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Essential DataFrame Operations

In this chapter, we will cover the following topics:

Selecting multiple DataFrame columns
Selecting columns with methods
Ordering column names sensibly
Operating on the entire DataFrame
Chaining DataFrame methods together
Working with operators on a DataFrame
Comparing missing values
Transposing the direction of a DataFrame operation
Determining college campus diversity

Key benefits

Use the power of pandas 0.20 to solve most complex scientific computing problems with ease

Leverage fast, robust data structures in pandas 0.20 to gain useful insights from your data

Practical, easy to implement recipes for quick solutions to common problems in data using pandas 0.20

Description

This book will provide you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas 0.20. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands like one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through practical situations that you are highly likely to encounter. Many advanced recipes combine several different features across the pandas 0.20 library to generate results.

Who is this book for?

This book is for data scientists, analysts and Python developers who wish to explore data analysis and scientific computing in a practical, hands-on manner. The recipes included in this book are suitable for both novice and advanced users, and contain helpful tips, tricks and caveats wherever necessary. Some understanding of pandas will be helpful, but not mandatory.

What you will learn

Master the fundamentals of pandas 0.20 to quickly begin exploring any dataset

Isolate any subset of data by properly selecting and querying the data

Split data into independent groups before applying aggregations and transformations to each group

Restructure data into tidy form to make data analysis and visualization easier

Prepare real-world messy datasets for machine learning

Combine and merge data from different sources through pandas SQL-like operations

Utilize pandas unparalleled time series functionality

Create beautiful and insightful visualizations through pandas 0.20 direct hooks to Matplotlib and Seaborn

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Tomas Dec 12, 2018

This is an excellent book if you want to learn pandas and if you want to understand pandas. It covers all cases, clearly explains what and why pandas do, and the chapters are organized really well and it depends on you if you just want to stay on surface or go deeper.

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Scott B. Oct 26, 2017

Ted Petrou in Pandas Cookbook gives real world problems easy to follow and implement recipes for data wrangling success. This books does an excellent job in highlighting the foundations of Pandas, critical operations in Pandas, how to use Pandas in data analysis, and data visualization with Matplotlib, Pandas and Seaborn libraries.Ted uses actual datasets in his examples. If you go to download supporting files with this book, you will get a copy of his Jupyter Notebooks for each chapter and over 30 real datasets for analysis.This is a must have book when I want to find actual solutions to problems with unruly data.

Dimitri Shvorob Nov 12, 2017

A self-regarding preface, if I may. This is my second attempt at reviewing "Pandas Cookbook". The first one was written in a dyspeptic mood - thank you, Kindle Unlimited free trial, for exposing me to the horrors of self-published rip-offs - did not put its emphases right, and was in one regard simply misleading. This prompted criticism from the book's author, supported by detailed objections, and led me to reconsider. I know that there will be a Version 3, already in December, as I will want to compare "Pandas Cookbook" and Daniel Chen's "Pandas for Everyone", which I expect to be both similar and good. I will leave speculation at that, and focus on the present.Before "Pandas Cookbook", I had seen five books about Pandas:"Python for Data Analysis" by Wes McKinney, 2nd ed., 544 pages, 2017"Learning the Pandas Library" by Matt Harrison, 212 pages, 2016"Learning pandas" by Michael Heydt, 504 pages, Packt, 2015"Mastering pandas" by Femi Anthony, 364 pages, Packt, 2015"Python Data Analytics" by Fabio Nelli, 364 pages, Apress, 2015I can confidently say that (a) you don't need to consider books other than McKinney's and Petrou's, and (b) you want to see both, and possibly leave both, depending on your budget and personal preference.The one wrong suggestion in my original review was that PC was "far behind" PDA in terms of coverage. Having checked PDA, however, I realized that PDA did not have many things which I thought I learned from it, but in fact picked up from other sources - Pandas online doc, Stack Overflow, and, early on, Chris Albon's site. Surprisingly, the bread-and-butter "nunique" function, for example, is not in PDA, and neither is "filter" or "query". (I actually learned about "query" from "Pandas Cookbook"; my office Python environment predates Pandas 0.18.0). "Behind" is debatable - or moot: you have bits in one book, and not the other, either way you look - and "far" is false. The upshot is that you can get a good handle on Pandas with either reference.The word "reference" fits PDA better - it has a methodical, clearly structured, but somewhat terse style, reminding me of O'Reilly's "Nutshell" series. PC, on the other hand, is pretty relaxed, and goes at a slower pace, with illustrations that are much more likely to stay with you than McKinney's, because (a) they use real datasets, as opposed to quick artificial ones, (b) often are part of a sequence of steps, providing context and identifying the use case. Packt's no-frills typesetting puts PC at a disadvantage, but it is not too bad.Comparing "Pandas Cookbook" to what was available before, I see and appreciate the qualitative change from (a) reductive digests of McKinney's book, to (b) something that builds on, and complements, McKinney's book. If Chen's book does the same, the Pandas newbie will get even more options. For now, kudos to Ted Petrou for an original and useful book.

Risiko Lektor May 27, 2019

This is certainly a great book. Like something we've been waiting for or at least I've been. It has clear structure and very knowledgeable content. Everything is clearly presented and above all it contains lots of stuff that is not presented elsewhere (at least to my knowledge). It's a little thick but that's alright. It is now my faithful companion on my desk to Pandas.

Petter Dischington Dec 24, 2017

This is a comprehensive volume covering many aspects of the Pandas data science library for the Python programming language. It's not merely a reference work, but can also be used as a way to learn effective data analysis methodology as you are following the the instructions. The book finds that holy middle ground between being a complete beginners guide, a reference guide, and the specific guidance offered by a stack overflow answer.In my experience stack overflow can be too specific if you are unsure about what exactly you need to do to your data in order prepare it for further analysis, and that is really where this book shines. It explains the whys as well as the hows, and the author will give explanations of what are the most efficient ways to do so from a human workload/intuition perspective, as well as what is most computationally efficient, highlighting the preceding and following steps.Finally, the author himself is very accessible in case of problems, and there are a number of supporting materials to the book available.

Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python

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