numpy.where — NumPy v1.26 Manual
numpy.where# numpy.where(condition, [x, y, ]/)# Return elements chosen from x or y depending on condition. Note When only condition is provided, this function is a shorthand for np.asarray(condition).nonzero(). Using nonzero directly should be preferred, as it behaves correctly for subclasses. The rest of this documentation covers only the case where all three arguments are provided. Parameters: c
numpy.cumsum — NumPy v1.26 Manual
numpy.cumsum# numpy.cumsum(a, axis=None, dtype=None, out=None)[source]# Return the cumulative sum of the elements along a given axis. Parameters: aarray_likeInput array. axisint, optionalAxis along which the cumulative sum is computed. The default (None) is to compute the cumsum over the flattened array. dtypedtype, optionalType of the returned array and of the accumulator in which the elements ar
numpy.ravel — NumPy v1.26 Manual
numpy.ravel# numpy.ravel(a, order='C')[source]# Return a contiguous flattened array. A 1-D array, containing the elements of the input, is returned. A copy is made only if needed. As of NumPy 1.10, the returned array will have the same type as the input array. (for example, a masked array will be returned for a masked array input) Parameters: aarray_likeInput array. The elements in a are read in t
numpy.corrcoef — NumPy v1.26 Manual
numpy.corrcoef# numpy.corrcoef(x, y=None, rowvar=True, bias=<no value>, ddof=<no value>, *, dtype=None)[source]# Return Pearson product-moment correlation coefficients. Please refer to the documentation for cov for more detail. The relationship between the correlation coefficient matrix, R, and the covariance matrix, C, is The values of R are between -1 and 1, inclusive. Parameters: xarray_likeA 1
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numpy.percentile — NumPy v1.26 Manual
numpy.empty_like — NumPy v1.26 Manual
numpy.random.poisson — NumPy v1.26 Manual
numpy.histogram — NumPy v1.26 Manual
numpy.hstack — NumPy v1.26 Manual
numpy.random.Generator.choice — NumPy v1.26 Manual
numpy.prod — NumPy v1.26 Manual
numpy.random.Generator.normal — NumPy v1.26 Manual
numpy.logical_not — NumPy v1.26 Manual
numpy.reshape — NumPy v1.26 Manual
numpy.mean — NumPy v1.26 Manual
numpy.absolute — NumPy v1.26 Manual
numpy.zeros — NumPy v1.26 Manual
numpy.stack — NumPy v1.26 Manual
numpy.flipud — NumPy v1.26 Manual
numpy.median — NumPy v1.26 Manual