Transforming and cleaning DataFrames
Once data has been curated into a cleaned Python data structure, such as a list of lists, it's easy to convert this into a pandas DataFrame. You'll practice doing just this with the data that was curated in the last exercise.
Per usual, pandas has been imported as pd, and the normalized_testing_scores variable stores the list of each schools testing data, as shown below.
[
['01M539', '111 Columbia Street', 'Manhattan', 657.0, 601.0, 601.0],
...
]
This exercise is part of the course
ETL and ELT in Python
Exercise instructions
- Create a
pandasDataFrame from the list of lists stored in thenormalized_testing_scoresvariable. - Set the columns names for the
normalized_dataDataFrame.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a DataFrame from the normalized_testing_scores list
normalized_data = ____(normalized_testing_scores)
# Set the column names
normalized_data.____ = ["school_id", "street_address", "city", "avg_score_math", "avg_score_reading", "avg_score_writing"]
normalized_data = normalized_data.set_index("school_id")
print(normalized_data.head())