data-munging-with-pandas-key.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data mungling with pandas\n",
    "\n",
    "_Adapted from original materials by [manishamde](https://github.com/manishamde)._\n",
    "\n",
    "Data munging or data wrangling is loosely the process of manually converting or mapping data from one \n",
    "\"raw\" form into another format that allows for more convenient consumption of the data with the help of \n",
    "semi-automated tools.\n",
    "\n",
    "Data munging is basically the hip term for cleaning up a messy data set.\n",
    "\n",
    "Data munging involves common operations such as: \n",
    " - Indexing\n",
    " - Renaming\n",
    " - Handling missing values\n",
    " - map(), apply(), applymap()\n",
    " - New Columns = f(Existing Columns)\n",
    " - Basic stats\n",
    " - Merge, join\n",
    " - Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll try the above operations on a very simple dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col\n",
       "0        0.1        1       a\n",
       "1        0.2        2       b\n",
       "2        0.2        6    None\n",
       "3       10.1        8       c\n",
       "4        NaN       -1       a"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def defdf():\n",
    "    df = pd.DataFrame({'int_col' : [1, 2, 6, 8, -1], \n",
    "                   'float_col' : [0.1, 0.2, 0.2, 10.1, None], \n",
    "                   'str_col' : ['a', 'b', None, 'c', 'a']})\n",
    "    return df\n",
    "\n",
    "df = defdf()\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexing\n",
    "\n",
    "### Selecting a subset of columns\n",
    " * Select only the float and string columns of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col\n",
       "0        0.1        1\n",
       "1        0.2        2\n",
       "2        0.2        6\n",
       "3       10.1        8\n",
       "4        NaN       -1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['float_col', 'int_col']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional indexing\n",
    " * Using boolean indexing, select the rows of the dataframe for which float column is larger than 0.15\n",
    " * Select the rows for which float column is larger than 0.1 and integer column is larger than 2. Change 'and' by 'or' \n",
    " * Select the rows for which string column is not 'a'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col\n",
       "1        0.2        2       b\n",
       "2        0.2        6    None\n",
       "3       10.1        8       c"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.float_col>0.15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col\n",
       "2        0.2        6    None\n",
       "3       10.1        8       c"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.float_col>0.1) & (df.int_col>2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col\n",
       "1        0.2        2       b\n",
       "2        0.2        6    None\n",
       "3       10.1        8       c"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.float_col>0.1) | (df.int_col>2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col\n",
       "1        0.2        2       b\n",
       "2        0.2        6    None\n",
       "3       10.1        8       c"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[~(df.str_col=='a')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Renaming\n",
    "\n",
    " * Use the rename method to rename all three columns\n",
    " * Set inplace=True for the changes to affect the existing dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FLO</th>\n",
       "      <th>INT</th>\n",
       "      <th>STR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FLO  INT   STR\n",
       "0   0.1    1     a\n",
       "1   0.2    2     b\n",
       "2   0.2    6  None\n",
       "3  10.1    8     c\n",
       "4   NaN   -1     a"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'float_col': 'FLO', 'int_col': 'INT', 'str_col': 'STR'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FLO</th>\n",
       "      <th>INT</th>\n",
       "      <th>STR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2</td>\n",
       "      <td>6</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FLO  INT   STR\n",
       "0   0.1    1     a\n",
       "1   0.2    2     b\n",
       "2   0.2    6  None\n",
       "3  10.1    8     c\n",
       "4   NaN   -1     a"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns={'float_col': 'FLO', 'int_col': 'INT', 'str_col': 'STR'}, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = defdf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling missing values\n",
    "\n",
    "### Drop missing values\n",
    "\n",
    " * Use dropna to drop all rows with missing data (NaN). From now on, perform the rest of the exercises on this modified dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.1</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col\n",
       "0        0.1        1       a\n",
       "1        0.2        2       b\n",
       "3       10.1        8       c"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill missing values\n",
    " * Use fillna to fill missing data. Fill float column with mean of column and string column with a character of your choosing. Use inplace to alter the value in the original dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>6</td>\n",
       "      <td>fill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.10</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col\n",
       "0       0.10        1       a\n",
       "1       0.20        2       b\n",
       "2       0.20        6    fill\n",
       "3      10.10        8       c\n",
       "4       2.65       -1       a"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_float = df.float_col.mean()\n",
    "df.float_col.fillna(mean_float, inplace=True)\n",
    "df.str_col.fillna('fill', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def filldf():\n",
    "    df = defdf()\n",
    "    df.float_col.fillna(mean_float, inplace=True)\n",
    "    df.str_col.fillna('fill', inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = filldf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vectorized operations: map, apply\n",
    "### map\n",
    "The map operation maps the values of a series iterating over each element\n",
    " * Use map to generate a series that equals each element of integer column squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     4\n",
       "2    36\n",
       "3    64\n",
       "4     1\n",
       "Name: int_col, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['int_col'].map(lambda i: i*i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### apply\n",
    "The apply operation applies a function along any axis of the dataframe. \n",
    " - axis=0: apply function to each column\n",
    " - axis=1: apply function to each row\n",
    "Depending on the return type of the function passed to apply(), the result will either be of lower dimension or the same dimension.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " * Use apply on columns to compute the square root of the float and integer columns\n",
    " * Use apply on rows to compute the cumulative sum by rows of the elements of the float and integer columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.316228</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.447214</td>\n",
       "      <td>1.414214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.447214</td>\n",
       "      <td>2.449490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.178050</td>\n",
       "      <td>2.828427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.627882</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col   int_col\n",
       "0   0.316228  1.000000\n",
       "1   0.447214  1.414214\n",
       "2   0.447214  2.449490\n",
       "3   3.178050  2.828427\n",
       "4   1.627882       NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['float_col', 'int_col']].apply(np.sqrt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>6.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.10</td>\n",
       "      <td>18.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.65</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col\n",
       "0       0.10     1.10\n",
       "1       0.20     2.20\n",
       "2       0.20     6.20\n",
       "3      10.10    18.10\n",
       "4       2.65     1.65"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['float_col', 'int_col']].apply(np.cumsum, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### applymap\n",
    "The applymap operation applies a function to a dataframe that is intended to operate elementwise\n",
    "\n",
    " * Use applymap to transform the dataframe in the following manner: duplicate elements of type string ('z' -> 'zz') and compute the exponential of numerical elements. Hint: define first the function fn that needs to be applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.105171</td>\n",
       "      <td>2.718282</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.221403</td>\n",
       "      <td>7.389056</td>\n",
       "      <td>bb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.221403</td>\n",
       "      <td>403.428793</td>\n",
       "      <td>fillfill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24343.009424</td>\n",
       "      <td>2980.957987</td>\n",
       "      <td>cc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.154039</td>\n",
       "      <td>0.367879</td>\n",
       "      <td>aa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      float_col      int_col   str_col\n",
       "0      1.105171     2.718282        aa\n",
       "1      1.221403     7.389056        bb\n",
       "2      1.221403   403.428793  fillfill\n",
       "3  24343.009424  2980.957987        cc\n",
       "4     14.154039     0.367879        aa"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fn(x): \n",
    "    if isinstance(x, str):\n",
    "        return x*2\n",
    "    else:\n",
    "        return np.exp(x)\n",
    "\n",
    "df.applymap(fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Columns = f(Existing Columns)\n",
    "\n",
    "Generating new columns from existing columns in a data frame is an integral part of the data mungling workflow. \n",
    "\n",
    "### multiple columns as a function of a single column\n",
    "\n",
    " * Use map in combination with zip to construct two new columns being the square and third power of the integer column. [Help](http://stackoverflow.com/questions/12356501/pandas-create-two-new-columns-in-a-dataframe-with-values-calculated-from-a-pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "      <th>int**2</th>\n",
       "      <th>int**3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>6</td>\n",
       "      <td>fill</td>\n",
       "      <td>36</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.10</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "      <td>64</td>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col  int**2  int**3\n",
       "0       0.10        1       a       1       1\n",
       "1       0.20        2       b       4       8\n",
       "2       0.20        6    fill      36     216\n",
       "3      10.10        8       c      64     512\n",
       "4       2.65       -1       a       1      -1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def pow23(x): \n",
    "    return x**2, x**3\n",
    "\n",
    "df['int**2'], df['int**3'] = zip(*df['int_col'].map(pow23))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = filldf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### single column as a function of multiple columns\n",
    "\n",
    " * Use apply to construct a new column product of the float and integer columns. [Help](http://stackoverflow.com/questions/13331698/how-to-apply-a-function-to-two-columns-of-pandas-dataframe?lq=1)\n",
    " * Use apply to construct a new column composed of the concatenation of the string column and the integer column cast to string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "      <th>sum_float_int</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>6</td>\n",
       "      <td>fill</td>\n",
       "      <td>1.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.10</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "      <td>80.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "      <td>-2.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col  sum_float_int\n",
       "0       0.10        1       a           0.10\n",
       "1       0.20        2       b           0.40\n",
       "2       0.20        6    fill           1.20\n",
       "3      10.10        8       c          80.80\n",
       "4       2.65       -1       a          -2.65"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sum_float_int'] = df.apply(lambda d: d['float_col'] * d['int_col'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = filldf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "      <th>concat_int_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>6</td>\n",
       "      <td>fill</td>\n",
       "      <td>6fill</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.10</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "      <td>8c</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "      <td>-1a</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col concat_int_str\n",
       "0       0.10        1       a             1a\n",
       "1       0.20        2       b             2b\n",
       "2       0.20        6    fill          6fill\n",
       "3      10.10        8       c             8c\n",
       "4       2.65       -1       a            -1a"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['concat_int_str'] = df.apply(lambda d: str(d['int_col']) + d['str_col'], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = filldf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### multiple columns as a function of multiple columns\n",
    "\n",
    " * Use apply and a function that returns a Series to construct two new columns being the square root of the float and integer columns. [Help](http://stackoverflow.com/questions/10751127/returning-multiple-values-from-pandas-apply-on-a-dataframe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "      <th>int_sq</th>\n",
       "      <th>flt_sq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>6</td>\n",
       "      <td>fill</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.10</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "      <td>102.0100</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "      <td>7.0225</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col    int_sq  flt_sq\n",
       "0       0.10        1       a    0.0100       1\n",
       "1       0.20        2       b    0.0400       4\n",
       "2       0.20        6    fill    0.0400      36\n",
       "3      10.10        8       c  102.0100      64\n",
       "4       2.65       -1       a    7.0225       1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def int_float_squares(series):\n",
    "   return pd.Series({'int_sq' : series['int_col']**2, 'flt_sq' : series['float_col']**2})\n",
    "   \n",
    "df[['int_sq', 'flt_sq']] = df.apply(int_float_squares, axis = 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = filldf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic stats\n",
    "\n",
    "### describe\n",
    "\n",
    " * Use describe to gather information on the distribution of the float and integer columns.\n",
    " * Use boxplot for a visual representation of the same information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.650000</td>\n",
       "      <td>3.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.301453</td>\n",
       "      <td>3.701351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.100000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.650000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.100000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       float_col   int_col\n",
       "count   5.000000  5.000000\n",
       "mean    2.650000  3.200000\n",
       "std     4.301453  3.701351\n",
       "min     0.100000 -1.000000\n",
       "25%     0.200000  1.000000\n",
       "50%     0.200000  2.000000\n",
       "75%     2.650000  6.000000\n",
       "max    10.100000  8.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['float_col', 'int_col']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b17d810>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW0AAAEBCAYAAACzN/QDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMbQlqSGGtiQ1xNCWpIb8PxgE5sYUCl+6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b17d050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='float_col', return_type='axes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b250e10>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V8EjSiyU9JOlW2y+PiPfZfrukKiJOzuvA9nMkXS/psoi43/Zg+tZhSXdGxBtsv0rSRyVd\nuLKZAKeBlTdKdHtEPDg9QP6YpOH0dWv35xEukXQ0Iu6XpIjYPPTkMm0UbEXEEUnPtr1/FQMHThfF\nGyX60Y6/P65m/8KcV9xnc20eOMPaUbxRitMpqI9KeuYu798m6RW2z5M2su7p638n6c3T1ypJ/xkR\nj535UIHlkXmjFIt2fex8/f2SPm/7uxFxxSkXRnzP9m9KunF6zPF/SLpSG5n3h2wfl/Q/kt6SduhA\nczxhCQAZIjYBgAwRm6CXbN8m6azNpjbild+IiLvXNyrg9BGbAECGiE0AIEMUbwDIEMUbADJE8QaA\nDFG8ASBD/wcDkh16Z5baAgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b31cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot(column='int_col', return_type='axes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge and Join\n",
    "\n",
    "Pandas supports database-like joins which makes it easy to link data frames.\n",
    "\n",
    " * Perform inner, outer, left and right joins of the dataframe with the second dataframe defined below, using the common key 'str_col' as key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>int_col_2</th>\n",
       "      <th>str_col</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   int_col_2 str_col\n",
       "0          1       a\n",
       "1          2       b"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({'str_col' : ['a','b'], 'int_col_2' : [1, 2]})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "      <th>int_col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col  int_col_2\n",
       "0       0.10        1       a          1\n",
       "1       2.65       -1       a          1\n",
       "2       0.20        2       b          2"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df, df2, on='str_col', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "      <th>int_col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.20</td>\n",
       "      <td>6</td>\n",
       "      <td>fill</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.10</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col  int_col_2\n",
       "0       0.10        1       a          1\n",
       "1       2.65       -1       a          1\n",
       "2       0.20        2       b          2\n",
       "3       0.20        6    fill        NaN\n",
       "4      10.10        8       c        NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df, df2, on='str_col', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "      <th>int_col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>6</td>\n",
       "      <td>fill</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.10</td>\n",
       "      <td>8</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col  int_col_2\n",
       "0       0.10        1       a          1\n",
       "1       0.20        2       b          2\n",
       "2       0.20        6    fill        NaN\n",
       "3      10.10        8       c        NaN\n",
       "4       2.65       -1       a          1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df, df2, on='str_col', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>float_col</th>\n",
       "      <th>int_col</th>\n",
       "      <th>str_col</th>\n",
       "      <th>int_col_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.10</td>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.65</td>\n",
       "      <td>-1</td>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.20</td>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   float_col  int_col str_col  int_col_2\n",
       "0       0.10        1       a          1\n",
       "1       2.65       -1       a          1\n",
       "2       0.20        2       b          2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df, df2, on='str_col', how='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot\n",
    "\n",
    "Pandas is equiped with straightforward wrappers for quick plotting of data\n",
    "\n",
    " * Use plot to visualize the columns of the dataframe defined below\n",
    " * Use hist to visualize the distribution of the data in the form of a histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.862863</td>\n",
       "      <td>-0.150650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.019300</td>\n",
       "      <td>1.669627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.196366</td>\n",
       "      <td>2.062641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.620009</td>\n",
       "      <td>1.143993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.458392</td>\n",
       "      <td>0.506314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y\n",
       "0 -0.862863 -0.150650\n",
       "1 -0.019300  1.669627\n",
       "2 -0.196366  2.062641\n",
       "3  0.620009  1.143993\n",
       "4  0.458392  0.506314"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_df = pd.DataFrame(np.random.randn(1000,2),columns=['x','y'])\n",
    "plot_df['y'] = plot_df['y'].map(lambda x : x + 1)\n",
    "plot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10b3dbad0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WLnJVWoaVg65OmUwX4rWfLP4EN4xmS0qKIX2HOgAx2/R181M8LbVujVk4JpD3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\n0kSu9joz6QcKdt7pwN0bkEnrlaNlAbuKpk7iH6cUwLjb0ev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9k8fAAJw17yz47/hglsig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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b3d0410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10b58de10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x10b7ee450>]], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fAu8Efgt4r6Q3lrGvF+u4zdq2t1KbyXU/hrlNTU2X0MfRjG/M5/29THp9/jY6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\ntZAndzOzFvLkbmbWQv8f/qGQtbBPJUUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b59e390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}