <td>  6.0</td>\n",
       "      <td> c</td>\n",
       "      <td> charlie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  group  ounces label    value\n",
       "0     a     4.0     a    alpha\n",
       "1     a     3.0     a    alpha\n",
       "2     a    12.0     a    alpha\n",
       "3     b     6.0     b     beta\n",
       "4     b     7.5     b     beta\n",
       "5     b     8.0     b     beta\n",
       "6     c     3.0     c  charlie\n",
       "7     c     5.0     c  charlie\n",
       "8     c     6.0     c  charlie"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inner_joined = pd.merge(left, right, how='inner', left_on='group', right_on='label')\n",
    "inner_joined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** To merge 2 Series, you have to use [pd.concat()](http://stackoverflow.com/questions/18062135/combining-two-series-into-a-dataframe-in-pandas) instead"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For more on merge, join, and concatenate, see the official [docs](http://pandas.pydata.org/pandas-docs/stable/merging.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"missing\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding rows of a data frame containing missing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> 0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td> 0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td> 0</td>\n",
       "      <td>  1</td>\n",
       "      <td>  2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2\n",
       "0  0   1   2\n",
       "1  0 NaN   0\n",
       "2  0   0 NaN\n",
       "3  0   1   2\n",
       "4  0   1   2"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "df = pd.DataFrame([range(3), [0, np.NaN, 0], [0, 0, np.NaN], range(3), range(3)])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().any(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> 0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>  0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> 0</td>\n",
       "      <td>  0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0   1   2\n",
       "1  0 NaN   0\n",
       "2  0   0 NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='convert_type'>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting a data type of a column in a data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td> a</td>\n",
       "      <td> 1.2</td>\n",
       "      <td>  4.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td> b</td>\n",
       "      <td>  70</td>\n",
       "      <td> 0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td> x</td>\n",
       "      <td>   5</td>\n",
       "      <td>    0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  one  two three\n",
       "0   a  1.2   4.2\n",
       "1   b   70  0.03\n",
       "2   x    5     0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "a = [['a', '1.2', '4.2'], ['b', '70', '0.03'], ['x', '5', '0']]\n",
    "df = pd.DataFrame(a, columns=['one', 'two', 'three'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one      object\n",
       "two      object\n",
       "three    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "df[['two', 'three']] = df[['two', 'three']].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one       object\n",
       "two      float64\n",
       "three    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"matplotlib\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting data frames using MATPLOTLIB version 1.5 and up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Starting with version 1.5, you can now pass a \"data\" parameter inside MATPLOTLIB's plot() function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prior to version 1.5:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEACAYAAACatzzfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09eb0b2978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.DataFrame({\"var1\":[1,2,3,4,5,6], \"var2\":[1,2,3,4,5,6]})\n",
    "plt.plot(df[\"var1\"], df[\"var2\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With version 1.5+"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWoAAAEACAYAAACatzzfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f09d9ee8940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(\"var1\", \"var2\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"subplots\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting data frame directly and creating sub-plots or grouping using column=, by= parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top]](#top)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**When making histograms, pandas knows to make sub-plots based on the by=column option:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfIAAAFiCAYAAAAN91qTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPwMAwFsR5AAAGIwgBwDAYAQ5AAAG+z8J8qd/oxPcpQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff5084679e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_excel('/home/pybokeh/Downloads/temp.xlsx', sheetname='Claims')\n",
    "df.hist(column='DTF', by='GRADE_SHORT')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**For box plots, instead of making sub-plots, pandas intelligently splits the data using the by= parameter\"**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfgAAAF5CAYAAABzxDgCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff505ae2d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='DTF', by='GRADE_SHORT')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"bokeh\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a Bokeh Chart from a Data Frame - see the [docs](http://bokeh.pydata.org/en/latest/docs/reference/charts.html) for more examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "conda install bokeh or pip instal bokeh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-banner\">\n",
       "        <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"37c43c6f-467e-4acd-a87d-c74e36144275\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(global) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "    window._bokeh_onload_callbacks = [];\n",
       "  }\n",
       "\n",
       "  function run_callbacks() {\n",
       "    window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    delete window._bokeh_onload_callbacks\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    window._bokeh_onload_callbacks.push(callback);\n",
       "    if (window._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    window._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        window._bokeh_is_loading--;\n",
       "        if (window._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };\n",
       "\n",
       "  var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      Bokeh.$(\"#37c43c6f-467e-4acd-a87d-c74e36144275\").text(\"BokehJS successfully loaded\");\n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    for (var i = 0; i < inline_js.length; i++) {\n",
       "      inline_js[i](window.Bokeh);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  if (window._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(this));"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"plotdiv\" id=\"6a3bab8a-bf08-44b6-9efe-a37c1d6923f6\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"6a3bab8a-bf08-44b6-9efe-a37c1d6923f6\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '6a3bab8a-bf08-44b6-9efe-a37c1d6923f6' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        Bokeh.$(function() {\n",
       "            var docs_json = {\"54f906a7-0a5a-40c0-8d25-503cc40b925e\":{\"roots\":{\"references\":[{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"f69724d4-38cd-419b-851c-8c290328edf9\",\"type\":\"Segment\"},{\"attributes\":{\"data_source\":{\"id\":\"66e782f2-2357-416c-9473-0a2927a0f70c\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"f69724d4-38cd-419b-851c-8c290328edf9\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"77de1789-8361-4037-95f0-0c0aefe52488\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"4c901a8c-8b59-4349-828d-798188bd43a2\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":6}],\"color\":[\"#df5320\"],\"cyl\":[6],\"fill_alpha\":[0.8],\"height\":[2.0],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"6\"],\"y\":[20.0]}},\"id\":\"cb870202-6622-485b-93e3-7e88721c77af\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":8}],\"color\":[\"DimGrey\"],\"cyl\":[8],\"fill_alpha\":[0.8],\"height\":[2.0],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"8\"],\"y\":[15.0]}},\"id\":\"0697a673-c48f-4fa5-8dfd-c9da5e5a463b\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":8}],\"color\":[\"#00ad9c\"],\"cyl\":[8],\"fill_alpha\":[0.8],\"height\":[2.0],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"8\"],\"y\":[15.0]}},\"id\":\"6e320b7e-0ed5-464c-adb3-dcb0ba7c56d3\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"f2e2aa69-91b9-4905-a7b3-bfefee0a4337\",\"type\":\"Rect\"},{\"attributes\":{},\"id\":\"ee0e6786-8884-4301-a9a5-d09242af8737\",\"type\":\"ToolEvents\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":4},{\"cyl\":4},{\"cyl\":4},{\"cyl\":4}],\"cyl\":[4,4,4,4],\"x0s\":[\"4:0.5\",\"4:0.65\",\"4:0.5\",\"4:0.65\"],\"x1s\":[\"4:0.5\",\"4:0.35\",\"4:0.5\",\"4:0.35\"],\"y0s\":[18.0,18.0,32.95,44.87500000000001],\"y1s\":[25.0,18.0,44.87500000000001,44.87500000000001]}},\"id\":\"50b5df1b-3706-48bc-8ffa-72ee5f219caf\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":4}],\"color\":[\"#5ab738\"],\"cyl\":[4],\"fill_alpha\":[0.8],\"height\":[4.550000000000004],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"4\"],\"y\":[30.675]}},\"id\":\"19c2c850-a954-44d1-9706-4088ac750cd2\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"b788ad0e-78be-47bf-9d17-8ef680854abe\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.7},\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":5},\"x\":{\"field\":\"x_values\"},\"y\":{\"field\":\"y_values\"}},\"id\":\"ab4a2e7a-0de9-47fc-b38d-817c67950446\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"f0d221e8-5080-4a84-9ae3-01cb59f9eea1\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"c0bce6c7-e423-434e-bdae-2f0236933b4c\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"487d8378-02aa-4e90-9494-b2d569da9eee\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"2a65ad57-3c7d-4ad0-8552-84bdca7f4721\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6035d187-53d7-4496-916d-d383861a80ff\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"290573a4-df81-4dec-a185-de6e79a7914a\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"cb870202-6622-485b-93e3-7e88721c77af\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"f2e2aa69-91b9-4905-a7b3-bfefee0a4337\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"6e77d140-cd53-45a2-8119-468fa1f5b6d2\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"overlay\":{\"id\":\"5503ad4b-0339-4a4b-9a4a-42d7de827189\",\"type\":\"BoxAnnotation\"},\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"9116968d-e0f1-4902-af1d-bdf449a33da0\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"below\":[{\"id\":\"019ad21e-b956-4ba8-b0a1-b65a432bd290\",\"type\":\"CategoricalAxis\"}],\"left\":[{\"id\":\"4b1e8e8d-0f09-4446-9457-609988c565b6\",\"type\":\"LinearAxis\"}],\"legend\":null,\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"6ebe606d-c8ec-4fe6-8db8-783557595f5e\",\"type\":\"BoxAnnotation\"},{\"id\":\"e37f60f2-7f4b-4f66-a7ec-57e1b518a099\",\"type\":\"GlyphRenderer\"},{\"id\":\"eaf9fce9-1897-4199-ad06-21bb20c8124b\",\"type\":\"GlyphRenderer\"},{\"id\":\"f6845efa-9b44-477e-91fe-2433f8fcd97e\",\"type\":\"GlyphRenderer\"},{\"id\":\"25e1ffef-8cb4-4762-a1cd-ee50283e62f8\",\"type\":\"GlyphRenderer\"},{\"id\":\"64722678-9fde-44e9-ba01-3c6b3495a7ea\",\"type\":\"GlyphRenderer\"},{\"id\":\"eefb0bac-cd2d-461b-803d-3d3a3be37eaa\",\"type\":\"GlyphRenderer\"},{\"id\":\"82f17246-048f-462b-ae49-a3b962732286\",\"type\":\"GlyphRenderer\"},{\"id\":\"00e6ec09-9cb6-41c6-a025-6f26b5c97527\",\"type\":\"GlyphRenderer\"},{\"id\":\"5b23bf00-5dd8-4372-81d1-821a29d1794b\",\"type\":\"GlyphRenderer\"},{\"id\":\"290573a4-df81-4dec-a185-de6e79a7914a\",\"type\":\"GlyphRenderer\"},{\"id\":\"84ad5ccc-2221-4262-bbc7-bf2da53041fa\",\"type\":\"GlyphRenderer\"},{\"id\":\"aa81e482-0589-4b15-8a98-8096e5056e32\",\"type\":\"GlyphRenderer\"},{\"id\":\"ae861430-7952-412a-a89d-4da90043c965\",\"type\":\"GlyphRenderer\"},{\"id\":\"d64909b4-e521-41d4-8cd5-bfc6013665d3\",\"type\":\"GlyphRenderer\"},{\"id\":\"77de1789-8361-4037-95f0-0c0aefe52488\",\"type\":\"GlyphRenderer\"},{\"id\":\"9ea841f4-862b-4f08-89d5-1ec80ec02259\",\"type\":\"GlyphRenderer\"},{\"id\":\"3d779d4b-9bbe-4291-83da-6526e257fd4a\",\"type\":\"GlyphRenderer\"},{\"id\":\"b9c7a470-523e-4881-947f-6318598c9e40\",\"type\":\"GlyphRenderer\"},{\"id\":\"019ad21e-b956-4ba8-b0a1-b65a432bd290\",\"type\":\"CategoricalAxis\"},{\"id\":\"4b1e8e8d-0f09-4446-9457-609988c565b6\",\"type\":\"LinearAxis\"},{\"id\":\"301587f4-b59c-493c-b386-267d103b8d4d\",\"type\":\"Grid\"}],\"title\":\"Auto MPG Box Plot\",\"title_text_font_size\":{\"value\":\"14pt\"},\"tool_events\":{\"id\":\"ee0e6786-8884-4301-a9a5-d09242af8737\",\"type\":\"ToolEvents\"},\"tools\":[{\"id\":\"8ddc2e3d-814b-4384-a65d-500648fde0e1\",\"type\":\"PanTool\"},{\"id\":\"f0cce7ae-3e4a-42d6-bf07-62707051075d\",\"type\":\"WheelZoomTool\"},{\"id\":\"feefcc95-c59b-4f8d-9c36-c4aef10b574a\",\"type\":\"BoxZoomTool\"},{\"id\":\"239eceb4-31b6-4bcd-9951-9f2a89c1fd96\",\"type\":\"PreviewSaveTool\"},{\"id\":\"f77c0dae-23bd-4afd-99a4-9760866ae9b1\",\"type\":\"ResizeTool\"},{\"id\":\"4974460b-6dfa-419d-9054-1e35a5ff4710\",\"type\":\"ResetTool\"},{\"id\":\"d0818983-6ad5-497e-912c-1b8c637bf720\",\"type\":\"HelpTool\"}],\"x_mapper_type\":\"auto\",\"x_range\":{\"id\":\"5ab4d262-cd40-4f4b-9c20-cee109783bd2\",\"type\":\"FactorRange\"},\"xgrid\":false,\"xscale\":\"auto\",\"y_mapper_type\":\"auto\",\"y_range\":{\"id\":\"82f2d8fd-eb0c-43a3-ad28-38ca16e85868\",\"type\":\"Range1d\"},\"yscale\":\"auto\"},\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"},{\"attributes\":{\"data_source\":{\"id\":\"a0c6f58d-91cc-4f27-bf4d-adaecb9ae6b7\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"8b1eac7f-f727-4846-94d3-a5fcfb5cbffe\",\"type\":\"Circle\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"f593be62-dcaf-453d-8c6f-060ccfe355ef\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"5f197628-e56e-4ea7-9885-49637c5ce088\",\"type\":\"Segment\"},{\"attributes\":{},\"id\":\"74610cb9-7668-4369-a6e0-fdf62574cdf8\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":6},{\"cyl\":6},{\"cyl\":6},{\"cyl\":6}],\"cyl\":[6,6,6,6],\"x0s\":[\"6:0.5\",\"6:0.65\",\"6:0.5\",\"6:0.65\"],\"x1s\":[\"6:0.5\",\"6:0.35\",\"6:0.5\",\"6:0.35\"],\"y0s\":[15.0,15.0,21.0,25.5],\"y1s\":[18.0,15.0,25.5,25.5]}},\"id\":\"36f3335e-1e84-4301-bbfa-b3a5afa2c5ef\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"4f570051-f65c-4265-a82e-0664a8869485\",\"type\":\"Segment\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":5}],\"color\":[\"DimGrey\"],\"cyl\":[5],\"fill_alpha\":[0.8],\"height\":[2.549999999999997],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"5\"],\"y\":[24.125]}},\"id\":\"9ef59340-c4de-4ec8-8b35-b5d69f81dc2c\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"5bdbe694-e0ba-45e6-9f09-76904a9d5442\",\"type\":\"ResizeTool\"},{\"attributes\":{\"data_source\":{\"id\":\"c69740d4-93d6-479a-ac0e-5d9727b1a76e\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"401e8df7-8761-49a7-b6c1-5da669c4f914\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"a2959b88-8a8e-4b12-914b-8ffb9478459b\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"factors\":[\"3\",\"4\",\"5\",\"6\",\"8\"]},\"id\":\"b8778478-164c-422e-9929-6d2213c98506\",\"type\":\"FactorRange\"},{\"attributes\":{\"data_source\":{\"id\":\"b3d40512-a591-4125-b445-d36e1c8b834d\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"c7482222-9202-4178-a480-4ed1a4c0766b\",\"type\":\"Circle\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"84ad5ccc-2221-4262-bbc7-bf2da53041fa\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":5},{\"cyl\":5},{\"cyl\":5},{\"cyl\":5}],\"cyl\":[5,5,5,5],\"x0s\":[\"5:0.5\",\"5:0.65\",\"5:0.5\",\"5:0.65\"],\"x1s\":[\"5:0.5\",\"5:0.35\",\"5:0.5\",\"5:0.35\"],\"y0s\":[20.3,20.3,30.9,36.4],\"y1s\":[22.85,20.3,36.4,36.4]}},\"id\":\"16199327-4c9a-4766-960d-bd317e082e25\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"9252edc6-7168-4d78-9279-db373ae58cb0\",\"type\":\"BasicTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"50b5df1b-3706-48bc-8ffa-72ee5f219caf\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"90aa0bfa-f3fa-4c70-a1d7-ae6549ee70b8\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"8e986351-f5b1-4460-b372-02604948f72b\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis_label\":\"Mpg\",\"formatter\":{\"id\":\"00fbb171-8f4b-48ed-a7c2-1e9554df06a7\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"9252edc6-7168-4d78-9279-db373ae58cb0\",\"type\":\"BasicTicker\"}},\"id\":\"d13e02a3-9424-4a70-8368-5b7bce9aa2fd\",\"type\":\"LinearAxis\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"f4603fae-990a-476b-a675-f614083aefa8\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"596716b6-8085-44c1-a71b-aaa1c6ed75ac\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"4c901a8c-8b59-4349-828d-798188bd43a2\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"4d69027c-e23d-4b41-a599-c96b1e4c2cec\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":6},{\"cyl\":6},{\"cyl\":6},{\"cyl\":6}],\"cyl\":[6,6,6,6],\"x0s\":[\"6:0.5\",\"6:0.65\",\"6:0.5\",\"6:0.65\"],\"x1s\":[\"6:0.5\",\"6:0.35\",\"6:0.5\",\"6:0.35\"],\"y0s\":[15.0,15.0,21.0,25.5],\"y1s\":[18.0,15.0,25.5,25.5]}},\"id\":\"613e4f4b-7532-4897-a12e-f017f04370bd\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":4}],\"color\":[\"DimGrey\"],\"cyl\":[4],\"fill_alpha\":[0.8],\"height\":[3.3999999999999986],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"4\"],\"y\":[26.7]}},\"id\":\"63a27ef7-c306-4cad-b849-b6c2888a43aa\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"452a127e-9dbf-4dac-afbd-e189341f2702\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"ccf6b78c-9890-48a0-b6f7-7f87da49180b\",\"type\":\"Circle\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"aca63adb-515a-4aa8-9ba0-99874b1bf0d0\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"90aa0bfa-f3fa-4c70-a1d7-ae6549ee70b8\",\"type\":\"Segment\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"ea266d28-627b-491a-8246-d52c1e88355a\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":8},{\"cyl\":8},{\"cyl\":8},{\"cyl\":8}],\"cyl\":[8,8,8,8],\"x0s\":[\"8:0.5\",\"8:0.65\",\"8:0.5\",\"8:0.65\"],\"x1s\":[\"8:0.5\",\"8:0.35\",\"8:0.5\",\"8:0.35\"],\"y0s\":[9.0,9.0,16.0,20.5],\"y1s\":[13.0,9.0,20.5,20.5]}},\"id\":\"959a8e09-bab7-4d26-874e-fe7ddeff715f\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"613e4f4b-7532-4897-a12e-f017f04370bd\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"4f570051-f65c-4265-a82e-0664a8869485\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"aa81e482-0589-4b15-8a98-8096e5056e32\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"6259e9ec-f9a9-4484-8902-4f0e93ebb6f7\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"b5d3bbaf-3eda-4376-badb-d00f84afb5f1\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e2966eae-ec83-44ed-904a-772c65c58ecd\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"8ddc2e3d-814b-4384-a65d-500648fde0e1\",\"type\":\"PanTool\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"9252edc6-7168-4d78-9279-db373ae58cb0\",\"type\":\"BasicTicker\"}},\"id\":\"bbe31629-72ca-4290-b7a0-85d546e92ca3\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":3}],\"color\":[\"#f22c40\"],\"cyl\":[3],\"fill_alpha\":[0.8],\"height\":[1.8000000000000007],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"3\"],\"y\":[21.15]}},\"id\":\"6259e9ec-f9a9-4484-8902-4f0e93ebb6f7\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"children\":[{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"},{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"}]},\"id\":\"a170dc67-b1c1-4603-aa11-6a8ce00c5147\",\"type\":\"HBox\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":3}],\"color\":[\"#f22c40\"],\"cyl\":[3],\"fill_alpha\":[0.8],\"height\":[1.5],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"3\"],\"y\":[19.5]}},\"id\":\"1426920e-98d8-4bf0-b48b-72660cd39c9c\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"1b620b1f-05c7-40f2-ac39-be36dd7ea6ad\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"453e29c7-9431-4db4-9364-0585fc9977a2\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"b9c7a470-523e-4881-947f-6318598c9e40\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"4974460b-6dfa-419d-9054-1e35a5ff4710\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"772c0ae0-7f9a-4ceb-9438-1a1478db181c\",\"type\":\"CategoricalTickFormatter\"},{\"attributes\":{},\"id\":\"00fbb171-8f4b-48ed-a7c2-1e9554df06a7\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":4}],\"color\":[\"DimGrey\"],\"cyl\":[4],\"fill_alpha\":[0.8],\"height\":[4.550000000000004],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"4\"],\"y\":[30.675]}},\"id\":\"567ad86c-fe25-42d9-a544-73eebf9b2cd2\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.7},\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":5},\"x\":{\"field\":\"x_values\"},\"y\":{\"field\":\"y_values\"}},\"id\":\"ccf6b78c-9890-48a0-b6f7-7f87da49180b\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"abfdf2bb-7069-46ff-bb42-4ec4a801864d\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"ab4a2e7a-0de9-47fc-b38d-817c67950446\",\"type\":\"Circle\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"703dfc55-147e-49de-a94a-b16aed4eee39\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"19c2c850-a954-44d1-9706-4088ac750cd2\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"98eea508-0f20-41c4-b5bc-8e04c948efc7\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"9631c7c4-11c5-4b16-9ef7-0ebbd7409844\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"6ebe606d-c8ec-4fe6-8db8-783557595f5e\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6035d187-53d7-4496-916d-d383861a80ff\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x_values\",\"y_values\"],\"data\":{\"chart_index\":[{\"cyl\":8},{\"cyl\":8},{\"cyl\":8}],\"cyl\":[8,8,8],\"x_values\":[\"8:0.5\",\"8:0.5\",\"8:0.5\"],\"y_values\":[23.0,23.9,26.6]}},\"id\":\"452a127e-9dbf-4dac-afbd-e189341f2702\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"69aea16f-6bdc-47b4-a098-65c0cd7712a6\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"eff62764-f4b1-493b-8f42-42d512385bcf\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"b494c11c-e99e-4c55-a267-afb2a1a02643\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"79262a47-13f6-4eba-a3b3-9cc3ba1005f6\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"9ef59340-c4de-4ec8-8b35-b5d69f81dc2c\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"8df51f78-55a9-453b-b97d-049ad93b5a51\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"9ea841f4-862b-4f08-89d5-1ec80ec02259\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"38d290e0-3caf-441d-89c7-4b51e6f3d94a\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"6a697de0-88bc-4cb7-b76b-f8b490a7c3b5\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"5b23bf00-5dd8-4372-81d1-821a29d1794b\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x_values\",\"y_values\"],\"data\":{\"chart_index\":[{\"cyl\":8},{\"cyl\":8},{\"cyl\":8}],\"cyl\":[8,8,8],\"x_values\":[\"8:0.5\",\"8:0.5\",\"8:0.5\"],\"y_values\":[23.0,23.9,26.6]}},\"id\":\"e0eca516-6d5d-4089-8014-3916f959c914\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"c0bce6c7-e423-434e-bdae-2f0236933b4c\",\"type\":\"Segment\"},{\"attributes\":{\"data_source\":{\"id\":\"3d007d9d-5025-4b65-a89e-334ca742e3c8\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"79262a47-13f6-4eba-a3b3-9cc3ba1005f6\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e825d345-7055-486b-89ee-9f53d99a2950\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"13a2c589-6e55-4bac-80ed-3a6883863370\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"26761f8d-37df-40e3-9d01-ddcdb2a0d5c3\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"139bf107-90f7-4be4-9eb5-b0c19a450c34\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"ae861430-7952-412a-a89d-4da90043c965\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"9186ab1a-31d0-4f81-a3cd-9f201b8d20c0\",\"type\":\"Rect\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"dc8311c6-1b65-4994-bb6c-d2fd14086363\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":3}],\"color\":[\"DimGrey\"],\"cyl\":[3],\"fill_alpha\":[0.8],\"height\":[1.5],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"3\"],\"y\":[19.5]}},\"id\":\"26761f8d-37df-40e3-9d01-ddcdb2a0d5c3\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"c40b326e-156c-4d68-903b-427b3e1fbb4c\",\"type\":\"PreviewSaveTool\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":5}],\"color\":[\"#407ee7\"],\"cyl\":[5],\"fill_alpha\":[0.8],\"height\":[5.5],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"5\"],\"y\":[28.15]}},\"id\":\"ff6b332f-e6a5-4fdb-97c1-e621318680be\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":4}],\"color\":[\"#5ab738\"],\"cyl\":[4],\"fill_alpha\":[0.8],\"height\":[3.3999999999999986],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"4\"],\"y\":[26.7]}},\"id\":\"c495769a-76c2-402a-befd-a37156248297\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x_values\",\"y_values\"],\"data\":{\"chart_index\":[{\"cyl\":4}],\"cyl\":[4],\"x_values\":[\"4:0.5\"],\"y_values\":[46.6]}},\"id\":\"abfdf2bb-7069-46ff-bb42-4ec4a801864d\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"1426920e-98d8-4bf0-b48b-72660cd39c9c\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"7f41521a-e021-458c-b4c1-2861e5bac1d8\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"3e63ca18-555f-4141-b8ca-1b8ff7a02fcb\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"factors\":[\"3\",\"4\",\"5\",\"6\",\"8\"]},\"id\":\"5ab4d262-cd40-4f4b-9c20-cee109783bd2\",\"type\":\"FactorRange\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":8},{\"cyl\":8},{\"cyl\":8},{\"cyl\":8}],\"cyl\":[8,8,8,8],\"x0s\":[\"8:0.5\",\"8:0.65\",\"8:0.5\",\"8:0.65\"],\"x1s\":[\"8:0.5\",\"8:0.35\",\"8:0.5\",\"8:0.35\"],\"y0s\":[9.0,9.0,16.0,20.5],\"y1s\":[13.0,9.0,20.5,20.5]}},\"id\":\"69aea16f-6bdc-47b4-a098-65c0cd7712a6\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":5}],\"color\":[\"DimGrey\"],\"cyl\":[5],\"fill_alpha\":[0.8],\"height\":[5.5],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"5\"],\"y\":[28.15]}},\"id\":\"b0e51823-3e75-4dbf-bd27-8d1dac258560\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"8df51f78-55a9-453b-b97d-049ad93b5a51\",\"type\":\"Rect\"},{\"attributes\":{\"below\":[{\"id\":\"3d49e58b-9e55-4348-9de8-df343370c725\",\"type\":\"CategoricalAxis\"}],\"left\":[{\"id\":\"d13e02a3-9424-4a70-8368-5b7bce9aa2fd\",\"type\":\"LinearAxis\"}],\"legend\":null,\"plot_height\":400,\"plot_width\":400,\"renderers\":[{\"id\":\"5503ad4b-0339-4a4b-9a4a-42d7de827189\",\"type\":\"BoxAnnotation\"},{\"id\":\"a2959b88-8a8e-4b12-914b-8ffb9478459b\",\"type\":\"GlyphRenderer\"},{\"id\":\"e5466171-8ec7-4472-bbf4-acebb97eedd8\",\"type\":\"GlyphRenderer\"},{\"id\":\"aca63adb-515a-4aa8-9ba0-99874b1bf0d0\",\"type\":\"GlyphRenderer\"},{\"id\":\"b494c11c-e99e-4c55-a267-afb2a1a02643\",\"type\":\"GlyphRenderer\"},{\"id\":\"ad6b2b17-3c6e-4a25-a012-6df78eb37470\",\"type\":\"GlyphRenderer\"},{\"id\":\"9631c7c4-11c5-4b16-9ef7-0ebbd7409844\",\"type\":\"GlyphRenderer\"},{\"id\":\"703dfc55-147e-49de-a94a-b16aed4eee39\",\"type\":\"GlyphRenderer\"},{\"id\":\"8e986351-f5b1-4460-b372-02604948f72b\",\"type\":\"GlyphRenderer\"},{\"id\":\"e825d345-7055-486b-89ee-9f53d99a2950\",\"type\":\"GlyphRenderer\"},{\"id\":\"6e77d140-cd53-45a2-8119-468fa1f5b6d2\",\"type\":\"GlyphRenderer\"},{\"id\":\"f593be62-dcaf-453d-8c6f-060ccfe355ef\",\"type\":\"GlyphRenderer\"},{\"id\":\"af876eb3-ce8c-4cf4-8e5b-493f576dcab7\",\"type\":\"GlyphRenderer\"},{\"id\":\"3e63ca18-555f-4141-b8ca-1b8ff7a02fcb\",\"type\":\"GlyphRenderer\"},{\"id\":\"e2966eae-ec83-44ed-904a-772c65c58ecd\",\"type\":\"GlyphRenderer\"},{\"id\":\"487d8378-02aa-4e90-9494-b2d569da9eee\",\"type\":\"GlyphRenderer\"},{\"id\":\"4d69027c-e23d-4b41-a599-c96b1e4c2cec\",\"type\":\"GlyphRenderer\"},{\"id\":\"e02b3565-1f30-4d6a-a51a-4d4e78f8be83\",\"type\":\"GlyphRenderer\"},{\"id\":\"d91ca993-b448-419d-b543-6e4663b8702a\",\"type\":\"GlyphRenderer\"},{\"id\":\"3d49e58b-9e55-4348-9de8-df343370c725\",\"type\":\"CategoricalAxis\"},{\"id\":\"d13e02a3-9424-4a70-8368-5b7bce9aa2fd\",\"type\":\"LinearAxis\"},{\"id\":\"bbe31629-72ca-4290-b7a0-85d546e92ca3\",\"type\":\"Grid\"}],\"title\":\"MPG Box Plot by Cylinder Count\",\"title_text_font_size\":{\"value\":\"14pt\"},\"tool_events\":{\"id\":\"21799b12-012d-4e96-a1e6-4b9b029dea3c\",\"type\":\"ToolEvents\"},\"tools\":[{\"id\":\"716464e4-0787-4c5e-9d00-f4239dea6c51\",\"type\":\"PanTool\"},{\"id\":\"afb8a6e9-4ca8-40e3-b7fd-32f5be7714c7\",\"type\":\"WheelZoomTool\"},{\"id\":\"9116968d-e0f1-4902-af1d-bdf449a33da0\",\"type\":\"BoxZoomTool\"},{\"id\":\"c40b326e-156c-4d68-903b-427b3e1fbb4c\",\"type\":\"PreviewSaveTool\"},{\"id\":\"5bdbe694-e0ba-45e6-9f09-76904a9d5442\",\"type\":\"ResizeTool\"},{\"id\":\"75341395-5ed6-47e1-ac8f-cddd4538be29\",\"type\":\"ResetTool\"},{\"id\":\"3a8a1ceb-8f8d-4b51-9c6c-08c706b930ee\",\"type\":\"HelpTool\"}],\"x_mapper_type\":\"auto\",\"x_range\":{\"id\":\"b8778478-164c-422e-9929-6d2213c98506\",\"type\":\"FactorRange\"},\"xgrid\":false,\"xscale\":\"auto\",\"y_mapper_type\":\"auto\",\"y_range\":{\"id\":\"a6460822-cb76-42d1-87b8-511e5cb80f05\",\"type\":\"Range1d\"},\"yscale\":\"auto\"},\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":5},{\"cyl\":5},{\"cyl\":5},{\"cyl\":5}],\"cyl\":[5,5,5,5],\"x0s\":[\"5:0.5\",\"5:0.65\",\"5:0.5\",\"5:0.65\"],\"x1s\":[\"5:0.5\",\"5:0.35\",\"5:0.5\",\"5:0.35\"],\"y0s\":[20.3,20.3,30.9,36.4],\"y1s\":[22.85,20.3,36.4,36.4]}},\"id\":\"1b620b1f-05c7-40f2-ac39-be36dd7ea6ad\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"f0cce7ae-3e4a-42d6-bf07-62707051075d\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"data_source\":{\"id\":\"63a27ef7-c306-4cad-b849-b6c2888a43aa\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"82e7f374-81d8-49a7-958f-30c9cdceea18\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"64722678-9fde-44e9-ba01-3c6b3495a7ea\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":8}],\"color\":[\"#00ad9c\"],\"cyl\":[8],\"fill_alpha\":[0.8],\"height\":[1.0],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"8\"],\"y\":[13.5]}},\"id\":\"c69740d4-93d6-479a-ac0e-5d9727b1a76e\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"end\":49.38},\"id\":\"82f2d8fd-eb0c-43a3-ad28-38ca16e85868\",\"type\":\"Range1d\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":5}],\"color\":[\"#407ee7\"],\"cyl\":[5],\"fill_alpha\":[0.8],\"height\":[2.549999999999997],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"5\"],\"y\":[24.125]}},\"id\":\"596716b6-8085-44c1-a71b-aaa1c6ed75ac\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"e0eca516-6d5d-4089-8014-3916f959c914\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"5601900c-8ed1-40cf-a80a-898db9b783fe\",\"type\":\"Circle\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"f6845efa-9b44-477e-91fe-2433f8fcd97e\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"401e8df7-8761-49a7-b6c1-5da669c4f914\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"ff6b332f-e6a5-4fdb-97c1-e621318680be\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"9186ab1a-31d0-4f81-a3cd-9f201b8d20c0\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e02b3565-1f30-4d6a-a51a-4d4e78f8be83\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":6}],\"color\":[\"DimGrey\"],\"cyl\":[6],\"fill_alpha\":[0.8],\"height\":[2.0],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"6\"],\"y\":[20.0]}},\"id\":\"2a65ad57-3c7d-4ad0-8552-84bdca7f4721\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":3}],\"color\":[\"DimGrey\"],\"cyl\":[3],\"fill_alpha\":[0.8],\"height\":[1.8000000000000007],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"3\"],\"y\":[21.15]}},\"id\":\"574dadf3-e953-4b87-a100-c40974e18012\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"b5d3bbaf-3eda-4376-badb-d00f84afb5f1\",\"type\":\"Rect\"},{\"attributes\":{\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"f77c0dae-23bd-4afd-99a4-9760866ae9b1\",\"type\":\"ResizeTool\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"82e7f374-81d8-49a7-958f-30c9cdceea18\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":4},{\"cyl\":4},{\"cyl\":4},{\"cyl\":4}],\"cyl\":[4,4,4,4],\"x0s\":[\"4:0.5\",\"4:0.65\",\"4:0.5\",\"4:0.65\"],\"x1s\":[\"4:0.5\",\"4:0.35\",\"4:0.5\",\"4:0.35\"],\"y0s\":[18.0,18.0,32.95,44.87500000000001],\"y1s\":[25.0,18.0,44.87500000000001,44.87500000000001]}},\"id\":\"70c3b27b-c8af-4242-a600-190e8c67a5b5\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"aae63103-21cc-4058-af52-cd4e5a7b0246\",\"type\":\"Rect\"},{\"attributes\":{\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"3a8a1ceb-8f8d-4b51-9c6c-08c706b930ee\",\"type\":\"HelpTool\"},{\"attributes\":{\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"239eceb4-31b6-4bcd-9951-9f2a89c1fd96\",\"type\":\"PreviewSaveTool\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":8}],\"color\":[\"DimGrey\"],\"cyl\":[8],\"fill_alpha\":[0.8],\"height\":[1.0],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"8\"],\"y\":[13.5]}},\"id\":\"3f1774c8-eae1-45b2-b21e-4b926c7cbdf2\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"overlay\":{\"id\":\"6ebe606d-c8ec-4fe6-8db8-783557595f5e\",\"type\":\"BoxAnnotation\"},\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"feefcc95-c59b-4f8d-9c36-c4aef10b574a\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.7},\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":5},\"x\":{\"field\":\"x_values\"},\"y\":{\"field\":\"y_values\"}},\"id\":\"c7482222-9202-4178-a480-4ed1a4c0766b\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"6e320b7e-0ed5-464c-adb3-dcb0ba7c56d3\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"13bc6947-5ac5-4920-8b5b-c8333a3649f5\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e5466171-8ec7-4472-bbf4-acebb97eedd8\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.7},\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":5},\"x\":{\"field\":\"x_values\"},\"y\":{\"field\":\"y_values\"}},\"id\":\"8b1eac7f-f727-4846-94d3-a5fcfb5cbffe\",\"type\":\"Circle\"},{\"attributes\":{\"data_source\":{\"id\":\"0697a673-c48f-4fa5-8dfd-c9da5e5a463b\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"13a2c589-6e55-4bac-80ed-3a6883863370\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"eaf9fce9-1897-4199-ad06-21bb20c8124b\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"68170fdc-2891-4604-a2ac-246c6cf5496b\",\"type\":\"Segment\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.7},\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":5},\"x\":{\"field\":\"x_values\"},\"y\":{\"field\":\"y_values\"}},\"id\":\"5601900c-8ed1-40cf-a80a-898db9b783fe\",\"type\":\"Circle\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":3},{\"cyl\":3},{\"cyl\":3},{\"cyl\":3}],\"cyl\":[3,3,3,3],\"x0s\":[\"3:0.5\",\"3:0.65\",\"3:0.5\",\"3:0.65\"],\"x1s\":[\"3:0.5\",\"3:0.35\",\"3:0.5\",\"3:0.35\"],\"y0s\":[18.0,18.0,22.05,23.7],\"y1s\":[18.75,18.0,23.7,23.7]}},\"id\":\"f0d221e8-5080-4a84-9ae3-01cb59f9eea1\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"36f3335e-1e84-4301-bbfa-b3a5afa2c5ef\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"68170fdc-2891-4604-a2ac-246c6cf5496b\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"af876eb3-ce8c-4cf4-8e5b-493f576dcab7\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x_values\",\"y_values\"],\"data\":{\"chart_index\":[{\"cyl\":4}],\"cyl\":[4],\"x_values\":[\"4:0.5\"],\"y_values\":[46.6]}},\"id\":\"749e8190-b432-4fa8-9e60-bae40720a886\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"7f41521a-e021-458c-b4c1-2861e5bac1d8\",\"type\":\"Rect\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"98eea508-0f20-41c4-b5bc-8e04c948efc7\",\"type\":\"Rect\"},{\"attributes\":{},\"id\":\"21799b12-012d-4e96-a1e6-4b9b029dea3c\",\"type\":\"ToolEvents\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"6a697de0-88bc-4cb7-b76b-f8b490a7c3b5\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":6}],\"color\":[\"#df5320\"],\"cyl\":[6],\"fill_alpha\":[0.8],\"height\":[1.0],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"6\"],\"y\":[18.5]}},\"id\":\"3d007d9d-5025-4b65-a89e-334ca742e3c8\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"c495769a-76c2-402a-befd-a37156248297\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"f4603fae-990a-476b-a675-f614083aefa8\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"ad6b2b17-3c6e-4a25-a012-6df78eb37470\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"716464e4-0787-4c5e-9d00-f4239dea6c51\",\"type\":\"PanTool\"},{\"attributes\":{\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"75341395-5ed6-47e1-ac8f-cddd4538be29\",\"type\":\"ResetTool\"},{\"attributes\":{\"axis_label\":\"Mpg\",\"formatter\":{\"id\":\"c6802719-b077-4225-b603-0f44130ce02d\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"e94c89a5-d628-4345-acbc-e80f62ee3957\",\"type\":\"BasicTicker\"}},\"id\":\"4b1e8e8d-0f09-4446-9457-609988c565b6\",\"type\":\"LinearAxis\"},{\"attributes\":{},\"id\":\"e94c89a5-d628-4345-acbc-e80f62ee3957\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"c6802719-b077-4225-b603-0f44130ce02d\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"5503ad4b-0339-4a4b-9a4a-42d7de827189\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x_values\",\"y_values\"],\"data\":{\"chart_index\":[{\"cyl\":6},{\"cyl\":6},{\"cyl\":6},{\"cyl\":6},{\"cyl\":6}],\"cyl\":[6,6,6,6,6],\"x_values\":[\"6:0.5\",\"6:0.5\",\"6:0.5\",\"6:0.5\",\"6:0.5\"],\"y_values\":[28.8,26.8,32.7,30.7,38.0]}},\"id\":\"b3d40512-a591-4125-b445-d36e1c8b834d\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"574dadf3-e953-4b87-a100-c40974e18012\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"dc8311c6-1b65-4994-bb6c-d2fd14086363\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"d64909b4-e521-41d4-8cd5-bfc6013665d3\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"16199327-4c9a-4766-960d-bd317e082e25\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"1ecdfd2e-16aa-4fc5-a1d1-7da575ed4066\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"d91ca993-b448-419d-b543-6e4663b8702a\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"data_source\":{\"id\":\"70c3b27b-c8af-4242-a600-190e8c67a5b5\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"5f197628-e56e-4ea7-9885-49637c5ce088\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"00e6ec09-9cb6-41c6-a025-6f26b5c97527\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"eff62764-f4b1-493b-8f42-42d512385bcf\",\"type\":\"Segment\"},{\"attributes\":{},\"id\":\"5e3f3d2a-b130-44a1-8dd6-ded1b1846768\",\"type\":\"CategoricalTicker\"},{\"attributes\":{\"data_source\":{\"id\":\"567ad86c-fe25-42d9-a544-73eebf9b2cd2\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"ded2e2ef-e925-41a8-a6b7-284f6ff911d4\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"eefb0bac-cd2d-461b-803d-3d3a3be37eaa\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis_label\":\"Cyl\",\"formatter\":{\"id\":\"772c0ae0-7f9a-4ceb-9438-1a1478db181c\",\"type\":\"CategoricalTickFormatter\"},\"major_label_orientation\":0.7853981633974483,\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"5e3f3d2a-b130-44a1-8dd6-ded1b1846768\",\"type\":\"CategoricalTicker\"}},\"id\":\"019ad21e-b956-4ba8-b0a1-b65a432bd290\",\"type\":\"CategoricalAxis\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"width\",\"fill_alpha\",\"line_alpha\",\"height\",\"y\",\"color\",\"line_color\",\"x\"],\"data\":{\"chart_index\":[{\"cyl\":6}],\"color\":[\"DimGrey\"],\"cyl\":[6],\"fill_alpha\":[0.8],\"height\":[1.0],\"line_alpha\":[1.0],\"line_color\":[\"black\"],\"width\":[0.8],\"x\":[\"6\"],\"y\":[18.5]}},\"id\":\"38d290e0-3caf-441d-89c7-4b51e6f3d94a\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.7},\"fill_color\":{\"value\":\"red\"},\"line_color\":{\"value\":\"red\"},\"size\":{\"units\":\"screen\",\"value\":5},\"x\":{\"field\":\"x_values\"},\"y\":{\"field\":\"y_values\"}},\"id\":\"b53e26f0-2fbe-4612-8407-1b80a6592537\",\"type\":\"Circle\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"13bc6947-5ac5-4920-8b5b-c8333a3649f5\",\"type\":\"Rect\"},{\"attributes\":{\"axis_label\":\"Cyl\",\"formatter\":{\"id\":\"74610cb9-7668-4369-a6e0-fdf62574cdf8\",\"type\":\"CategoricalTickFormatter\"},\"major_label_orientation\":0.7853981633974483,\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"b788ad0e-78be-47bf-9d17-8ef680854abe\",\"type\":\"CategoricalTicker\"}},\"id\":\"3d49e58b-9e55-4348-9de8-df343370c725\",\"type\":\"CategoricalAxis\"},{\"attributes\":{\"plot\":{\"id\":\"847b27ca-745a-4a68-a809-cc9aca9e0c72\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"afb8a6e9-4ca8-40e3-b7fd-32f5be7714c7\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"e94c89a5-d628-4345-acbc-e80f62ee3957\",\"type\":\"BasicTicker\"}},\"id\":\"301587f4-b59c-493c-b386-267d103b8d4d\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"3f1774c8-eae1-45b2-b21e-4b926c7cbdf2\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"aae63103-21cc-4058-af52-cd4e5a7b0246\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"e37f60f2-7f4b-4f66-a7ec-57e1b518a099\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"81d15f00-8a01-4b18-9c19-9c272751d0e3\",\"type\":\"Segment\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"ded2e2ef-e925-41a8-a6b7-284f6ff911d4\",\"type\":\"Rect\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x1s\",\"y1s\",\"x0s\",\"y0s\"],\"data\":{\"chart_index\":[{\"cyl\":3},{\"cyl\":3},{\"cyl\":3},{\"cyl\":3}],\"cyl\":[3,3,3,3],\"x0s\":[\"3:0.5\",\"3:0.65\",\"3:0.5\",\"3:0.65\"],\"x1s\":[\"3:0.5\",\"3:0.35\",\"3:0.5\",\"3:0.35\"],\"y0s\":[18.0,18.0,22.05,23.7],\"y1s\":[18.75,18.0,23.7,23.7]}},\"id\":\"66e782f2-2357-416c-9473-0a2927a0f70c\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"453e29c7-9431-4db4-9364-0585fc9977a2\",\"type\":\"Segment\"},{\"attributes\":{\"callback\":null,\"column_names\":[\"x_values\",\"y_values\"],\"data\":{\"chart_index\":[{\"cyl\":6},{\"cyl\":6},{\"cyl\":6},{\"cyl\":6},{\"cyl\":6}],\"cyl\":[6,6,6,6,6],\"x_values\":[\"6:0.5\",\"6:0.5\",\"6:0.5\",\"6:0.5\",\"6:0.5\"],\"y_values\":[28.8,26.8,32.7,30.7,38.0]}},\"id\":\"a0c6f58d-91cc-4f27-bf4d-adaecb9ae6b7\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"end\":49.38},\"id\":\"a6460822-cb76-42d1-87b8-511e5cb80f05\",\"type\":\"Range1d\"},{\"attributes\":{\"data_source\":{\"id\":\"959a8e09-bab7-4d26-874e-fe7ddeff715f\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"81d15f00-8a01-4b18-9c19-9c272751d0e3\",\"type\":\"Segment\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"25e1ffef-8cb4-4762-a1cd-ee50283e62f8\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"plot\":{\"id\":\"eab14ec9-b04f-4965-9de4-a1ae494a8fa0\",\"subtype\":\"Chart\",\"type\":\"Plot\"}},\"id\":\"d0818983-6ad5-497e-912c-1b8c637bf720\",\"type\":\"HelpTool\"},{\"attributes\":{\"data_source\":{\"id\":\"b0e51823-3e75-4dbf-bd27-8d1dac258560\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"ea266d28-627b-491a-8246-d52c1e88355a\",\"type\":\"Rect\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"3d779d4b-9bbe-4291-83da-6526e257fd4a\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"fill_alpha\":{\"field\":\"fill_alpha\"},\"fill_color\":{\"field\":\"color\"},\"height\":{\"field\":\"height\",\"units\":\"data\"},\"line_color\":{\"field\":\"line_color\"},\"width\":{\"field\":\"width\",\"units\":\"data\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"139bf107-90f7-4be4-9eb5-b0c19a450c34\",\"type\":\"Rect\"},{\"attributes\":{\"data_source\":{\"id\":\"749e8190-b432-4fa8-9e60-bae40720a886\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"b53e26f0-2fbe-4612-8407-1b80a6592537\",\"type\":\"Circle\"},\"hover_glyph\":null,\"nonselection_glyph\":null,\"selection_glyph\":null},\"id\":\"82f17246-048f-462b-ae49-a3b962732286\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_width\":{\"value\":2},\"x0\":{\"field\":\"x0s\"},\"x1\":{\"field\":\"x1s\"},\"y0\":{\"field\":\"y0s\"},\"y1\":{\"field\":\"y1s\"}},\"id\":\"1ecdfd2e-16aa-4fc5-a1d1-7da575ed4066\",\"type\":\"Segment\"}],\"root_ids\":[\"a170dc67-b1c1-4603-aa11-6a8ce00c5147\"]},\"title\":\"Bokeh Application\",\"version\":\"0.11.1\"}};\n",
       "            var render_items = [{\"docid\":\"54f906a7-0a5a-40c0-8d25-503cc40b925e\",\"elementid\":\"6a3bab8a-bf08-44b6-9efe-a37c1d6923f6\",\"modelid\":\"a170dc67-b1c1-4603-aa11-6a8ce00c5147\",\"notebook_comms_target\":\"7bc9d316-b8d3-4c93-8d22-04955145c195\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[1]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7fdf95a2e7f0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from bokeh.sampledata.autompg import autompg as df\n",
    "from bokeh.charts import BoxPlot, output_notebook, show, hplot\n",
    "\n",
    "box = BoxPlot(df, values='mpg', label='cyl', title=\"Auto MPG Box Plot\", plot_width=400, plot_height=400)\n",
    "box2 = BoxPlot(df, values='mpg', label='cyl', color='cyl',\n",
    "               title=\"MPG Box Plot by Cylinder Count\", plot_width=400, plot_height=400)\n",
    "\n",
    "output_notebook()\n",
    "show(hplot(box, box2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"plotly\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making plotly charts directly from data frame.  See plotly [documentation](https://plot.ly/python/offline/) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Need to first install plotly and cufflinks: pip install plotly and pip install cufflinks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id=\"02c2718a-4fcb-4c34-a840-2177194e19fa\" style=\"height: 500; width: 800px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"02c2718a-4fcb-4c34-a840-2177194e19fa\", [{\"y\": [0.49485701567718243, 0.1489900774917795, 0.5635171458472495, 0.2861311328196805, 0.5105892817155726, 0.5328847535069048, 0.5603148235411177, 0.7038216929804261, 0.17598284708098488, 0.6340096701843306], \"line\": {\"width\": 1.3}, \"type\": \"box\", \"name\": \"A\", \"boxpoints\": false, \"marker\": {\"color\": \"rgba(255, 153, 51, 1.0)\"}}, {\"y\": [0.09156786998787936, 0.9357055851799271, 0.8439149119908194, 0.8169742102755223, 0.12349825408504711, 0.27174371168654543, 0.39367666237655397, 0.8826322128984038, 0.7945657873159881, 0.10257505837286018], \"line\": {\"width\": 1.3}, \"type\": \"box\", \"name\": \"B\", \"boxpoints\": false, \"marker\": {\"color\": \"rgba(55, 128, 191, 1.0)\"}}, {\"y\": [0.3711625785889209, 0.26883118332507383, 0.97178129162547, 0.5934611037580209, 0.9011558548424885, 0.5499763858930155, 0.4670281961696965, 0.6195063603950746, 0.837155424449581, 0.5750595384976235], \"line\": {\"width\": 1.3}, \"type\": \"box\", \"name\": \"C\", \"boxpoints\": false, \"marker\": {\"color\": \"rgba(50, 171, 96, 1.0)\"}}, {\"y\": [0.9949155513071452, 0.3586675353497122, 0.7420385481419092, 0.5937226313101065, 0.8926029930573889, 0.45075482429114155, 0.9192897387494157, 0.4282922137481152, 0.3834206671884558, 0.9690353468781382], \"line\": {\"width\": 1.3}, \"type\": \"box\", \"name\": \"D\", \"boxpoints\": false, \"marker\": {\"color\": \"rgba(128, 0, 128, 1.0)\"}}, {\"y\": [0.04769579750183928, 0.76052099151547, 0.39675617708273214, 0.45782000674201007, 0.9008630096606345, 0.0996807138853395, 0.4506166089444035, 0.9477033146830205, 0.49277334566308173, 0.17116978887530798], \"line\": {\"width\": 1.3}, \"type\": \"box\", \"name\": \"E\", \"boxpoints\": false, \"marker\": {\"color\": \"rgba(219, 64, 82, 1.0)\"}}], {\"plot_bgcolor\": \"#F5F6F9\", \"yaxis1\": {\"title\": \"\", \"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"zerolinecolor\": \"#E1E5ED\", \"tickfont\": {\"color\": \"#4D5663\"}, \"titlefont\": {\"color\": \"#4D5663\"}}, \"title\": \"Plotly Pandas\", \"xaxis1\": {\"title\": \"\", \"gridcolor\": \"#E1E5ED\", \"showgrid\": true, \"zerolinecolor\": \"#E1E5ED\", \"tickfont\": {\"color\": \"#4D5663\"}, \"titlefont\": {\"color\": \"#4D5663\"}}, \"width\": 800, \"paper_bgcolor\": \"#F5F6F9\", \"legend\": {\"bgcolor\": \"#F5F6F9\", \"font\": {\"color\": \"#4D5663\"}}, \"height\": 500, \"titlefont\": {\"color\": \"#4D5663\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, iplot\n",
    "import numpy as np\n",
    "import cufflinks as cf\n",
    "init_notebook_mode()\n",
    "\n",
    "df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])\n",
    "\n",
    "iplot(df.iplot(asFigure=True, kind='box', title='Plotly Pandas', dimensions=(800,500)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"chaining\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method Chaining"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With method chaining, it eliminates the need for making intermediary variables.  You can process your data frame in a series of method calls when you enclose your data frame with parenthesis. Here's a very contrived example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEMCAYAAAAxoErWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa40ee46438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = (pd.read_csv('/home/pybokeh/temp/vehicles.csv',\n",
    "                 usecols=['year', 'make', 'model', 'comb08', 'fuelType', 'fuelType1', \n",
    "                          'fuelType2', 'atvType', 'cylinders', 'VClass'])\n",
    "      .rename(columns={'comb08':'combmpg'})\n",
    "      .query(\"make in('Honda','Acura','Toyota','Lexus') \\\n",
    "              & fuelType1 in('Regular Gasoline','Premium Gasoline','Midgrade Gasoline') \\\n",
    "              & cylinders in(4, 6) \\\n",
    "              & VClass in('Compact Cars','Subcompact Cars','Midsize Cars','Large Cars','Sport Utility','Minivan') \\\n",
    "              & ~(fuelType2 in('E85','Electricity','Natural Gas','Propane'))\")\n",
    "      ['combmpg'].plot.hist(alpha=0.5, label='Honda Motor Co')\n",
    "     )\n",
    "plt.title(\"Histogram of Combined Hwy+City MPG\", weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Also check out [pipe() method](http://pandas.pydata.org/pandas-docs/stable/basics.html#tablewise-function-application).**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"rpy2\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sending Pandas data frame to R using rpy2 IPython notebook extension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assuming you have [R](http://www.r-project.org/) and [rpy2](http://rpy.sourceforge.net/rpy2/doc-2.4/html/interactive.html#module-rpy2.ipython.rmagic) installed, we can send a Pandas data frame to R using the rpy2 IPython notebook extension to take advantage of R's various statistical and graphing packages.\n",
    "\n",
    "#### Simple example where I will pull Yen currency exchange rate data from the web and graph it using R's ggplot2 library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# load the rpy2 extension\n",
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's fetch the exchange rate data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pandas_df = pd.read_csv('http://research.stlouisfed.org/fred2/data/EXJPUS.txt', sep='  ', skiprows=28, engine='python',\n",
    "                 parse_dates=[0], header=None, names=['Month','Value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sanity check on the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month    datetime64[ns]\n",
       "Value           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>118.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>2015-02-01</td>\n",
       "      <td>118.7600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>2015-03-01</td>\n",
       "      <td>120.3945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>2015-04-01</td>\n",
       "      <td>119.5095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>2015-05-01</td>\n",
       "      <td>119.7064</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Month     Value\n",
       "528 2015-01-01  118.2500\n",
       "529 2015-02-01  118.7600\n",
       "530 2015-03-01  120.3945\n",
       "531 2015-04-01  119.5095\n",
       "532 2015-05-01  119.7064"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We'll use this one-liner %R magic command to send the Pandas data frame to R as input using the \"-i\" flag.  See [here](http://rpy.sourceforge.net/rpy2/doc-2.4/html/interactive.html#module-rpy2.ipython.rmagic) for other flags."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "%R -i pandas_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alternatively, you can use pandas built-in [facility](http://pandas.pydata.org/pandas-docs/stable/r_interface.html) to do this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "from rpy2.robjects import pandas2ri\n",
    "pandas2ri.activate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Next, we'll use %%R magic command which will make the entire code cell R executable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAyAAAAH0CAMAAADynrlKAAADAFBMVEUAAAABAQECAgIDAwMEBAQF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\ngkAwAkAQCEb8H3kVDrK/9cqUAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -w 800 -h 500 -u px # instead of px, you can also choose 'in', 'cm', or 'mm'\n",
    "df = as.data.frame(pandas_df)  # ensure data frame that was passed in from Python will be R's data frame type\n",
    "library(ggplot2)        # plotting library\n",
    "library(scales)         # enables us to change the x label time scale\n",
    "\n",
    "p = ggplot(pandas_df, aes(pandas_df$Month, pandas_df$Value))\n",
    "p = p + \n",
    "    geom_line() +\n",
    "    scale_x_datetime(labels=date_format(\"%Y-%m\"), breaks=\"5 years\", minor_breaks=\"1 year\") +\n",
    "    ggtitle(\"Yen to US Dollars\") +\n",
    "    xlab(\"Time\") +\n",
    "    ylab(\"Yen Value\")\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[[back to top](#top)]"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.5.1"
  },
  "toc": {
   "toc_cell": false,
   "toc_number_sections": true,
   "toc_threshold": 6,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}