Top 10 Python Books Of 2018 That You Should Read!
There are always many reasons to publish a book about IT and programming. The bitcoin rate has soared – a dozen blockbusters appear on the market for blocking and mining technology.
The machine on Go has beaten a person into some kind of intellectual game – get a pile of “the most complete guides” in the Google language.
But there are eternal themes. One of them is Python. It’s understandable, the language is perfect for those who are not familiar with programming at all; You can study all the main libraries in a couple of weeks, and the possibilities are practically unlimited. We looked at the electronic shelves of the Amazon store to see what useful was released or reissued by Python in the first half of 2018.
Computer Programming for Beginners: Fundamentals of Programming Terms and Concepts, Nathan Clark
Before we go directly to the language, let me recommend this book. From what has emerged in recent years, perhaps, this is the most complete and readable introduction to programming.
It is perfect for those who do not understand anything at all in programming. Just two hundred pages are very laconic, but at the same time it is clearly described:
- All the main terms of programming with definitions.
- What are states, operators, functions, scopes, and uses?
- How programs look in different languages: structure, assembly and debugging.
- What is OOP, what is it different from functional programming, and when to use it?
- How to work with memory: recording, management, cleaning, handling.
All this is accompanied by examples in the languages C #, Python and Java, so you not only get acquainted with the programming in theory but also begin to delve into it in practice.
Python Programming: For the Beginners, Navi Feroz
Judging only by feedback, this is one of the best books for beginners. Here, without a deeper understanding of the mechanics of processes (with this you can see in the previous book), you give basic knowledge of programming in general, and only then on the syntax of Python, the structure of programs, the main libraries, and capabilities. Everything is written in plain language, so you can read it at least in front of the computer, even while lying on the beach on vacation.
Undoubtedly, at the exit, you will not feel like a ready-made specialist, but definitely, you will be fired by the desire to continue your education in this direction. In addition, the book is replete with basic examples, so in the future practice, you will often return to a re-study of individual chapters.
Python Programming: A Step By Step Guide For Beginners, Brian Jenkins
This book is more suitable for those who want not to delve into the jungle of theory, but to comprehend a new language, strictly following the steps from installation to writing the first serious program in Python 3. Of course, this is not a book that can be studied far from the computer.
The material is extremely chewed and accompanied by a bunch of examples, so you will not have white spots after reading, everything will be assimilated even at the level of mechanical memory. In general, this is a good guide for education if you have only a few days off.
Python: — The Bible- 3 Manuscripts in 1 book: -Python Programming For Beginners -Python Programming For Intermediates -Python Programming for Advanced, Maurice J. Thompson
A collection of three books, which will suit those who are sure that its future will be connected with Python. Each part is designed for a certain level of preparation. The declared duration of training on this manual is 21 days. However, do not think that this time will be enough to become a ready-made specialist.
The first part deals with basic questions:
- What is Python? What are its advantages over other languages?
- How to get started?
- What are variables, strings, lists, dictionaries, etc.?
- What does the Python program look like? Why is it important to follow certain style rules?
- How to create the first project?
The second and third parts reveal deeper questions, for example:
- How does Python work with memory?
- What does OOP look like in Python?
- How should I debug and test programs?
- What are iterators and generators? How to use them?
This is only a small list of topics under consideration – in the Bible, there are answers to all questions about the language, but exactly in the volume that can be accommodated in just three books.
Python Programming: The Basic, Blackhat, Intermediary and Advanced Guide to Python Programming, Richard Ozer
Similar to the previous collection, but consisting of four parts. Here a little more information, a little more examples, in the “advanced” parts a little more emphasis on the mechanisms of Python. In general, it is the same full-fledged language guide that is useful to keep on hand for years of programming.
Python Machine Learning: A Guide For Beginners, Leonard Eddison
From the general books on language, we turn to the manual for beginners, who are interested not only in Python but in its use in the field of artificial intelligence. Although the title says that the book is for beginners, it is better to get acquainted with the language in advance, because the main emphasis here will be made not so much on it, as on the work with data.
The main goal of the author is to show how the world of Data Science works, how information is structured, processed and placed in algorithms that subsequently use artificial intelligence. That is, this is not a classical step-by-step guide, so the book will be interesting even for those who study other languages, or do not program at all.
Python Programming Illustrated For Beginners & Intermediates:: “Learn By Doing” Approach-Step By Step Ultimate Guide To Mastering Python: The Future Is Here!, William Sullivan
Another step-by-step guide with examples of the working code. The principal difference from most other books is the amount of information. This means that you do not have to run to the store for the next book immediately after the creation of the first program. With the help of this guide, you will be able to practice functional programming, learn to build informative diagrams, master the professional style of writing code. And all these thanks to a huge number of examples, flavored with a detailed description of the processes.
Coding: Raspberry Pi &Python: A Guide For Beginners, Leonard Eddison
Another very high-quality book from Addison, consisting of two parts. The first one is devoted to the educational program on Python – it can be used in conjunction with the above-mentioned book “Python Machine Learning …”. The second part – the knowledge of using Python the capabilities of one of the most popular gigs platforms. In general, this is a very convenient desktop guide for those who decide to create their own robot or make their own home a little smarter.
Direct examples from the category “how to assemble a machine for watering plants” is not here, but if you do not understand much in programming – the book will be very useful.
Coding: The Bible: 2 Manuscripts — Python and Raspberry PI, Larry Lutz
Similar in structure to a collection of two books, but a little larger. Here, not only questions relating to the language itself are discussed in detail, but also important points such as code optimization, reliability, and modularity. After reading the question with Python you can actually close – everything else will lie in the field of engineering and the desire to independently find the answers.
The second part introduces the platform Raspberry Pi. You will learn how to put an operating system on the device and start working with it using Python. Unlike the previous book, there are practical examples, but still not in the amount that did not have to strain the brain.
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python, Rudolph Russell
We finish our review with another book on the current topic – machine learning. Unlike Addison’s book, here it is initially assumed that you have the knowledge and experience of working with Python. The book will appeal to everyone who is close to the subject of artificial intelligence and large data, but because of the abundance of examples using libraries such as pandas, matplotlib, and sklearn, it is of particular value to pythonists. There are a lot of illustrations and code examples (as far as possible in a 100-page book), as well as explanations through which the author explains the main algorithms for data processing. In general, this is one of the best books on MO in Python.
