Treading On Python Volume 1 Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Treading On Python Volume 1 book. This book definitely worth reading, it is an incredibly well-written.
Treading on Python is designed to bring developers and others who are anxious to learn Python up to speed quickly. Not only does it teach the basics of syntax, but it condenses years of experience. You will learn warts, gotchas, best practices and hints that have been gleaned through the years in days. You will hit the ground running and running in the right way.
Do you want to take your Python to the next level? Python is easy to learn. You can learn the basics in a day and be productive with it. But there are more advanced constructs that you will eventually run across if you spend enough time with it. Don't be confused by these. Learn them, embrace them, and improve your code and others.
Illustrated Guide to Python 3 by Matt Harrison Pdf
Introducing Your Guide to Learning PythonIllustrated Guide to Learning Python is designed to bring developers and others who are anxious to learn Python up to speed quickly. Not only does it teach the basics of syntax, but it condenses years of experience. You will learn warts, gotchas, best practices and hints that have been gleaned through the years in days. You will hit the ground running and running in the right way.Learn Python QuicklyPython is an incredible language. It is powerful and applicable in many areas. It is used for automation of simple or complex tasks, numerical processing, web development, interactive games and more. Whether you are a programmer coming to Python from another language, managing Python programmers or wanting to learn to program, it makes sense to cut to the chase and learn Python the right way. You could scour blogs, websites and much longer tomes if you have time. Treading on Python lets you learn the hints and tips to be Pythonic quickly.Packed with Useful Hints and TipsYou'll learn the best practices without wasting time searching or trying to force Python to be like other languages. I've collected all the gems I've gleaned over years of writing and teaching Python for you.A No Nonsense Guide to Mastering Basic PythonPython is a programming language that lets you work more quickly and integrate your systems more effectively. You can learn to use Python and see almost immediate gains in productivity and lower maintenance costs.What you will learn: Distilled best practices and tips How interpreted languages work Using basic types such as Strings, Integers, and Floats Best practices for using the interpreter during development The difference between mutable and immutable data Sets, Lists, and Dictionaries, and when to use each Gathering keyboard input How to define a class Looping constructs Handling Exceptions in code Slicing sequences Creating modular code Using libraries Laying out code Community prescribed conventions
Python is one of the top 3 tools that Data Scientists use. One of the tools in their arsenal is the Pandas library. This tool is popular because it gives you so much functionality out of the box. In addition, you can use all the power of Python to make the hard stuff easy! Learning the Pandas Library is designed to bring developers and aspiring data scientists who are anxious to learn Pandas up to speed quickly. It starts with the fundamentals of the data structures. Then, it covers the essential functionality. It includes many examples, graphics, code samples, and plots from real world examples. The Content Covers: Installation Data Structures Series CRUD Series Indexing Series Methods Series Plotting Series Examples DataFrame Methods DataFrame Statistics Grouping, Pivoting, and Reshaping Dealing with Missing Data Joining DataFrames DataFrame Examples Preliminary Reviews This is an excellent introduction benefitting from clear writing and simple examples. The pandas documentation itself is large and sometimes assumes too much knowledge, in my opinion. Learning the Pandas Library bridges this gap for new users and even for those with some pandas experience such as me. -Garry C. I have finished reading Learning the Pandas Library and I liked it... very useful and helpful tips even for people who use pandas regularly. -Tom Z.
You Will Learn Python 3! Zed Shaw has perfected the world’s best system for learning Python 3. Follow it and you will succeed—just like the millions of beginners Zed has taught to date! You bring the discipline, commitment, and persistence; the author supplies everything else. In Learn Python 3 the Hard Way, you’ll learn Python by working through 52 brilliantly crafted exercises. Read them. Type their code precisely. (No copying and pasting!) Fix your mistakes. Watch the programs run. As you do, you’ll learn how a computer works; what good programs look like; and how to read, write, and think about code. Zed then teaches you even more in 5+ hours of video where he shows you how to break, fix, and debug your code—live, as he’s doing the exercises. Install a complete Python environment Organize and write code Fix and break code Basic mathematics Variables Strings and text Interact with users Work with files Looping and logic Data structures using lists and dictionaries Program design Object-oriented programming Inheritance and composition Modules, classes, and objects Python packaging Automated testing Basic game development Basic web development It’ll be hard at first. But soon, you’ll just get it—and that will feel great! This course will reward you for every minute you put into it. Soon, you’ll know one of the world’s most powerful, popular programming languages. You’ll be a Python programmer. This Book Is Perfect For Total beginners with zero programming experience Junior developers who know one or two languages Returning professionals who haven’t written code in years Seasoned professionals looking for a fast, simple, crash course in Python 3
Beginner coders often gravitate to the easy-to-use Python language for its versatility and usability. Games, robots, and Web sites—including those of Google and YouTube—and much more run on Python, and developers are constantly collaborating to improve the language and address problem areas. This volume introduces readers to Python, exploring its various applications and the history of its development. Side-by-side comparisons with other languages are also included to show the benefits of Python, while interviews with programmers highlight its many real-world applications.
Effective PyCharm by Michael Kennedy,Matt Harrison Pdf
Hello and welcome to Effective PyCharm. In this book, we're going to look at all the different features of one of the very best environments for interacting and creating Python code, PyCharm. PyCharm is an IDE (integrated development environment) and this book will teach you how you can make the most of this super powerful editor.The first thing we are going to talk about is why do we want to use an IDE in the first place? What value does a relatively heavyweight application like PyCharm bring and why would we want to use it? There are many features that make PyCharm valuable. However, let's begin by talking about the various types of editors we can use and what the trade-offs are there.We're going to start by focusing on creating new projects and working with all the files in them. You'll see there's a bunch of configuration switcheswe can set to be more effective. Then we're going to jump right intowhat I would say is the star of the show--the editor.If you're writing code, you need an editor. You will be writing a lot of code. This includes typing new text and manipulating existing text. The editor has to be awesome and aid you in these tasks. We're going to focus on all the cool features that the PyCharm editor offers. We'll see that source control in particular, Git and Subversion are deeply integrated into PyCharm. There are all sorts of powerful things we can do beyond git, including actual GitHub integration. We are going to focus on source control and the features right inside the IDE.PyCharm is great at *refactoring*. Refactoring code is changing our code to restructure it in a different way, to use a slightly different algorithm, while not actually changing the behavior of the code. There are many powerful techniques in PyCharm that you can use to do this. Because it understands all of your files at once, it can safely refactor. It will even refactor doc strings and other items that could be overlooked without a deep understanding of code structures.There is powerful database tooling in PyCharm. You can interact with most databases including SQLite, MySQL, and Postgres. You can edit the data, edit the schemes, run queries and more. Because PyCharm has a deep understanding of your code, there is even integration between your database schema and the Python text editor. Note that PyCharm has a free version and a professional version. The database features are only available in the professional version.PyCharm is excellent at building web applications using libraries like Django, Pyramid, or Flask. It also has a full JavaScript editor and environment so you can use TypeScript or CoffeeScript. We'll look into both server-side and client-side features.PyCharm has a great visual debugger, and we are going to look at all the different features of it. You can use it to debug and understand your application. It has powerful breakpoint operations and data visualization that typically editors don't have.Profiling is a common task if you want to understand how your code is running. If your application is slow and you want it to go faster, you shouldn't guess where it is slow. PyCharm makes it easy to look at the code determine what it fast and slow, rather than relying on our intuition which may be flawed. PyCharm has some tremendous built-in visual types of tools for us to fundamentally understand the performance of our app.PyCharm has built-in test runners for pytest, unittest, and a number of Python testing frameworks. If you are doing any unit testing or integration testing, PyCharm will come to your aid. For example, one feature you can turn on is auto test execution. If you are changing certain parts of your code, PyCharm will automatically re-run the tests. There are a couple of additional tools that don't really land in any of the above categories. There is a chapter with the additional tools at the end.
Advanced Guide to Python 3 Programming by John Hunt Pdf
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.
"Focusing on Python 3.6 and higher, this concise handbook focuses on the essential core of the language, with updated code examples to illuminate how Python works and how to structure programs that can be more easily explained, tested, and debugged. Throughout, Beazley reflects all he's learned teaching Python to scientists, engineers, and developers, and pushing the envelope of what makes Python tick."--Page 4 of cover.
Learning Professional Python by Usharani Bhimavarapu,Jude D. Hemanth Pdf
Volume 1 of Learning Professional Python is a resource for students who want to learn Python even if they don’t have any programming knowledge and for teachers who want a comprehensive introduction to Python to use with their students. This book helps the students achieve their dream job in IT Industry and teaches the students in an easy, understandable manner while strengthening coding skills. Learning Professional Python: Volume 1 Objectives Become familiar with the features of Python programming language Introduce the object-oriented programming concepts Discover how to write Python code by following the object-oriented programming concepts Become comfortable with concepts such as classes, objects, inheritance, dynamic dispatch, interfaces, and packages Learn the Python generics and collections Develop exception handling and the multithreaded applications Design graphical user interface (GUI) applications
Python for Probability, Statistics, and Machine Learning by José Unpingco Pdf
This book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. All the figures and numerical results are reproducible using the Python codes provided. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Detailed proofs for certain important results are also provided. Modern Python modules like Pandas, Sympy, Scikit-learn, Tensorflow, and Keras are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This updated edition now includes the Fisher Exact Test and the Mann-Whitney-Wilcoxon Test. A new section on survival analysis has been included as well as substantial development of Generalized Linear Models. The new deep learning section for image processing includes an in-depth discussion of gradient descent methods that underpin all deep learning algorithms. As with the prior edition, there are new and updated *Programming Tips* that the illustrate effective Python modules and methods for scientific programming and machine learning. There are 445 run-able code blocks with corresponding outputs that have been tested for accuracy. Over 158 graphical visualizations (almost all generated using Python) illustrate the concepts that are developed both in code and in mathematics. We also discuss and use key Python modules such as Numpy, Scikit-learn, Sympy, Scipy, Lifelines, CvxPy, Theano, Matplotlib, Pandas, Tensorflow, Statsmodels, and Keras. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming.
The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.
Machine Learning for Algorithmic Trading by Stefan Jansen Pdf
Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key FeaturesDesign, train, and evaluate machine learning algorithms that underpin automated trading strategiesCreate a research and strategy development process to apply predictive modeling to trading decisionsLeverage NLP and deep learning to extract tradeable signals from market and alternative dataBook Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance. What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek's high-quality trades and quotes dataWho this book is for If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.
Learn web scraping and crawling techniques to access unlimited data from any web source in any format. With this practical guide, you’ll learn how to use Python scripts and web APIs to gather and process data from thousands—or even millions—of web pages at once. Ideal for programmers, security professionals, and web administrators familiar with Python, this book not only teaches basic web scraping mechanics, but also delves into more advanced topics, such as analyzing raw data or using scrapers for frontend website testing. Code samples are available to help you understand the concepts in practice. Learn how to parse complicated HTML pages Traverse multiple pages and sites Get a general overview of APIs and how they work Learn several methods for storing the data you scrape Download, read, and extract data from documents Use tools and techniques to clean badly formatted data Read and write natural languages Crawl through forms and logins Understand how to scrape JavaScript Learn image processing and text recognition