Untitled10.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = mat([1,2,3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(a[0,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "numpy.int64"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = np.array(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d = np.random.normal(size=(10000,2))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "AttributeError",
       "evalue": "'numpy.ndarray' object has no attribute 'hist'",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-53-697e61cebd1b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'hist'"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dd = pd.DataFrame(d[d[0]==0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "d[:,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "array([-0.97988586, -0.09590988,  1.47455414, ...,  1.84963906,\n",
        "        1.72808041, -0.51254215])"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dd = pd.DataFrame(d[abs(d[:,0]-0.0)<0.01])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dd.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "array([[<matplotlib.axes.AxesSubplot object at 0x4c6e750>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x4c8f7d0>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ixAlUVVUN7ZiZ+IAxzMSN0XMemV7ET548iWAwiC+++MJUI/FwEY8x0iW9um0RB4BQKIT5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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x38c3ed0>"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit for i in xrange(1000000): pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 33.9 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit for i in range(1000000): pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 54.9 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit for i in np.arange(100000): pass"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "10 loops, best of 3: 19.1 ms per loop\n"
       ]
      }
     ],
     "prompt_number": 73
    }
   ],
   "metadata": {}
  }
 ]
}