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Exceptions and Warnings (:mod:`numpy.exceptions`)
=================================================
General exceptions used by NumPy. Note that some exceptions may be module
specific, such as linear algebra errors.
.. versionadded:: NumPy 1.25
The exceptions module is new in NumPy 1.25. Older exceptions remain
available through the main NumPy namespace for compatibility.
.. currentmodule:: numpy.exceptions
Warnings
--------
.. autosummary::
:toctree: generated/
ComplexWarning Given when converting complex to real.
VisibleDeprecationWarning Same as a DeprecationWarning, but more visible.
Exceptions
----------
.. autosummary::
:toctree: generated/
AxisError Given when an axis was invalid.
DTypePromotionError Given when no common dtype could be found.
TooHardError Error specific to `numpy.shares_memory`.
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The warning raised when casting a complex dtype to a real dtype.
As implemented, casting a complex number to a real discards its imaginary
part, but this behavior may not be what the user actually wants.
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.. warning::
This warning should not be used, since nose testing is not relevant
anymore.
The nose tester turns ordinary Deprecation warnings into test failures.
That makes it hard to deprecate whole modules, because they get
imported by default. So this is a special Deprecation warning that the
nose tester will let pass without making tests fail.
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By default, python will not show deprecation warnings, so this class
can be used when a very visible warning is helpful, for example because
the usage is most likely a user bug.
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This is raised whenever the maximum number of candidate solutions
to consider specified by the ``max_work`` parameter is exceeded.
Assigning a finite number to max_work may have caused the operation
to fail.
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This is raised whenever an ``axis`` parameter is specified that is larger
than the number of array dimensions.
For compatibility with code written against older numpy versions, which
raised a mixture of `ValueError` and `IndexError` for this situation, this
exception subclasses both to ensure that ``except ValueError`` and
``except IndexError`` statements continue to catch `AxisError`.
.. versionadded:: 1.13
Parameters
----------
axis : int or str
The out of bounds axis or a custom exception message.
If an axis is provided, then `ndim` should be specified as well.
ndim : int, optional
The number of array dimensions.
msg_prefix : str, optional
A prefix for the exception message.
Attributes
----------
axis : int, optional
The out of bounds axis or ``None`` if a custom exception
message was provided. This should be the axis as passed by
the user, before any normalization to resolve negative indices.
.. versionadded:: 1.22
ndim : int, optional
The number of array dimensions or ``None`` if a custom exception
message was provided.
.. versionadded:: 1.22
Examples
--------
>>> array_1d = np.arange(10)
>>> np.cumsum(array_1d, axis=1)
Traceback (most recent call last):
...
numpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1
Negative axes are preserved:
>>> np.cumsum(array_1d, axis=-2)
Traceback (most recent call last):
...
numpy.exceptions.AxisError: axis -2 is out of bounds for array of dimension 1
The class constructor generally takes the axis and arrays'
dimensionality as arguments:
>>> print(np.AxisError(2, 1, msg_prefix='error'))
error: axis 2 is out of bounds for array of dimension 1
Alternatively, a custom exception message can be passed:
>>> print(np.AxisError('Custom error message'))
Custom error message
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This exception derives from ``TypeError`` and is raised whenever dtypes
cannot be converted to a single common one. This can be because they
are of a different category/class or incompatible instances of the same
one (see Examples).
Notes
-----
Many functions will use promotion to find the correct result and
implementation. For these functions the error will typically be chained
with a more specific error indicating that no implementation was found
for the input dtypes.
Typically promotion should be considered "invalid" between the dtypes of
two arrays when `arr1 == arr2` can safely return all ``False`` because the
dtypes are fundamentally different.
Examples
--------
Datetimes and complex numbers are incompatible classes and cannot be
promoted:
>>> np.result_type(np.dtype("M8[s]"), np.complex128)
DTypePromotionError: The DType <class 'numpy.dtype[datetime64]'> could not
be promoted by <class 'numpy.dtype[complex128]'>. This means that no common
DType exists for the given inputs. For example they cannot be stored in a
single array unless the dtype is `object`. The full list of DTypes is:
(<class 'numpy.dtype[datetime64]'>, <class 'numpy.dtype[complex128]'>)
For example for structured dtypes, the structure can mismatch and the
same ``DTypePromotionError`` is given when two structured dtypes with
a mismatch in their number of fields is given:
>>> dtype1 = np.dtype([("field1", np.float64), ("field2", np.int64)])
>>> dtype2 = np.dtype([("field1", np.float64)])
>>> np.promote_types(dtype1, dtype2)
DTypePromotionError: field names `('field1', 'field2')` and `('field1',)`
mismatch.
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