Section-3-Python-for-Data-Scientists/Lec-3.06(NumPy-06-ArrayManipulation).ipynb

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   "source": [
    "---   \n",
    " <img align=\"left\" width=\"75\" height=\"75\"  src=\"https://upload.wikimedia.org/wikipedia/en/c/c8/University_of_the_Punjab_logo.png\"> \n",
    "\n",
    "<h1 align=\"center\">Department of Data Science</h1>\n",
    "<h1 align=\"center\">Course: Tools and Techniques for Data Science</h1>\n",
    "\n",
    "---\n",
    "<h3><div align=\"right\">Instructor: Muhammad Arif Butt, Ph.D.</div></h3>    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7ee3b8",
   "metadata": {},
   "source": [
    "<h1 align=\"center\">Lecture 3.6 (NumPy-06)</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ba925a",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/arifpucit/data-science/blob/master/Section-3-Python-for-Data-Scientists/Lec-3.06(NumPy-06-ArrayManipulation).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa6dcb19",
   "metadata": {},
   "source": [
    "# _Manipulating Array Elements.ipynb_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c97f0f67",
   "metadata": {},
   "source": [
    "<img align=\"right\" width=\"400\" height=\"400\"  src=\"images/numpy123D.png\" > \n",
    "\n",
    "# Learning agenda of this notebook\n",
    "\n",
    "1. Updating existing values of NumPy array elements\n",
    "2. Append new elements to a NumPy array using np.append()\n",
    "3. Insert new elements in a NumPy array using np.insert()\n",
    "4. Delete elements of a NumPy array using np.delete()\n",
    "5. Alias vs Shallow Copy vs Deep Copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaf623d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f9a8b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To install this library in Jupyter notebook\n",
    "#import sys\n",
    "#!{sys.executable} -m pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6fa14827",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('1.19.5',\n",
       " ['/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/numpy'])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.__version__ , np.__path__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a55cc9b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9800e96a",
   "metadata": {},
   "source": [
    "## 1. Updating Existing Values of Numpy Array Elements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c75b1aa",
   "metadata": {},
   "source": [
    "### a. 1-D Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c22d47e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Array \n",
      " [21 64 42 91 22 79 40 84 91 92]\n",
      "Updated Array \n",
      " [ 21  64 333  91  22  79  40  84  91 777]\n"
     ]
    }
   ],
   "source": [
    "arr = np.random.randint(low = 1, high = 100, size = 10)\n",
    "print(\"Original Array \\n\", arr)\n",
    "arr[2] = 333\n",
    "arr[-1] = 777\n",
    "print(\"Updated Array \\n\", arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd1f603",
   "metadata": {},
   "source": [
    "### b. 2-D Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e3b0f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Array \n",
      " [[7 4 7 1]\n",
      " [3 9 2 3]\n",
      " [7 4 8 5]\n",
      " [4 5 5 9]]\n",
      "Updated Array \n",
      " [[ 7 77  7  1]\n",
      " [ 3  9 66  3]\n",
      " [ 7  4  8 22]\n",
      " [ 4  5  5  9]]\n"
     ]
    }
   ],
   "source": [
    "# Creating 2-D array of size 4x4 of int type b/w interval 1 to 9\n",
    "arr = np.random.randint(low = 1, high = 10, size = (4, 4))\n",
    "print(\"Original Array \\n\", arr)\n",
    "arr[0][1] = 77\n",
    "arr[1][2] = 66\n",
    "arr[2][-1] = 22\n",
    "print(\"Updated Array \\n\", arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec4398ea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73186f77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a22f0d64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf452b4",
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c7b21ead",
   "metadata": {},
   "source": [
    "## 2. Append New Elements to Numpy Arrays\n",
    "- The `np.append()` method allows us to insert new values at the end of a NumPy array.\n",
    "- The method always returns a copy of the existing numpy array with the values appended to the given axis.\n",
    "```\n",
    "np.append(arr, values, axis=None)\n",
    "```\n",
    "- Where,\n",
    "    - `arr` is the array in which we want to append\n",
    "    - `values` must be of the correct shape (the same shape as `arr` excluding `axis`)\n",
    "    - If `axis` is not specified, both `arr` and `values` are flattened before use.\n",
    "    - If `axis` is specified, then `values` must be of the correct shape (the same shape as `arr` excluding `axis`)\n",
    "- The original array remains as such, as it does not occur in-place."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b7fa54",
   "metadata": {},
   "source": [
    "### a. Appending Elements in 1-D Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "22b89811",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 =  [27 43 66 95 39 38 42  7 25 37]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 100, size = 10)\n",
    "print(\"arr1 = \", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b966077f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After append:\n",
      "arr1 =  [27 43 66 95 39 38 42  7 25 37]\n",
      "arr2 =  [ 27  43  66  95  39  38  42   7  25  37 101 202 303]\n"
     ]
    }
   ],
   "source": [
    "# You can add a scalar value or a list of values at the end of a 1-D array\n",
    "arr2 = np.append(arr1, [101, 202,303])\n",
    "print(\"After append:\")\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f809838c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(id(arr1))\n",
    "print(id(arr2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f29ee507",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c42b712a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "196785fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b71d3c60",
   "metadata": {},
   "source": [
    "### b. Appending Elements in 2-D Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d9cf4a",
   "metadata": {},
   "source": [
    "**Example:** In case of 2-D Arrays if `axis` is not mentioned both `arr` and `values` are flattened before use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9fdcd017",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 = \n",
      " [[3 3 1]\n",
      " [7 7 3]\n",
      " [6 8 7]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (3,3))\n",
    "print(\"arr1 = \\n\", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dfdb6a95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After append:\n",
      "arr1 = \n",
      " [[3 3 1]\n",
      " [7 7 3]\n",
      " [6 8 7]]\n",
      "arr2 =  [  3   3   1   7   7   3   6   8   7 101 202 303 404 505]\n"
     ]
    }
   ],
   "source": [
    "# If the axis is not mentioned, values can be of any shape and both `arr` and `values` are flattened before use.\n",
    "arr2 = np.append(arr1, [101, 202,303, 404, 505])\n",
    "print(\"After append:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "302c7f71",
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "137dc887",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5de2401",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c44d2ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "130e285f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "28824ef2",
   "metadata": {},
   "source": [
    "**Example:** Appending a Row to a 2-D array (`axis=0`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "469af8c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 = \n",
      " [[2 7 2]\n",
      " [8 7 3]\n",
      " [4 1 9]\n",
      " [9 8 1]]\n",
      "shape:  (4, 3)\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (4,3))\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"shape: \", arr1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "43aea3c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After append:\n",
      "arr1 = \n",
      " [[2 7 2]\n",
      " [8 7 3]\n",
      " [4 1 9]\n",
      " [9 8 1]]\n",
      "arr2 = \n",
      " [[  2   7   2]\n",
      " [  8   7   3]\n",
      " [  4   1   9]\n",
      " [  9   8   1]\n",
      " [101 202 303]]\n"
     ]
    }
   ],
   "source": [
    "# For appending at axis 0, the values argument must the same shape as `arr` excluding `axis`\n",
    "# so the values should be a row vector, and in this case of shape (1,3), having 1 row and 3 columns\n",
    "arr2 = np.append(arr1, [[101, 202,303]], axis=0)\n",
    "print(\"After append:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8b37d3f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "b385e069",
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d04630e",
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d820ca37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "154a8a0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1e3ef38e",
   "metadata": {},
   "source": [
    "**Example:** Appending a Column to a 2-D array (`axis=1`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "70c4b2dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 = \n",
      " [[4 5 6]\n",
      " [8 3 5]\n",
      " [4 6 9]\n",
      " [4 2 7]]\n",
      "shape:  (4, 3)\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (4,3))\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"shape: \", arr1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4d32417b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After append:\n",
      "arr1 = \n",
      " [[4 5 6]\n",
      " [8 3 5]\n",
      " [4 6 9]\n",
      " [4 2 7]]\n",
      "arr2 = \n",
      " [[  4   5   6 101]\n",
      " [  8   3   5 202]\n",
      " [  4   6   9 303]\n",
      " [  4   2   7 404]]\n"
     ]
    }
   ],
   "source": [
    "# For appending at axis 1, the values argument must the same shape as `arr` excluding `axis`\n",
    "# so the values should be a column vector of shape (4,1), having 4 rows and 1 column\n",
    "arr2 = np.append(arr1, [[101], [202], [303], [404]], axis=1)\n",
    "print(\"After append:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "277823a5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a969f309",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eaadbd59",
   "metadata": {},
   "outputs": [],
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  },
  {
   "cell_type": "markdown",
   "id": "66964303",
   "metadata": {},
   "source": [
    "## 3. Inserting New Elements in Numpy Arrays\n",
    "- The `np.insert()` method allows us to insert new values along the given axis before the given index.\n",
    "- The method always returns a copy of the existing numpy array with the values inserted to the given axis.\n",
    "```\n",
    "np.insert(arr, index, values, axis=None)\n",
    "```\n",
    "- Where,\n",
    "    - `arr` is the array in which we want to insert\n",
    "    - `index` is the index before which we want to insert\n",
    "    - `values` [array_like] values to be added in the `arr`\n",
    "    - If `axis` is not specified, both `arr` and `values` are flattened before use.\n",
    "    - If `axis` is zero, a row is inserted (For 2-D arrays)\n",
    "    - If `axis` is one, a column is inserted (For 2-D arrays)\n",
    "- The original array remains as such, as it does not occur in-place."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "962d7c69",
   "metadata": {},
   "source": [
    "### a. Inserting Elements in 1-D Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "668f2e4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 =  [15 58 34 72 77]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 100, size = 5)\n",
    "print(\"arr1 = \", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6c59fdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After insert:\n",
      "arr1 =  [15 58 34 72 77]\n",
      "arr2 =  [15 58 34 55 66 77 72 77]\n"
     ]
    }
   ],
   "source": [
    "# You can insert a scalar value or a list of values in between array elements before the mentioned index\n",
    "arr2 = np.insert(arr1, 3, [55, 66,77])\n",
    "print(\"After insert:\")\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a513d985",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5edc791f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bf0a3ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e468e72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "06a5cb18",
   "metadata": {},
   "source": [
    "### b. Inserting Elements in 2-D Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de9ed861",
   "metadata": {},
   "source": [
    "**Example:** In case of 2-D array, if `axis` is not mentioned the array is flattened first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3284a269",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 = \n",
      " [[5 5 1 9]\n",
      " [8 3 1 1]\n",
      " [2 7 9 2]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (3,4))\n",
    "print(\"arr1 = \\n\", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cfad3dcf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After insert:\n",
      "arr1 = \n",
      " [[5 5 1 9]\n",
      " [8 3 1 1]\n",
      " [2 7 9 2]]\n",
      "arr2 =  [ 5  5  1  9 55  8  3  1  1  2  7  9  2]\n"
     ]
    }
   ],
   "source": [
    "# Inserting a single value\n",
    "arr2 = np.insert(arr1, 4, 55)\n",
    "print(\"After insert:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40317d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inserting a multiple values\n",
    "arr2 = np.insert(arr1, 4, [55, 66])\n",
    "print(\"After insert:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ade890b3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ef7679e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "556b2299",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "816fd274",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f2a9adba",
   "metadata": {},
   "source": [
    "**Example:** If axis=0, value(s) are added as a row before mentioned index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fc4f9a6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 = \n",
      " [[8 9 3 7]\n",
      " [9 8 7 7]\n",
      " [8 6 1 9]]\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (3,4))\n",
    "print(\"arr1 = \\n\", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a11a09b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After insert:\n",
      "arr1 = \n",
      " [[8 9 3 7]\n",
      " [9 8 7 7]\n",
      " [8 6 1 9]]\n",
      "arr2 = \n",
      " [[ 8  9  3  7]\n",
      " [ 9  8  7  7]\n",
      " [55 55 55 55]\n",
      " [ 8  6  1  9]]\n"
     ]
    }
   ],
   "source": [
    "# For axis = 0, note how the scalar value is replicated before insertion\n",
    "arr2 = np.insert(arr1, 2, 55, axis=0)\n",
    "print(\"After insert:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49fbdaad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For axis=0, note the size of values has to be 4 in this case (equal to number of columns)\n",
    "arr2 = np.insert(arr1, 2, [55, 66, 77, 88], axis=0)\n",
    "print(\"After insert:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bfca2c79",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f584165",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ea4dcf0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "87670040",
   "metadata": {},
   "source": [
    "**Example:** If axis=1, value(s) are added as a column at mentioned index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a92e0c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (3,4))\n",
    "print(\"arr1 = \\n\", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6d02c09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For axis = 1, note how the scalar value is replicated before insertion\n",
    "arr2 = np.insert(arr1, 2, 55, axis=1)\n",
    "print(\"After insert:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "08c536be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# For axis=1, note the size of values has to be 3 in this case (equal to number of rows)\n",
    "arr2 = np.insert(arr1, 2, [55, 66, 77], axis=1)\n",
    "print(\"After insert:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9c796a3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "835da82c",
   "metadata": {},
   "source": [
    "## 4. Deleting  Elements of Numpy Arrays\n",
    "- The `np.delete()` method allows us to delete value(s) from an array at the given index\n",
    "- This function always returns a copy of the existing numpy array with the values deleted from the given axis.\n",
    "- If axis is not specified, values can be of any shape and will be flattened before use\n",
    "```\n",
    "np.delete(arr, index, axis=None)\n",
    "```\n",
    "- The original array remains as such, as it does not occur in-place."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8179e2e5",
   "metadata": {},
   "source": [
    "### a. Deleting Elements from a 1-D Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6c6747",
   "metadata": {},
   "source": [
    "**Example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe38b972",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = 5)\n",
    "print(\"arr1 = \", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e988364b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can delete a scalar value from a specific index\n",
    "arr2 = np.delete(arr1, 3)\n",
    "print(\"After delete:\")\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8b82c88",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3ce1cc1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "123f4850",
   "metadata": {},
   "source": [
    "**Example:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1911555",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 100, size = 10)\n",
    "print(\"arr1 = \", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73ce969a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can delete a list of values in between array elements from specific indices\n",
    "arr2 = np.delete(arr1, [2,5])\n",
    "print(\"After delete:\")\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10436aac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20dd4fec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8cf1d87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "569a9b74",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f5fefe0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9fd1c3ac",
   "metadata": {},
   "source": [
    "### b. Deleting Elements from a 2-D Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bc492dc",
   "metadata": {},
   "source": [
    "**Example:** Delete a specific element from a 2-D array, don't mention the axis. The resulting array is flattened before use"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b859729",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (3,3))\n",
    "print(\"arr1 = \\n\", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe24666f",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.delete(arr1, 5)\n",
    "print(\"After delete:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dcac4ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a179b1ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dad6e7d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa78d4b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25e85c4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "572970be",
   "metadata": {},
   "source": [
    "**Example:**  Delete a specific row from an existing 2-D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c787db6",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (4,4))\n",
    "print(\"arr1 = \\n\", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aa44191",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.delete(arr1, 2, axis=0)\n",
    "print(\"After delete:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46b11add",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "467e9c34",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59461c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c2a31aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12e50b96",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a67df0fe",
   "metadata": {},
   "source": [
    "**Example:**  Delete a specific column from an existing 2-D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e08589e",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = (4,4))\n",
    "print(\"arr1 = \\n\", arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6da91c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.delete(arr1, 2, axis=1)\n",
    "print(\"After delete:\")\n",
    "print(\"arr1 = \\n\", arr1)\n",
    "print(\"arr2 = \\n\", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "977612c1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d25f455",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "770bac4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "7f00bded",
   "metadata": {},
   "source": [
    "## 5. Assigning vs Coping NumPy Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e0e6a49",
   "metadata": {},
   "source": [
    "### a. Assigning two NumPy Arrays (Create an alias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "382c3f2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 =  [8 6 3 4 9 3 4 3 2 9]\n",
      "arr2 =  [8 6 3 4 9 3 4 3 2 9]\n",
      "140657482813808\n",
      "140657482813808\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = 10)\n",
    "\n",
    "# Creating a copy using assignment operator, both variables point at the same array\n",
    "arr2 = arr1\n",
    "\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)\n",
    "print(id(arr1))\n",
    "print(id(arr2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2ec4f294",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change value in arr1 will also occur in arr2\n",
    "arr2[2] = 55\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8fd525a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5dffdcd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa2d253d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d6aa14c7",
   "metadata": {},
   "source": [
    "### b. View/Shallow Copy\n",
    "Arrays that share some data. The view method creates an object looking at the same data. Slicing an array returns a view of that array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "670adc44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 =  [6 9 5 9 6 5 4 8 3 3]\n",
      "arr2 =  [6 9 5 9 6 5 4 8 3 3]\n",
      "140657482814368\n",
      "140657482813312\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr1 = np.random.randint(low = 1, high = 10, size = 10)\n",
    "\n",
    "# Creating a shallow copy (view) using slice operator\n",
    "arr2 = arr1[:]\n",
    "\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)\n",
    "print(id(arr1))\n",
    "print(id(arr2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ede79da2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 =  [ 6  9 55  9  6  5  4  8  3  3]\n",
      "arr2 =  [ 6  9 55  9  6  5  4  8  3  3]\n"
     ]
    }
   ],
   "source": [
    "# Change value in arr1 will occur in arr2\n",
    "arr2[2] = 55\n",
    "\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c5bf0ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3371fba8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "341a0304",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3f007f4f",
   "metadata": {},
   "source": [
    "### c. Deep Copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1abe5e75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 =  [1 5 9 4 1 5 9 8 6 6]\n",
      "arr2 =  [1 5 9 4 1 5 9 8 6 6]\n",
      "140657482815488\n",
      "140657482769472\n"
     ]
    }
   ],
   "source": [
    "arr1 = np.random.randint(low = 1, high = 10, size = 10)\n",
    "\n",
    "# Create a Deep copy using copy() method, which will create a new copy of the array\n",
    "arr2 = arr1.copy()\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)\n",
    "print(id(arr1))\n",
    "print(id(arr2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b11e005",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "arr1 =  [1 5 9 4 1 5 9 8 6 6]\n",
      "arr2 =  [ 1  5 55  4  1  5  9  8  6  6]\n"
     ]
    }
   ],
   "source": [
    "# Change value in array 1 will NOT occur in array 2\n",
    "arr2[2] = 55\n",
    "\n",
    "print(\"arr1 = \", arr1)\n",
    "print(\"arr2 = \", arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ec509b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06a4ad16",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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