Thinking in Pandas
- Author: Hannah Stepanek
- ISBN: 148425838X
- Year: 2020
- Pages: 197
- Language: English
- File size: 2.3 MB
- File format: PDF, ePub
- Category: Python
Understand and execute big data analysis solutions in pandas having an emphasis on performance. This book strengthens your intuition for working with pandas, the Python data analysis library, by researching its underlying execution and data structures.
Believing in Pandas presents the subject of big information and demonstrates concepts by studying stimulating and impactful projects that pandas assisted to solve. From that point, you will learn to assess your own projects by size and type to see if pandas is the appropriate library for your needs. Writer Hannah Stepanek describes how to load and normalize data in pandas efficiently, and reviews a few of the most commonly used loaders and a number of the most powerful options. You will then learn how to access and change data efficiently, what approaches to avoid, and when to employ more advanced performance methods. You will also go over fundamental information access and munging from pandas and the intuitive dictionary syntax. Choosing the right DataFrame format, working with multi-level DataFrames, and also how pandas might be improved upon in the future will also be covered.
From the end of the book, you’ll have a good understanding of the way the pandas library functions beneath the hood. Get prepared to make confident decisions in your own projects by using pandas–the ideal way.
- Understand the underlying data structure of pandas and why it performs the way it does under particular circumstances
- Discover the best way to utilize pandas to extract, transform, and load data properly with an emphasis on functionality
- Pick the ideal DataFrame so that the data evaluation is easy and productive.
- Boost performance of pandas operations together with other Python libraries
Who This Book Is For
computer software engineers with fundamental programming skills in Python keen on using pandas to get a significant data evaluation project. Python application developers are interested in big data.