QuickPythonDemo.ipynb

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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MeredithGal/BAN-502-Course-Files/blob/main/QuickPythonDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w-kRT6Oc3t-p"
      },
      "source": [
        "### Quick Demo of Colab Python Notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rA2c9mGF4LGc"
      },
      "source": [
        "Start by loading necessary Python libraries."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysFvhPvr3l0l"
      },
      "source": [
        "import numpy as np #for NumPy\n",
        "import pandas as pd #for Pandas\n",
        "import seaborn as sns #for prettier plots\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing #for scaling data\n",
        "from sklearn.datasets import load_boston #loads the Boston house price dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_dLj6bq4djn"
      },
      "source": [
        "Next we load in a sample dataset. The dataset that we are using in this example is the Boston housing price dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Cx2n2dSA4d7a",
        "outputId": "ee31eba4-9d21-4487-db7c-a8ded12af57f"
      },
      "source": [
        "boston = load_boston() #assign the Boston dataset to an object\n",
        "# Load the dataset into a dataframe and set the field names\n",
        "df = pd.DataFrame(boston['data'], columns=boston['feature_names']) #gives the data frame a name of \"df\"\n",
        "df['PRICE']= boston.target #adds the response variable from the Boston dataset \n",
        "df.head() #shows first five rows of the data frame"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>PRICE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX  ...    TAX  PTRATIO       B  LSTAT  PRICE\n",
              "0  0.00632  18.0   2.31   0.0  0.538  ...  296.0     15.3  396.90   4.98   24.0\n",
              "1  0.02731   0.0   7.07   0.0  0.469  ...  242.0     17.8  396.90   9.14   21.6\n",
              "2  0.02729   0.0   7.07   0.0  0.469  ...  242.0     17.8  392.83   4.03   34.7\n",
              "3  0.03237   0.0   2.18   0.0  0.458  ...  222.0     18.7  394.63   2.94   33.4\n",
              "4  0.06905   0.0   2.18   0.0  0.458  ...  222.0     18.7  396.90   5.33   36.2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-T1Co0sC5HNH"
      },
      "source": [
        "Demonstrate a couple of quick plots in Seaborn. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "YbfAtfsT5KDR",
        "outputId": "d104e2fd-c52b-4e4d-d843-1e2732e90dcb"
      },
      "source": [
        "#histogram\n",
        "sns.histplot(data=df, x=\"PRICE\", binwidth=1)\n",
        "\n",
        "#scatterplot\n",
        "sns.relplot(x=\"AGE\", y=\"PRICE\", data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f6ea23b7e48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}