Introduction to the conditional_impute function

The conditional_impute function is a Python function that can be used to impute missing values in a given dataset. It takes an input DataFrame as its argument and provides the option to choose between different imputation methods, with the default method being the median.

Function Signature

def conditional_impute(input_df, choice='median')

Arguments

• input_df (DataFrame): The input DataFrame containing the dataset with missing values.
• choice (string, optional): The choice of imputation method. Default is set to 'median'.

Imputation methods

The conditional_impute function provides several options for imputing missing values:

• Median: If choice is set to 'median', the missing values are replaced with the median value of the corresponding column.
• Mean: If choice is set to 'mean', the missing values are replaced with the mean value of the corresponding column.
• Mode: If choice is set to 'mode', the missing values are replaced with the mode (most frequent value) of the corresponding column.

Returns

The conditional_impute function returns a new DataFrame with the missing values imputed using the specified method.

Example Usage

# Importing necessary libraries
import pandas as pd

# Creating a sample DataFrame
data = {'A': [1, 2, None, 4, 5],
        'B': [1, 2, 3, None, None],
        'C': [None, 2, 3, 4, 5]}
df = pd.DataFrame(data)

# Imputing missing values using the 'median' method
imputed_df = conditional_impute(df, choice='median')

# Printing the imputed DataFrame
print(imputed_df)

The above code will output the following DataFrame with the missing values imputed with the median:

     A    B    C
0  1.0  1.0  3.5
1  2.0  2.0  2.0
2  3.0  3.0  3.0
3  4.0  2.0  4.0
4  5.0  2.0  5.0

In the above result, the missing values in column 'A' are replaced with the median of [1, 2, 3, 4, 5], which is 3. The missing values in column 'B' are replaced with the median of [1, 2, 3, 2, 2], which is 2. The missing value in column 'C' is replaced with the median of [3.5, 2, 3, 4, 5], which is 3.5.

This function can be useful in preprocessing datasets with missing values, ensuring that the missing values are appropriately handled before further analysis or modeling.