chararray.view(dtype=None, type=None) New view of array with the same data. Parameters: dtype : data-type or ndarray sub-class, optional Data-type descriptor of the returned view, e.g., float32 or int16. The default, None, results in the view having the same data-type as a. This argument can also be specified as an ndarray sub-class, which then specifies the type of the returned object (this is equivalent to setting the type parameter). type : Python type, optional Type of the returned

class numpy.errstate(**kwargs) [source] Context manager for floating-point error handling. Using an instance of errstate as a context manager allows statements in that context to execute with a known error handling behavior. Upon entering the context the error handling is set with seterr and seterrcall, and upon exiting it is reset to what it was before. Parameters: kwargs : {divide, over, under, invalid} Keyword arguments. The valid keywords are the possible floating-point exceptions. Ea

An ndarray is a (usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape, which is a tuple of N positive integers that specify the sizes of each dimension. The type of items in the array is specified by a separate data-type object (dtype), one of which is associated with each ndarray. As with other container objects in Python, the contents of an ndarray can be accessed and modified by indexing or

RandomState.f(dfnum, dfden, size=None) Draw samples from an F distribution. Samples are drawn from an F distribution with specified parameters, dfnum (degrees of freedom in numerator) and dfden (degrees of freedom in denominator), where both parameters should be greater than zero. The random variate of the F distribution (also known as the Fisher distribution) is a continuous probability distribution that arises in ANOVA tests, and is the ratio of two chi-square variates. Parameters: dfnum

MaskedArray.__rlshift__ x.__rlshift__(y) <==> y<<x

numpy.busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None) Counts the number of valid days between begindates and enddates, not including the day of enddates. If enddates specifies a date value that is earlier than the corresponding begindates date value, the count will be negative. New in version 1.7.0. Parameters: begindates : array_like of datetime64[D] The array of the first dates for counting. enddates : array_like of datetime64[D] The arr

numpy.partition(a, kth, axis=-1, kind='introselect', order=None) [source] Return a partitioned copy of an array. Creates a copy of the array with its elements rearranged in such a way that the value of the element in kth position is in the position it would be in a sorted array. All elements smaller than the kth element are moved before this element and all equal or greater are moved behind it. The ordering of the elements in the two partitions is undefined. New in version 1.8.0. Paramete

numpy.average(a, axis=None, weights=None, returned=False) [source] Compute the weighted average along the specified axis. Parameters: a : array_like Array containing data to be averaged. If a is not an array, a conversion is attempted. axis : int, optional Axis along which to average a. If None, averaging is done over the flattened array. weights : array_like, optional An array of weights associated with the values in a. Each value in a contributes to the average according to its asso

ndarray.__irshift__ x.__irshift__(y) <==> x>>=y

generic.flatten() 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