{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0.],\n       [0., 0., 0., 0., 0.]])"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((5,5))\n",
    "a"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.,  0.,  0.,  0.,  0.],\n       [ 0.,  0.,  0.,  0.,  0.],\n       [ 0.,  0., 10.,  0.,  0.],\n       [ 0.,  0.,  0.,  0.,  0.],\n       [ 0.,  0.,  0.,  0.,  0.]])"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2,2] = 10\n",
    "a"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "index_array = np.array([2,2], dtype=int)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "234 ns ± 7.47 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n",
      "311 ns ± 2.15 ns per loop (mean ± std. dev. of 7 runs, 1,000,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit a[tuple(index_array.tolist())]\n",
    "%timeit a[index_array[0], index_array[1]]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "def array_change(array):\n",
    "    array[1] = 1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.,  0.,  0.,  0.,  0.],\n       [ 1.,  1.,  1.,  1.,  1.],\n       [ 0.,  1., 10.,  0.,  0.],\n       [ 1.,  1.,  1.,  1.,  1.],\n       [ 0.,  0.,  0.,  0.,  0.]])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_change(a[2:])\n",
    "a"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ 0.,  0.,  0.,  0.,  0.],\n       [ 1.,  1.,  1.,  1.,  1.],\n       [ 0.,  1., 10.,  0.,  0.],\n       [ 0.,  0.,  0.,  0.,  0.],\n       [ 0.,  0.,  0.,  0.,  0.]])"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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