Appendix A - Advanced NumPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "#### We will go deeper into the NumPy library for array computing.\n",
    "#### More internal details about ndarray type and advanced array manipulations and algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ndarray Object Internals\n",
    "#### The NumPy ndarray provides way to interpret block of homogenous data (continous or strided) as multi-dimensional array object.\n",
    "#### The data-type (dtype) determines how data is interpreted (floating point, integer, boolean, etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ndarray is flexible because every array object is a 'strided' view on a block of data. eg - arr[::2, ::-1] does not copy the data.\n",
    "#### ndarray is more than just chunk of memory and a dtype, it also has striding information. This allows array to move through memory with varying step sizes.\n",
    "#### Internally, ndarray consists of the following:\n",
    "####     1. Pointer to data i.e. block of RAM or memory-mapped file\n",
    "####     2. Data type or dtype, describing fixed-size value cells in array\n",
    "####     3. Tuple indicating array's 'shape'\n",
    "####     4. A tuple of strides, integers indicating number of 'steps' to advance to the next element."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Image of ndarray internal structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(\"images/ndarrayInnards.png\", width=1000, height=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A 10 * 5 array would have shape (10, 5).\n",
    "#### A typical order of 3 * 4 * 5 array of float64(8-byte) has strides (160, 40, 8). The larger the strides along an axis, the costlier to perform computation along it.\n",
    "#### Knowing strides is critical ingredient in constructing 'zero-copy' array views. Strides can be negative, enabling to move backward through memory. eg - obj[:, ::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 5)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.ones((10, 5)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160, 40, 8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3, 4, 5), dtype=np.float64).strides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NumPy dtype Hierarchy\n",
    "#### You may need to check if array contains integers, float, strings, etc.\n",
    "#### There are multiples of floating-point numbers (float16 through float128). So checking a dtype would be very verbose.\n",
    "#### For this reason, dtypes have superclasses (eg - np.integer, np.floating) which can be used in conjunction with 'np.issubdtype' function.\n",
    "#### To know parent classes of specific dtype, call the type's 'mro' method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ints = np.ones(10, dtype=np.uint16)\n",
    "\n",
    "floats = np.ones(10, dtype=np.float32)\n",
    "\n",
    "np.issubdtype(ints.dtype, np.integer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.issubdtype(floats.dtype, np.floating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[numpy.float64,\n",
       " numpy.floating,\n",
       " numpy.inexact,\n",
       " numpy.number,\n",
       " numpy.generic,\n",
       " float,\n",
       " object]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.float64.mro()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.issubdtype(ints.dtype, np.number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph of dtype hierarchy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"images/dtypeHierarchy.png\", width=1000, height=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Array Manipulation\n",
    "#### There are many ways to work with arrays other than indexing, slicing, etc.\n",
    "#### Much of the heavy-lifting for data analysis is handled by higher-level functions.\n",
    "#### But at some point you may need to write an algorithm that is not existing in any library."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping Arrays\n",
    "#### In most cases, we can convert an array from one shape to another without copying data. Pass a tuple indicating new shape to 'reshape' instance method.\n",
    "#### A multidimensional array can also be reshaped. If we pass -1 to a shape dimension, the value used for that dimension will be inferred from data.\n",
    "#### As array's shape is a tuple, it can be passed to 'reshape' too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(8)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape((4, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape((4, 2)).reshape((2, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11],\n",
       "       [12, 13, 14]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(15)\n",
    "arr.reshape((5, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_arr = np.ones((3, 5))\n",
    "other_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(other_arr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The opposite operation to reshape , from 1-dimensional to higher, is known as 'flattening' or 'raveling'.\n",
    "#### ravel does not produce a copy of underlying values if values in result were contiguous in original array.\n",
    "#### flatten behaves like ravel except it always return copy of data. Data can be reshaped or raveled in different orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11],\n",
       "       [12, 13, 14]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(15).reshape((5, 3))\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C Versus Fortran Order\n",
    "#### NumPy gives control and flexibility over layout of data in memory. By default, arrays created are in 'row major' order.\n",
    "#### Spatially, this means that for a 2-dimensional array, items in each row are stored in adjacent memory locations.\n",
    "#### Alternative is 'column major' order, values within each column are stored in adjacent memory locations.\n",
    "#### 'Row major' is known as 'C order' and 'Column major' is known as 'Fortran order'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Functions like reshape and ravel accept 'order' argument, indicating order to use data in the array. It is usually set to 'C' or 'F'.\n",
    "#### Reshaping arrays with more than 2 dimensions can be mind-bending. Key difference between C and Fortran order is the way in which dimensions are walked:\n",
    "####     1. C / row major order - Traverse higher dimensions first (eg - axis 1 before advancing on axis 0)\n",
    "####     2. Fortran / column major order - Traverse higher dimensions last (eg - axis 0 before advancing on axis 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(12).reshape((3, 4))\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4,  8,  1,  5,  9,  2,  6, 10,  3,  7, 11])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ravel('F')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate and Splitting Arrays\n",
    "#### 'numpy.concatenate' takes a sequence (eg - tuple, list, etc.) of arrays and joins them together along input axis.\n",
    "#### Convinience functions like 'vstack' and 'hstack' are also used for common concatenation operations.\n",
    "#### 'split' slices apart an array into multiple arrays along given axis. The array passed to the method indicates at which indices the array needs to be split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "arr2 = np.array([[7, 8, 9], [10, 11, 12]])\n",
    "\n",
    "np.concatenate([arr1, arr2], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  7,  8,  9],\n",
       "       [ 4,  5,  6, 10, 11, 12]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate([arr1, arr2], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((arr1, arr2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  7,  8,  9],\n",
       "       [ 4,  5,  6, 10, 11, 12]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((arr1, arr2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.44651107,  0.33143854],\n",
       "       [-0.64444668, -0.57585883],\n",
       "       [-1.59243462, -0.92526308],\n",
       "       [ 0.67906636,  0.73352484],\n",
       "       [ 0.88586877, -1.71940675]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(5, 2)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.44651107, 0.33143854]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first, second, third = np.split(arr, [1, 3])\n",
    "\n",
    "first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.64444668, -0.57585883],\n",
       "       [-1.59243462, -0.92526308]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.67906636,  0.73352484],\n",
       "       [ 0.88586877, -1.71940675]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "third"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stacking Helpers : r_ and c_\n",
    "#### There are 2 special objects in NumPy namespace, r_ and c_, that make stacking arrays more concise.\n",
    "#### r_ is row-wise stacking i.e. vstack or concatenating along axis=0.\n",
    "#### c_ is row-wise stacking i.e. hstack or concatenating along axis=1.\n",
    "#### They can also translate slices into arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ],\n",
       "       [ 2.        ,  3.        ],\n",
       "       [ 4.        ,  5.        ],\n",
       "       [-0.9369448 , -0.28500204],\n",
       "       [-1.61286672,  0.37753844],\n",
       "       [ 1.79143154,  0.03675487]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(6)\n",
    "arr1 = arr.reshape((3, 2))\n",
    "arr2 = np.random.randn(3, 2)\n",
    "\n",
    "np.r_[arr1, arr2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ,  0.        ],\n",
       "       [ 2.        ,  3.        ,  1.        ],\n",
       "       [ 4.        ,  5.        ,  2.        ],\n",
       "       [-0.9369448 , -0.28500204,  3.        ],\n",
       "       [-1.61286672,  0.37753844,  4.        ],\n",
       "       [ 1.79143154,  0.03675487,  5.        ]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.c_[np.r_[arr1, arr2], arr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1, -10],\n",
       "       [  2,  -9],\n",
       "       [  3,  -8],\n",
       "       [  4,  -7],\n",
       "       [  5,  -6]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.c_[1:6, -10:-5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeating Elements: tile and repeat\n",
    "#### 'repeat' and 'tile' are 2 useful tools for repeating or replicating arrays to produce larger arrays.\n",
    "#### 'repeat' replicates each element in array some number of times, producing larger array.\n",
    "#### If you pass an integer to the method, each element of the array will be repeated that number of times. If you pass an array of integers, each element will be repeated different number of times.\n",
    "#### Multidimensional arrays can have elements repeated along a particular axis. If no axis is passed, the array will be flattened.\n",
    "#### If you pass an array of integers for multidimensional array, then different slices will be repeated different number of times.\n",
    "#### NOTE - Replicating or Repeating is not such a necessity with NumPy than it is with other languages like MATLAB. Features like 'broadcasting' often fill such needs better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(3)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 2, 2, 2])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.repeat(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 1, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.repeat([2, 3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(2, 2)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24616211, -1.67000187],\n",
       "       [-1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758],\n",
       "       [-0.36830961,  0.71553758]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.repeat(2, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24616211, -1.67000187],\n",
       "       [-1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758],\n",
       "       [-0.36830961,  0.71553758],\n",
       "       [-0.36830961,  0.71553758]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.repeat([2, 3], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24616211, -1.24616211, -1.67000187, -1.67000187, -1.67000187],\n",
       "       [-0.36830961, -0.36830961,  0.71553758,  0.71553758,  0.71553758]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.repeat([2, 3], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 'tile' on the other hand, is shortcut for stocking copies of an array along an axis. Visually, it is similar to 'laying down tiles'.\n",
    "#### Second argument is number of tiles. For scalar value, tiling is done row-by-row. We can also pass a tuple indicating the layout of the tiling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24616211, -1.67000187, -1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758, -0.36830961,  0.71553758]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(arr, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758],\n",
       "       [-1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(arr, (2, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24616211, -1.67000187, -1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758, -0.36830961,  0.71553758],\n",
       "       [-1.24616211, -1.67000187, -1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758, -0.36830961,  0.71553758],\n",
       "       [-1.24616211, -1.67000187, -1.24616211, -1.67000187],\n",
       "       [-0.36830961,  0.71553758, -0.36830961,  0.71553758]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(arr, (3, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fancy Indexing Equivalents: take and put\n",
    "#### One way to get and set subsets of arrays is by 'fancy' indexing using integer arrays.\n",
    "#### Some alternative ndarray methods to this are useful in special case of only making selection on a single axis. eg - 'put'\n",
    "#### To use 'take' along other axes, you can pass 'axis' keyword.\n",
    "#### 'put' does not accept an axis argument but rather indexes into flattened (1-Dimensional, C order) version of the array. So, if you want to set elements using index array on other axes, it is often easier to use fancy indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([700, 100, 200, 600])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fancy indexing\n",
    "\n",
    "arr = np.arange(10) * 100\n",
    "inds = [7, 1, 2, 6]\n",
    "\n",
    "arr[inds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([700, 100, 200, 600])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.take(inds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,  42,  42, 300, 400, 500,  42,  42, 800, 900])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.put(inds, 42)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,  41,  42, 300, 400, 500,  43,  40, 800, 900])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.put(inds, [40, 41, 42, 43])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.65304121,  0.50898693,  0.60680346, -1.25271469],\n",
       "       [ 0.2077261 ,  0.22888488, -0.1354729 , -1.40332399]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inds = [2, 0, 2, 1]\n",
    "arr = np.random.randn(2, 4)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.60680346,  0.65304121,  0.60680346,  0.50898693],\n",
       "       [-0.1354729 ,  0.2077261 , -0.1354729 ,  0.22888488]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.take(inds, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting\n",
    "#### Broadcasting describes how arithemetic works between arrays of different shapes. It can be a powerful feature but also cause confusion.\n",
    "#### The simplest example of broadcasting use is combining scalar value with an array. We can say that the scalar value (eg - 4) has been broadcast to all elements in the array operation (eg - multiplication)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  4,  8, 12, 16])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr * 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### But using broadcasting for other operations (eg - demeaning row means from each column of a 2-dimensional array) requires understanding of the Broadcasting Rule.\n",
    "### The Broadcasting Rule:\n",
    "### \"Two arrays are compatible for broadcasting if for each trailing dimension (i.e. starting from end) the axis lengths match or if either of the lengths is 1. Broadcasting is then performed over the missing or length 1 dimensions\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.94091687, -0.87204289,  1.75735047],\n",
       "       [ 0.63509543,  0.08422455, -0.84315407],\n",
       "       [ 0.34604284, -0.39524823,  1.13449633],\n",
       "       [ 0.31821292, -1.34353227, -0.96158149]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(4, 3)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.56006702, -0.63164971,  0.27177781])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.38084986, -0.24039318,  1.48557266],\n",
       "       [ 0.07502842,  0.71587426, -1.11493188],\n",
       "       [-0.21402418,  0.23640148,  0.86271852],\n",
       "       [-0.24185409, -0.71188256, -1.2333593 ]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demeaned = arr - arr.mean(0)\n",
    "demeaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-9.71445147e-17, -8.32667268e-17, -1.11022302e-16])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demeaned.mean(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.94091687, -0.87204289,  1.75735047],\n",
       "       [ 0.63509543,  0.08422455, -0.84315407],\n",
       "       [ 0.34604284, -0.39524823,  1.13449633],\n",
       "       [ 0.31821292, -1.34353227, -0.96158149]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Demeaning row mean from columns\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_means = arr.mean(1)\n",
    "\n",
    "row_means.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.60874149],\n",
       "       [-0.04127803],\n",
       "       [ 0.36176365],\n",
       "       [-0.66230028]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_means.reshape((4, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, -7.40148683e-17])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demeaned = arr - row_means.reshape((4, 1))\n",
    "\n",
    "demeaned.mean(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting Over Other Axes\n",
    "#### Broadcasting with higher-dimensional arrays seems to be very complicated, but is simply a matter of following rules.\n",
    "#### In some scenarios, you may want to perform arithemetic operation with lower dimension array on an axes other than 0.\n",
    "#### According to Broadcast rule, the 'broadcast dimensions' must be 1 in the smaller array. eg - Reshaping row to (4, 1) instead of (4,)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (4,3) (4,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-92-195a30ae1a36>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Error when broadcast rules are broken\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0marr\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,3) (4,) "
     ]
    }
   ],
   "source": [
    "# Error when broadcast rules are broken\n",
    "arr - arr.mean(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.33217539, -1.48078437,  1.14860899],\n",
       "       [ 0.67637346,  0.12550258, -0.80187604],\n",
       "       [-0.01572081, -0.75701187,  0.77273268],\n",
       "       [ 0.9805132 , -0.68123199, -0.29928121]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr - arr.mean(1).reshape((4, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Even when broadcasting over three dimensions, it is only a matter of reshaping the data to be shape-compatible.\n",
    "#### Below diagram shows the shapes required to broadcast over each axis of a 3-dimensional array."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compatible 2D array shapes for broadcasting over 3D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 95,
     "metadata": {
      "image/png": {
       "height": 1000,
       "width": 1000
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"images/broadcast3d.png\", width=1000, height=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### A common issue is needing to add a new axis with length 1 specifically for broadcasting purposes.\n",
    "#### 'reshape' is an option, but inserting an axis requires constructing a tuple indicating new shape. This can be quite tedious.\n",
    "#### NumPy arrays offer special syntax 'np.newaxis' which we can use with \"full\" slices to insert new axis.\n",
    "#### We can even generalize \"demeaning\" over an axis without sacrificing performance. But it requires lots of indexing work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 1, 4)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding new axis\n",
    "arr = np.zeros((4, 4))\n",
    "arr_3d = arr[:, np.newaxis, :]\n",
    "arr_3d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.01879652],\n",
       "       [ 0.11816453],\n",
       "       [-1.29511066]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d = np.random.normal(size=3)\n",
    "arr_1d[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.01879652,  0.11816453, -1.29511066]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_1d[np.newaxis, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.08010598,  0.38483531, -0.09249923,  0.1210798 ],\n",
       "       [ 0.67287436, -0.44134396, -0.17579773, -0.12343376],\n",
       "       [ 0.39927106,  0.74153769,  0.24253089, -0.08141809]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Demeaning axis 2 in a 3D array\n",
    "arr = np.random.randn(3, 4, 5)\n",
    "depth_means = arr.mean(2)\n",
    "\n",
    "depth_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth_means.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.52655666e-17,  0.00000000e+00, -4.44089210e-17,\n",
       "        -4.44089210e-17],\n",
       "       [ 0.00000000e+00, -4.44089210e-17,  1.66533454e-17,\n",
       "         4.44089210e-17],\n",
       "       [-2.22044605e-17, -8.88178420e-17, -2.22044605e-17,\n",
       "        -2.22044605e-17]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demeaned = arr - depth_means[:, :, np.newaxis]\n",
    "demeaned.mean(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generalization of demeaning\n",
    "def demean_axis(arr, axis=0):\n",
    "    means = arr.mean(axis)\n",
    "    \n",
    "    # This generalizes [:, :, np.newaxis] to N dimensions\n",
    "    indexer = [slice(None)] * arr.ndim\n",
    "    indexer[axis] = np.newaxis\n",
    "    return arr - means[indexer]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Array Values by Broadcasting\n",
    "#### The same broadcasting rule governing arithematic operations also applies to setting values via array indexing.\n",
    "#### But if we have a 1D array that we want to set into columns of the array, we can still do it as long as shape is compatible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 5., 5.],\n",
       "       [5., 5., 5.],\n",
       "       [5., 5., 5.],\n",
       "       [5., 5., 5.]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.zeros((4, 3))\n",
    "arr[:] = 5\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.28,  1.28,  1.28],\n",
       "       [-0.42, -0.42, -0.42],\n",
       "       [ 0.44,  0.44,  0.44],\n",
       "       [ 1.6 ,  1.6 ,  1.6 ]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = np.array([1.28, -0.42, 0.44, 1.6])\n",
    "\n",
    "arr[:] = col[:, np.newaxis]\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.37 , -1.37 , -1.37 ],\n",
       "       [ 0.509,  0.509,  0.509],\n",
       "       [ 0.44 ,  0.44 ,  0.44 ],\n",
       "       [ 1.6  ,  1.6  ,  1.6  ]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:2] = [[-1.37], [0.509]]\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced ufunc Usage\n",
    "#### Although universal functions provide fast element-wise operations, There are a number of additional features that occasionally help write more concise code without loops."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ufunc Instance Methods\n",
    "#### Each of NumPy's binary ufuncs has special methods for performing certain kinds of special vectorized operations.\n",
    "#### 'reduce' takes a single array and aggregates it to its values, optionally along its axis, by performing binary operations. eg - To add elements in an array call 'np.add.reduce'.\n",
    "#### The starting value depends on the ufunc (0 for add). If an axis is passed, the reduction is done along that axis. This provides concise answers to some questions. eg - 'np.logical_and' can check if values in each row are sorted.\n",
    "#### 'np.logical_and.reduce' is equivalent to 'all' method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10)\n",
    "np.add.reduce(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True],\n",
       "       [ True, False,  True, False],\n",
       "       [ True,  True,  True,  True],\n",
       "       [False,  True, False, False],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(123456)\n",
    "\n",
    "arr = np.random.randn(5, 5)\n",
    "arr[::2].sort(1) # Sort some rows\n",
    "\n",
    "arr[:, :-1] < arr[:, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, False,  True])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logical_and.reduce(arr[:, :-1] < arr[:, 1:], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 'accumulate' produces an array of the same size with 'intermediate accumulated' values. Like cumsum does for sum.\n",
    "#### 'outer' performs pairwise cross-product between two arrays. Its output will have a dimension that is sum of the dimensions of inputs.\n",
    "#### 'reduceat' performs 'local reduce' i.e a 'groupby' in which slices of array are aggregated together. It accepts 'bin edges' which indicate how to split and aggregate values. eg - For [0, 5, 8] reductions are performed over [0:5], [5:8] and [8:]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  3,  6, 10],\n",
       "       [ 5, 11, 18, 26, 35],\n",
       "       [10, 21, 33, 46, 60]], dtype=int32)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(15).reshape((3, 5))\n",
    "np.add.accumulate(arr, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 2, 2])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(3).repeat([1, 2, 2])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 1, 2, 3, 4],\n",
       "       [0, 2, 4, 6, 8],\n",
       "       [0, 2, 4, 6, 8]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.multiply.outer(arr, np.arange(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 5)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = np.random.randn(3, 4), np.random.randn(5)\n",
    "\n",
    "result = np.subtract.outer(x, y)\n",
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 18, 17], dtype=int32)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10)\n",
    "np.add.reduceat(arr, [0, 5, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0,  0,  0],\n",
       "       [ 0,  1,  2,  3,  4],\n",
       "       [ 0,  2,  4,  6,  8],\n",
       "       [ 0,  3,  6,  9, 12]])"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.multiply.outer(np.arange(4), np.arange(5))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  0,  0],\n",
       "       [ 1,  5,  4],\n",
       "       [ 2, 10,  8],\n",
       "       [ 3, 15, 12]], dtype=int32)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add.reduceat(arr, [0, 2, 4], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing New ufuncs in Python\n",
    "#### There are many facilities to create your own NumPy ufuncs. Most general is NumPy C API. But here we will focus on pure Python ufuncs.\n",
    "#### 'numpy.frompyfunc' accepts a Python function along with specification for number of inputs and outputs.\n",
    "#### Functions created in this way always return arrays of Python objects, which can be inconvinient. But 'numpy.vectorize' allows us to specify output type.\n",
    "#### These functions allow us to create ufunc-like functions, but they are very slow because they require Python function call to compute each element, which is slower than NumPy's C-based ufunc loops."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8, 10, 12, 14], dtype=object)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add_elements(x, y):\n",
    "    return x + y\n",
    "\n",
    "add_them = np.frompyfunc(add_elements, 2, 1)\n",
    "add_them(np.arange(8), np.arange(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  2.,  4.,  6.,  8., 10., 12., 14.])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.vectorize allows to determine output\n",
    "add_them = np.vectorize(add_elements, otypes=[np.float64])\n",
    "\n",
    "add_them(np.arange(8), np.arange(8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.58 ms ± 17.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Python ufuncs are slow\n",
    "arr = np.random.randn(10000)\n",
    "\n",
    "% timeit add_them(arr, arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.77 µs ± 71.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Default numpy C based functions, comparatively are very fast\n",
    "%timeit np.add(arr, arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Structured and Record Arrays\n",
    "#### ndarray is a homogenous data container i.e. each element takes up same number of bytes based on dtype.\n",
    "#### On surface, this does not allow you to represent heterogenous or tabular-like data.\n",
    "#### a 'structured' array is an ndarray where each element can be looked at as representing a 'struct' in C or a row in SQL table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1.5       ,  6), (3.14159265, -2)],\n",
       "      dtype=[('x', '<f8'), ('y', '<i4')])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtype = [('x', np.float64), ('y', np.int32)]\n",
    "\n",
    "sarr = np.array([(1.5, 6), (np.pi, -2)], dtype=dtype)\n",
    "\n",
    "sarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are many ways to specify a structured dtype. One way is as list of tuples with '(field_name, field_data_type)'.\n",
    "#### The elements of such structure are tuple-like objects, whose elements can be accessed like a dictionary.\n",
    "#### field names are stored in 'dtype.names' attribute. When accessing a field on structured array, strided view is returned, copying nothing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5, 6)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarr[0]['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.5       , 3.14159265])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sarr['x']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nested dtypes and Multidimensional Fields\n",
    "#### When specifying structured dtype, you can pass a shape (int or tuple).\n",
    "#### In below example, 'x' refers to an array of length 3 for each record. and accessing \"arr['x']\" returns a 2D array instead of 1D array.\n",
    "#### This enables us to express more complicated, nested structures as a single block of memory in an array.\n",
    "#### pandas DataFrame does not support this feature directly, but it is similar to hierarchical indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([([0, 0, 0], 0), ([0, 0, 0], 0), ([0, 0, 0], 0), ([0, 0, 0], 0)],\n",
       "      dtype=[('x', '<i8', (3,)), ('y', '<i4')])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 'x' is 3 elements of int64 and 'y' is 1 element of int32\n",
    "dtype = [('x', np.int64, 3), ('y', np.int32)]\n",
    "\n",
    "# 4 records of 'dtype'\n",
    "arr = np.zeros(4, dtype=dtype)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0]['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 0],\n",
       "       [0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1., 2.), (3., 4.)], dtype=[('a', '<f8'), ('b', '<f4')])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtype = [('x', [('a', 'f8'), ('b', 'f4')]), ('y', np.int32)]\n",
    "\n",
    "data = np.array([((1, 2), 5), ((3, 4), 6)], dtype=dtype)\n",
    "\n",
    "data['x']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 3.])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['x']['a']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Why use Structured Arrays?\n",
    "#### Compared to pandas DataFrame, NumPy structured arrays are comparatively low-level tool.\n",
    "#### They provide an opportunity to interpret a block of memory as tabular structure with arbitrarily complex nested columns.\n",
    "#### As each element in array is represented in memory as fixed number of bytes, structured arrays provide fast and efficient way of writing data to and from disk, transporting over network, etc.\n",
    "#### In legacy systems, writing data files as fixed-length record byte streams is common way to serialize data in C and C++.\n",
    "#### As long as file format is known (order, byte size, data type), data can be read into memory with 'np.fromfile'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More about Sorting\n",
    "#### Like 'list' ndarray's instance method 'sort' is an in-place sort i.e array contents are rearranged without creating new array.\n",
    "#### 1 thing to remember is that if the array is a view on a different ndarray, the original array will also be modified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.90893859, -0.0998147 ,  0.35584976,  0.43531326,  0.67911044,\n",
       "        1.2145555 ])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(6)\n",
    "arr.sort()\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.2625154 ,  0.02812367,  0.87458306,  0.01972901,  1.10790981],\n",
       "       [ 0.3825507 , -0.5565042 ,  0.78636179, -0.38964038, -0.56135084],\n",
       "       [-0.1724924 ,  0.24018256, -1.77194708,  0.82789246, -0.77915441]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(3, 5)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.2625154 ,  0.02812367,  0.87458306,  0.01972901,  1.10790981],\n",
       "       [-0.1724924 , -0.5565042 ,  0.78636179, -0.38964038, -0.56135084],\n",
       "       [ 0.3825507 ,  0.24018256, -1.77194708,  0.82789246, -0.77915441]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort first column\n",
    "arr[:, 0].sort()\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### On other hand, 'numpy.sort' creates a new, sorted copy of an array. Else, it can also accept same arguments (eg - 'kind') as 'ndarray.sort'.\n",
    "#### All the sorting methods take an axis argument for sorting sections of data along passed axis independently.\n",
    "#### The sort methods do not have the option to sort in descending order. This is a problem, because array slicing produces views, thus not producing a copy or requiring computational work.\n",
    "#### In this case, the same trick that works for lists ([::-1]) will also work for ndarrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.00592983,  1.24347796,  0.70024873,  0.64729809,  0.31218427],\n",
       "       [-3.05614302,  0.1113004 , -0.01858619,  2.01359289,  0.81407983],\n",
       "       [-1.23442883,  0.91624336,  0.3461365 , -0.92729173,  0.38947689]])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(3, 5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.00592983,  0.31218427,  0.64729809,  0.70024873,  1.24347796],\n",
       "       [-3.05614302, -0.01858619,  0.1113004 ,  0.81407983,  2.01359289],\n",
       "       [-1.23442883, -0.92729173,  0.3461365 ,  0.38947689,  0.91624336]])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sort(axis=1)\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.24347796,  0.70024873,  0.64729809,  0.31218427, -1.00592983],\n",
       "       [ 2.01359289,  0.81407983,  0.1113004 , -0.01858619, -3.05614302],\n",
       "       [ 0.91624336,  0.38947689,  0.3461365 , -0.92729173, -1.23442883]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:, ::-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indirect Sorts: argsort and lexsort\n",
    "#### In some cases you need to reorder datsets by one or more keys. This is an example of 'indirect' sort.\n",
    "#### Given a key or keys (an array of values or multiple arrays of values) we need to obtain array of integer indices that tells how to reorder data to be in sorted order.\n",
    "#### 2 methods for this are 'argsort' and 'numpy.lexsort'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 4, 3, 0], dtype=int64)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = np.array([5, 0, 1, 3, 2])\n",
    "\n",
    "indexer = values.argsort()\n",
    "\n",
    "indexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 5])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values[indexer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5.        ,  0.        ,  1.        ,  3.        ,  2.        ],\n",
       "       [-0.08944073,  0.68565292, -0.88063003,  1.52901185, -0.43166578],\n",
       "       [ 0.27258011,  0.44695264, -0.24457814,  0.2242489 , -0.36752189]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(3, 5)\n",
    "\n",
    "arr[0] = values\n",
    "\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  1.        ,  2.        ,  3.        ,  5.        ],\n",
       "       [ 0.68565292, -0.88063003, -0.43166578,  1.52901185, -0.08944073],\n",
       "       [ 0.44695264, -0.24457814, -0.36752189,  0.2242489 ,  0.27258011]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[:, arr[0].argsort()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 'lexsort' is similar to argsort, but performs indirect/'lexicographical' sort on multiple key arrays.\n",
    "#### It can be a bit confusing during first use because the order in which keys are used to order data starts with 'last' array passed as parameter.\n",
    "#### NOTE - pandas methods like 'sort_values' for Series and DataFrame objects are implemented with variants of argsort and lexsort."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0, 4], dtype=int64)"
      ]
     },
     "execution_count": 160,
     "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",
    "\n",
    "sorter = np.lexsort((first_name, last_name))\n",
    "sorter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zip at 0x24fa89d9c48>"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip(last_name[sorter], first_name[sorter])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Alternative Sort Algorithms\n",
    "#### A stable sorting algorithm preserves relative positions of equal elements. This is especially important in indirect sorts where relative ordering is meaningful.\n",
    "#### The only stable sort available is 'mergesort'. It has a guaranteed 'O(n log n)'performance, but its performance is on average worse than the default 'quicksort' method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values = np.array(['2:first', '2:second', '1:first', '1:second',\n",
    "                  '1:third'])\n",
    "key = np.array([2, 2, 1, 1, 1])\n",
    "\n",
    "indexer = key.argsort(kind='mergesort')\n",
    "\n",
    "indexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1:first', '1:second', '1:third', '2:first', '2:second'],\n",
       "      dtype='<U8')"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "values.take(indexer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The table of different sorts with relative performance is shown below -\n",
    "\n",
    "#### Kind             Speed         Stable       Work space        Worst case\n",
    "#### 'quicksort'       1               No            0                          O(n^2)\n",
    "#### 'mergesort'      2              Yes           n / 2                     O(n log n)\n",
    "#### 'heapsort'        3               No            0                          O(n log n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Partially Sorted Arrays\n",
    "#### A goal of sorting can be to determine the largest or smallest element in an array.\n",
    "#### NumPy has optimized methods, 'numpy.partition' and 'np.argpartition'for partitioning array around k-th smallest element.\n",
    "#### eg - For 'partition(arr, 3)', the first 3 elements in the result are smallest 3 elements in no particular order.\n",
    "#### 'numpy.argpartition' is similar to 'numpy.argsort' and returns indices that rearrange the data into equivalent order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.20470766,  0.47894334, -0.51943872, -0.5557303 ,  1.96578057,\n",
       "        1.39340583,  0.09290788,  0.28174615,  0.76902257,  1.24643474,\n",
       "        1.00718936, -1.29622111,  0.27499163,  0.22891288,  1.35291684,\n",
       "        0.88642934, -2.00163731, -0.37184254,  1.66902531, -0.43856974])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(12345)\n",
    "arr = np.random.randn(20)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.00163731, -1.29622111, -0.5557303 , -0.51943872, -0.37184254,\n",
       "       -0.43856974, -0.20470766,  0.28174615,  0.76902257,  0.47894334,\n",
       "        1.00718936,  0.09290788,  0.27499163,  0.22891288,  1.35291684,\n",
       "        0.88642934,  1.39340583,  1.96578057,  1.66902531,  1.24643474])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.partition(arr, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([16, 11,  3,  2, 17, 19,  0,  7,  8,  1, 10,  6, 12, 13, 14, 15,  5,\n",
       "        4, 18,  9], dtype=int64)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = np.argpartition(arr, 3)\n",
    "indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2.00163731, -1.29622111, -0.5557303 , -0.51943872, -0.37184254,\n",
       "       -0.43856974, -0.20470766,  0.28174615,  0.76902257,  0.47894334,\n",
       "        1.00718936,  0.09290788,  0.27499163,  0.22891288,  1.35291684,\n",
       "        0.88642934,  1.39340583,  1.96578057,  1.66902531,  1.24643474])"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.take(indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.searchsorted: Finding Elements in a Sorted Array\n",
    "#### 'searchsorted' is an array method that performs a binary search on sorted array, returning location where a value needs to be inserted to maintain sortedness.\n",
    "#### We can also pass array of values to get array of indices back. \n",
    "#### For values that are equal to existing elements, the default behaviour is to return index at left side of group of equal values. We can change this behaviour by passing 'side' argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([0, 1, 7, 12, 15])\n",
    "arr.searchsorted(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3, 3, 5], dtype=int64)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.searchsorted([0, 8, 11, 16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 3], dtype=int64)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search for Equal values\n",
    "arr = np.array([0, 0, 0, 1, 1, 1, 1])\n",
    "\n",
    "arr.searchsorted([0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 7], dtype=int64)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change default behaviour\n",
    "arr.searchsorted([0, 1], side='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Another application for searchsorted is to bin data using a seperate array of 'bucket edges'.\n",
    "#### We then use searchsorted to get a label of which interval each data point belonged to.\n",
    "#### This, combined with pandas's 'groupby' can be used to bin data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9940., 6768., 7908., 1709.,  268., 8003., 9037.,  246., 4917.,\n",
       "       5262., 5963.,  519., 8950., 7282., 8183., 5002., 8101.,  959.,\n",
       "       2189., 2587., 4681., 4593., 7095., 1780., 5314., 1677., 7688.,\n",
       "       9281., 6094., 1501., 4896., 3773., 8486., 9110., 3838., 3154.,\n",
       "       5683., 1878., 1258., 6875., 7996., 5735., 9732., 6340., 8884.,\n",
       "       4954., 3516., 7142., 5039., 2256.])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.floor(np.random.uniform(0, 10000, size=50))\n",
    "bins = np.array([0, 100, 1000, 5000, 10000])\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 3, 2, 4, 4, 2, 3, 4, 4, 2, 4, 4, 4, 4, 4, 2, 3, 3, 3, 3,\n",
       "       4, 3, 4, 3, 4, 4, 4, 3, 3, 3, 4, 4, 3, 3, 4, 3, 3, 4, 4, 4, 4, 4,\n",
       "       4, 3, 3, 4, 4, 3], dtype=int64)"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = bins.searchsorted(data)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2     498.000000\n",
       "3    3064.277778\n",
       "4    7389.035714\n",
       "dtype: float64"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pd.Series(data).groupby(labels).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing Fast NumPy Functions with Numba\n",
    "#### Numba is an open source project that creates fast functions for NumPy-like data using CPUs, GPUs or other hardware.\n",
    "#### It uses the 'LLVM Project' to translate Python code into compiled machine code.\n",
    "#### A simple function that computes '(x - y).mean()' using a for loop is very slow in pure Python version. The NumPy version is over 100 times faster.\n",
    "#### But when we convert the function into compiled Numba function using 'numba.jit' (also can be compiled as decorator). The resulting function is faster than vectorized NumPy version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pure Python function\n",
    "\n",
    "def mean_distance(x, y):\n",
    "    nx = len(x)\n",
    "    result = 0.0\n",
    "    count = 0\n",
    "    for i in range(nx):\n",
    "        result += x[i] - y[i]\n",
    "        count += 1\n",
    "    return result / count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.75 s ± 41.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# Comparison between NumPy and Pure Python\n",
    "x = np.random.randn(10000000)\n",
    "y = np.random.randn(10000000)\n",
    "\n",
    "# Pure Python\n",
    "%timeit mean_distance(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "49 ms ± 2.11 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# NumPy version\n",
    "%timeit (x - y).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compiling pure Python into Numba\n",
    "import numba as nb\n",
    "\n",
    "numba_mean_distance = nb.jit(mean_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Can also be compiled as decorator\n",
    "# @nb.jit\n",
    "# def mean_distance(x, y):\n",
    "#     nx = len(x)\n",
    "#     result = 0.0\n",
    "#     count = 0\n",
    "#     for i in range(nx):\n",
    "#         result += x[i] - y[i]\n",
    "#         count += 1\n",
    "#     return result / count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.2 ms ± 268 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "# Numba function faster than NumPy\n",
    "%timeit numba_mean_distance(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Numba cannot compile arbitrary Python code, but it does support significant subset of pure Python that is usefule for writing numerical algorithms.\n",
    "#### Numba is a deep library, supporting many kinds of hardware, modes of compilation, and user extensions.\n",
    "#### It can also compile substantial subset of NumPy Python API without explicit for loops.\n",
    "#### It can also recognize constructs that can be compiled to machine code, while substituting calls to CPython API that it does not know how to compile.\n",
    "#### Its 'jit' function also has the option 'nopython=True', which restricts allowed code to Python code that can be compiled to LLVM without any Python C API calls. 'jit(nopython=True)' has a shorter alias 'numba.njit'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Same code in shorter form\n",
    "from numba import float64, njit\n",
    "\n",
    "@njit(float64(float64[:], float64[:]))\n",
    "def mean_distance(x, y):\n",
    "    return (x - y).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Array Input and Output\n",
    "#### Just like 'np.save' and 'np.load' are used for storing and retrieving arrays in binary format on disk, there are number of additional options for more sophisticated use.\n",
    "#### 'Memory maps' in particular have additional benefir of enabling to work with datasetstaht do not fit into RAM."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Memory-Mapped Files\n",
    "#### A 'memory-mapped' file is a method for interacting with binary data on disk as though it is in-memory.\n",
    "#### NumPy implements 'memmap' object that is ndarray-like, enabling small segments of large file to be read and written without reading whole array into memory.\n",
    "#### Also, memmap has same methods as in-memory array and can be substituted into many algorithms where ndarray is expected."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### To create a memmap, use the method 'np.memmap' and pass a file path, stype, shape and file mode.\n",
    "#### Slicing a memmap will return views on the data on disk.\n",
    "#### If you assign data to memmap, it will be buffered in memory (like Python file object). You can write it to disk by calling 'flush'.\n",
    "#### When a memmap falls out of scope and is garbage-collected, any changes will be flushed to disk also.\n",
    "#### When opening existing memmap, we still have to specify dtype and shape as the file is only a block of binary data with no metadata on disk.\n",
    "#### memmap also work with structured or nested dtypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "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": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmap = np.memmap('mymmap', dtype='float64', mode='w+',\n",
    "                shape=(10000, 10000))\n",
    "mmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "section = mmap[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "memmap([[ 1.37140985,  0.93127837,  0.60573747, ..., -0.62115557,\n",
       "         -0.46780136,  0.47874865],\n",
       "        [ 0.42296545,  0.83060431,  0.69976547, ...,  1.28831447,\n",
       "          0.58858679, -1.42755372],\n",
       "        [ 2.16005954, -1.24616489,  2.44470054, ...,  0.86866129,\n",
       "          0.28019716,  2.13008671],\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": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "section[:] = np.random.randn(5, 10000)\n",
    "\n",
    "mmap.flush()\n",
    "\n",
    "mmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "del mmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "memmap([[ 1.37140985,  0.93127837,  0.60573747, ..., -0.62115557,\n",
       "         -0.46780136,  0.47874865],\n",
       "        [ 0.42296545,  0.83060431,  0.69976547, ...,  1.28831447,\n",
       "          0.58858679, -1.42755372],\n",
       "        [ 2.16005954, -1.24616489,  2.44470054, ...,  0.86866129,\n",
       "          0.28019716,  2.13008671],\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": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mmap = np.memmap('mymmap', dtype='float64', shape=(10000, 10000))\n",
    "mmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HDF5 and Other Array Storage Options\n",
    "#### PyTables and h5py are 2 projects that provide NumPy-friendly interfaces for storing array data in efficient and compressible HDF5 (Hierarchical Data Format) format.\n",
    "#### We can store hundreds of gigabytes or even terabytes of data in HDF5 format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance Tips\n",
    "#### Getting good performance out of code using NumPy is quite straightforward, as array operations typically replace otherwise  slow pure Python loops.\n",
    "#### Some things to keep in mind:\n",
    "####     1. Convert Python loops and conditional logic to array operations and boolean array operations\n",
    "####     2. Use broadcasting whenever possible.\n",
    "####     3. Use array views (slicing) to avoid copying data\n",
    "####     4. Utilize ufuncs and ufunc methods\n",
    "#### If you do not get the required performance after exhausting all of the above options, consider writing code in C, Fortran or Cython.\n",
    "#### Cython provides an easy way to get C-like performance with minimal development."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Importance of Contiguous Memory\n",
    "#### Memory layout of an array can significantly affect the speed of computations.\n",
    "#### This is based partly on performance differences related to cache hierarchy of the CPU. \n",
    "#### So, operations accessing contiguous blocks of memory will generally be fastest because memory subsystems will buffer appropriate blocks of memory into ultrafast L1 or L2 CPU cache.\n",
    "#### Also certain code paths inside NumPy's C codebase have been optimized for contiguous case in which generic strided memory accesses can be avoided."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### By saying that an array's memory layout is contiguous means that the elements are stored in memory in the same order they appear in array with respect to Fortran (column major) or C (row major).\n",
    "#### By default, NumPy arrays are C-contiguous or simply contiguous. A column major array, like a transpose of a C-major array, is thus called Fortran contiguous.\n",
    "#### These properties can be checked via the 'flags' attribute on ndarray."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : True\n",
       "  F_CONTIGUOUS : False\n",
       "  OWNDATA : True\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  WRITEBACKIFCOPY : False\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_c = np.ones((1000, 1000), order='C')\n",
    "arr_f = np.ones((1000, 1000), order='F')\n",
    "\n",
    "arr_c.flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : False\n",
       "  F_CONTIGUOUS : True\n",
       "  OWNDATA : True\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  WRITEBACKIFCOPY : False\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_f.flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_f.flags.f_contiguous"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summing of the rows of the above arrays should, in theory, be faster for arr_c than arr_f since rows are contiguous in memory. But the difference looks very slight.\n",
    "#### But if you want to squeeze more performance out of NumPy, this is a good place for some effort.\n",
    "#### If an array does not have the right order, you can use 'copy' and pass either 'C' or 'F' based on requirement.\n",
    "#### When constructing a view on an array, keep in mind that the result is not guaranteed to be contiguous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "744 µs ± 28.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Performance difference between C and F major arrays\n",
    "\n",
    "%timeit arr_c.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "632 µs ± 10.7 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit arr_f.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : True\n",
       "  F_CONTIGUOUS : False\n",
       "  OWNDATA : True\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  WRITEBACKIFCOPY : False\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Copying array with a particular order\n",
    "arr_f.copy('C').flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_c[:50].flags.contiguous"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  C_CONTIGUOUS : False\n",
       "  F_CONTIGUOUS : False\n",
       "  OWNDATA : False\n",
       "  WRITEABLE : True\n",
       "  ALIGNED : True\n",
       "  WRITEBACKIFCOPY : False\n",
       "  UPDATEIFCOPY : False"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_c[:, :50].flags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}