Data Science Tutorials

Explore 4 ways to detect NaN values in Python, using NumPy and Pandas. Learn key differences between NaN and None to clean and analyze data efficiently.

Most open-source LLMs have one huge drawback - short context length. Since context length is essentially the “memory” of LLMs, this issue needs to be addressed urgently. That’s exactly what Salesforce XGen 7B LLM does - provide an impressive 8k context length. This article is a tutorial on how to use and fine-tune it.

Learn all you need to know about the pandas .explode() method, covering single and multiple columns, handling nested data, and common pitfalls with practical Python code examples.

Learn how fine-tuning large language models (LLMs) improves their performance in tasks like language translation, sentiment analysis, and text generation.

There are several ways to find the size of a DataFrame in Python to fit different coding needs. Check out this tutorial for a quick primer on finding the size of a DataFrame. This tutorial presents several ways to check DataFrame size, so you’re sure to find a way that fits your needs.

Comparing values in Python to check if they are not equal is simple with the not equal operator. Check out this quick tutorial on how to use the not equal Python operator, as well as alternatives for comparing floats.

Unlock the power of SQL CONTAINS for advanced text searches. Dive into logical operators, proximity searches, and wildcard uses for precise data analysis.

Master SQL's NOT IN operator with this beginner's guide. Learn to filter data effectively, avoid common pitfalls, and explore efficient alternatives

File management is a crucial aspect of code handling. Part of this skill set is knowing how to delete a file. In this tutorial, we cover multiple ways to delete a file in Python, along with best practices in doing so.

Discover everything you need to know about LMQL, short for Language Models Query Language, an innovative programming language for LLMs.

Unlock the power of SQL NOT EQUAL with our expert guide. Learn to refine data queries with practical examples and optimization tips for better analysis.

Learn an end-to-end workflow to monitor any model in your Jupyter notebook in production environments.