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Speed Up your Python Skills
Seven tips to take you to the next level
Python is the most widely used programming language in the data science domain, and its popularity continues to grow. The entire data science field has grown enormously in recent years.
In this article, we will show you seven tips on how to improve your Python skills. It’s often the little things that make a big difference. The tips will enrich your life as a Data Scientist. That’s why we give you seven tips that you can put into practice right now. Be curious!
As a Data Scientist, you often have to deal with large amounts of data. For this reason, you must code efficiently in terms of run time and memory. Your Python code should also be well-structured and easy to read. The tips will help you to write efficient and readable Python code.
Tip 1: Speed up NumPy
NumPy is a Python library to work efficiently with arrays. It also offers fast and optimised vectorised operations. But! It does not support parallel processing. As an alternative to NumPy, you can use NumExpr.
NumExpr achieves significantly better performance than NumPy because it supports multi-threading. Furthermore, it avoids allocating memory for intermediate results.
