How to Efficiently Create Multiple New Columns from a Function in Pandas?

Creating Multiple New Columns from a Function Using Pandas

In Pandas, you can encounter situations where you need to create multiple new columns based on a custom function applied to an existing column. The task may seem straightforward, but unexpected challenges can arise due to the expected return type of the function.

Original Approach: Assigning to Index Range

Initially, you might attempt to assign the output of a function directly to a range of indices in a DataFrame using the df.ix[: ,10:16] = df.textcol.map(extract_text_features) syntax. However, this approach can often result in errors due to the incompatible return type of the function.

Iterable Solution

One potential solution is to iterate over each row of the DataFrame using df.iterrows(). This method allows you to apply the function to each row individually and capture the results as a tuple. However, this approach can be significantly slower than other options.

Using zip()

A more efficient and flexible approach is to use the zip() function in conjunction with map() to create the new columns. The zip() function combines the output of the function into a tuple, which can then be unpacked into individual columns. For instance, the following code demonstrates how to create six new columns using the zip() method:

<code class="python">df['p1'], df['p2'], df['p3'], df['p4'], df['p5'], df['p6'] = zip(*df['num'].map(powers))</code>

Improved DataFrame Methods

Recent updates to Pandas have introduced more convenient methods for applying functions to columns and creating new columns. For instance, the df.apply() method allows you to specify the output format (DataFrame, Series, or list) and handle additional parameters. Additionally, the df.assign() method enables you to create new columns directly without explicitly assigning the output. These newer methods provide more flexibility and efficiency in creating multiple new columns based on a function.

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