Sea Level Predictor Test

I am trying to resolve the last error, but I am not able to locate the issue.
I think it is the way I set up the x-values, but can’t pinpoint what the issue is.

here is the error message I get:

FAIL: test_plot_lines (test_module.LinePlotTestCase)

Traceback (most recent call last):
File “/home/runner/boilerplate-sea-level-predictor/”, line 34, in test_plot_lines
self.assertEqual(actual, expected, “Expected different line for first line of best fit.”)
AssertionError: Lists differ: [-0.5421240249263519, -0.4179452988418433, -0.293766572757[1880 chars]9947] != [-0.5421240249263661, -0.4790794409142336, -0.416034856902[3240 chars]2443]

First differing element 0:

Second list contains 70 additional elements.
First extra element 100:

Diff is 6123 characters long. Set self.maxDiff to None to see it. : Expected different line for first line of best fit.

Ran 4 tests in 9.435s

FAILED (failures=1)

and here is the code:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import linregress
import seaborn as sns

def draw_plot():
# Read data from file
df = pd.read_csv(‘epa-sea-level.csv’)

# Create scatter plot
fig, ax = plt.subplots(figsize=(16, 9))
x = plt.scatter(data=df, x='Year', y='CSIRO Adjusted Sea Level')

# Create first line of best fit
lin_func = linregress(df['Year'], df['CSIRO Adjusted Sea Level'])

x_fit = np.linspace(np.min(df['Year']), np.max(2050))
y_fit = x_fit * lin_func[0] + lin_func[1]
ax = sns.regplot(data=df, x=x_fit, y=y_fit)

xlim = [1850,2075]

# Create second line of best fit
df_after2000 = df.iloc[120:]

lin_func2 = linregress(df_after2000['Year'], df_after2000['CSIRO Adjusted Sea Level'])

x_fit2 = np.linspace(np.min(2000), np.max(2050))
y_fit2 = lin_func2[0] * x_fit2 + lin_func2[1]
ax = sns.regplot(data=df, x=x_fit2, y=y_fit2)

# Add labels and title
ax.set_ylabel('Sea Level (inches)')
ax.set_title('Rise in Sea Level')

# Save plot and return data for testing (DO NOT MODIFY)
return plt.gca()

and here is the link to my code:

any help would be great!
Thanks you very much :slight_smile:

This line of code is not going to produce the necessary number of data points to satisfy the test cases, additionally the test cases are expecting whole numbers or ints for the year, x-data, and your code produces two ints and 48 floats. If you use the num parameter with the correct argument, this code will produce only ints. You could also use np.arange() with the parameters used in your code, with a reminder that np.arange() will give you a range of numbers exclusive of the endpoint.