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Apply custom functions to each element of a Series in PySpark:Series.apply()

   April 4, 2024
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PySpark-Pandas Series.apply() 

apply() function, which allows users to apply custom functions to each element of a Series. In this article, we’ll explore the capabilities of Series.apply() through a practical example and delve into its significance in data transformation tasks.

Significance of Series.apply():

  • Flexibility: apply() allows users to define and apply any custom function to each element, offering unparalleled flexibility.
  • Efficiency: Leveraging vectorized operations in Pandas ensures efficient computation, even for large datasets.
  • Readability: apply() enhances code readability by encapsulating complex transformations into concise functions.

Usage:

  • Data Cleaning: Applying custom cleaning functions to standardize data formats or handle missing values.
  • Feature Engineering: Creating new features based on existing ones using user-defined transformation logic.
  • Statistical Analysis: Applying statistical functions to compute summary statistics or derive new insights from data.

Considerations:

  • Vectorized Alternatives: Where possible, prefer Pandas’ built-in vectorized functions for improved performance.
  • Performance Optimization: Avoid inefficient operations within custom functions to optimize computation time.
  • Type Consistency: Ensure consistency in data types returned by the custom function to prevent unexpected behavior.

Sample code

from pyspark.sql import SparkSession
from pyspark.sql.functions import col, udf
from pyspark.sql.types import IntegerType

# Create a SparkSession
spark = SparkSession.builder \
    .appName("Learning @ Freshers.in : Pandas API on Spark-Series.apply()") \
    .getOrCreate()

# Sample data
data = [("London", 20), ("New York", 21), ("Helsinki", 12)]
columns = ['city', 'numbers']
# Create a DataFrame
df = spark.createDataFrame(data, schema=columns)
# Define a custom function
def square(x):
    return x ** 2
# Register the custom function as a Spark UDF
square_udf = udf(square, IntegerType())
# Apply the function using the UDF
result_df = df.withColumn('squared_numbers', square_udf(col('numbers')))
# Show the result
result_df.show()

Output

+--------+-------+---------------+
|    city|numbers|squared_numbers|
+--------+-------+---------------+
|  London|     20|            400|
|New York|     21|            441|
|Helsinki|     12|            144|
+--------+-------+---------------+
  • We import the necessary modules from PySpark.
  • Sample data is defined as a list of tuples.
  • A Spark DataFrame is created using createDataFrame.
  • A custom function square() is defined to square each element.
  • The function is registered as a Spark UDF (User Defined Function) using udf.
  • The UDF is applied to the ‘numbers’ column using withColumn.
  • Finally, the transformed DataFrame is displayed using show().

Spark important urls to refer

  1. Spark Examples
  2. PySpark Blogs
  3. Bigdata Blogs
  4. Spark Interview Questions
  5. Official Page
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