matrix.conj() Complex-conjugate all elements. Refer to numpy.conjugate for full documentation. See also numpy.conjugate equivalent function

nditer.remove_multi_index() When the ?multi_index? flag was specified, this removes it, allowing the internal iteration structure to be optimized further.

generic.std() 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

RandomState.noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral 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 > 1. nonc is the non-centrality parameter. Parameters: dfnum : int Parameter, should be > 1. dfden : int Parameter, should be > 1. nonc : float Parameter, should be >= 0. size : int or tuple of

numpy.around(a, decimals=0, out=None) [source] Evenly round to the given number of decimals. Parameters: a : array_like Input data. decimals : int, optional Number of decimal places to round to (default: 0). If decimals is negative, it specifies the number of positions to the left of the decimal point. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output, but the type of the output values will be cast if nece

chararray.title() [source] For each element in self, return a titlecased version of the string: words start with uppercase characters, all remaining cased characters are lowercase. See also char.title

record.all() 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

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

Common test support for all numpy test scripts. This single module should provide all the common functionality for numpy tests in a single location, so that test scripts can just import it and work right away. Asserts assert_almost_equal(actual, desired[, ...]) Raises an AssertionError if two items are not equal up to desired precision. assert_approx_equal(actual, desired[, ...]) Raises an AssertionError if two items are not equal up to significant digits. assert_array_almost_equal(x, y[,

ndarray.__ipow__ x.__ipow__(y) <==> x**=y