Impute Missing Contract Metadata | Dataford Interview Questions

Given a dataset of contract metadata in a Pandas DataFrame, write a function to identify and impute missing values based on historical category averages. For grading, represent the DataFrame as a dictionary of column names to equal-length lists, with missing values represented by None. Return a new dictionary with missing entries in value_col replaced by the mean of observed values in the same category; leave them as None when no observed value exists. Implement def impute_missing_by_category(df, category_col, value_col): using only the Python standard library.

Constraints

df contains category_col and value_col.
Both selected columns have the same length.
Values in value_col are numbers or None.
Category values are hashable.
Return a new dictionary without changing df.

Function Signature

def impute_missing_by_category(df, category_col, value_col):

Your question is Impute Missing Contract Metadata. Start with the requirements in the Question tab.

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Problem

Constraints

df contains category_col and value_col.
Both selected columns have the same length.
Values in value_col are numbers or None.
Category values are hashable.
Return a new dictionary without changing df.

Function Signature

def impute_missing_by_category(df, category_col, value_col):

Your question is Impute Missing Contract Metadata. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.