Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.
In Pandas in Action you will learn how to:
- Import datasets, identify issues with their data structures, and optimize them for efficiency
- Sort, filter, pivot, and draw conclusions from a dataset and its subsets
- Identify trends from text-based and time-based data
- Organize, group, merge, and join separate datasets
- Use a GroupBy object to store multiple DataFrames
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Data analysis with Python doesn't have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It's a perfect way to up your data game.
About the book
Pandas in Action introduces Python-based data analysis using the amazing pandas library. You'll learn to automate repetitive operations and gain deeper insights into your data that would be impractical—or impossible—in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you'll find in the real world.
What's inside
- Organize, group, merge, split, and join datasets
- Find trends in text-based and time-based data
- Sort, filter, pivot, optimize, and draw conclusions
- Apply aggregate operations
About the reader
For readers experienced with spreadsheets and basic Python programming.
About the author
Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries.
Table of Contents
PART 1 CORE PANDAS
1 Introducing pandas
2 The Series object
3 Series methods
4 The DataFrame object
5 Filtering a DataFrame
PART 2 APPLIED PANDAS
6 Working with text data
7 MultiIndex DataFrames
8 Reshaping and pivoting
9 The GroupBy object
10 Merging, joining, and concatenating
11 Working with dates and times
12 Imports and exports
13 Configuring pandas
14 Visualization