Impute missing values by group data

import pandas as pd

params = {'dataframe':       df,
      'cat_col_1':       'categorical_column from df', 
      'cat_col_2':       'categorical_column from df', 
      'num_col':         'numerical_column from df', 
      'impute_strategy': 'mean, median, etc.'}

def conditional_fillna(dataframe, cat_col_1, cat_col_2, num_col, impute_strategy):
  """Impute the missing values in a column using central tendency. 
    The values of the central tendency is obtained by the statistic of the grouped data

  Args:
    dataframe (DataFrame): Two-dimensional data structure stored in a tabular format.
    cat_col_1 (Series): Categorical column to be used in data grouping
    cat_col_2 (Series): Categorical column to be used in data grouping
    num_col (Series): Numerical Column to be used to compute for numeric values among different data groups
    impute_strategy (String): The method on which to impute the missing values

  Returns:
    DataFrame: Imputed missing values based on the statistic from other column/s
  """

  # Create a grouping for the dataset
  grouped_data = dataframe.groupby([cat_col_1, cat_col_2])[num_col]

  # Fill missing values in a column using the defined impute strategy
  dataframe[num_col].fillna(grouped_data.transform(impute_strategy), inplace=True)

  # Optional: Covert the imputed numerical values into an integer
  dataframe[num_col] = dataframe[num_col].round(0).astype(int)

  return dataframe