Real_world_data_distributions.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNNFBJwLsIIYzLdWJZJurJJ",
      "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/singhsourav0/DP100_learning/blob/main/Real_world_data_distributions.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "0F9jrMtvNh1l",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4f9d3134-b885-482d-c749-2f3f5adc6527"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-03-21 22:43:13--  https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 322 [text/plain]\n",
            "Saving to: ‘grades.csv’\n",
            "\n",
            "grades.csv          100%[===================>]     322  --.-KB/s    in 0s      \n",
            "\n",
            "2023-03-21 22:43:13 (10.7 MB/s) - ‘grades.csv’ saved [322/322]\n",
            "\n",
            "         Name  StudyHours  Grade   Pass\n",
            "0         Dan       10.00   50.0  False\n",
            "1       Joann       11.50   50.0  False\n",
            "2       Pedro        9.00   47.0  False\n",
            "3       Rosie       16.00   97.0   True\n",
            "4       Ethan        9.25   49.0  False\n",
            "5       Vicky        1.00    3.0  False\n",
            "6    Frederic       11.50   53.0  False\n",
            "7      Jimmie        9.00   42.0  False\n",
            "8      Rhonda        8.50   26.0  False\n",
            "9    Giovanni       14.50   74.0   True\n",
            "10  Francesca       15.50   82.0   True\n",
            "11      Rajab       13.75   62.0   True\n",
            "12    Naiyana        9.00   37.0  False\n",
            "13       Kian        8.00   15.0  False\n",
            "14      Jenny       15.50   70.0   True\n",
            "15     Jakeem        8.00   27.0  False\n",
            "16     Helena        9.00   36.0  False\n",
            "17      Ismat        6.00   35.0  False\n",
            "18      Anila       10.00   48.0  False\n",
            "19       Skye       12.00   52.0  False\n",
            "20     Daniel       12.50   63.0   True\n",
            "21      Aisha       12.00   64.0   True\n",
            "Minimum:3.00\n",
            "Mean:49.18\n",
            "Median:49.50\n",
            "Mode:50.00\n",
            "Maximum:97.00\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "\n",
        "# Load data from a text file\n",
        "!wget https://raw.githubusercontent.com/MicrosoftDocs/mslearn-introduction-to-machine-learning/main/Data/ml-basics/grades.csv\n",
        "df_students = pd.read_csv('grades.csv',delimiter=',',header='infer')\n",
        "\n",
        "# Remove any rows with missing data\n",
        "df_students = df_students.dropna(axis=0, how='any')\n",
        "\n",
        "# Calculate who passed, assuming '60' is the grade needed to pass\n",
        "passes  = pd.Series(df_students['Grade'] >= 60)\n",
        "\n",
        "# Save who passed to the Pandas dataframe\n",
        "df_students = pd.concat([df_students, passes.rename(\"Pass\")], axis=1)\n",
        "\n",
        "\n",
        "# Print the result out into this notebook\n",
        "print(df_students)\n",
        "\n",
        "\n",
        "# Create a function that we can re-use\n",
        "def show_distribution(var_data):\n",
        "    '''\n",
        "    This function will make a distribution (graph) and display it\n",
        "    '''\n",
        "\n",
        "    # Get statistics\n",
        "    min_val = var_data.min()\n",
        "    max_val = var_data.max()\n",
        "    mean_val = var_data.mean()\n",
        "    med_val = var_data.median()\n",
        "    mod_val = var_data.mode()[0]\n",
        "\n",
        "    print('Minimum:{:.2f}\\nMean:{:.2f}\\nMedian:{:.2f}\\nMode:{:.2f}\\nMaximum:{:.2f}\\n'.format(min_val,\n",
        "                                                                                            mean_val,\n",
        "                                                                                            med_val,\n",
        "                                                                                            mod_val,\n",
        "                                                                                            max_val))\n",
        "\n",
        "    # Create a figure for 2 subplots (2 rows, 1 column)\n",
        "    fig, ax = plt.subplots(2, 1, figsize = (10,4))\n",
        "\n",
        "    # Plot the histogram   \n",
        "    ax[0].hist(var_data)\n",
        "    ax[0].set_ylabel('Frequency')\n",
        "\n",
        "    # Add lines for the mean, median, and mode\n",
        "    ax[0].axvline(x=min_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
        "    ax[0].axvline(x=mean_val, color = 'cyan', linestyle='dashed', linewidth = 2)\n",
        "    ax[0].axvline(x=med_val, color = 'red', linestyle='dashed', linewidth = 2)\n",
        "    ax[0].axvline(x=mod_val, color = 'yellow', linestyle='dashed', linewidth = 2)\n",
        "    ax[0].axvline(x=max_val, color = 'gray', linestyle='dashed', linewidth = 2)\n",
        "\n",
        "    # Plot the boxplot   \n",
        "    ax[1].boxplot(var_data, vert=False)\n",
        "    ax[1].set_xlabel('Value')\n",
        "\n",
        "    # Add a title to the Figure\n",
        "    fig.suptitle('Data Distribution')\n",
        "\n",
        "    # Show the figure\n",
        "    fig.show()\n",
        "\n",
        "\n",
        "show_distribution(df_students['Grade'])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the variable to examine\n",
        "col = df_students['StudyHours']\n",
        "# Call the function\n",
        "show_distribution(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "HhsfgdBqV9G2",
        "outputId": "92ebfb36-e218-40dc-a00b-8d85e74e1cc4"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum:1.00\n",
            "Mean:10.52\n",
            "Median:10.00\n",
            "Mode:9.00\n",
            "Maximum:16.00\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the variable to examine\n",
        "# We will only get students who have studied more than one hour\n",
        "col = df_students[df_students.StudyHours>1]['StudyHours']\n",
        "\n",
        "# Call the function\n",
        "show_distribution(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "03tueF0MWDbv",
        "outputId": "4beac70b-c553-4818-ef84-d6a68b2c2212"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum:6.00\n",
            "Mean:10.98\n",
            "Median:10.00\n",
            "Mode:9.00\n",
            "Maximum:16.00\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate the 0.01th percentile\n",
        "q01 = df_students.StudyHours.quantile(0.01)\n",
        "# Get the variable to examine\n",
        "col = df_students[df_students.StudyHours>q01]['StudyHours']\n",
        "# Call the function\n",
        "show_distribution(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "YcQbzY3FWHDf",
        "outputId": "0c81cc0c-98d3-456a-cb72-880c62d6c99a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Minimum:6.00\n",
            "Mean:10.98\n",
            "Median:10.00\n",
            "Mode:9.00\n",
            "Maximum:16.00\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def show_density(var_data):\n",
        "    fig = plt.figure(figsize=(10,4))\n",
        "\n",
        "    # Plot density\n",
        "    var_data.plot.density()\n",
        "\n",
        "    # Add titles and labels\n",
        "    plt.title('Data Density')\n",
        "\n",
        "    # Show the mean, median, and mode\n",
        "    plt.axvline(x=var_data.mean(), color = 'cyan', linestyle='dashed', linewidth = 2)\n",
        "    plt.axvline(x=var_data.median(), color = 'red', linestyle='dashed', linewidth = 2)\n",
        "    plt.axvline(x=var_data.mode()[0], color = 'yellow', linestyle='dashed', linewidth = 2)\n",
        "\n",
        "    # Show the figure\n",
        "    plt.show()\n",
        "\n",
        "# Get the density of StudyHours\n",
        "show_density(col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "MmKW6ys2WMCg",
        "outputId": "ba62f5fa-606d-49e1-f9e2-e68b6e0b308b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col_name in ['Grade','StudyHours']:\n",
        "    col = df_students[col_name]\n",
        "    rng = col.max() - col.min()\n",
        "    var = col.var()\n",
        "    std = col.std()\n",
        "    print('\\n{}:\\n - Range: {:.2f}\\n - Variance: {:.2f}\\n - Std.Dev: {:.2f}'.format(col_name, rng, var, std))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98LyD8u5WOzi",
        "outputId": "5b265672-6d58-46df-8e6e-a19b518a1712"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Grade:\n",
            " - Range: 94.00\n",
            " - Variance: 472.54\n",
            " - Std.Dev: 21.74\n",
            "\n",
            "StudyHours:\n",
            " - Range: 15.00\n",
            " - Variance: 12.16\n",
            " - Std.Dev: 3.49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "\n",
        "# Get the Grade column\n",
        "col = df_students['Grade']\n",
        "\n",
        "# get the density\n",
        "density = stats.gaussian_kde(col)\n",
        "\n",
        "# Plot the density\n",
        "col.plot.density()\n",
        "\n",
        "# Get the mean and standard deviation\n",
        "s = col.std()\n",
        "m = col.mean()\n",
        "\n",
        "# Annotate 1 stdev\n",
        "x1 = [m-s, m+s]\n",
        "y1 = density(x1)\n",
        "plt.plot(x1,y1, color='magenta')\n",
        "plt.annotate('1 std (68.26%)', (x1[1],y1[1]))\n",
        "\n",
        "# Annotate 2 stdevs\n",
        "x2 = [m-(s*2), m+(s*2)]\n",
        "y2 = density(x2)\n",
        "plt.plot(x2,y2, color='green')\n",
        "plt.annotate('2 std (95.45%)', (x2[1],y2[1]))\n",
        "\n",
        "# Annotate 3 stdevs\n",
        "x3 = [m-(s*3), m+(s*3)]\n",
        "y3 = density(x3)\n",
        "plt.plot(x3,y3, color='orange')\n",
        "plt.annotate('3 std (99.73%)', (x3[1],y3[1]))\n",
        "\n",
        "# Show the location of the mean\n",
        "plt.axvline(col.mean(), color='cyan', linestyle='dashed', linewidth=1)\n",
        "\n",
        "plt.axis('off')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "9p6xe0BRWVKG",
        "outputId": "b0e59a42-27af-4977-d0b5-73cc712189fd"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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ITMANsZT3uE0X2A1B064oyfrib/gUJTEDFCUxexOA/rix5JpQ1beAK1oXoRSRHy6ZZIm55yw5m458DTfndVLgOD5HCjBbo8Q03JBGTWttqOrN+tkilMJRN9/6utBx1DNLzqYje+JqN98VOpAym4UOoFWexi24G2q7Rkk2KHQ8pnFYcjZt8vvk7QXc44vMm/ZNwM1Q2CV0IKZxWHI27dkCWAO4JXQgbXgrdABlWpdvWxlRUzGWnE179sZNsQq1X1+71E3jKow8jRfgalyPjpJsQOh4TGOw5GwW47ej2guYkqfxB4HDWYzAGaFjaMOtuOJChZ7eZuqHJWfTli1wRXeKOKQBrkBQ0dyLq4Wxf+hATGOw5GzashduSKOZ9wrsFj8P/FZgrM3aMJVgydl8jh/S2Bu4t4hDGgV3E7AUMCp0IKb+WXI25YYDQynukAa4GIvoAdxMkv1CB2LqnyVnU25vYCGBd9iuR3kaLwJuBkZGSbZcZ+cb0xFLzuY/SmZp3JencYfbLgX2WOgAOnATrkj97qEDMfXNkrMptQVu26ObQwdSx6bh6h/b0IbpFUvOptSBwKe45cimB/I0VlzveacoyQZ3dr4x7bHkbACIkqwfruD75FrVJe6FcaED6MRNuF1V9g4diKlflpxNq28CqwC/Dx1IZ7SYKwRLPYPbKsqGNkyPWXI2rQ4EPqR45UEXI25vvMLyQxs3AttFSTY0dDymPllyNvgVbbsDt+Zp/EnoeLpgSOgAuuB6/3xw0ChM3bLkbAB2A5amDoY06kWexjluUcohfoqiMd1iydmAG9KYiatLXA+eCB1AF10HrAtsGToQU38sOTe5KMlWBnYFbvQr3ApPYfPQMXTRrcC/gf8OHYipP5aczQFAP1wvry4IXB46hq7w23tNBPaLkmyJ0PGY+mLJuYn5sdDDgcfzNH46dDzdcEToALrhWmB53Li+MV1mybm5bQpsDFwVOpAGdj9uPP+Q0IGY+mLJubkdhluufWPoQBqVH8e/Htg1SrJVQsdj6ocl5yYVJdlA3HjzxDyNZ4eOp5tWDx1AN12HG9e3LaxMl1lybl5jgBWozyGNepmtAUCexjNwZU4PtznPpqssOTevw4DXgKmhA+mBetwI4ApgI1xZVmM6Zcm5CUVJthawM3BNvcxtbgA3AnOpr5kmJiBLzs3pSP9cj0MadSlP449wCXr/KMmWDR2PKT5Lzk0mSrIBuN7b5DyNXw0dTw8dFTqAHrocGIS7EWtMhyw5N589cHWbfx06kJ7SOlkh2IbHgKf47JOLMe2y5Nx8jgZeBqaEDqSnBDR0DD3h6zxfDmwaJVldzTgxtWfJuYlESbYhsB3wmzyNW0LH06R+jyuGZL1n0yFLzs3le7gVgVeHDqRZ+f0ZxwMH2I1B0xFLzk0iSrLlcfUdxudpPCtwOL01OXQAvfRr3OYGVkrUtMuSc/M4EpcQLgodSG9pnVd4y9P4UeAR4Ngoyez/oGmT/WA0AT997jhgap7G0wOH02sCd4SOoQIuxu2S8q3QgZhisuTcHPbBFQv6eehAKmRU6AAq4DZcKdHjQgdiismSc4PzhXZOBJ4D7g4cjvHyNF4AXAqMiJLsK6HjMcVjybnx7QAMAy606XOFczkwHzgmdCCmeCw5N74fAu/gCr43BIWGKLuZp/E7wA3AoVGSrRA6HlMslpwbWJRkWwAjgJ/nafxJ6HgqRRprAcdFwFK4lZvG/Icl58Z2KvA+bmyzkVwWOoBK8Rvr3gkcHyXZoNDxmOKw5NygoiTbFDer4SJfrtIUVwoMBr4dOhBTHJacG9f/Ah8CvwwdiOnUX4C/AidHSdY/dDCmGCw5N6AoyTbClQa92NdyaDSjQwdQSb5aXQoMxc1JN8aSc4M6G5iDW4XWiB4PHUAVZMAMILFNYA1Ycm44UZJtg+tZnp+n8fuh46mSN0MHUGl+Dvr5wIbA2LDRmCKw5NxAfI8rBd6mcXvNjexG4EXgTCuIZOwHoLHEwLbAuDyN54YOxnRPnsYLgdNxvWcbe25ylpwbRJRkfYHzgJeA3wYOp9quCB1AFd0MPAuMi5KsX+hgTDiWnBvHd3A9rp/4ojoNSxtrheDn+LHn04D1gAMDh2MCsuTcAHxdhnOAB4BbA4dTddKYszVKTcJ9j+OiJBsYOBYTiCXnxjAOWAE4zs+ZbXSbhQ6gmvy/YYKb92z1npuUqDbD/+XG5XfUng5clqfx9wOHUxMC2iiV6ToSJdkdwNeBdX0FO9NErOdcx/zUuUtwy7RPCxxOLb0VOoAaORlXse6MwHGYACw517dDccX0f5yn8XuBY6kZhdVCx1ALeRo/j6soeFSUZBuEjsfUliXnOhUl2aq4PQH/TGNPLVuMNFdPchzwEXCxLetuLpac69cvcB95j2zC7adODx1AreRpPAv4MfBNYP/A4ZgasuRch6Iki4H9gLP9R1/T2C4DHgUusu2smocl5zoTJdnywG9wFczODxuNqYU8jRcBRwErA+cGDsfUiCXn+vNLYAjw7TyN54cOJpDhoQOotTyNn8QVszoqSrKtQ8djqs+Scx2Jkmwf4CDgzDyNHw0dj6m504HXgGtsv8HGZ8m5TkRJtjpuOONv2Efbx0IHEILfC/LbwLq40rCmgVlyrgN+CtVVwBLAwb60pGlCeRr/Ebfw6NgoyXYMHY+pHkvO9eEEYBfgpDyNXwwdjAnuR7jSsFf7G8T/ISJrisgfReTvIjJDRI7vaqMiMkxERnbwei4iK7dxXERkqogs678+XkSe9df/Qcl5Z4jImyIy3T86ulZfEXlSRCaXHLtGRF4pef8wf3xPf60/i8hK/tg6IjK+5L0DRORBEambMqyWnAsuSrKtcLMyJuKGNYxbmNG08jSeBxyMWyn527LFKQuBE1V1fWAr4Psisn4Xmx4GtJswOzASeEpV54jIhsARwBbAJsAoEflSybkXqeow/7izgzaPB55r4/jJJe+f7o8dC3wVN+XwAH/sbNwO9ACo6nzgfmDf7n97YVhyLrAoyVYExgNvAIc1ScW5TmlzrRBsU57G03CV6/YAjmk9rqpvqeoT/s8f4RLc6uXvF5G9fe/2Kd+jHACcCezre6X7ishKIjLF90qvpP1iUwcCf/B//i9gmqrOU9WFuDK2e3TnexORNXC7+lzZxbe04Ib8BgELRGQ74G1VfansvEnUUY1sS84F5XtD1+Kmze2Tp/EHYSMqDoGZoWMoiAuBycDPoyRbbHqhiETApsC0Nt57GjBCVTcBRvue5WnAeN8rHY+bHfIXVd0A98ltrXbi2IbPamw/C2znE/sgXK96zZJzjxGRp0XkKhFpb0HNL4Af4pJuuXP8+y8SkSX8sfOA+4DdcPswngqc1cZ7n8X1sOuClQwtqGVOjW/ur0P2/lT+MWFevwceABbhflhbn1u6eKy751esDT29Oj9czVIytCuiJFsJeBI3nDG8dcd1EVka12s9R1UnlL9PRH4DrIPbFmuCqr4nIocCw1X1GH/OdGAPVX3Zf/0+sJ6qzipr6yNVXabk68OBo4G5uMVSn6rqD0RkVWAWoLjkOURVDytraxQwUlWPFpHtgZNUdZR/bQhu8+IBwOXAP1X1zLL3HwKsCDwCnATMBo5X1Xn+9TeBr/hPFYVmybmAoiTb4c0lvjN1YZ+3Q4dSCZX8JeH+fLpuzjh5pILt1nUbK8z/zsZLtmxxvbJg2vv9f7nbp2c9PxfXo75HVS9s7x9GRLbEDR8cAmyO63n2JDnPBlZS1cV6uiJyLvCGqv667HgETFbVDcuOn4cbT18IDASWxf3yOKjsvO0pSdz+2CD/fY/wz3sAewEDVPUKf84s3C+Fwm/lVjd3LptFlGQRcMuQTy95fk6/23b6sP/4uUBf3BBU63OfNo519FpXj1WijerHNuuFOcCcsvP748Ydqx1H4cwecCWz3fDstkvPHzPhU55/A3iuk8S8jqpOA6aJyK64oYePgGVKTnsQd4PtbH9Oe8MQLwBfBP7h215FVd8RkbVwCXIrf3yIqrbW4t4dN8zwOar6I9xslNIEfFDp+0VEgLFtvP9k4BJVXSAiS+J66C24sWj8TI5Z9ZCYwZJzofhVXxOBfn0YNOaDs296E24KHVYxnR7mE5+Mk9akXbhfYMsu2Oeg/q+uucvHLtRnfM8X4MdtzIz4qYisixseuh94Crf6MPHvOw83K+ZGEZkB/NW/3pYM2B6fnIHbfCJcAHxfVT/wxy/w098UyHH1QhCR1YArVbWzmSK/F5HBPubpwHdbX/BtbKGqrTN5fokrFvUBLpGDq32edXKNwrBhjYLwNwB/j6s2NypP446mGTU1gcsbeQfunoqSrB/u4/yOwE55Gj9Yi+v6seDrVHXnWlyvp0RkApCoal2sFSjkx7QmdSKuXu9PLDF36ojQARSRXzm6H/BP4A+12j3FD1Vc0boIpYj8VMFJ9ZKYwXrOhRAl2QjgTmACbtqc/aN0wGZrdCxKsqHAw7gbh1vnafx64JBMD1hyDszvnv0QbgxumzyNPw4bUfFZcu5clGQb47YwewPYNk/j2YFDMt1kwxoB+X0AJ+Pmg46yxNxli614M5+Xp/HTwBjgS8DtVmK0/lhyDiRKsiWB24HBwG720bNbNg8dQD3I0/hPuPrf2wAToyQbGDYi0x02rBFAlGR9cDUz9gR2z9P4D528xZSwYY3uiZLsUOBq4A5grybeQaeuWM85jHNwK5dOssRsqi1P42twy6l3A27wU+5MwVlyrrEoyY7AVRO7DLgocDimSeRpfCnwP7hPa9dGSdY3cEimE5acayhKsrG4msx3AsfalLkeOyp0APUoT+OLgB/jlmRfYz3oYrMx5xqJkmw74F7cstNv5mk8N2xEpllFSfYTXDH6m4GD8jSui1oTzcaScw1ESbYRrojM27g5p+8FDqmu2Q3B3ouS7CTgp7gi+fvmafxp4JBMGUvOVeZXa/3Vf/m1PI3bKx5jusiSc2VESXYMrkDQncCeeRp/EjgkU8LGnKvILzK5B1eycIQlZlMkeRr/Cjd+vytwR5RkSwUOyZSw5FwlUZKtjNs6Z03c6r/FateaHpvc+SmmK/I0vhz4Nq6S3X1+ZxVTADasUQV+u/qpuM0u4zyNp4aNyJiORUm2O27/vVewT3mFYD3nCouSbBngbmAD3Oo/S8wVJm6lm6mgPI0n4rZ3Wg14KEqy9QOH1PSs51xBUZIti9tpYSvcMllb/VcFdkOweqIk2wTXuVgC96nv4cAhNS3rOVdIlGQr4rb72QrY3xKzqUd5Gj+FK5T0HnB/lGR7BA6paVnPuQL8rIx7gfVwPWa7YVVF1nOuvijJVsHNgd4K+Alwnq1orS1Lzr3kd8u+B1djeEyexveHjciYyvAlRq/CbZ/2O+AIW6xSOzas0QtRkg0HHgFWAXaxxFwbYpu71oRflHIgcBpwMDDVf0o0NWA95x6Kkmw0burRO8DIPI2fCxxS07BhjdqLkmxv4FrgA2C/Wu3s3cys59xNUZL1iZIsASYCM4CtLDGbRpen8S248eePcT3oU/ymEe0SkYEi8jcReUpEZojIuK5eT0S2F5GtO3i9zS3dRGRJEXlARPr6r88XkWf9Y9+S83YUkSf88WtFZLEKfSKyg4hML3l8IiJj/Wu/9d/X0yJyq4gs7Y8f69u80+/4jYhsKyIXlbQ7WETu7uzvwJJzN/jFJROB84BbgB3yNP5X0KCMqRG/L+Fw3C7xKW5vwo6GOT4FdlTVTYBhwLdEZKsuXm57oN3k3IHDgAmqukhEYmAzf+0tgZNEZFkR6YP7FLCfqm4IvAr8d3lDqvpHVR2mqsNwKyjnAVP8yyeo6iaqujHwGnCMP34gsDGuns4IERHgVOCsknbfBd4SkW06+kYsOXdRlGSbA48CI4HjcdPlrOxnGKNDB9Cs8jSeA+wLHAvsBMzwQx6LUae1h9vfPxYbRxWR40Tk774XepOIRMB3gRN8j3U7EVlbRB4WkWdE5OwOQjwQN8sEYH3gQVVdqKpzgaeBbwErAfNV9UV/3r24TQg6shdwl6rO89/bHB+7AEuWfF/iv89BwALcHo53qer7Ze1N8rG2y8acOxElWX/cVKL/xZX83DdP44fCRtXcBFZTmBk6jmbnVxFeA3wVtyfmsXkav1t6jh9eeBy3C/j/qeop5e2IyExgbVX9VESWV9UPROQM4GNV/Zk/53bgVlW9TkS+D5yvqkuXtTMAeE1Vv+C/3gU4HdgZlyz/BvwfcCGQA3uq6mMicjGuh79Re9+riEwFLlTVySXHrsZ11v4OxKo6T0QOxu04MwP4Hu4XxQhVXVDW3urA3R1e05Jz+6Ik2wK4FPfR6HrguDyNZ4eNytgNweLwu6n8EDgD97H/NODXeRovLD1PRJbHDQkeq6rPlr12N24sexIwSVU/biM5vwd8QVUXiMiywMw2kvNqwFRV/UrJsZ8AewPv4m7eP6qqvxCRrwEX4FZCTgFG+eGLxYjIEFyve7U2kmxfXNnVR1X16rLXTvPvawEOAV4HTlTVFhHpD7ytqu0WmrJhjTZESTY4SrIrgWm4WgN75Wl8sCVmYz4vT+OFeRqfixtnnQZcDEyPkmxklGT/+QWqqh8Af8QNK5SLcT3azYBH27o519pMJ+H8Gxj4uTeonuPHjXfG/UJ/0R9/WFW3U9UtcBthvLhYa5/ZB5hYnph9O4uAmygbFvG/KLZQ1UnAibihoA+Ab/pTBvp422V7iJXY4Me3rPhJyxL/0weOFxjYTxZetPOy08791dALPuYGlsT94wrul5qUPexYra5zgMINMq4g37Mdgz75xogqosiiFmSDPmh2/etbvrvat/959IBV17nt1fNHDcQNL5xPCX9zbk1V/aOI/AXYD1ga+AhYtuTUh/xr19POWK2qzhaRviIyUFU/8b3a5VX1PRHZGPcLZIq/7iqq+o6ILAGcApzTVpve/sCPSmIWYB1V/Yf/82jg+bL3nIX7FAGfjUm34IZXwK0m7rCMsCVnIEqytQf3e//4GRsecnzZSyf4hymQI/5xOXz2g9/6Q69lj3o9tqhAsXTrmAgqqC7SPjz08cYbTX7zy1vPuvPiW3Thp5/0WXKZ93Xh/Kta5n9SXtqgL3C9iCyHS/SX+DHnO4BbRWQM7ubj8cANInIKn93wa8sUYFtcLfX+wJ9d/mQOcJCqtg63nCwio3C/bC5V1akAIjIc+K6qfsd/HeFqsj9Qcg0BrvXDKwI8hRtfxr9nUwBVfcIfugF4BjescYE/tgOuSFq7mnbM2e/6MAq38mnkAFnQcsZqv3l+q6WfeeCLS8z8FwX5gS/AsaLF444d0KQ/uHUkSrIBuP9fxwKbAB/iFm6NB/6cp/GiSl9TRDbDTXM7uNJtV5KIPAiMUdXZ7Z7TTMnZ7+e3I25bnlG4jxtvAVcCl+Vp/GbA8EwXCTyusHnoOEzX+LHnrYGjgbG4j/Zv43a0uQ+YWj7LozdE5DDgWj8eXDgiMhjYxo9Ht39eIyZn/8MwGNgQ2BR3o2FLYB1/yr+A23Bbw/+lGr/BTfUINlujXvlPrDHuJttOwHL+pRnAY8AT/vEi8G4zV8IrfHL2U3WWxP22bX0eBCyDS8CDgZX98xrAF/2jdLPKN3BzLf+Iq7k8o5n/0eudJefG4P9vb46bwbAdrhO1SskpHwP/BF7GfcJ9h8+mxL3vX59b8jwX+KRR/m9XJTlHSTYCuAg32N7Vh7RxrHVVUVfMxv0Dtv5jvoy7g/pkJT8ymfAEZqqb4mgaiP/EOwT3afdLuE+66wBr45J2VzefbQEW+seCkj+3PlrvZUDb91haj1+Rp/GFvfqmeqFaszXm4KaJtHThoR28thA3sX0ebk5g6Z8/xv0WfRd4r3zSu2lclpgbk+/xzqSd1Z9+te5KuES9Au7T8dIlz0sDA3B5rb9/Ln/057P1HeVTAqXs+NuV/P66q/DDGsaUEzhD3Yo0YxqWJWdTd2zM2TQDW75tjDEFZMnZGGMKyJKzqUfDQwdgTLVZcjbGmAKyG4Km7tgNQdMMrOdsjDEFZMnZGGMKyJKzqTs2pGGagSVnY4wpIEvOxhhTQJacjTGmgCw5G2NMAVlyNsaYArLkbIwxBfT/b/p4cMcrbr4AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_students.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lLiYaBhBWWsH",
        "outputId": "a1f351b0-1e78-480f-aea3-918c8a5ae4dc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       StudyHours      Grade\n",
              "count   22.000000  22.000000\n",
              "mean    10.522727  49.181818\n",
              "std      3.487144  21.737912\n",
              "min      1.000000   3.000000\n",
              "25%      9.000000  36.250000\n",
              "50%     10.000000  49.500000\n",
              "75%     12.375000  62.750000\n",
              "max     16.000000  97.000000"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StudyHours</th>\n",
              "      <th>Grade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>10.522727</td>\n",
              "      <td>49.181818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.487144</td>\n",
              "      <td>21.737912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>9.000000</td>\n",
              "      <td>36.250000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>49.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.375000</td>\n",
              "      <td>62.750000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sample = df_students[df_students['StudyHours']>1]\n",
        "df_sample"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "93LyE5iKWcZS",
        "outputId": "e26f9879-c87e-4636-b2cc-4071a54b8095"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Name  StudyHours  Grade   Pass\n",
              "0         Dan       10.00   50.0  False\n",
              "1       Joann       11.50   50.0  False\n",
              "2       Pedro        9.00   47.0  False\n",
              "3       Rosie       16.00   97.0   True\n",
              "4       Ethan        9.25   49.0  False\n",
              "6    Frederic       11.50   53.0  False\n",
              "7      Jimmie        9.00   42.0  False\n",
              "8      Rhonda        8.50   26.0  False\n",
              "9    Giovanni       14.50   74.0   True\n",
              "10  Francesca       15.50   82.0   True\n",
              "11      Rajab       13.75   62.0   True\n",
              "12    Naiyana        9.00   37.0  False\n",
              "13       Kian        8.00   15.0  False\n",
              "14      Jenny       15.50   70.0   True\n",
              "15     Jakeem        8.00   27.0  False\n",
              "16     Helena        9.00   36.0  False\n",
              "17      Ismat        6.00   35.0  False\n",
              "18      Anila       10.00   48.0  False\n",
              "19       Skye       12.00   52.0  False\n",
              "20     Daniel       12.50   63.0   True\n",
              "21      Aisha       12.00   64.0   True"
            ],
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              "      <td>26.0</td>\n",
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              "      <th>9</th>\n",
              "      <td>Giovanni</td>\n",
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              "      <td>74.0</td>\n",
              "      <td>True</td>\n",
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              "      <th>10</th>\n",
              "      <td>Francesca</td>\n",
              "      <td>15.50</td>\n",
              "      <td>82.0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Rajab</td>\n",
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              "      <td>62.0</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
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              "      <th>12</th>\n",
              "      <td>Naiyana</td>\n",
              "      <td>9.00</td>\n",
              "      <td>37.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Kian</td>\n",
              "      <td>8.00</td>\n",
              "      <td>15.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Jenny</td>\n",
              "      <td>15.50</td>\n",
              "      <td>70.0</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Jakeem</td>\n",
              "      <td>8.00</td>\n",
              "      <td>27.0</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Helena</td>\n",
              "      <td>9.00</td>\n",
              "      <td>36.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>Ismat</td>\n",
              "      <td>6.00</td>\n",
              "      <td>35.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
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              "      <td>Anila</td>\n",
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              "      <td>48.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Skye</td>\n",
              "      <td>12.00</td>\n",
              "      <td>52.0</td>\n",
              "      <td>False</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Daniel</td>\n",
              "      <td>12.50</td>\n",
              "      <td>63.0</td>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Aisha</td>\n",
              "      <td>12.00</td>\n",
              "      <td>64.0</td>\n",
              "      <td>True</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_sample.boxplot(column='StudyHours', by='Pass', figsize=(8,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
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        "id": "UbcIz8XoWgUs",
        "outputId": "d89478dc-00b4-4a23-ea10-518654e28cb0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'StudyHours'}, xlabel='Pass'>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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Y3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDVo26AElSZ+2WHcNp6GMLa+eYww8dTh1aFAa3JB0Edm89fSjtrN2yY2ht6eDkpXJJkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSGzBneSC5LcmuS6gWVvSnJDkn9I8udJHraoVUqSJGBuZ9wXAs+ZsuzjwElV9W+AzwOvG3JdkiRpGrMGd1VdBtwxZdklVXV///RK4PhFqE2SJE0xjM+4XwH81RDakSRJs1i1kBcnORu4H3jPAbbZDGwGWLNmDePj4wvpUktkw4YNQ2ln586dQ2lH0tyPy7zxwOs9Lts27+BOcibwPODUqqqZtquqbcA2gPXr19fY2Nh8u9QSOsBbCsDaLTvYvfX0JapGEsx+XAKMj4/jv7PL27yCO8lzgN8GnlVV3xxuSZIkaSZz+TrYduAK4MQke5JsAt4GHA18PMk1Sd6+yHVKkiTmcMZdVRunWXz+ItQiSZJm4chpkiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDVk1uBOckGSW5NcN7Ds4Uk+nuSf+v8eu7hlSpIkmNsZ94XAc6Ys2wJcWlXfB1zaP5ckSYts1uCuqsuAO6YsfgFwUf/4IuCFwy1LkiRNZ9U8X7emqr7SP74FWDPThkk2A5sB1qxZw/j4+Dy71LD8+qV3c/d9C29n7ZYdC3r9kYfCH5965MILkfSAiYkJ/51d5uYb3A+oqkpSB1i/DdgGsH79+hobG1tol1qguz+2g91bT19QG+Pj4yz0vVy7ZceC25C0r2Ecmzq4zfeu8q8meTRA/99bh1eSJEmayXyD+y+AM/rHZwAfGU45kiTpQObydbDtwBXAiUn2JNkEbAV+Msk/Aaf1zyVJ0iKb9TPuqto4w6pTh1yLJEmahSOnSZLUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNWTVqAvQ0jt63RaectGWhTd00ULrADh94XVI0gpicK9Ad+3ayu6tCwvM8fFxxsbGFtTG2i07FvR6SVqJvFQuSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ3xe9wr1FC+Q/2xhbVxzOGHLrwGSVphDO4VaKGDr0AX/MNoR5L04HipXJKkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNWRBwZ3kN5Jcn+S6JNuTPHRYhUmSpP3NO7iTfC/wKmB9VZ0EHAL8/LAKkyRJ+1vopfJVwOFJVgFHADcvvCRJkjSTec8OVlVfTvJm4IvAPcAlVXXJ1O2SbAY2A6xZs4bx8fH5dqkltGHDhlm3yRtnb2fnzp1DqEbSXE1MTPjv7DKXqprfC5NjgQ8CLwbuBD4AXFxV757pNevXr6+rr756Xv3p4DI+Ps7Y2Nioy5A0hcfm8pHk01W1furyhVwqPw24sapuq6r7gA8BP7KA9iRJ0iwWEtxfBH44yRFJApwK7BpOWZIkaTrzDu6qugq4GPgMcG3f1rYh1SVJkqYx75vTAKrq9cDrh1SLJEmahSOnSZLUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDTG4JUlqiMEtSVJDDG5JkhpicEuS1BCDW5KkhhjckiQ1xOCWJKkhBrckSQ0xuCVJaojBLUlSQwxuSZIasqDgTvKwJBcnuSHJriTPGFZhkiRpfws94/4j4GNV9WTgqcCuhZekg9n27ds56aSTOPXUUznppJPYvn37qEuSpBVl1XxfmOQY4MeBMwGq6l7g3uGUpYPR9u3bOfvsszn//PPZu3cvhxxyCJs2bQJg48aNI65OklaGhZxxPx64DfjTJJ9N8s4kRw6pLh2EzjvvPM4//3w2bNjAqlWr2LBhA+effz7nnXfeqEuTpBVj3mfc/WtPBs6qqquS/BGwBfjdwY2SbAY2A6xZs4bx8fEFdKlR2rVrF3v37mV8fJyJiQnGx8fZu3cvu3bt8n2VDhKTx6aWr4UE9x5gT1Vd1T+/mC6491FV24BtAOvXr6+xsbEFdKlRWrduHYcccghjY2OMj48zNjbGzp07WbduHb6v0sFh8tjU8jXvS+VVdQvwpSQn9otOBf5xKFXpoHT22WezadMmdu7cyf3338/OnTvZtGkTZ5999qhLk6QVYyFn3ABnAe9JchjwBeDlCy9JB6vJG9DOOussdu3axbp16zjvvPO8MU2SltCCgruqrgHWD6cUtWDjxo1s3LjRy3GSNCKOnCZJUkMMbkmSGmJwS5LUEINbkqSGGNySJDXE4JYkqSEGtyRJDUlVLV1nyW3ATUvWoRbTauD2URchaT8em8vH46rqkVMXLmlwa/lIcnVVOfiOdJDx2Fz+vFQuSVJDDG5JkhpicGu+to26AEnT8thc5vyMW5KkhnjGLUlSQxY6H7eWkSR7gWsHFr2wqnbPsO1EVR21JIVJK1ySRwCX9k+/B9gL3NY//8GqunckhWkkvFSuBzyYMDa4pdFIci4wUVVvHli2qqruH11VWkpeKteMkhyV5NIkn0lybZIXTLPNo5NcluSaJNcl+bF++bOTXNG/9gNJDHlpiJJcmOTtSa4C/iDJuUn+48D665Ks7R+/NMmn+uP0fyY5ZFR1a+EMbg06vD+wr0ny58C3gJ+pqpOBDcB/S5Ipr/kF4P9U1dOApwLXJFkNnAOc1r/2auA3l2wvpJXjeOBHqmrG4yvJOuDFwI/2x+le4CVLU54Wg59xa9A9/YENQJJDgd9P8uPAd4DvBdYAtwy85u+BC/ptP1xV1yR5FvD9wOV9zh8GXLE0uyCtKB+oqr2zbHMq8HTg7/vj8XDg1sUuTIvH4NaBvAR4JPD0qrovyW7goYMbVNVlfbCfDlyY5A+BrwMfr6qNS12wtMLcPfD4fva9ijp5rAa4qKpet2RVaVF5qVwHcgxwax/aG4DHTd0gyeOAr1bVO4B3AicDVwI/muSEfpsjkzxpCeuWVqLddMcfSU4GHt8vvxT490ke1a97eH/cqlGecetA3gN8NMm1dJ9T3zDNNmPAbyW5D5gAXlZVtyU5E9ie5Lv67c4BPr/4JUsr1geBlyW5HriK/nirqn9Mcg5wSZKHAPcBv44zNTbLr4NJktQQL5VLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkN8etg0jI1MNvbKmAXcEZVfXO0VUlaKM+4peXrnqp6WlWdBNwL/MqoC5K0cAa3tDJ8EjghyfOTXJXks0k+kWQNQJJnDUww89kkR88085uk0XIAFmmZmpwzPckqulG1Pga8F7izqirJK4F1VfXaJB8FtlbV5f0UrN8CXg08tKrO66eBPKKq7hrV/kjq+Bm3tHwdnuSa/vEngfOBE4H3JXk03axtN/brLwf+MMl7gA9V1Z4k+838tqTVS5qWl8ql5WvyM+6nVdVZVXUv8FbgbVX1FOCX6WeQqqqtwCvppny8PMmTq+oy4MeBL9PN/Pay0eyGpEGecUsryzF0QQxwxuTCJE+sqmuBa5OcAjw5yT3Anqp6Rz9ZzMnAu5a8Ykn78IxbWlnOBT6Q5NPA7QPLX9PfgPYPdLNH/RXdzG+fS/JZ4MXAHy1xrZKm4c1pkiQ1xDNuSZIaYnBLktQQg1uSpIYY3JIkNcTgliSpIQa3JEkNMbglSWqIwS1JUkP+PyNNtJ346DNWAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a bar plot of name vs grade and study hours\n",
        "df_sample.plot(x='Name', y=['Grade','StudyHours'], kind='bar', figsize=(8,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "fmvPNtcvWjpP",
        "outputId": "e1c00327-f446-4f36-8e72-47d1575cbfab"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Name'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "# Get a scaler object\n",
        "scaler = MinMaxScaler()\n",
        "\n",
        "# Create a new dataframe for the scaled values\n",
        "df_normalized = df_sample[['Name', 'Grade', 'StudyHours']].copy()\n",
        "\n",
        "# Normalize the numeric columns\n",
        "df_normalized[['Grade','StudyHours']] = scaler.fit_transform(df_normalized[['Grade','StudyHours']])\n",
        "\n",
        "# Plot the normalized values\n",
        "df_normalized.plot(x='Name', y=['Grade','StudyHours'], kind='bar', figsize=(8,5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "WLzEbKz8WmrY",
        "outputId": "701e95d5-cb09-468b-987c-d5c4424caeed"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Name'>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_normalized.Grade.corr(df_normalized.StudyHours)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z2mZH49iWo6-",
        "outputId": "a95e400e-7754-47f2-de63-460b84f775ba"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9117666413789677"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a scatter plot\n",
        "df_sample.plot.scatter(title='Study Time vs Grade', x='StudyHours', y='Grade')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "gIwskc59WsJY",
        "outputId": "080c1deb-8b4b-4c7e-cfeb-0db8bdfa9cde"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Study Time vs Grade'}, xlabel='StudyHours', ylabel='Grade'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "\n",
        "#\n",
        "df_regression = df_sample[['Grade', 'StudyHours']].copy()\n",
        "\n",
        "# Get the regression slope and intercept\n",
        "m, b, r, p, se = stats.linregress(df_regression['StudyHours'], df_regression['Grade'])\n",
        "print('slope: {:.4f}\\ny-intercept: {:.4f}'.format(m,b))\n",
        "print('so...\\n f(x) = {:.4f}x + {:.4f}'.format(m,b))\n",
        "\n",
        "# Use the function (mx + b) to calculate f(x) for each x (StudyHours) value\n",
        "df_regression['fx'] = (m * df_regression['StudyHours']) + b\n",
        "\n",
        "# Calculate the error between f(x) and the actual y (Grade) value\n",
        "df_regression['error'] = df_regression['fx'] - df_regression['Grade']\n",
        "\n",
        "# Create a scatter plot of Grade vs StudyHours\n",
        "df_regression.plot.scatter(x='StudyHours', y='Grade')\n",
        "\n",
        "# Plot the regression line\n",
        "plt.plot(df_regression['StudyHours'],df_regression['fx'], color='cyan')\n",
        "\n",
        "# Display the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "9cU74Z0xWvKh",
        "outputId": "2c892ccf-479d-4242-bd0e-5676d6344e2d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "slope: 6.3134\n",
            "y-intercept: -17.9164\n",
            "so...\n",
            " f(x) = 6.3134x + -17.9164\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/pandas/plotting/_matplotlib/core.py:1114: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  scatter = ax.scatter(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show the original x,y values, the f(x) value, and the error\n",
        "df_regression[['StudyHours', 'Grade', 'fx', 'error']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "FGppfKbOWx-8",
        "outputId": "c69141e9-1fc6-4030-b438-1a00a92431d0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    StudyHours  Grade         fx      error\n",
              "0        10.00   50.0  45.217846  -4.782154\n",
              "1        11.50   50.0  54.687985   4.687985\n",
              "2         9.00   47.0  38.904421  -8.095579\n",
              "3        16.00   97.0  83.098400 -13.901600\n",
              "4         9.25   49.0  40.482777  -8.517223\n",
              "6        11.50   53.0  54.687985   1.687985\n",
              "7         9.00   42.0  38.904421  -3.095579\n",
              "8         8.50   26.0  35.747708   9.747708\n",
              "9        14.50   74.0  73.628262  -0.371738\n",
              "10       15.50   82.0  79.941687  -2.058313\n",
              "11       13.75   62.0  68.893193   6.893193\n",
              "12        9.00   37.0  38.904421   1.904421\n",
              "13        8.00   15.0  32.590995  17.590995\n",
              "14       15.50   70.0  79.941687   9.941687\n",
              "15        8.00   27.0  32.590995   5.590995\n",
              "16        9.00   36.0  38.904421   2.904421\n",
              "17        6.00   35.0  19.964144 -15.035856\n",
              "18       10.00   48.0  45.217846  -2.782154\n",
              "19       12.00   52.0  57.844698   5.844698\n",
              "20       12.50   63.0  61.001410  -1.998590\n",
              "21       12.00   64.0  57.844698  -6.155302"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-afadc08d-cca0-43b1-8c1e-0ff0b0fff6b6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StudyHours</th>\n",
              "      <th>Grade</th>\n",
              "      <th>fx</th>\n",
              "      <th>error</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.00</td>\n",
              "      <td>50.0</td>\n",
              "      <td>45.217846</td>\n",
              "      <td>-4.782154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11.50</td>\n",
              "      <td>50.0</td>\n",
              "      <td>54.687985</td>\n",
              "      <td>4.687985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9.00</td>\n",
              "      <td>47.0</td>\n",
              "      <td>38.904421</td>\n",
              "      <td>-8.095579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16.00</td>\n",
              "      <td>97.0</td>\n",
              "      <td>83.098400</td>\n",
              "      <td>-13.901600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.25</td>\n",
              "      <td>49.0</td>\n",
              "      <td>40.482777</td>\n",
              "      <td>-8.517223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>11.50</td>\n",
              "      <td>53.0</td>\n",
              "      <td>54.687985</td>\n",
              "      <td>1.687985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>9.00</td>\n",
              "      <td>42.0</td>\n",
              "      <td>38.904421</td>\n",
              "      <td>-3.095579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>8.50</td>\n",
              "      <td>26.0</td>\n",
              "      <td>35.747708</td>\n",
              "      <td>9.747708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>14.50</td>\n",
              "      <td>74.0</td>\n",
              "      <td>73.628262</td>\n",
              "      <td>-0.371738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>15.50</td>\n",
              "      <td>82.0</td>\n",
              "      <td>79.941687</td>\n",
              "      <td>-2.058313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>13.75</td>\n",
              "      <td>62.0</td>\n",
              "      <td>68.893193</td>\n",
              "      <td>6.893193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>9.00</td>\n",
              "      <td>37.0</td>\n",
              "      <td>38.904421</td>\n",
              "      <td>1.904421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>8.00</td>\n",
              "      <td>15.0</td>\n",
              "      <td>32.590995</td>\n",
              "      <td>17.590995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15.50</td>\n",
              "      <td>70.0</td>\n",
              "      <td>79.941687</td>\n",
              "      <td>9.941687</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>8.00</td>\n",
              "      <td>27.0</td>\n",
              "      <td>32.590995</td>\n",
              "      <td>5.590995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>9.00</td>\n",
              "      <td>36.0</td>\n",
              "      <td>38.904421</td>\n",
              "      <td>2.904421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>6.00</td>\n",
              "      <td>35.0</td>\n",
              "      <td>19.964144</td>\n",
              "      <td>-15.035856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>10.00</td>\n",
              "      <td>48.0</td>\n",
              "      <td>45.217846</td>\n",
              "      <td>-2.782154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>12.00</td>\n",
              "      <td>52.0</td>\n",
              "      <td>57.844698</td>\n",
              "      <td>5.844698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>12.50</td>\n",
              "      <td>63.0</td>\n",
              "      <td>61.001410</td>\n",
              "      <td>-1.998590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>12.00</td>\n",
              "      <td>64.0</td>\n",
              "      <td>57.844698</td>\n",
              "      <td>-6.155302</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afadc08d-cca0-43b1-8c1e-0ff0b0fff6b6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-afadc08d-cca0-43b1-8c1e-0ff0b0fff6b6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-afadc08d-cca0-43b1-8c1e-0ff0b0fff6b6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function based on our regression coefficients\n",
        "def f(x):\n",
        "    m = 6.3134\n",
        "    b = -17.9164\n",
        "    return m*x + b\n",
        "\n",
        "study_time = 14\n",
        "\n",
        "# Get f(x) for study time\n",
        "prediction = f(study_time)\n",
        "\n",
        "# Grade can't be less than 0 or more than 100\n",
        "expected_grade = max(0,min(100,prediction))\n",
        "\n",
        "#Print the estimated grade\n",
        "print ('Studying for {} hours per week may result in a grade of {:.0f}'.format(study_time, expected_grade))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FyhAMyrPW3Yn",
        "outputId": "e73c2994-dec2-493f-a034-fe621dc2974f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Studying for 14 hours per week may result in a grade of 70\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XB4QbTS1W5Xq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}