{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using HDF5 with Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pandas import (\n",
    "    DataFrame, HDFStore\n",
    ")\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = DataFrame(np.random.randn(5,3), columns=['A', 'B', 'C',])"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.092894</td>\n",
       "      <td>0.480401</td>\n",
       "      <td>-0.967241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.003829</td>\n",
       "      <td>0.012645</td>\n",
       "      <td>0.527720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.060884</td>\n",
       "      <td>-0.088839</td>\n",
       "      <td>-0.269744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.729817</td>\n",
       "      <td>-0.042234</td>\n",
       "      <td>0.229410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.117705</td>\n",
       "      <td>-0.778368</td>\n",
       "      <td>-1.280790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.092894  0.480401 -0.967241\n",
       "1 -1.003829  0.012645  0.527720\n",
       "2 -0.060884 -0.088839 -0.269744\n",
       "3  0.729817 -0.042234  0.229410\n",
       "4 -1.117705 -0.778368 -1.280790"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a HDF5 format file for saving th dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store = HDFStore('dataset.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: dataset.h5\n",
       "Empty"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add the dataframe to the HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store.put('d1', df, format='table', data_columns=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: dataset.h5\n",
       "/d1            frame_table  (typ->appendable,nrows->5,ncols->3,indexers->[index],dc->[A,B,C])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing the dataframe from HDF5 file"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.092894</td>\n",
       "      <td>0.480401</td>\n",
       "      <td>-0.967241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.003829</td>\n",
       "      <td>0.012645</td>\n",
       "      <td>0.527720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.060884</td>\n",
       "      <td>-0.088839</td>\n",
       "      <td>-0.269744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.729817</td>\n",
       "      <td>-0.042234</td>\n",
       "      <td>0.229410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.117705</td>\n",
       "      <td>-0.778368</td>\n",
       "      <td>-1.280790</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.092894  0.480401 -0.967241\n",
       "1 -1.003829  0.012645  0.527720\n",
       "2 -0.060884 -0.088839 -0.269744\n",
       "3  0.729817 -0.042234  0.229410\n",
       "4 -1.117705 -0.778368 -1.280790"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store['d1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Appending another dataframe to already exisiting dataframe in HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store.append('d1', DataFrame(np.random.randn(5,3), columns=['A', 'B', 'C']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: dataset.h5\n",
       "/d1            frame_table  (typ->appendable,nrows->10,ncols->3,indexers->[index],dc->[A,B,C])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store"
   ]
  },
  {
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.092894</td>\n",
       "      <td>0.480401</td>\n",
       "      <td>-0.967241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.003829</td>\n",
       "      <td>0.012645</td>\n",
       "      <td>0.527720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.060884</td>\n",
       "      <td>-0.088839</td>\n",
       "      <td>-0.269744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.729817</td>\n",
       "      <td>-0.042234</td>\n",
       "      <td>0.229410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.117705</td>\n",
       "      <td>-0.778368</td>\n",
       "      <td>-1.280790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.396292</td>\n",
       "      <td>0.205355</td>\n",
       "      <td>0.995982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.450495</td>\n",
       "      <td>-0.744076</td>\n",
       "      <td>-1.320831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.003412</td>\n",
       "      <td>-0.876143</td>\n",
       "      <td>1.677286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.395701</td>\n",
       "      <td>-0.465095</td>\n",
       "      <td>0.287003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.442614</td>\n",
       "      <td>0.818815</td>\n",
       "      <td>-0.378552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.092894  0.480401 -0.967241\n",
       "1 -1.003829  0.012645  0.527720\n",
       "2 -0.060884 -0.088839 -0.269744\n",
       "3  0.729817 -0.042234  0.229410\n",
       "4 -1.117705 -0.778368 -1.280790\n",
       "0 -0.396292  0.205355  0.995982\n",
       "1  0.450495 -0.744076 -1.320831\n",
       "2  1.003412 -0.876143  1.677286\n",
       "3 -0.395701 -0.465095  0.287003\n",
       "4  1.442614  0.818815 -0.378552"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store['d1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Closing the HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: dataset.h5\n",
       "File is CLOSED"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Opening HDF5 file - Method 1 (not advised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_hdf('dataset.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.092894</td>\n",
       "      <td>0.480401</td>\n",
       "      <td>-0.967241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.003829</td>\n",
       "      <td>0.012645</td>\n",
       "      <td>0.527720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.060884</td>\n",
       "      <td>-0.088839</td>\n",
       "      <td>-0.269744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.729817</td>\n",
       "      <td>-0.042234</td>\n",
       "      <td>0.229410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.117705</td>\n",
       "      <td>-0.778368</td>\n",
       "      <td>-1.280790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.396292</td>\n",
       "      <td>0.205355</td>\n",
       "      <td>0.995982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.450495</td>\n",
       "      <td>-0.744076</td>\n",
       "      <td>-1.320831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.003412</td>\n",
       "      <td>-0.876143</td>\n",
       "      <td>1.677286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.395701</td>\n",
       "      <td>-0.465095</td>\n",
       "      <td>0.287003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.442614</td>\n",
       "      <td>0.818815</td>\n",
       "      <td>-0.378552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.092894  0.480401 -0.967241\n",
       "1 -1.003829  0.012645  0.527720\n",
       "2 -0.060884 -0.088839 -0.269744\n",
       "3  0.729817 -0.042234  0.229410\n",
       "4 -1.117705 -0.778368 -1.280790\n",
       "0 -0.396292  0.205355  0.995982\n",
       "1  0.450495 -0.744076 -1.320831\n",
       "2  1.003412 -0.876143  1.677286\n",
       "3 -0.395701 -0.465095  0.287003\n",
       "4  1.442614  0.818815 -0.378552"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Opening HDF5 file - Method 2 (recommended way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store = HDFStore('dataset.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: dataset.h5\n",
       "/d1            frame_table  (typ->appendable,nrows->10,ncols->3,indexers->[index],dc->[A,B,C])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding dataframe to the opened HDF5, using the default format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store.put('d2', DataFrame(np.random.randn(7,4)))\n",
    "store.put('d3', DataFrame(np.random.randn(14,3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: dataset.h5\n",
       "/d1            frame_table  (typ->appendable,nrows->10,ncols->3,indexers->[index],dc->[A,B,C])\n",
       "/d2            frame        (shape->[7,4])                                                    \n",
       "/d3            frame        (shape->[14,3])                                                   "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Difference between frame_table and frame formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### frame_table format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store.append('d1', pd.DataFrame(np.random.randn(3,3), columns=['A', 'B', 'C']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.003829</td>\n",
       "      <td>0.012645</td>\n",
       "      <td>0.527720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.060884</td>\n",
       "      <td>-0.088839</td>\n",
       "      <td>-0.269744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.729817</td>\n",
       "      <td>-0.042234</td>\n",
       "      <td>0.229410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.117705</td>\n",
       "      <td>-0.778368</td>\n",
       "      <td>-1.280790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.396292</td>\n",
       "      <td>0.205355</td>\n",
       "      <td>0.995982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.450495</td>\n",
       "      <td>-0.744076</td>\n",
       "      <td>-1.320831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.003412</td>\n",
       "      <td>-0.876143</td>\n",
       "      <td>1.677286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.395701</td>\n",
       "      <td>-0.465095</td>\n",
       "      <td>0.287003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.442614</td>\n",
       "      <td>0.818815</td>\n",
       "      <td>-0.378552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.196617</td>\n",
       "      <td>0.585134</td>\n",
       "      <td>1.052465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.386223</td>\n",
       "      <td>-0.932965</td>\n",
       "      <td>0.749832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.721093</td>\n",
       "      <td>1.453562</td>\n",
       "      <td>-0.426798</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C\n",
       "0 -0.092894  0.480401 -0.967241\n",
       "1 -1.003829  0.012645  0.527720\n",
       "2 -0.060884 -0.088839 -0.269744\n",
       "3  0.729817 -0.042234  0.229410\n",
       "4 -1.117705 -0.778368 -1.280790\n",
       "0 -0.396292  0.205355  0.995982\n",
       "1  0.450495 -0.744076 -1.320831\n",
       "2  1.003412 -0.876143  1.677286\n",
       "3 -0.395701 -0.465095  0.287003\n",
       "4  1.442614  0.818815 -0.378552\n",
       "0 -0.196617  0.585134  1.052465\n",
       "1  0.386223 -0.932965  0.749832\n",
       "2  0.721093  1.453562 -0.426798"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store['d1']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### frame format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Can only append to Tables",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-966a756e5b6e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mstore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'd2'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Users\\Jeri_Dabba\\pandas\\io\\pytables.pyc\u001b[0m in \u001b[0;36mappend\u001b[0;34m(self, key, value, format, append, columns, dropna, **kwargs)\u001b[0m\n\u001b[1;32m    917\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_format\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m         self._write_to_group(key, value, append=append, dropna=dropna,\n\u001b[0;32m--> 919\u001b[0;31m                              **kwargs)\n\u001b[0m\u001b[1;32m    920\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    921\u001b[0m     def append_to_multiple(self, d, value, selector, data_columns=None,\n",
      "\u001b[0;32mC:\\Users\\Jeri_Dabba\\pandas\\io\\pytables.pyc\u001b[0m in \u001b[0;36m_write_to_group\u001b[0;34m(self, key, value, format, index, append, complib, encoding, **kwargs)\u001b[0m\n\u001b[1;32m   1250\u001b[0m             if (not s.is_table or\n\u001b[1;32m   1251\u001b[0m                     (s.is_table and format == 'fixed' and s.is_exists)):\n\u001b[0;32m-> 1252\u001b[0;31m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Can only append to Tables'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1253\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_exists\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1254\u001b[0m                 \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_object_info\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Can only append to Tables"
     ]
    }
   ],
   "source": [
    "store.append('d2', pd.DataFrame(np.random.randn(4,4)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The frame format (default) is faster than frame_table format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To view the dataframe with ordered index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <th>C</th>\n",
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       "      <th>4</th>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>3</td>\n",
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       "      <td>-0.465095</td>\n",
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       "      <th>9</th>\n",
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       "      <th>10</th>\n",
       "      <td>0</td>\n",
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       "      <td>1.052465</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0.386223</td>\n",
       "      <td>-0.932965</td>\n",
       "      <td>0.749832</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>0.721093</td>\n",
       "      <td>1.453562</td>\n",
       "      <td>-0.426798</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "    index         A         B         C\n",
       "0       0 -0.092894  0.480401 -0.967241\n",
       "1       1 -1.003829  0.012645  0.527720\n",
       "2       2 -0.060884 -0.088839 -0.269744\n",
       "3       3  0.729817 -0.042234  0.229410\n",
       "4       4 -1.117705 -0.778368 -1.280790\n",
       "5       0 -0.396292  0.205355  0.995982\n",
       "6       1  0.450495 -0.744076 -1.320831\n",
       "7       2  1.003412 -0.876143  1.677286\n",
       "8       3 -0.395701 -0.465095  0.287003\n",
       "9       4  1.442614  0.818815 -0.378552\n",
       "10      0 -0.196617  0.585134  1.052465\n",
       "11      1  0.386223 -0.932965  0.749832\n",
       "12      2  0.721093  1.453562 -0.426798"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "store['d1/table']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To get the dataframe from the HDF5 file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = store['d1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "          A         B         C\n",
       "0 -0.092894  0.480401 -0.967241\n",
       "1 -1.003829  0.012645  0.527720\n",
       "2 -0.060884 -0.088839 -0.269744\n",
       "3  0.729817 -0.042234  0.229410\n",
       "4 -1.117705 -0.778368 -1.280790\n",
       "0 -0.396292  0.205355  0.995982\n",
       "1  0.450495 -0.744076 -1.320831\n",
       "2  1.003412 -0.876143  1.677286\n",
       "3 -0.395701 -0.465095  0.287003\n",
       "4  1.442614  0.818815 -0.378552\n",
       "0 -0.196617  0.585134  1.052465\n",
       "1  0.386223 -0.932965  0.749832\n",
       "2  0.721093  1.453562 -0.426798"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "store.close()"
   ]
  }
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