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Modifying DataFrame columns

Previously, you filtered out any rows that didn't conform to something generally resembling a name. Now based on your earlier work, your manager has asked you to create two new columns - first_name and last_name. She asks you to split the VOTER_NAME column into words on any space character. You'll treat the last word as the last_name, and all other words as the first_name. You'll be using some new functions in this exercise including .split(), .size(), and .getItem(). The .getItem(index) takes an integer value to return the appropriately numbered item in the column. The functions .split() and .size() are in the pyspark.sql.functions library.

Please note that these operations are always somewhat specific to the use case. Having your data conform to a format often matters more than the specific details of the format. Rarely is a data cleaning task meant just for one person - matching a defined format allows for easier sharing of the data later (ie, Paul doesn't need to worry about names - Mary already cleaned the dataset).

The filtered voter DataFrame from your previous exercise is available as voter_df. The pyspark.sql.functions library is available under the alias F.

Talimatlar

100 XP
  • Add a new column called splits holding the list of possible names.
  • Use the getItem() method and create a new column called first_name.
  • Get the last entry of the splits list and create a column called last_name.
  • Drop the splits column and show the new voter_df.