Nearly every scientist working in Python draws on the power of NumPy. NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to …

The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the Anaconda Distribution - it includes …

numpy.where # numpy.where(condition, [x, y, ]/) # Return elements chosen from x or y depending on condition.

In such cases, the use of numpy.linspace should be preferred. The built-in range generates Python built-in integers that have arbitrary size, while numpy.arange produces numpy.int32 or …

The reference guide contains a detailed description of the functions, modules, and objects included in NumPy. The reference describes how the methods work and which parameters …

The NumPy library contains multidimensional array data structures, such as the homogeneous, N-dimensional ndarray, and a large library of functions that operate efficiently on these data …

Since version 1.4, the new polynomial API defined in numpy.polynomial is preferred. A summary of the differences can be found in the transition guide. Fit a polynomial p(x) = p[0] * x**deg + ...

That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones. Create an array of the given shape and populate …

numpy.argsort # numpy.argsort(a, axis=-1, kind=None, order=None, *, stable=None) [source] # Returns the indices that would sort an array. Perform an indirect sort along the given axis …

numpy.random.normal # random.normal(loc=0.0, scale=1.0, size=None) # Draw random samples from a normal (Gaussian) distribution.