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.
Bu egzersiz
Big Data Fundamentals with PySpark
kursunun bir parçasıdırEgzersiz talimatları
- Filter the
people_dfDataFrame to select all rows where sex is female intopeople_df_femaleDataFrame. - Filter the
people_dfDataFrame to select all rows where sex is male intopeople_df_maleDataFrame. - Count the number of rows in
people_df_femaleandpeople_df_maleDataFrames.
Uygulamalı interaktif egzersiz
Bu örnek kodu tamamlayarak bu egzersizi bitirin.
# 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.____()))