modin.pandas.DataFrame.isin¶
- DataFrame.isin(values: ListLike | Series | DataFrame | dict[Hashable, ListLike]) DataFrame [source]¶
Whether each element in the DataFrame is contained in values.
- Parameters:
values (list-like, 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.
Snowpark pandas assumes that in the case of values being Series or DataFrame that the index of values is unique (i.e., values.index.is_unique() = True)
- Returns:
DataFrame of booleans showing whether each element in the DataFrame is contained in values.
- Return type:
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
Caution
Snowpark pandas does not perform a check for the case that values is a DataFrame or Series nor does it check whether the index is unique. Snowpark pandas preserves NULL values; if the DataFrame contains NULL in a cell the output cell will be NULL.