{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NExnbqXuNPbZ"
   },
   "source": [
    "# Python Tutorial Notebook\n",
    "\n",
    "## 1. Python Syntax\n",
    "\n",
    "### Text: Python Syntax Overview\n",
    "Python is known for its clean and easy-to-read syntax. Unlike languages such as C or C++, Python does not use curly brackets (`{}`) to define blocks of code. Instead, it uses indentation to indicate a block of code. This makes Python code visually neat and eliminates the need for excessive symbols.\n",
    "\n",
    "Python is an **interpreted language**, meaning the code is executed line by line, making it easy to debug and test. In contrast, C and C++ are **compiled languagess**, meaning the entire code is converted into machine code before execution. This often results in faster performance for C/C++ programs but makes debugging slightly more complex.\n",
    "\n",
    "#### Example: Print Function\n",
    "The `print()` function is used to output text in Python. This is simpler than the `printf` function in C or C++.\n",
    "\n",
    "**C Example:**\n",
    "```c\n",
    "#include <stdio.h>\n",
    "int main() {\n",
    "    printf(\"Hello, World!\\n\");\n",
    "    return 0;\n",
    "}\n",
    "```\n",
    "\n",
    "**Python Equivalent:**\n",
    "```python\n",
    "print(\"Hello, World!\")\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OTfQYI5uNYiM",
    "outputId": "476630e1-5169-44b1-d7a4-7907ac0cc7e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is correctly indented.\n"
     ]
    }
   ],
   "source": [
    "# Example 1: Proper indentation\n",
    "if True:\n",
    "    print(\"This is correctly indented.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "D0iZkL3oNi5E"
   },
   "outputs": [],
   "source": [
    "# Example 2: Incorrect indentation will throw an error\n",
    "# Uncomment the following code to see the error\n",
    "# if True:\n",
    "# print(\"This will cause an IndentationError.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "He6gHS7FNk9H",
    "outputId": "652ede80-211d-45c8-a858-dfd9ead8c639"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 is less than 20\n"
     ]
    }
   ],
   "source": [
    "# Example 3: Conditional statement\n",
    "x = 10\n",
    "y = 20\n",
    "if x < y:\n",
    "    print(f\"{x} is less than {y}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "rvenwS1RNm0d",
    "outputId": "ed1a4b9a-b0ac-47c1-bb42-34070d0ba641"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current number: 0\n",
      "Current number: 1\n",
      "Current number: 2\n",
      "Current number: 3\n",
      "Current number: 4\n"
     ]
    }
   ],
   "source": [
    "# Example 4: Loop with proper indentation\n",
    "for i in range(5):\n",
    "    print(f\"Current number: {i}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_fmRCfRANoZI",
    "outputId": "edb530f6-3d89-4e6a-8221-9eeff9b42553"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello, Alice!\n"
     ]
    }
   ],
   "source": [
    "# Example 5: Function definition and indentation\n",
    "def greet(name):\n",
    "    print(f\"Hello, {name}!\")\n",
    "greet(\"Alice\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Yg9aThXlNrsY",
    "outputId": "a1d6489a-5f2b-4a2b-f983-d3b78c63a4f0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 is even\n",
      "2 is even\n"
     ]
    }
   ],
   "source": [
    "# Example 6: Nested indentation\n",
    "for i in range(3):\n",
    "    if i % 2 == 0:\n",
    "        print(f\"{i} is even\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "digXeQakNsnr",
    "outputId": "ba542ed8-fb73-4a05-c1ef-13649b85836b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Both conditions are True\n"
     ]
    }
   ],
   "source": [
    "# Example 7: Multi-line statement\n",
    "if (10 > 5 and\n",
    "    20 > 15):\n",
    "    print(\"Both conditions are True\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "L2nSxLvPNup4",
    "outputId": "c10adf3e-95f1-40d9-a3a4-9285f8606a74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# Example 8: Inline comments\n",
    "x = 10  # Assigning value to x\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mEQ28tDnNxQ-",
    "outputId": "d9c03d94-17c5-44ec-8853-e6b8c5c6e625"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "# Example 9: Using pass\n",
    "for i in range(3):\n",
    "    if i == 1:\n",
    "        pass  # Do nothing\n",
    "    else:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tj54BCicN1Fb",
    "outputId": "0389c490-8444-403c-da80-62b344f1b3e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "Loop completed!\n"
     ]
    }
   ],
   "source": [
    "# Example 10: Using else with loops\n",
    "for i in range(3):\n",
    "    print(i)\n",
    "else:\n",
    "    print(\"Loop completed!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R4V_4QBNN5Sb"
   },
   "source": [
    "\n",
    "## 2. Python Variables\n",
    "\n",
    "### Text: Introduction to Variables\n",
    "In Python, variables are used to store data. Unlike C or C++, you don’t need to declare the type of the variable. Python automatically infers the type based on the assigned value.\n",
    "\n",
    "#### Casting\n",
    "To explicitly convert a variable to a specific type, you can use casting.\n",
    "\n",
    "#### Case-Sensitive Strings\n",
    "Variable names in Python are case-sensitive. For instance, `Name` and `name` would be treated as two different variables.\n",
    "\n",
    "#### Lists in Python\n",
    "Python lists are versatile and can store multiple items in a single variable. Lists allow duplicates, and their elements are indexed. Lists can store elements of different data types, but you can also choose to use lists with consistent data types for clarity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sG0r-DvNN_Hf",
    "outputId": "85ceb83c-732c-459d-a0f6-1fc40c2ba38f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'int'>\n",
      "<class 'float'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# Example 1: Variable assignment and type inference\n",
    "x = 5       # Integer\n",
    "y = 3.14    # Float\n",
    "z = \"Hello\" # String\n",
    "print(type(x))\n",
    "print(type(y))\n",
    "print(type(z))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PaDrSTA_OBmk",
    "outputId": "8212a372-867a-4e26-fb32-d97511da8a84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n",
      "123\n",
      "4.56\n"
     ]
    }
   ],
   "source": [
    "# Example 2: Casting\n",
    "x = int(3.14)  # Cast float to int\n",
    "print(x)\n",
    "y = str(123)   # Cast int to string\n",
    "print(y)\n",
    "z = float(\"4.56\")  # Cast string to float\n",
    "print(z)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7Mg9zbOlODb0",
    "outputId": "cccabbb4-3fe9-46b3-d467-e99f17d837a0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alice\n",
      "Bob\n"
     ]
    }
   ],
   "source": [
    "# Example 3: Case sensitivity\n",
    "Name = \"Alice\"\n",
    "name = \"Bob\"\n",
    "print(Name)\n",
    "print(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "9Q1zoJt2OGdQ",
    "outputId": "8b882d08-370e-41a4-a772-a7d22005f06a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List elements: [1, 2, 3, 3, 4]\n",
      "List length: 5\n"
     ]
    }
   ],
   "source": [
    "# Example 4: Lists\n",
    "my_list = [1, 2, 3, 3, 4]\n",
    "print(\"List elements:\", my_list)\n",
    "print(\"List length:\", len(my_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4jl1cJZyOHEQ",
    "outputId": "0e22fc4f-f254-48e5-8edc-8b69118f51e8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Alice', 30, True]\n"
     ]
    }
   ],
   "source": [
    "# Example 5: Lists with mixed data types\n",
    "mixed_list = [\"Alice\", 30, True]\n",
    "print(mixed_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hI9HYbESOItP",
    "outputId": "e063ee57-0464-49cf-dcb7-05c11914f1f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First fruit: apple\n",
      "Last fruit: cherry\n"
     ]
    }
   ],
   "source": [
    "# Example 6: List indexing\n",
    "fruits = [\"apple\", \"banana\", \"cherry\"]\n",
    "print(\"First fruit:\", fruits[0])\n",
    "print(\"Last fruit:\", fruits[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SwdgbETaOLXY",
    "outputId": "c1cee213-18f2-4922-f28d-b9d899f3a044"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First three numbers: [0, 1, 2]\n",
      "Last two numbers: [4, 5]\n"
     ]
    }
   ],
   "source": [
    "# Example 7: List slicing\n",
    "numbers = [0, 1, 2, 3, 4, 5]\n",
    "print(\"First three numbers:\", numbers[:3])\n",
    "print(\"Last two numbers:\", numbers[-2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SxDPZppnONkm",
    "outputId": "99406304-2999-48dd-9288-7d6a23866525"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modified fruits list: ['apple', 'blueberry', 'cherry']\n"
     ]
    }
   ],
   "source": [
    "# Example 8: Modifying list elements\n",
    "fruits[1] = \"blueberry\"\n",
    "print(\"Modified fruits list:\", fruits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H16KtLLGOPZ5",
    "outputId": "d3eca463-e1e0-4952-9f48-0156e22f8c3c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After appending: [0, 1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "# Example 9: Appending items to a list\n",
    "numbers.append(6)\n",
    "print(\"After appending:\", numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "P-t3eljHORQy",
    "outputId": "eb2f3800-4bbd-4f38-d0ca-f4f2adc052cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After inserting at index 2: [0, 1, 99, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "# Example 10: Inserting items into a list\n",
    "numbers.insert(2, 99)\n",
    "print(\"After inserting at index 2:\", numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AEKgcq4AOR4-",
    "outputId": "4f9cd5b4-be44-4826-c97a-39fa290bce47"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After removing 99: [0, 1, 2, 3, 4, 5, 6]\n"
     ]
    }
   ],
   "source": [
    "# Example 11: Removing items from a list\n",
    "numbers.remove(99)\n",
    "print(\"After removing 99:\", numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dIdReNMnOUcE",
    "outputId": "0dece636-4cb3-4956-ba4c-69452ab301a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# Example 12: Checking existence in a list\n",
    "print(\"apple\" in fruits)\n",
    "print(\"grape\" not in fruits)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K04FUkLTOWBD"
   },
   "source": [
    "\n",
    "## 3. Python Operations\n",
    "\n",
    "### Text: Mathematical Operations in Python\n",
    "Python provides all basic mathematical operations, such as addition (`+`), subtraction (`-`), multiplication (`*`), division (`/`), and modulo (`%`).\n",
    "\n",
    "#### Multiplying Lists\n",
    "If you multiply a list by a number, Python creates a new list that repeats the original list. To perform elementwise operations on lists, you need a library like NumPy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oZ34cSrROYLt",
    "outputId": "5a8b653f-e8e3-438c-be09-82232cd1305f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Addition: 13\n",
      "Subtraction: 7\n",
      "Multiplication: 30\n",
      "Division: 3.3333333333333335\n",
      "Modulo: 1\n"
     ]
    }
   ],
   "source": [
    "# Example 1: Basic mathematical operations\n",
    "x = 10\n",
    "y = 3\n",
    "print(\"Addition:\", x + y)\n",
    "print(\"Subtraction:\", x - y)\n",
    "print(\"Multiplication:\", x * y)\n",
    "print(\"Division:\", x / y)\n",
    "print(\"Modulo:\", x % y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fla_OtMaOd-Z",
    "outputId": "366df1e9-9cbc-4dc7-cbe4-eb1dbe4aabb0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Exponentiation: 1000\n",
      "Floor Division: 3\n"
     ]
    }
   ],
   "source": [
    "# Example 2: Exponentiation and floor division\n",
    "print(\"Exponentiation:\", x ** y)\n",
    "print(\"Floor Division:\", x // y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jTuoFIFcOfo6",
    "outputId": "a8f2cf1a-fa7f-4c85-c0e3-1d245ba388ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List multiplied by 3: [1, 2, 3, 1, 2, 3, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "# Example 3: List multiplication\n",
    "my_list = [1, 2, 3]\n",
    "print(\"List multiplied by 3:\", my_list * 3)  # Repeats the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "HAYapBtFOhM2"
   },
   "outputs": [],
   "source": [
    "# Example 4: Elementwise multiplication (requires NumPy)\n",
    "# Uncomment the following to see the error if NumPy is not used\n",
    "# print(\"Elementwise multiplication:\", [i * 3 for i in my_list])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7ZAitl0VOuou",
    "outputId": "2318ee2c-e14a-46ba-ad47-2ef2861c15d1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rounded division result: 3.33\n"
     ]
    }
   ],
   "source": [
    "# Example 5: Using the round function\n",
    "c = 10 / 3\n",
    "print(\"Rounded division result:\", round(c, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UvEbSKGJOv1N",
    "outputId": "37a016d8-af32-49a7-fdea-7a540f834e98"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Incremented x: 11\n",
      "Decremented x: 10\n"
     ]
    }
   ],
   "source": [
    "# Example 6: Increment and decrement\n",
    "x += 1\n",
    "print(\"Incremented x:\", x)\n",
    "x -= 1\n",
    "print(\"Decremented x:\", x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "F9OFkp6-Oya1",
    "outputId": "563a8047-21a7-4d88-fbb3-e7f2151d8386"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x is greater than y: True\n",
      "x is equal to y: False\n"
     ]
    }
   ],
   "source": [
    "# Example 7: Comparison operators\n",
    "print(\"x is greater than y:\", x > y)\n",
    "print(\"x is equal to y:\", x == y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eOZAeB7XO7e_",
    "outputId": "2e17b931-765d-419c-ba35-c2e06b11736a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Squared numbers: [0, 1, 4, 9, 16]\n"
     ]
    }
   ],
   "source": [
    "# Example 8: List comprehensions with operations\n",
    "squared_numbers = [i**2 for i in range(5)]\n",
    "print(\"Squared numbers:\", squared_numbers)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L6ns17PbPBGS"
   },
   "source": [
    "# NumPy Tutorial\n",
    "\n",
    "## 1. Introduction:\n",
    "\n",
    "### What is NumPy?\n",
    "\n",
    "NumPy is a Python library used for working with arrays.\n",
    "\n",
    "It also has functions for working in the domain of linear algebra, Fourier transform, and matrices.\n",
    "\n",
    "NumPy was created in 2005 by Travis Oliphant. It is an open-source project, and you can use it freely.\n",
    "\n",
    "NumPy stands for Numerical Python.\n",
    "\n",
    "In Python, we have lists that serve the purpose of arrays, but they are slow to process.\n",
    "\n",
    "NumPy aims to provide an array object that is up to 50x faster than traditional Python lists.\n",
    "\n",
    "The array object in NumPy is called `ndarray`. It provides a lot of supporting functions that make working with `ndarray` very easy.\n",
    "\n",
    "Arrays are very frequently used in data science, where speed and resources are very important.\n",
    "\n",
    "### Why is NumPy Faster Than Lists?\n",
    "\n",
    "NumPy arrays are stored at one continuous place in memory, unlike lists, so processes can access and manipulate them very efficiently.\n",
    "\n",
    "This behavior is called **locality of reference** in computer science.\n",
    "\n",
    "This is the main reason why NumPy is faster than lists. Also, it is optimized to work with the latest CPU architectures.\n",
    "\n",
    "### Which Language is NumPy written in?\n",
    "\n",
    "NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are written in C or C++.\n",
    "\n",
    "### Where is the NumPy Codebase?\n",
    "\n",
    "The source code for NumPy is located at this GitHub repository: [https://github.com/numpy/numpy](https://github.com/numpy/numpy)\n",
    "\n",
    "### Array Creation in NumPy\n",
    "\n",
    "Below are some examples of how to create arrays in NumPy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "snYP7FesQpbU"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XOjKR5gcQtK1",
    "outputId": "2b760034-dca7-486c-abc3-ea75366577af"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1D Array: [1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "# Creating a 1D array\n",
    "array_1d = np.array([1, 2, 3, 4, 5])\n",
    "print(\"1D Array:\", array_1d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q5Zjk0MwQuxY",
    "outputId": "6a1ea387-badc-4c3f-fd9a-73d82ae3cf68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2D Array:\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "# Creating a 2D array\n",
    "array_2d = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "print(\"\\n2D Array:\")\n",
    "print(array_2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ua2CaVSeQwHa",
    "outputId": "f77728ae-bfc0-42a4-ea32-bff11ddf5fab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3D Array:\n",
      "[[[1 2]\n",
      "  [3 4]]\n",
      "\n",
      " [[5 6]\n",
      "  [7 8]]]\n"
     ]
    }
   ],
   "source": [
    "# Creating a 3D array\n",
    "array_3d = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
    "print(\"\\n3D Array:\")\n",
    "print(array_3d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mj85AYuLQxhh",
    "outputId": "dd6f796f-9be8-4f53-8042-57fa8eb9a4ff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Zeros Array:\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "# Creating an array with zeros\n",
    "zeros_array = np.zeros((3, 3))\n",
    "print(\"\\nZeros Array:\")\n",
    "print(zeros_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RatTh4_rQzc0",
    "outputId": "0d473126-485f-4d63-eba4-bc6ef77b70a5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ones Array:\n",
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# Creating an array with ones\n",
    "ones_array = np.ones((2, 4))\n",
    "print(\"\\nOnes Array:\")\n",
    "print(ones_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O8g6uG7OQ1FK",
    "outputId": "b7565eb9-17cd-451e-d9be-b857aa0ebd67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Range Array: [1 3 5 7 9]\n"
     ]
    }
   ],
   "source": [
    "# Creating an array with a range of numbers\n",
    "range_array = np.arange(1, 10, 2)  # start, stop, step\n",
    "print(\"\\nRange Array:\", range_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "52ONd0t2Q3Ks",
    "outputId": "5cad3c64-40c8-47f8-859d-9e7a82ba3189"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Linspace Array: [0.   0.25 0.5  0.75 1.  ]\n"
     ]
    }
   ],
   "source": [
    "# Creating an array with evenly spaced numbers\n",
    "linspace_array = np.linspace(0, 1, 5)  # start, stop, number of points\n",
    "print(\"\\nLinspace Array:\", linspace_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-Eg_BbspQ4wq",
    "outputId": "63b7a793-0566-4af8-aa71-1a3e98e33722"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Identity Matrix:\n",
      "[[1. 0. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# Creating an identity matrix\n",
    "identity_matrix = np.eye(3)\n",
    "print(\"\\nIdentity Matrix:\")\n",
    "print(identity_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "o7GZTUrTQ85j"
   },
   "source": [
    "## 2. Array Indexing and Array Slicing\n",
    "\n",
    "### Indexing in NumPy Arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "EjRFrZV5Q9_d",
    "outputId": "2d2cb90a-6596-4e6b-eb8a-3f5a0910da79"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# Accessing elements in a 1D array\n",
    "print(array_1d[0])  # First element\n",
    "print(array_1d[-1]) # Last element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "n-VjXICtRAP-",
    "outputId": "2b30f7d3-b930-4ce8-c14c-56d7c01cd7b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "# Accessing elements in a 2D array\n",
    "print(array_2d[0, 1])  # Element in the first row and second column\n",
    "print(array_2d[1, -1]) # Element in the last row and last column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "42vRnw0oRDZl"
   },
   "source": [
    "### Slicing NumPy Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IJsdCV2FRER8",
    "outputId": "433adb03-0b8a-45f9-b5b8-cdbf8002ab4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 3 4]\n"
     ]
    }
   ],
   "source": [
    "# Slicing a 1D array\n",
    "print(array_1d[1:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FGWlyZ5xRIzg",
    "outputId": "d66754c4-1ea5-46fe-8d4e-7d76e7c1294a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 3]\n",
      " [5 6]]\n"
     ]
    }
   ],
   "source": [
    "# Slicing a 2D array\n",
    "print(array_2d[0:2, 1:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PgXqWutlRMmQ"
   },
   "source": [
    "### Array Shape and Iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ns-CFffTRNNA",
    "outputId": "470a7554-d5a7-449a-eefd-72cb75ab86bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n"
     ]
    }
   ],
   "source": [
    "# Shape of an array\n",
    "print(array_2d.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vBye85fcRPFK",
    "outputId": "b18261f2-6d1b-4752-c868-4549bd9b99e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3]\n",
      "[4 5 6]\n"
     ]
    }
   ],
   "source": [
    "# Iterating through an array\n",
    "for row in array_2d:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Esb7g6_2RRui",
    "outputId": "3663512e-7d2d-41de-9147-9a8d263186a9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "# Flattening and iterating\n",
    "for element in array_2d.flat:\n",
    "    print(element)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eE4fYcICRWjg"
   },
   "source": [
    "### Joining Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zr2VI2OVRXNl",
    "outputId": "ff0d9fee-87e7-4e6b-e85d-9b554590d3eb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Vertical Stack:\n",
      "[[1 2 3 4 5]\n",
      " [1 2 3 4 5]]\n"
     ]
    }
   ],
   "source": [
    "# Joining arrays vertically\n",
    "vstack_array = np.vstack((array_1d, array_1d))\n",
    "print(\"\\nVertical Stack:\")\n",
    "print(vstack_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CN8WDE0SRahw",
    "outputId": "77c6e82d-fd19-4616-fc0c-84842590eb5b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Horizontal Stack:\n",
      "[1 2 3 4 5 1 2 3 4 5]\n"
     ]
    }
   ],
   "source": [
    "# Joining arrays horizontally\n",
    "hstack_array = np.hstack((array_1d, array_1d))\n",
    "print(\"\\nHorizontal Stack:\")\n",
    "print(hstack_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xuObp1d4Rcwq"
   },
   "source": [
    "## 3. Random\n",
    "\n",
    "### Properties of Random in NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CJlHfIohRdZv",
    "outputId": "59afb12c-f3ef-49c1-99f5-c870544a916b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Array:\n",
      "[[0.19613654 0.66699566 0.64752084]\n",
      " [0.41378751 0.77344783 0.09557272]\n",
      " [0.68367106 0.46480407 0.16145855]]\n"
     ]
    }
   ],
   "source": [
    "# Generating random numbers\n",
    "random_array = np.random.rand(3, 3)  # Uniform distribution between 0 and 1\n",
    "print(\"\\nRandom Array:\")\n",
    "print(random_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gUXUyeqERfKn",
    "outputId": "ece5ac61-7413-44f7-8b01-a8ddb369edae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Normal Distribution: [-0.01018828  0.17432043  0.63089233 -0.35371924 -0.54080941]\n"
     ]
    }
   ],
   "source": [
    "# Normal distribution\n",
    "normal_array = np.random.normal(0, 1, 5)  # mean, standard deviation, size\n",
    "print(\"\\nNormal Distribution:\", normal_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Kc6rw1ARgo7",
    "outputId": "98e479ed-83a1-45f3-a0de-4a5afd789d2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Binomial Distribution: [4 4 5 3 5]\n"
     ]
    }
   ],
   "source": [
    "# Binomial distribution\n",
    "binomial_array = np.random.binomial(n=10, p=0.5, size=5)  # trials, probability, size\n",
    "print(\"\\nBinomial Distribution:\", binomial_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "m3Xy5K3vRi-B",
    "outputId": "399f6583-1f05-45f7-e350-d37dfde7415f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Poisson Distribution: [4 2 1 2 2]\n"
     ]
    }
   ],
   "source": [
    "# Poisson distribution\n",
    "poisson_array = np.random.poisson(lam=3, size=5)  # lambda, size\n",
    "print(\"\\nPoisson Distribution:\", poisson_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "REyQlk2MRmTl"
   },
   "source": [
    "## 4. Daily Use Functions\n",
    "\n",
    "### Trigonometric Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "br1UQSbgRnBs",
    "outputId": "c0a1c2fd-3070-48db-bef1-4a971f47f6cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Sine Values: [0.0000000e+00 1.0000000e+00 1.2246468e-16]\n",
      "Cosine Values: [ 1.000000e+00  6.123234e-17 -1.000000e+00]\n",
      "\n",
      "Tangent Values: [ 0.00000000e+00  1.63312394e+16 -1.22464680e-16]\n"
     ]
    }
   ],
   "source": [
    "angles = np.array([0, np.pi / 2, np.pi])\n",
    "\n",
    "# Sine and cosine\n",
    "sin_values = np.sin(angles)\n",
    "cos_values = np.cos(angles)\n",
    "print(\"\\nSine Values:\", sin_values)\n",
    "print(\"Cosine Values:\", cos_values)\n",
    "\n",
    "# Tangent\n",
    "tan_values = np.tan(angles)\n",
    "print(\"\\nTangent Values:\", tan_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zpKlTJhfRveJ"
   },
   "source": [
    "### Other Useful Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q3XaLJaaRrN9",
    "outputId": "50b726c0-d74b-4e1b-af67-411054206fa0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Square Roots: [1.         1.41421356 1.73205081 2.         2.23606798]\n",
      "\n",
      "Exponential: [  2.71828183   7.3890561   20.08553692  54.59815003 148.4131591 ]\n",
      "\n",
      "Logarithm: [0.         0.69314718 1.09861229 1.38629436 1.60943791]\n",
      "\n",
      "Mean: 3.0\n",
      "Median: 3.0\n",
      "Standard Deviation: 1.4142135623730951\n"
     ]
    }
   ],
   "source": [
    "# Square root\n",
    "sqrt_array = np.sqrt(array_1d)\n",
    "print(\"\\nSquare Roots:\", sqrt_array)\n",
    "\n",
    "# Exponent\n",
    "exp_array = np.exp(array_1d)\n",
    "print(\"\\nExponential:\", exp_array)\n",
    "\n",
    "# Logarithm\n",
    "log_array = np.log(array_1d)\n",
    "print(\"\\nLogarithm:\", log_array)\n",
    "\n",
    "# Mean, Median, and Standard Deviation\n",
    "mean_value = np.mean(array_1d)\n",
    "median_value = np.median(array_1d)\n",
    "std_value = np.std(array_1d)\n",
    "print(\"\\nMean:\", mean_value)\n",
    "print(\"Median:\", median_value)\n",
    "print(\"Standard Deviation:\", std_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "55A_6hjWRy0_"
   },
   "source": [
    "# Matplotlib Python Tutorial\n",
    "\n",
    "## Introduction:\n",
    "\n",
    "### What is Matplotlib?\n",
    "Matplotlib is a low-level graph plotting library in Python that serves as a visualization utility.\n",
    "\n",
    "- Matplotlib was created by John D. Hunter.\n",
    "- Matplotlib is open source, and we can use it freely.\n",
    "- It is mostly written in Python, with a few segments written in C, Objective-C, and JavaScript for platform compatibility.\n",
    "\n",
    "### Where is the Matplotlib Codebase?\n",
    "The source code for Matplotlib is located at this GitHub repository: [Matplotlib GitHub Repository](https://github.com/matplotlib/matplotlib)\n",
    "\n",
    "---\n",
    "\n",
    "## Pyplot\n",
    "\n",
    "The `pyplot` module in Matplotlib provides a state-based interface to Matplotlib. It contains functions that are closely aligned with MATLAB-style plotting commands.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "bdTAb_dWSoIv",
    "outputId": "8470088a-76c3-4ff3-8330-236ad48020a1"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot Example 1: Simple Line Plot\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Simple Line Plot\n",
    "x = [0, 1, 2, 3, 4, 5]\n",
    "y = [0, 1, 4, 9, 16, 25]\n",
    "\n",
    "plt.plot(x, y, label='y = x^2', color='blue', marker='o')\n",
    "plt.title('Simple Line Plot')\n",
    "plt.xlabel('X-Axis')\n",
    "plt.ylabel('Y-Axis')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "Ehie60Z6Stbs",
    "outputId": "ef77dc37-9eb7-4b34-e689-e801897c8fb1"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot Example 2: Multiple Lines in One Plot\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# Data for multiple lines\n",
    "x = np.linspace(0, 10, 100)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "plt.plot(x, y1, label='sin(x)', color='red')\n",
    "plt.plot(x, y2, label='cos(x)', color='green')\n",
    "plt.title('Sine and Cosine Waves')\n",
    "plt.xlabel('X-Axis')\n",
    "plt.ylabel('Y-Axis')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 472
    },
    "id": "J0YuOJvuS253",
    "outputId": "0b474255-33b6-4b9a-ca90-c4face39da42"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Plot Example 3: Scatter Plot\n",
    "\n",
    "# Scatter Plot\n",
    "x = [1, 2, 3, 4, 5, 6, 7, 8]\n",
    "y = [5, 7, 6, 8, 7, 5, 9, 6]\n",
    "\n",
    "plt.scatter(x, y, label='Data Points', color='purple', marker='*', s=100)\n",
    "plt.title('Scatter Plot Example')\n",
    "plt.xlabel('X-Axis')\n",
    "plt.ylabel('Y-Axis')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "nMn0tGHkS5gf",
    "outputId": "1109e263-2f00-47d0-9023-1d754bf34ecf"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Imshow Example 1: Visualizing a 2D Array\n",
    "\n",
    "# Imshow with Random Data\n",
    "import numpy as np\n",
    "\n",
    "# Generating random data\n",
    "random_data = np.random.rand(10, 10)\n",
    "\n",
    "plt.imshow(random_data, cmap='viridis', interpolation='nearest')\n",
    "plt.colorbar(label='Random Values')\n",
    "plt.title('Imshow Example: Random Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "B-wE6490S80r",
    "outputId": "9898032f-6f7e-4900-dc08-36e0cd05217a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Imshow Example 2: Heatmap Representation\n",
    "\n",
    "\n",
    "# Imshow with Heatmap\n",
    "heatmap_data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n",
    "\n",
    "plt.imshow(heatmap_data, cmap='hot', interpolation='nearest')\n",
    "plt.colorbar(label='Heatmap Values')\n",
    "plt.title('Imshow Example: Heatmap')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}