ch12forth.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pandas import DataFrame, Series\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy.random import randn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.66252951, -0.0956943 , -0.01814607,  0.27970656,  0.29199334,\n",
       "        0.57142159])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = randn(6)\n",
    "arr.sort()\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.06456385e-01,  -1.45108574e-01,   2.23752910e-03,\n",
       "         -7.93935233e-01,  -1.25261759e+00],\n",
       "       [ -3.86344202e-02,  -8.90350295e-01,  -1.28592908e-01,\n",
       "          4.70966622e-01,   2.60679866e+00],\n",
       "       [ -1.63683063e+00,  -1.52783083e+00,   2.17446836e+00,\n",
       "          5.90761795e-01,   1.23646359e+00]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = randn(3, 5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.63683063e+00,  -1.45108574e-01,   2.23752910e-03,\n",
       "         -7.93935233e-01,  -1.25261759e+00],\n",
       "       [ -3.86344202e-02,  -8.90350295e-01,  -1.28592908e-01,\n",
       "          4.70966622e-01,   2.60679866e+00],\n",
       "       [  1.06456385e-01,  -1.52783083e+00,   2.17446836e+00,\n",
       "          5.90761795e-01,   1.23646359e+00]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:, 0].sort() # sort first column values in-place\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.52812602, -0.47370947, -0.71279049, -0.96333218, -0.01685095])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = randn(5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.96333218, -0.71279049, -0.47370947, -0.01685095,  0.52812602])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.52812602, -0.47370947, -0.71279049, -0.96333218, -0.01685095])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.07547164,  1.40813197, -0.41124863,  0.97566277, -0.84580314],\n",
       "       [-1.80792429,  0.24792603,  0.55171647,  0.3270743 , -0.76419124],\n",
       "       [-2.119253  , -0.36267734,  0.14620263, -0.33330987,  0.13864297]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = randn(3, 5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.84580314, -0.41124863,  0.07547164,  0.97566277,  1.40813197],\n",
       "       [-1.80792429, -0.76419124,  0.24792603,  0.3270743 ,  0.55171647],\n",
       "       [-2.119253  , -0.36267734, -0.33330987,  0.13864297,  0.14620263]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sort(axis=1)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.40813197,  0.97566277,  0.07547164, -0.41124863, -0.84580314],\n",
       "       [ 0.55171647,  0.3270743 ,  0.24792603, -0.76419124, -1.80792429],\n",
       "       [ 0.14620263,  0.13864297, -0.33330987, -0.36267734, -2.119253  ]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 4, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = np.array([5, 0, 1, 3, 2])\n",
    "indexer = values.argsort()\n",
    "indexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 5])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values[indexer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.        ,  0.        ,  1.        ,  3.        ,  2.        ],\n",
       "       [ 1.99951353, -0.53906493,  0.69579683,  0.03258102,  0.11506788],\n",
       "       [-0.7260661 ,  0.2464319 , -1.63473778, -0.73729395, -0.51551055]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = randn(3, 5)\n",
    "arr[0] = values\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ,  2.        ,  3.        ,  5.        ],\n",
       "       [-0.53906493,  0.69579683,  0.11506788,  0.03258102,  1.99951353],\n",
       "       [ 0.2464319 , -1.63473778, -0.51551055, -0.73729395, -0.7260661 ]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:, arr[0].argsort()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('Arnold', 'Jane'),\n",
       " ('Arnold', 'Steve'),\n",
       " ('Jones', 'Bill'),\n",
       " ('Jones', 'Bob'),\n",
       " ('Walters', 'Barbara')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_name = np.array(['Bob', 'Jane', 'Steve', 'Bill', 'Barbara'])\n",
    "last_name = np.array(['Jones', 'Arnold', 'Arnold', 'Jones', 'Walters'])\n",
    "sorter = np.lexsort((first_name, last_name))\n",
    "zip(last_name[sorter], first_name[sorter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = np.array(['2:first', '2:second', '1:first', '1:second', '1:third'])\n",
    "key = np.array([2, 2, 1, 1, 1])\n",
    "indexer = key.argsort(kind='mergesort')\n",
    "indexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1:first', '1:second', '1:third', '2:first', '2:second'], \n",
       "      dtype='|S8')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values.take(indexer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([0 ,1,7, 12, 15])\n",
    "arr.searchsorted(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 3, 5], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.searchsorted([0, 8, 11, 16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([0, 0, 0, 1, 1, 1, 1])\n",
    "arr.searchsorted([0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 7], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.searchsorted([0, 1], side='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9986.,  3677.,  9889.,  3336.,  9837.,  7351.,   137.,   585.,\n",
       "        7422.,  1782.,  2792.,  8997.,  1590.,   964.,  9309.,  7001.,\n",
       "        4902.,  3578.,  2276.,  9510.,  1883.,  2554.,   103.,  2502.,\n",
       "        8483.,  2172.,  6500.,   479.,  1694.,  9716.,  2895.,  3034.,\n",
       "        4348.,  5333.,  6565.,  1070.,  8722.,  2138.,  6676.,  2649.,\n",
       "         972.,  9377.,  3012.,  8127.,  9651.,  9553.,  7732.,  4135.,\n",
       "        2381.,  2343.])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.floor(np.random.uniform(0, 10000, size=50))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9986.,  3677.,  9889.,  3336.,  9837.,  7351.,   137.,   585.,\n",
       "        7422.,  1782.,  2792.,  8997.,  1590.,   964.,  9309.,  7001.,\n",
       "        4902.,  3578.,  2276.,  9510.,  1883.,  2554.,   103.,  2502.,\n",
       "        8483.,  2172.,  6500.,   479.,  1694.,  9716.,  2895.,  3034.,\n",
       "        4348.,  5333.,  6565.,  1070.,  8722.,  2138.,  6676.,  2649.,\n",
       "         972.,  9377.,  3012.,  8127.,  9651.,  9553.,  7732.,  4135.,\n",
       "        2381.,  2343.])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.array([0, 100, 1000, 5000, 10000])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 4, 2, 2, 4, 3, 3, 4, 3, 2, 4, 4, 3, 3, 3, 4, 3, 3, 2,\n",
       "       3, 4, 3, 4, 2, 3, 4, 3, 3, 3, 4, 4, 3, 4, 3, 4, 3, 2, 4, 3, 4, 4, 4,\n",
       "       4, 3, 3, 3], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = bins.searchsorted(data)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     540.000000\n",
       "3    2727.956522\n",
       "4    8368.428571\n",
       "dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Series(data).groupby(labels).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 4, 3, 4, 4, 2, 2, 4, 3, 3, 4, 3, 2, 4, 4, 3, 3, 3, 4, 3, 3, 2,\n",
       "       3, 4, 3, 4, 2, 3, 4, 3, 3, 3, 4, 4, 3, 4, 3, 4, 3, 2, 4, 3, 4, 4, 4,\n",
       "       4, 3, 3, 3], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.digitize(data, bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.45557471,  -2.3522518 ,   3.29241388,  -3.63930622],\n",
       "       [  3.01906633,  17.58707481,   9.83978684, -12.16680305],\n",
       "       [ -7.41403321,   3.5149984 ,   2.58014277,   0.43401077],\n",
       "       [ -6.92100309,  -5.45322914,  15.57843171,   4.24318544]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = randn(4, 4)* 10.0\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.45557471,  3.01906633, -7.41403321, -6.92100309])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4.45557471,  -2.3522518 ,   3.29241388,  -3.63930622],\n",
       "       [  3.01906633,  17.58707481,   9.83978684, -12.16680305],\n",
       "       [ -7.41403321,   3.5149984 ,   2.58014277,   0.43401077],\n",
       "       [ -6.92100309,  -5.45322914,  15.57843171,   4.24318544]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = X[:, :1] # 切片操作可产生二维结果\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4.45557471],\n",
       "       [ 3.01906633],\n",
       "       [-7.41403321],\n",
       "       [-6.92100309]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1955.48360634]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(y.T, np.dot(X, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[  4.45557471,  -2.3522518 ,   3.29241388,  -3.63930622],\n",
       "        [  3.01906633,  17.58707481,   9.83978684, -12.16680305],\n",
       "        [ -7.41403321,   3.5149984 ,   2.58014277,   0.43401077],\n",
       "        [ -6.92100309,  -5.45322914,  15.57843171,   4.24318544]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xm = np.matrix(X)\n",
    "ym = Xm[:, 0]\n",
    "Xm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 4.45557471],\n",
       "        [ 3.01906633],\n",
       "        [-7.41403321],\n",
       "        [-6.92100309]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1955.48360634]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ym.T * Xm * ym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[  1.00000000e+00,   1.94289029e-16,   1.11022302e-16,\n",
       "          -1.11022302e-16],\n",
       "        [  2.77555756e-17,   1.00000000e+00,   5.55111512e-17,\n",
       "          -2.63677968e-16],\n",
       "        [  0.00000000e+00,  -5.55111512e-17,   1.00000000e+00,\n",
       "           0.00000000e+00],\n",
       "        [  1.11022302e-16,   1.11022302e-16,   4.44089210e-16,\n",
       "           1.00000000e+00]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xm.I * X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "memmap([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmap = np.memmap('mymmap', dtype='float64', mode='w+', shape=(10000, 10000))\n",
    "mmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "memmap([[-1.41147797,  1.23117177, -1.86859027, ..., -0.79980231,\n",
       "        -2.24514314,  1.23074082],\n",
       "       [ 0.44013742, -0.30032517, -1.25904478, ...,  0.0916274 ,\n",
       "         0.4481306 , -1.47859898],\n",
       "       [ 0.08378055,  0.00412298,  0.29392474, ...,  0.03898427,\n",
       "        -0.41463042,  0.90307623],\n",
       "       ..., \n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "section = mmap[:5]\n",
    "section[:] = np.random.randn(5, 10000)\n",
    "mmap.flush()\n",
    "mmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del mmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "memmap([[-1.41147797,  1.23117177, -1.86859027, ..., -0.79980231,\n",
       "        -2.24514314,  1.23074082],\n",
       "       [ 0.44013742, -0.30032517, -1.25904478, ...,  0.0916274 ,\n",
       "         0.4481306 , -1.47859898],\n",
       "       [ 0.08378055,  0.00412298,  0.29392474, ...,  0.03898427,\n",
       "        -0.41463042,  0.90307623],\n",
       "       ..., \n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "         0.        ,  0.        ]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmap = np.memmap('mymmap', dtype='float64', shape=(10000, 10000))\n",
    "mmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : True\n",
       "  F_CONTIGUOUS : False\n",
       "  OWNDATA : True\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_c = np.ones((1000, 1000), order='C')\n",
    "arr_f = np.ones((1000, 1000), order='F')\n",
    "arr_c.flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : False\n",
       "  F_CONTIGUOUS : True\n",
       "  OWNDATA : True\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_f.flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_f.flags.f_contiguous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 1.28 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit arr_c.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 828 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit arr_f.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : True\n",
       "  F_CONTIGUOUS : False\n",
       "  OWNDATA : True\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_f.copy('C').flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_c[:50].flags.contiguous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : False\n",
       "  F_CONTIGUOUS : False\n",
       "  OWNDATA : False\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_c[:, :50].flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "name": "python",
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