sea_level_predictor.py

import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import linregress

def draw_plot():
    # Read data
    df = pd.read_csv("epa-sea-level.csv")

    # Create scatter plot
    fig, ax = plt.subplots(figsize=(10, 6))
    ax.scatter(df['Year'], df['CSIRO Adjusted Sea Level'], color='blue', label='Data')

    # -----------------------
    # Line of best fit (all years)
    # -----------------------
    slope_all, intercept_all, r_value, p_value, std_err = linregress(df['Year'], df['CSIRO Adjusted Sea Level'])
    # Extend the line to 2050
    years_all = pd.Series(range(int(df['Year'].min()), 2051))
    line_all = intercept_all + slope_all * years_all
    ax.plot(years_all, line_all, color='red', label='Best Fit (All Years)')

    # -----------------------
    # Line of best fit (from 2000)
    # -----------------------
    df_recent = df[df['Year'] >= 2000]
    slope_recent, intercept_recent, r_value, p_value, std_err = linregress(df_recent['Year'], df_recent['CSIRO Adjusted Sea Level'])
    years_recent = pd.Series(range(2000, 2051))
    line_recent = intercept_recent + slope_recent * years_recent
    ax.plot(years_recent, line_recent, color='green', label='Best Fit (2000+)')

    # -----------------------
    # Labels and title
    # -----------------------
    ax.set_xlabel("Year")
    ax.set_ylabel("Sea Level (inches)")
    ax.set_title("Rise in Sea Level")
    ax.legend()

    # Save figure
    fig.savefig('sea_level_plot.png')

    return fig