How pandas assign works in Python? Best example

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If you’ve ever needed to add new columns to a Pandas DataFrame in a clean and efficient way, then pandas.assign() is your new best friend. This powerful method allows you to create new columns without modifying the original DataFrame, making your code more readable and functional. Let’s explore how it works and why you should use it.

Understanding `pandas.assign()`

The assign() method in Pandas provides a convenient way to add new columns to a DataFrame. Unlike directly assigning columns using square brackets ([]), assign() returns a new DataFrame with the added columns while leaving the original DataFrame unchanged.

Here’s the basic syntax:


import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

new_df = df.assign(C=df['A'] + df['B'])

Now, new_df contains an additional column C, which is the sum of columns A and B.

Why Use `assign()` Instead of Direct Assignment?

Many Pandas users wonder why they should use assign() when they can simply do:


df['C'] = df['A'] + df['B']

Here’s why assign() can be a better approach:

Immutable Operations: Since assign() returns a new DataFrame, your original data remains unchanged.
Method Chaining: You can link multiple operations together in a single readable statement.
Consistent Functional Style: Many Pandas methods follow a functional programming approach, keeping transformations isolated.

Using Multiple Assignments at Once

The assign() method allows multiple columns to be added in one go. Here’s an example:


df = df.assign(
    C=df['A'] * 2,
    D=df['B'] + 10
)

Now, df has columns C, where each value in A is doubled, and D, where 10 has been added to each value in column B.

Using Lambda Functions Inside `assign()`

A powerful feature of assign() is that it allows you to use lambda functions, which helps in complex computation:


df = df.assign(
    E=lambda x: x['A'] * x['B']
)

Here, column E is created by multiplying columns A and B, but instead of referencing df multiple times, we use x as an alias for the DataFrame inside the lambda function.

Chaining `assign()` with Other Methods

One of the best aspects of assign() is how well it integrates with Pandas method chaining:


(df
    .assign(C=lambda x: x['A'] + x['B'])
    .query("C > 5")
    .sort_values(by='C', ascending=False)
)

This concise approach eliminates intermediate variables, making the code more readable.

Performance Considerations

While assign() is convenient, repeatedly calling it for large DataFrames can lead to unnecessary copies and slower performance compared to direct assignment. If performance is a concern, consider:

Using df['new_column'] = values for in-place modifications when appropriate.
Assigning multiple columns at once instead of calling assign() sequentially.

Comparison: `assign()` vs Direct Assignment

Method	Modifies Original?	Supports Chaining?
`df.assign()`	No	Yes
`df['new_column'] = value`	Yes	No

Final Thoughts

Using pandas.assign() in Python is an elegant way to add new columns to your DataFrame while maintaining a functional, chainable approach. Whether you’re a beginner or an experienced Pandas user, understanding how to leverage assign() effectively can make your data transformations more readable and efficient. Next time you need to create new columns, give assign() a try!

“` Other interesting article: How pandas eval works in Python? Best example

How pandas assign works in Python? Best example

Understanding pandas.assign()

Why Use assign() Instead of Direct Assignment?