Numpy is a fundamental programming library for Python, which serves as one of the cornerstones of data science activities. It has been an indispensable tool in the data scientist and machine learning practitioner’s arsenal for providing data processing, analysis, and manipulation capabilities. In this post, we’ll dive into the basics of Numpy and discuss the following five reasons why it is an essential component of the professional data scientist’s toolkit:
1. Array Processing: Numpy performs operations on arrays, enabling efficient data manipulation. Numpy can perform complex vector and matrix operations quickly and efficiently, meaning users can apply mathematical functions to arrays without additional work.
2. Advanced Math Features: Numpy also provides advanced mathematical features such as linear algebra, calculus, and probability functions. These tools allow data scientists to explore and analyze data more deeply, quickly, and accurately.
3. Integrates with Popular Tools: Numpy can integrate with other popular data science tools such as SciPy, Pandas and SciKits, allowing users to explore more data sources with more feature engineering toolsets quickly and efficiently.
4. Speed and Efficiency: Numpy uses vectorization; that is, it can process multiple elements on an array simultaneously rather than iterating through each element single by single. This leads to significant speed increases, as well as a reduction in memory use.
5. Industry Recognition: Numpy is at the centre of today’s leading data science and machine learning platforms. Many of the industry’s leaders, including Google, Facebook, and Amazon, all base their data science algorithms on Numpy libraries.
Ultimately, a data scientist’s success starts in the fundamentals. From training models and creating algorithms to crafting machine learning projects, having a thorough understanding of Numpy’s features is essential to any data science project. We hope that this post has provided a better understanding of why.
