maybe_cast_to_integer_array(arr: 'list | np.ndarray', dtype: 'np.dtype', copy: 'bool' = False) -> 'np.ndarray'

Parameters

arr : np.ndarray or list

The array to cast.

dtype : np.dtype

The integer dtype to cast the array to.

copy: bool, default False :

Whether to make a copy of the array before returning.

Raises

OverflowError : the dtype is incompatible with the data

ValueError : loss of precision has occurred during casting

Returns

ndarray

Array of integer or unsigned integer dtype.

Takes any dtype and returns the casted version, raising for when data is incompatible with integer/unsigned integer dtypes.

Examples

If you try to coerce negative values to unsigned integers, it raises:

This example is valid syntax, but we were not able to check execution

>>> pd.Series([-1], dtype="uint64")
Traceback (most recent call last):
    ...
OverflowError: Trying to coerce negative values to unsigned integers

Also, if you try to coerce float values to integers, it raises:

This example is valid syntax, but we were not able to check execution

>>> pd.Series([1, 2, 3.5], dtype="int64")
Traceback (most recent call last):
    ...
ValueError: Trying to coerce float values to integers

See :

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File: /pandas/core/dtypes/cast.py#1985
type: <class 'function'>
Commit: