Python pandas.DataFrame.to_dict

pandas_DataFrame_to_dict.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_dict.html\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
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    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_mu</th>\n",
       "      <th>_sigma</th>\n",
       "      <th>normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>100</td>\n",
       "      <td>886.616167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>100</td>\n",
       "      <td>1038.431919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>100</td>\n",
       "      <td>1149.655378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>100</td>\n",
       "      <td>964.461770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>100</td>\n",
       "      <td>921.246646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    _mu  _sigma       normal\n",
       "0  1000     100   886.616167\n",
       "1  1000     100  1038.431919\n",
       "2  1000     100  1149.655378\n",
       "3  1000     100   964.461770\n",
       "4  1000     100   921.246646"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(111)\n",
    "mu, sigma, size = 1000, 100, 5\n",
    "x = pd.DataFrame({\n",
    "        '_mu':mu,\n",
    "        '_sigma':sigma,\n",
    "        'normal':np.random.normal(mu,sigma,size)\n",
    "    })\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_mu': {0: 1000, 1: 1000, 2: 1000, 3: 1000, 4: 1000},\n",
       " '_sigma': {0: 100, 1: 100, 2: 100, 3: 100, 4: 100},\n",
       " 'normal': {0: 886.61616656909678,\n",
       "  1: 1038.4319193399647,\n",
       "  2: 1149.6553776370522,\n",
       "  3: 964.46177028375462,\n",
       "  4: 921.24664590713189}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.to_dict(orient=\"dict\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_mu': [1000, 1000, 1000, 1000, 1000],\n",
       " '_sigma': [100, 100, 100, 100, 100],\n",
       " 'normal': [886.6161665690968,\n",
       "  1038.4319193399647,\n",
       "  1149.6553776370522,\n",
       "  964.4617702837546,\n",
       "  921.2466459071319]}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.to_dict(orient=\"list\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_mu': 0    1000\n",
       " 1    1000\n",
       " 2    1000\n",
       " 3    1000\n",
       " 4    1000\n",
       " Name: _mu, dtype: int64, '_sigma': 0    100\n",
       " 1    100\n",
       " 2    100\n",
       " 3    100\n",
       " 4    100\n",
       " Name: _sigma, dtype: int64, 'normal': 0     886.616167\n",
       " 1    1038.431919\n",
       " 2    1149.655378\n",
       " 3     964.461770\n",
       " 4     921.246646\n",
       " Name: normal, dtype: float64}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.to_dict(orient=\"series\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'columns': ['_mu', '_sigma', 'normal'],\n",
       " 'data': [[1000.0, 100.0, 886.6161665690968],\n",
       "  [1000.0, 100.0, 1038.4319193399647],\n",
       "  [1000.0, 100.0, 1149.6553776370522],\n",
       "  [1000.0, 100.0, 964.4617702837546],\n",
       "  [1000.0, 100.0, 921.2466459071319]],\n",
       " 'index': [0, 1, 2, 3, 4]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.to_dict(orient=\"split\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'_mu': 1000.0, '_sigma': 100.0, 'normal': 886.61616656909678},\n",
       " {'_mu': 1000.0, '_sigma': 100.0, 'normal': 1038.4319193399647},\n",
       " {'_mu': 1000.0, '_sigma': 100.0, 'normal': 1149.6553776370522},\n",
       " {'_mu': 1000.0, '_sigma': 100.0, 'normal': 964.46177028375462},\n",
       " {'_mu': 1000.0, '_sigma': 100.0, 'normal': 921.24664590713189}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.to_dict(orient=\"records\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'_mu': 1000.0, '_sigma': 100.0, 'normal': 886.61616656909678},\n",
       " 1: {'_mu': 1000.0, '_sigma': 100.0, 'normal': 1038.4319193399647},\n",
       " 2: {'_mu': 1000.0, '_sigma': 100.0, 'normal': 1149.6553776370522},\n",
       " 3: {'_mu': 1000.0, '_sigma': 100.0, 'normal': 964.46177028375462},\n",
       " 4: {'_mu': 1000.0, '_sigma': 100.0, 'normal': 921.24664590713189}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.to_dict(orient=\"index\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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    "collapsed": true
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