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Pandas in Action
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ISBN13: 9781617297434
Publisher: Manning Publications
Published: Oct 5 2021
Pages: 440
Weight: 1.58
Height: 0.63 Width: 7.32 Depth: 9.13
Language: English
Identify trends from text-based and time-based data
Organize, group, merge, and join separate datasets
Use a GroupBy object to store multiple DataFrames Pandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You'll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data. 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
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