Get startedGet started for free

Filtering your DataFrame

In the previous exercise, you have subset the data using select() operator which is mainly used to subset the DataFrame column-wise. What if you want to subset the DataFrame based on a condition (for example, select all rows where the sex is Female). In this exercise, you will filter the rows in the people_df DataFrame in which 'sex' is female and male and create two different datasets. Finally, you'll count the number of rows in each of those datasets.

Remember, you already have a SparkSession spark and a DataFrame people_df available in your workspace.

This exercise is part of the course

Big Data Fundamentals with PySpark

View Course

Exercise instructions

  • Filter the people_df DataFrame to select all rows where sex is female into people_df_female DataFrame.
  • Filter the people_df DataFrame to select all rows where sex is male into people_df_male DataFrame.
  • Count the number of rows in people_df_female and people_df_male DataFrames.

Hands-on interactive exercise

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

# Filter people_df to select females 
people_df_female = people_df.____(people_df.____ == "female")

# Filter people_df to select males
people_df_male = people_df.____(____ == "____")

# Count the number of rows 
print("There are {} rows in the people_df_female DataFrame and {} rows in the people_df_male DataFrame".format(people_df_female.____(), people_df_male.____()))
Edit and Run Code