check_array_indexer(array: 'AnyArrayLike', indexer: 'Any') -> 'Any'
For a boolean mask, array
and :None:None:`indexer` are checked to have the same length. The dtype is validated, and if it is an integer or boolean ExtensionArray, it is checked if there are missing values present, and it is converted to the appropriate numpy array. Other dtypes will raise an error.
Non-array indexers (integer, slice, Ellipsis, tuples, ..) are passed through as is.
The array that is being indexed (only used for the length).
The array-like that's used to index. List-like input that is not yet a numpy array or an ExtensionArray is converted to one. Other input types are passed through as is.
When the lengths don't match.
When :None:None:`indexer` cannot be converted to a numpy ndarray to index (e.g. presence of missing values).
The validated indexer as a numpy array that can be used to index.
Check if :None:None:`indexer` is a valid array indexer for array
.
api.types.is_bool_dtype
Check if :None:None:`key` is of boolean dtype.
When checking a boolean mask, a boolean ndarray is returned when the arguments are all valid.
This example is valid syntax, but we were not able to check execution>>> mask = pd.array([True, False])
... arr = pd.array([1, 2])
... pd.api.indexers.check_array_indexer(arr, mask) array([ True, False])
An IndexError is raised when the lengths don't match.
This example is valid syntax, but we were not able to check execution>>> mask = pd.array([True, False, True])
... pd.api.indexers.check_array_indexer(arr, mask) Traceback (most recent call last): ... IndexError: Boolean index has wrong length: 3 instead of 2.
NA values in a boolean array are treated as False.
This example is valid syntax, but we were not able to check execution>>> mask = pd.array([True, pd.NA])
... pd.api.indexers.check_array_indexer(arr, mask) array([ True, False])
A numpy boolean mask will get passed through (if the length is correct):
This example is valid syntax, but we were not able to check execution>>> mask = np.array([True, False])
... pd.api.indexers.check_array_indexer(arr, mask) array([ True, False])
Similarly for integer indexers, an integer ndarray is returned when it is a valid indexer, otherwise an error is (for integer indexers, a matching length is not required):
This example is valid syntax, but we were not able to check execution>>> indexer = pd.array([0, 2], dtype="Int64")This example is valid syntax, but we were not able to check execution
... arr = pd.array([1, 2, 3])
... pd.api.indexers.check_array_indexer(arr, indexer) array([0, 2])
>>> indexer = pd.array([0, pd.NA], dtype="Int64")
... pd.api.indexers.check_array_indexer(arr, indexer) Traceback (most recent call last): ... ValueError: Cannot index with an integer indexer containing NA values
For non-integer/boolean dtypes, an appropriate error is raised:
This example is valid syntax, but we were not able to check execution>>> indexer = np.array([0., 2.], dtype="float64")See :
... pd.api.indexers.check_array_indexer(arr, indexer) Traceback (most recent call last): ... IndexError: arrays used as indices must be of integer or boolean type
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.common.is_bool_indexer
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