dask_expr._collection.Index.isin
dask_expr._collection.Index.isin¶
- Index.isin(values)¶
Whether each element in the DataFrame is contained in values.
This docstring was copied from pandas.core.frame.DataFrame.isin.
Some inconsistencies with the Dask version may exist.
- 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