d2207197/random.ipynb

## random.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matplotlib.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import  numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x4792290>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4fcb790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(np.random.rand(10000)).hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5f7a6d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EdXX1iPXr16/H+vXrR6xrbW3FjRs3UFlZCbfbjbKyMpw5cwaCIMQ2NRERjUvEbpzs7GzM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5e4f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(np.random.randn(10000)).hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x645c390>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x634c250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(random.random() for _ in range(10000)).hist(bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://gist.github.com/4691a1336fbf4523ea55\r\n"
     ]
    }
   ],
   "source": [
    "!gist random.ipynb"
   ]
  },
  {
   "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": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}