cctk.OneIndexedArray¶

class
cctk.
OneIndexedArray
(obj, **kwargs)[source]¶ Bases:
numpy.ndarray
Wrapper for
np.ndarray
that’s indexed from one, not zero, to store atomic numbers and geometries. This only works on 1D or 2D arrays. Additionally, only the first index of a 2D array will be 1indexed.Note that
enumerate(one_indexed_array)
will throwIndexError
– instead, useenumerate(one_indexed_array, start=1)
.
__init__
()¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
()Initialize self.
all
([axis, out, keepdims, where])Returns True if all elements evaluate to True.
any
([axis, out, keepdims, where])Returns True if any of the elements of a evaluate to True.
argmax
([axis, out])Return indices of the maximum values along the given axis.
argmin
([axis, out])Return indices of the minimum values along the given axis.
argpartition
(kth[, axis, kind, order])Returns the indices that would partition this array.
argsort
([axis, kind, order])Returns the indices that would sort this array.
astype
(dtype[, order, casting, subok, copy])Copy of the array, cast to a specified type.
byteswap
([inplace])Swap the bytes of the array elements
choose
(choices[, out, mode])Use an index array to construct a new array from a set of choices.
clip
([min, max, out])Return an array whose values are limited to
[min, max]
.compress
(condition[, axis, out])Return selected slices of this array along given axis.
conj
()Complexconjugate all elements.
Return the complex conjugate, elementwise.
copy
([order])Return a copy of the array.
cumprod
([axis, dtype, out])Return the cumulative product of the elements along the given axis.
cumsum
([axis, dtype, out])Return the cumulative sum of the elements along the given axis.
diagonal
([offset, axis1, axis2])Return specified diagonals.
dot
(b[, out])Dot product of two arrays.
dump
(file)Dump a pickle of the array to the specified file.
dumps
()Returns the pickle of the array as a string.
fill
(value)Fill the array with a scalar value.
flatten
([order])Return a copy of the array collapsed into one dimension.
getfield
(dtype[, offset])Returns a field of the given array as a certain type.
item
(*args)Copy an element of an array to a standard Python scalar and return it.
itemset
(*args)Insert scalar into an array (scalar is cast to array’s dtype, if possible)
max
([axis, out, keepdims, initial, where])Return the maximum along a given axis.
mean
([axis, dtype, out, keepdims, where])Returns the average of the array elements along given axis.
min
([axis, out, keepdims, initial, where])Return the minimum along a given axis.
newbyteorder
([new_order])Return the array with the same data viewed with a different byte order.
nonzero
()Return the indices of the elements that are nonzero.
partition
(kth[, axis, kind, order])Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array.
prod
([axis, dtype, out, keepdims, initial, …])Return the product of the array elements over the given axis
ptp
([axis, out, keepdims])Peak to peak (maximum  minimum) value along a given axis.
put
(indices, values[, mode])Set
a.flat[n] = values[n]
for all n in indices.ravel
([order])Return a flattened array.
repeat
(repeats[, axis])Repeat elements of an array.
reshape
(shape[, order])Returns an array containing the same data with a new shape.
resize
(new_shape[, refcheck])Change shape and size of array inplace.
round
([decimals, out])Return a with each element rounded to the given number of decimals.
searchsorted
(v[, side, sorter])Find indices where elements of v should be inserted in a to maintain order.
setfield
(val, dtype[, offset])Put a value into a specified place in a field defined by a datatype.
setflags
([write, align, uic])Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively.
sort
([axis, kind, order])Sort an array inplace.
squeeze
([axis])Remove axes of length one from a.
std
([axis, dtype, out, ddof, keepdims, where])Returns the standard deviation of the array elements along given axis.
sum
([axis, dtype, out, keepdims, initial, where])Return the sum of the array elements over the given axis.
swapaxes
(axis1, axis2)Return a view of the array with axis1 and axis2 interchanged.
take
(indices[, axis, out, mode])Return an array formed from the elements of a at the given indices.
tobytes
([order])Construct Python bytes containing the raw data bytes in the array.
tofile
(fid[, sep, format])Write array to a file as text or binary (default).
tolist
()Return the array as an
a.ndim
levels deep nested list of Python scalars.tostring
([order])A compatibility alias for tobytes, with exactly the same behavior.
trace
([offset, axis1, axis2, dtype, out])Return the sum along diagonals of the array.
transpose
(*axes)Returns a view of the array with axes transposed.
var
([axis, dtype, out, ddof, keepdims, where])Returns the variance of the array elements, along given axis.
view
([dtype][, type])New view of array with the same data.
Attributes
The transposed array.
Base object if memory is from some other object.
An object to simplify the interaction of the array with the ctypes module.
Python buffer object pointing to the start of the array’s data.
Datatype of the array’s elements.
Information about the memory layout of the array.
A 1D iterator over the array.
The imaginary part of the array.
Length of one array element in bytes.
Total bytes consumed by the elements of the array.
Number of array dimensions.
The real part of the array.
Tuple of array dimensions.
Number of elements in the array.
Tuple of bytes to step in each dimension when traversing an array.

T
¶ The transposed array.
Same as
self.transpose()
.Examples
>>> x = np.array([[1.,2.],[3.,4.]]) >>> x array([[ 1., 2.], [ 3., 4.]]) >>> x.T array([[ 1., 3.], [ 2., 4.]]) >>> x = np.array([1.,2.,3.,4.]) >>> x array([ 1., 2., 3., 4.]) >>> x.T array([ 1., 2., 3., 4.])
See also

all
(axis=None, out=None, keepdims=False, *, where=True)¶ Returns True if all elements evaluate to True.
Refer to numpy.all for full documentation.
See also
numpy.all
equivalent function

any
(axis=None, out=None, keepdims=False, *, where=True)¶ Returns True if any of the elements of a evaluate to True.
Refer to numpy.any for full documentation.
See also
numpy.any
equivalent function

argmax
(axis=None, out=None)¶ Return indices of the maximum values along the given axis.
Refer to numpy.argmax for full documentation.
See also
numpy.argmax
equivalent function

argmin
(axis=None, out=None)¶ Return indices of the minimum values along the given axis.
Refer to numpy.argmin for detailed documentation.
See also
numpy.argmin
equivalent function

argpartition
(kth, axis= 1, kind='introselect', order=None)¶ Returns the indices that would partition this array.
Refer to numpy.argpartition for full documentation.
New in version 1.8.0.
See also
numpy.argpartition
equivalent function

argsort
(axis= 1, kind=None, order=None)¶ Returns the indices that would sort this array.
Refer to numpy.argsort for full documentation.
See also
numpy.argsort
equivalent function

astype
(dtype, order='K', casting='unsafe', subok=True, copy=True)¶ Copy of the array, cast to a specified type.
 Parameters
dtype (str or dtype) – Typecode or datatype to which the array is cast.
order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout order of the result. ‘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. Default is ‘K’.
casting ({'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional) –
Controls what kind of data casting may occur. Defaults to ‘unsafe’ for backwards compatibility.
’no’ means the data types should not be cast at all.
’equiv’ means only byteorder 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.
subok (bool, optional) – If True, then subclasses will be passedthrough (default), otherwise the returned array will be forced to be a baseclass array.
copy (bool, optional) – By default, astype always returns a newly allocated array. If this is set to false, and the dtype, order, and subok requirements are satisfied, the input array is returned instead of a copy.
 Returns
arr_t – Unless copy is False and the other conditions for returning the input array are satisfied (see description for copy input parameter), arr_t is a new array of the same shape as the input array, with dtype, order given by dtype, order.
 Return type
ndarray
Notes
Changed in version 1.17.0: Casting between a simple data type and a structured one is possible only for “unsafe” casting. Casting to multiple fields is allowed, but casting from multiple fields is not.
Changed in version 1.9.0: Casting from numeric to string types in ‘safe’ casting mode requires that the string dtype length is long enough to store the max integer/float value converted.
 Raises
ComplexWarning – When casting from complex to float or int. To avoid this, one should use
a.real.astype(t)
.
Examples
>>> x = np.array([1, 2, 2.5]) >>> x array([1. , 2. , 2.5])
>>> x.astype(int) array([1, 2, 2])

base
¶ Base object if memory is from some other object.
Examples
The base of an array that owns its memory is None:
>>> x = np.array([1,2,3,4]) >>> x.base is None True
Slicing creates a view, whose memory is shared with x:
>>> y = x[2:] >>> y.base is x True

byteswap
(inplace=False)¶ Swap the bytes of the array elements
Toggle between lowendian and bigendian data representation by returning a byteswapped array, optionally swapped inplace. Arrays of bytestrings are not swapped. The real and imaginary parts of a complex number are swapped individually.
 Parameters
inplace (bool, optional) – If
True
, swap bytes inplace, default isFalse
. Returns
out – The byteswapped array. If inplace is
True
, this is a view to self. Return type
ndarray
Examples
>>> A = np.array([1, 256, 8755], dtype=np.int16) >>> list(map(hex, A)) ['0x1', '0x100', '0x2233'] >>> A.byteswap(inplace=True) array([ 256, 1, 13090], dtype=int16) >>> list(map(hex, A)) ['0x100', '0x1', '0x3322']
Arrays of bytestrings are not swapped
>>> A = np.array([b'ceg', b'fac']) >>> A.byteswap() array([b'ceg', b'fac'], dtype='S3')
A.newbyteorder().byteswap()
produces an array with the same valuesbut different representation in memory
>>> A = np.array([1, 2, 3]) >>> A.view(np.uint8) array([1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0], dtype=uint8) >>> A.newbyteorder().byteswap(inplace=True) array([1, 2, 3]) >>> A.view(np.uint8) array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3], dtype=uint8)

choose
(choices, out=None, mode='raise')¶ Use an index array to construct a new array from a set of choices.
Refer to numpy.choose for full documentation.
See also
numpy.choose
equivalent function

clip
(min=None, max=None, out=None, **kwargs)¶ Return an array whose values are limited to
[min, max]
. One of max or min must be given.Refer to numpy.clip for full documentation.
See also
numpy.clip
equivalent function

compress
(condition, axis=None, out=None)¶ Return selected slices of this array along given axis.
Refer to numpy.compress for full documentation.
See also
numpy.compress
equivalent function

conj
()¶ Complexconjugate all elements.
Refer to numpy.conjugate for full documentation.
See also
numpy.conjugate
equivalent function

conjugate
()¶ Return the complex conjugate, elementwise.
Refer to numpy.conjugate for full documentation.
See also
numpy.conjugate
equivalent function

copy
(order='C')¶ Return a copy of the array.
 Parameters
order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout of the copy. ‘C’ means Corder, ‘F’ means Forder, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible. (Note that this function and
numpy.copy()
are very similar, but have different default values for their order= arguments.)
See also
numpy.copy
,numpy.copyto
Examples
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x array([[0, 0, 0], [0, 0, 0]])
>>> y array([[1, 2, 3], [4, 5, 6]])
>>> y.flags['C_CONTIGUOUS'] True

ctypes
¶ An object to simplify the interaction of the array with the ctypes module.
This attribute creates an object that makes it easier to use arrays when calling shared libraries with the ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes below) which themselves return ctypes objects that can be used as arguments to a shared library.
 Parameters
None –
 Returns
c – Possessing attributes data, shape, strides, etc.
 Return type
Python object
See also
numpy.ctypeslib
Notes
Below are the public attributes of this object which were documented in “Guide to NumPy” (we have omitted undocumented public attributes, as well as documented private attributes):

_ctypes.
data
A pointer to the memory area of the array as a Python integer. This memory area may contain data that is not aligned, or not in correct byteorder. The memory area may not even be writeable. The array flags and datatype of this array should be respected when passing this attribute to arbitrary Ccode to avoid trouble that can include Python crashing. User Beware! The value of this attribute is exactly the same as
self._array_interface_['data'][0]
.Note that unlike
data_as
, a reference will not be kept to the array: code likectypes.c_void_p((a + b).ctypes.data)
will result in a pointer to a deallocated array, and should be spelt(a + b).ctypes.data_as(ctypes.c_void_p)

_ctypes.
shape
A ctypes array of length self.ndim where the basetype is the Cinteger corresponding to
dtype('p')
on this platform. This basetype could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The c_intp type is defined accordingly in numpy.ctypeslib. The ctypes array contains the shape of the underlying array. Type
(c_intp*self.ndim)

_ctypes.
strides
A ctypes array of length self.ndim where the basetype is the same as for the shape attribute. This ctypes array contains the strides information from the underlying array. This strides information is important for showing how many bytes must be jumped to get to the next element in the array.
 Type
(c_intp*self.ndim)

_ctypes.
data_as
(obj) Return the data pointer cast to a particular ctypes object. For example, calling
self._as_parameter_
is equivalent toself.data_as(ctypes.c_void_p)
. Perhaps you want to use the data as a pointer to a ctypes array of floatingpoint data:self.data_as(ctypes.POINTER(ctypes.c_double))
.The returned pointer will keep a reference to the array.

_ctypes.
shape_as
(obj) Return the shape tuple as an array of some other ctypes type. For example:
self.shape_as(ctypes.c_short)
.

_ctypes.
strides_as
(obj) Return the strides tuple as an array of some other ctypes type. For example:
self.strides_as(ctypes.c_longlong)
.
If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful, but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have the
as_parameter
attribute which will return an integer equal to the data attribute.Examples
>>> import ctypes >>> x = np.array([[0, 1], [2, 3]], dtype=np.int32) >>> x array([[0, 1], [2, 3]], dtype=int32) >>> x.ctypes.data 31962608 # may vary >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)) <__main__.LP_c_uint object at 0x7ff2fc1fc200> # may vary >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint32)).contents c_uint(0) >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_uint64)).contents c_ulong(4294967296) >>> x.ctypes.shape <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1fce60> # may vary >>> x.ctypes.strides <numpy.core._internal.c_long_Array_2 object at 0x7ff2fc1ff320> # may vary

cumprod
(axis=None, dtype=None, out=None)¶ Return the cumulative product of the elements along the given axis.
Refer to numpy.cumprod for full documentation.
See also
numpy.cumprod
equivalent function

cumsum
(axis=None, dtype=None, out=None)¶ Return the cumulative sum of the elements along the given axis.
Refer to numpy.cumsum for full documentation.
See also
numpy.cumsum
equivalent function

data
¶ Python buffer object pointing to the start of the array’s data.

diagonal
(offset=0, axis1=0, axis2=1)¶ Return specified diagonals. In NumPy 1.9 the returned array is a readonly view instead of a copy as in previous NumPy versions. In a future version the readonly restriction will be removed.
Refer to
numpy.diagonal()
for full documentation.See also
numpy.diagonal
equivalent function

dot
(b, out=None)¶ Dot product of two arrays.
Refer to numpy.dot for full documentation.
See also
numpy.dot
equivalent function
Examples
>>> a = np.eye(2) >>> b = np.ones((2, 2)) * 2 >>> a.dot(b) array([[2., 2.], [2., 2.]])
This array method can be conveniently chained:
>>> a.dot(b).dot(b) array([[8., 8.], [8., 8.]])

dtype
¶ Datatype of the array’s elements.
 Parameters
None –
 Returns
d
 Return type
numpy dtype object
See also
numpy.dtype
Examples
>>> x array([[0, 1], [2, 3]]) >>> x.dtype dtype('int32') >>> type(x.dtype) <type 'numpy.dtype'>

dump
(file)¶ Dump a pickle of the array to the specified file. The array can be read back with pickle.load or numpy.load.
 Parameters
file (str or Path) –
A string naming the dump file.
Changed in version 1.17.0: pathlib.Path objects are now accepted.

dumps
()¶ Returns the pickle of the array as a string. pickle.loads or numpy.loads will convert the string back to an array.
 Parameters
None –

fill
(value)¶ Fill the array with a scalar value.
 Parameters
value (scalar) – All elements of a will be assigned this value.
Examples
>>> a = np.array([1, 2]) >>> a.fill(0) >>> a array([0, 0]) >>> a = np.empty(2) >>> a.fill(1) >>> a array([1., 1.])

flags
¶ Information about the memory layout of the array.

C_CONTIGUOUS
(C)¶ The data is in a single, Cstyle contiguous segment.

F_CONTIGUOUS
(F)¶ The data is in a single, Fortranstyle contiguous segment.

OWNDATA
(O)¶ The array owns the memory it uses or borrows it from another object.

WRITEABLE
(W)¶ The data area can be written to. Setting this to False locks the data, making it readonly. A view (slice, etc.) inherits WRITEABLE from its base array at creation time, but a view of a writeable array may be subsequently locked while the base array remains writeable. (The opposite is not true, in that a view of a locked array may not be made writeable. However, currently, locking a base object does not lock any views that already reference it, so under that circumstance it is possible to alter the contents of a locked array via a previously created writeable view onto it.) Attempting to change a nonwriteable array raises a RuntimeError exception.

ALIGNED
(A)¶ The data and all elements are aligned appropriately for the hardware.

WRITEBACKIFCOPY
(X)¶ This array is a copy of some other array. The CAPI function PyArray_ResolveWritebackIfCopy must be called before deallocating to the base array will be updated with the contents of this array.

UPDATEIFCOPY
(U)¶ (Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array. When this array is deallocated, the base array will be updated with the contents of this array.

FNC
¶ F_CONTIGUOUS and not C_CONTIGUOUS.

FORC
¶ F_CONTIGUOUS or C_CONTIGUOUS (onesegment test).

BEHAVED
(B)¶ ALIGNED and WRITEABLE.

CARRAY
(CA)¶ BEHAVED and C_CONTIGUOUS.

FARRAY
(FA)¶ BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
Notes
The flags object can be accessed dictionarylike (as in
a.flags['WRITEABLE']
), or by using lowercased attribute names (as ina.flags.writeable
). Short flag names are only supported in dictionary access.Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by the user, via direct assignment to the attribute or dictionary entry, or by calling ndarray.setflags.
The array flags cannot be set arbitrarily:
UPDATEIFCOPY can only be set
False
.WRITEBACKIFCOPY can only be set
False
.ALIGNED can only be set
True
if the data is truly aligned.WRITEABLE can only be set
True
if the array owns its own memory or the ultimate owner of the memory exposes a writeable buffer interface or is a string.
Arrays can be both Cstyle and Fortranstyle contiguous simultaneously. This is clear for 1dimensional arrays, but can also be true for higher dimensional arrays.
Even for contiguous arrays a stride for a given dimension
arr.strides[dim]
may be arbitrary ifarr.shape[dim] == 1
or the array has no elements. It does not generally hold thatself.strides[1] == self.itemsize
for Cstyle contiguous arrays orself.strides[0] == self.itemsize
for Fortranstyle contiguous arrays is true.

flat
¶ A 1D iterator over the array.
This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s builtin iterator object.
Examples
>>> x = np.arange(1, 7).reshape(2, 3) >>> x array([[1, 2, 3], [4, 5, 6]]) >>> x.flat[3] 4 >>> x.T array([[1, 4], [2, 5], [3, 6]]) >>> x.T.flat[3] 5 >>> type(x.flat) <class 'numpy.flatiter'>
An assignment example:
>>> x.flat = 3; x array([[3, 3, 3], [3, 3, 3]]) >>> x.flat[[1,4]] = 1; x array([[3, 1, 3], [3, 1, 3]])

flatten
(order='C')¶ Return a copy of the array collapsed into one dimension.
 Parameters
order ({'C', 'F', 'A', 'K'}, optional) – ‘C’ means to flatten in rowmajor (Cstyle) order. ‘F’ means to flatten in columnmajor (Fortran style) order. ‘A’ means to flatten in columnmajor order if a is Fortran contiguous in memory, rowmajor order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.
 Returns
y – A copy of the input array, flattened to one dimension.
 Return type
ndarray
Examples
>>> a = np.array([[1,2], [3,4]]) >>> a.flatten() array([1, 2, 3, 4]) >>> a.flatten('F') array([1, 3, 2, 4])

getfield
(dtype, offset=0)¶ Returns a field of the given array as a certain type.
A field is a view of the array data with a given datatype. The values in the view are determined by the given type and the offset into the current array in bytes. The offset needs to be such that the view dtype fits in the array dtype; for example an array of dtype complex128 has 16byte elements. If taking a view with a 32bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.
 Parameters
dtype (str or dtype) – The data type of the view. The dtype size of the view can not be larger than that of the array itself.
offset (int) – Number of bytes to skip before beginning the element view.
Examples
>>> x = np.diag([1.+1.j]*2) >>> x[1, 1] = 2 + 4.j >>> x array([[1.+1.j, 0.+0.j], [0.+0.j, 2.+4.j]]) >>> x.getfield(np.float64) array([[1., 0.], [0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the array for our view:
>>> x.getfield(np.float64, offset=8) array([[1., 0.], [0., 4.]])

imag
¶ The imaginary part of the array.
Examples
>>> x = np.sqrt([1+0j, 0+1j]) >>> x.imag array([ 0. , 0.70710678]) >>> x.imag.dtype dtype('float64')

item
(*args)¶ Copy an element of an array to a standard Python scalar and return it.
 Parameters
*args (Arguments (variable number and type)) –
none: in this case, the method only works for arrays with one element (a.size == 1), which element is copied into a standard Python scalar object and returned.
int_type: this argument is interpreted as a flat index into the array, specifying which element to copy and return.
tuple of int_types: functions as does a single int_type argument, except that the argument is interpreted as an ndindex into the array.
 Returns
z – A copy of the specified element of the array as a suitable Python scalar
 Return type
Standard Python scalar object
Notes
When the data type of a is longdouble or clongdouble, item() returns a scalar array object because there is no available Python scalar that would not lose information. Void arrays return a buffer object for item(), unless fields are defined, in which case a tuple is returned.
item is very similar to a[args], except, instead of an array scalar, a standard Python scalar is returned. This can be useful for speeding up access to elements of the array and doing arithmetic on elements of the array using Python’s optimized math.
Examples
>>> np.random.seed(123) >>> x = np.random.randint(9, size=(3, 3)) >>> x array([[2, 2, 6], [1, 3, 6], [1, 0, 1]]) >>> x.item(3) 1 >>> x.item(7) 0 >>> x.item((0, 1)) 2 >>> x.item((2, 2)) 1

itemset
(*args)¶ Insert scalar into an array (scalar is cast to array’s dtype, if possible)
There must be at least 1 argument, and define the last argument as item. Then,
a.itemset(*args)
is equivalent to but faster thana[args] = item
. The item should be a scalar value and args must select a single item in the array a. Parameters
*args (Arguments) – If one argument: a scalar, only used in case a is of size 1. If two arguments: the last argument is the value to be set and must be a scalar, the first argument specifies a single array element location. It is either an int or a tuple.
Notes
Compared to indexing syntax, itemset provides some speed increase for placing a scalar into a particular location in an ndarray, if you must do this. However, generally this is discouraged: among other problems, it complicates the appearance of the code. Also, when using itemset (and item) inside a loop, be sure to assign the methods to a local variable to avoid the attribute lookup at each loop iteration.
Examples
>>> np.random.seed(123) >>> x = np.random.randint(9, size=(3, 3)) >>> x array([[2, 2, 6], [1, 3, 6], [1, 0, 1]]) >>> x.itemset(4, 0) >>> x.itemset((2, 2), 9) >>> x array([[2, 2, 6], [1, 0, 6], [1, 0, 9]])

itemsize
¶ Length of one array element in bytes.
Examples
>>> x = np.array([1,2,3], dtype=np.float64) >>> x.itemsize 8 >>> x = np.array([1,2,3], dtype=np.complex128) >>> x.itemsize 16

max
(axis=None, out=None, keepdims=False, initial=<no value>, where=True)¶ Return the maximum along a given axis.
Refer to numpy.amax for full documentation.
See also
numpy.amax
equivalent function

mean
(axis=None, dtype=None, out=None, keepdims=False, *, where=True)¶ Returns the average of the array elements along given axis.
Refer to numpy.mean for full documentation.
See also
numpy.mean
equivalent function

min
(axis=None, out=None, keepdims=False, initial=<no value>, where=True)¶ Return the minimum along a given axis.
Refer to numpy.amin for full documentation.
See also
numpy.amin
equivalent function

nbytes
¶ Total bytes consumed by the elements of the array.
Notes
Does not include memory consumed by nonelement attributes of the array object.
Examples
>>> x = np.zeros((3,5,2), dtype=np.complex128) >>> x.nbytes 480 >>> np.prod(x.shape) * x.itemsize 480

ndim
¶ Number of array dimensions.
Examples
>>> x = np.array([1, 2, 3]) >>> x.ndim 1 >>> y = np.zeros((2, 3, 4)) >>> y.ndim 3

newbyteorder
(new_order='S', /)¶ Return the array with the same data viewed with a different byte order.
Equivalent to:
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and subarrays of the array data type.
 Parameters
new_order (string, optional) –
Byte order to force; a value from the byte order specifications below. new_order codes can be any of:
’S’  swap dtype from current to opposite endian
{‘<’, ‘little’}  little endian
{‘>’, ‘big’}  big endian
’=’  native order, equivalent to sys.byteorder
{‘’, ‘I’}  ignore (no change to byte order)
The default value (‘S’) results in swapping the current byte order.
 Returns
new_arr – New array object with the dtype reflecting given change to the byte order.
 Return type
array

nonzero
()¶ Return the indices of the elements that are nonzero.
Refer to numpy.nonzero for full documentation.
See also
numpy.nonzero
equivalent function

partition
(kth, axis= 1, kind='introselect', order=None)¶ Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined.
New in version 1.8.0.
 Parameters
kth (int or sequence of ints) – Element index to partition by. The kth element value will be in its final sorted position and all smaller elements will be moved before it and all equal or greater elements behind it. The order of all elements in the partitions is undefined. If provided with a sequence of kth it will partition all elements indexed by kth of them into their sorted position at once.
axis (int, optional) – Axis along which to sort. Default is 1, which means sort along the last axis.
kind ({'introselect'}, optional) – Selection algorithm. Default is ‘introselect’.
order (str or list of str, optional) – When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need to be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.
See also
numpy.partition
Return a parititioned copy of an array.
argpartition
Indirect partition.
sort
Full sort.
Notes
See
np.partition
for notes on the different algorithms.Examples
>>> a = np.array([3, 4, 2, 1]) >>> a.partition(3) >>> a array([2, 1, 3, 4])
>>> a.partition((1, 3)) >>> a array([1, 2, 3, 4])

prod
(axis=None, dtype=None, out=None, keepdims=False, initial=1, where=True)¶ Return the product of the array elements over the given axis
Refer to numpy.prod for full documentation.
See also
numpy.prod
equivalent function

ptp
(axis=None, out=None, keepdims=False)¶ Peak to peak (maximum  minimum) value along a given axis.
Refer to numpy.ptp for full documentation.
See also
numpy.ptp
equivalent function

put
(indices, values, mode='raise')¶ Set
a.flat[n] = values[n]
for all n in indices.Refer to numpy.put for full documentation.
See also
numpy.put
equivalent function

ravel
([order])¶ Return a flattened array.
Refer to numpy.ravel for full documentation.
See also
numpy.ravel
equivalent function
ndarray.flat
a flat iterator on the array.

real
¶ The real part of the array.
Examples
>>> x = np.sqrt([1+0j, 0+1j]) >>> x.real array([ 1. , 0.70710678]) >>> x.real.dtype dtype('float64')
See also
numpy.real
equivalent function

repeat
(repeats, axis=None)¶ Repeat elements of an array.
Refer to numpy.repeat for full documentation.
See also
numpy.repeat
equivalent function

reshape
(shape, order='C')¶ Returns an array containing the same data with a new shape.
Refer to numpy.reshape for full documentation.
See also
numpy.reshape
equivalent function
Notes
Unlike the free function numpy.reshape, this method on ndarray allows the elements of the shape parameter to be passed in as separate arguments. For example,
a.reshape(10, 11)
is equivalent toa.reshape((10, 11))
.

resize
(new_shape, refcheck=True)¶ Change shape and size of array inplace.
 Parameters
new_shape (tuple of ints, or n ints) – Shape of resized array.
refcheck (bool, optional) – If False, reference count will not be checked. Default is True.
 Returns
 Return type
None
 Raises
ValueError – If a does not own its own data or references or views to it exist, and the data memory must be changed. PyPy only: will always raise if the data memory must be changed, since there is no reliable way to determine if references or views to it exist.
SystemError – If the order keyword argument is specified. This behaviour is a bug in NumPy.
See also
resize
Return a new array with the specified shape.
Notes
This reallocates space for the data area if necessary.
Only contiguous arrays (data elements consecutive in memory) can be resized.
The purpose of the reference count check is to make sure you do not use this array as a buffer for another Python object and then reallocate the memory. However, reference counts can increase in other ways so if you are sure that you have not shared the memory for this array with another Python object, then you may safely set refcheck to False.
Examples
Shrinking an array: array is flattened (in the order that the data are stored in memory), resized, and reshaped:
>>> a = np.array([[0, 1], [2, 3]], order='C') >>> a.resize((2, 1)) >>> a array([[0], [1]])
>>> a = np.array([[0, 1], [2, 3]], order='F') >>> a.resize((2, 1)) >>> a array([[0], [2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]]) >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple >>> b array([[0, 1, 2], [3, 0, 0]])
Referencing an array prevents resizing…
>>> c = a >>> a.resize((1, 1)) Traceback (most recent call last): ... ValueError: cannot resize an array that references or is referenced ...
Unless refcheck is False:
>>> a.resize((1, 1), refcheck=False) >>> a array([[0]]) >>> c array([[0]])

round
(decimals=0, out=None)¶ Return a with each element rounded to the given number of decimals.
Refer to numpy.around for full documentation.
See also
numpy.around
equivalent function

searchsorted
(v, side='left', sorter=None)¶ Find indices where elements of v should be inserted in a to maintain order.
For full documentation, see numpy.searchsorted
See also
numpy.searchsorted
equivalent function

setfield
(val, dtype, offset=0)¶ Put a value into a specified place in a field defined by a datatype.
Place val into a’s field defined by dtype and beginning offset bytes into the field.
 Parameters
val (object) – Value to be placed in field.
dtype (dtype object) – Datatype of the field in which to place val.
offset (int, optional) – The number of bytes into the field at which to place val.
 Returns
 Return type
None
See also
Examples
>>> x = np.eye(3) >>> x.getfield(np.float64) array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) >>> x.setfield(3, np.int32) >>> x.getfield(np.int32) array([[3, 3, 3], [3, 3, 3], [3, 3, 3]], dtype=int32) >>> x array([[1.0e+000, 1.5e323, 1.5e323], [1.5e323, 1.0e+000, 1.5e323], [1.5e323, 1.5e323, 1.0e+000]]) >>> x.setfield(np.eye(3), np.int32) >>> x array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])

setflags
(write=None, align=None, uic=None)¶ Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively.
These Booleanvalued flags affect how numpy interprets the memory area used by a (see Notes below). The ALIGNED flag can only be set to True if the data is actually aligned according to the type. The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set to True. The flag WRITEABLE can only be set to True if the array owns its own memory, or the ultimate owner of the memory exposes a writeable buffer interface, or is a string. (The exception for string is made so that unpickling can be done without copying memory.)
 Parameters
write (bool, optional) – Describes whether or not a can be written to.
align (bool, optional) – Describes whether or not a is aligned properly for its type.
uic (bool, optional) – Describes whether or not a is a copy of another “base” array.
Notes
Array flags provide information about how the memory area used for the array is to be interpreted. There are 7 Boolean flags in use, only four of which can be changed by the user: WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.
WRITEABLE (W) the data area can be written to;
ALIGNED (A) the data and strides are aligned appropriately for the hardware (as determined by the compiler);
UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;
WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced by .base). When the CAPI function PyArray_ResolveWritebackIfCopy is called, the base array will be updated with the contents of this array.
All flags can be accessed using the single (upper case) letter as well as the full name.
Examples
>>> y = np.array([[3, 1, 7], ... [2, 0, 0], ... [8, 5, 9]]) >>> y array([[3, 1, 7], [2, 0, 0], [8, 5, 9]]) >>> y.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False >>> y.setflags(write=0, align=0) >>> y.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : True WRITEABLE : False ALIGNED : False WRITEBACKIFCOPY : False UPDATEIFCOPY : False >>> y.setflags(uic=1) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: cannot set WRITEBACKIFCOPY flag to True

shape
¶ Tuple of array dimensions.
The shape property is usually used to get the current shape of an array, but may also be used to reshape the array inplace by assigning a tuple of array dimensions to it. As with numpy.reshape, one of the new shape dimensions can be 1, in which case its value is inferred from the size of the array and the remaining dimensions. Reshaping an array inplace will fail if a copy is required.
Examples
>>> x = np.array([1, 2, 3, 4]) >>> x.shape (4,) >>> y = np.zeros((2, 3, 4)) >>> y.shape (2, 3, 4) >>> y.shape = (3, 8) >>> y array([[ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0., 0., 0., 0.]]) >>> y.shape = (3, 6) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: total size of new array must be unchanged >>> np.zeros((4,2))[::2].shape = (1,) Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: Incompatible shape for inplace modification. Use `.reshape()` to make a copy with the desired shape.
See also
numpy.reshape
similar function
ndarray.reshape
similar method

size
¶ Number of elements in the array.
Equal to
np.prod(a.shape)
, i.e., the product of the array’s dimensions.Notes
a.size returns a standard arbitrary precision Python integer. This may not be the case with other methods of obtaining the same value (like the suggested
np.prod(a.shape)
, which returns an instance ofnp.int_
), and may be relevant if the value is used further in calculations that may overflow a fixed size integer type.Examples
>>> x = np.zeros((3, 5, 2), dtype=np.complex128) >>> x.size 30 >>> np.prod(x.shape) 30

sort
(axis= 1, kind=None, order=None)¶ Sort an array inplace. Refer to numpy.sort for full documentation.
 Parameters
axis (int, optional) – Axis along which to sort. Default is 1, which means sort along the last axis.
kind ({'quicksort', 'mergesort', 'heapsort', 'stable'}, optional) –
Sorting algorithm. The default is ‘quicksort’. Note that both ‘stable’ and ‘mergesort’ use timsort under the covers and, in general, the actual implementation will vary with datatype. The ‘mergesort’ option is retained for backwards compatibility.
Changed in version 1.15.0.: The ‘stable’ option was added.
order (str or list of str, optional) – When a is an array with fields defined, this argument specifies which fields to compare first, second, etc. A single field can be specified as a string, and not all fields need be specified, but unspecified fields will still be used, in the order in which they come up in the dtype, to break ties.
See also
numpy.sort
Return a sorted copy of an array.
numpy.argsort
Indirect sort.
numpy.lexsort
Indirect stable sort on multiple keys.
numpy.searchsorted
Find elements in sorted array.
numpy.partition
Partial sort.
Notes
See numpy.sort for notes on the different sorting algorithms.
Examples
>>> a = np.array([[1,4], [3,1]]) >>> a.sort(axis=1) >>> a array([[1, 4], [1, 3]]) >>> a.sort(axis=0) >>> a array([[1, 3], [1, 4]])
Use the order keyword to specify a field to use when sorting a structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)]) >>> a.sort(order='y') >>> a array([(b'c', 1), (b'a', 2)], dtype=[('x', 'S1'), ('y', '<i8')])

squeeze
(axis=None)¶ Remove axes of length one from a.
Refer to numpy.squeeze for full documentation.
See also
numpy.squeeze
equivalent function

std
(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)¶ Returns the standard deviation of the array elements along given axis.
Refer to numpy.std for full documentation.
See also
numpy.std
equivalent function

strides
¶ Tuple of bytes to step in each dimension when traversing an array.
The byte offset of element
(i[0], i[1], ..., i[n])
in an array a is:offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the “ndarray.rst” file in the NumPy reference guide.
Notes
Imagine an array of 32bit integers (each 4 bytes):
x = np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory). The strides of an array tell us how many bytes we have to skip in memory to move to the next position along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20 bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be
(20, 4)
.See also
numpy.lib.stride_tricks.as_strided
Examples
>>> y = np.reshape(np.arange(2*3*4), (2,3,4)) >>> y array([[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]) >>> y.strides (48, 16, 4) >>> y[1,1,1] 17 >>> offset=sum(y.strides * np.array((1,1,1))) >>> offset/y.itemsize 17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0) >>> x.strides (32, 4, 224, 1344) >>> i = np.array([3,5,2,2]) >>> offset = sum(i * x.strides) >>> x[3,5,2,2] 813 >>> offset / x.itemsize 813

sum
(axis=None, dtype=None, out=None, keepdims=False, initial=0, where=True)¶ Return the sum of the array elements over the given axis.
Refer to numpy.sum for full documentation.
See also
numpy.sum
equivalent function

swapaxes
(axis1, axis2)¶ Return a view of the array with axis1 and axis2 interchanged.
Refer to numpy.swapaxes for full documentation.
See also
numpy.swapaxes
equivalent function

take
(indices, axis=None, out=None, mode='raise')¶ Return an array formed from the elements of a at the given indices.
Refer to numpy.take for full documentation.
See also
numpy.take
equivalent function

tobytes
(order='C')¶ Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of data memory. The bytes object is produced in Corder by default. This behavior is controlled by the
order
parameter.New in version 1.9.0.
 Parameters
order ({'C', 'F', 'A'}, optional) – Controls the memory layout of the bytes object. ‘C’ means Corder, ‘F’ means Forder, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.
 Returns
s – Python bytes exhibiting a copy of a’s raw data.
 Return type
bytes
Examples
>>> x = np.array([[0, 1], [2, 3]], dtype='<u2') >>> x.tobytes() b'\x00\x00\x01\x00\x02\x00\x03\x00' >>> x.tobytes('C') == x.tobytes() True >>> x.tobytes('F') b'\x00\x00\x02\x00\x01\x00\x03\x00'

tofile
(fid, sep='', format='%s')¶ Write array to a file as text or binary (default).
Data is always written in ‘C’ order, independent of the order of a. The data produced by this method can be recovered using the function fromfile().
 Parameters
fid (file or str or Path) –
An open file object, or a string containing a filename.
Changed in version 1.17.0: pathlib.Path objects are now accepted.
sep (str) – Separator between array items for text output. If “” (empty), a binary file is written, equivalent to
file.write(a.tobytes())
.format (str) – Format string for text file output. Each entry in the array is formatted to text by first converting it to the closest Python type, and then using “format” % item.
Notes
This is a convenience function for quick storage of array data. Information on endianness and precision is lost, so this method is not a good choice for files intended to archive data or transport data between machines with different endianness. Some of these problems can be overcome by outputting the data as text files, at the expense of speed and file size.
When fid is a file object, array contents are directly written to the file, bypassing the file object’s
write
method. As a result, tofile cannot be used with files objects supporting compression (e.g., GzipFile) or filelike objects that do not supportfileno()
(e.g., BytesIO).

tolist
()¶ Return the array as an
a.ndim
levels deep nested list of Python scalars.Return a copy of the array data as a (nested) Python list. Data items are converted to the nearest compatible builtin Python type, via the ~numpy.ndarray.item function.
If
a.ndim
is 0, then since the depth of the nested list is 0, it will not be a list at all, but a simple Python scalar. Parameters
none –
 Returns
y – The possibly nested list of array elements.
 Return type
object, or list of object, or list of list of object, or ..
Notes
The array may be recreated via
a = np.array(a.tolist())
, although this may sometimes lose precision.Examples
For a 1D array,
a.tolist()
is almost the same aslist(a)
, except thattolist
changes numpy scalars to Python scalars:>>> a = np.uint32([1, 2]) >>> a_list = list(a) >>> a_list [1, 2] >>> type(a_list[0]) <class 'numpy.uint32'> >>> a_tolist = a.tolist() >>> a_tolist [1, 2] >>> type(a_tolist[0]) <class 'int'>
Additionally, for a 2D array,
tolist
applies recursively:>>> a = np.array([[1, 2], [3, 4]]) >>> list(a) [array([1, 2]), array([3, 4])] >>> a.tolist() [[1, 2], [3, 4]]
The base case for this recursion is a 0D array:
>>> a = np.array(1) >>> list(a) Traceback (most recent call last): ... TypeError: iteration over a 0d array >>> a.tolist() 1

tostring
(order='C')¶ A compatibility alias for tobytes, with exactly the same behavior.
Despite its name, it returns bytes not strs.
Deprecated since version 1.19.0.

trace
(offset=0, axis1=0, axis2=1, dtype=None, out=None)¶ Return the sum along diagonals of the array.
Refer to numpy.trace for full documentation.
See also
numpy.trace
equivalent function

transpose
(*axes)¶ Returns a view of the array with axes transposed.
For a 1D array this has no effect, as a transposed vector is simply the same vector. To convert a 1D array into a 2D column vector, an additional dimension must be added. np.atleast2d(a).T achieves this, as does a[:, np.newaxis]. For a 2D array, this is a standard matrix transpose. For an nD array, if axes are given, their order indicates how the axes are permuted (see Examples). If axes are not provided and
a.shape = (i[0], i[1], ... i[n2], i[n1])
, thena.transpose().shape = (i[n1], i[n2], ... i[1], i[0])
. Parameters
axes (None, tuple of ints, or n ints) –
None or no argument: reverses the order of the axes.
tuple of ints: i in the jth place in the tuple means a’s ith axis becomes a.transpose()’s jth axis.
n ints: same as an ntuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)
 Returns
out – View of a, with axes suitably permuted.
 Return type
ndarray
See also
ndarray.T
Array property returning the array transposed.
ndarray.reshape
Give a new shape to an array without changing its data.
Examples
>>> a = np.array([[1, 2], [3, 4]]) >>> a array([[1, 2], [3, 4]]) >>> a.transpose() array([[1, 3], [2, 4]]) >>> a.transpose((1, 0)) array([[1, 3], [2, 4]]) >>> a.transpose(1, 0) array([[1, 3], [2, 4]])

var
(axis=None, dtype=None, out=None, ddof=0, keepdims=False, *, where=True)¶ Returns the variance of the array elements, along given axis.
Refer to numpy.var for full documentation.
See also
numpy.var
equivalent function

view
([dtype][, type])¶ New view of array with the same data.
Note
Passing None for
dtype
is different from omitting the parameter, since the former invokesdtype(None)
which is an alias fordtype('float_')
. Parameters
dtype (datatype or ndarray subclass, optional) – Datatype descriptor of the returned view, e.g., float32 or int16. Omitting it results in the view having the same datatype as a. This argument can also be specified as an ndarray subclass, which then specifies the type of the returned object (this is equivalent to setting the
type
parameter).type (Python type, optional) – Type of the returned view, e.g., ndarray or matrix. Again, omission of the parameter results in type preservation.
Notes
a.view()
is used two different ways:a.view(some_dtype)
ora.view(dtype=some_dtype)
constructs a view of the array’s memory with a different datatype. This can cause a reinterpretation of the bytes of memory.a.view(ndarray_subclass)
ora.view(type=ndarray_subclass)
just returns an instance of ndarray_subclass that looks at the same array (same shape, dtype, etc.) This does not cause a reinterpretation of the memory.For
a.view(some_dtype)
, ifsome_dtype
has a different number of bytes per entry than the previous dtype (for example, converting a regular array to a structured array), then the behavior of the view cannot be predicted just from the superficial appearance ofa
(shown byprint(a)
). It also depends on exactly howa
is stored in memory. Therefore ifa
is Cordered versus fortranordered, versus defined as a slice or transpose, etc., the view may give different results.Examples
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> y = x.view(dtype=np.int16, type=np.matrix) >>> y matrix([[513]], dtype=int16) >>> print(type(y)) <class 'numpy.matrix'>
Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)]) >>> xv = x.view(dtype=np.int8).reshape(1,2) >>> xv array([[1, 2], [3, 4]], dtype=int8) >>> xv.mean(0) array([2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20 >>> x array([(1, 20), (3, 4)], dtype=[('a', 'i1'), ('b', 'i1')])
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray) >>> z.a array([1, 3], dtype=int8)
Views share data:
>>> x[0] = (9, 10) >>> z[0] (9, 10)
Views that change the dtype size (bytes per entry) should normally be avoided on arrays defined by slices, transposes, fortranordering, etc.:
>>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16) >>> y = x[:, 0:2] >>> y array([[1, 2], [4, 5]], dtype=int16) >>> y.view(dtype=[('width', np.int16), ('length', np.int16)]) Traceback (most recent call last): ... ValueError: To change to a dtype of a different size, the array must be Ccontiguous >>> z = y.copy() >>> z.view(dtype=[('width', np.int16), ('length', np.int16)]) array([[(1, 2)], [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])
