{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Mini Python Practice Lab\n",
        "\n",
        "Run the basic workflow: load the data, inspect it, summarize it, graph it, and write a cautious interpretation."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_csv(\"wage_sample.csv\")\n",
        "print(df.head())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "summary = df[[\"wage\", \"education\", \"experience\"]].describe()\n",
        "print(summary)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "print(\"Correlation between wage and education:\")\n",
        "print(round(df[\"wage\"].corr(df[\"education\"]), 3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "plt.scatter(df[\"education\"], df[\"wage\"])\n",
        "plt.xlabel(\"Education\")\n",
        "plt.ylabel(\"Wage\")\n",
        "plt.title(\"Practice lab: wage and education\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Interpretation prompt\n",
        "\n",
        "Describe the pattern in the sample. Then add one caution about why this is not automatically causal."
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.11"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}