Standard array subclasses — NumPy v2.2 Manual (original) (raw)

Note

Subclassing a numpy.ndarray is possible but if your goal is to create an array with modified behavior, as do dask arrays for distributed computation and cupy arrays for GPU-based computation, subclassing is discouraged. Instead, using numpy’sdispatch mechanism is recommended.

The ndarray can be inherited from (in Python or in C) if desired. Therefore, it can form a foundation for many useful classes. Often whether to sub-class the array object or to simply use the core array component as an internal part of a new class is a difficult decision, and can be simply a matter of choice. NumPy has several tools for simplifying how your new object interacts with other array objects, and so the choice may not be significant in the end. One way to simplify the question is by asking yourself if the object you are interested in can be replaced as a single array or does it really require two or more arrays at its core.

Note that asarray always returns the base-class ndarray. If you are confident that your use of the array object can handle any subclass of an ndarray, then asanyarray can be used to allow subclasses to propagate more cleanly through your subroutine. In principal a subclass could redefine any aspect of the array and therefore, under strict guidelines, asanyarray would rarely be useful. However, most subclasses of the array object will not redefine certain aspects of the array object such as the buffer interface, or the attributes of the array. One important example, however, of why your subroutine may not be able to handle an arbitrary subclass of an array is that matrices redefine the “*” operator to be matrix-multiplication, rather than element-by-element multiplication.

Special attributes and methods#

NumPy provides several hooks that classes can customize:

class.__array_ufunc__(ufunc, method, *inputs, **kwargs)#

Any class, ndarray subclass or not, can define this method or set it to None in order to override the behavior of NumPy’s ufuncs. This works quite similarly to Python’s __mul__ and other binary operation routines.

The method should return either the result of the operation, orNotImplemented if the operation requested is not implemented.

If one of the input, output, or where arguments has a __array_ufunc__method, it is executed instead of the ufunc. If more than one of the arguments implements __array_ufunc__, they are tried in the order: subclasses before superclasses, inputs before outputs, outputs before where, otherwise left to right. The first routine returning something other thanNotImplemented determines the result. If all of the__array_ufunc__ operations return NotImplemented, aTypeError is raised.

Note

We intend to re-implement numpy functions as (generalized) Ufunc, in which case it will become possible for them to be overridden by the __array_ufunc__ method. A prime candidate ismatmul, which currently is not a Ufunc, but could be relatively easily be rewritten as a (set of) generalized Ufuncs. The same may happen with functions such as median,amin, and argsort.

Like with some other special methods in python, such as __hash__ and__iter__, it is possible to indicate that your class does _not_support ufuncs by setting __array_ufunc__ = None. Ufuncs always raiseTypeError when called on an object that sets__array_ufunc__ = None.

The presence of __array_ufunc__ also influences howndarray handles binary operations like arr + obj and arr < obj when arr is an ndarray and obj is an instance of a custom class. There are two possibilities. Ifobj.__array_ufunc__ is present and not None, thenndarray.__add__ and friends will delegate to the ufunc machinery, meaning that arr + obj becomes np.add(arr, obj), and thenadd invokes obj.__array_ufunc__. This is useful if you want to define an object that acts like an array.

Alternatively, if obj.__array_ufunc__ is set to None, then as a special case, special methods like ndarray.__add__ will notice this and unconditionally raise TypeError. This is useful if you want to create objects that interact with arrays via binary operations, but are not themselves arrays. For example, a units handling system might have an object m representing the “meters” unit, and want to support the syntax arr * m to represent that the array has units of “meters”, but not want to otherwise interact with arrays via ufuncs or otherwise. This can be done by setting __array_ufunc__ = None and defining __mul__and __rmul__ methods. (Note that this means that writing an__array_ufunc__ that always returns NotImplemented is not quite the same as setting __array_ufunc__ = None: in the former case, arr + obj will raise TypeError, while in the latter case it is possible to define a __radd__ method to prevent this.)

The above does not hold for in-place operators, for which ndarraynever returns NotImplemented. Hence, arr += obj would always lead to a TypeError. This is because for arrays in-place operations cannot generically be replaced by a simple reverse operation. (For instance, by default, arr += obj would be translated to arr = arr + obj, i.e., arr would be replaced, contrary to what is expected for in-place array operations.)

Note

If you define __array_ufunc__:

class.__array_function__(func, types, args, kwargs)#

As a convenience for __array_function__ implementers, typesprovides all argument types with an '__array_function__' attribute. This allows implementers to quickly identify cases where they should defer to __array_function__ implementations on other arguments. Implementations should not rely on the iteration order of types.

Most implementations of __array_function__ will start with two checks:

  1. Is the given function something that we know how to overload?
  2. Are all arguments of a type that we know how to handle?

If these conditions hold, __array_function__ should return the result from calling its implementation for func(*args, **kwargs). Otherwise, it should return the sentinel value NotImplemented, indicating that the function is not implemented by these types.

There are no general requirements on the return value from__array_function__, although most sensible implementations should probably return array(s) with the same type as one of the function’s arguments.

It may also be convenient to define a custom decorators (implementsbelow) for registering __array_function__ implementations.

HANDLED_FUNCTIONS = {}

class MyArray: def array_function(self, func, types, args, kwargs): if func not in HANDLED_FUNCTIONS: return NotImplemented # Note: this allows subclasses that don't override # array_function to handle MyArray objects if not all(issubclass(t, MyArray) for t in types): return NotImplemented return HANDLED_FUNCTIONS[func](*args, **kwargs)

def implements(numpy_function): """Register an array_function implementation for MyArray objects.""" def decorator(func): HANDLED_FUNCTIONS[numpy_function] = func return func return decorator

@implements(np.concatenate) def concatenate(arrays, axis=0, out=None): ... # implementation of concatenate for MyArray objects

@implements(np.broadcast_to) def broadcast_to(array, shape): ... # implementation of broadcast_to for MyArray objects

Note that it is not required for __array_function__ implementations to include all of the corresponding NumPy function’s optional arguments (e.g., broadcast_to above omits the irrelevant subok argument). Optional arguments are only passed in to __array_function__ if they were explicitly used in the NumPy function call.

Just like the case for builtin special methods like __add__, properly written __array_function__ methods should always returnNotImplemented when an unknown type is encountered. Otherwise, it will be impossible to correctly override NumPy functions from another object if the operation also includes one of your objects.

For the most part, the rules for dispatch with __array_function__match those for __array_ufunc__. In particular:

If no __array_function__ methods exists, NumPy will default to calling its own implementation, intended for use on NumPy arrays. This case arises, for example, when all array-like arguments are Python numbers or lists. (NumPy arrays do have a __array_function__ method, given below, but it always returns NotImplemented if any argument other than a NumPy array subclass implements __array_function__.)

One deviation from the current behavior of __array_ufunc__ is that NumPy will only call __array_function__ on the first argument of each unique type. This matches Python’s rule for calling reflected methods, and this ensures that checking overloads has acceptable performance even when there are a large number of overloaded arguments.

class.__array_finalize__(obj)#

This method is called whenever the system internally allocates a new array from obj, where obj is a subclass (subtype) of thendarray. It can be used to change attributes of _self_after construction (so as to ensure a 2-d matrix for example), or to update meta-information from the “parent.” Subclasses inherit a default implementation of this method that does nothing.

class.__array_wrap__(array, context=None, return_scalar=False)#

At the end of every ufunc, this method is called on the input object with the highest array priority, or the output object if one was specified. The ufunc-computed array is passed in and whatever is returned is passed to the user. Subclasses inherit a default implementation of this method, which transforms the array into a new instance of the object’s class. Subclasses may opt to use this method to transform the output array into an instance of the subclass and update metadata before returning the array to the user.

NumPy may also call this function without a context from non-ufuncs to allow preserving subclass information.

Changed in version 2.0: return_scalar is now passed as either False (usually) or Trueindicating that NumPy would return a scalar. Subclasses may ignore the value, or return array[()] to behave more like NumPy.

Note

It is hoped to eventually deprecate this method in favour of__array_ufunc__ for ufuncs (and __array_function__for a few other functions like numpy.squeeze).

class.__array_priority__#

The value of this attribute is used to determine what type of object to return in situations where there is more than one possibility for the Python type of the returned object. Subclasses inherit a default value of 0.0 for this attribute.

Note

For ufuncs, it is hoped to eventually deprecate this method in favour of __array_ufunc__.

class.__array__(dtype=None, copy=None)#

If defined on an object, should return an ndarray. This method is called by array-coercion functions like np.array() if an object implementing this interface is passed to those functions. The third-party implementations of __array__ must take dtype andcopy keyword arguments, as ignoring them might break third-party code or NumPy itself.

Please refer to Interoperability with NumPyfor the protocol hierarchy, of which __array__ is the oldest and least desirable.

Note

If a class (ndarray subclass or not) having the __array__method is used as the output object of an ufunc, results will not be written to the object returned by __array__. This practice will return TypeError.

Matrix objects#

Note

It is strongly advised not to use the matrix subclass. As described below, it makes writing functions that deal consistently with matrices and regular arrays very difficult. Currently, they are mainly used for interacting with scipy.sparse. We hope to provide an alternative for this use, however, and eventually remove the matrix subclass.

matrix objects inherit from the ndarray and therefore, they have the same attributes and methods of ndarrays. There are six important differences of matrix objects, however, that may lead to unexpected results when you use matrices but expect them to act like arrays:

  1. Matrix objects can be created using a string notation to allow Matlab-style syntax where spaces separate columns and semicolons (‘;’) separate rows.
  2. Matrix objects are always two-dimensional. This has far-reaching implications, in that m.ravel() is still two-dimensional (with a 1 in the first dimension) and item selection returns two-dimensional objects so that sequence behavior is fundamentally different than arrays.
  3. Matrix objects over-ride multiplication to be matrix-multiplication. Make sure you understand this for functions that you may want to receive matrices. Especially in light of the fact that asanyarray(m) returns a matrix when m is a matrix.
  4. Matrix objects over-ride power to be matrix raised to a power. The same warning about using power inside a function that uses asanyarray(…) to get an array object holds for this fact.
  5. The default __array_priority__ of matrix objects is 10.0, and therefore mixed operations with ndarrays always produce matrices.
  6. Matrices have special attributes which make calculations easier. These are

Warning

Matrix objects over-ride multiplication, ‘*’, and power, ‘**’, to be matrix-multiplication and matrix power, respectively. If your subroutine can accept sub-classes and you do not convert to base- class arrays, then you must use the ufuncs multiply and power to be sure that you are performing the correct operation for all inputs.

The matrix class is a Python subclass of the ndarray and can be used as a reference for how to construct your own subclass of the ndarray. Matrices can be created from other matrices, strings, and anything else that can be converted to an ndarray . The name “mat “is an alias for “matrix “in NumPy.

Example 1: Matrix creation from a string

import numpy as np a = np.asmatrix('1 2 3; 4 5 3') print((a*a.T).I) [[ 0.29239766 -0.13450292] [-0.13450292 0.08187135]]

Example 2: Matrix creation from a nested sequence

import numpy as np np.asmatrix([[1,5,10],[1.0,3,4j]]) matrix([[ 1.+0.j, 5.+0.j, 10.+0.j], [ 1.+0.j, 3.+0.j, 0.+4.j]])

Example 3: Matrix creation from an array

import numpy as np np.asmatrix(np.random.rand(3,3)).T matrix([[4.17022005e-01, 3.02332573e-01, 1.86260211e-01], [7.20324493e-01, 1.46755891e-01, 3.45560727e-01], [1.14374817e-04, 9.23385948e-02, 3.96767474e-01]])

Memory-mapped file arrays#

Memory-mapped files are useful for reading and/or modifying small segments of a large file with regular layout, without reading the entire file into memory. A simple subclass of the ndarray uses a memory-mapped file for the data buffer of the array. For small files, the over-head of reading the entire file into memory is typically not significant, however for large files using memory mapping can save considerable resources.

Memory-mapped-file arrays have one additional method (besides those they inherit from the ndarray): .flush() which must be called manually by the user to ensure that any changes to the array actually get written to disk.

Example:

a = np.memmap('newfile.dat', dtype=float, mode='w+', shape=1000) a[10] = 10.0 a[30] = 30.0 del a

b = np.fromfile('newfile.dat', dtype=float) print(b[10], b[30]) 10.0 30.0

a = np.memmap('newfile.dat', dtype=float) print(a[10], a[30]) 10.0 30.0

Character arrays (numpy.char)#

Note

The chararray class exists for backwards compatibility with Numarray, it is not recommended for new development. Starting from numpy 1.4, if one needs arrays of strings, it is recommended to use arrays ofdtype object_, bytes_ or str_, and use the free functions in the numpy.char module for fast vectorized string operations.

These are enhanced arrays of either str_ type orbytes_ type. These arrays inherit from thendarray, but specially-define the operations +, *, and % on a (broadcasting) element-by-element basis. These operations are not available on the standard ndarray of character type. In addition, the chararray has all of the standard str (and bytes) methods, executing them on an element-by-element basis. Perhaps the easiest way to create a chararray is to use self.view(chararray) where self is an ndarray of str or unicode data-type. However, a chararray can also be created using thechararray constructor, or via thenumpy.char.array function:

Another difference with the standard ndarray of str data-type is that the chararray inherits the feature introduced by Numarray that white-space at the end of any element in the array will be ignored on item retrieval and comparison operations.

Record arrays#

NumPy provides the recarray class which allows accessing the fields of a structured array as attributes, and a corresponding scalar data type object record.

Note

The pandas DataFrame is more powerful than record array. If possible, please use pandas DataFrame instead.

Masked arrays (numpy.ma)#

Standard container class#

For backward compatibility and as a standard “container “class, the UserArray from Numeric has been brought over to NumPy and namednumpy.lib.user_array.container The container class is a Python class whose self.array attribute is an ndarray. Multiple inheritance is probably easier with numpy.lib.user_array.container than with the ndarray itself and so it is included by default. It is not documented here beyond mentioning its existence because you are encouraged to use the ndarray class directly if you can.

Array iterators#

Iterators are a powerful concept for array processing. Essentially, iterators implement a generalized for-loop. If myiter is an iterator object, then the Python code:

for val in myiter: ... some code involving val ...

calls val = next(myiter) repeatedly until StopIteration is raised by the iterator. There are several ways to iterate over an array that may be useful: default iteration, flat iteration, and\(N\)-dimensional enumeration.

Default iteration#

The default iterator of an ndarray object is the default Python iterator of a sequence type. Thus, when the array object itself is used as an iterator. The default behavior is equivalent to:

for i in range(arr.shape[0]): val = arr[i]

This default iterator selects a sub-array of dimension \(N-1\)from the array. This can be a useful construct for defining recursive algorithms. To loop over the entire array requires \(N\) for-loops.

import numpy as np a = np.arange(24).reshape(3,2,4) + 10 for val in a: ... print('item:', val) item: [[10 11 12 13] [14 15 16 17]] item: [[18 19 20 21] [22 23 24 25]] item: [[26 27 28 29] [30 31 32 33]]

Flat iteration#

As mentioned previously, the flat attribute of ndarray objects returns an iterator that will cycle over the entire array in C-style contiguous order.

import numpy as np a = np.arange(24).reshape(3,2,4) + 10 for i, val in enumerate(a.flat): ... if i%5 == 0: print(i, val) 0 10 5 15 10 20 15 25 20 30

Here, I’ve used the built-in enumerate iterator to return the iterator index as well as the value.

N-dimensional enumeration#

Sometimes it may be useful to get the N-dimensional index while iterating. The ndenumerate iterator can achieve this.

import numpy as np for i, val in np.ndenumerate(a): ... if sum(i)%5 == 0: print(i, val) (0, 0, 0) 10 (1, 1, 3) 25 (2, 0, 3) 29 (2, 1, 2) 32

Iterator for broadcasting#

The general concept of broadcasting is also available from Python using the broadcast iterator. This object takes \(N\)objects as inputs and returns an iterator that returns tuples providing each of the input sequence elements in the broadcasted result.

import numpy as np for val in np.broadcast([[1, 0], [2, 3]], [0, 1]): ... print(val) (np.int64(1), np.int64(0)) (np.int64(0), np.int64(1)) (np.int64(2), np.int64(0)) (np.int64(3), np.int64(1))