chararray.imag
  • References/Python/NumPy/Routines/String operations/numpy.core.defchararray.chararray

chararray.imag The imaginary part of the array. Examples

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numpy.ma.std()
  • References/Python/NumPy/Routines/Masked array operations

numpy.ma.std(self, axis=None, dtype=None, out=None, ddof=0) = Compute the standard deviation along the specified axis. Returns

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numpy.polynomial.laguerre.lagsub()
  • References/Python/NumPy/Routines/Polynomials/Polynomial Package/Laguerre Module

numpy.polynomial.laguerre.lagsub(c1, c2)

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numpy.random.noncentral_f()
  • References/Python/NumPy/Routines/Random sampling

numpy.random.noncentral_f(dfnum, dfden, nonc, size=None) Draw samples from the noncentral F distribution. Samples

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numpy.asfarray()
  • References/Python/NumPy/Routines/Array manipulation routines

numpy.asfarray(a, dtype=)

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numpy.pmt()
  • References/Python/NumPy/Routines/Financial functions

numpy.pmt(rate, nper, pv, fv=0, when='end')

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RandomState.beta()
  • References/Python/NumPy/Routines/Random sampling

RandomState.beta(a, b, size=None) Draw samples from a Beta distribution. The Beta distribution is a special

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RandomState.get_state()
  • References/Python/NumPy/Routines/Random sampling

RandomState.get_state() Return a tuple representing the internal state of the generator. For more details

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numpy.setdiff1d()
  • References/Python/NumPy/Routines/Set routines

numpy.setdiff1d(ar1, ar2, assume_unique=False)

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numpy.ma.getdata()
  • References/Python/NumPy/Routines/Masked array operations

numpy.ma.getdata(a, subok=True)

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