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.

Deze oefening maakt deel uit van de cursus

Big Data Fundamentals with PySpark

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Oefeninstructies

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.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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.____()))

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