How to Use Pandas' `.isin()` for DataFrame Filtering: IN and NOT IN Operations?

Using Pandas' .isin() for DataFrame Filtering

In SQL, the IN and NOT IN operators allow you to filter data based on a list of values. Pandas' DataFrame provides a convenient method, .isin(), that enables similar functionality.

How to Use .isin()

To use .isin():

• For IN: Use something.isin(somewhere)
• For NOT IN: Use ~something.isin(somewhere)

Example Usage

Consider the following DataFrame:

df = pd.DataFrame({'country': ['US', 'UK', 'Germany', 'China']})

And a list of countries to keep:

countries_to_keep = ['UK', 'China']

To filter the DataFrame using the equivalent of SQL's IN:

df[df.country.isin(countries_to_keep)]

This will return:

    country
1        UK
3     China

For the equivalent of SQL's NOT IN:

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

This will return:

    country
0        US
2   Germany

This method avoids the use of clumsy kludges and provides a straightforward way to filter DataFrames based on a list of values.

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