# NumPy 1.23.0 Release Notes#

The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are:

Implementation of

`loadtxt`

in C, greatly improving its performance.Exposing DLPack at the Python level for easy data exchange.

Changes to the promotion and comparisons of structured dtypes.

Improvements to f2py.

See below for the details,

## New functions#

A masked array specialization of

`ndenumerate`

is now available as`numpy.ma.ndenumerate`

. It provides an alternative to`numpy.ndenumerate`

and skips masked values by default.(gh-20020)

`numpy.from_dlpack`

has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the`__dlpack__`

and`__dlpack_device__`

methods and returns a ndarray object which is generally the view of the data of the input object.(gh-21145)

## Deprecations#

Setting

`__array_finalize__`

to`None`

is deprecated. It must now be a method and may wish to call`super().__array_finalize__(obj)`

after checking for`None`

or if the NumPy version is sufficiently new.(gh-20766)

Using

`axis=32`

(`axis=np.MAXDIMS`

) in many cases had the same meaning as`axis=None`

. This is deprecated and`axis=None`

must be used instead.(gh-20920)

The hook function

`PyDataMem_SetEventHook`

has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via`tracemalloc`

.(gh-20394)

`numpy.distutils`

has been deprecated, as a result of`distutils`

itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see Status of numpy.distutils and migration advice.(gh-20875)

`numpy.loadtxt`

will now give a`DeprecationWarning`

when an integer`dtype`

is requested but the value is formatted as a floating point number.(gh-21663)

## Expired deprecations#

The

`NpzFile.iteritems()`

and`NpzFile.iterkeys()`

methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15.(gh-16830)

The

`alen`

and`asscalar`

functions have been removed.(gh-20414)

The

`UPDATEIFCOPY`

array flag has been removed together with the enum`NPY_ARRAY_UPDATEIFCOPY`

. The associated (and deprecated)`PyArray_XDECREF_ERR`

was also removed. These were all deprecated in 1.14. They are replaced by`WRITEBACKIFCOPY`

, that requires calling`PyArray_ResoveWritebackIfCopy`

before the array is deallocated.(gh-20589)

Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes

`__array__`

or`__array_interface__`

, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised.(gh-20835)

Multidimensional indexing with non-tuple values is not allowed. Previously, code such as

`arr[ind]`

where`ind = [[0, 1], [0, 1]]`

produced a`FutureWarning`

and was interpreted as a multidimensional index (i.e.,`arr[tuple(ind)]`

). Now this example is treated like an array index over a single dimension (`arr[array(ind)]`

). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15.(gh-21029)

Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change.

(gh-20722)

## New Features#

### crackfortran has support for operator and assignment overloading#

`crackfortran`

parser now understands operator and assignment
definitions in a module. They are added in the `body`

list of the
module which contains a new key `implementedby`

listing the names
of the subroutines or functions implementing the operator or
assignment.

(gh-15006)

### f2py supports reading access type attributes from derived type statements#

As a result, one does not need to use `public`

or `private`

statements to
specify derived type access properties.

(gh-15844)

### New parameter `ndmin`

added to `genfromtxt`

#

This parameter behaves the same as `ndmin`

from `numpy.loadtxt`

.

(gh-20500)

`np.loadtxt`

now supports quote character and single converter function#

`numpy.loadtxt`

now supports an additional `quotechar`

keyword argument
which is not set by default. Using `quotechar='"'`

will read quoted fields
as used by the Excel CSV dialect.

Further, it is now possible to pass a single callable rather than a dictionary
for the `converters`

argument.

(gh-20580)

### Changing to dtype of a different size now requires contiguity of only the last axis#

Previously, viewing an array with a dtype of a different item size required that the entire array be C-contiguous. This limitation would unnecessarily force the user to make contiguous copies of non-contiguous arrays before being able to change the dtype.

This change affects not only `ndarray.view`

, but other construction
mechanisms, including the discouraged direct assignment to `ndarray.dtype`

.

This change expires the deprecation regarding the viewing of F-contiguous arrays, described elsewhere in the release notes.

(gh-20722)

### Deterministic output files for F2PY#

For F77 inputs, `f2py`

will generate `modname-f2pywrappers.f`

unconditionally, though these may be empty. For free-form inputs,
`modname-f2pywrappers.f`

, `modname-f2pywrappers2.f90`

will both be generated
unconditionally, and may be empty. This allows writing generic output rules in
`cmake`

or `meson`

and other build systems. Older behavior can be restored
by passing `--skip-empty-wrappers`

to `f2py`

. Using via meson details usage.

(gh-21187)

`keepdims`

parameter for `average`

#

The parameter `keepdims`

was added to the functions `numpy.average`

and `numpy.ma.average`

. The parameter has the same meaning as it
does in reduction functions such as `numpy.sum`

or `numpy.mean`

.

(gh-21485)

### New parameter `equal_nan`

added to `np.unique`

#

`np.unique`

was changed in 1.21 to treat all `NaN`

values as equal and return
a single `NaN`

. Setting `equal_nan=False`

will restore pre-1.21 behavior
to treat `NaNs`

as unique. Defaults to `True`

.

(gh-21623)

## Compatibility notes#

### 1D `np.linalg.norm`

preserves float input types, even for scalar results#

Previously, this would promote to `float64`

when the `ord`

argument was
not one of the explicitly listed values, e.g. `ord=3`

:

```
>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2).dtype
dtype('float32')
>>> np.linalg.norm(f32, 3)
dtype('float64') # numpy 1.22
dtype('float32') # numpy 1.23
```

This change affects only `float32`

and `float16`

vectors with `ord`

other than `-Inf`

, `0`

, `1`

, `2`

, and `Inf`

.

(gh-17709)

### Changes to structured (void) dtype promotion and comparisons#

In general, NumPy now defines correct, but slightly limited, promotion for structured dtypes by promoting the subtypes of each field instead of raising an exception:

```
>>> np.result_type(np.dtype("i,i"), np.dtype("i,d"))
dtype([('f0', '<i4'), ('f1', '<f8')])
```

For promotion matching field names, order, and titles are enforced, however
padding is ignored.
Promotion involving structured dtypes now always ensures native byte-order for
all fields (which may change the result of `np.concatenate`

)
and ensures that the result will be “packed”, i.e. all fields are ordered
contiguously and padding is removed.
See Structure Comparison and Promotion for further details.

The `repr`

of aligned structures will now never print the long form including
`offsets`

and `itemsize`

unless the structure includes padding not
guaranteed by `align=True`

.

In alignment with the above changes to the promotion logic, the casting safety has been updated:

`"equiv"`

enforces matching names and titles. The itemsize is allowed to differ due to padding.`"safe"`

allows mismatching field names and titlesThe cast safety is limited by the cast safety of each included field.

The order of fields is used to decide cast safety of each individual field. Previously, the field names were used and only unsafe casts were possible when names mismatched.

The main important change here is that name mismatches are now considered “safe” casts.

(gh-19226)

`NPY_RELAXED_STRIDES_CHECKING`

has been removed#

NumPy cannot be compiled with `NPY_RELAXED_STRIDES_CHECKING=0`

anymore. Relaxed strides have been the default for many years and
the option was initially introduced to allow a smoother transition.

(gh-20220)

`np.loadtxt`

has recieved several changes#

The row counting of `numpy.loadtxt`

was fixed. `loadtxt`

ignores fully
empty lines in the file, but counted them towards `max_rows`

.
When `max_rows`

is used and the file contains empty lines, these will now
not be counted. Previously, it was possible that the result contained fewer
than `max_rows`

rows even though more data was available to be read.
If the old behaviour is required, `itertools.islice`

may be used:

```
import itertools
lines = itertools.islice(open("file"), 0, max_rows)
result = np.loadtxt(lines, ...)
```

While generally much faster and improved, `numpy.loadtxt`

may now fail to
converter certain strings to numbers that were previously successfully read.
The most important cases for this are:

Parsing floating point values such as

`1.0`

into integers is now deprecated.Parsing hexadecimal floats such as

`0x3p3`

will failAn

`_`

was previously accepted as a thousands delimiter`100_000`

. This will now result in an error.

If you experience these limitations, they can all be worked around by passing
appropriate `converters=`

. NumPy now supports passing a single converter
to be used for all columns to make this more convenient.
For example, `converters=float.fromhex`

can read hexadecimal float numbers
and `converters=int`

will be able to read `100_000`

.

Further, the error messages have been generally improved. However, this means
that error types may differ. In particularly, a `ValueError`

is now always
raised when parsing of a single entry fails.

(gh-20580)

## Improvements#

`ndarray.__array_finalize__`

is now callable#

This means subclasses can now use `super().__array_finalize__(obj)`

without worrying whether `ndarray`

is their superclass or not.
The actual call remains a no-op.

(gh-20766)

### Add support for VSX4/Power10#

With VSX4/Power10 enablement, the new instructions available in Power ISA 3.1 can be used to accelerate some NumPy operations, e.g., floor_divide, modulo, etc.

(gh-20821)

`np.fromiter`

now accepts objects and subarrays#

The `numpy.fromiter`

function now supports object and
subarray dtypes. Please see he function documentation for
examples.

(gh-20993)

### Math C library feature detection now uses correct signatures#

Compiling is preceded by a detection phase to determine whether the
underlying libc supports certain math operations. Previously this code
did not respect the proper signatures. Fixing this enables compilation
for the `wasm-ld`

backend (compilation for web assembly) and reduces
the number of warnings.

(gh-21154)

`np.kron`

now maintains subclass information#

`np.kron`

maintains subclass information now such as masked arrays
while computing the Kronecker product of the inputs

```
>>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]])
>>> np.kron(x,x)
masked_array(
data=[[1, --, --, --],
[--, 4, --, --],
[--, --, 4, --],
[--, --, --, 16]],
mask=[[False, True, True, True],
[ True, False, True, True],
[ True, True, False, True],
[ True, True, True, False]],
fill_value=999999)
```

Warning

`np.kron`

output now follows `ufunc`

ordering (`multiply`

)
to determine the output class type

```
>>> class myarr(np.ndarray):
>>> __array_priority__ = -1
>>> a = np.ones([2, 2])
>>> ma = myarray(a.shape, a.dtype, a.data)
>>> type(np.kron(a, ma)) == np.ndarray
False # Before it was True
>>> type(np.kron(a, ma)) == myarr
True
```

(gh-21262)

## Performance improvements and changes#

### Faster `np.loadtxt`

#

`numpy.loadtxt`

is now generally much faster than previously as most of it
is now implemented in C.

(gh-20580)

### Faster reduction operators#

Reduction operations like `numpy.sum`

, `numpy.prod`

, `numpy.add.reduce`

,
`numpy.logical_and.reduce`

on contiguous integer-based arrays are now
much faster.

(gh-21001)

### Faster `np.where`

#

`numpy.where`

is now much faster than previously on unpredictable/random
input data.

(gh-21130)

### Faster operations on NumPy scalars#

Many operations on NumPy scalars are now significantly faster, although
rare operations (e.g. with 0-D arrays rather than scalars) may be slower
in some cases.
However, even with these improvements users who want the best performance
for their scalars, may want to convert a known NumPy scalar into a Python
one using `scalar.item()`

.

(gh-21188)

### Faster `np.kron`

#

`numpy.kron`

is about 80% faster as the product is now computed
using broadcasting.

(gh-21354)