What are some useful functions in NumPy?

What are some useful functions in NumPy?

NumPy offers a variety of functions for numerical operations. Some useful ones include numpy.array(), numpy.mean(), numpy.median(), and numpy.dot(). Would you like to know more about any specific function?

Can you explain how to use numpy.mean()?

Certainly! The numpy.mean() function calculates the average of an array. You can use it like this: numpy.mean(array), where array is your input data. It can also take an optional axis parameter to compute the mean along a specific axis.

What about numpy.dot()? How is it different from numpy.multiply()?

numpy.dot() performs dot product of two arrays, while numpy.multiply() performs element-wise multiplication. For example, numpy.dot(a, b) computes the sum of products of corresponding elements, whereas numpy.multiply(a, b) multiplies each element of a with the corresponding element of b.

Are there any functions for linear algebra in NumPy?

Yes, NumPy has a submodule called numpy.linalg that includes functions for linear algebra, such as numpy.linalg.inv() for matrix inversion and numpy.linalg.eig() for eigenvalues and eigenvectors.