7. Manipulate an Array.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.random.rand(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### transpose an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.98440661 0.95953312 0.30401979]\n",
      " [0.15315942 0.01093673 0.59247041]\n",
      " [0.3330772  0.90659178 0.81129995]\n",
      " [0.45128527 0.99198992 0.82223538]]\n",
      "[[0.98440661 0.95953312 0.30401979]\n",
      " [0.15315942 0.01093673 0.59247041]\n",
      " [0.3330772  0.90659178 0.81129995]\n",
      " [0.45128527 0.99198992 0.82223538]]\n",
      "[[0.98440661 0.95953312 0.30401979]\n",
      " [0.15315942 0.01093673 0.59247041]\n",
      " [0.3330772  0.90659178 0.81129995]\n",
      " [0.45128527 0.99198992 0.82223538]]\n"
     ]
    }
   ],
   "source": [
    "# the following methods return a copy\n",
    "print(arr.T)\n",
    "# or \n",
    "print(np.transpose(arr))\n",
    "# or\n",
    "print(arr.transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### transpose of a high dimensional array with specified order of axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]]\n",
      "\n",
      " [[ 8  9 10 11]\n",
      "  [12 13 14 15]]]\n",
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]]\n",
      "\n",
      " [[ 8  9 10 11]\n",
      "  [12 13 14 15]]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.arange(16).reshape((2,2,4))\n",
    "print(arr1)\n",
    "\n",
    "arr1.transpose((1,0,2))\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### swap axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  4]\n",
      "  [ 1  5]\n",
      "  [ 2  6]\n",
      "  [ 3  7]]\n",
      "\n",
      " [[ 8 12]\n",
      "  [ 9 13]\n",
      "  [10 14]\n",
      "  [11 15]]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.arange(16).reshape((2,2,4))\n",
    "print(arr1.swapaxes(1,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### change the shape of an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.98440661, 0.15315942, 0.3330772 , 0.45128527, 0.95953312,\n",
       "        0.01093673],\n",
       "       [0.90659178, 0.99198992, 0.30401979, 0.59247041, 0.81129995,\n",
       "        0.82223538]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change the shape of an array and return a copy\n",
    "arr.reshape((2,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change the shape of an array in place\n",
    "arr.resize((2,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### flatten an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98440661, 0.15315942, 0.3330772 , 0.45128527, 0.95953312,\n",
       "       0.01093673, 0.90659178, 0.99198992, 0.30401979, 0.59247041,\n",
       "       0.81129995, 0.82223538])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return a copy\n",
    "arr.flatten()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.98440661, 0.15315942, 0.3330772 , 0.45128527, 0.95953312,\n",
       "       0.01093673, 0.90659178, 0.99198992, 0.30401979, 0.59247041,\n",
       "       0.81129995, 0.82223538])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# return a view\n",
    "# change any element in the view will change the initial array\n",
    "arr.ravel()      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### append elements to an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "# append a scalar and return a copy\n",
    "arr1 = np.append(arr, 4)    \n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "# append an array and return a copy\n",
    "arr2 = np.append(arr, [4,5,6])    \n",
    "print(arr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### insert elements into an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100   1   2   3]\n"
     ]
    }
   ],
   "source": [
    "# np.insert(array, position, element)\n",
    "\n",
    "# insert a scalar at a certain position\n",
    "arr3 = np.insert(arr, 0, 100)    \n",
    "print(arr3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 1 2 3]\n"
     ]
    }
   ],
   "source": [
    "# insert multiple values at a certain position\n",
    "arr3 = np.insert(arr, 0, [1,2,3])    \n",
    "print(arr3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### delete elements from an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3]\n"
     ]
    }
   ],
   "source": [
    "# remove the element at position 0\n",
    "arr4 = np.delete(arr, 0)    \n",
    "print(arr4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2]\n"
     ]
    }
   ],
   "source": [
    "# remove the element at multiple positions\n",
    "arr4 = np.delete(arr, [0,2])    \n",
    "print(arr4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### copy an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the following methods are all deep copy\n",
    "arr1 = np.copy(arr)\n",
    "# or \n",
    "arr1 = arr.copy()\n",
    "# or \n",
    "arr1 = np.array(arr, copy=True)"
   ]
  }
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