Given a pandas DataFrame containing nan:

import numpy as np
import pandas as pd

bad_df = pd.DataFrame({'foo': [np.nan, 1.0, 2.0], 'bar': ['a', 'b', 'c']})

And also given a dictionary with lookup keys that may exist in the bar column:

replace_values = {'a': 7.0, 'd': 10.0}

How can the nan values in the foo column be replace with values in the replace_values dictionary based on the lookup of bar? The resulting DataFrame would look like:

expected_df = pd.DataFrame({'foo': [7.0, 1.0, 2.0], 'bar': ['a', 'b', 'c']})

The typical fillna and replace methods on DataFrames don’t seem to have this functionality since they will replace all na with the same value as opposed to a lookup value.

Thank you in advance for your consideration and response.