codes/08_array-creation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "📝 **Author:** Amirhossein Heydari - 📧 **Email:** AmirhosseinHeydari78@gmail.com - 📍 **Linktree:** [linktr.ee/mr_pylin](https://linktr.ee/mr_pylin)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NumPy - Array Creation\n",
    "   - Array creation routines in NumPy provide various methods to create arrays with different properties and initial values.\n",
    "\n",
    "📝 Doc:\n",
    "   - Array creation routines: [numpy.org/doc/stable/reference/routines.array-creation.html](https://numpy.org/doc/stable/reference/routines.array-creation.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Shape or Value\n",
    "\n",
    "<table style=\"margin: 0 auto;\">\n",
    "   <thead>\n",
    "      <tr>\n",
    "         <th style=\"text-align: center;\">Function</th>\n",
    "         <th style=\"text-align: center;\">Description</th>\n",
    "         <th style=\"text-align: center;\">Details</th>\n",
    "      </tr>\n",
    "   </thead>\n",
    "   <tbody>\n",
    "      <tr>\n",
    "         <td><code>np.zeros</code></td>\n",
    "         <td>Return a new array of given shape and type, filled with zeros</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.zeros.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.zeros_like</code></td>\n",
    "         <td>Return an array of zeros with the same shape and type as a given array</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.zeros_like.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.ones</code></td>\n",
    "         <td>Return a new array of given shape and type, filled with ones</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.ones.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.ones_like</code></td>\n",
    "         <td>Return an array of ones with the same shape and type as a given array</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.ones_like.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.full</code></td>\n",
    "         <td>Return a new array of given shape and type, filled with fill_value</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.full.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.full_like</code></td>\n",
    "         <td>Return a full array with the same shape and type as a given array</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.full_like.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.empty</code></td>\n",
    "         <td>Return a new array of given shape and type, without initializing entries</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.empty.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.empty_like</code></td>\n",
    "         <td>Return a new array with the same shape and type as a given array</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.empty_like.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.eye</code></td>\n",
    "         <td>Return a 2-D array with ones on the diagonal and zeros elsewhere</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.eye.html\">link</a></td>\n",
    "      </tr>\n",
    "   </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.zeros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zeros_1 :\n",
      "[0. 0. 0. 0.]\n",
      "--------------------------------------------------\n",
      "zeros_2 :\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "--------------------------------------------------\n",
      "zeros_3 :\n",
      "[[[0 0]\n",
      "  [0 0]\n",
      "  [0 0]]\n",
      "\n",
      " [[0 0]\n",
      "  [0 0]\n",
      "  [0 0]]]\n"
     ]
    }
   ],
   "source": [
    "zeros_1 = np.zeros(4)\n",
    "zeros_2 = np.zeros((2, 3))\n",
    "zeros_3 = np.zeros(shape=(2, 3, 2), dtype=np.int_)\n",
    "\n",
    "# log\n",
    "print(f\"zeros_1 :\\n{zeros_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"zeros_2 :\\n{zeros_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"zeros_3 :\\n{zeros_3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.zeros_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "zeros_like_1 :\n",
      "[0. 0. 0. 0.]\n",
      "--------------------------------------------------\n",
      "zeros_like_2 :\n",
      "[[False False]\n",
      " [False False]]\n",
      "--------------------------------------------------\n",
      "zeros_like_3 :\n",
      "[[[0 0]\n",
      "  [0 0]\n",
      "  [0 0]]\n",
      "\n",
      " [[0 0]\n",
      "  [0 0]\n",
      "  [0 0]]]\n"
     ]
    }
   ],
   "source": [
    "zeros_like_1 = np.zeros_like(zeros_1)\n",
    "zeros_like_2 = np.zeros_like(zeros_2, bool, shape=(2, 2))\n",
    "zeros_like_3 = np.zeros_like(a=zeros_3, dtype=int)\n",
    "\n",
    "# log\n",
    "print(f\"zeros_like_1 :\\n{zeros_like_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"zeros_like_2 :\\n{zeros_like_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"zeros_like_3 :\\n{zeros_like_3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ones_1 :\n",
      "[1. 1. 1. 1.]\n",
      "--------------------------------------------------\n",
      "ones_2 :\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]]\n",
      "--------------------------------------------------\n",
      "ones_3 :\n",
      "[[[1 1]\n",
      "  [1 1]\n",
      "  [1 1]]\n",
      "\n",
      " [[1 1]\n",
      "  [1 1]\n",
      "  [1 1]]]\n"
     ]
    }
   ],
   "source": [
    "ones_1 = np.ones(4)\n",
    "ones_2 = np.ones((2, 3))\n",
    "ones_3 = np.ones(shape=(2, 3, 2), dtype=np.int_)\n",
    "\n",
    "\n",
    "# log\n",
    "print(f\"ones_1 :\\n{ones_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"ones_2 :\\n{ones_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"ones_3 :\\n{ones_3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.ones_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ones_like_1 :\n",
      "[1. 1. 1. 1.]\n",
      "--------------------------------------------------\n",
      "ones_like_2 :\n",
      "[ True  True  True]\n",
      "--------------------------------------------------\n",
      "ones_like_3 :\n",
      "[[[1 1]\n",
      "  [1 1]\n",
      "  [1 1]]\n",
      "\n",
      " [[1 1]\n",
      "  [1 1]\n",
      "  [1 1]]]\n"
     ]
    }
   ],
   "source": [
    "ones_like_1 = np.ones_like(ones_1)\n",
    "ones_like_2 = np.ones_like(ones_2, bool, shape=3)\n",
    "ones_like_3 = np.ones_like(a=ones_3, dtype=int)\n",
    "\n",
    "# log\n",
    "print(f\"ones_like_1 :\\n{ones_like_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"ones_like_2 :\\n{ones_like_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"ones_like_3 :\\n{ones_like_3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "full_1 :\n",
      "[0 0 0 0]\n",
      "--------------------------------------------------\n",
      "full_2 :\n",
      "[[1 1 1]\n",
      " [1 1 1]]\n",
      "--------------------------------------------------\n",
      "full_3 :\n",
      "[[[2 2]\n",
      "  [2 2]\n",
      "  [2 2]]\n",
      "\n",
      " [[2 2]\n",
      "  [2 2]\n",
      "  [2 2]]]\n"
     ]
    }
   ],
   "source": [
    "full_1 = np.full(4, 0)\n",
    "full_2 = np.full((2, 3), 1)\n",
    "full_3 = np.full((2, 3, 2), fill_value=2)\n",
    "\n",
    "# log\n",
    "print(f\"full_1 :\\n{full_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"full_2 :\\n{full_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"full_3 :\\n{full_3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.full_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "full_like_1 :\n",
      "[2 2 2 2]\n",
      "--------------------------------------------------\n",
      "full_like_2 :\n",
      "[[1 1 1]\n",
      " [1 1 1]]\n",
      "--------------------------------------------------\n",
      "full_like_3 :\n",
      "[[[0. 0.]\n",
      "  [0. 0.]\n",
      "  [0. 0.]]\n",
      "\n",
      " [[0. 0.]\n",
      "  [0. 0.]\n",
      "  [0. 0.]]]\n"
     ]
    }
   ],
   "source": [
    "full_like_1 = np.full_like(full_1, 2)\n",
    "full_like_2 = np.full_like(full_2, 1)\n",
    "full_like_3 = np.full_like(full_3, 0, dtype=float)\n",
    "\n",
    "# log\n",
    "print(f\"full_like_1 :\\n{full_like_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"full_like_2 :\\n{full_like_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"full_like_3 :\\n{full_like_3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "empty_1 :\n",
      "[1. 1. 1. 1.]\n",
      "--------------------------------------------------\n",
      "empty_2 :\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]]\n",
      "--------------------------------------------------\n",
      "empty_3 :\n",
      "[[[4128860 6029375]\n",
      "  [3801155 5570652]\n",
      "  [6619251 7536754]]\n",
      "\n",
      " [[6357084 6881389]\n",
      "  [6815858 4259932]\n",
      "  [7340144 6357060]]]\n"
     ]
    }
   ],
   "source": [
    "empty_1 = np.empty(4)\n",
    "empty_2 = np.empty((2, 3))\n",
    "empty_3 = np.empty(shape=(2, 3, 2), dtype=np.int_)\n",
    "\n",
    "# log\n",
    "print(f\"empty_1 :\\n{empty_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"empty_2 :\\n{empty_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"empty_3 :\\n{empty_3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.empty_like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "empty_like_1 :\n",
      "[1. 1. 1. 1.]\n",
      "--------------------------------------------------\n",
      "empty_like_2 :\n",
      "[[1. 1. 1.]\n",
      " [1. 1. 1.]]\n",
      "--------------------------------------------------\n",
      "empty_like_3 :\n",
      "[[ -734228164    81272836 -2125560053]\n",
      " [-1758162866  -734228164    81272836]]\n"
     ]
    }
   ],
   "source": [
    "empty_like_1 = np.empty_like(empty_1)\n",
    "empty_like_2 = np.empty_like(empty_2, float)\n",
    "empty_like_3 = np.empty_like(prototype=empty_3, shape=(2, 3))\n",
    "\n",
    "# log\n",
    "print(f\"empty_like_1 :\\n{empty_like_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"empty_like_2 :\\n{empty_like_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"empty_like_3 :\\n{empty_like_3}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.eye"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eye_1 :\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n",
      "--------------------------------------------------\n",
      "eye_2 :\n",
      "[[1. 0. 0. 0.]\n",
      " [0. 1. 0. 0.]\n",
      " [0. 0. 1. 0.]]\n",
      "--------------------------------------------------\n",
      "eye_3 :\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]]\n",
      "--------------------------------------------------\n",
      "eye_4 :\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n",
      "--------------------------------------------------\n",
      "eye_5 :\n",
      "[[0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "eye_1 = np.eye(3)\n",
    "eye_2 = np.eye(3, 4)\n",
    "eye_3 = np.eye(N=2, M=3)\n",
    "eye_4 = np.eye(3, 3, 0)\n",
    "eye_5 = np.eye(3, 3, 1)\n",
    "eye_6 = np.eye(3, 3, -1)\n",
    "\n",
    "# log\n",
    "print(f\"eye_1 :\\n{eye_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"eye_2 :\\n{eye_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"eye_3 :\\n{eye_3}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"eye_4 :\\n{eye_4}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"eye_5 :\\n{eye_5}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Existing Data\n",
    "\n",
    "<table style=\"margin: 0 auto;\">\n",
    "   <thead>\n",
    "      <tr>\n",
    "         <th style=\"text-align: center;\">Function</th>\n",
    "         <th style=\"text-align: center;\">Description</th>\n",
    "         <th style=\"text-align: center;\">Details</th>\n",
    "      </tr>\n",
    "   </thead>\n",
    "   <tbody>\n",
    "      <tr>\n",
    "         <td><code>np.array</code></td>\n",
    "         <td>Create an array</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.array.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.asarray</code></td>\n",
    "         <td>Convert the input to an array</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.asarray.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.astype</code></td>\n",
    "         <td>Copies an array to a specified data type</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.astype.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.copy</code></td>\n",
    "         <td>Return an array copy of the given object</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.copy.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.fromfile</code></td>\n",
    "         <td>Construct an array from data in a text or binary file</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.fromfile.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.loadtxt</code></td>\n",
    "         <td>Load data from a text file</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html\">link</a></td>\n",
    "      </tr>\n",
    "   </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.array\n",
    "   - Always creates a new array, even if the input is already a NumPy array.\n",
    "   - It's useful when you need a new array that is independent of the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr_1d_1 :\n",
      "[1 2 3 4]\n",
      "--------------------------------------------------\n",
      "arr_2d_1 :\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "--------------------------------------------------\n",
      "arr_3d_1 :\n",
      "[[[1 2]\n",
      "  [3 4]]\n",
      "\n",
      " [[5 6]\n",
      "  [7 8]]]\n"
     ]
    }
   ],
   "source": [
    "arr_1d_1 = np.array([1, 2, 3, 4])\n",
    "arr_2d_1 = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "arr_3d_1 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
    "\n",
    "# log\n",
    "print(f\"arr_1d_1 :\\n{arr_1d_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"arr_2d_1 :\\n{arr_2d_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"arr_3d_1 :\\n{arr_3d_1}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.asarray\n",
    "   - Converts the input to an array, but does not create a copy if the input is already a NumPy array with the same dtype.\n",
    "   - It's useful when you want to avoid unnecessary copies and work directly with the original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asarr_1d_1: [1 2 3 4]\n",
      "asarr_1d_2: [0 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "list_1d_1 = [1, 2, 3, 4]\n",
    "arr_1d_2 = np.array([1, 2, 3, 4])\n",
    "\n",
    "# asarray\n",
    "asarr_1d_1 = np.asarray(list_1d_1)\n",
    "asarr_1d_2 = np.asarray(arr_1d_2)\n",
    "\n",
    "# index assignment\n",
    "list_1d_1[0] = 0\n",
    "arr_1d_2[0] = 0\n",
    "\n",
    "# log\n",
    "print(f\"asarr_1d_1: {asarr_1d_1}\")\n",
    "print(f\"asarr_1d_2: {asarr_1d_2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr_2d_2 :\n",
      "[[ 3  2  1]\n",
      " [-1  5  6]]\n",
      "--------------------------------------------------\n",
      "arr_2d_3 :\n",
      "[[ 3  2  1]\n",
      " [-1  5  6]]\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "arr_2d_2 = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "arr_2d_3 = arr_2d_2\n",
    "\n",
    "# index assignment\n",
    "arr_2d_2[1, 0] = -1\n",
    "arr_2d_3[0] = [3, 2, 1]\n",
    "\n",
    "# log\n",
    "print(f\"arr_2d_2 :\\n{arr_2d_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"arr_2d_3 :\\n{arr_2d_3}\", end=f\"\\n{'-' * 50}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr_2d_4 :\n",
      "[[1 2 3]\n",
      " [0 5 6]]\n",
      "--------------------------------------------------\n",
      "arr_2d_5 :\n",
      "[[3 2 1]\n",
      " [4 5 6]]\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "arr_2d_4 = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "arr_2d_5 = np.copy(arr_2d_4)\n",
    "\n",
    "# index assignment\n",
    "arr_2d_4[1, 0] = 0\n",
    "arr_2d_5[0] = [3, 2, 1]\n",
    "\n",
    "# log\n",
    "print(f\"arr_2d_4 :\\n{arr_2d_4}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"arr_2d_5 :\\n{arr_2d_5}\", end=f\"\\n{'-' * 50}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numerical Ranges\n",
    "\n",
    "<table style=\"margin: 0 auto;\">\n",
    "   <thead>\n",
    "      <tr>\n",
    "         <th style=\"text-align: center;\">Function</th>\n",
    "         <th style=\"text-align: center;\">Description</th>\n",
    "         <th style=\"text-align: center;\">Details</th>\n",
    "      </tr>\n",
    "   </thead>\n",
    "   <tbody>\n",
    "      <tr>\n",
    "         <td><code>np.arange</code></td>\n",
    "         <td>Return evenly spaced values within a given interval</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.arange.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.linspace</code></td>\n",
    "         <td>Return evenly spaced numbers over a specified interval</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.linspace.html\">link</a></td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "         <td><code>np.meshgrid</code></td>\n",
    "         <td>Return a tuple of coordinate matrices from coordinate vectors</td>\n",
    "         <td style=\"text-align: center;\"><a href=\"https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html\">link</a></td>\n",
    "      </tr>\n",
    "   </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arange_1: [0 1 2]\n",
      "arange_2: [0 1 2 3 4]\n",
      "arange_3: [2 3 4]\n",
      "arange_4: [0 2 4 6]\n",
      "arange_5: [0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9]\n"
     ]
    }
   ],
   "source": [
    "arange_1 = np.arange(3)\n",
    "arange_2 = np.arange(5)\n",
    "arange_3 = np.arange(2, 5)\n",
    "arange_4 = np.arange(0, 7, 2)\n",
    "arange_5 = np.arange(0, 1, .1)\n",
    "\n",
    "# log\n",
    "print(f\"arange_1: {arange_1}\")\n",
    "print(f\"arange_2: {arange_2}\")\n",
    "print(f\"arange_3: {arange_3}\")\n",
    "print(f\"arange_4: {arange_4}\")\n",
    "print(f\"arange_5: {arange_5}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.linspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linspace_1 :\n",
      "[0. 1. 2.]\n",
      "--------------------------------------------------\n",
      "linspace_2 :\n",
      "[0.   0.25 0.5  0.75 1.  ]\n",
      "--------------------------------------------------\n",
      "linspace_3 :\n",
      "[0 1 2]\n",
      "--------------------------------------------------\n",
      "linspace_4 :\n",
      "[[0. 3.]\n",
      " [1. 4.]\n",
      " [2. 5.]]\n",
      "--------------------------------------------------\n",
      "linspace_5 :\n",
      "[[0. 1. 2.]\n",
      " [3. 4. 5.]]\n"
     ]
    }
   ],
   "source": [
    "linspace_1 = np.linspace(0, 2, 3)\n",
    "linspace_2 = np.linspace(0, 1, 5)\n",
    "linspace_3 = np.linspace(0, 2, 3, dtype=int)\n",
    "linspace_4 = np.linspace((0, 3), (2, 5), 3, axis=0)\n",
    "linspace_5 = np.linspace((0, 3), (2, 5), 3, axis=1)\n",
    "\n",
    "# log\n",
    "print(f\"linspace_1 :\\n{linspace_1}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"linspace_2 :\\n{linspace_2}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"linspace_3 :\\n{linspace_3}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"linspace_4 :\\n{linspace_4}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"linspace_5 :\\n{linspace_5}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### numpy.meshgrid\n",
    "   - It is primarily used to create coordinate grids for vectorized evaluations of functions over a grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X :\n",
      "[[0. 1. 2.]\n",
      " [0. 1. 2.]\n",
      " [0. 1. 2.]\n",
      " [0. 1. 2.]\n",
      " [0. 1. 2.]]\n",
      "--------------------------------------------------\n",
      "Y :\n",
      "[[0.   0.   0.  ]\n",
      " [0.25 0.25 0.25]\n",
      " [0.5  0.5  0.5 ]\n",
      " [0.75 0.75 0.75]\n",
      " [1.   1.   1.  ]]\n",
      "--------------------------------------------------\n",
      "Z :\n",
      "[[1.         1.84147098 1.90929743]\n",
      " [0.96891242 1.81038341 1.87820985]\n",
      " [0.87758256 1.71905355 1.78687999]\n",
      " [0.73168887 1.57315985 1.6409863 ]\n",
      " [0.54030231 1.38177329 1.44959973]]\n"
     ]
    }
   ],
   "source": [
    "# define the coordinate arrays\n",
    "x = np.linspace(0, 2, 3)\n",
    "y = np.linspace(0, 1, 5)\n",
    "\n",
    "# create the meshgrid\n",
    "X, Y = np.meshgrid(x, y)\n",
    "\n",
    "# log\n",
    "print(f\"X :\\n{X}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "print(f\"Y :\\n{Y}\", end=f\"\\n{'-' * 50}\\n\")\n",
    "\n",
    "# example of using the meshgrid to evaluate a function\n",
    "Z = np.sin(X) + np.cos(Y)\n",
    "print(f\"Z :\\n{Z}\")"
   ]
  }
 ],
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  "author_email": "AmirhosseinHeydari78@gmail.com",
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  "author_name": "Amirhossein Heydari",
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