Free Download Python Data Science Handbook: Essential Tools for Working with Data
Desember 25, 2018linkin@juwang33Free Download Python Data Science Handbook: Essential Tools for Working with Data
You can alter point of how reading will certainly offer you much better selection. Yeah, Python Data Science Handbook: Essential Tools For Working With Data is a book produced by a specialist author. You can take this kind of publication in this website. Why? We provide the billions types and also brochures of the books worldwide. So, really, it is not only this book. You can find various other book types to be yours. The means is really basic, discover the web link that we give and obtain guide sooner. Always try to be the first individual to read this publication is extremely enjoyable.
Python Data Science Handbook: Essential Tools for Working with Data
Free Download Python Data Science Handbook: Essential Tools for Working with Data
Success is a selection. It's what lots of people say and also suggest making others be doing well. When someone decides to be success, they will certainly try big effort to recognize. Several methods are intended and also undertaken. Absolutely nothing limited, however there is something that may b forgotten. Seeking for knowledge and experience must remain in the plan as well as process. When you always more these 2, you can finish your strategies.
Lots of tasks in this current era require guide not only from the most recent book, however additionally from the old book collections. Why not? We offer you all collections from the earliest to the most recent books worldwide collections. So, it is very completed. When you feel that guide that you have is actually book that you intend to review currently, it's so pleasured. Yet, we truly recommend you to check out Python Data Science Handbook: Essential Tools For Working With Data for your own requirement.
So, should you review it rapidly? Obviously, yes! Need to you read this Python Data Science Handbook: Essential Tools For Working With Data as well as finish it fast? Never! You could get the satisfying reading when you are reading this publication while enjoying the spare time. Even you don't review the published publication as right here, you can still hold your tablet computer and also read it throughout. After obtaining the choice for you to obtain included in this sort of models, you could take some means to review.
After getting guide, you could begin your activity to review it, even in your extra time every where you are. You can recognize why we ready make it as recommended publication for you. This is not only regarding the appropriate topic for your analysis source however also the preferable book with excellent quality components. So, it will certainly not make puzzled to feel concerned not to get anything from Python Data Science Handbook: Essential Tools For Working With Data
About the Author
Jake VanderPlas is a long-time user and developer of the Python scientific stack. He currently works as an interdisciplinary research director at the University of Washington, conducts his own astronomy research, and spends time advising and consulting with local scientists from a wide range of fields.
Read more
Product details
Paperback: 548 pages
Publisher: O'Reilly Media; 1 edition (December 10, 2016)
Language: English
ISBN-10: 9781491912058
ISBN-13: 978-1491912058
ASIN: 1491912057
Product Dimensions:
7 x 1.2 x 10 inches
Shipping Weight: 1.8 pounds (View shipping rates and policies)
Average Customer Review:
4.6 out of 5 stars
44 customer reviews
Amazon Best Sellers Rank:
#4,146 in Books (See Top 100 in Books)
The figures were generated in color, but printed black and white, so they are often unintelligible. It's hard to tell the red dots from the blue when they are both grey.Apart from that major oversight, the book is ok. If you want to learn data science, this is not for you; it doesn't get into the fundamentals much at all. If you are an experienced R user looking for how to translate into python, this will get you started. The rest of my review comes from this perspective.The book spends far too much time on low-level ipython, numpy, and matplotlib functionality (chapters 1, 2, and 4). You are rarely going to use this stuff.The pandas section (chapter 3) is fine, but I was a little disappointed in the treatment of the grouping/aggregation functions. The book mentions the split-apply-combine paradigm of Hadley Wickham, but doesn't cover the topic in nearly as much detail as the paper of the same name. I was hoping to learn how to translate the dplyr verbs (group_by, filter, select, mutate, summarize, arrange) into pandas, but this book doesn't provide that. You will learn the basics of grouping and aggregation, but your code is going to be a lot more verbose than it was in R.The machine learning case studies in chapter 5 are pretty nice - probably the only reason I would recommend this book. The chapter provides a good overview of the scikit-learn API and effective patterns for machine learning problems.
I am currently taking a Machine Learning course from Udacity and this book has proven to be a great reference guide for several projects and quizes. Although it does not go in depth in regards to machine learning (although almost half of the book is dedicated to it), it does give an understanding of essential concepts. For those interested in machine learning I would recommend bying "Hands-On Machine Learning with Scikit-Learn and TensorFlow" by Geron as well as this book.There is no one book for data science, and this one is no exception. Just keep that in mind before buying it.Other than that, I am really happy with my purchase.P.S. For those complaining about black and white graphs and diagrams - check the author's GitHub.
This is an excellent reference book for people working with data science. Remember, 80% of the effort in machine learning, data analysis or data science in general is about processing data and understanding data. This book is for that purpose and I think it's the best book out there about data processing, analysis and visualization using python. If you look for hardcore machine learning, go for other books. Highly recommended!
I have used R for a few years and this was my first book that covered Python for data science. Even though it does not go into super great depth in any area, it is definitely a super book. It covers everything from Pandas, Matplotlib, and scikit-learn. I would highly recommend it for anyone that is new to Python and/or data science. The book is written with Jupyter Notebooks so it is easy to follow along and try code from the book in your own notebook.
When I first received this book, I was surprised that it didn't get to scikit-learn until the last third of the book. The first third is about numpy and pandas, and the middle third is about matplotlib. Now that I've been applying it at work, however, I've found that the items covered in the first two thirds were really essential. I wouldn't be nearly as productive if I had just jumped straight to the sections on scikit-learn. The author does an excellent job covering broad terrain with enough detail that you are able to apply it to your problems. You will find yourself going back to use this book as a reference.
I really enjoyed this book. I had not much experience with python prior to reading the book however I was able to pick it up quickly. Before long I was plotting distributions of real time statistics and prototyped a predictive modeling micro service. I consider this a must have book for any aspiring data scientist.
This book is well written and easy to follow. It's saved me from spending hours searching the internet to get acquainted with the standard libraries.I have used it extensively for the intro to ML at Berkeley and for now the book belongs to my short list of desk reference books.
I love the presentation style and the treatment of the subject in this book. This is a must have for experienced programmers breaking into the Data Science/ Machine Learning in Python. The book could have been organized better into more chapters instead of five.
Python Data Science Handbook: Essential Tools for Working with Data PDF
Python Data Science Handbook: Essential Tools for Working with Data EPub
Python Data Science Handbook: Essential Tools for Working with Data Doc
Python Data Science Handbook: Essential Tools for Working with Data iBooks
Python Data Science Handbook: Essential Tools for Working with Data rtf
Python Data Science Handbook: Essential Tools for Working with Data Mobipocket
Python Data Science Handbook: Essential Tools for Working with Data Kindle
0 komentar