Reductions (or aggregations) are a family of NumPy functions that operate over an array returning a result with fewer dimensions.

Many of these functions perform typical statistical operations on arrays, while others perform dimensionality-reductions.

In this article, we will learn about some of the most common aggregations, but before we

Universal functions (ufunc) are special NumPy functions that operate on ndarrays in an element-by-element fashion.

They represent a vast array of vectorized functions that perform much better than iterative implementations and let you write concise code. Most ufuncs achieve this by providing a Python wrapper around a C implementation.

In

NumPy array indexing is a big topic, and there are many different ways of selecting elements from an array.

Let's start with the simplest case: selecting an entry from a 1-dimensional array.

import numpy as np

arr = np.arange(10)
print(arr)

[0 1 2 3 4 5 6 7

In the last article, we learned many different ways in which we can create ndarrays. Now that we know how to create NumPy arrays it's time to start playing around with them.

We will learn to perform basic operations, a task you can divide into 3 categories:

• Operations between arrays

NumPy (an acronym for Numeric Python) is a library for working with multi-dimensional arrays and matrices. It was created in 2005 by Travis Oliphant, and since then received numerous contributions from the community that enabled it to grow into one of the most used tools in data science.

NumPy lets