How to Melt a PySpark DataFrame: A Step-by-Step Guide

Introduction: Melting or unpivoting a DataFrame is a common data manipulation task that involves converting a wide-format DataFrame into a long-format one. In this tutorial, we will walk you through the process of melting a PySpark DataFrame using the stack function combined with the select and expr methods. We will provide a step-by-step guide along with a complete example to help you master this essential skill.

Table of Contents:

  1. Creating a PySpark DataFrame

  2. Defining Identifier and Value Columns

  3. Melting the DataFrame

  4. Displaying the Melted DataFrame

  5. Performance Considerations

  6. Conclusion

Creating a PySpark DataFrame

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First, let's create a sample DataFrame to demonstrate the melting process. You can either read data from a file (CSV, Parquet, JSON, etc.) or create a DataFrame from a list of dictionaries:

from pyspark.sql import Row 
data = [ Row(id=1, A=10, B=20, C=30), Row(id=2, A=40, B=50, C=60), Row(id=3, A=70, B=80, C=90), ] 
columns = ["id", "A", "B", "C"] 

df = spark.createDataFrame(data, columns) 

Defining Identifier and Value Columns

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Next, we need to define the columns that will remain constant (identifier columns) and the columns that will be melted (value columns):

identifier_columns = ["id"] 
value_columns = ["A", "B", "C"] 

Melting the DataFrame

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To melt the DataFrame, we will use the stack function in combination with the select and expr methods. The stack function takes the number of columns to be melted, followed by pairs of column name and column value expressions. The expr method is used to create the required expression string for the stack function:

from pyspark.sql import functions as F 
melt_expression = f"stack({len(value_columns)}, {', '.join([f'\'{col}\', {col}' for col in value_columns])})" 

melted_df = + [F.expr(melt_expression).alias("variable", "value")]) 

Displaying the Melted DataFrame

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Now that the DataFrame has been melted, let's display the result: 

The resulting melted DataFrame will have three columns: "id", "variable", and "value". The "variable" column contains the original column names, while the "value" column contains the corresponding values.

Performance Considerations Keep in mind

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that melting a DataFrame can significantly increase its size, as it creates additional rows for each value column. This may impact performance and memory usage when working with large DataFrames. Always be cautious and consider the implications of melting a large DataFrame.


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In this tutorial, we demonstrated how to melt a PySpark DataFrame using the stack function, select method, and expr method. By following these steps and being mindful of performance implications, you can efficiently manipulate your PySpark DataFrames to suit your data analysis needs.