MatplotLib_Intro.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wh-8deLojXJb"
      },
      "source": [
        "# MatplotLib short introduction\n",
        "Matplotlib is a powerful library in Python for creating static, animated, and interactive visualizations.\n",
        "\n",
        "Matplotlib is often used to visualize datasets being fed into PyTorch models, especially when working with images\n",
        "\n",
        "This guide will help you get started with the basics of matplotlib, like how to create line plots, bar charts, and scatter plots.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aGbld7_Jl1vO"
      },
      "source": [
        "Prerequisites\n",
        "\n",
        "Ensure you have the matplotlib library installed. If not, run:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B3KE5e4HjnyD",
        "outputId": "aea2af90-0b8c-4ffa-eff3-204a277516b6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (3.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (4.54.1)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.4.7)\n",
            "Requirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (24.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (10.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (3.1.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
          ]
        }
      ],
      "source": [
        "pip install matplotlib\n",
        "\n",
        "#or if you are using conda\n",
        "#conda install matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zu6MSojPjqf0"
      },
      "source": [
        "Importing Matplotlib\n",
        "To start using matplotlib, import it in your Python script or Jupyter notebook:\n",
        "Python\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zYWBpLcCjs2A"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IoZTHoNjugu"
      },
      "source": [
        "Creating a Line Plot\n",
        "A line plot is one of the most basic types of plots:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IR-coFp6jxXK",
        "outputId": "6ce9c624-0b50-4b20-b1d2-e3305981a7af"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Sample data\n",
        "x = [1, 2, 3, 4, 5]\n",
        "y = [10, 15, 13, 18, 16]\n",
        "\n",
        "#Creating the plot\n",
        "plt.plot(x, y, marker='o')\n",
        "\n",
        "#Adding labels and title\n",
        "plt.xlabel('X-axis')\n",
        "plt.ylabel('Y-axis')\n",
        "plt.title('Line Plot')\n",
        "\n",
        "#Display the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HqbgYJ7tj-9N"
      },
      "source": [
        "# Bar charts are useful for comparing categorical data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fEb4DvU3kJET",
        "outputId": "9b549b7b-4f16-42da-8735-484a074f38fd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Sample data\n",
        "categories = ['A', 'B', 'C', 'D']\n",
        "values = [1, 2, 3, 4]\n",
        "\n",
        "#Creating the bar chart\n",
        "plt.bar(categories, values, color='green')\n",
        "\n",
        "#Adding labels and title\n",
        "plt.xlabel('Categories')\n",
        "plt.ylabel('Values')\n",
        "plt.title('Bar Chart Example')\n",
        "\n",
        "#Display the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q94wb2n5kVAz"
      },
      "source": [
        "Creating a Scatter Plot\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "BP75joOnkXbD",
        "outputId": "4bc7b7a8-1ff8-4c30-fca8-6403a0db9f2f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Sample data\n",
        "x = [5, 7, 8, 9, 10]\n",
        "y = [10, 14, 13, 17, 19]\n",
        "\n",
        "#Creating the scatter plot\n",
        "plt.scatter(x, y, color='red')\n",
        "\n",
        "#Adding labels and title\n",
        "plt.xlabel('X-axis')\n",
        "plt.ylabel('Y-axis')\n",
        "plt.title('Scatter Plot Example')\n",
        "\n",
        "#Display the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hKwSQNzk1e_"
      },
      "source": [
        "Once you're comfortable with these basics, you can explore more advanced topics like subplots, histograms, and customizing plot styles. The official [Matplotlib documentation](https://matplotlib.org/stable/contents.html) is a great resource for deep dives.\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}