دانلود کتاب پایتون 3 و بصری سازی داده ها

WHAT IS THE PRIMARY VALUE PROPOSITION FOR THIS BOOK? This book contains a fast-paced introduction to relevant information about Pythonbased data visualization. You will learn how to generate graphics using Pandas, Matplotlib, and Seaborn. In addition, an appendix contains SVG-based and D3-based graphics effects, along with links for many additional code samples. This book is intended primarily for those who have worked with Python and are interested in learning about graphics effects with Python libraries. It is also intended to reach an international audience of readers with highly diverse backgrounds in various age groups. Consequently, the book uses standard English rather than colloquial expressions that might be confusing to those readers. It provides a comfortable and meaningful learning experience for the intended readers. The first chapter contains a quick tour of basic Python 3, followed by a chapter that introduces you to NumPy. The third and fourth chapters introduce you to Pandas as well as Pandas with JSON data. MySQL and SQL. The fifth chapter delves into data visualization with Matplotlib and also working with SweetViz and Skimpy. The final chapter of this book shows you how to create graphics effects with Seaborn, and an example of a rendering graphics effects in Bokeh. In addition, an appendix is included with graphics effects based on SVG and D3. Most of the code samples are short (usually less than one page and sometimes less than half a page), and if need be, you can easily and quickly copy/paste the code into a new Jupyter notebook. For the Python code samples that reference a CSV file, you do not need any additional code in the corresponding Jupyter notebook to access the CSV file. Moreover, the code samples execute quickly, so you won’t need to avail yourself of the free GPU that is provided in Google Colaboratory. If you do decide to use Google Colaboratory, you can easily copy/paste the Python code into a notebook, and also use the upload feature to upload existing Jupyter notebooks. Keep in mind the following point: if the Python code references a CSV file, make sure that you include the appropriate code snippet (details are available online) to access the CSV file in the corresponding Jupyter notebook in Google Colaboratory. First, keep in mind that the Sklearn material in this book is minimalistic, because it is not about machine learning. Second, the Sklearn material is located in chapter 6 where you will learn about some of the Sklearn built-in datasets. If you decide to study machine learning, you will have already been introduced to some aspects of Sklearn. Some programmers learn well from prose, others learn well from sample code (and lots of it), which means that there’s no single style that can be used for everyone. Moreover, some programmers want to run the code first, see what it does, and then return to the code to delve into the details (and others use the opposite approach). Consequently, there are various types of code samples in this book: some are short, some are long, and other code samples “build” from earlier code samples. Current knowledge of Python 3.x is the most helpful skill. Knowledge of other programming languages (such as Java) can also be helpful because of the exposure to programming concepts and constructs. The less technical knowledge that you have, the more diligence will be required in order to understand the various topics that are covered. As for the non-technical skills, it’s very important to have a strong desire to learn about data visualization, along with the motivation and discipline to read and understand the code samples. The companion files contain all the code samples to save you time and effort from the error-prone process of manually typing code into a text file. In addition, there are situations in which you might not have easy access to these files. Furthermore, the code samples in the book provide explanations that are not available in the companion files.