numpy.nditer

class numpy.nditer

Efficient multi-dimensional iterator object to iterate over arrays. To get started using this object, see the introductory guide to array iteration.

Parameters :

op : ndarray or sequence of array_like

The array(s) to iterate over.

flags : sequence of str, optional

Flags to control the behavior of the iterator.

• “buffered” enables buffering when required.
• “c_index” causes a C-order index to be tracked.
• “f_index” causes a Fortran-order index to be tracked.
• “multi_index” causes a multi-index, or a tuple of indices with one per iteration dimension, to be tracked.
• “common_dtype” causes all the operands to be converted to a common data type, with copying or buffering as necessary.
• “delay_bufalloc” delays allocation of the buffers until a reset() call is made. Allows “allocate” operands to be initialized before their values are copied into the buffers.
• “external_loop” causes the values given to be one-dimensional arrays with multiple values instead of zero-dimensional arrays.
• “grow_inner” allows the value array sizes to be made larger than the buffer size when both “buffered” and “external_loop” is used.
• “ranged” allows the iterator to be restricted to a sub-range of the iterindex values.
• “refs_ok” enables iteration of reference types, such as object arrays.
• “reduce_ok” enables iteration of “readwrite” operands which are broadcasted, also known as reduction operands.
• “zerosize_ok” allows itersize to be zero.

op_flags : list of list of str, optional

This is a list of flags for each operand. At minimum, one of “readonly”, “readwrite”, or “writeonly” must be specified.

• “readonly” indicates the operand will only be read from.
• “readwrite” indicates the operand will be read from and written to.
• “writeonly” indicates the operand will only be written to.
• “no_broadcast” prevents the operand from being broadcasted.
• “contig” forces the operand data to be contiguous.
• “aligned” forces the operand data to be aligned.
• “nbo” forces the operand data to be in native byte order.
• “copy” allows a temporary read-only copy if required.
• “updateifcopy” allows a temporary read-write copy if required.
• “allocate” causes the array to be allocated if it is None in the op parameter.
• “no_subtype” prevents an “allocate” operand from using a subtype.
• “arraymask” indicates that this operand is the mask to use for selecting elements when writing to operands with the ‘writemasked’ flag set. The iterator does not enforce this, but when writing from a buffer back to the array, it only copies those elements indicated by this mask.
• ‘writemasked’ indicates that only elements where the chosen ‘arraymask’ operand is True will be written to.

op_dtypes : dtype or tuple of dtype(s), optional

The required data type(s) of the operands. If copying or buffering is enabled, the data will be converted to/from their original types.

order : {‘C’, ‘F’, ‘A’, or ‘K’}, optional

Controls the iteration order. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. This also affects the element memory order of “allocate” operands, as they are allocated to be compatible with iteration order. Default is ‘K’.

casting : {‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

Controls what kind of data casting may occur when making a copy or buffering. Setting this to ‘unsafe’ is not recommended, as it can adversely affect accumulations.

• ‘no’ means the data types should not be cast at all.
• ‘equiv’ means only byte-order changes are allowed.
• ‘safe’ means only casts which can preserve values are allowed.
• ‘same_kind’ means only safe casts or casts within a kind, like float64 to float32, are allowed.
• ‘unsafe’ means any data conversions may be done.

op_axes : list of list of ints, optional

If provided, is a list of ints or None for each operands. The list of axes for an operand is a mapping from the dimensions of the iterator to the dimensions of the operand. A value of -1 can be placed for entries, causing that dimension to be treated as “newaxis”.

itershape : tuple of ints, optional

The desired shape of the iterator. This allows “allocate” operands with a dimension mapped by op_axes not corresponding to a dimension of a different operand to get a value not equal to 1 for that dimension.

buffersize : int, optional

When buffering is enabled, controls the size of the temporary buffers. Set to 0 for the default value.

Notes

nditer supersedes flatiter. The iterator implementation behind nditer is also exposed by the Numpy C API.

The Python exposure supplies two iteration interfaces, one which follows the Python iterator protocol, and another which mirrors the C-style do-while pattern. The native Python approach is better in most cases, but if you need the iterator’s coordinates or index, use the C-style pattern.

Examples

Here is how we might write an iter_add function, using the Python iterator protocol:

def iter_add_py(x, y, out=None):
    addop = np.add
    it = np.nditer([x, y, out], [],
                [['readonly'], ['readonly'], ['writeonly','allocate']])
    for (a, b, c) in it:
        addop(a, b, out=c)
    return it.operands[2]

Here is the same function, but following the C-style pattern:

def iter_add(x, y, out=None):
    addop = np.add

    it = np.nditer([x, y, out], [],
                [['readonly'], ['readonly'], ['writeonly','allocate']])

    while not it.finished:
        addop(it[0], it[1], out=it[2])
        it.iternext()

    return it.operands[2]

Here is an example outer product function:

def outer_it(x, y, out=None):
    mulop = np.multiply

    it = np.nditer([x, y, out], ['external_loop'],
            [['readonly'], ['readonly'], ['writeonly', 'allocate']],
            op_axes=[range(x.ndim)+[-1]*y.ndim,
                     [-1]*x.ndim+range(y.ndim),
                     None])

    for (a, b, c) in it:
        mulop(a, b, out=c)

    return it.operands[2]

>>> a = np.arange(2)+1
>>> b = np.arange(3)+1
>>> outer_it(a,b)
array([[1, 2, 3],
       [2, 4, 6]])

Here is an example function which operates like a “lambda” ufunc:

def luf(lamdaexpr, *args, **kwargs):
    "luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)"
    nargs = len(args)
    op = (kwargs.get('out',None),) + args
    it = np.nditer(op, ['buffered','external_loop'],
            [['writeonly','allocate','no_broadcast']] +
                            [['readonly','nbo','aligned']]*nargs,
            order=kwargs.get('order','K'),
            casting=kwargs.get('casting','safe'),
            buffersize=kwargs.get('buffersize',0))
    while not it.finished:
        it[0] = lamdaexpr(*it[1:])
        it.iternext()
    return it.operands[0]

>>> a = np.arange(5)
>>> b = np.ones(5)
>>> luf(lambda i,j:i*i + j/2, a, b)
array([  0.5,   1.5,   4.5,   9.5,  16.5])

Attributes

dtypes
finished
has_delayed_bufalloc
has_index
has_multi_index
iterationneedsapi
iterindex
itersize
ndim
nop
operands
shape

Methods

copy()	Get a copy of the iterator in its current state.
debug_print()	Print the current state of the nditer instance and debug info to stdout.
enable_external_loop()	When the “external_loop” was not used during construction, but
iternext()	Check whether iterations are left, and perform a single internal iteration without returning the result.
next	x.next() -> the next value, or raise StopIteration
remove_axis(i)	Removes axis i from the iterator.
remove_multi_index()	When the “multi_index” flag was specified, this removes it, allowing
reset()	Reset the iterator to its initial state.