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Pandas Cookbook: Recipes for Scientific Computing, Time Series Analysis and Data Visualization using Python Paperback – October 23, 2017

by Theodore Petrou (Author)

17 customer reviews

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Key Features

Use the power of pandas to solve most complex scientific computing problems with ease
Leverage fast, robust data structures in pandas to gain useful insights from your data
Practical, easy to implement recipes for quick solutions to common problems in data using pandas

Book Description

Pandas is one of the most powerful, flexible, and efficient scientific computing packages in Python. With this book, you will explore data in pandas through dozens of practice problems with detailed solutions in iPython notebooks.

This book will provide you with clean, clear recipes, and solutions that explain how to handle common data manipulation and scientific computing tasks with pandas. You will work with different types of datasets, and perform data manipulation and data wrangling effectively. You will explore the power of pandas DataFrames and find out about boolean and multi-indexing. Tasks related to statistical and time series computations, and how to implement them in financial and scientific applications are also covered in this book.

By the end of this book, you will have all the knowledge you need to master pandas, and perform fast and accurate scientific computing.

What you will learn

Master the fundamentals of pandas 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 a tidy form to make data analysis and visualization easier
Prepare messy real-world 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 direct hooks to matplotlib and seaborn

About the Author

Theodore Petrou is a data scientist and the founder of Dunder Data, a professional educational company focusing on exploratory data analysis. He is also the head of Houston Data Science, a meetup group with more than 2,000 members that has the primary goal of getting local data enthusiasts together in the same room to practice data science. Before founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services company, where he spent the vast majority of his time exploring data.

Some of his projects included using targeted sentiment analysis to discover the root cause of part failure from engineer text, developing customized client/server dashboarding applications, and real-time web services to avoid the mispricing of sales items. Ted received his masters degree in statistics from Rice University, and used his analytical skills to play poker professionally and teach math before becoming a data scientist. Ted is a strong supporter of learning through practice and can often be found answering questions about pandas on Stack Overflow.

Pandas Foundations
Essential DataFrame Operations
Beginning Data Analysis
Selecting Subsets of Data
Boolean Indexing
Index Alignment
Grouping for Aggregation, Filtration and Transformation
Restructuring Data into Tidy Form
Joining multiple pandas objects
Time Series
Visualization

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Editorial Reviews

About the Author

Paperback: 538 pages
Publisher: Packt Publishing (October 23, 2017)
Language: English
ISBN-10: 9781784393878
ISBN-13: 978-1784393878
ASIN: 1784393878
Product Dimensions: 7.5 x 1.2 x 9.2 inches
Shipping Weight: 1.6 pounds (View shipping rates and policies)
Average Customer Review: 17 customer reviews
Amazon Best Sellers Rank: #41,344 in Books (See Top 100 in Books)
- #18 in Books > Computers & Technology > Software > Mathematical & Statistical
- #25 in Books > Computers & Technology > Databases & Big Data > Data Mining
- #34 in Books > Computers & Technology > Databases & Big Data > Data Processing

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Timothy

For such a large book, it's EASY to read...

November 9, 2018

Format: PaperbackVerified Purchase

This book is over 500 pages, but the layout is outstanding.

I am the type of person to read 1-3 page of a book each day at least.
The book has chapters, topics, and subsections.
Each subsection is organized the same throughout the ENTIRE book...
Chater 1...
Topic 1
1. Getting ready
2. How to do it.
3. How it works...
4. There's more...
5. See also
Topic 2
1. Getting ready
2. How to do it.
3. How it works...
4. There's more...
5. See also.
etc.

Meaning, that you can pick up from anywhere and learn a little piece at a time with a consistent layout for each topic.
I HIGHLY recommend this book, not because it's easy, but it streamlines the information for you in a consistent manner. You can make a "class" or "course" based on one subsection at a time and do it consisitently, developing your skils. Because we ALL understand simple things get done and consistent actions produce the results in our lives.

Much luck in your decision to purchase this book for yourself. ^__^

3 people found this helpful

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Brandon i

Excellent book with good explanations

January 28, 2019

Format: Kindle EditionVerified Purchase

This book has helped me out a lot. I am new to python and pandas but this book has made things much clearer. Good explanations with example of codes. Author also explains how each part of the code works and reinforces material learned in previous chapters. Was intimidated by some of the chapters before i started reading once i got to those parts I was no longer worried if i would understand the subject matter.

One person found this helpful

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Tomas

Exellent book

December 12, 2018

Format: PaperbackVerified Purchase

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.

2 people found this helpful

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BDC

Kindle formatting is simply awful

November 15, 2018

Format: Kindle EditionVerified Purchase

I'm trying to read this book on kindle cloud reader, have it open in one screen while I code in another. The formatting is so bad that the book is unreadable.

One person found this helpful

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Chan Chee Siong

Very good reference book

October 11, 2018

Format: Kindle EditionVerified Purchase

After complete reading the book, I constantly use it for reference whenever I need. Very easy to search for the code snippets I need

One person found this helpful

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Dimitri Shvorob

Value added

November 12, 2017

Format: Kindle Edition

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, 2015

I 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.

43 people found this helpful

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Lindsay

Five Stars

April 11, 2018

Format: Kindle EditionVerified Purchase

Very useful as an introduction for data manuipilation in python.

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R Roberts

The useful content is crippled by lack of indexing

March 23, 2018

Format: PaperbackVerified Purchase

Splitting this review into two parts: 5 for Petrou's content in the recipes themselves, 1 for Packt publisher's cookbook format which severely cripples the content with its lack of relevant indexing & labeling. Packt seems obsessed with cut/paste of non-value-added structure titles (Getting Ready, How to do It, How it works, There's more, See also). Those 5 phases are repeated 98 times in the Table of contents (blowing it up to 15 pages instead of concise 3). The skimpy index adds little value to find the relevant sections, unlike any well designed cookbook. Scanning the book is frustrating because the 98 repetitions of the structure headings are bigger and bolder than any other content. I've actually had to take the time to pencil in useful headings on the top-right corner of the pages and essentially create my own index so I can find what I need. I spent the time because the examples were useful and relevant -- if I could only find them when I need them...

9 people found this helpful

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