Download PDF Pandas Cookbook Recipes for Scientific Computing Time Series Analysis and Data Visualization using Python Theodore Petrou 9781784393878 Books

Product details Paperback 538 pages Publisher Packt Publishing (October 23, 2017) Language English ISBN-10 9781784393878

Pandas Cookbook Recipes for Scientific Computing Time Series Analysis and Data Visualization using Python Theodore Petrou 9781784393878 Books Reviews

• 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. ^__^
• 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.
• 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.
• 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.
• On Cloud reader with Firefox (latest), after the first 170 pages, the formatting becomes narrower with each page. Eventually it shows one character per line in a single column. I'm sure , Firefox, and Packt will put the blame on each other. Packt also disallows downloading the book, so I can't try other options for viewing. Bottom line is I paid for something I can't read.
• After complete reading the book, I constantly use it for reference whenever I need. Very easy to search for the code snippets I need
• 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, 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.
• Very useful as an introduction for data manuipilation in python.