Master Data Wrangling First: Top 20 Python Libraries + Best Practices

Author(s): Anil Tilbe

Originally published on Towards AI.

Processes, approaches, Top 20 libraries, along with important best practices — how to do it all with Python

By Pixabay from Pexels

Data Wrangling is transforming data from one format to another. Python is a powerful tool for data wrangling.

1. Import data from a variety of sources

2. Clean data by identifying and removing invalid or incorrect values

Data cleaning. By https://www.coursereport.com/blog/ultimate-guide-to-data-cleaning-with-python-lighthouse-labs

3. Format data for ease of use and consistency

4. Enrich data by adding derived or calculated values

5. Integrate data from multiple sources

6. Normalize data to correct for differences in units or cases

7. Reshape data to facilitate analysis

8. Split data into training and test sets

9. Aggregate data to summarize or describe it

10. Visualize data to explore and understand it

1. Web scraping:… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI