Applying advanced transformations to DataFrames

pandas has a plethora of built-in transformation tools, but sometimes, more advanced logic needs to be used in a transformation. The apply function lets you apply a user-defined function to a row or column of a DataFrame, opening the door for advanced transformation and feature generation.

The find_street_name() function parses the street name from the "street_address", dropping the street number from the string. This function has been loaded into memory, and is ready to be applied to the raw_testing_scores DataFrame.

This exercise is part of the course

ETL and ELT in Python

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Exercise instructions

In the definition of the transform() function, use the find_street_name() function to create a new column with the name "street_name".
Use the transform() function to clean the raw_testing_scores DataFrame.
Print the head of the cleaned_testing_scores DataFrame, observing the new "street_name" column.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

def transform(raw_data):
	# Use the apply function to extract the street_name from the street_address
    raw_data["street_name"] = raw_data.____(
   		# Pass the correct function to the apply method
        ____,
        axis=1
    )
    return raw_data

# Transform the raw_testing_scores DataFrame
cleaned_testing_scores = ____(raw_testing_scores)

# Print the head of the cleaned_testing_scores DataFrame
print(cleaned_testing_scores.____())

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