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.

See also

DataFrame.eq: Equality test for DataFrame.
Series.isin: Equivalent method on Series.
Series.str.contains: Test if pattern or regex is contained within a string of a Series or Index.

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