Useful Python
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Useful Python
Author: Stuart Langridge
language: en
Publisher: SitePoint Pty Ltd
Release Date: 2023-07-07
Python is a versatile and powerful language that can be used for a wide variety of tasks. In this book, we'll look at how Python can be used for various tasks that will make your life easier: Python as a “glue” language: it helps us combine skills and programs we already know how to use by allowing us to easily convert data from one format to another. This means that we can take data in one format that we don’t have tools to manipulate and change it into data for tools that we’re comfortable with. Whether we need to process a CSV, web page, or JSON file, Python can help us get the data into a format we can use. Python for stitching together other things. Sometimes we need to do more than process the data in an Excel file we’ve been sent. For example, we may want to fetch some pages from the Web, or work with an online API, or control our computer itself (such as renaming a batch of files, or changing how our operating system works). Python is great at these tasks. Words and numbers. Python is a powerful tool that can be used to solve a wide variety of problems, both big and small. In this tutorial, we’ll explore how to use Python to answer questions, solve puzzles, and simulate various scenarios. Running someone else’s Python code. Unlike many other languages, Python actually comes with modules for all sorts of useful functions. But for the times when we want to do things not covered by the standard library, we can use PyPI, a vast collection of open-source software libraries, frameworks and applications that can be easily installed and used in Python projects. We'll look at some examples. We’re also going to assume a little knowledge of Python and programming already—such as what a variable is, what a dictionary is, and how to import a module.
Advanced Guide to Python 3 Programming
Advanced Guide to Python 3 Programming 2nd Edition delves deeply into a host of subjects that you need to understand if you are to develop sophisticated real-world programs. Each topic is preceded by an introduction followed by more advanced topics, along with numerous examples, that take you to an advanced level. This second edition has been significantly updated with two new sections on advanced Python language concepts and data analytics and machine learning. The GUI chapters have been rewritten to use the Tkinter UI library and a chapter on performance monitoring and profiling has been added. In total there are 18 new chapters, and all remaining chapters have been updated for the latest version of Python as well as for any of the libraries they use. There are eleven sections within the book covering Python Language Concepts, Computer Graphics (including GUIs), Games, Testing, File Input and Output, Databases Access, Logging, Concurrency and Parallelism, Reactive Programming, Networking and Data Analytics. Each section is self-contained and can either be read on its own or as part of the book as a whole. It is aimed at those who have learnt the basics of the Python 3 language but wish to delve deeper into Python’s eco system of additional libraries and modules.
Python Tools for Scientists
An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration. Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data. Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms. You’ll also learn how to: Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development features Use Python’s built-in data types, write custom functions and classes, and document your code Represent data with the essential NumPy, Matplotlib, and pandas libraries Use Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizations Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.