How to apply a function to two columns of a Pandas dataframe

Efficient implementation of function application

Best coding practices ensure that your function application across multiple dataframe columns is both safe and efficient.

Empower Lambda for custom functions

For execution of dynamic operations over multiple columns, a lambda function is your best friend. With axis=1 set in the apply method, it applies the function row-wise.

Correct column access

To confidently access columns within apply and lambda, especially those with spaces or special characters in their names or colliding with DataFrame attribute names, adhere to the square bracket notation with string column names:

df.apply(lambda row: row['Column A'] + row['Column B'], axis=1)  # As easy as A + B
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Exception handling and data type compatibility

Make sure your function can handle different data types gracefully. Also, to catch exceptions and keep your code productive, do something like this:

df.apply(lambda row: try_your_function(row['A'], row['B']), axis=1)  # Try and try again until you...catch an exception

def try_your_function(val1, val2):
    try:
        # Your operation here
        return some_result
    except Exception as e:
        # Process or log the exception
        return np.nan  # When real life gives you an exception, make NaN-aide
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Finding an ally in Series.combine

For carrying out more elaborate element-wise operations, Series.combine works wonders. Remember to convert both series to the correct type using astype(object) if needed:

df['new_column'] = df['A'].combine(df['B'], func=your_function)  # Two series, one function, and a whole new column
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Optimize through list comprehension

For dealing with larger datasets, implementing list comprehension can work wonders for creating a new column:

df['new_column'] = [your_function(a, b) for a, b in zip(df['A'], df['B'])]  # Zip and unzip the power of list comprehension
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Trust but verify your function

Before applying your function to the entire dataframe, a sanity check using sample data should be on your checklist:

sample_result = your_function(df['A'].iloc[0], df['B'].iloc[0]);
print(sample_result)  # Veni, vidi, vici, err… verify!
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Building robust and maintainable code

Your solution is more than immediate code; it's about ensuring robustness and maintainability.

Avoiding column name conflicts

Steer clear of numerical indices and use string column names instead, which are safer especially when the data layout might change. Otherwise, you may bang your head against alignment issues.

Consistent return type

Keep the return type of your applied function consistent with the desired type of your new column. It saves you from data type related surprises downstream.

Rigorous Testing

Test the waters (or rather your code) by running rigorous tests on a subset of your data. It's more fun if you think of it as a game.

Clever use of examples

Remember the adage, "A picture is worth a thousand words"? The effect of clear, relatable examples is no different in illustrating your answer.