How Can I Simulate SQL's IN and NOT IN Operators Using Pandas' isin() Method?

Simulating SQL's IN/NOT IN with Pandas' isin() Method

In data analysis, it's common to need to filter a DataFrame based on a set of values, akin to SQL's IN and NOT IN operators. Pandas offers a straightforward solution with the isin() method.

The isin() method operates on Pandas Series and tests if each element in the series is contained in a specified list or set. To replicate SQL's IN, simply apply isin(list) to the desired column:

>>> countries_to_keep = ['UK', 'China']
>>> df.country.isin(countries_to_keep)

For NOT IN, use the negation operator (~):

>>> df[~df.country.isin(countries_to_keep)]

The isin() method simplifies data filtering, eliminating the need for cumbersome merge operations as seen in the initial code sample. Its syntax mirrors SQL's IN/NOT IN, making it easy to incorporate into your Pandas workflow.

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