numpy.issubdtype   numpy.issubdtype(arg1, arg2)[source]

Returns True if first argument is a typecode lower/equal in type hierarchy. This is like the builtin issubclass, but for dtypes.  Parameters

arg1, arg2dtype_like

dtype or object coercible to one    Returns

outbool

    See also  Scalars

Overview of the numpy type hierarchy.
issubsctype, issubclass_

  Examples issubdtype can be used to check the type of arrays: >>> ints = np.array([1, 2, 3], dtype=np.int32)
>>> np.issubdtype(ints.dtype, np.integer)
True
>>> np.issubdtype(ints.dtype, np.floating)
False
 >>> floats = np.array([1, 2, 3], dtype=np.float32)
>>> np.issubdtype(floats.dtype, np.integer)
False
>>> np.issubdtype(floats.dtype, np.floating)
True
 Similar types of different sizes are not subdtypes of each other: >>> np.issubdtype(np.float64, np.float32)
False
>>> np.issubdtype(np.float32, np.float64)
False
 but both are subtypes of floating: >>> np.issubdtype(np.float64, np.floating)
True
>>> np.issubdtype(np.float32, np.floating)
True
 For convenience, dtype-like objects are allowed too: >>> np.issubdtype('S1', np.string_)
True
>>> np.issubdtype('i4', np.signedinteger)
True


numpy.issubsctype   numpy.issubsctype(arg1, arg2)[source]

Determine if the first argument is a subclass of the second argument.  Parameters

arg1, arg2dtype or dtype specifier

Data-types.    Returns

outbool

The result.      See also
issctype, issubdtype, obj2sctype

  Examples >>> np.issubsctype('S8', str)
False
>>> np.issubsctype(np.array([1]), int)
True
>>> np.issubsctype(np.array([1]), float)
False


numpy.ix_   numpy.ix_(*args)[source]

Construct an open mesh from multiple sequences. This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the non-unit shape value cycles through all N dimensions. Using ix_ one can quickly construct index arrays that will index the cross product. a[np.ix_([1,3],[2,5])] returns the array [[a[1,2] a[1,5]], [a[3,2] a[3,5]]].  Parameters

args1-D sequences

Each sequence should be of integer or boolean type. Boolean sequences will be interpreted as boolean masks for the corresponding dimension (equivalent to passing in np.nonzero(boolean_sequence)).    Returns

outtuple of ndarrays

N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh.      See also
ogrid, mgrid, meshgrid

  Examples >>> a = np.arange(10).reshape(2, 5)
>>> a
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
>>> ixgrid = np.ix_([0, 1], [2, 4])
>>> ixgrid
(array([[0],
       [1]]), array([[2, 4]]))
>>> ixgrid[0].shape, ixgrid[1].shape
((2, 1), (1, 2))
>>> a[ixgrid]
array([[2, 4],
       [7, 9]])
 >>> ixgrid = np.ix_([True, True], [2, 4])
>>> a[ixgrid]
array([[2, 4],
       [7, 9]])
>>> ixgrid = np.ix_([True, True], [False, False, True, False, True])
>>> a[ixgrid]
array([[2, 4],
       [7, 9]])


numpy.kaiser   numpy.kaiser(M, beta)[source]

Return the Kaiser window. The Kaiser window is a taper formed by using a Bessel function.  Parameters

Mint

Number of points in the output window. If zero or less, an empty array is returned.
betafloat

Shape parameter for window.    Returns

outarray

The window, with the maximum value normalized to one (the value one appears only if the number of samples is odd).      See also
bartlett, blackman, hamming, hanning

  Notes The Kaiser window is defined as  \[w(n) = I_0\left( \beta \sqrt{1-\frac{4n^2}{(M-1)^2}} \right)/I_0(\beta)\] with  \[\quad -\frac{M-1}{2} \leq n \leq \frac{M-1}{2},\] where \(I_0\) is the modified zeroth-order Bessel function. The Kaiser was named for Jim Kaiser, who discovered a simple approximation to the DPSS window based on Bessel functions. The Kaiser window is a very good approximation to the Digital Prolate Spheroidal Sequence, or Slepian window, which is the transform which maximizes the energy in the main lobe of the window relative to total energy. The Kaiser can approximate many other windows by varying the beta parameter.
beta Window shape
0 Rectangular
5 Similar to a Hamming
6 Similar to a Hanning
8.6 Similar to a Blackman   A beta value of 14 is probably a good starting point. Note that as beta gets large, the window narrows, and so the number of samples needs to be large enough to sample the increasingly narrow spike, otherwise NaNs will get returned. Most references to the Kaiser window come from the signal processing literature, where it is used as one of many windowing functions for smoothing values. It is also known as an apodization (which means “removing the foot”, i.e. smoothing discontinuities at the beginning and end of the sampled signal) or tapering function. References  1
J. F. Kaiser, “Digital Filters” - Ch 7 in “Systems analysis by digital computer”, Editors: F.F. Kuo and J.F. Kaiser, p 218-285. John Wiley and Sons, New York, (1966).  2
E.R. Kanasewich, “Time Sequence Analysis in Geophysics”, The University of Alberta Press, 1975, pp. 177-178.  3
Wikipedia, “Window function”, https://en.wikipedia.org/wiki/Window_function   Examples >>> import matplotlib.pyplot as plt
>>> np.kaiser(12, 14)
 array([7.72686684e-06, 3.46009194e-03, 4.65200189e-02, # may vary
        2.29737120e-01, 5.99885316e-01, 9.45674898e-01,
        9.45674898e-01, 5.99885316e-01, 2.29737120e-01,
        4.65200189e-02, 3.46009194e-03, 7.72686684e-06])
 Plot the window and the frequency response: >>> from numpy.fft import fft, fftshift
>>> window = np.kaiser(51, 14)
>>> plt.plot(window)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Kaiser window")
Text(0.5, 1.0, 'Kaiser window')
>>> plt.ylabel("Amplitude")
Text(0, 0.5, 'Amplitude')
>>> plt.xlabel("Sample")
Text(0.5, 0, 'Sample')
>>> plt.show()
    >>> plt.figure()
<Figure size 640x480 with 0 Axes>
>>> A = fft(window, 2048) / 25.5
>>> mag = np.abs(fftshift(A))
>>> freq = np.linspace(-0.5, 0.5, len(A))
>>> response = 20 * np.log10(mag)
>>> response = np.clip(response, -100, 100)
>>> plt.plot(freq, response)
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.title("Frequency response of Kaiser window")
Text(0.5, 1.0, 'Frequency response of Kaiser window')
>>> plt.ylabel("Magnitude [dB]")
Text(0, 0.5, 'Magnitude [dB]')
>>> plt.xlabel("Normalized frequency [cycles per sample]")
Text(0.5, 0, 'Normalized frequency [cycles per sample]')
>>> plt.axis('tight')
(-0.5, 0.5, -100.0, ...) # may vary
>>> plt.show()


numpy.kron   numpy.kron(a, b)[source]

Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first.  Parameters

a, barray_like

  Returns

outndarray

    See also  outer

The outer product    Notes The function assumes that the number of dimensions of a and b are the same, if necessary prepending the smallest with ones. If a.shape = (r0,r1,..,rN) and b.shape = (s0,s1,...,sN), the Kronecker product has shape (r0*s0, r1*s1, ..., rN*SN). The elements are products of elements from a and b, organized explicitly by: kron(a,b)[k0,k1,...,kN] = a[i0,i1,...,iN] * b[j0,j1,...,jN]
 where: kt = it * st + jt,  t = 0,...,N
 In the common 2-D case (N=1), the block structure can be visualized: [[ a[0,0]*b,   a[0,1]*b,  ... , a[0,-1]*b  ],
 [  ...                              ...   ],
 [ a[-1,0]*b,  a[-1,1]*b, ... , a[-1,-1]*b ]]
 Examples >>> np.kron([1,10,100], [5,6,7])
array([  5,   6,   7, ..., 500, 600, 700])
>>> np.kron([5,6,7], [1,10,100])
array([  5,  50, 500, ...,   7,  70, 700])
 >>> np.kron(np.eye(2), np.ones((2,2)))
array([[1.,  1.,  0.,  0.],
       [1.,  1.,  0.,  0.],
       [0.,  0.,  1.,  1.],
       [0.,  0.,  1.,  1.]])
 >>> a = np.arange(100).reshape((2,5,2,5))
>>> b = np.arange(24).reshape((2,3,4))
>>> c = np.kron(a,b)
>>> c.shape
(2, 10, 6, 20)
>>> I = (1,3,0,2)
>>> J = (0,2,1)
>>> J1 = (0,) + J             # extend to ndim=4
>>> S1 = (1,) + b.shape
>>> K = tuple(np.array(I) * np.array(S1) + np.array(J1))
>>> c[K] == a[I]*b[J]
True


numpy.lcm   numpy.lcm(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'lcm'>

Returns the lowest common multiple of |x1| and |x2|  Parameters

x1, x2array_like, int

Arrays of values. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).    Returns

yndarray or scalar

The lowest common multiple of the absolute value of the inputs This is a scalar if both x1 and x2 are scalars.      See also  gcd

The greatest common divisor    Examples >>> np.lcm(12, 20)
60
>>> np.lcm.reduce([3, 12, 20])
60
>>> np.lcm.reduce([40, 12, 20])
120
>>> np.lcm(np.arange(6), 20)
array([ 0, 20, 20, 60, 20, 20])


numpy.ldexp   numpy.ldexp(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'ldexp'>

Returns x1 * 2**x2, element-wise. The mantissas x1 and twos exponents x2 are used to construct floating point numbers x1 * 2**x2.  Parameters

x1array_like

Array of multipliers.
x2array_like, int

Array of twos exponents. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
wherearray_like, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.  **kwargs

For other keyword-only arguments, see the ufunc docs.    Returns

yndarray or scalar

The result of x1 * 2**x2. This is a scalar if both x1 and x2 are scalars.      See also  frexp

Return (y1, y2) from x = y1 * 2**y2, inverse to ldexp.    Notes Complex dtypes are not supported, they will raise a TypeError. ldexp is useful as the inverse of frexp, if used by itself it is more clear to simply use the expression x1 * 2**x2. Examples >>> np.ldexp(5, np.arange(4))
array([ 5., 10., 20., 40.], dtype=float16)
 >>> x = np.arange(6)
>>> np.ldexp(*np.frexp(x))
array([ 0.,  1.,  2.,  3.,  4.,  5.])


numpy.left_shift   numpy.left_shift(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'left_shift'>

Shift the bits of an integer to the left. Bits are shifted to the left by appending x2 0s at the right of x1. Since the internal representation of numbers is in binary format, this operation is equivalent to multiplying x1 by 2**x2.  Parameters

x1array_like of integer type

Input values.
x2array_like of integer type

Number of zeros to append to x1. Has to be non-negative. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
wherearray_like, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.  **kwargs

For other keyword-only arguments, see the ufunc docs.    Returns

outarray of integer type

Return x1 with bits shifted x2 times to the left. This is a scalar if both x1 and x2 are scalars.      See also  right_shift

Shift the bits of an integer to the right.  binary_repr

Return the binary representation of the input number as a string.    Examples >>> np.binary_repr(5)
'101'
>>> np.left_shift(5, 2)
20
>>> np.binary_repr(20)
'10100'
 >>> np.left_shift(5, [1,2,3])
array([10, 20, 40])
 Note that the dtype of the second argument may change the dtype of the result and can lead to unexpected results in some cases (see Casting Rules): >>> a = np.left_shift(np.uint8(255), 1) # Expect 254
>>> print(a, type(a)) # Unexpected result due to upcasting
510 <class 'numpy.int64'>
>>> b = np.left_shift(np.uint8(255), np.uint8(1))
>>> print(b, type(b))
254 <class 'numpy.uint8'>
 The << operator can be used as a shorthand for np.left_shift on ndarrays. >>> x1 = 5
>>> x2 = np.array([1, 2, 3])
>>> x1 << x2
array([10, 20, 40])


numpy.less   numpy.less(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'less'>

Return the truth value of (x1 < x2) element-wise.  Parameters

x1, x2array_like

Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
wherearray_like, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.  **kwargs

For other keyword-only arguments, see the ufunc docs.    Returns

outndarray or scalar

Output array, element-wise comparison of x1 and x2. Typically of type bool, unless dtype=object is passed. This is a scalar if both x1 and x2 are scalars.      See also
greater, less_equal, greater_equal, equal, not_equal

  Examples >>> np.less([1, 2], [2, 2])
array([ True, False])
 The < operator can be used as a shorthand for np.less on ndarrays. >>> a = np.array([1, 2])
>>> b = np.array([2, 2])
>>> a < b
array([ True, False])


numpy.less_equal   numpy.less_equal(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'less_equal'>

Return the truth value of (x1 <= x2) element-wise.  Parameters

x1, x2array_like

Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).
outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
wherearray_like, optional

This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.  **kwargs

For other keyword-only arguments, see the ufunc docs.    Returns

outndarray or scalar

Output array, element-wise comparison of x1 and x2. Typically of type bool, unless dtype=object is passed. This is a scalar if both x1 and x2 are scalars.      See also
greater, less, greater_equal, equal, not_equal

  Examples >>> np.less_equal([4, 2, 1], [2, 2, 2])
array([False,  True,  True])
 The <= operator can be used as a shorthand for np.less_equal on ndarrays. >>> a = np.array([4, 2, 1])
>>> b = np.array([2, 2, 2])
>>> a <= b
array([False,  True,  True])


numpy.lexsort   numpy.lexsort(keys, axis=- 1)

Perform an indirect stable sort using a sequence of keys. Given multiple sorting keys, which can be interpreted as columns in a spreadsheet, lexsort returns an array of integer indices that describes the sort order by multiple columns. The last key in the sequence is used for the primary sort order, the second-to-last key for the secondary sort order, and so on. The keys argument must be a sequence of objects that can be converted to arrays of the same shape. If a 2D array is provided for the keys argument, its rows are interpreted as the sorting keys and sorting is according to the last row, second last row etc.  Parameters

keys(k, N) array or tuple containing k (N,)-shaped sequences

The k different “columns” to be sorted. The last column (or row if keys is a 2D array) is the primary sort key.
axisint, optional

Axis to be indirectly sorted. By default, sort over the last axis.    Returns

indices(N,) ndarray of ints

Array of indices that sort the keys along the specified axis.      See also  argsort

Indirect sort.  ndarray.sort

In-place sort.  sort

Return a sorted copy of an array.    Examples Sort names: first by surname, then by name. >>> surnames =    ('Hertz',    'Galilei', 'Hertz')
>>> first_names = ('Heinrich', 'Galileo', 'Gustav')
>>> ind = np.lexsort((first_names, surnames))
>>> ind
array([1, 2, 0])
 >>> [surnames[i] + ", " + first_names[i] for i in ind]
['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']
 Sort two columns of numbers: >>> a = [1,5,1,4,3,4,4] # First column
>>> b = [9,4,0,4,0,2,1] # Second column
>>> ind = np.lexsort((b,a)) # Sort by a, then by b
>>> ind
array([2, 0, 4, 6, 5, 3, 1])
 >>> [(a[i],b[i]) for i in ind]
[(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]
 Note that sorting is first according to the elements of a. Secondary sorting is according to the elements of b. A normal argsort would have yielded: >>> [(a[i],b[i]) for i in np.argsort(a)]
[(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]
 Structured arrays are sorted lexically by argsort: >>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
...              dtype=np.dtype([('x', int), ('y', int)]))
 >>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
array([2, 0, 4, 6, 5, 3, 1])


numpy.lib.arraysetops Set operations for arrays based on sorting.  Notes For floating point arrays, inaccurate results may appear due to usual round-off and floating point comparison issues. Speed could be gained in some operations by an implementation of numpy.sort, that can provide directly the permutation vectors, thus avoiding calls to numpy.argsort. Original author: Robert Cimrman


numpy.lib.Arrayterator   class numpy.lib.Arrayterator(var, buf_size=None)[source]

Buffered iterator for big arrays. Arrayterator creates a buffered iterator for reading big arrays in small contiguous blocks. The class is useful for objects stored in the file system. It allows iteration over the object without reading everything in memory; instead, small blocks are read and iterated over. Arrayterator can be used with any object that supports multidimensional slices. This includes NumPy arrays, but also variables from Scientific.IO.NetCDF or pynetcdf for example.  Parameters

vararray_like

The object to iterate over.
buf_sizeint, optional

The buffer size. If buf_size is supplied, the maximum amount of data that will be read into memory is buf_size elements. Default is None, which will read as many element as possible into memory.      See also  ndenumerate

Multidimensional array iterator.  flatiter

Flat array iterator.  memmap

Create a memory-map to an array stored in a binary file on disk.    Notes The algorithm works by first finding a “running dimension”, along which the blocks will be extracted. Given an array of dimensions (d1, d2, ..., dn), e.g. if buf_size is smaller than d1, the first dimension will be used. If, on the other hand, d1 < buf_size < d1*d2 the second dimension will be used, and so on. Blocks are extracted along this dimension, and when the last block is returned the process continues from the next dimension, until all elements have been read. Examples >>> a = np.arange(3 * 4 * 5 * 6).reshape(3, 4, 5, 6)
>>> a_itor = np.lib.Arrayterator(a, 2)
>>> a_itor.shape
(3, 4, 5, 6)
 Now we can iterate over a_itor, and it will return arrays of size two. Since buf_size was smaller than any dimension, the first dimension will be iterated over first: >>> for subarr in a_itor:
...     if not subarr.all():
...         print(subarr, subarr.shape)
>>> # [[[[0 1]]]] (1, 1, 1, 2)
  Attributes
 var
buf_size
start
stop
step
shape

The shape of the array to be iterated over.  flat

A 1-D flat iterator for Arrayterator objects.


numpy.lib.mixins.NDArrayOperatorsMixin   class numpy.lib.mixins.NDArrayOperatorsMixin[source]

Mixin defining all operator special methods using __array_ufunc__. This class implements the special methods for almost all of Python’s builtin operators defined in the operator module, including comparisons (==, >, etc.) and arithmetic (+, *, -, etc.), by deferring to the __array_ufunc__ method, which subclasses must implement. It is useful for writing classes that do not inherit from numpy.ndarray, but that should support arithmetic and numpy universal functions like arrays as described in A Mechanism for Overriding Ufuncs. As an trivial example, consider this implementation of an ArrayLike class that simply wraps a NumPy array and ensures that the result of any arithmetic operation is also an ArrayLike object: class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
    def __init__(self, value):
        self.value = np.asarray(value)

    # One might also consider adding the built-in list type to this
    # list, to support operations like np.add(array_like, list)
    _HANDLED_TYPES = (np.ndarray, numbers.Number)

    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
        out = kwargs.get('out', ())
        for x in inputs + out:
            # Only support operations with instances of _HANDLED_TYPES.
            # Use ArrayLike instead of type(self) for isinstance to
            # allow subclasses that don't override __array_ufunc__ to
            # handle ArrayLike objects.
            if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)):
                return NotImplemented

        # Defer to the implementation of the ufunc on unwrapped values.
        inputs = tuple(x.value if isinstance(x, ArrayLike) else x
                       for x in inputs)
        if out:
            kwargs['out'] = tuple(
                x.value if isinstance(x, ArrayLike) else x
                for x in out)
        result = getattr(ufunc, method)(*inputs, **kwargs)

        if type(result) is tuple:
            # multiple return values
            return tuple(type(self)(x) for x in result)
        elif method == 'at':
            # no return value
            return None
        else:
            # one return value
            return type(self)(result)

    def __repr__(self):
        return '%s(%r)' % (type(self).__name__, self.value)
 In interactions between ArrayLike objects and numbers or numpy arrays, the result is always another ArrayLike: >>> x = ArrayLike([1, 2, 3])
>>> x - 1
ArrayLike(array([0, 1, 2]))
>>> 1 - x
ArrayLike(array([ 0, -1, -2]))
>>> np.arange(3) - x
ArrayLike(array([-1, -1, -1]))
>>> x - np.arange(3)
ArrayLike(array([1, 1, 1]))
 Note that unlike numpy.ndarray, ArrayLike does not allow operations with arbitrary, unrecognized types. This ensures that interactions with ArrayLike preserve a well-defined casting hierarchy.  New in version 1.13.


numpy.lib.NumpyVersion   class numpy.lib.NumpyVersion(vstring)[source]

Parse and compare numpy version strings. NumPy has the following versioning scheme (numbers given are examples; they can be > 9 in principle):  Released version: ‘1.8.0’, ‘1.8.1’, etc. Alpha: ‘1.8.0a1’, ‘1.8.0a2’, etc. Beta: ‘1.8.0b1’, ‘1.8.0b2’, etc. Release candidates: ‘1.8.0rc1’, ‘1.8.0rc2’, etc. Development versions: ‘1.8.0.dev-f1234afa’ (git commit hash appended)
 Development versions after a1: ‘1.8.0a1.dev-f1234afa’,

‘1.8.0b2.dev-f1234afa’, ‘1.8.1rc1.dev-f1234afa’, etc.    Development versions (no git hash available): ‘1.8.0.dev-Unknown’  Comparing needs to be done against a valid version string or other NumpyVersion instance. Note that all development versions of the same (pre-)release compare equal.  New in version 1.9.0.   Parameters

vstringstr

NumPy version string (np.__version__).     Examples >>> from numpy.lib import NumpyVersion
>>> if NumpyVersion(np.__version__) < '1.7.0':
...     print('skip')
>>> # skip
 >>> NumpyVersion('1.7')  # raises ValueError, add ".0"
Traceback (most recent call last):
    ...
ValueError: Not a valid numpy version string


Structured arrays  Introduction Structured arrays are ndarrays whose datatype is a composition of simpler datatypes organized as a sequence of named fields. For example, >>> x = np.array([('Rex', 9, 81.0), ('Fido', 3, 27.0)],
...              dtype=[('name', 'U10'), ('age', 'i4'), ('weight', 'f4')])
>>> x
array([('Rex', 9, 81.), ('Fido', 3, 27.)],
      dtype=[('name', 'U10'), ('age', '<i4'), ('weight', '<f4')])
 Here x is a one-dimensional array of length two whose datatype is a structure with three fields: 1. A string of length 10 or less named ‘name’, 2. a 32-bit integer named ‘age’, and 3. a 32-bit float named ‘weight’. If you index x at position 1 you get a structure: >>> x[1]
('Fido', 3, 27.0)
 You can access and modify individual fields of a structured array by indexing with the field name: >>> x['age']
array([9, 3], dtype=int32)
>>> x['age'] = 5
>>> x
array([('Rex', 5, 81.), ('Fido', 5, 27.)],
      dtype=[('name', 'U10'), ('age', '<i4'), ('weight', '<f4')])
 Structured datatypes are designed to be able to mimic ‘structs’ in the C language, and share a similar memory layout. They are meant for interfacing with C code and for low-level manipulation of structured buffers, for example for interpreting binary blobs. For these purposes they support specialized features such as subarrays, nested datatypes, and unions, and allow control over the memory layout of the structure. Users looking to manipulate tabular data, such as stored in csv files, may find other pydata projects more suitable, such as xarray, pandas, or DataArray. These provide a high-level interface for tabular data analysis and are better optimized for that use. For instance, the C-struct-like memory layout of structured arrays in numpy can lead to poor cache behavior in comparison.   Structured Datatypes A structured datatype can be thought of as a sequence of bytes of a certain length (the structure’s itemsize) which is interpreted as a collection of fields. Each field has a name, a datatype, and a byte offset within the structure. The datatype of a field may be any numpy datatype including other structured datatypes, and it may also be a subarray data type which behaves like an ndarray of a specified shape. The offsets of the fields are arbitrary, and fields may even overlap. These offsets are usually determined automatically by numpy, but can also be specified.  Structured Datatype Creation Structured datatypes may be created using the function numpy.dtype. There are 4 alternative forms of specification which vary in flexibility and conciseness. These are further documented in the Data Type Objects reference page, and in summary they are:
A list of tuples, one tuple per field Each tuple has the form (fieldname, datatype, shape) where shape is optional. fieldname is a string (or tuple if titles are used, see Field Titles below), datatype may be any object convertible to a datatype, and shape is a tuple of integers specifying subarray shape. >>> np.dtype([('x', 'f4'), ('y', np.float32), ('z', 'f4', (2, 2))])
dtype([('x', '<f4'), ('y', '<f4'), ('z', '<f4', (2, 2))])
 If fieldname is the empty string '', the field will be given a default name of the form f#, where # is the integer index of the field, counting from 0 from the left: >>> np.dtype([('x', 'f4'), ('', 'i4'), ('z', 'i8')])
dtype([('x', '<f4'), ('f1', '<i4'), ('z', '<i8')])
 The byte offsets of the fields within the structure and the total structure itemsize are determined automatically.
A string of comma-separated dtype specifications In this shorthand notation any of the string dtype specifications may be used in a string and separated by commas. The itemsize and byte offsets of the fields are determined automatically, and the field names are given the default names f0, f1, etc. >>> np.dtype('i8, f4, S3')
dtype([('f0', '<i8'), ('f1', '<f4'), ('f2', 'S3')])
>>> np.dtype('3int8, float32, (2, 3)float64')
dtype([('f0', 'i1', (3,)), ('f1', '<f4'), ('f2', '<f8', (2, 3))])

A dictionary of field parameter arrays This is the most flexible form of specification since it allows control over the byte-offsets of the fields and the itemsize of the structure. The dictionary has two required keys, ‘names’ and ‘formats’, and four optional keys, ‘offsets’, ‘itemsize’, ‘aligned’ and ‘titles’. The values for ‘names’ and ‘formats’ should respectively be a list of field names and a list of dtype specifications, of the same length. The optional ‘offsets’ value should be a list of integer byte-offsets, one for each field within the structure. If ‘offsets’ is not given the offsets are determined automatically. The optional ‘itemsize’ value should be an integer describing the total size in bytes of the dtype, which must be large enough to contain all the fields. >>> np.dtype({'names': ['col1', 'col2'], 'formats': ['i4', 'f4']})
dtype([('col1', '<i4'), ('col2', '<f4')])
>>> np.dtype({'names': ['col1', 'col2'],
...           'formats': ['i4', 'f4'],
...           'offsets': [0, 4],
...           'itemsize': 12})
dtype({'names': ['col1', 'col2'], 'formats': ['<i4', '<f4'], 'offsets': [0, 4], 'itemsize': 12})
 Offsets may be chosen such that the fields overlap, though this will mean that assigning to one field may clobber any overlapping field’s data. As an exception, fields of numpy.object_ type cannot overlap with other fields, because of the risk of clobbering the internal object pointer and then dereferencing it. The optional ‘aligned’ value can be set to True to make the automatic offset computation use aligned offsets (see Automatic Byte Offsets and Alignment), as if the ‘align’ keyword argument of numpy.dtype had been set to True. The optional ‘titles’ value should be a list of titles of the same length as ‘names’, see Field Titles below.
A dictionary of field names The use of this form of specification is discouraged, but documented here because older numpy code may use it. The keys of the dictionary are the field names and the values are tuples specifying type and offset: >>> np.dtype({'col1': ('i1', 0), 'col2': ('f4', 1)})
dtype([('col1', 'i1'), ('col2', '<f4')])
 This form is discouraged because Python dictionaries do not preserve order in Python versions before Python 3.6, and the order of the fields in a structured dtype has meaning. Field Titles may be specified by using a 3-tuple, see below.     Manipulating and Displaying Structured Datatypes The list of field names of a structured datatype can be found in the names attribute of the dtype object: >>> d = np.dtype([('x', 'i8'), ('y', 'f4')])
>>> d.names
('x', 'y')
 The field names may be modified by assigning to the names attribute using a sequence of strings of the same length. The dtype object also has a dictionary-like attribute, fields, whose keys are the field names (and Field Titles, see below) and whose values are tuples containing the dtype and byte offset of each field. >>> d.fields
mappingproxy({'x': (dtype('int64'), 0), 'y': (dtype('float32'), 8)})
 Both the names and fields attributes will equal None for unstructured arrays. The recommended way to test if a dtype is structured is with if dt.names is not None rather than if dt.names, to account for dtypes with 0 fields. The string representation of a structured datatype is shown in the “list of tuples” form if possible, otherwise numpy falls back to using the more general dictionary form.   Automatic Byte Offsets and Alignment Numpy uses one of two methods to automatically determine the field byte offsets and the overall itemsize of a structured datatype, depending on whether align=True was specified as a keyword argument to numpy.dtype. By default (align=False), numpy will pack the fields together such that each field starts at the byte offset the previous field ended, and the fields are contiguous in memory. >>> def print_offsets(d):
...     print("offsets:", [d.fields[name][1] for name in d.names])
...     print("itemsize:", d.itemsize)
>>> print_offsets(np.dtype('u1, u1, i4, u1, i8, u2'))
offsets: [0, 1, 2, 6, 7, 15]
itemsize: 17
 If align=True is set, numpy will pad the structure in the same way many C compilers would pad a C-struct. Aligned structures can give a performance improvement in some cases, at the cost of increased datatype size. Padding bytes are inserted between fields such that each field’s byte offset will be a multiple of that field’s alignment, which is usually equal to the field’s size in bytes for simple datatypes, see PyArray_Descr.alignment. The structure will also have trailing padding added so that its itemsize is a multiple of the largest field’s alignment. >>> print_offsets(np.dtype('u1, u1, i4, u1, i8, u2', align=True))
offsets: [0, 1, 4, 8, 16, 24]
itemsize: 32
 Note that although almost all modern C compilers pad in this way by default, padding in C structs is C-implementation-dependent so this memory layout is not guaranteed to exactly match that of a corresponding struct in a C program. Some work may be needed, either on the numpy side or the C side, to obtain exact correspondence. If offsets were specified using the optional offsets key in the dictionary-based dtype specification, setting align=True will check that each field’s offset is a multiple of its size and that the itemsize is a multiple of the largest field size, and raise an exception if not. If the offsets of the fields and itemsize of a structured array satisfy the alignment conditions, the array will have the ALIGNED flag set. A convenience function numpy.lib.recfunctions.repack_fields converts an aligned dtype or array to a packed one and vice versa. It takes either a dtype or structured ndarray as an argument, and returns a copy with fields re-packed, with or without padding bytes.   Field Titles In addition to field names, fields may also have an associated title, an alternate name, which is sometimes used as an additional description or alias for the field. The title may be used to index an array, just like a field name. To add titles when using the list-of-tuples form of dtype specification, the field name may be specified as a tuple of two strings instead of a single string, which will be the field’s title and field name respectively. For example: >>> np.dtype([(('my title', 'name'), 'f4')])
dtype([(('my title', 'name'), '<f4')])
 When using the first form of dictionary-based specification, the titles may be supplied as an extra 'titles' key as described above. When using the second (discouraged) dictionary-based specification, the title can be supplied by providing a 3-element tuple (datatype, offset, title) instead of the usual 2-element tuple: >>> np.dtype({'name': ('i4', 0, 'my title')})
dtype([(('my title', 'name'), '<i4')])
 The dtype.fields dictionary will contain titles as keys, if any titles are used. This means effectively that a field with a title will be represented twice in the fields dictionary. The tuple values for these fields will also have a third element, the field title. Because of this, and because the names attribute preserves the field order while the fields attribute may not, it is recommended to iterate through the fields of a dtype using the names attribute of the dtype, which will not list titles, as in: >>> for name in d.names:
...     print(d.fields[name][:2])
(dtype('int64'), 0)
(dtype('float32'), 8)
   Union types Structured datatypes are implemented in numpy to have base type numpy.void by default, but it is possible to interpret other numpy types as structured types using the (base_dtype, dtype) form of dtype specification described in Data Type Objects. Here, base_dtype is the desired underlying dtype, and fields and flags will be copied from dtype. This dtype is similar to a ‘union’ in C.    Indexing and Assignment to Structured arrays  Assigning data to a Structured Array There are a number of ways to assign values to a structured array: Using python tuples, using scalar values, or using other structured arrays.  Assignment from Python Native Types (Tuples) The simplest way to assign values to a structured array is using python tuples. Each assigned value should be a tuple of length equal to the number of fields in the array, and not a list or array as these will trigger numpy’s broadcasting rules. The tuple’s elements are assigned to the successive fields of the array, from left to right: >>> x = np.array([(1, 2, 3), (4, 5, 6)], dtype='i8, f4, f8')
>>> x[1] = (7, 8, 9)
>>> x
array([(1, 2., 3.), (7, 8., 9.)],
     dtype=[('f0', '<i8'), ('f1', '<f4'), ('f2', '<f8')])
   Assignment from Scalars A scalar assigned to a structured element will be assigned to all fields. This happens when a scalar is assigned to a structured array, or when an unstructured array is assigned to a structured array: >>> x = np.zeros(2, dtype='i8, f4, ?, S1')
>>> x[:] = 3
>>> x
array([(3, 3., True, b'3'), (3, 3., True, b'3')],
      dtype=[('f0', '<i8'), ('f1', '<f4'), ('f2', '?'), ('f3', 'S1')])
>>> x[:] = np.arange(2)
>>> x
array([(0, 0., False, b'0'), (1, 1., True, b'1')],
      dtype=[('f0', '<i8'), ('f1', '<f4'), ('f2', '?'), ('f3', 'S1')])
 Structured arrays can also be assigned to unstructured arrays, but only if the structured datatype has just a single field: >>> twofield = np.zeros(2, dtype=[('A', 'i4'), ('B', 'i4')])
>>> onefield = np.zeros(2, dtype=[('A', 'i4')])
>>> nostruct = np.zeros(2, dtype='i4')
>>> nostruct[:] = twofield
Traceback (most recent call last):
...
TypeError: Cannot cast array data from dtype([('A', '<i4'), ('B', '<i4')]) to dtype('int32') according to the rule 'unsafe'
   Assignment from other Structured Arrays Assignment between two structured arrays occurs as if the source elements had been converted to tuples and then assigned to the destination elements. That is, the first field of the source array is assigned to the first field of the destination array, and the second field likewise, and so on, regardless of field names. Structured arrays with a different number of fields cannot be assigned to each other. Bytes of the destination structure which are not included in any of the fields are unaffected. >>> a = np.zeros(3, dtype=[('a', 'i8'), ('b', 'f4'), ('c', 'S3')])
>>> b = np.ones(3, dtype=[('x', 'f4'), ('y', 'S3'), ('z', 'O')])
>>> b[:] = a
>>> b
array([(0., b'0.0', b''), (0., b'0.0', b''), (0., b'0.0', b'')],
      dtype=[('x', '<f4'), ('y', 'S3'), ('z', 'O')])
   Assignment involving subarrays When assigning to fields which are subarrays, the assigned value will first be broadcast to the shape of the subarray.    Indexing Structured Arrays  Accessing Individual Fields Individual fields of a structured array may be accessed and modified by indexing the array with the field name. >>> x = np.array([(1, 2), (3, 4)], dtype=[('foo', 'i8'), ('bar', 'f4')])
>>> x['foo']
array([1, 3])
>>> x['foo'] = 10
>>> x
array([(10, 2.), (10, 4.)],
      dtype=[('foo', '<i8'), ('bar', '<f4')])
 The resulting array is a view into the original array. It shares the same memory locations and writing to the view will modify the original array. >>> y = x['bar']
>>> y[:] = 11
>>> x
array([(10, 11.), (10, 11.)],
      dtype=[('foo', '<i8'), ('bar', '<f4')])
 This view has the same dtype and itemsize as the indexed field, so it is typically a non-structured array, except in the case of nested structures. >>> y.dtype, y.shape, y.strides
(dtype('float32'), (2,), (12,))
 If the accessed field is a subarray, the dimensions of the subarray are appended to the shape of the result: >>> x = np.zeros((2, 2), dtype=[('a', np.int32), ('b', np.float64, (3, 3))])
>>> x['a'].shape
(2, 2)
>>> x['b'].shape
(2, 2, 3, 3)
   Accessing Multiple Fields One can index and assign to a structured array with a multi-field index, where the index is a list of field names.  Warning The behavior of multi-field indexes changed from Numpy 1.15 to Numpy 1.16.  The result of indexing with a multi-field index is a view into the original array, as follows: >>> a = np.zeros(3, dtype=[('a', 'i4'), ('b', 'i4'), ('c', 'f4')])
>>> a[['a', 'c']]
array([(0, 0.), (0, 0.), (0, 0.)],
     dtype={'names':['a','c'], 'formats':['<i4','<f4'], 'offsets':[0,8], 'itemsize':12})
 Assignment to the view modifies the original array. The view’s fields will be in the order they were indexed. Note that unlike for single-field indexing, the dtype of the view has the same itemsize as the original array, and has fields at the same offsets as in the original array, and unindexed fields are merely missing.  Warning In Numpy 1.15, indexing an array with a multi-field index returned a copy of the result above, but with fields packed together in memory as if passed through numpy.lib.recfunctions.repack_fields. The new behavior as of Numpy 1.16 leads to extra “padding” bytes at the location of unindexed fields compared to 1.15. You will need to update any code which depends on the data having a “packed” layout. For instance code such as: >>> a[['a', 'c']].view('i8')  # Fails in Numpy 1.16
Traceback (most recent call last):
   File "<stdin>", line 1, in <module>
ValueError: When changing to a smaller dtype, its size must be a divisor of the size of original dtype
 will need to be changed. This code has raised a FutureWarning since Numpy 1.12, and similar code has raised FutureWarning since 1.7. In 1.16 a number of functions have been introduced in the numpy.lib.recfunctions module to help users account for this change. These are numpy.lib.recfunctions.repack_fields. numpy.lib.recfunctions.structured_to_unstructured, numpy.lib.recfunctions.unstructured_to_structured, numpy.lib.recfunctions.apply_along_fields, numpy.lib.recfunctions.assign_fields_by_name, and numpy.lib.recfunctions.require_fields. The function numpy.lib.recfunctions.repack_fields can always be used to reproduce the old behavior, as it will return a packed copy of the structured array. The code above, for example, can be replaced with: >>> from numpy.lib.recfunctions import repack_fields
>>> repack_fields(a[['a', 'c']]).view('i8')  # supported in 1.16
array([0, 0, 0])
 Furthermore, numpy now provides a new function numpy.lib.recfunctions.structured_to_unstructured which is a safer and more efficient alternative for users who wish to convert structured arrays to unstructured arrays, as the view above is often indeded to do. This function allows safe conversion to an unstructured type taking into account padding, often avoids a copy, and also casts the datatypes as needed, unlike the view. Code such as: >>> b = np.zeros(3, dtype=[('x', 'f4'), ('y', 'f4'), ('z', 'f4')])
>>> b[['x', 'z']].view('f4')
array([0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)
 can be made safer by replacing with: >>> from numpy.lib.recfunctions import structured_to_unstructured
>>> structured_to_unstructured(b[['x', 'z']])
array([0, 0, 0])
  Assignment to an array with a multi-field index modifies the original array: >>> a[['a', 'c']] = (2, 3)
>>> a
array([(2, 0, 3.), (2, 0, 3.), (2, 0, 3.)],
      dtype=[('a', '<i4'), ('b', '<i4'), ('c', '<f4')])
 This obeys the structured array assignment rules described above. For example, this means that one can swap the values of two fields using appropriate multi-field indexes: >>> a[['a', 'c']] = a[['c', 'a']]
   Indexing with an Integer to get a Structured Scalar Indexing a single element of a structured array (with an integer index) returns a structured scalar: >>> x = np.array([(1, 2., 3.)], dtype='i, f, f')
>>> scalar = x[0]
>>> scalar
(1, 2., 3.)
>>> type(scalar)
<class 'numpy.void'>
 Unlike other numpy scalars, structured scalars are mutable and act like views into the original array, such that modifying the scalar will modify the original array. Structured scalars also support access and assignment by field name: >>> x = np.array([(1, 2), (3, 4)], dtype=[('foo', 'i8'), ('bar', 'f4')])
>>> s = x[0]
>>> s['bar'] = 100
>>> x
array([(1, 100.), (3, 4.)],
      dtype=[('foo', '<i8'), ('bar', '<f4')])
 Similarly to tuples, structured scalars can also be indexed with an integer: >>> scalar = np.array([(1, 2., 3.)], dtype='i, f, f')[0]
>>> scalar[0]
1
>>> scalar[1] = 4
 Thus, tuples might be thought of as the native Python equivalent to numpy’s structured types, much like native python integers are the equivalent to numpy’s integer types. Structured scalars may be converted to a tuple by calling numpy.ndarray.item: >>> scalar.item(), type(scalar.item())
((1, 4.0, 3.0), <class 'tuple'>)
    Viewing Structured Arrays Containing Objects In order to prevent clobbering object pointers in fields of object type, numpy currently does not allow views of structured arrays containing objects.   Structure Comparison If the dtypes of two void structured arrays are equal, testing the equality of the arrays will result in a boolean array with the dimensions of the original arrays, with elements set to True where all fields of the corresponding structures are equal. Structured dtypes are equal if the field names, dtypes and titles are the same, ignoring endianness, and the fields are in the same order: >>> a = np.zeros(2, dtype=[('a', 'i4'), ('b', 'i4')])
>>> b = np.ones(2, dtype=[('a', 'i4'), ('b', 'i4')])
>>> a == b
array([False, False])
 Currently, if the dtypes of two void structured arrays are not equivalent the comparison fails, returning the scalar value False. This behavior is deprecated as of numpy 1.10 and will raise an error or perform elementwise comparison in the future. The < and > operators always return False when comparing void structured arrays, and arithmetic and bitwise operations are not supported.    Record Arrays As an optional convenience numpy provides an ndarray subclass, numpy.recarray that allows access to fields of structured arrays by attribute instead of only by index. Record arrays use a special datatype, numpy.record, that allows field access by attribute on the structured scalars obtained from the array. The numpy.rec module provides functions for creating recarrays from various objects. Additional helper functions for creating and manipulating structured arrays can be found in numpy.lib.recfunctions. The simplest way to create a record array is with numpy.rec.array: >>> recordarr = np.rec.array([(1, 2., 'Hello'), (2, 3., "World")],
...                    dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'S10')])
>>> recordarr.bar
array([ 2.,  3.], dtype=float32)
>>> recordarr[1:2]
rec.array([(2, 3., b'World')],
      dtype=[('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')])
>>> recordarr[1:2].foo
array([2], dtype=int32)
>>> recordarr.foo[1:2]
array([2], dtype=int32)
>>> recordarr[1].baz
b'World'
 numpy.rec.array can convert a wide variety of arguments into record arrays, including structured arrays: >>> arr = np.array([(1, 2., 'Hello'), (2, 3., "World")],
...             dtype=[('foo', 'i4'), ('bar', 'f4'), ('baz', 'S10')])
>>> recordarr = np.rec.array(arr)
 The numpy.rec module provides a number of other convenience functions for creating record arrays, see record array creation routines. A record array representation of a structured array can be obtained using the appropriate view: >>> arr = np.array([(1, 2., 'Hello'), (2, 3., "World")],
...                dtype=[('foo', 'i4'),('bar', 'f4'), ('baz', 'a10')])
>>> recordarr = arr.view(dtype=np.dtype((np.record, arr.dtype)),
...                      type=np.recarray)
 For convenience, viewing an ndarray as type numpy.recarray will automatically convert to numpy.record datatype, so the dtype can be left out of the view: >>> recordarr = arr.view(np.recarray)
>>> recordarr.dtype
dtype((numpy.record, [('foo', '<i4'), ('bar', '<f4'), ('baz', 'S10')]))
 To get back to a plain ndarray both the dtype and type must be reset. The following view does so, taking into account the unusual case that the recordarr was not a structured type: >>> arr2 = recordarr.view(recordarr.dtype.fields or recordarr.dtype, np.ndarray)
 Record array fields accessed by index or by attribute are returned as a record array if the field has a structured type but as a plain ndarray otherwise. >>> recordarr = np.rec.array([('Hello', (1, 2)), ("World", (3, 4))],
...                 dtype=[('foo', 'S6'),('bar', [('A', int), ('B', int)])])
>>> type(recordarr.foo)
<class 'numpy.ndarray'>
>>> type(recordarr.bar)
<class 'numpy.recarray'>
 Note that if a field has the same name as an ndarray attribute, the ndarray attribute takes precedence. Such fields will be inaccessible by attribute but will still be accessible by index.  Recarray Helper Functions Collection of utilities to manipulate structured arrays. Most of these functions were initially implemented by John Hunter for matplotlib. They have been rewritten and extended for convenience.   numpy.lib.recfunctions.append_fields(base, names, data, dtypes=None, fill_value=- 1, usemask=True, asrecarray=False)[source]

Add new fields to an existing array. The names of the fields are given with the names arguments, the corresponding values with the data arguments. If a single field is appended, names, data and dtypes do not have to be lists but just values.  Parameters

basearray

Input array to extend.
namesstring, sequence

String or sequence of strings corresponding to the names of the new fields.
dataarray or sequence of arrays

Array or sequence of arrays storing the fields to add to the base.
dtypessequence of datatypes, optional

Datatype or sequence of datatypes. If None, the datatypes are estimated from the data.
fill_value{float}, optional

Filling value used to pad missing data on the shorter arrays.
usemask{False, True}, optional

Whether to return a masked array or not.
asrecarray{False, True}, optional

Whether to return a recarray (MaskedRecords) or not.
   numpy.lib.recfunctions.apply_along_fields(func, arr)[source]

Apply function ‘func’ as a reduction across fields of a structured array. This is similar to apply_along_axis, but treats the fields of a structured array as an extra axis. The fields are all first cast to a common type following the type-promotion rules from numpy.result_type applied to the field’s dtypes.  Parameters

funcfunction

Function to apply on the “field” dimension. This function must support an axis argument, like np.mean, np.sum, etc.
arrndarray

Structured array for which to apply func.    Returns

outndarray

Result of the recution operation     Examples >>> from numpy.lib import recfunctions as rfn
>>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
>>> rfn.apply_along_fields(np.mean, b)
array([ 2.66666667,  5.33333333,  8.66666667, 11.        ])
>>> rfn.apply_along_fields(np.mean, b[['x', 'z']])
array([ 3. ,  5.5,  9. , 11. ])

   numpy.lib.recfunctions.assign_fields_by_name(dst, src, zero_unassigned=True)[source]

Assigns values from one structured array to another by field name. Normally in numpy >= 1.14, assignment of one structured array to another copies fields “by position”, meaning that the first field from the src is copied to the first field of the dst, and so on, regardless of field name. This function instead copies “by field name”, such that fields in the dst are assigned from the identically named field in the src. This applies recursively for nested structures. This is how structure assignment worked in numpy >= 1.6 to <= 1.13.  Parameters

dstndarray

srcndarray

The source and destination arrays during assignment.
zero_unassignedbool, optional

If True, fields in the dst for which there was no matching field in the src are filled with the value 0 (zero). This was the behavior of numpy <= 1.13. If False, those fields are not modified.
   numpy.lib.recfunctions.drop_fields(base, drop_names, usemask=True, asrecarray=False)[source]

Return a new array with fields in drop_names dropped. Nested fields are supported.  Changed in version 1.18.0: drop_fields returns an array with 0 fields if all fields are dropped, rather than returning None as it did previously.   Parameters

basearray

Input array
drop_namesstring or sequence

String or sequence of strings corresponding to the names of the fields to drop.
usemask{False, True}, optional

Whether to return a masked array or not.
asrecarraystring or sequence, optional

Whether to return a recarray or a mrecarray (asrecarray=True) or a plain ndarray or masked array with flexible dtype. The default is False.     Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
...   dtype=[('a', np.int64), ('b', [('ba', np.double), ('bb', np.int64)])])
>>> rfn.drop_fields(a, 'a')
array([((2., 3),), ((5., 6),)],
      dtype=[('b', [('ba', '<f8'), ('bb', '<i8')])])
>>> rfn.drop_fields(a, 'ba')
array([(1, (3,)), (4, (6,))], dtype=[('a', '<i8'), ('b', [('bb', '<i8')])])
>>> rfn.drop_fields(a, ['ba', 'bb'])
array([(1,), (4,)], dtype=[('a', '<i8')])

   numpy.lib.recfunctions.find_duplicates(a, key=None, ignoremask=True, return_index=False)[source]

Find the duplicates in a structured array along a given key  Parameters

aarray-like

Input array
key{string, None}, optional

Name of the fields along which to check the duplicates. If None, the search is performed by records
ignoremask{True, False}, optional

Whether masked data should be discarded or considered as duplicates.
return_index{False, True}, optional

Whether to return the indices of the duplicated values.     Examples >>> from numpy.lib import recfunctions as rfn
>>> ndtype = [('a', int)]
>>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3],
...         mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
>>> rfn.find_duplicates(a, ignoremask=True, return_index=True)
(masked_array(data=[(1,), (1,), (2,), (2,)],
             mask=[(False,), (False,), (False,), (False,)],
       fill_value=(999999,),
            dtype=[('a', '<i8')]), array([0, 1, 3, 4]))

   numpy.lib.recfunctions.flatten_descr(ndtype)[source]

Flatten a structured data-type description. Examples >>> from numpy.lib import recfunctions as rfn
>>> ndtype = np.dtype([('a', '<i4'), ('b', [('ba', '<f8'), ('bb', '<i4')])])
>>> rfn.flatten_descr(ndtype)
(('a', dtype('int32')), ('ba', dtype('float64')), ('bb', dtype('int32')))

   numpy.lib.recfunctions.get_fieldstructure(adtype, lastname=None, parents=None)[source]

Returns a dictionary with fields indexing lists of their parent fields. This function is used to simplify access to fields nested in other fields.  Parameters

adtypenp.dtype

Input datatype
lastnameoptional

Last processed field name (used internally during recursion).
parentsdictionary

Dictionary of parent fields (used interbally during recursion).     Examples >>> from numpy.lib import recfunctions as rfn
>>> ndtype =  np.dtype([('A', int),
...                     ('B', [('BA', int),
...                            ('BB', [('BBA', int), ('BBB', int)])])])
>>> rfn.get_fieldstructure(ndtype)
... # XXX: possible regression, order of BBA and BBB is swapped
{'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'], 'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']}

   numpy.lib.recfunctions.get_names(adtype)[source]

Returns the field names of the input datatype as a tuple.  Parameters

adtypedtype

Input datatype     Examples >>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names(np.empty((1,), dtype=int))
Traceback (most recent call last):
    ...
AttributeError: 'numpy.ndarray' object has no attribute 'names'
 >>> rfn.get_names(np.empty((1,), dtype=[('A',int), ('B', float)]))
Traceback (most recent call last):
    ...
AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names(adtype)
('a', ('b', ('ba', 'bb')))

   numpy.lib.recfunctions.get_names_flat(adtype)[source]

Returns the field names of the input datatype as a tuple. Nested structure are flattened beforehand.  Parameters

adtypedtype

Input datatype     Examples >>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names_flat(np.empty((1,), dtype=int)) is None
Traceback (most recent call last):
    ...
AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', float)]))
Traceback (most recent call last):
    ...
AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names_flat(adtype)
('a', 'b', 'ba', 'bb')

   numpy.lib.recfunctions.join_by(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None, usemask=True, asrecarray=False)[source]

Join arrays r1 and r2 on key key. The key should be either a string or a sequence of string corresponding to the fields used to join the array. An exception is raised if the key field cannot be found in the two input arrays. Neither r1 nor r2 should have any duplicates along key: the presence of duplicates will make the output quite unreliable. Note that duplicates are not looked for by the algorithm.  Parameters

key{string, sequence}

A string or a sequence of strings corresponding to the fields used for comparison.
r1, r2arrays

Structured arrays.
jointype{‘inner’, ‘outer’, ‘leftouter’}, optional

If ‘inner’, returns the elements common to both r1 and r2. If ‘outer’, returns the common elements as well as the elements of r1 not in r2 and the elements of not in r2. If ‘leftouter’, returns the common elements and the elements of r1 not in r2.
r1postfixstring, optional

String appended to the names of the fields of r1 that are present in r2 but absent of the key.
r2postfixstring, optional

String appended to the names of the fields of r2 that are present in r1 but absent of the key.
defaults{dictionary}, optional

Dictionary mapping field names to the corresponding default values.
usemask{True, False}, optional

Whether to return a MaskedArray (or MaskedRecords is asrecarray==True) or a ndarray.
asrecarray{False, True}, optional

Whether to return a recarray (or MaskedRecords if usemask==True) or just a flexible-type ndarray.     Notes  The output is sorted along the key. A temporary array is formed by dropping the fields not in the key for the two arrays and concatenating the result. This array is then sorted, and the common entries selected. The output is constructed by filling the fields with the selected entries. Matching is not preserved if there are some duplicates…
   numpy.lib.recfunctions.merge_arrays(seqarrays, fill_value=- 1, flatten=False, usemask=False, asrecarray=False)[source]

Merge arrays field by field.  Parameters

seqarrayssequence of ndarrays

Sequence of arrays
fill_value{float}, optional

Filling value used to pad missing data on the shorter arrays.
flatten{False, True}, optional

Whether to collapse nested fields.
usemask{False, True}, optional

Whether to return a masked array or not.
asrecarray{False, True}, optional

Whether to return a recarray (MaskedRecords) or not.     Notes
Without a mask, the missing value will be filled with something, depending on what its corresponding type:
-1 for integers
-1.0 for floating point numbers
'-' for characters
'-1' for strings
True for boolean values   XXX: I just obtained these values empirically  Examples >>> from numpy.lib import recfunctions as rfn
>>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])))
array([( 1, 10.), ( 2, 20.), (-1, 30.)],
      dtype=[('f0', '<i8'), ('f1', '<f8')])
 >>> rfn.merge_arrays((np.array([1, 2], dtype=np.int64),
...         np.array([10., 20., 30.])), usemask=False)
 array([(1, 10.0), (2, 20.0), (-1, 30.0)],
         dtype=[('f0', '<i8'), ('f1', '<f8')])
>>> rfn.merge_arrays((np.array([1, 2]).view([('a', np.int64)]),
...               np.array([10., 20., 30.])),
...              usemask=False, asrecarray=True)
rec.array([( 1, 10.), ( 2, 20.), (-1, 30.)],
          dtype=[('a', '<i8'), ('f1', '<f8')])

   numpy.lib.recfunctions.rec_append_fields(base, names, data, dtypes=None)[source]

Add new fields to an existing array. The names of the fields are given with the names arguments, the corresponding values with the data arguments. If a single field is appended, names, data and dtypes do not have to be lists but just values.  Parameters

basearray

Input array to extend.
namesstring, sequence

String or sequence of strings corresponding to the names of the new fields.
dataarray or sequence of arrays

Array or sequence of arrays storing the fields to add to the base.
dtypessequence of datatypes, optional

Datatype or sequence of datatypes. If None, the datatypes are estimated from the data.    Returns

appended_arraynp.recarray

    See also  append_fields

   numpy.lib.recfunctions.rec_drop_fields(base, drop_names)[source]

Returns a new numpy.recarray with fields in drop_names dropped.
   numpy.lib.recfunctions.rec_join(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None)[source]

Join arrays r1 and r2 on keys. Alternative to join_by, that always returns a np.recarray.  See also  join_by

equivalent function
   numpy.lib.recfunctions.recursive_fill_fields(input, output)[source]

Fills fields from output with fields from input, with support for nested structures.  Parameters

inputndarray

Input array.
outputndarray

Output array.     Notes
output should be at least the same size as input
  Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', np.int64), ('B', np.float64)])
>>> b = np.zeros((3,), dtype=a.dtype)
>>> rfn.recursive_fill_fields(a, b)
array([(1, 10.), (2, 20.), (0,  0.)], dtype=[('A', '<i8'), ('B', '<f8')])

   numpy.lib.recfunctions.rename_fields(base, namemapper)[source]

Rename the fields from a flexible-datatype ndarray or recarray. Nested fields are supported.  Parameters

basendarray

Input array whose fields must be modified.
namemapperdictionary

Dictionary mapping old field names to their new version.     Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
...   dtype=[('a', int),('b', [('ba', float), ('bb', (float, 2))])])
>>> rfn.rename_fields(a, {'a':'A', 'bb':'BB'})
array([(1, (2., [ 3., 30.])), (4, (5., [ 6., 60.]))],
      dtype=[('A', '<i8'), ('b', [('ba', '<f8'), ('BB', '<f8', (2,))])])

   numpy.lib.recfunctions.repack_fields(a, align=False, recurse=False)[source]

Re-pack the fields of a structured array or dtype in memory. The memory layout of structured datatypes allows fields at arbitrary byte offsets. This means the fields can be separated by padding bytes, their offsets can be non-monotonically increasing, and they can overlap. This method removes any overlaps and reorders the fields in memory so they have increasing byte offsets, and adds or removes padding bytes depending on the align option, which behaves like the align option to np.dtype. If align=False, this method produces a “packed” memory layout in which each field starts at the byte the previous field ended, and any padding bytes are removed. If align=True, this methods produces an “aligned” memory layout in which each field’s offset is a multiple of its alignment, and the total itemsize is a multiple of the largest alignment, by adding padding bytes as needed.  Parameters

andarray or dtype

array or dtype for which to repack the fields.
alignboolean

If true, use an “aligned” memory layout, otherwise use a “packed” layout.
recurseboolean

If True, also repack nested structures.    Returns

repackedndarray or dtype

Copy of a with fields repacked, or a itself if no repacking was needed.     Examples >>> from numpy.lib import recfunctions as rfn
>>> def print_offsets(d):
...     print("offsets:", [d.fields[name][1] for name in d.names])
...     print("itemsize:", d.itemsize)
...
>>> dt = np.dtype('u1, <i8, <f8', align=True)
>>> dt
dtype({'names': ['f0', 'f1', 'f2'], 'formats': ['u1', '<i8', '<f8'], 'offsets': [0, 8, 16], 'itemsize': 24}, align=True)
>>> print_offsets(dt)
offsets: [0, 8, 16]
itemsize: 24
>>> packed_dt = rfn.repack_fields(dt)
>>> packed_dt
dtype([('f0', 'u1'), ('f1', '<i8'), ('f2', '<f8')])
>>> print_offsets(packed_dt)
offsets: [0, 1, 9]
itemsize: 17

   numpy.lib.recfunctions.require_fields(array, required_dtype)[source]

Casts a structured array to a new dtype using assignment by field-name. This function assigns from the old to the new array by name, so the value of a field in the output array is the value of the field with the same name in the source array. This has the effect of creating a new ndarray containing only the fields “required” by the required_dtype. If a field name in the required_dtype does not exist in the input array, that field is created and set to 0 in the output array.  Parameters

andarray

array to cast
required_dtypedtype

datatype for output array    Returns

outndarray

array with the new dtype, with field values copied from the fields in the input array with the same name     Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.ones(4, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
>>> rfn.require_fields(a, [('b', 'f4'), ('c', 'u1')])
array([(1., 1), (1., 1), (1., 1), (1., 1)],
  dtype=[('b', '<f4'), ('c', 'u1')])
>>> rfn.require_fields(a, [('b', 'f4'), ('newf', 'u1')])
array([(1., 0), (1., 0), (1., 0), (1., 0)],
  dtype=[('b', '<f4'), ('newf', 'u1')])

   numpy.lib.recfunctions.stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False, autoconvert=False)[source]

Superposes arrays fields by fields  Parameters

arraysarray or sequence

Sequence of input arrays.
defaultsdictionary, optional

Dictionary mapping field names to the corresponding default values.
usemask{True, False}, optional

Whether to return a MaskedArray (or MaskedRecords is asrecarray==True) or a ndarray.
asrecarray{False, True}, optional

Whether to return a recarray (or MaskedRecords if usemask==True) or just a flexible-type ndarray.
autoconvert{False, True}, optional

Whether automatically cast the type of the field to the maximum.     Examples >>> from numpy.lib import recfunctions as rfn
>>> x = np.array([1, 2,])
>>> rfn.stack_arrays(x) is x
True
>>> z = np.array([('A', 1), ('B', 2)], dtype=[('A', '|S3'), ('B', float)])
>>> zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
...   dtype=[('A', '|S3'), ('B', np.double), ('C', np.double)])
>>> test = rfn.stack_arrays((z,zz))
>>> test
masked_array(data=[(b'A', 1.0, --), (b'B', 2.0, --), (b'a', 10.0, 100.0),
                   (b'b', 20.0, 200.0), (b'c', 30.0, 300.0)],
             mask=[(False, False,  True), (False, False,  True),
                   (False, False, False), (False, False, False),
                   (False, False, False)],
       fill_value=(b'N/A', 1.e+20, 1.e+20),
            dtype=[('A', 'S3'), ('B', '<f8'), ('C', '<f8')])

   numpy.lib.recfunctions.structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe')[source]

Converts an n-D structured array into an (n+1)-D unstructured array. The new array will have a new last dimension equal in size to the number of field-elements of the input array. If not supplied, the output datatype is determined from the numpy type promotion rules applied to all the field datatypes. Nested fields, as well as each element of any subarray fields, all count as a single field-elements.  Parameters

arrndarray

Structured array or dtype to convert. Cannot contain object datatype.
dtypedtype, optional

The dtype of the output unstructured array.
copybool, optional

See copy argument to ndarray.astype. If true, always return a copy. If false, and dtype requirements are satisfied, a view is returned.
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

See casting argument of ndarray.astype. Controls what kind of data casting may occur.    Returns

unstructuredndarray

Unstructured array with one more dimension.     Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
>>> a
array([(0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.]),
       (0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.])],
      dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))])
>>> rfn.structured_to_unstructured(a)
array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])
 >>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
>>> np.mean(rfn.structured_to_unstructured(b[['x', 'z']]), axis=-1)
array([ 3. ,  5.5,  9. , 11. ])

   numpy.lib.recfunctions.unstructured_to_structured(arr, dtype=None, names=None, align=False, copy=False, casting='unsafe')[source]

Converts an n-D unstructured array into an (n-1)-D structured array. The last dimension of the input array is converted into a structure, with number of field-elements equal to the size of the last dimension of the input array. By default all output fields have the input array’s dtype, but an output structured dtype with an equal number of fields-elements can be supplied instead. Nested fields, as well as each element of any subarray fields, all count towards the number of field-elements.  Parameters

arrndarray

Unstructured array or dtype to convert.
dtypedtype, optional

The structured dtype of the output array
nameslist of strings, optional

If dtype is not supplied, this specifies the field names for the output dtype, in order. The field dtypes will be the same as the input array.
alignboolean, optional

Whether to create an aligned memory layout.
copybool, optional

See copy argument to ndarray.astype. If true, always return a copy. If false, and dtype requirements are satisfied, a view is returned.
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

See casting argument of ndarray.astype. Controls what kind of data casting may occur.    Returns

structuredndarray

Structured array with fewer dimensions.     Examples >>> from numpy.lib import recfunctions as rfn
>>> dt = np.dtype([('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
>>> a = np.arange(20).reshape((4,5))
>>> a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19]])
>>> rfn.unstructured_to_structured(a, dt)
array([( 0, ( 1.,  2), [ 3.,  4.]), ( 5, ( 6.,  7), [ 8.,  9.]),
       (10, (11., 12), [13., 14.]), (15, (16., 17), [18., 19.])],
      dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))])


numpy.lib.recfunctions.apply_along_fields(func, arr)[source]

Apply function ‘func’ as a reduction across fields of a structured array. This is similar to apply_along_axis, but treats the fields of a structured array as an extra axis. The fields are all first cast to a common type following the type-promotion rules from numpy.result_type applied to the field’s dtypes.  Parameters

funcfunction

Function to apply on the “field” dimension. This function must support an axis argument, like np.mean, np.sum, etc.
arrndarray

Structured array for which to apply func.    Returns

outndarray

Result of the recution operation     Examples >>> from numpy.lib import recfunctions as rfn
>>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
>>> rfn.apply_along_fields(np.mean, b)
array([ 2.66666667,  5.33333333,  8.66666667, 11.        ])
>>> rfn.apply_along_fields(np.mean, b[['x', 'z']])
array([ 3. ,  5.5,  9. , 11. ])


numpy.lib.recfunctions.assign_fields_by_name(dst, src, zero_unassigned=True)[source]

Assigns values from one structured array to another by field name. Normally in numpy >= 1.14, assignment of one structured array to another copies fields “by position”, meaning that the first field from the src is copied to the first field of the dst, and so on, regardless of field name. This function instead copies “by field name”, such that fields in the dst are assigned from the identically named field in the src. This applies recursively for nested structures. This is how structure assignment worked in numpy >= 1.6 to <= 1.13.  Parameters

dstndarray

srcndarray

The source and destination arrays during assignment.
zero_unassignedbool, optional

If True, fields in the dst for which there was no matching field in the src are filled with the value 0 (zero). This was the behavior of numpy <= 1.13. If False, those fields are not modified.


numpy.lib.recfunctions.drop_fields(base, drop_names, usemask=True, asrecarray=False)[source]

Return a new array with fields in drop_names dropped. Nested fields are supported.  Changed in version 1.18.0: drop_fields returns an array with 0 fields if all fields are dropped, rather than returning None as it did previously.   Parameters

basearray

Input array
drop_namesstring or sequence

String or sequence of strings corresponding to the names of the fields to drop.
usemask{False, True}, optional

Whether to return a masked array or not.
asrecarraystring or sequence, optional

Whether to return a recarray or a mrecarray (asrecarray=True) or a plain ndarray or masked array with flexible dtype. The default is False.     Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, 3.0)), (4, (5, 6.0))],
...   dtype=[('a', np.int64), ('b', [('ba', np.double), ('bb', np.int64)])])
>>> rfn.drop_fields(a, 'a')
array([((2., 3),), ((5., 6),)],
      dtype=[('b', [('ba', '<f8'), ('bb', '<i8')])])
>>> rfn.drop_fields(a, 'ba')
array([(1, (3,)), (4, (6,))], dtype=[('a', '<i8'), ('b', [('bb', '<i8')])])
>>> rfn.drop_fields(a, ['ba', 'bb'])
array([(1,), (4,)], dtype=[('a', '<i8')])


numpy.lib.recfunctions.find_duplicates(a, key=None, ignoremask=True, return_index=False)[source]

Find the duplicates in a structured array along a given key  Parameters

aarray-like

Input array
key{string, None}, optional

Name of the fields along which to check the duplicates. If None, the search is performed by records
ignoremask{True, False}, optional

Whether masked data should be discarded or considered as duplicates.
return_index{False, True}, optional

Whether to return the indices of the duplicated values.     Examples >>> from numpy.lib import recfunctions as rfn
>>> ndtype = [('a', int)]
>>> a = np.ma.array([1, 1, 1, 2, 2, 3, 3],
...         mask=[0, 0, 1, 0, 0, 0, 1]).view(ndtype)
>>> rfn.find_duplicates(a, ignoremask=True, return_index=True)
(masked_array(data=[(1,), (1,), (2,), (2,)],
             mask=[(False,), (False,), (False,), (False,)],
       fill_value=(999999,),
            dtype=[('a', '<i8')]), array([0, 1, 3, 4]))


numpy.lib.recfunctions.flatten_descr(ndtype)[source]

Flatten a structured data-type description. Examples >>> from numpy.lib import recfunctions as rfn
>>> ndtype = np.dtype([('a', '<i4'), ('b', [('ba', '<f8'), ('bb', '<i4')])])
>>> rfn.flatten_descr(ndtype)
(('a', dtype('int32')), ('ba', dtype('float64')), ('bb', dtype('int32')))


numpy.lib.recfunctions.get_fieldstructure(adtype, lastname=None, parents=None)[source]

Returns a dictionary with fields indexing lists of their parent fields. This function is used to simplify access to fields nested in other fields.  Parameters

adtypenp.dtype

Input datatype
lastnameoptional

Last processed field name (used internally during recursion).
parentsdictionary

Dictionary of parent fields (used interbally during recursion).     Examples >>> from numpy.lib import recfunctions as rfn
>>> ndtype =  np.dtype([('A', int),
...                     ('B', [('BA', int),
...                            ('BB', [('BBA', int), ('BBB', int)])])])
>>> rfn.get_fieldstructure(ndtype)
... # XXX: possible regression, order of BBA and BBB is swapped
{'A': [], 'B': [], 'BA': ['B'], 'BB': ['B'], 'BBA': ['B', 'BB'], 'BBB': ['B', 'BB']}


numpy.lib.recfunctions.get_names(adtype)[source]

Returns the field names of the input datatype as a tuple.  Parameters

adtypedtype

Input datatype     Examples >>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names(np.empty((1,), dtype=int))
Traceback (most recent call last):
    ...
AttributeError: 'numpy.ndarray' object has no attribute 'names'
 >>> rfn.get_names(np.empty((1,), dtype=[('A',int), ('B', float)]))
Traceback (most recent call last):
    ...
AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names(adtype)
('a', ('b', ('ba', 'bb')))


numpy.lib.recfunctions.get_names_flat(adtype)[source]

Returns the field names of the input datatype as a tuple. Nested structure are flattened beforehand.  Parameters

adtypedtype

Input datatype     Examples >>> from numpy.lib import recfunctions as rfn
>>> rfn.get_names_flat(np.empty((1,), dtype=int)) is None
Traceback (most recent call last):
    ...
AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> rfn.get_names_flat(np.empty((1,), dtype=[('A',int), ('B', float)]))
Traceback (most recent call last):
    ...
AttributeError: 'numpy.ndarray' object has no attribute 'names'
>>> adtype = np.dtype([('a', int), ('b', [('ba', int), ('bb', int)])])
>>> rfn.get_names_flat(adtype)
('a', 'b', 'ba', 'bb')


numpy.lib.recfunctions.join_by(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None, usemask=True, asrecarray=False)[source]

Join arrays r1 and r2 on key key. The key should be either a string or a sequence of string corresponding to the fields used to join the array. An exception is raised if the key field cannot be found in the two input arrays. Neither r1 nor r2 should have any duplicates along key: the presence of duplicates will make the output quite unreliable. Note that duplicates are not looked for by the algorithm.  Parameters

key{string, sequence}

A string or a sequence of strings corresponding to the fields used for comparison.
r1, r2arrays

Structured arrays.
jointype{‘inner’, ‘outer’, ‘leftouter’}, optional

If ‘inner’, returns the elements common to both r1 and r2. If ‘outer’, returns the common elements as well as the elements of r1 not in r2 and the elements of not in r2. If ‘leftouter’, returns the common elements and the elements of r1 not in r2.
r1postfixstring, optional

String appended to the names of the fields of r1 that are present in r2 but absent of the key.
r2postfixstring, optional

String appended to the names of the fields of r2 that are present in r1 but absent of the key.
defaults{dictionary}, optional

Dictionary mapping field names to the corresponding default values.
usemask{True, False}, optional

Whether to return a MaskedArray (or MaskedRecords is asrecarray==True) or a ndarray.
asrecarray{False, True}, optional

Whether to return a recarray (or MaskedRecords if usemask==True) or just a flexible-type ndarray.     Notes  The output is sorted along the key. A temporary array is formed by dropping the fields not in the key for the two arrays and concatenating the result. This array is then sorted, and the common entries selected. The output is constructed by filling the fields with the selected entries. Matching is not preserved if there are some duplicates…


numpy.lib.recfunctions.merge_arrays(seqarrays, fill_value=- 1, flatten=False, usemask=False, asrecarray=False)[source]

Merge arrays field by field.  Parameters

seqarrayssequence of ndarrays

Sequence of arrays
fill_value{float}, optional

Filling value used to pad missing data on the shorter arrays.
flatten{False, True}, optional

Whether to collapse nested fields.
usemask{False, True}, optional

Whether to return a masked array or not.
asrecarray{False, True}, optional

Whether to return a recarray (MaskedRecords) or not.     Notes
Without a mask, the missing value will be filled with something, depending on what its corresponding type:
-1 for integers
-1.0 for floating point numbers
'-' for characters
'-1' for strings
True for boolean values   XXX: I just obtained these values empirically  Examples >>> from numpy.lib import recfunctions as rfn
>>> rfn.merge_arrays((np.array([1, 2]), np.array([10., 20., 30.])))
array([( 1, 10.), ( 2, 20.), (-1, 30.)],
      dtype=[('f0', '<i8'), ('f1', '<f8')])
 >>> rfn.merge_arrays((np.array([1, 2], dtype=np.int64),
...         np.array([10., 20., 30.])), usemask=False)
 array([(1, 10.0), (2, 20.0), (-1, 30.0)],
         dtype=[('f0', '<i8'), ('f1', '<f8')])
>>> rfn.merge_arrays((np.array([1, 2]).view([('a', np.int64)]),
...               np.array([10., 20., 30.])),
...              usemask=False, asrecarray=True)
rec.array([( 1, 10.), ( 2, 20.), (-1, 30.)],
          dtype=[('a', '<i8'), ('f1', '<f8')])


numpy.lib.recfunctions.rec_append_fields(base, names, data, dtypes=None)[source]

Add new fields to an existing array. The names of the fields are given with the names arguments, the corresponding values with the data arguments. If a single field is appended, names, data and dtypes do not have to be lists but just values.  Parameters

basearray

Input array to extend.
namesstring, sequence

String or sequence of strings corresponding to the names of the new fields.
dataarray or sequence of arrays

Array or sequence of arrays storing the fields to add to the base.
dtypessequence of datatypes, optional

Datatype or sequence of datatypes. If None, the datatypes are estimated from the data.    Returns

appended_arraynp.recarray

    See also  append_fields


numpy.lib.recfunctions.rec_drop_fields(base, drop_names)[source]

Returns a new numpy.recarray with fields in drop_names dropped.


numpy.lib.recfunctions.rec_join(key, r1, r2, jointype='inner', r1postfix='1', r2postfix='2', defaults=None)[source]

Join arrays r1 and r2 on keys. Alternative to join_by, that always returns a np.recarray.  See also  join_by

equivalent function


numpy.lib.recfunctions.recursive_fill_fields(input, output)[source]

Fills fields from output with fields from input, with support for nested structures.  Parameters

inputndarray

Input array.
outputndarray

Output array.     Notes
output should be at least the same size as input
  Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, 10.), (2, 20.)], dtype=[('A', np.int64), ('B', np.float64)])
>>> b = np.zeros((3,), dtype=a.dtype)
>>> rfn.recursive_fill_fields(a, b)
array([(1, 10.), (2, 20.), (0,  0.)], dtype=[('A', '<i8'), ('B', '<f8')])


numpy.lib.recfunctions.rename_fields(base, namemapper)[source]

Rename the fields from a flexible-datatype ndarray or recarray. Nested fields are supported.  Parameters

basendarray

Input array whose fields must be modified.
namemapperdictionary

Dictionary mapping old field names to their new version.     Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.array([(1, (2, [3.0, 30.])), (4, (5, [6.0, 60.]))],
...   dtype=[('a', int),('b', [('ba', float), ('bb', (float, 2))])])
>>> rfn.rename_fields(a, {'a':'A', 'bb':'BB'})
array([(1, (2., [ 3., 30.])), (4, (5., [ 6., 60.]))],
      dtype=[('A', '<i8'), ('b', [('ba', '<f8'), ('BB', '<f8', (2,))])])


numpy.lib.recfunctions.repack_fields(a, align=False, recurse=False)[source]

Re-pack the fields of a structured array or dtype in memory. The memory layout of structured datatypes allows fields at arbitrary byte offsets. This means the fields can be separated by padding bytes, their offsets can be non-monotonically increasing, and they can overlap. This method removes any overlaps and reorders the fields in memory so they have increasing byte offsets, and adds or removes padding bytes depending on the align option, which behaves like the align option to np.dtype. If align=False, this method produces a “packed” memory layout in which each field starts at the byte the previous field ended, and any padding bytes are removed. If align=True, this methods produces an “aligned” memory layout in which each field’s offset is a multiple of its alignment, and the total itemsize is a multiple of the largest alignment, by adding padding bytes as needed.  Parameters

andarray or dtype

array or dtype for which to repack the fields.
alignboolean

If true, use an “aligned” memory layout, otherwise use a “packed” layout.
recurseboolean

If True, also repack nested structures.    Returns

repackedndarray or dtype

Copy of a with fields repacked, or a itself if no repacking was needed.     Examples >>> from numpy.lib import recfunctions as rfn
>>> def print_offsets(d):
...     print("offsets:", [d.fields[name][1] for name in d.names])
...     print("itemsize:", d.itemsize)
...
>>> dt = np.dtype('u1, <i8, <f8', align=True)
>>> dt
dtype({'names': ['f0', 'f1', 'f2'], 'formats': ['u1', '<i8', '<f8'], 'offsets': [0, 8, 16], 'itemsize': 24}, align=True)
>>> print_offsets(dt)
offsets: [0, 8, 16]
itemsize: 24
>>> packed_dt = rfn.repack_fields(dt)
>>> packed_dt
dtype([('f0', 'u1'), ('f1', '<i8'), ('f2', '<f8')])
>>> print_offsets(packed_dt)
offsets: [0, 1, 9]
itemsize: 17


numpy.lib.recfunctions.require_fields(array, required_dtype)[source]

Casts a structured array to a new dtype using assignment by field-name. This function assigns from the old to the new array by name, so the value of a field in the output array is the value of the field with the same name in the source array. This has the effect of creating a new ndarray containing only the fields “required” by the required_dtype. If a field name in the required_dtype does not exist in the input array, that field is created and set to 0 in the output array.  Parameters

andarray

array to cast
required_dtypedtype

datatype for output array    Returns

outndarray

array with the new dtype, with field values copied from the fields in the input array with the same name     Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.ones(4, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')])
>>> rfn.require_fields(a, [('b', 'f4'), ('c', 'u1')])
array([(1., 1), (1., 1), (1., 1), (1., 1)],
  dtype=[('b', '<f4'), ('c', 'u1')])
>>> rfn.require_fields(a, [('b', 'f4'), ('newf', 'u1')])
array([(1., 0), (1., 0), (1., 0), (1., 0)],
  dtype=[('b', '<f4'), ('newf', 'u1')])


numpy.lib.recfunctions.stack_arrays(arrays, defaults=None, usemask=True, asrecarray=False, autoconvert=False)[source]

Superposes arrays fields by fields  Parameters

arraysarray or sequence

Sequence of input arrays.
defaultsdictionary, optional

Dictionary mapping field names to the corresponding default values.
usemask{True, False}, optional

Whether to return a MaskedArray (or MaskedRecords is asrecarray==True) or a ndarray.
asrecarray{False, True}, optional

Whether to return a recarray (or MaskedRecords if usemask==True) or just a flexible-type ndarray.
autoconvert{False, True}, optional

Whether automatically cast the type of the field to the maximum.     Examples >>> from numpy.lib import recfunctions as rfn
>>> x = np.array([1, 2,])
>>> rfn.stack_arrays(x) is x
True
>>> z = np.array([('A', 1), ('B', 2)], dtype=[('A', '|S3'), ('B', float)])
>>> zz = np.array([('a', 10., 100.), ('b', 20., 200.), ('c', 30., 300.)],
...   dtype=[('A', '|S3'), ('B', np.double), ('C', np.double)])
>>> test = rfn.stack_arrays((z,zz))
>>> test
masked_array(data=[(b'A', 1.0, --), (b'B', 2.0, --), (b'a', 10.0, 100.0),
                   (b'b', 20.0, 200.0), (b'c', 30.0, 300.0)],
             mask=[(False, False,  True), (False, False,  True),
                   (False, False, False), (False, False, False),
                   (False, False, False)],
       fill_value=(b'N/A', 1.e+20, 1.e+20),
            dtype=[('A', 'S3'), ('B', '<f8'), ('C', '<f8')])


numpy.lib.recfunctions.structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe')[source]

Converts an n-D structured array into an (n+1)-D unstructured array. The new array will have a new last dimension equal in size to the number of field-elements of the input array. If not supplied, the output datatype is determined from the numpy type promotion rules applied to all the field datatypes. Nested fields, as well as each element of any subarray fields, all count as a single field-elements.  Parameters

arrndarray

Structured array or dtype to convert. Cannot contain object datatype.
dtypedtype, optional

The dtype of the output unstructured array.
copybool, optional

See copy argument to ndarray.astype. If true, always return a copy. If false, and dtype requirements are satisfied, a view is returned.
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

See casting argument of ndarray.astype. Controls what kind of data casting may occur.    Returns

unstructuredndarray

Unstructured array with one more dimension.     Examples >>> from numpy.lib import recfunctions as rfn
>>> a = np.zeros(4, dtype=[('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
>>> a
array([(0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.]),
       (0, (0., 0), [0., 0.]), (0, (0., 0), [0., 0.])],
      dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))])
>>> rfn.structured_to_unstructured(a)
array([[0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.],
       [0., 0., 0., 0., 0.]])
 >>> b = np.array([(1, 2, 5), (4, 5, 7), (7, 8 ,11), (10, 11, 12)],
...              dtype=[('x', 'i4'), ('y', 'f4'), ('z', 'f8')])
>>> np.mean(rfn.structured_to_unstructured(b[['x', 'z']]), axis=-1)
array([ 3. ,  5.5,  9. , 11. ])


numpy.lib.recfunctions.unstructured_to_structured(arr, dtype=None, names=None, align=False, copy=False, casting='unsafe')[source]

Converts an n-D unstructured array into an (n-1)-D structured array. The last dimension of the input array is converted into a structure, with number of field-elements equal to the size of the last dimension of the input array. By default all output fields have the input array’s dtype, but an output structured dtype with an equal number of fields-elements can be supplied instead. Nested fields, as well as each element of any subarray fields, all count towards the number of field-elements.  Parameters

arrndarray

Unstructured array or dtype to convert.
dtypedtype, optional

The structured dtype of the output array
nameslist of strings, optional

If dtype is not supplied, this specifies the field names for the output dtype, in order. The field dtypes will be the same as the input array.
alignboolean, optional

Whether to create an aligned memory layout.
copybool, optional

See copy argument to ndarray.astype. If true, always return a copy. If false, and dtype requirements are satisfied, a view is returned.
casting{‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

See casting argument of ndarray.astype. Controls what kind of data casting may occur.    Returns

structuredndarray

Structured array with fewer dimensions.     Examples >>> from numpy.lib import recfunctions as rfn
>>> dt = np.dtype([('a', 'i4'), ('b', 'f4,u2'), ('c', 'f4', 2)])
>>> a = np.arange(20).reshape((4,5))
>>> a
array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14],
       [15, 16, 17, 18, 19]])
>>> rfn.unstructured_to_structured(a, dt)
array([( 0, ( 1.,  2), [ 3.,  4.]), ( 5, ( 6.,  7), [ 8.,  9.]),
       (10, (11., 12), [13., 14.]), (15, (16., 17), [18., 19.])],
      dtype=[('a', '<i4'), ('b', [('f0', '<f4'), ('f1', '<u2')]), ('c', '<f4', (2,))])


numpy.lib.user_array.container   class numpy.lib.user_array.container(data, dtype=None, copy=True)[source]

Standard container-class for easy multiple-inheritance. Methods
copy
tostring
byteswap
astype


numpy.linspace   numpy.linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None, axis=0)[source]

Return evenly spaced numbers over a specified interval. Returns num evenly spaced samples, calculated over the interval [start, stop]. The endpoint of the interval can optionally be excluded.  Changed in version 1.16.0: Non-scalar start and stop are now supported.   Changed in version 1.20.0: Values are rounded towards -inf instead of 0 when an integer dtype is specified. The old behavior can still be obtained with np.linspace(start, stop, num).astype(int)   Parameters

startarray_like

The starting value of the sequence.
stoparray_like

The end value of the sequence, unless endpoint is set to False. In that case, the sequence consists of all but the last of num + 1 evenly spaced samples, so that stop is excluded. Note that the step size changes when endpoint is False.
numint, optional

Number of samples to generate. Default is 50. Must be non-negative.
endpointbool, optional

If True, stop is the last sample. Otherwise, it is not included. Default is True.
retstepbool, optional

If True, return (samples, step), where step is the spacing between samples.
dtypedtype, optional

The type of the output array. If dtype is not given, the data type is inferred from start and stop. The inferred dtype will never be an integer; float is chosen even if the arguments would produce an array of integers.  New in version 1.9.0.
axisint, optional

The axis in the result to store the samples. Relevant only if start or stop are array-like. By default (0), the samples will be along a new axis inserted at the beginning. Use -1 to get an axis at the end.  New in version 1.16.0.     Returns

samplesndarray

There are num equally spaced samples in the closed interval [start, stop] or the half-open interval [start, stop) (depending on whether endpoint is True or False).
stepfloat, optional

Only returned if retstep is True Size of spacing between samples.      See also  arange

Similar to linspace, but uses a step size (instead of the number of samples).  geomspace

Similar to linspace, but with numbers spaced evenly on a log scale (a geometric progression).  logspace

Similar to geomspace, but with the end points specified as logarithms.    Examples >>> np.linspace(2.0, 3.0, num=5)
array([2.  , 2.25, 2.5 , 2.75, 3.  ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([2. ,  2.2,  2.4,  2.6,  2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([2.  ,  2.25,  2.5 ,  2.75,  3.  ]), 0.25)
 Graphical illustration: >>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()


numpy.load   numpy.load(file, mmap_mode=None, allow_pickle=False, fix_imports=True, encoding='ASCII')[source]

Load arrays or pickled objects from .npy, .npz or pickled files.  Warning Loading files that contain object arrays uses the pickle module, which is not secure against erroneous or maliciously constructed data. Consider passing allow_pickle=False to load data that is known not to contain object arrays for the safer handling of untrusted sources.   Parameters

filefile-like object, string, or pathlib.Path

The file to read. File-like objects must support the seek() and read() methods. Pickled files require that the file-like object support the readline() method as well.
mmap_mode{None, ‘r+’, ‘r’, ‘w+’, ‘c’}, optional

If not None, then memory-map the file, using the given mode (see numpy.memmap for a detailed description of the modes). A memory-mapped array is kept on disk. However, it can be accessed and sliced like any ndarray. Memory mapping is especially useful for accessing small fragments of large files without reading the entire file into memory.
allow_picklebool, optional

Allow loading pickled object arrays stored in npy files. Reasons for disallowing pickles include security, as loading pickled data can execute arbitrary code. If pickles are disallowed, loading object arrays will fail. Default: False  Changed in version 1.16.3: Made default False in response to CVE-2019-6446.
fix_importsbool, optional

Only useful when loading Python 2 generated pickled files on Python 3, which includes npy/npz files containing object arrays. If fix_imports is True, pickle will try to map the old Python 2 names to the new names used in Python 3.
encodingstr, optional

What encoding to use when reading Python 2 strings. Only useful when loading Python 2 generated pickled files in Python 3, which includes npy/npz files containing object arrays. Values other than ‘latin1’, ‘ASCII’, and ‘bytes’ are not allowed, as they can corrupt numerical data. Default: ‘ASCII’    Returns

resultarray, tuple, dict, etc.

Data stored in the file. For .npz files, the returned instance of NpzFile class must be closed to avoid leaking file descriptors.    Raises
 OSError

If the input file does not exist or cannot be read.  UnpicklingError

If allow_pickle=True, but the file cannot be loaded as a pickle.  ValueError

The file contains an object array, but allow_pickle=False given.      See also
save, savez, savez_compressed, loadtxt

memmap

Create a memory-map to an array stored in a file on disk.  lib.format.open_memmap

Create or load a memory-mapped .npy file.    Notes  If the file contains pickle data, then whatever object is stored in the pickle is returned. If the file is a .npy file, then a single array is returned. If the file is a .npz file, then a dictionary-like object is returned, containing {filename: array} key-value pairs, one for each file in the archive.
If the file is a .npz file, the returned value supports the context manager protocol in a similar fashion to the open function: with load('foo.npz') as data:
    a = data['a']
 The underlying file descriptor is closed when exiting the ‘with’ block.   Examples Store data to disk, and load it again: >>> np.save('/tmp/123', np.array([[1, 2, 3], [4, 5, 6]]))
>>> np.load('/tmp/123.npy')
array([[1, 2, 3],
       [4, 5, 6]])
 Store compressed data to disk, and load it again: >>> a=np.array([[1, 2, 3], [4, 5, 6]])
>>> b=np.array([1, 2])
>>> np.savez('/tmp/123.npz', a=a, b=b)
>>> data = np.load('/tmp/123.npz')
>>> data['a']
array([[1, 2, 3],
       [4, 5, 6]])
>>> data['b']
array([1, 2])
>>> data.close()
 Mem-map the stored array, and then access the second row directly from disk: >>> X = np.load('/tmp/123.npy', mmap_mode='r')
>>> X[1, :]
memmap([4, 5, 6])