How Can I Efficiently Filter Pandas DataFrames Using 'IN' and 'NOT IN' Operators?

Filtering Pandas Dataframes with "In" and "Not In": A Simpler Solution

When working with Pandas dataframes, it is often necessary to filter data based on specific criteria. One common requirement is to find rows where a particular column matches or does not match a set of predefined values, similar to the SQL "IN" and "NOT IN" operators.

Alternative to the Merge-Based Approach

Traditionally, some users have employed a merge-based approach to achieve this filtering. While functional, this method is considered inefficient and needlessly complex.

Using pd.Series.isin

The ideal solution lies in utilizing the pd.Series.isin function. It provides straightforward functionality for both "IN" and "NOT IN" filtering.

"IN" Filtering

To filter rows where a specific column matches any value in a provided list, use:

something.isin(somewhere)

"NOT IN" Filtering

Alternatively, to filter rows where a column value does not match any value in a given list, use:

~something.isin(somewhere)

Example Usage

Consider the following example:

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

countries_to_keep = ['UK', 'China']

df_in = df[df.country.isin(countries_to_keep)]
df_not_in = df[~df.country.isin(countries_to_keep)]

print(df_in)
print(df_not_in)

Output:

    country
1        UK
3     China
    country
0        US
2   Germany

As demonstrated, pd.Series.isin provides a concise and efficient method for filtering Pandas dataframes. It eliminates the need for convoluted merge-based approaches, making the filtering process both simpler and more performant.

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