How to Efficiently Filter Pandas DataFrames Using a List of Values?

Filtering Pandas Dataframes with a List of Values

In data manipulation tasks, selecting specific rows from a Pandas dataframe based on a list of values is a common requirement. This article demonstrates how to efficiently achieve this operation.

Using the isin() Method

To select rows where the values of a specified column are present in a given list, the isin() method is a straightforward solution. Let's consider the following Pandas dataframe:

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

To retrieve rows where column 'A' contains values 3 or 6, we can use:

list_of_values = [3, 6]
result = df[df['A'].isin(list_of_values)]
print(result)

This operation yields the rows with matching 'A' column values:

   A  B
1  6  2
2  3  3

Excluding Values with the ~ Operator

To exclude rows where the 'A' column values are not present in the list, the ~ operator can be employed in conjunction with isin(). For example:

result = df[~df['A'].isin(list_of_values)]
print(result)

This operation excludes rows with 'A' values of 3 or 6:

   A  B
0  5  1
3  4  5

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