Basics of NumPy in Python

NumPy is a library written for scientific computing and data analysis. It stands for Numerical Python.

Let’s hear from Vaidehi about the advantages of NumPy over the standard ways.

The most basic object in NumPy is a NumPy Array. As Vaidehi mentioned, Python is incapable of operating over the entire data stored in lists. This is where NumPy arrays come to the rescue. Let’s see how NumPy is helpful in overcoming this issue.

You can download the Python notebooks used in the lecture from the link below. As mentioned in the introduction, you are expected to code along with the instructor in the notebooks. These notebooks are provided with proper spaces to fill your code.

In this video, you were introduced to the NumPy Arrays. 
 

Creating NumPy Arrays

There are two ways to create NumPy arrays:

1. Converting the existing lists or tuples to arrays using np.array
2. Initialising fixed-length arrays using the NumPy functions

As a part of this session, you will learn about both the methods. 

The key advantage of NumPy arrays over lists is that the array allows you to operate over the entire data, unlike lists. However, if you look at the structure, NumPy arrays are very similar to the lists. If you try to run the print() command over an array, you get the following output:

[element_1  element_2  element_3…]

The only difference is that the elements are separated with space instead of a comma.

An important thing to note is that the above array is a one-dimensional array. You will learn about multidimensional arrays in the coming lectures.

Another feature of NumPy arrays is that they are homogeneous in nature. By homogenous, we mean that all the elements in a NumPy array have to be of the same data type, which could be an integer, float, string, etc. In the example discussed in the video, you saw that all the elements were converted to a string when stored in the array.

In the next segment, you will learn about different operations that can be performed over one-dimensional NumPy arrays.