NumPy for Data Science - Part 1

NumPy for Data Science – Part 1

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.

Numpy vs Python List

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

Wide verity of mathematical operations on Arrays.
It supplies an enormous library of high-level mathematical function that operate on these Arrays and metrices.
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.

NumPy for Data Science – Part 2

Author

Naveen

Naveen Pandey has more than 2 years of experience in data science and machine learning. He is an experienced Machine Learning Engineer with a strong background in data analysis, natural language processing, and machine learning. Holding a Bachelor of Science in Information Technology from Sikkim Manipal University, he excels in leveraging cutting-edge technologies such as Large Language Models (LLMs), TensorFlow, PyTorch, and Hugging Face to develop innovative solutions.
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