### NumPy for Data Science – Part 1

• • 0

## What is NumPy Array?

An array is a grid of values and it contains information about the raw data, how to locate an element, and how to interpret an element.

## Advantages of using NumPy Arrays over Python List:

• Consumes less memory.
• Fast as compared to the Python List.
• Convenient to use.

Let’s look at the example of NumPy Array and Python List.

## Importance of NumPy Array in Python

1. Wide verity of mathematical operations on Arrays.
2. It supplies an enormous library of high-level mathematical function that operate on these Arrays and metrices.
3. Mathematical, logical, shape, manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, random simulation and much more.

## Creating NumPy Arrays

• To create a NumPy Array, you can use the function np.array().

## Dimensions in Array

• 1-D Arrays
• 2-D Arrays
• 3-D Arrays
• High Dimensional Arrays

## Special NumPy Arrays

• Array filled with 0’s
• Array files with 1’s
• An Array with a range of elements
• Array diagonal element filled with 1’s
• Create an array with values that are spaced linearly in a specified interval.

## Conclusion

In this article we have learned what is NumPy and how to create 1-D, 2-D, 3-D and High dimensional arrays in it, in the next part we will be learning how to create NumPy Arrays with Random Numbers.