---
product_id: 31343218
title: "Data Visualization with Python and JavaScript: Scrape, Clean, Explore & Transform Your Data 1st Edition"
brand: "kyran dale"
price: "3213 kr"
currency: ISK
in_stock: true
reviews_count: 13
url: https://www.desertcart.is/products/31343218-data-visualization-with-python-and-javascript-scrape-clean-explore-transform
store_origin: IS
region: Iceland
---

# Data Visualization with Python and JavaScript: Scrape, Clean, Explore & Transform Your Data 1st Edition

**Brand:** kyran dale
**Price:** 3213 kr
**Availability:** ✅ In Stock

## Quick Answers

- **What is this?** Data Visualization with Python and JavaScript: Scrape, Clean, Explore & Transform Your Data 1st Edition by kyran dale
- **How much does it cost?** 3213 kr with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.is](https://www.desertcart.is/products/31343218-data-visualization-with-python-and-javascript-scrape-clean-explore-transform)

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## Customer Reviews

### ⭐⭐⭐⭐⭐ 







  
  
    Outstanding Coverage of a Powerful Toolchain
  

*by J***Y on Reviewed in the United States on December 14, 2016*

Forgive the long winded review, but this book really resonated with me because (I think) I happen to align just about spot-on with its intended audience. I needed to setup a visualization dashboard to take in GBs of data weekly, and allow users to easily view it at various levels of granularity and slice through various directions in data space. This was for chip design data, so the data elements at their worst could represent hundreds of millions of transistors and connections, and the various data spaces could be timing, power, layout (physical space), time (timestamps) and so forth. But the specifics of the data are not important - the point is that there was a lot of it, and that users needed to interactively examine it.After introductory chapters covering foundational matters in python, javascript, html, css, and svg, Dale works through each stage of the data acquisition, processing, and visualization flow, following a nontrivial example project from the very beginning all the way through to completion.The first sections cover data procurement, cleaning techniques, and exploratory data visualization with pandas and matplotlib. Although in my case this portion of the flow was not as crucial (my data was more or less easy pickings and familiar), I did get a nice instructive window into some the issues that the "data science" crowd routinely worry about. I expect to benefit much more from this material in the future, as it is certain I'll have to confront less familiar datasets at some point. See the TOC online, as there is a lot of nice material here that, as it happens, I didn't yet need to dig into for my particular project. This time, that is.Since I already knew most of the requirements for my visualizations, my work started in earnest with the web app. This is where Dale's book really laid out a clear, detailed path for me.On the server side, his choice of the Flask micro web framework was a good fit, and the explanations were clear and helpful. Like many, I have in recent years become enamored of scripting in python, and Flask is a nice lightweight framework in which to easily code up a solid data delivery interface. I opted for MongoDB storage, but SQL is supported too; both are covered in the book. It's all spelled out, and it was straightforward to apply his techniques to my problem. One of the keys to making this work for me was his prescription for how to make a clean so-called REST interface, including - crucially - pagination, to throttle loads of JSON data from the server to the client via ajax.Then, on the client side, crossfilter.js and d3.js make it possible to produce some very sophisticated (and beautiful) visualizations. Crossfilter and d3, with all their subtleties and moving parts, are not easy tools to master - at least not for me. And there is perhaps an over abundance of code snippets out there on the web, a kind of fast food diet that does not provide sufficient understanding necessary to prevent the inevitable frustration later on when this approach starts to let you down. So Dale's step by step, screenshot by screenshot walkthroughs, with excellent color diagrams, really helped tremendously here. I have not seen better explanations anywhere.Final notes: The various sections of the book are relatively independent and can stand alone; depending on your background, you may be able to skip around and hunt for exactly what you need. If you are in a short term deadline situation, this may very well be the best approach initially. A reasonable skim of the appropriate section(s) of the book and a bit of copy-paste with the code should get you on your way.But, speaking generally, I would tend to advise against such an approach, especially when you're not under time pressure. This book is more of a running storyline and workshop than a quick-reference cookbook. A hunt-copy-paste approach could very well blind you to the whole point, and lead you to toss the book aside, incorrectly concluding that it won't help you after all. And even if you reap some quick early victories, you may still miss out on unexpected goodies that you'll later wish you hadn't. Instead, a more careful, active study is required to gain the fluency with these tools that you actually need so that they won't crumble when you try to apply them in new circumstances. If you make the effort, this extraordinary book will show you how to gain this fluency.

### ⭐⭐⭐⭐⭐ 







  
  
    Tour De Force Of Data Visualization
  

*by W***S on Reviewed in the United States on September 14, 2016*

This book does an excellent job of showing how to create a website for Data Visualization. Python and Javascript are the choosen languages along with many libraries. The choice of Python was for its strength in manipulating data, and Javascript is used for the front-end, particularly the D3 library.Throughout the book there is an example which uses data about Nobel Prize winners. The data is pulled from Wikipedia, cleaned, analyzed, hosted, and visualized. Each step of the process was explained well.The beginning of the book starts by introducing Python and Javascript; how they are different and how they are similar. Then each step of the 5-step process is explained with multiple chapters dedicated to each step. I was surprised how often the author had the meta-view to know which libraries were best, and to be able to link to all the right places. There are no random links, or extraneous information, everything was relevant to the goal of creating the site.Language in the book is better than most as the author takes times to explain in interesting ways. On page 274 when introducting different graph types he says: "The humble bar chart is a staple for a lot of visual data exploration." What a nice way to explain the importance of the bar chart.At the end I had a clear picture of how to build an impressive data visualization website form scratch. It was great to see an author who can provide a full-stack implementation and explain each aspect with ease.This book is in color which I really appreciated. It helped a lot to have syntax highlighting and I hope O'rielly does this for all their other books. The dimensions of the book is smaller than other O'rielly books as well; it was comfortable.

### ⭐⭐⭐⭐ 







  
  
    This is a really useful book. But be prepared for a long journey ...
  

*by D***S on Reviewed in the United States on February 5, 2018*

This is a really useful book. But be prepared for a long journey with it, it is really ambitious. I'm still working though it after a year, and for each chapter that I go into I have to do a lot of research on the side to make everything make sense. Of course the packages are changing so fast that you can't rely on the code examples to function perfectly without doing your own debugging and syntax updating, this isn't the author's fault. The value of this book is the general pipeline it presents, not the specific instructions in each chapter.I think of this book as a general guide to how to navigate the modern landscape of web-based data visualization, a broad map for a pretty confusing landscape. I don't know of any other book that does that, even if you do have to delve into other resources on the side as you go.

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*Store origin: IS*
*Last updated: 2026-02-20*