numpy.log10()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.log10(x[, out]) = Return the base 10 logarithm of the input array, element-wise.

2025-01-10 15:47:30
numpy.round_()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.round_(a, decimals=0, out=None)

2025-01-10 15:47:30
numpy.remainder()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.remainder(x1, x2[, out]) = Return element-wise remainder of division. Computes the remainder complementary to the

2025-01-10 15:47:30
numpy.i0()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.i0(x)

2025-01-10 15:47:30
numpy.gradient()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.gradient(f, *varargs, **kwargs)

2025-01-10 15:47:30
numpy.expm1()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.expm1(x[, out]) = Calculate exp(x) - 1 for all elements in the array.

2025-01-10 15:47:30
numpy.sinh()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.sinh(x[, out]) = Hyperbolic sine, element-wise. Equivalent to 1/2 * (np.exp(x) - np.exp(-x)) or -1j

2025-01-10 15:47:30
numpy.diff()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.diff(a, n=1, axis=-1)

2025-01-10 15:47:30
numpy.interp()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.interp(x, xp, fp, left=None, right=None, period=None)

2025-01-10 15:47:30
numpy.nansum()
  • References/Python/NumPy/Routines/Mathematical functions

numpy.nansum(a, axis=None, dtype=None, out=None, keepdims=0)

2025-01-10 15:47:30