import pandas as pd
import matplotlib.pyplot as plt
from scipy.stats import linregress
def draw_plot():
  # Read data from file
  df = pd.read_csv('epa-sea-level.csv')
  print(df[df['Year'][df['Year']==2000].index[0]:len(df['Year'])])
  # Create scatter plot
  fig = plt.figure(figsize=(20, 10))
  ax = plt.axes()
  # Create first line of best fit
  l1 = linregress(df['Year'],df['CSIRO Adjusted Sea Level'])
  plt.plot([df['Year'][0],2050],[l1.slope*df['Year'][0]+l1.intercept,l1.slope*2050+l1.intercept])
  # Create second line of best fit
  l2 = linregress(df[df['Year'][df['Year']==2000].index[0]:len(df['Year'])]['Year'],df[df['Year'][df['Year']==2000].index[0]:len(df['Year'])]['CSIRO Adjusted Sea Level'])
  plt.plot([2000,2050],[l2.slope*2000+l2.intercept,l2.slope*2050+l2.intercept])
  print(ax.get_lines()[0].get_ydata().tolist())
  print(ax.get_lines()[1].get_ydata().tolist())
  # Add labels and title
  ax.set_xlabel("Year")
  ax.set_ylabel("Sea Level (inches)")
  ax.set_title("Rise in Sea Level")
# Save plot and return data for testing (DO NOT MODIFY)
plt.savefig('sea_level_plot.png')
return plt.gca()
sea_level_predictor.draw_plot()