NumPy; element-wise operations, broadcast.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n",
      "[5 6 7 8]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2, 3, 4])\n",
    "y = np.array([5, 6, 7, 8])\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 6  8 10 12]\n",
      "[ 6  8 10 12]\n"
     ]
    }
   ],
   "source": [
    "print(x + y)\n",
    "print(np.add(x, y)) # This method has more options available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-4 -4 -4 -4]\n",
      "[-4 -4 -4 -4]\n",
      "[ 5 12 21 32]\n",
      "[ 5 12 21 32]\n",
      "[0.2        0.33333333 0.42857143 0.5       ]\n",
      "[0.2        0.33333333 0.42857143 0.5       ]\n"
     ]
    }
   ],
   "source": [
    "print(x - y)\n",
    "print(np.subtract(x, y))\n",
    "print(x * y)\n",
    "print(np.multiply(x, y))\n",
    "print(x / y)\n",
    "print(np.divide(x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "X = x.reshape(2,2)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5 6]\n",
      " [7 8]]\n"
     ]
    }
   ],
   "source": [
    "Y = y.reshape(2,2)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6  8]\n",
      " [10 12]] \n",
      "\n",
      "[[-4 -4]\n",
      " [-4 -4]] \n",
      "\n",
      "[[ 5 12]\n",
      " [21 32]] \n",
      "\n",
      "[[0.2        0.33333333]\n",
      " [0.42857143 0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "print(X + Y, '\\n')\n",
    "print(X - Y, '\\n')\n",
    "print(X * Y, '\\n')\n",
    "print(X / Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Applying functions to elements in array**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n",
      "[1.         1.41421356 1.73205081 2.        ]\n",
      "[ 2.71828183  7.3890561  20.08553692 54.59815003]\n",
      "[ 1  4  9 16]\n"
     ]
    }
   ],
   "source": [
    "print(x)\n",
    "print(np.sqrt(x))\n",
    "print(np.exp(x))\n",
    "print(np.power(x, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Some basic statistics**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]] \n",
      "\n",
      "average of all:  2.5\n"
     ]
    }
   ],
   "source": [
    "print(X, '\\n')\n",
    "print('average of all: ', X.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average of columns:  [2. 3.]\n",
      "average of rows:  [1.5 3.5]\n"
     ]
    }
   ],
   "source": [
    "print('average of columns: ', X.mean(axis= 0))\n",
    "print('average of rows: ', X.mean(axis= 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.5\n",
      "1.118033988749895\n",
      "4\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "print(np.median(X))\n",
    "print(np.std(X))\n",
    "print(np.max(X))\n",
    "print(np.min(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Scalar -> (operation) -> Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 5]\n",
      " [6 7]]\n"
     ]
    }
   ],
   "source": [
    "print(3 + X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3  6]\n",
      " [ 9 12]]\n"
     ]
    }
   ],
   "source": [
    "print(3 * X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "A = np.arange(9).reshape(3, 3)\n",
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2]\n"
     ]
    }
   ],
   "source": [
    "b = np.arange(3)\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  2  4]\n",
      " [ 3  5  7]\n",
      " [ 6  8 10]]\n"
     ]
    }
   ],
   "source": [
    "print(A + b) # Broadcasting to adapt array b to array A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n",
      "(4, 1)\n",
      "(4, 4)\n",
      "[[1. 2. 3. 4.]\n",
      " [1. 2. 3. 4.]\n",
      " [1. 2. 3. 4.]\n",
      " [1. 2. 3. 4.]]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(4)\n",
    "b = np.ones(4).reshape(4,1)\n",
    "print(a.shape)\n",
    "print(b.shape)\n",
    "print((a + b).shape)\n",
    "print(a + b)"
   ]
  }
 ],
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