How to apply a custom function in Polars that does the processing row by row?

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04-09-2024

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Row-by-Row Processing in Polars: Unleashing the Power of Custom Functions

Polars, a lightning-fast data manipulation library for Python, offers incredible performance but sometimes requires custom logic that goes beyond its built-in functions. This is where applying custom functions to process each row of your DataFrame comes in handy.

Let's explore how to leverage this powerful technique to enhance your data analysis workflow.

The Challenge:

Imagine you have a complex data transformation that involves custom logic, calculations, or even external API calls. You want to apply this logic to every row of your Polars DataFrame, creating a new column with the results.

The Solution: apply and row

Polars provides the apply function, which allows you to apply a custom function to each row of your DataFrame. Here's how it works:

import polars as pl

def my_complicated_function(row):
    """
    This custom function takes a row as input and performs
    some complex calculation.
    """
    result = row["foo"] + row["bar"] * row["baz"]
    return result

df = pl.DataFrame({
    "foo": [1, 2, 3], 
    "bar": [4, 5, 6], 
    "baz": [7, 8, 9]
})

# Apply the function to each row
df = df.with_column(
    pl.col("*").apply(my_complicated_function).alias("result")
)

print(df)

Breakdown:

1. Define your custom function: Create a function that takes a row object as input and performs your desired processing. In this example, we add foo to the product of bar and baz.

2. Apply the function: The apply method is applied to the entire DataFrame (using pl.col("*")) to process every row. You can specify columns using the pl.col function if you want to apply the function to specific columns.

3. New column creation: The result of applying the custom function is assigned to a new column named "result" using the alias method.

Important Notes:

• Row Object: The row object passed to your custom function is a Series object, representing a single row of your DataFrame. You can access individual values from the row using square brackets and column names (row["foo"]).

• Returning Values: The custom function must return a value that can be assigned to the new column. This can be a scalar value, a list, a tuple, or any other data type supported by Polars.

• Efficiency: While apply can be powerful, it can be less efficient than using built-in Polars functions for operations that can be vectorized. Consider leveraging Polars' optimized functions for common data manipulation tasks to ensure optimal performance.

Beyond the Basics: Additional Tips

• Handling Multiple Columns: If your custom function requires data from multiple columns, you can pass these columns as arguments:

  def my_function(foo, bar):
      return foo * bar
  
  df = df.with_column(
      pl.col("foo").apply(my_function, args=(df["bar"],)).alias("result")
  )
  

• Performance Optimization: For large datasets, you might consider using pl.map in conjunction with pl.Series.to_list to improve efficiency.

• Error Handling: Use try-except blocks within your custom function to handle potential errors gracefully and prevent your program from crashing.

Example Use Cases:

• Custom String Formatting: Apply a custom function to format strings in a column based on specific conditions.
• Advanced Calculations: Perform complex mathematical operations or calculations on data from multiple columns.
• External API Integration: Use a custom function to make API calls based on data in each row, enriching your DataFrame with external information.

By mastering the apply function, you can unleash the full power of Polars for even the most complex data manipulation tasks. Remember, this function provides flexibility and control, allowing you to extend Polars' capabilities and achieve your data analysis goals efficiently.