03_Numpy_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:3.75em;color:purple; font-style:bold\"><br>\n",
    "Introduction to numpy:\n",
    "</p><br>\n",
    "\n",
    "<p style=\"font-family: Arial; font-size:1.25em;color:#2462C0; font-style:bold\"><br>\n",
    "Package for scientific computing with Python\n",
    "</p><br>\n",
    "\n",
    "Numerical Python, or \"Numpy\" for short, is a foundational package on which many of the most common data science packages are built.  Numpy provides us with high performance multi-dimensional arrays which we can use as vectors or matrices.  \n",
    "\n",
    "The key features of numpy are:\n",
    "\n",
    "- ndarrays: n-dimensional arrays of the same data type which are fast and space-efficient.  There are a number of built-in methods for ndarrays which allow for rapid processing of data without using loops (e.g., compute the mean).\n",
    "- Broadcasting: a useful tool which defines implicit behavior between multi-dimensional arrays of different sizes.\n",
    "- Vectorization: enables numeric operations on ndarrays.\n",
    "- Input/Output: simplifies reading and writing of data from/to file.\n",
    "\n",
    "<b>Additional Recommended Resources:</b><br>\n",
    "<a href=\"https://docs.scipy.org/doc/numpy/reference/\">Numpy Documentation</a><br>\n",
    "<i>Python for Data Analysis</i> by Wes McKinney<br>\n",
    "<i>Python Data science Handbook</i> by Jake VanderPlas\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Getting started with ndarray<br><br></p>\n",
    "\n",
    "**ndarrays** are time and space-efficient multidimensional arrays at the core of numpy.  Like the data structures in Week 2, let's get started by creating ndarrays using the numpy package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "How to create Rank 1 numpy arrays:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "an_array = np.array([3, 33, 333])  # Create a rank 1 array\n",
    "\n",
    "print(type(an_array))              # The type of an ndarray is: \"<class 'numpy.ndarray'>\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test the shape of the array we just created, it should have just one dimension (Rank 1)\n",
    "print(an_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# because this is a 1-rank array, we need only one index to accesss each element\n",
    "print(an_array[0], an_array[1], an_array[2]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "an_array[0] =888            # ndarrays are mutable, here we change an element of the array\n",
    "\n",
    "print(an_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "How to create a Rank 2 numpy array:</p>\n",
    "\n",
    "A rank 2 **ndarray** is one with two dimensions.  Notice the format below of [ [row] , [row] ].  2 dimensional arrays are great for representing matrices which are often useful in data science."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "another = np.array([[11,12,13],[21,22,23]])   # Create a rank 2 array\n",
    "\n",
    "print(another)  # print the array\n",
    "\n",
    "print(\"The shape is 2 rows, 3 columns: \", another.shape)  # rows x columns                   \n",
    "\n",
    "print(\"Accessing elements [0,0], [0,1], and [1,0] of the ndarray: \", another[0, 0], \", \",another[0, 1],\", \", another[1, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "There are many way to create numpy arrays:\n",
    "</p>\n",
    "\n",
    "Here we create a number of different size arrays with different shapes and different pre-filled values.  numpy has a number of built in methods which help us quickly and easily create multidimensional arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.]\n",
      " [ 0.  0.]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# create a 2x2 array of zeros\n",
    "ex1 = np.zeros((2,2))      \n",
    "print(ex1)                              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a 2x2 array filled with 9.0\n",
    "ex2 = np.full((2,2), 9.0)  \n",
    "print(ex2)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a 2x2 matrix with the diagonal 1s and the others 0\n",
    "ex3 = np.eye(2,2)\n",
    "print(ex3)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an array of ones\n",
    "ex4 = np.ones((1,2))\n",
    "print(ex4)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# notice that the above ndarray (ex4) is actually rank 2, it is a 2x1 array\n",
    "print(ex4.shape)\n",
    "\n",
    "# which means we need to use two indexes to access an element\n",
    "print()\n",
    "print(ex4[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an array of random floats between 0 and 1\n",
    "ex5 = np.random.random((2,2))\n",
    "print(ex5)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Array Indexing\n",
    "<br><br></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "Slice indexing:\n",
    "</p>\n",
    "\n",
    "Similar to the use of slice indexing with lists and strings, we can use slice indexing to pull out sub-regions of ndarrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11 12 13 14]\n",
      " [21 22 23 24]\n",
      " [31 32 33 34]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Rank 2 array of shape (3, 4)\n",
    "an_array = np.array([[11,12,13,14], [21,22,23,24], [31,32,33,34]])\n",
    "print(an_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use array slicing to get a subarray consisting of the first 2 rows x 2 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[12 13]\n",
      " [22 23]]\n"
     ]
    }
   ],
   "source": [
    "a_slice = an_array[:2, 1:3]\n",
    "print(a_slice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you modify a slice, you actually modify the underlying array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before: 12\n",
      "After: 1000\n"
     ]
    }
   ],
   "source": [
    "print(\"Before:\", an_array[0, 1])   #inspect the element at 0, 1  \n",
    "a_slice[0, 0] = 1000    # a_slice[0, 0] is the same piece of data as an_array[0, 1]\n",
    "print(\"After:\", an_array[0, 1])    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Use both integer indexing & slice indexing\n",
    "</p>\n",
    "\n",
    "We can use combinations of integer indexing and slice indexing to create different shaped matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Rank 2 array of shape (3, 4)\n",
    "an_array = np.array([[11,12,13,14], [21,22,23,24], [31,32,33,34]])\n",
    "print(an_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using both integer indexing & slicing generates an array of lower rank\n",
    "row_rank1 = an_array[1, :]    # Rank 1 view \n",
    "\n",
    "print(row_rank1, row_rank1.shape)  # notice only a single []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing alone: generates an array of the same rank as the an_array\n",
    "row_rank2 = an_array[1:2, :]  # Rank 2 view \n",
    "\n",
    "print(row_rank2, row_rank2.shape)   # Notice the [[ ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We can do the same thing for columns of an array:\n",
    "\n",
    "print()\n",
    "col_rank1 = an_array[:, 1]\n",
    "col_rank2 = an_array[:, 1:2]\n",
    "\n",
    "print(col_rank1, col_rank1.shape)  # Rank 1\n",
    "print()\n",
    "print(col_rank2, col_rank2.shape)  # Rank 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Array Indexing for changing elements:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it's useful to use an array of indexes to access or change elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new array\n",
    "an_array = np.array([[11,12,13], [21,22,23], [31,32,33], [41,42,43]])\n",
    "\n",
    "print('Original Array:')\n",
    "print(an_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an array of indices\n",
    "col_indices = np.array([0, 1, 2, 0])\n",
    "print('\\nCol indices picked : ', col_indices)\n",
    "\n",
    "row_indices = np.arange(4)\n",
    "print('\\nRows indices picked : ', row_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Examine the pairings of row_indices and col_indices.  These are the elements we'll change next.\n",
    "for row,col in zip(row_indices,col_indices):\n",
    "    print(row, \", \",col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select one element from each row\n",
    "print('Values in the array at those indices: ',an_array[row_indices, col_indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change one element from each row using the indices selected\n",
    "an_array[row_indices, col_indices] += 100000\n",
    "\n",
    "print('\\nChanged Array:')\n",
    "print(an_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "Boolean Indexing\n",
    "\n",
    "<br><br></p>\n",
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Array Indexing for changing elements:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a 3x2 array\n",
    "an_array = np.array([[11,12], [21, 22], [31, 32]])\n",
    "print(an_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a filter which will be boolean values for whether each element meets this condition\n",
    "filter = (an_array > 15)\n",
    "filter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the filter is a same size ndarray as an_array which is filled with True for each element whose corresponding element in an_array which is greater than 15 and False for those elements whose value is less than 15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can now select just those elements which meet that criteria\n",
    "print(an_array[filter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For short, we could have just used the approach below without the need for the separate filter array.\n",
    "\n",
    "an_array[(an_array % 2 == 0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is particularly useful is that we can actually change elements in the array applying a similar logical filter.  Let's add 100 to all the even values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "an_array[an_array % 2 == 0] +=100\n",
    "print(an_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Datatypes and Array Operations\n",
    "<br><br></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Datatypes:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex1 = np.array([11, 12]) # Python assigns the  data type\n",
    "print(ex1.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex2 = np.array([11.0, 12.0]) # Python assigns the  data type\n",
    "print(ex2.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex3 = np.array([11, 21], dtype=np.int64) #You can also tell Python the  data type\n",
    "print(ex3.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can use this to force floats into integers (using floor function)\n",
    "ex4 = np.array([11.1,12.7], dtype=np.int64)\n",
    "print(ex4.dtype)\n",
    "print()\n",
    "print(ex4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# you can use this to force integers into floats if you anticipate\n",
    "# the values may change to floats later\n",
    "ex5 = np.array([11, 21], dtype=np.float64)\n",
    "print(ex5.dtype)\n",
    "print()\n",
    "print(ex5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Arithmetic Array Operations:\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[111,112],[121,122]], dtype=np.int)\n",
    "y = np.array([[211.1,212.1],[221.1,222.1]], dtype=np.float64)\n",
    "\n",
    "print(x)\n",
    "print()\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add\n",
    "print(x + y)         # The plus sign works\n",
    "print()\n",
    "print(np.add(x, y))  # so does the numpy function \"add\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subtract\n",
    "print(x - y)\n",
    "print()\n",
    "print(np.subtract(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# multiply\n",
    "print(x * y)\n",
    "print()\n",
    "print(np.multiply(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# divide\n",
    "print(x / y)\n",
    "print()\n",
    "print(np.divide(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# square root\n",
    "print(np.sqrt(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exponent (e ** x)\n",
    "print(np.exp(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Statistical Methods, Sorting, and <br> <br> Set Operations:\n",
    "<br><br>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Basic Statistical Operations:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# setup a random 2 x 4 matrix\n",
    "arr = 10 * np.random.randn(2,5)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the mean for all elements\n",
    "print(arr.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the means by row\n",
    "print(arr.mean(axis = 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the means by column\n",
    "print(arr.mean(axis = 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sum all the elements\n",
    "print(arr.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the medians\n",
    "print(np.median(arr, axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Sorting:\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a 10 element array of randoms\n",
    "unsorted = np.random.randn(10)\n",
    "\n",
    "print(unsorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create copy and sort\n",
    "sorted = np.array(unsorted)\n",
    "sorted.sort()\n",
    "\n",
    "print(sorted)\n",
    "print()\n",
    "print(unsorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inplace sorting\n",
    "unsorted.sort() \n",
    "\n",
    "print(unsorted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Finding Unique elements:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.array([1,2,1,4,2,1,4,2])\n",
    "\n",
    "print(np.unique(array))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Set Operations with np.array data type:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1 = np.array(['desk','chair','bulb'])\n",
    "s2 = np.array(['lamp','bulb','chair'])\n",
    "print(s1, s2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print( np.intersect1d(s1, s2) ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print( np.union1d(s1, s2) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print( np.setdiff1d(s1, s2) )# elements in s1 that are not in s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print( np.in1d(s1, s2) )#which element of s1 is also in s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Broadcasting:\n",
    "<br><br>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduction to broadcasting. <br>\n",
    "For more details, please see: <br>\n",
    "https://docs.scipy.org/doc/numpy-1.10.1/user/basics.broadcasting.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "start = np.zeros((4,3))\n",
    "print(start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a rank 1 ndarray with 3 values\n",
    "add_rows = np.array([1, 0, 2])\n",
    "print(add_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = start + add_rows  # add to each row of 'start' using broadcasting\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an ndarray which is 4 x 1 to broadcast across columns\n",
    "add_cols = np.array([[0,1,2,3]])\n",
    "add_cols = add_cols.T\n",
    "\n",
    "print(add_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add to each column of 'start' using broadcasting\n",
    "y = start + add_cols \n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this will just broadcast in both dimensions\n",
    "add_scalar = np.array([1])  \n",
    "print(start+add_scalar)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example from the slides:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create our 3x4 matrix\n",
    "arrA = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12]])\n",
    "print(arrA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create our 4x1 array\n",
    "arrB = [0,1,0,2]\n",
    "print(arrB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the two together using broadcasting\n",
    "print(arrA + arrB)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Speedtest: ndarrays vs lists\n",
    "<br><br>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First setup paramaters for the speed test. We'll be testing time to sum elements in an ndarray versus a list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import arange\n",
    "from timeit import Timer\n",
    "\n",
    "size    = 1000000\n",
    "timeits = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the ndarray with values 0,1,2...,size-1\n",
    "nd_array = arange(size)\n",
    "print( type(nd_array) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# timer expects the operation as a parameter, \n",
    "# here we pass nd_array.sum()\n",
    "timer_numpy = Timer(\"nd_array.sum()\", \"from __main__ import nd_array\")\n",
    "\n",
    "print(\"Time taken by numpy ndarray: %f seconds\" % \n",
    "      (timer_numpy.timeit(timeits)/timeits))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create the list with values 0,1,2...,size-1\n",
    "a_list = list(range(size))\n",
    "print (type(a_list) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# timer expects the operation as a parameter, here we pass sum(a_list)\n",
    "timer_list = Timer(\"sum(a_list)\", \"from __main__ import a_list\")\n",
    "\n",
    "print(\"Time taken by list:  %f seconds\" % \n",
    "      (timer_list.timeit(timeits)/timeits))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Read or Write to Disk:\n",
    "<br><br>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.3em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Binary Format:</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.array([ 23.23, 24.24] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.save('an_array', x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.load('an_array.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.3em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Text Format:</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.savetxt('array.txt', X=x, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cat array.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "np.loadtxt('array.txt', delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:2.75em;color:purple; font-style:bold\"><br>\n",
    "\n",
    "Additional Common ndarray Operations\n",
    "<br><br></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Dot Product on Matrices and Inner Product on Vectors:\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determine the dot product of two matrices\n",
    "x2d = np.array([[1,1],[1,1]])\n",
    "y2d = np.array([[2,2],[2,2]])\n",
    "\n",
    "print(x2d.dot(y2d))\n",
    "print()\n",
    "print(np.dot(x2d, y2d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determine the inner product of two vectors\n",
    "a1d = np.array([9 , 9 ])\n",
    "b1d = np.array([10, 10])\n",
    "\n",
    "print(a1d.dot(b1d))\n",
    "print()\n",
    "print(np.dot(a1d, b1d))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dot produce on an array and vector\n",
    "print(x2d.dot(a1d))\n",
    "print()\n",
    "print(np.dot(x2d, a1d))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Sum:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sum elements in the array\n",
    "ex1 = np.array([[11,12],[21,22]])\n",
    "\n",
    "print(np.sum(ex1))          # add all members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(ex1, axis=0))  # columnwise sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(np.sum(ex1, axis=1))  # rowwise sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Element-wise Functions: </p>\n",
    "\n",
    "For example, let's compare two arrays values to get the maximum of each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# random array\n",
    "x = np.random.randn(8)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# another random array\n",
    "y = np.random.randn(8)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns element wise maximum between two arrays\n",
    "\n",
    "np.maximum(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Reshaping array:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grab values from 0 through 19 in an array\n",
    "arr = np.arange(20)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reshape to be a 4 x 5 matrix\n",
    "arr.reshape(4,5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Transpose:\n",
    "\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# transpose\n",
    "ex1 = np.array([[11,12],[21,22]])\n",
    "\n",
    "ex1.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Indexing using where():</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_1 = np.array([1,2,3,4,5])\n",
    "\n",
    "y_1 = np.array([11,22,33,44,55])\n",
    "\n",
    "filter = np.array([True, False, True, False, True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out = np.where(filter, x_1, y_1)\n",
    "print(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.random.rand(5,5)\n",
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.where( mat > 0.5, 1000, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "\"any\" or \"all\" conditionals:</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr_bools = np.array([ True, False, True, True, False ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_bools.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "arr_bools.all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Random Number Generation:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = np.random.normal(size = (1,5))[0]\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.random.randint(low=2,high=50,size=4)\n",
    "print(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.permutation(Z) #return a new ordering of elements in Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.uniform(size=4) #uniform distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.normal(size=4) #normal distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"font-family: Arial; font-size:1.75em;color:#2462C0; font-style:bold\"><br>\n",
    "\n",
    "Merging data sets:\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "K = np.random.randint(low=2,high=50,size=(2,2))\n",
    "print(K)\n",
    "\n",
    "print()\n",
    "M = np.random.randint(low=2,high=50,size=(2,2))\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.vstack((K,M))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.hstack((K,M))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.concatenate([K, M], axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.concatenate([K, M.T], axis = 1)"
   ]
  }
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