diff --git a/02_exercises/Day_4_Exercises.ipynb b/02_exercises/Day_4_Exercises.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7c0bd9da",
+      "metadata": {
+        "id": "7c0bd9da"
+      },
+      "source": [
+        "# Day 4 Exercises\n",
+        "## Object Oriented Programming"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "6bdfa3e9",
+      "metadata": {
+        "id": "6bdfa3e9"
+      },
+      "source": [
+        "#### 1. Create a class called `Book` that has the attributes title, author, pages, price. It also has the method get_price() which returns price, and set_price() to ensure price is numeric.\n",
+        "\n",
+        "Then create two instances of this class:\n",
+        "\n",
+        "b1:\n",
+        "* title -> 'Practical Programming'\n",
+        "* author -> 'Gries, Campbell, Montojo'\n",
+        "* pages -> 383\n",
+        "* price -> 50\n",
+        "\n",
+        "b2:\n",
+        "* title -> 'Building a Career in Data Science'\n",
+        "* author -> 'Robinson, Nolis'\n",
+        "* pages -> 322\n",
+        "* price -> 40"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "4f378a91",
+      "metadata": {
+        "id": "4f378a91"
+      },
+      "outputs": [],
+      "source": [
+        "class Book:\n",
+        "    def __init__(self, title, author, pages, price):\n",
+        "        self.title = title\n",
+        "        self.author = author\n",
+        "        self.pages = pages\n",
+        "        self.price = price\n",
+        "    def get_price(self):\n",
+        "        return self.price\n",
+        "    def set_price(self, price):\n",
+        "        self.price = price\n",
+        "        return self.price\n",
+        "\n",
+        "b1 = Book('Practical Programming', 'Gries, Campbell, Montojo', 383, 50)\n",
+        "b2 = Book('Building a Career in Data Science', 'Robinson, Nolis', 322, 40)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "eeeea49e",
+      "metadata": {
+        "id": "eeeea49e"
+      },
+      "source": [
+        "#### 2. Create a `Phone` class that has the attributes model, id, name, brand and price. Create the getters and setters for each attribute. As well as the following methods: calculate_total which takes the price and calculates it by the tax argument."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "id": "ef1d6c93",
+      "metadata": {
+        "id": "ef1d6c93"
+      },
+      "outputs": [],
+      "source": [
+        "class Phone:\n",
+        "    def __init__(self, model, id, name, brand, price):\n",
+        "        self.model = model\n",
+        "        self.id = id\n",
+        "        self.name = name\n",
+        "        self.brand = brand\n",
+        "        self.price = price\n",
+        "\n",
+        "    def get_model(self):\n",
+        "        return self.model\n",
+        "    def set_model(self, model):\n",
+        "        self.model = model\n",
+        "        return self.model\n",
+        "\n",
+        "    def get_id(self):\n",
+        "        return self.id\n",
+        "    def set_id(self, id):\n",
+        "        self.id = id\n",
+        "        return self.id\n",
+        "\n",
+        "    def get_name(self):\n",
+        "        return self.name\n",
+        "    def set_name(self, name):\n",
+        "        self.name = name\n",
+        "        return self.name\n",
+        "\n",
+        "    def get_brand(self):\n",
+        "        return self.brand\n",
+        "    def set_brand(self, brand):\n",
+        "        self.brand = brand\n",
+        "        return self.brand\n",
+        "\n",
+        "    def get_price(self):\n",
+        "        return self.price\n",
+        "    def set_price(self, price):\n",
+        "        self.price = price\n",
+        "        return self.price\n",
+        "\n",
+        "    def calculate_total(self, tax):\n",
+        "        return self.price + (self.price * tax)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1ad55cea",
+      "metadata": {
+        "id": "1ad55cea"
+      },
+      "source": [
+        "## Numpy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "id": "9b1f4547",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9b1f4547",
+        "outputId": "68366b32-089f-4f90-ffec-93aa7d0e81c8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1 2 3 4 5]\n",
+            "<class 'numpy.ndarray'>\n",
+            "1\n",
+            "(5,)\n",
+            "int64\n",
+            "5\n",
+            "8\n",
+            "40\n",
+            "(8,)\n",
+            "<memory at 0x7a89eee7e2c0>\n",
+            "  C_CONTIGUOUS : True\n",
+            "  F_CONTIGUOUS : True\n",
+            "  OWNDATA : True\n",
+            "  WRITEABLE : True\n",
+            "  ALIGNED : True\n",
+            "  WRITEBACKIFCOPY : False\n",
+            "\n",
+            "<numpy._core._internal._ctypes object at 0x7a89ed884b60>\n",
+            "None\n",
+            "[1 2 3 4 5]\n",
+            "[1 2 3 4 5]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "arr = np.array([1, 2, 3, 4, 5])\n",
+        "\n",
+        "print(arr)\n",
+        "\n",
+        "print(type(arr))\n",
+        "\n",
+        "print(arr.ndim)\n",
+        "\n",
+        "print(arr.shape)\n",
+        "\n",
+        "print(arr.dtype)\n",
+        "\n",
+        "print(arr.size)\n",
+        "\n",
+        "print(arr.itemsize)\n",
+        "\n",
+        "print(arr.nbytes)\n",
+        "\n",
+        "print(arr.strides)\n",
+        "\n",
+        "print(arr.data)\n",
+        "\n",
+        "print(arr.flags)\n",
+        "\n",
+        "print(arr.ctypes)\n",
+        "\n",
+        "print(arr.base)\n",
+        "\n",
+        "print(arr.view())\n",
+        "\n",
+        "print(arr.copy())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a7784ff1",
+      "metadata": {
+        "id": "a7784ff1"
+      },
+      "source": [
+        "#### 1. Write a code to convert a 1D array to a 2D array with 2 rows.\n",
+        "\n",
+        "Original Array: [0 1 2 3 4 5]\n",
+        "\n",
+        "Reshaped 2x3 Array:\n",
+        "\n",
+        "[[0 1 2]\n",
+        "\n",
+        "[3 4 5]]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "id": "92b04fb4",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "92b04fb4",
+        "outputId": "df20db44-8b45-4da4-91b4-1b69033a4a4f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[0 1 2]\n",
+            " [3 4 5]]\n",
+            "(2, 3)\n",
+            "2\n"
+          ]
+        }
+      ],
+      "source": [
+        "arr = np.array([0, 1, 2, 3, 4, 5])\n",
+        "arr = arr.reshape(2, 3)\n",
+        "print(arr)\n",
+        "print(arr.shape)\n",
+        "print(arr.ndim)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cf064844",
+      "metadata": {
+        "id": "cf064844"
+      },
+      "source": [
+        "#### 2. Create a 3×3 NumPy array of all True\n",
+        "\n",
+        "*Hint! Use np.ones() with dtype=bool.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "803e014e",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "803e014e",
+        "outputId": "61830063-13f3-4649-8d48-5f42937dcc38"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ True  True  True]\n",
+            " [ True  True  True]\n",
+            " [ True  True  True]]\n"
+          ]
+        }
+      ],
+      "source": [
+        "true_arr = np.ones((3, 3), dtype=bool)\n",
+        "print(true_arr)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "f7e3f9c7",
+      "metadata": {
+        "id": "f7e3f9c7"
+      },
+      "source": [
+        "#### 3. Create a 1D array filled with zeros and another filled with ones"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "id": "e550ec75",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e550ec75",
+        "outputId": "370a884b-a61d-4fbf-b15b-46a572bd5184"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+            "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n"
+          ]
+        }
+      ],
+      "source": [
+        "zero_arr = np.zeros(10)\n",
+        "one_arr = np.ones(10)\n",
+        "print(zero_arr)\n",
+        "print(one_arr)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "1673c3d0",
+      "metadata": {
+        "id": "1673c3d0"
+      },
+      "source": [
+        "#### 4. Reverse a 1D NumPy array\n",
+        "Ex. arr = np.arange(10)\n",
+        "\n",
+        "Output is [9 8 7 6 5 4 3 2 1 0]\n",
+        "\n",
+        "*Hint! Use slicing with step = -1.*"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "id": "9877bbc5",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9877bbc5",
+        "outputId": "85461368-5b29-4448-aaee-37dfbb1646de"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0 1 2 3 4 5 6 7 8 9]\n",
+            "[9 8 7 6 5 4 3 2 1 0]\n"
+          ]
+        }
+      ],
+      "source": [
+        "arr = np.arange(10)\n",
+        "print(arr)\n",
+        "print(arr[::-1])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "39556d59",
+      "metadata": {
+        "id": "39556d59"
+      },
+      "source": [
+        "#### 5. Stack these arrays horizontally\n",
+        "\n",
+        "\n",
+        "a = np.array([1, 2, 3])\n",
+        "\n",
+        "b = np.array([4, 5, 6])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "id": "4df2d740",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4df2d740",
+        "outputId": "2ed3df9e-d3a3-4a02-f4f9-be3e83eff12a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[1 2 3 4 5 6]\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = np.array([1, 2, 3])\n",
+        "b = np.array([4, 5, 6])\n",
+        "print(np.hstack((a, b)))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "dea8db04",
+      "metadata": {
+        "id": "dea8db04"
+      },
+      "source": [
+        "#### 6. Perform arithmetic operations on two NumPy arrays element-wise\n",
+        "\n",
+        "a = np.array([1, 2, 3])\n",
+        "\n",
+        "b = np.array([4, 5, 6])\n",
+        "\n",
+        "*   Add two NumPy arrays element by element.\n",
+        "*   Multiply two NumPy arrays element by element."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "d4fb55a9",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "d4fb55a9",
+        "outputId": "42ab099f-4660-4d26-cd0d-513c6b3724be"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[5 7 9]\n",
+            "[ 4 10 18]\n"
+          ]
+        }
+      ],
+      "source": [
+        "a = np.array([1, 2, 3])\n",
+        "b = np.array([4, 5, 6])\n",
+        "print(a + b)\n",
+        "print(a * b)"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "lcr-env",
+      "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": "3.11.13"
+    },
+    "colab": {
+      "provenance": []
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file