Iris_Visualizations.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMU0OtwepPmZOYvtnVNY611",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/frm1789/100DaysOfPython/blob/main/Iris_Visualizations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "0MrS-EHmLxH6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "GHRsM3c1LkcW",
        "outputId": "bba4a3aa-93e8-4214-d8c4-a17f5c8f9d8d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# Load the Iris dataset\n",
        "iris = pd.read_csv('https://raw.githubusercontent.com/frm1789/100DaysOfPython/main/iris.data')\n",
        "\n",
        "# Create the correlation matrix\n",
        "corr = iris.corr()\n",
        "\n",
        "# Create a heat map of the correlation matrix\n",
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "\n",
        "#Add lines between cells:\n",
        "sns.heatmap(corr, cmap=\"Blues\", linewidth=.5,\n",
        "            vmin=-1,\n",
        "            annot=True)\n",
        "\n",
        "# Add a title\n",
        "plt.title('Heatmap of Iris Dataset')\n",
        "\n",
        "\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mask = np.zeros_like(corr, dtype=np.bool)\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(corr,\n",
        "            vmin=-1,\n",
        "            cmap='coolwarm',\n",
        "            annot=True,\n",
        "            mask = mask);\n",
        "\n",
        "# Add a title\n",
        "plt.title('Heatmap of Iris Dataset')\n",
        "\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 679
        },
        "id": "QU5CoF3gRrkI",
        "outputId": "d827309c-1b23-41b4-cfee-374202b5be0e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-15-48ddcc64a297>:1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
            "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
            "  mask = np.zeros_like(corr, dtype=np.bool)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}