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Showing content from https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.isin.html below:

pandas.DataFrame.isin — pandas 2.3.1 documentation

pandas.DataFrame.isin#
DataFrame.isin(values)[source]#

Whether each element in the DataFrame is contained in values.

Parameters:
valuesiterable, Series, DataFrame or dict

The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then both the index and column labels must match.

Returns:
DataFrame

DataFrame of booleans showing whether each element in the DataFrame is contained in values.

Examples

>>> df = pd.DataFrame({'num_legs': [2, 4], 'num_wings': [2, 0]},
...                   index=['falcon', 'dog'])
>>> df
        num_legs  num_wings
falcon         2          2
dog            4          0

When values is a list check whether every value in the DataFrame is present in the list (which animals have 0 or 2 legs or wings)

>>> df.isin([0, 2])
        num_legs  num_wings
falcon      True       True
dog        False       True

To check if values is not in the DataFrame, use the ~ operator:

>>> ~df.isin([0, 2])
        num_legs  num_wings
falcon     False      False
dog         True      False

When values is a dict, we can pass values to check for each column separately:

>>> df.isin({'num_wings': [0, 3]})
        num_legs  num_wings
falcon     False      False
dog        False       True

When values is a Series or DataFrame the index and column must match. Note that ‘falcon’ does not match based on the number of legs in other.

>>> other = pd.DataFrame({'num_legs': [8, 3], 'num_wings': [0, 2]},
...                      index=['spider', 'falcon'])
>>> df.isin(other)
        num_legs  num_wings
falcon     False       True
dog        False      False

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