numpy.ma.zeros_like   ma.zeros_like(*args, **kwargs) = <numpy.ma.core._convert2ma object>

Return an array of zeros with the same shape and type as a given array.  Parameters

aarray_like

The shape and data-type of a define these same attributes of the returned array.
dtypedata-type, optional

Overrides the data type of the result.  New in version 1.6.0.
order{‘C’, ‘F’, ‘A’, or ‘K’}, optional

Overrides the memory layout of the result. ‘C’ means C-order, ‘F’ means F-order, ‘A’ means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. ‘K’ means match the layout of a as closely as possible.  New in version 1.6.0.
subokbool, optional.

If True, then the newly created array will use the sub-class type of a, otherwise it will be a base-class array. Defaults to True.
shapeint or sequence of ints, optional.

Overrides the shape of the result. If order=’K’ and the number of dimensions is unchanged, will try to keep order, otherwise, order=’C’ is implied.  New in version 1.17.0.     Returns

outMaskedArray

Array of zeros with the same shape and type as a.      See also  empty_like

Return an empty array with shape and type of input.  ones_like

Return an array of ones with shape and type of input.  full_like

Return a new array with shape of input filled with value.  zeros

Return a new array setting values to zero.    Examples >>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.zeros_like(x)
array([[0, 0, 0],
       [0, 0, 0]])
 >>> y = np.arange(3, dtype=float)
>>> y
array([0., 1., 2.])
>>> np.zeros_like(y)
array([0.,  0.,  0.])


make_config_py(name='__config__')[source]

Generate package __config__.py file containing system_info information used during building the package. This file is installed to the package installation directory.


make_svn_version_py(delete=True)[source]

Appends a data function to the data_files list that will generate __svn_version__.py file to the current package directory. Generate package __svn_version__.py file from SVN revision number, it will be removed after python exits but will be available when sdist, etc commands are executed. Notes If __svn_version__.py existed before, nothing is done. This is intended for working with source directories that are in an SVN repository.


Constants of the numpy.ma module In addition to the MaskedArray class, the numpy.ma module defines several constants.   numpy.ma.masked

The masked constant is a special case of MaskedArray, with a float datatype and a null shape. It is used to test whether a specific entry of a masked array is masked, or to mask one or several entries of a masked array: >>> x = ma.array([1, 2, 3], mask=[0, 1, 0])
>>> x[1] is ma.masked
True
>>> x[-1] = ma.masked
>>> x
masked_array(data=[1, --, --],
             mask=[False,  True,  True],
       fill_value=999999)

   numpy.ma.nomask

Value indicating that a masked array has no invalid entry. nomask is used internally to speed up computations when the mask is not needed. It is represented internally as np.False_.
   numpy.ma.masked_print_options

String used in lieu of missing data when a masked array is printed. By default, this string is '--'.


numpy.matlib.empty   matlib.empty(shape, dtype=None, order='C')[source]

Return a new matrix of given shape and type, without initializing entries.  Parameters

shapeint or tuple of int

Shape of the empty matrix.
dtypedata-type, optional

Desired output data-type.
order{‘C’, ‘F’}, optional

Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory.      See also
empty_like, zeros

  Notes empty, unlike zeros, does not set the matrix values to zero, and may therefore be marginally faster. On the other hand, it requires the user to manually set all the values in the array, and should be used with caution. Examples >>> import numpy.matlib
>>> np.matlib.empty((2, 2))    # filled with random data
matrix([[  6.76425276e-320,   9.79033856e-307], # random
        [  7.39337286e-309,   3.22135945e-309]])
>>> np.matlib.empty((2, 2), dtype=int)
matrix([[ 6600475,        0], # random
        [ 6586976, 22740995]])


numpy.matlib.eye   matlib.eye(n, M=None, k=0, dtype=<class 'float'>, order='C')[source]

Return a matrix with ones on the diagonal and zeros elsewhere.  Parameters

nint

Number of rows in the output.
Mint, optional

Number of columns in the output, defaults to n.
kint, optional

Index of the diagonal: 0 refers to the main diagonal, a positive value refers to an upper diagonal, and a negative value to a lower diagonal.
dtypedtype, optional

Data-type of the returned matrix.
order{‘C’, ‘F’}, optional

Whether the output should be stored in row-major (C-style) or column-major (Fortran-style) order in memory.  New in version 1.14.0.     Returns

Imatrix

A n x M matrix where all elements are equal to zero, except for the k-th diagonal, whose values are equal to one.      See also  numpy.eye

Equivalent array function.  identity

Square identity matrix.    Examples >>> import numpy.matlib
>>> np.matlib.eye(3, k=1, dtype=float)
matrix([[0.,  1.,  0.],
        [0.,  0.,  1.],
        [0.,  0.,  0.]])


numpy.matlib.identity   matlib.identity(n, dtype=None)[source]

Returns the square identity matrix of given size.  Parameters

nint

Size of the returned identity matrix.
dtypedata-type, optional

Data-type of the output. Defaults to float.    Returns

outmatrix

n x n matrix with its main diagonal set to one, and all other elements zero.      See also  numpy.identity

Equivalent array function.  matlib.eye

More general matrix identity function.    Examples >>> import numpy.matlib
>>> np.matlib.identity(3, dtype=int)
matrix([[1, 0, 0],
        [0, 1, 0],
        [0, 0, 1]])


numpy.matlib.ones   matlib.ones(shape, dtype=None, order='C')[source]

Matrix of ones. Return a matrix of given shape and type, filled with ones.  Parameters

shape{sequence of ints, int}

Shape of the matrix
dtypedata-type, optional

The desired data-type for the matrix, default is np.float64.
order{‘C’, ‘F’}, optional

Whether to store matrix in C- or Fortran-contiguous order, default is ‘C’.    Returns

outmatrix

Matrix of ones of given shape, dtype, and order.      See also  ones

Array of ones.  matlib.zeros

Zero matrix.    Notes If shape has length one i.e. (N,), or is a scalar N, out becomes a single row matrix of shape (1,N). Examples >>> np.matlib.ones((2,3))
matrix([[1.,  1.,  1.],
        [1.,  1.,  1.]])
 >>> np.matlib.ones(2)
matrix([[1.,  1.]])


numpy.matlib.rand   matlib.rand(*args)[source]

Return a matrix of random values with given shape. Create a matrix of the given shape and propagate it with random samples from a uniform distribution over [0, 1).  Parameters

*argsArguments

Shape of the output. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape.    Returns

outndarray

The matrix of random values with shape given by *args.      See also
randn, numpy.random.RandomState.rand

  Examples >>> np.random.seed(123)
>>> import numpy.matlib
>>> np.matlib.rand(2, 3)
matrix([[0.69646919, 0.28613933, 0.22685145],
        [0.55131477, 0.71946897, 0.42310646]])
>>> np.matlib.rand((2, 3))
matrix([[0.9807642 , 0.68482974, 0.4809319 ],
        [0.39211752, 0.34317802, 0.72904971]])
 If the first argument is a tuple, other arguments are ignored: >>> np.matlib.rand((2, 3), 4)
matrix([[0.43857224, 0.0596779 , 0.39804426],
        [0.73799541, 0.18249173, 0.17545176]])


numpy.matlib.randn   matlib.randn(*args)[source]

Return a random matrix with data from the “standard normal” distribution. randn generates a matrix filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1.  Parameters

*argsArguments

Shape of the output. If given as N integers, each integer specifies the size of one dimension. If given as a tuple, this tuple gives the complete shape.    Returns

Zmatrix of floats

A matrix of floating-point samples drawn from the standard normal distribution.      See also
rand, numpy.random.RandomState.randn

  Notes For random samples from \(N(\mu, \sigma^2)\), use: sigma * np.matlib.randn(...) + mu Examples >>> np.random.seed(123)
>>> import numpy.matlib
>>> np.matlib.randn(1)
matrix([[-1.0856306]])
>>> np.matlib.randn(1, 2, 3)
matrix([[ 0.99734545,  0.2829785 , -1.50629471],
        [-0.57860025,  1.65143654, -2.42667924]])
 Two-by-four matrix of samples from \(N(3, 6.25)\): >>> 2.5 * np.matlib.randn((2, 4)) + 3
matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462],
        [2.76322758, 6.72847407, 1.40274501, 1.8900451 ]])


numpy.matlib.repmat   matlib.repmat(a, m, n)[source]

Repeat a 0-D to 2-D array or matrix MxN times.  Parameters

aarray_like

The array or matrix to be repeated.
m, nint

The number of times a is repeated along the first and second axes.    Returns

outndarray

The result of repeating a.     Examples >>> import numpy.matlib
>>> a0 = np.array(1)
>>> np.matlib.repmat(a0, 2, 3)
array([[1, 1, 1],
       [1, 1, 1]])
 >>> a1 = np.arange(4)
>>> np.matlib.repmat(a1, 2, 2)
array([[0, 1, 2, 3, 0, 1, 2, 3],
       [0, 1, 2, 3, 0, 1, 2, 3]])
 >>> a2 = np.asmatrix(np.arange(6).reshape(2, 3))
>>> np.matlib.repmat(a2, 2, 3)
matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2],
        [3, 4, 5, 3, 4, 5, 3, 4, 5],
        [0, 1, 2, 0, 1, 2, 0, 1, 2],
        [3, 4, 5, 3, 4, 5, 3, 4, 5]])


numpy.matlib.zeros   matlib.zeros(shape, dtype=None, order='C')[source]

Return a matrix of given shape and type, filled with zeros.  Parameters

shapeint or sequence of ints

Shape of the matrix
dtypedata-type, optional

The desired data-type for the matrix, default is float.
order{‘C’, ‘F’}, optional

Whether to store the result in C- or Fortran-contiguous order, default is ‘C’.    Returns

outmatrix

Zero matrix of given shape, dtype, and order.      See also  numpy.zeros

Equivalent array function.  matlib.ones

Return a matrix of ones.    Notes If shape has length one i.e. (N,), or is a scalar N, out becomes a single row matrix of shape (1,N). Examples >>> import numpy.matlib
>>> np.matlib.zeros((2, 3))
matrix([[0.,  0.,  0.],
        [0.,  0.,  0.]])
 >>> np.matlib.zeros(2)
matrix([[0.,  0.]])


numpy.matrix.all method   matrix.all(axis=None, out=None)[source]

Test whether all matrix elements along a given axis evaluate to True.  Parameters
 See `numpy.all` for complete descriptions
    See also  numpy.all
  Notes This is the same as ndarray.all, but it returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> y = x[0]; y
matrix([[0, 1, 2, 3]])
>>> (x == y)
matrix([[ True,  True,  True,  True],
        [False, False, False, False],
        [False, False, False, False]])
>>> (x == y).all()
False
>>> (x == y).all(0)
matrix([[False, False, False, False]])
>>> (x == y).all(1)
matrix([[ True],
        [False],
        [False]])


numpy.matrix.any method   matrix.any(axis=None, out=None)[source]

Test whether any array element along a given axis evaluates to True. Refer to numpy.any for full documentation.  Parameters

axisint, optional

Axis along which logical OR is performed
outndarray, optional

Output to existing array instead of creating new one, must have same shape as expected output    Returns

anybool, ndarray

Returns a single bool if axis is None; otherwise, returns ndarray


numpy.matrix.argmax method   matrix.argmax(axis=None, out=None)[source]

Indexes of the maximum values along an axis. Return the indexes of the first occurrences of the maximum values along the specified axis. If axis is None, the index is for the flattened matrix.  Parameters
 See `numpy.argmax` for complete descriptions
    See also  numpy.argmax
  Notes This is the same as ndarray.argmax, but returns a matrix object where ndarray.argmax would return an ndarray. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.argmax()
11
>>> x.argmax(0)
matrix([[2, 2, 2, 2]])
>>> x.argmax(1)
matrix([[3],
        [3],
        [3]])


numpy.matrix.argmin method   matrix.argmin(axis=None, out=None)[source]

Indexes of the minimum values along an axis. Return the indexes of the first occurrences of the minimum values along the specified axis. If axis is None, the index is for the flattened matrix.  Parameters
 See `numpy.argmin` for complete descriptions.
    See also  numpy.argmin
  Notes This is the same as ndarray.argmin, but returns a matrix object where ndarray.argmin would return an ndarray. Examples >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
matrix([[  0,  -1,  -2,  -3],
        [ -4,  -5,  -6,  -7],
        [ -8,  -9, -10, -11]])
>>> x.argmin()
11
>>> x.argmin(0)
matrix([[2, 2, 2, 2]])
>>> x.argmin(1)
matrix([[3],
        [3],
        [3]])


numpy.matrix.argpartition method   matrix.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


numpy.matrix.argsort method   matrix.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


numpy.matrix.astype method   matrix.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

Copy of the array, cast to a specified type.  Parameters

dtypestr or dtype

Typecode or data-type 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 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.
subokbool, optional

If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.
copybool, 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_tndarray

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.    Raises
 ComplexWarning

When casting from complex to float or int. To avoid this, one should use a.real.astype(t).     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.  Examples >>> x = np.array([1, 2, 2.5])
>>> x
array([1. ,  2. ,  2.5])
 >>> x.astype(int)
array([1, 2, 2])


numpy.matrix.base attribute   matrix.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


numpy.matrix.byteswap method   matrix.byteswap(inplace=False)

Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.  Parameters

inplacebool, optional

If True, swap bytes in-place, default is False.    Returns

outndarray

The byteswapped array. If inplace is True, this is a view to self.     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 byte-strings 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 values

but 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)


numpy.matrix.choose method   matrix.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


numpy.matrix.clip method   matrix.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


numpy.matrix.compress method   matrix.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


numpy.matrix.conj method   matrix.conj()

Complex-conjugate all elements. Refer to numpy.conjugate for full documentation.  See also  numpy.conjugate

equivalent function


numpy.matrix.conjugate method   matrix.conjugate()

Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation.  See also  numpy.conjugate

equivalent function


numpy.matrix.copy method   matrix.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 C-order, ‘F’ means F-order, ‘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, and this function always passes sub-classes through.)      See also  numpy.copy

Similar function with different default behavior  numpy.copyto
  Notes This function is the preferred method for creating an array copy. The function numpy.copy is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default. 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


numpy.matrix.ctypes attribute   matrix.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

cPython object

Possessing attributes data, shape, strides, etc.      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 byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code 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 like ctypes.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

(c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform (see c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.
   _ctypes.strides

(c_intp*self.ndim): 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.
   _ctypes.data_as(obj)[source]

Return the data pointer cast to a particular c-types object. For example, calling self._as_parameter_ is equivalent to self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a pointer to a ctypes array of floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)). The returned pointer will keep a reference to the array.
   _ctypes.shape_as(obj)[source]

Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).
   _ctypes.strides_as(obj)[source]

Return the strides tuple as an array of some other c-types 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


numpy.matrix.cumprod method   matrix.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


numpy.matrix.cumsum method   matrix.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


numpy.matrix.data attribute   matrix.data

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


numpy.matrix.diagonal method   matrix.diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed. Refer to numpy.diagonal for full documentation.  See also  numpy.diagonal

equivalent function


numpy.matrix.dump method   matrix.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

filestr or Path

A string naming the dump file.  Changed in version 1.17.0: pathlib.Path objects are now accepted.


numpy.matrix.dumps method   matrix.dumps()

Returns the pickle of the array as a string. pickle.loads will convert the string back to an array.  Parameters
 None


numpy.matrix.fill method   matrix.fill(value)

Fill the array with a scalar value.  Parameters

valuescalar

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.])


numpy.matrix.flags attribute   matrix.flags

Information about the memory layout of the array. Notes The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.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 C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional 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 if arr.shape[dim] == 1 or the array has no elements. It does not generally hold that self.strides[-1] == self.itemsize for C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true.  Attributes
 C_CONTIGUOUS (C)

The data is in a single, C-style contiguous segment.  F_CONTIGUOUS (F)

The data is in a single, Fortran-style 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 read-only. 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 non-writeable 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 C-API 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 (one-segment test).  BEHAVED (B)

ALIGNED and WRITEABLE.  CARRAY (CA)

BEHAVED and C_CONTIGUOUS.  FARRAY (FA)

BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.


numpy.matrix.flat attribute   matrix.flat

A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.  See also  flatten

Return a copy of the array collapsed into one dimension.  flatiter
  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]])


numpy.matrix.flatten method   matrix.flatten(order='C')[source]

Return a flattened copy of the matrix. All N elements of the matrix are placed into a single row.  Parameters

order{‘C’, ‘F’, ‘A’, ‘K’}, optional

‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran-style) order. ‘A’ means to flatten in column-major order if m is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten m in the order the elements occur in memory. The default is ‘C’.    Returns

ymatrix

A copy of the matrix, flattened to a (1, N) matrix where N is the number of elements in the original matrix.      See also  ravel

Return a flattened array.  flat

A 1-D flat iterator over the matrix.    Examples >>> m = np.matrix([[1,2], [3,4]])
>>> m.flatten()
matrix([[1, 2, 3, 4]])
>>> m.flatten('F')
matrix([[1, 3, 2, 4]])


numpy.matrix.getA method   matrix.getA()[source]

Return self as an ndarray object. Equivalent to np.asarray(self).  Parameters
 None
  Returns

retndarray

self as an ndarray     Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.getA()
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])


numpy.matrix.getA1 method   matrix.getA1()[source]

Return self as a flattened ndarray. Equivalent to np.asarray(x).ravel()  Parameters
 None
  Returns

retndarray

self, 1-D, as an ndarray     Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.getA1()
array([ 0,  1,  2, ...,  9, 10, 11])


numpy.matrix.getfield method   matrix.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 data-type. 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 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.  Parameters

dtypestr or dtype

The data type of the view. The dtype size of the view can not be larger than that of the array itself.
offsetint

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.]])


numpy.matrix.getH method   matrix.getH()[source]

Returns the (complex) conjugate transpose of self. Equivalent to np.transpose(self) if self is real-valued.  Parameters
 None
  Returns

retmatrix object

complex conjugate transpose of self     Examples >>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[  0. +0.j,   1. -1.j,   2. -2.j,   3. -3.j],
        [  4. -4.j,   5. -5.j,   6. -6.j,   7. -7.j],
        [  8. -8.j,   9. -9.j,  10.-10.j,  11.-11.j]])
>>> z.getH()
matrix([[ 0. -0.j,  4. +4.j,  8. +8.j],
        [ 1. +1.j,  5. +5.j,  9. +9.j],
        [ 2. +2.j,  6. +6.j, 10.+10.j],
        [ 3. +3.j,  7. +7.j, 11.+11.j]])


numpy.matrix.getI method   matrix.getI()[source]

Returns the (multiplicative) inverse of invertible self.  Parameters
 None
  Returns

retmatrix object

If self is non-singular, ret is such that ret * self == self * ret == np.matrix(np.eye(self[0,:].size)) all return True.    Raises
 numpy.linalg.LinAlgError: Singular matrix

If self is singular.      See also  linalg.inv
  Examples >>> m = np.matrix('[1, 2; 3, 4]'); m
matrix([[1, 2],
        [3, 4]])
>>> m.getI()
matrix([[-2. ,  1. ],
        [ 1.5, -0.5]])
>>> m.getI() * m
matrix([[ 1.,  0.], # may vary
        [ 0.,  1.]])


numpy.matrix.getT method   matrix.getT()[source]

Returns the transpose of the matrix. Does not conjugate! For the complex conjugate transpose, use .H.  Parameters
 None
  Returns

retmatrix object

The (non-conjugated) transpose of the matrix.      See also
transpose, getH

  Examples >>> m = np.matrix('[1, 2; 3, 4]')
>>> m
matrix([[1, 2],
        [3, 4]])
>>> m.getT()
matrix([[1, 3],
        [2, 4]])


numpy.matrix.item method   matrix.item(*args)

Copy an element of an array to a standard Python scalar and return it.  Parameters

*argsArguments (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 nd-index into the array.     Returns

zStandard Python scalar object

A copy of the specified element of the array as a suitable Python scalar     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


numpy.matrix.itemset method   matrix.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 than a[args] = item. The item should be a scalar value and args must select a single item in the array a.  Parameters

*argsArguments

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 look-up 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]])


numpy.matrix.itemsize attribute   matrix.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


numpy.matrix.max method   matrix.max(axis=None, out=None)[source]

Return the maximum value along an axis.  Parameters
 See `amax` for complete descriptions
    See also
amax, ndarray.max

  Notes This is the same as ndarray.max, but returns a matrix object where ndarray.max would return an ndarray. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.max()
11
>>> x.max(0)
matrix([[ 8,  9, 10, 11]])
>>> x.max(1)
matrix([[ 3],
        [ 7],
        [11]])


numpy.matrix.mean method   matrix.mean(axis=None, dtype=None, out=None)[source]

Returns the average of the matrix elements along the given axis. Refer to numpy.mean for full documentation.  See also  numpy.mean
  Notes Same as ndarray.mean except that, where that returns an ndarray, this returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.mean()
5.5
>>> x.mean(0)
matrix([[4., 5., 6., 7.]])
>>> x.mean(1)
matrix([[ 1.5],
        [ 5.5],
        [ 9.5]])


numpy.matrix.min method   matrix.min(axis=None, out=None)[source]

Return the minimum value along an axis.  Parameters
 See `amin` for complete descriptions.
    See also
amin, ndarray.min

  Notes This is the same as ndarray.min, but returns a matrix object where ndarray.min would return an ndarray. Examples >>> x = -np.matrix(np.arange(12).reshape((3,4))); x
matrix([[  0,  -1,  -2,  -3],
        [ -4,  -5,  -6,  -7],
        [ -8,  -9, -10, -11]])
>>> x.min()
-11
>>> x.min(0)
matrix([[ -8,  -9, -10, -11]])
>>> x.min(1)
matrix([[ -3],
        [ -7],
        [-11]])


numpy.matrix.nbytes attribute   matrix.nbytes

Total bytes consumed by the elements of the array. Notes Does not include memory consumed by non-element 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


numpy.matrix.ndim attribute   matrix.ndim

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


numpy.matrix.newbyteorder method   matrix.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 sub-arrays of the array data type.  Parameters

new_orderstring, 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’} - 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_arrarray

New array object with the dtype reflecting given change to the byte order.


numpy.matrix.nonzero method   matrix.nonzero()

Return the indices of the elements that are non-zero. Refer to numpy.nonzero for full documentation.  See also  numpy.nonzero

equivalent function


numpy.matrix.partition method   matrix.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

kthint 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.  Deprecated since version 1.22.0: Passing booleans as index is deprecated.
axisint, optional

Axis along which to sort. Default is -1, which means sort along the last axis.
kind{‘introselect’}, optional

Selection algorithm. Default is ‘introselect’.
orderstr 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])


numpy.matrix.prod method   matrix.prod(axis=None, dtype=None, out=None)[source]

Return the product of the array elements over the given axis. Refer to prod for full documentation.  See also
prod, ndarray.prod

  Notes Same as ndarray.prod, except, where that returns an ndarray, this returns a matrix object instead. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[  0,  45, 120, 231]])
>>> x.prod(1)
matrix([[   0],
        [ 840],
        [7920]])


numpy.matrix.ptp method   matrix.ptp(axis=None, out=None)[source]

Peak-to-peak (maximum - minimum) value along the given axis. Refer to numpy.ptp for full documentation.  See also  numpy.ptp
  Notes Same as ndarray.ptp, except, where that would return an ndarray object, this returns a matrix object. Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.ptp()
11
>>> x.ptp(0)
matrix([[8, 8, 8, 8]])
>>> x.ptp(1)
matrix([[3],
        [3],
        [3]])


numpy.matrix.put method   matrix.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


numpy.matrix.ravel method   matrix.ravel(order='C')[source]

Return a flattened matrix. Refer to numpy.ravel for more documentation.  Parameters

order{‘C’, ‘F’, ‘A’, ‘K’}, optional

The elements of m are read using this index order. ‘C’ means to index the elements in C-like order, with the last axis index changing fastest, back to the first axis index changing slowest. ‘F’ means to index the elements in Fortran-like index order, with the first index changing fastest, and the last index changing slowest. Note that the ‘C’ and ‘F’ options take no account of the memory layout of the underlying array, and only refer to the order of axis indexing. ‘A’ means to read the elements in Fortran-like index order if m is Fortran contiguous in memory, C-like order otherwise. ‘K’ means to read the elements in the order they occur in memory, except for reversing the data when strides are negative. By default, ‘C’ index order is used.    Returns

retmatrix

Return the matrix flattened to shape (1, N) where N is the number of elements in the original matrix. A copy is made only if necessary.      See also  matrix.flatten

returns a similar output matrix but always a copy  matrix.flat

a flat iterator on the array.  numpy.ravel

related function which returns an ndarray


numpy.matrix.repeat method   matrix.repeat(repeats, axis=None)

Repeat elements of an array. Refer to numpy.repeat for full documentation.  See also  numpy.repeat

equivalent function


numpy.matrix.reshape method   matrix.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 to a.reshape((10, 11)).


numpy.matrix.resize method   matrix.resize(new_shape, refcheck=True)

Change shape and size of array in-place.  Parameters

new_shapetuple of ints, or n ints

Shape of resized array.
refcheckbool, optional

If False, reference count will not be checked. Default is True.    Returns
 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]])


numpy.matrix.round method   matrix.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


numpy.matrix.searchsorted method   matrix.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


numpy.matrix.setfield method   matrix.setfield(val, dtype, offset=0)

Put a value into a specified place in a field defined by a data-type. Place val into a’s field defined by dtype and beginning offset bytes into the field.  Parameters

valobject

Value to be placed in field.
dtypedtype object

Data-type of the field in which to place val.
offsetint, optional

The number of bytes into the field at which to place val.    Returns
 None
    See also  getfield
  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, 1.5e-323, 1.5e-323],
       [1.5e-323, 1.0e+000, 1.5e-323],
       [1.5e-323, 1.5e-323, 1.0e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[1.,  0.,  0.],
       [0.,  1.,  0.],
       [0.,  0.,  1.]])


numpy.matrix.setflags method   matrix.setflags(write=None, align=None, uic=None)

Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY), respectively. These Boolean-valued 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

writebool, optional

Describes whether or not a can be written to.
alignbool, optional

Describes whether or not a is aligned properly for its type.
uicbool, 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 C-API 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


numpy.matrix.size attribute   matrix.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 of np.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


numpy.matrix.sort method   matrix.sort(axis=- 1, kind=None, order=None)

Sort an array in-place. Refer to numpy.sort for full documentation.  Parameters

axisint, 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.
orderstr 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')])


numpy.matrix.squeeze method   matrix.squeeze(axis=None)[source]

Return a possibly reshaped matrix. Refer to numpy.squeeze for more documentation.  Parameters

axisNone or int or tuple of ints, optional

Selects a subset of the axes of length one in the shape. If an axis is selected with shape entry greater than one, an error is raised.    Returns

squeezedmatrix

The matrix, but as a (1, N) matrix if it had shape (N, 1).      See also  numpy.squeeze

related function    Notes If m has a single column then that column is returned as the single row of a matrix. Otherwise m is returned. The returned matrix is always either m itself or a view into m. Supplying an axis keyword argument will not affect the returned matrix but it may cause an error to be raised. Examples >>> c = np.matrix([[1], [2]])
>>> c
matrix([[1],
        [2]])
>>> c.squeeze()
matrix([[1, 2]])
>>> r = c.T
>>> r
matrix([[1, 2]])
>>> r.squeeze()
matrix([[1, 2]])
>>> m = np.matrix([[1, 2], [3, 4]])
>>> m.squeeze()
matrix([[1, 2],
        [3, 4]])


numpy.matrix.std method   matrix.std(axis=None, dtype=None, out=None, ddof=0)[source]

Return the standard deviation of the array elements along the given axis. Refer to numpy.std for full documentation.  See also  numpy.std
  Notes This is the same as ndarray.std, except that where an ndarray would be returned, a matrix object is returned instead. Examples >>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.std()
3.4520525295346629 # may vary
>>> x.std(0)
matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]]) # may vary
>>> x.std(1)
matrix([[ 1.11803399],
        [ 1.11803399],
        [ 1.11803399]])


numpy.matrix.strides attribute   matrix.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.  See also  numpy.lib.stride_tricks.as_strided
  Notes Imagine an array of 32-bit 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). 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


numpy.matrix.sum method   matrix.sum(axis=None, dtype=None, out=None)[source]

Returns the sum of the matrix elements, along the given axis. Refer to numpy.sum for full documentation.  See also  numpy.sum
  Notes This is the same as ndarray.sum, except that where an ndarray would be returned, a matrix object is returned instead. Examples >>> x = np.matrix([[1, 2], [4, 3]])
>>> x.sum()
10
>>> x.sum(axis=1)
matrix([[3],
        [7]])
>>> x.sum(axis=1, dtype='float')
matrix([[3.],
        [7.]])
>>> out = np.zeros((2, 1), dtype='float')
>>> x.sum(axis=1, dtype='float', out=np.asmatrix(out))
matrix([[3.],
        [7.]])


numpy.matrix.swapaxes method   matrix.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


numpy.matrix.take method   matrix.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


numpy.matrix.tobytes method   matrix.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 C-order 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 C-order, ‘F’ means F-order, ‘A’ (short for Any) means ‘F’ if a is Fortran contiguous, ‘C’ otherwise. Default is ‘C’.    Returns

sbytes

Python bytes exhibiting a copy of a’s raw data.     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'


numpy.matrix.tofile method   matrix.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

fidfile 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.
sepstr

Separator between array items for text output. If “” (empty), a binary file is written, equivalent to file.write(a.tobytes()).
formatstr

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 file-like objects that do not support fileno() (e.g., BytesIO).


numpy.matrix.tolist method   matrix.tolist()[source]

Return the matrix as a (possibly nested) list. See ndarray.tolist for full documentation.  See also  ndarray.tolist
  Examples >>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.tolist()
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]


numpy.matrix.tostring method   matrix.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.


numpy.matrix.trace method   matrix.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


numpy.matrix.transpose method   matrix.transpose(*axes)

Returns a view of the array with axes transposed. For a 1-D array this has no effect, as a transposed vector is simply the same vector. To convert a 1-D 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 2-D array, this is a standard matrix transpose. For an n-D 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[n-2], i[n-1]), then a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0]).  Parameters

axesNone, tuple of ints, or n ints

 None or no argument: reverses the order of the axes. tuple of ints: i in the j-th place in the tuple means a’s i-th axis becomes a.transpose()’s j-th axis.
n ints: same as an n-tuple of the same ints (this form is intended simply as a “convenience” alternative to the tuple form)     Returns

outndarray

View of a, with axes suitably permuted.      See also  transpose

Equivalent function  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]])


numpy.matrix.var method   matrix.var(axis=None, dtype=None, out=None, ddof=0)[source]

Returns the variance of the matrix elements, along the given axis. Refer to numpy.var for full documentation.  See also  numpy.var
  Notes This is the same as ndarray.var, except that where an ndarray would be returned, a matrix object is returned instead. Examples >>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0,  1,  2,  3],
        [ 4,  5,  6,  7],
        [ 8,  9, 10, 11]])
>>> x.var()
11.916666666666666
>>> x.var(0)
matrix([[ 10.66666667,  10.66666667,  10.66666667,  10.66666667]]) # may vary
>>> x.var(1)
matrix([[1.25],
        [1.25],
        [1.25]])


numpy.matrix.view method   matrix.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 invokes dtype(None) which is an alias for dtype('float_').   Parameters

dtypedata-type or ndarray sub-class, optional

Data-type descriptor of the returned view, e.g., float32 or int16. Omitting it results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter).
typePython 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) or a.view(dtype=some_dtype) constructs a view of the array’s memory with a different data-type. This can cause a reinterpretation of the bytes of memory. a.view(ndarray_subclass) or a.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), if some_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 of a (shown by print(a)). It also depends on exactly how a is stored in memory. Therefore if a is C-ordered versus fortran-ordered, 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, fortran-ordering, 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 C-contiguous
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 2)],
       [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])


numpy.memmap.flush method   memmap.flush()[source]

Write any changes in the array to the file on disk. For further information, see memmap.  Parameters
 None
    See also  memmap


Miscellaneous  IEEE 754 Floating Point Special Values Special values defined in numpy: nan, inf, NaNs can be used as a poor-man’s mask (if you don’t care what the original value was) Note: cannot use equality to test NaNs. E.g.: >>> myarr = np.array([1., 0., np.nan, 3.])
>>> np.nonzero(myarr == np.nan)
(array([], dtype=int64),)
>>> np.nan == np.nan  # is always False! Use special numpy functions instead.
False
>>> myarr[myarr == np.nan] = 0. # doesn't work
>>> myarr
array([  1.,   0.,  NaN,   3.])
>>> myarr[np.isnan(myarr)] = 0. # use this instead find
>>> myarr
array([ 1.,  0.,  0.,  3.])
 Other related special value functions: isinf():    True if value is inf
isfinite(): True if not nan or inf
nan_to_num(): Map nan to 0, inf to max float, -inf to min float
 The following corresponds to the usual functions except that nans are excluded from the results: nansum()
nanmax()
nanmin()
nanargmax()
nanargmin()

>>> x = np.arange(10.)
>>> x[3] = np.nan
>>> x.sum()
nan
>>> np.nansum(x)
42.0
   How numpy handles numerical exceptions The default is to 'warn' for invalid, divide, and overflow and 'ignore' for underflow. But this can be changed, and it can be set individually for different kinds of exceptions. The different behaviors are:  ‘ignore’ : Take no action when the exception occurs. ‘warn’ : Print a RuntimeWarning (via the Python warnings module). ‘raise’ : Raise a FloatingPointError. ‘call’ : Call a function specified using the seterrcall function. ‘print’ : Print a warning directly to stdout. ‘log’ : Record error in a Log object specified by seterrcall.  These behaviors can be set for all kinds of errors or specific ones:  all : apply to all numeric exceptions invalid : when NaNs are generated divide : divide by zero (for integers as well!) overflow : floating point overflows underflow : floating point underflows  Note that integer divide-by-zero is handled by the same machinery. These behaviors are set on a per-thread basis.   Examples >>> oldsettings = np.seterr(all='warn')
>>> np.zeros(5,dtype=np.float32)/0.
invalid value encountered in divide
>>> j = np.seterr(under='ignore')
>>> np.array([1.e-100])**10
>>> j = np.seterr(invalid='raise')
>>> np.sqrt(np.array([-1.]))
FloatingPointError: invalid value encountered in sqrt
>>> def errorhandler(errstr, errflag):
...      print("saw stupid error!")
>>> np.seterrcall(errorhandler)
<function err_handler at 0x...>
>>> j = np.seterr(all='call')
>>> np.zeros(5, dtype=np.int32)/0
FloatingPointError: invalid value encountered in divide
saw stupid error!
>>> j = np.seterr(**oldsettings) # restore previous
...                              # error-handling settings
   Interfacing to C Only a survey of the choices. Little detail on how each works.  Bare metal, wrap your own C-code manually.
Plusses:  Efficient No dependencies on other tools
Minuses:
Lots of learning overhead:  need to learn basics of Python C API need to learn basics of numpy C API need to learn how to handle reference counting and love it.
Reference counting often difficult to get right.  getting it wrong leads to memory leaks, and worse, segfaults   API will change for Python 3.0!     Cython
Plusses:  avoid learning C API’s no dealing with reference counting can code in pseudo python and generate C code can also interface to existing C code should shield you from changes to Python C api has become the de-facto standard within the scientific Python community fast indexing support for arrays
Minuses:  Can write code in non-standard form which may become obsolete Not as flexible as manual wrapping     ctypes
Plusses:  part of Python standard library good for interfacing to existing shareable libraries, particularly Windows DLLs avoids API/reference counting issues
good numpy support: arrays have all these in their ctypes attribute: a.ctypes.data
a.ctypes.data_as
a.ctypes.shape
a.ctypes.shape_as
a.ctypes.strides
a.ctypes.strides_as

Minuses:  can’t use for writing code to be turned into C extensions, only a wrapper tool.     SWIG (automatic wrapper generator)
Plusses:  around a long time multiple scripting language support C++ support Good for wrapping large (many functions) existing C libraries
Minuses:  generates lots of code between Python and the C code can cause performance problems that are nearly impossible to optimize out interface files can be hard to write doesn’t necessarily avoid reference counting issues or needing to know API’s     scipy.weave
Plusses:  can turn many numpy expressions into C code dynamic compiling and loading of generated C code can embed pure C code in Python module and have weave extract, generate interfaces and compile, etc.
Minuses:  Future very uncertain: it’s the only part of Scipy not ported to Python 3 and is effectively deprecated in favor of Cython.     Psyco
Plusses:  Turns pure python into efficient machine code through jit-like optimizations very fast when it optimizes well
Minuses:  Only on intel (windows?) Doesn’t do much for numpy?      Interfacing to Fortran: The clear choice to wrap Fortran code is f2py. Pyfort is an older alternative, but not supported any longer. Fwrap is a newer project that looked promising but isn’t being developed any longer.   Interfacing to C++:  Cython CXX Boost.python SWIG SIP (used mainly in PyQT)


numpy.ndarray.__abs__ method   ndarray.__abs__(self)


numpy.ndarray.__add__ method   ndarray.__add__(value, /)

Return self+value.


numpy.ndarray.__and__ method   ndarray.__and__(value, /)

Return self&value.


numpy.ndarray.__array__ method   ndarray.__array__([dtype, ]/) → reference if type unchanged, copy otherwise.

Returns either a new reference to self if dtype is not given or a new array of provided data type if dtype is different from the current dtype of the array.


numpy.ndarray.__array_wrap__ method   ndarray.__array_wrap__(array, [context, ]/)

Returns a view of array with the same type as self.


numpy.ndarray.__bool__ method   ndarray.__bool__(/)

self != 0


numpy.ndarray.__class_getitem__ method   ndarray.__class_getitem__(item, /)

Return a parametrized wrapper around the ndarray type.  New in version 1.22.   Returns

aliastypes.GenericAlias

A parametrized ndarray type.      See also  PEP 585

Type hinting generics in standard collections.  numpy.typing.NDArray

An ndarray alias generic w.r.t. its dtype.type.    Notes This method is only available for python 3.9 and later. Examples >>> from typing import Any
>>> import numpy as np
 >>> np.ndarray[Any, np.dtype[Any]]
numpy.ndarray[typing.Any, numpy.dtype[typing.Any]]


numpy.ndarray.__complex__ method   ndarray.__complex__()


numpy.ndarray.__contains__ method   ndarray.__contains__(key, /)

Return key in self.


numpy.ndarray.__copy__ method   ndarray.__copy__()

Used if copy.copy is called on an array. Returns a copy of the array. Equivalent to a.copy(order='K').


numpy.ndarray.__deepcopy__ method   ndarray.__deepcopy__(memo, /) → Deep copy of array.

Used if copy.deepcopy is called on an array.


numpy.ndarray.__divmod__ method   ndarray.__divmod__(value, /)

Return divmod(self, value).


numpy.ndarray.__eq__ method   ndarray.__eq__(value, /)

Return self==value.


numpy.ndarray.__float__ method   ndarray.__float__(self)


numpy.ndarray.__floordiv__ method   ndarray.__floordiv__(value, /)

Return self//value.


numpy.ndarray.__ge__ method   ndarray.__ge__(value, /)

Return self>=value.


numpy.ndarray.__getitem__ method   ndarray.__getitem__(key, /)

Return self[key].


numpy.ndarray.__gt__ method   ndarray.__gt__(value, /)

Return self>value.


numpy.ndarray.__iadd__ method   ndarray.__iadd__(value, /)

Return self+=value.


numpy.ndarray.__iand__ method   ndarray.__iand__(value, /)

Return self&=value.


numpy.ndarray.__ifloordiv__ method   ndarray.__ifloordiv__(value, /)

Return self//=value.


numpy.ndarray.__ilshift__ method   ndarray.__ilshift__(value, /)

Return self<<=value.


numpy.ndarray.__imod__ method   ndarray.__imod__(value, /)

Return self%=value.


numpy.ndarray.__imul__ method   ndarray.__imul__(value, /)

Return self*=value.


numpy.ndarray.__int__ method   ndarray.__int__(self)


numpy.ndarray.__invert__ method   ndarray.__invert__(/)

~self


numpy.ndarray.__ior__ method   ndarray.__ior__(value, /)

Return self|=value.


numpy.ndarray.__ipow__ method   ndarray.__ipow__(value, /)

Return self**=value.


numpy.ndarray.__irshift__ method   ndarray.__irshift__(value, /)

Return self>>=value.


numpy.ndarray.__isub__ method   ndarray.__isub__(value, /)

Return self-=value.


numpy.ndarray.__itruediv__ method   ndarray.__itruediv__(value, /)

Return self/=value.


numpy.ndarray.__ixor__ method   ndarray.__ixor__(value, /)

Return self^=value.


numpy.ndarray.__le__ method   ndarray.__le__(value, /)

Return self<=value.


numpy.ndarray.__len__ method   ndarray.__len__(/)

Return len(self).


numpy.ndarray.__lshift__ method   ndarray.__lshift__(value, /)

Return self<<value.


numpy.ndarray.__lt__ method   ndarray.__lt__(value, /)

Return self<value.


numpy.ndarray.__matmul__ method   ndarray.__matmul__(value, /)

Return self@value.


numpy.ndarray.__mod__ method   ndarray.__mod__(value, /)

Return self%value.


numpy.ndarray.__mul__ method   ndarray.__mul__(value, /)

Return self*value.


numpy.ndarray.__ne__ method   ndarray.__ne__(value, /)

Return self!=value.


numpy.ndarray.__neg__ method   ndarray.__neg__(/)

-self


numpy.ndarray.__new__ method   ndarray.__new__(*args, **kwargs)


numpy.ndarray.__or__ method   ndarray.__or__(value, /)

Return self|value.


numpy.ndarray.__pos__ method   ndarray.__pos__(/)

+self


numpy.ndarray.__pow__ method   ndarray.__pow__(value, mod=None, /)

Return pow(self, value, mod).


numpy.ndarray.__reduce__ method   ndarray.__reduce__()

For pickling.


numpy.ndarray.__repr__ method   ndarray.__repr__(/)

Return repr(self).


numpy.ndarray.__rshift__ method   ndarray.__rshift__(value, /)

Return self>>value.


numpy.ndarray.__setitem__ method   ndarray.__setitem__(key, value, /)

Set self[key] to value.


numpy.ndarray.__setstate__ method   ndarray.__setstate__(state, /)

For unpickling. The state argument must be a sequence that contains the following elements:  Parameters

versionint

optional pickle version. If omitted defaults to 0.
shapetuple

dtypedata-type

isFortranbool

rawdatastring or list

a binary string with the data (or a list if ‘a’ is an object array)


numpy.ndarray.__str__ method   ndarray.__str__(/)

Return str(self).


numpy.ndarray.__sub__ method   ndarray.__sub__(value, /)

Return self-value.


numpy.ndarray.__truediv__ method   ndarray.__truediv__(value, /)

Return self/value.


numpy.ndarray.__xor__ method   ndarray.__xor__(value, /)

Return self^value.


numpy.ndarray.all method   ndarray.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


numpy.ndarray.any method   ndarray.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


numpy.ndarray.argmax method   ndarray.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


numpy.ndarray.argmin method   ndarray.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


numpy.ndarray.argpartition method   ndarray.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


numpy.ndarray.argsort method   ndarray.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


numpy.ndarray.astype method   ndarray.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)

Copy of the array, cast to a specified type.  Parameters

dtypestr or dtype

Typecode or data-type 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 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.
subokbool, optional

If True, then sub-classes will be passed-through (default), otherwise the returned array will be forced to be a base-class array.
copybool, 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_tndarray

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.    Raises
 ComplexWarning

When casting from complex to float or int. To avoid this, one should use a.real.astype(t).     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.  Examples >>> x = np.array([1, 2, 2.5])
>>> x
array([1. ,  2. ,  2.5])
 >>> x.astype(int)
array([1, 2, 2])


numpy.ndarray.base attribute   ndarray.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


numpy.ndarray.byteswap method   ndarray.byteswap(inplace=False)

Swap the bytes of the array elements Toggle between low-endian and big-endian data representation by returning a byteswapped array, optionally swapped in-place. Arrays of byte-strings are not swapped. The real and imaginary parts of a complex number are swapped individually.  Parameters

inplacebool, optional

If True, swap bytes in-place, default is False.    Returns

outndarray

The byteswapped array. If inplace is True, this is a view to self.     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 byte-strings 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 values

but 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)


numpy.ndarray.choose method   ndarray.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


numpy.ndarray.clip method   ndarray.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


numpy.ndarray.compress method   ndarray.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


numpy.ndarray.conj method   ndarray.conj()

Complex-conjugate all elements. Refer to numpy.conjugate for full documentation.  See also  numpy.conjugate

equivalent function


numpy.ndarray.conjugate method   ndarray.conjugate()

Return the complex conjugate, element-wise. Refer to numpy.conjugate for full documentation.  See also  numpy.conjugate

equivalent function


numpy.ndarray.copy method   ndarray.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 C-order, ‘F’ means F-order, ‘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, and this function always passes sub-classes through.)      See also  numpy.copy

Similar function with different default behavior  numpy.copyto
  Notes This function is the preferred method for creating an array copy. The function numpy.copy is similar, but it defaults to using order ‘K’, and will not pass sub-classes through by default. 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


numpy.ndarray.ctypes attribute   ndarray.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

cPython object

Possessing attributes data, shape, strides, etc.      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 byte-order. The memory area may not even be writeable. The array flags and data-type of this array should be respected when passing this attribute to arbitrary C-code 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 like ctypes.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

(c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the C-integer corresponding to dtype('p') on this platform (see c_intp). This base-type could be ctypes.c_int, ctypes.c_long, or ctypes.c_longlong depending on the platform. The ctypes array contains the shape of the underlying array.
   _ctypes.strides

(c_intp*self.ndim): 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.
   _ctypes.data_as(obj)[source]

Return the data pointer cast to a particular c-types object. For example, calling self._as_parameter_ is equivalent to self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a pointer to a ctypes array of floating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)). The returned pointer will keep a reference to the array.
   _ctypes.shape_as(obj)[source]

Return the shape tuple as an array of some other c-types type. For example: self.shape_as(ctypes.c_short).
   _ctypes.strides_as(obj)[source]

Return the strides tuple as an array of some other c-types 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


numpy.ndarray.cumprod method   ndarray.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


numpy.ndarray.cumsum method   ndarray.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


numpy.ndarray.data attribute   ndarray.data

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


numpy.ndarray.diagonal method   ndarray.diagonal(offset=0, axis1=0, axis2=1)

Return specified diagonals. In NumPy 1.9 the returned array is a read-only view instead of a copy as in previous NumPy versions. In a future version the read-only restriction will be removed. Refer to numpy.diagonal for full documentation.  See also  numpy.diagonal

equivalent function


numpy.ndarray.dtype attribute   ndarray.dtype

Data-type of the array’s elements.  Parameters
 None
  Returns

dnumpy dtype object

    See also  numpy.dtype
  Examples >>> x
array([[0, 1],
       [2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>


numpy.ndarray.dump method   ndarray.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

filestr or Path

A string naming the dump file.  Changed in version 1.17.0: pathlib.Path objects are now accepted.


numpy.ndarray.dumps method   ndarray.dumps()

Returns the pickle of the array as a string. pickle.loads will convert the string back to an array.  Parameters
 None


numpy.ndarray.fill method   ndarray.fill(value)

Fill the array with a scalar value.  Parameters

valuescalar

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.])


numpy.ndarray.flags attribute   ndarray.flags

Information about the memory layout of the array. Notes The flags object can be accessed dictionary-like (as in a.flags['WRITEABLE']), or by using lowercased attribute names (as in a.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 C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional 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 if arr.shape[dim] == 1 or the array has no elements. It does not generally hold that self.strides[-1] == self.itemsize for C-style contiguous arrays or self.strides[0] == self.itemsize for Fortran-style contiguous arrays is true.  Attributes
 C_CONTIGUOUS (C)

The data is in a single, C-style contiguous segment.  F_CONTIGUOUS (F)

The data is in a single, Fortran-style 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 read-only. 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 non-writeable 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 C-API 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 (one-segment test).  BEHAVED (B)

ALIGNED and WRITEABLE.  CARRAY (CA)

BEHAVED and C_CONTIGUOUS.  FARRAY (FA)

BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.


numpy.ndarray.flat attribute   ndarray.flat

A 1-D iterator over the array. This is a numpy.flatiter instance, which acts similarly to, but is not a subclass of, Python’s built-in iterator object.  See also  flatten

Return a copy of the array collapsed into one dimension.  flatiter
  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]])


numpy.ndarray.flatten method   ndarray.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 row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.    Returns

yndarray

A copy of the input array, flattened to one dimension.      See also  ravel

Return a flattened array.  flat

A 1-D flat iterator over the array.    Examples >>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])


numpy.ndarray.getfield method   ndarray.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 data-type. 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 16-byte elements. If taking a view with a 32-bit integer (4 bytes), the offset needs to be between 0 and 12 bytes.  Parameters

dtypestr or dtype

The data type of the view. The dtype size of the view can not be larger than that of the array itself.
offsetint

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.]])


numpy.ndarray.imag attribute   ndarray.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')


numpy.ndarray.item method   ndarray.item(*args)

Copy an element of an array to a standard Python scalar and return it.  Parameters

*argsArguments (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 nd-index into the array.     Returns

zStandard Python scalar object

A copy of the specified element of the array as a suitable Python scalar     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


numpy.ndarray.itemset method   ndarray.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 than a[args] = item. The item should be a scalar value and args must select a single item in the array a.  Parameters

*argsArguments

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 look-up 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]])


numpy.ndarray.itemsize attribute   ndarray.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


numpy.ndarray.max method   ndarray.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


numpy.ndarray.mean method   ndarray.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


numpy.ndarray.min method   ndarray.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


numpy.ndarray.nbytes attribute   ndarray.nbytes

Total bytes consumed by the elements of the array. Notes Does not include memory consumed by non-element 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


numpy.ndarray.ndim attribute   ndarray.ndim

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