Create a data frame with columns Name and Age.

data=[("Alice", 25), ("Bob", 30), ("Alice", 25), ("Kate", 22)]
cols= ["Name", "Age"]
df = spark.createDataFrame(data, cols)

Approach 1:- Using dropDuplicates()

#Approach 1
dedup_df=df.dropDuplicates(subset=["Name", "Age"])
dedup_df.show()

Approach 2:- Using distinct()

#Approach 2 
df.distinct().show()

Approach 3:- Using window function

#Approach 3
from pyspark.sql.functions import row_number
from pyspark.sql.window import Window

window_spec=Window.partitionBy("Name", "Age").orderBy("Name")

df_with_row_number=df.withColumn("row_number", row_number().over(window_spec))
dedup_df_windows=df_with_row_number.filter(df_with_row_number.row_number==1).drop("row_number")
dedup_df_windows.show()

Thank you ! Happy Learning !!