NumPy; Slicing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Slicing indexes:**\n",
    "1. array [start:end]\n",
    "2. array [start:]\n",
    "3. array [:end]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4  5]\n",
      " [ 6  7  8  9 10]\n",
      " [11 12 13 14 15]\n",
      " [16 17 18 19 20]]\n"
     ]
    }
   ],
   "source": [
    "X = np.arange(1, 21).reshape(4, 5)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8  9 10]\n",
      " [13 14 15]\n",
      " [18 19 20]]\n"
     ]
    }
   ],
   "source": [
    "y = X[1:, 2:]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "print(X[0,1])\n",
    "print(X[1,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 3]\n",
      " [ 8]\n",
      " [13]\n",
      " [18]]\n"
     ]
    }
   ],
   "source": [
    "z = X[:, 2:3]\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3  8 13 18]\n"
     ]
    }
   ],
   "source": [
    "z = X[:, 2]\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Slicing creates alias to the original array**\n",
    "\n",
    "Note: To work correctly this cell must be executed the last."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X Before:\n",
      " [[ 1  2  3  4  5]\n",
      " [ 6  7  8  9 10]\n",
      " [11 12 13 14 15]\n",
      " [16 17 18 19 20]] \n",
      "\n",
      "X After:\n",
      " [[  1   2 555   4   5]\n",
      " [  6   7   8   9  10]\n",
      " [ 11  12  13  14  15]\n",
      " [ 16  17  18  19  20]]\n"
     ]
    }
   ],
   "source": [
    "print('X Before:\\n', X, '\\n')\n",
    "z[0] = 555\n",
    "print('X After:\\n', X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Creating a copy of an NumPy array**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[555   8  13  18]\n"
     ]
    }
   ],
   "source": [
    "a = X[:,2].copy()\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using an array to index another array**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3]\n"
     ]
    }
   ],
   "source": [
    "indices = np.array([1, 3])\n",
    "print(indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6  7  8  9 10]\n",
      " [16 17 18 19 20]]\n"
     ]
    }
   ],
   "source": [
    "y = X[indices, :]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  4]\n",
      " [ 7  9]\n",
      " [12 14]\n",
      " [17 19]]\n"
     ]
    }
   ],
   "source": [
    "z = X[:, indices]\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NumPy diagonal function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1   2 555   4   5]\n",
      " [  6   7   8   9  10]\n",
      " [ 11  12  13  14  15]\n",
      " [ 16  17  18  19  20]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  7 13 19]\n"
     ]
    }
   ],
   "source": [
    "z = np.diag(X)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  8 14 20]\n",
      "[ 6 12 18]\n"
     ]
    }
   ],
   "source": [
    "z = np.diag(X, k= 1) # Selecting elements above the diagonal\n",
    "print(z)\n",
    "z = np.diag(X, k= -1) # Selecting elements below the diagonal\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Extracting unique values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 1. 1.]\n",
      " [1. 5. 1.]\n",
      " [1. 1. 7.]]\n"
     ]
    }
   ],
   "source": [
    "X = np.ones([3,3]) # Creating a matrix with some unique values\n",
    "X[1,1] = 5\n",
    "X[2,2] = 7\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 5. 7.]\n"
     ]
    }
   ],
   "source": [
    "print(np.unique(X))"
   ]
  }
 ],
 "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
}