numpy.matlib.empty(shape, dtype=None, order='C') [source] Return a new matrix of given shape and type, without initializing entries. Parameters: shape : int or tuple of int Shape of the empty matrix. dtype : data-type, optional Desired output data-type. order : {?C?, ?F?}, optional Whether to store multi-dimensional data in row-major (C-style) or column-major (Fortran-style) order in memory. See also empty_like, zeros Notes empty, unlike zeros, does not set the matrix values to ze

numpy.busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None) First adjusts the date to fall on a valid day according to the roll rule, then applies offsets to the given dates counted in valid days. New in version 1.7.0. Parameters: dates : array_like of datetime64[D] The array of dates to process. offsets : array_like of int The array of offsets, which is broadcast with dates. roll : {?raise?, ?nat?, ?forward?, ?following?, ?backward?

record.tolist() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

record.argsort() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The

MaskedArray.__float__() [source] Convert to float.

numpy.core.defchararray.decode(a, encoding=None, errors=None) [source] Calls str.decode element-wise. The set of available codecs comes from the Python standard library, and may be extended at runtime. For more information, see the codecs module. Parameters: a : array_like of str or unicode encoding : str, optional The name of an encoding errors : str, optional Specifies how to handle encoding errors Returns: out : ndarray See also str.decode Notes The type of the result will depe

numpy.less_equal(x1, x2[, out]) = Return the truth value of (x1 =< x2) element-wise. Parameters: x1, x2 : array_like Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape (which may be the shape of one or the other). Returns: out : bool or ndarray of bool Array of bools, or a single bool if 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,

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

numpy.testing.assert_array_equal(x, y, err_msg='', verbose=True) [source] Raises an AssertionError if two array_like objects are not equal. Given two array_like objects, check that the shape is equal and all elements of these objects are equal. An exception is raised at shape mismatch or conflicting values. In contrast to the standard usage in numpy, NaNs are compared like numbers, no assertion is raised if both objects have NaNs in the same positions. The usual caution for verifying equali

record.conjugate() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and possesses, albeit unimplemented, all the attributes of the ndarray class so as to provide a uniform API. See also The