Creating a Conditional Column: If-Elif-Else in Pandas

The given problem asks for a new column to be added to a DataFrame based on a series of conditional criteria. The challenge lies in implementing these conditions while maintaining code efficiency and readability.

Solution Using Function Application

One approach involves creating a function that maps each row to the desired result based on the conditions:

def f(row):
    if row['A'] == row['B']:
        return 0
    elif row['A'] > row['B']:
        return 1
    else:
        return -1

df['C'] = df.apply(f, axis=1)

This method is readable and easy to implement, but it is not vectorized and may lead to performance issues with large datasets.

Vectorized Solution

For efficiency, a vectorized approach using NumPy's np.where function is recommended:

df['C'] = np.where(
    df['A'] == df['B'], 0, np.where(
    df['A'] > df['B'], 1, -1))

This operation performs the conditional selection element-wise on the DataFrame, resulting in a new column with the desired values.

This vectorized approach provides significant performance benefits compared to the function-based method. It also allows for a more concise and readable implementation of the conditional criteria.