5 NumPy appealing functions that you'll easily remember and use.

Before getting deep insight on the 5 NumPy functions that will be covered in this tutorial, let's see a breaf introduction of NumPy.

To begin with, Numpy stands for Numerical Python. As it's name mentions, it is a library for numerical computing in Python, but in a way much more powerful than traditional Python functions. When it comes to calculating massive arrays of data, it's efficient not only in calculation speed but also in storing and retrieving data.

Its efficiency to process tremendous arrays of data is based, for example, in its low requirement of memory, or in the fact that it does not necessitate for loops of Python to execute numerical computations on large arrays.[1]

Finally, NumPy allows us to write shorter and more organized codes than we only use Python; additionally, there is a NumPy function for each possible numerial computation. The following list presents the 5 functions that will be explained with detail in this document.

• np.apply_along_axis
• np.where
• np.linalg.solve
• np.split
• np.transpose

The recommended way to run this notebook is to click the "Run" button at the top of this page, and select "Run on Binder". This will run the notebook on mybinder.org, a free online service for running Jupyter notebooks.

Let's begin by importing NumPy and listing out the functions covered in this notebook.