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It is crucial to learn Python from the best resources available. books are one of the best resources to learn anything so we are going to check out the 5 best Python books for beginners. the episode of this podcast is dedicated to the top 5 Python books to build a strong foundation for beginners.
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Mastering Python for Bioinformatics 1st Edition by Ken Youens-Clark, ISBN-13: 978-1098100889 [PDF eBook eTextbook] Publisher: O’Reilly Media; 1st edition (June 15, 2021) Language: English 454 pages ISBN-10: 1098100883 ISBN-13: 978-1098100889 Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained, usually by students and researchers who’ve never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software. You should read this book if you care about the craft of programming, and if you want to learn how to write programs that produce documentation, validate their parameters, fail gracefully, and work reliably. Testing is a key skill both for understanding your code and for verifying its correctness. I’ll show you how to use the tests I’ve written as well as how to write tests for your programs. To get the most out of this book, you should already have a solid understanding of Python. I will build on the skills I taught in Tiny Python Projects (Manning, 2020), where I show how to use Python data structures like strings, lists, tuples, dictionaries, sets, & named tuples. You need not be an expert in Python, but I definitely will push you to understand some advanced concepts I introduce in that book, such as types, regular expressions, and ideas about higher-order functions, along with testing and how to use tools like pylint, flake8, yapf, & pytest to check style, syntax, and correctness. One notable difference is that I will consistently use type annotations for all code in this book and will use the mypy tool to ensure the correct use of types. This book has been written for the aspiring bioinformatics programmer who wants to learn about Python’s best practices and tools such as the following: Since Python 3.6, you can add type hints to indicate, for instance, that a variable should be a type like a number or a list, and you can use the mypy tool to ensure the types are used correctly. Testing frameworks like pytest can exercise your code with both good and bad data to ensure that it reacts in some predictable way. Tools like pylint and flake8 can find potential errors and stylistic problems that would make your programs more difficult to understand. The argparse module can document and validate the arguments to your programs. The Python ecosystem allows you to leverage hundreds of existing modules like Biopython to shorten programs and make them more reliable. Using these tools practices individually will improve your programs, but combining them all will improve your code in compounding ways. This book is not a textbook on bioinformatics per se. The focus is on what Python offers that makes it suitable for writing scientific programs that are reproducible. That is, I’ll show you how to design and test programs that will always produce the same outputs given the same inputs. Bioinformatics is saturated with poorly written, undocumented programs, and my goal is to reverse this trend, one program at a time. Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You’ll learn the latest Python features and tools including linters, formatters, type checkers, and tests to create documented and tested programs. You’ll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming. Create command-line Python programs to document and validate parameters Write tests to verify refactor programs and confirm they’re correct Address bioinformatics ideas using Python data structures and modules such as Biopython Create reproducible shortcuts and workflows using makefiles Parse essential bioinformatics file formats such as FASTA and FASTQ Find patterns of text using regular expressions Use higher-order functions in Python like filter(), map(), and reduce() Table of Contents: Preface Who Should Read This? Programming Style: Why I Avoid OOP and Exceptions Structure Test-Driven Development Using the Command Line and Installing Python Getting the Code and Tests Installing Modules Installing the new.py Program Why Did I Write This Book? Conventions Used in This Book Using Code Examples OâReilly Online Learning How to Contact Us Acknowledgments I. The Rosalind.info Challenges 1. Tetranucleotide Frequency: Counting Things Getting Started Creating the Program Using new.py Using argparse Tools for Finding Errors in the Code Introducing Named Tuples Adding Types to Named Tuples Representing the Arguments with a NamedTuple Reading Input from the Command Line or a File Testing Your Program Running the Program to Test the Output Solution 1: Iterating and Counting the Characters in a String Counting the Nucleotides Writing and Verifying a Solution Additional Solutions Solution 2: Creating a count() Function and Adding a Unit Test Solution 3: Using str.count() Solution 4: Using a Dictionary to Count All the Characters Solution 5: Counting Only the Desired Bases Solution 6: Using collections.defaultdict() Solution 7: Using collections.Counter() Going Further Review 2. Transcribing DNA into mRNA: Mutating Strings, Reading and Writing Files Getting Started Defining the Programâs Parameters Defining an Optional Parameter Defining One or More Required Positional Parameters Using nargs to Define the Number of Arguments Using argparse.FileType() to Validate File Arguments Defining the Args Class Outlining the Program Using Pseudocode Iterating the Input Files Creating the Output Filenames Opening the Output Files Writing the Output Sequences Printing the Status Report Using the Test Suite Solutions Solution 1: Using str.replace() Solution 2: Using re.sub() Benchmarking Going Further Review 3. Reverse Complement of DNA: String Manipulation Getting Started Iterating Over a Reversed String Creating a Decision Tree Refactoring Solutions Solution 1: Using a for Loop and Decision Tree Solution 2: Using a Dictionary Lookup Solution 3: Using a List Comprehension Solution 4: Using str.translate() Solution 5: Using Bio.Seq Review 4. Creating the Fibonacci Sequence: Writing, Testing, and Benchmarking Algorithms Getting Started An Imperative Approach Solutions Solution 1: An Imperative Solution Using a List as a Stack Solution 2: Creating a Generator Function Solution 3: Using Recursion and Memoization Benchmarking the Solutions Testing the Good, the Bad, and the Ugly Running the Test Suite on All the Solutions Going Further Review 5. Computing GC Content: Parsing FASTA and Analyzing Sequences Getting Started Get Parsing FASTA Using Biopython Iterating the Sequences Using a for Loop Solutions Solution 1: Using a List Solution 2: Type Annotations and Unit Tests Solution 3: Keeping a Running Max Variable Solution 4: Using a List Comprehension with a Guard Solution 5: Using the filter() Function Solution 6: Using the map() Function and Summing Booleans Solution 7: Using Regular Expressions to Find Patterns Solution 8: A More Complex find_gc() Function Benchmarking Going Further Review 6. Finding the Hamming Distance: Counting Point Mutations Getting Started Iterating the Characters of Two Strings Solutions Solution 1: Iterating and Counting Solution 2: Creating a Unit Test Solution 3: Using the zip() Function Solution 4: Using the zip_longest() Function Solution 5: Using a List Comprehension Solution 6: Using the filter() Function Solution 7: Using the map() Function with zip_longest() Solution 8: Using the starmap() and operator.ne() Functions Going Further Review 7. Translating mRNA into Protein: More Functional Programming Getting Started K-mers and Codons Translating Codons Solutions Solution 1: Using a for Loop Solution 2: Adding Unit Tests Solution 3: Another Function and a List Comprehension Solution 4: Functional Programming with the map(), partial(), and takewhile() Functions Solution 5: Using Bio.Seq.translate() Benchmarking Going Further Review 8. Find a Motif in DNA: Exploring Sequence Similarity Getting Started Finding Subsequences Solutions Solution 1: Using the str.find() Method Solution 2: Using the str.index() Method Solution 3: A Purely Functional Approach Solution 4: Using K-mers Solution 5: Finding Overlapping Patterns Using Regular Expressions Benchmarking Going Further Review 9. Overlap Graphs: Sequence Assembly Using Shared K-mers Getting Started Managing Runtime Messages with STDOUT, STDERR, and Logging Finding Overlaps Grouping Sequences by the Overlap Solutions Solution 1: Using Set Intersections to Find Overlaps Solution 2: Using a Graph to Find All Paths Going Further Review 10. Finding the Longest Shared Subsequence: Finding K-mers, Writing Functions, and Using Binary Search Getting Started Finding the Shortest Sequence in a FASTA File Extracting K-mers from a Sequence Solutions Solution 1: Counting Frequencies of K-mers Solution 2: Speeding Things Up with a Binary Search Going Further Review 11. Finding a Protein Motif: Fetching Data and Using Regular Expressions Getting Started Downloading Sequences Files on the Command Line Downloading Sequences Files with Python Writing a Regular Expression to Find the Motif Solutions Solution 1: Using a Regular Expression Solution 2: Writing a Manual Solution Going Further Review 12. Inferring mRNA from Protein: Products and Reductions of Lists Getting Started Creating the Product of Lists Avoiding Overflow with Modular Multiplication Solutions Solution 1: Using a Dictionary for the RNA Codon Table Solution 2: Turn the Beat Around Solution 3: Encoding the Minimal Information Going Further Review 13. Location Restriction Sites: Using, Testing, and Sharing Code Getting Started Finding All Subsequences Using K-mers Finding All Reverse Complements Putting It All Together Solutions Solution 1: Using the zip() and enumerate() Functions Solution 2: Using the operator.eq() Function Solution 3: Writing a revp() Function Testing the Program Going Further Review 14. Finding Open Reading Frames Getting Started Translating Proteins Inside Each Frame Finding the ORFs in a Protein Sequence Solutions Solution 1: Using the str.index() Function Solution 2: Using the str.partition() Function Solution 3: Using a Regular Expression Going Further Review II. Other Programs 15. Seqmagique: Creating and Formatting Reports Using Seqmagick to Analyze Sequence Files Checking Files Using MD5 Hashes Getting Started Formatting Text Tables Using tabulate() Solutions Solution 1: Formatting with tabulate() Solution 2: Formatting with rich Going Further Review 16. FASTX grep: Creating a Utility Program to Select Sequences Finding Lines in a File Using grep The Structure of a FASTQ Record Getting Started Guessing the File Format Solution Going Further Review 17. DNA Synthesizer: Creating Synthetic Data with Markov Chains Understanding Markov Chains Getting Started Understanding Random Seeds Reading the Training Files Generating the Sequences Structuring the Program Solution Going Further Review 18. FASTX Sampler: Randomly Subsampling Sequence Files Getting Started Reviewing the Program Parameters Defining the Parameters Nondeterministic Sampling Structuring the Program Solutions Solution 1: Reading Regular Files Solution 2: Reading a Large Number of Compressed Files Going Further Review 19. Blastomatic: Parsing Delimited Text Files Introduction to BLAST Using csvkit and csvchk Getting Started Defining the Arguments Parsing Delimited Text Files Using the csv Module Parsing Delimited Text Files Using the pandas Module Solutions Solution 1: Manually Joining the Tables Using Dictionaries Solution 2: Writing the Output File with csv.DictWriter() Solution 3: Reading and Writing Files Using pandas Solution 4: Joining Files Using pandas Going Further Review A. Documenting Commands and Creating Workflows with make Makefiles Are Recipes Running a Specific Target Running with No Target Makefiles Create DAGs Using make to Compile a C Program Using make for a Shortcut Defining Variables Writing a Workflow Other Workflow Managers Further Reading B. Understanding $PATH and Installing Command-Line Programs Epilogue Index About the Author Ken Youens-Clark works as a Data Engineer at The Critical Path Institute where he helps partners in industry, academia, and government find novel drug therapies for diseases ranging from cancer and tuberculosis to thousands of rare diseases. His career in bioinformatics began in 2001 when he joined a plant genomics project at Cold Spring Harbor Laboratory under the direction of Dr. Lincoln Stein, a prominent author of books and modules in Perl and an early advocate for open software, data, and science. In 2014 Ken moved to Tucson, AZ, to work as a Senior Scientific Programmer at the University of Arizona where he completed a MS in Biosystems Engineering in 2019. While at UA, Ken enjoyed teaching programming and bioinformatics skills, and used some of those ideas in his first book, Tiny Python Projects (Manning, 2020), which uses a test-driven development approach to teaching Python. 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Systems & Cybernetics Site Map & Web Stuff
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Diego Azeta blogs:
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Sys•Cyber ✴️🔆✴️ blog:
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Fractal self-similarity
Principia Cybernetica
Simulation Modeling Software Analyze • Visualize • Optimize
AnyLogic - agent-based modeling (ABM), discrete event simulation (DES), system dynamics (SD) modeling
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Mesa - Python ABM
AgentPy - Python ABM library
Simantics System Dynamics - open source SD modeling & simulation software
Insight Maker - ABM & SD online simulation
Systems Dynamics Society - core software
Systems Dynamics Society - web-based tools
Capterra - best free simulation software
Online Resources
Computer Modeling and Simulation by Ángel A. Juan Pérez, Open University of Catalonia. A brief overview of DES. (PDF)
Gentle Intro Discrete Event Simulation
Intro Discrete Event Simulation (PDF)
Intro to Modeling and Analysis of Complex Systems by Hiroki Sayama, SUNY Binghamton
Complexity - Wikipedia
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Principles of Systems Science - U Wash PDF
Systems Theory - Wikipedia
Cybernetics and Systems (open journal)
International Encyclopedia of Systems and Cybernetics 2e
The Limits to Growth - The classic world sim, now more relevant than ever. 🌟🌟🌟🌟🌟
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What Is System Dynamics - SD Society
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The Beginnings of SD - Jay W. Forrester
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Descriptive Statistics - Bhandari @ Scribbr
Inferential Statistics - Bhandari @ Scribbr
Descriptive vs inferential Statistics - by Zach @ Statology
Bayesian Statistics - Wikipedia
Bayesian Statistics & Modeling - Gelman
Open Access Textbooks 📚
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Directory of Open Access Books
Math Open Textbooks - AI Math
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Guide to e-Books - UC Berkeley
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Recommended for learners of systems science, cyber systems analysis, and cyber systems engineering. Work at your own pace.
Probability & Statistics
Probability & Statistics - Carnegie-Mellon OLI
Intro Probability & Statistics - MIT OCW
• Introductory Statistics with Randomization and Simulation (Open Textbook Library)
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Linear Algebra
Linear Algebra - Strang, MIT OCW
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• Getting Started Python - Michigan Online
• Think Python 2e - Downey
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Linear Programming
Linear Programming (basic ideas) - OU
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Economics
Microeconomics - Gruber, MIT OCW
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Game Theory
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Logic
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• Logic I - MIT OCW
• Intro Logic and Critical Thinking (text) - Van Cleave, OTL
Systems Thinking
• Systems Thinking Books - Goodreads
• Intro Systems Thinking - UN ESCAP (PDF)
• Introduction to Industrial Engineering by Bonnie Boardman, U Texas at Arlington
Calculus & Differential Equations
Calculus Made Easy - An elegant introduction
Coursera Calculus courses
edX Calculus courses
MIT Calculus courses - MIT OCW
Diffy Qs: Differential Equations for Engineers - Jiří Lebl (textbook)
Differential Equations: An Introduction for Engineers - Matthew Charley (textbook)
Differential Equations - U Toronto (PDF)
You need not be an expert at solving DEs to work productively in cyber systems analysis. SD simulation software do that automatically with built-in numerical algorithms. But you should know what the DE means and does in the model. The same holds true for other technical details, such as random number generation and statistical measures, in different modeling and simulation paradigms. The conceptual ideas must be well understood; the computational aspects are best delegated to the modeling platform.
Thought of the day: Investing your time in becoming conversant with cybernetic system analysis methods is far superior to wasting it watching TV.
Cyber systems analysts think much better than non-analysts and are excellent at multi-factor problem solving, enterprise or institutional policy design, and strategic decision making. They are skilled in transforming conceptual insights into effective operational solutions.
Cyberneticians were key to the rise of cognitive science as a modern scientific discipline. They were pivotal in overcoming resistance to new ideas in psychiatry and psychology, especially in doctrinaire behaviorism.
Traditional fields of inquiry are often biased by conceiving problems and proposed solutions solely from their disciplinary perspective. This can turn out to be a hindrance. Cyber systems analysis is trans-disciplinary and eclectic. As such it tends to be more open in recognizing issues and ascertaining workable solutions to complex real-world problems.
If you want to change the world without waiting forever for change to happen, consider using cybernetic systems analysis. Effective results. Efficient and functional. Less prone to risk yet significantly more cost effective than traditional organizational reform approaches.
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(PDF/ePub) Elements of Programming Interviews in Python: The Insiders' Guide - Adnan Aziz
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This is the Python version of our book. See the website for links to the C++ and Java version.Have you ever...Wanted to work at an exciting futuristic company?Struggled with an interview problem thatcould have been solved in 15 minutes?Wished you could study real-world computing problems?If so, you need to read Elements of Programming Interviews (EPI).EPI is your comprehensive guide to interviewing for software development roles.The core of EPI is a collection of over 250 problems with detailed solutions. The problems are representative of interview questions asked at leading software companies. The problems are illustrated with 200 figures, 300 tested programs, and 150 additional variants.The book begins with a summary of the nontechnical aspects of interviewing, such as strategies for a great interview, common mistakes, perspectives from the other side of the table, tips on negotiating the best offer, and a guide to the best ways to use EPI. We also provide a summary of data
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[Download] Fluent Python, 2nd Edition: Clear, Concise, and Effective Programming - Luciano Ramalho
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Python's simplicity lets you become productive quickly, but often this means you aren't using everything it has to offer. With the updated edition of this hands-on guide, you'll learn how to write effective, modern Python 3 code by leveraging its best ideas.Don't waste time bending Python to fit patterns you learned in other languages. Discover and apply idiomatic Python 3 features beyond your past experience. Author Luciano Ramalho guides you through Python's core language features and libraries and teaches you how to make your code shorter, faster, and more readable.Featuring major updates throughout the book, Fluent Python, second edition, covers:Special methods: The key to the consistent behavior of Python objectsData structures: Sequences, dicts, sets, Unicode, and data classesFunctions as objects: First-class functions, related design patterns, and type hints in function declarationsObject-oriented idioms: Composition, inheritance, mixins, interfaces, operator overloading,
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[PDF Download] Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems - Aur?lien G?ron
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Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming
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PL/SQL ist eine prozedurale Sprache, die speziell für SQL-Anweisungen in ihrer Syntax entwickelt wurde. PL/SQL-Programmeinheiten werden vom Oracle Database- Konfigurieren Sie das Embedded-Plsql-Gateway der Datenbank als. Webserver. Außerdem wollen wir eine komplette Entwicklungsumgebung installieren. 6.1 PLSQL PPT CH6.pdf - View presentation slides online. PL/SQL Tutorial A package is an Oracle object, which holds other • Generic • Encapsulated denn das geht nicht direkt in SQL, sondern nur in PL/SQL. (aber auch englischsprachigen) PDF von Oracle: dem Data Warehousing Guide. In. Oracle Index Tuning &Admin Marco Patzwahl MuniQSoft GmbH München-Unterhaching Schlüsselworte: SQL, PL/SQL, DBA Zusammenfassung Indizes sind ein erprobtes Learn Python Programming for Beginners: Best Step-by-Step Guide for Coding with Python, Great for Kids and Adults. Includes Practical Exercises on Data Analysis 08.08.2013 — Book title: Oracle Database 10g PL/SQL Programming Аthor: Scott Urman, Ron Hardman Formаts: pdf, android, epub, ebook, text, ipad, audio (mit SQL-Kritik). [dcs.warwick.ac.uk/~hugh/CS253/CS253-TheAskewWall.pdf] Tutorial zu Oracle PL/SQL (von Yu-May Chang, Jeff Ullman u.a.). 21.01.2019 — locking, binding, and use of PL/SQL in the demonstrated a couple of database queries Bill's basic concept for Oracle Forms 1979.
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Plain text workflow
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That’s a lot of technobabble, let me break that down some. The major downside is that it is expensive, although the developer lets you use it without paying if you can put up with periodic scolding popups.Įverything is saved to a git repository which gets to be in a private repo on GitHub. But I like Sublime Text over those other alternatives because (a) it’s extremely fast, stable, and lightweight (basically the opposite of Word), and (b) it has a plug-in system based in Python, which is one of my preferred programming languages-so it’s very easy for me to directly program my editor. There are fine alternatives for editing plain text, like Atom, BBEdit (Mac-only), and Notepad++ (Windows-only). I use Sublime Text pretty much only for editing markdown or plain text prose-when I write code, I usually use emacs or vim-so I don’t have to muck around with complicated file-type-specific settings. On the Mac, I do most of my markdown writing in Sublime Text, which is a totally bulletproof programmer’s text editor. Typically, for longer works (big articles, books) or multi-stage projects, I’ll have multiple markdown files with different logically separated portions of text, plus files for notes, paragraphs that need to be discarded or moved to a better home, etc. etc.
#Plain text workflow how to
For people who are interested in Markdown, I wrote a tutorial on how to work with Markdown here. There are different flavors of Markdown (for reasons to be described below, I use the Pandoc version), but the basics are very simple.
#Plain text workflow pdf
It’s readily convertible to MSWord format, as well as PDF (if you’ve installed LaTeX), HTML, and all kinds of more arcane things-it’s very commonly used by programmers and bloggers, and I think it’s by far the best way to write initial drafts. Markdown, for those who aren’t familiar, is a plain text format with very lightweight markup for things like bold/italics, links, and the like. So my workflow has the following elements: I want to use git for version control so that I can recover prior versions if something gets horribly screwed up. I like being able to relatively seamlessly switch between writing on my MacBook and on my iPad.Į. I also hate manually formatting my citations.ĭ. Most of the major alternatives to word (OpenOffice, Google Docs, Pages) are crap.Ĭ. Word “features” like styled paste, auto-conversion of URLS to links, bizarre dictatorial bullet point numbering, etc. I also want something that doesn’t impose involuntary formatting on me.
#Plain text workflow code
My writing needs to be scriptable-I need to be able to read my writing into an ordinary programming language as a string, run code on it, and spit it back out again as a string. If I want to do something weird or automated with my content, I want to be able to do so. It takes longer to start than XCODE, which, for those of you who program, you’re probably screaming in horror at the very idea.)
#Plain text workflow pro
I have a brand new souped up 16-inch Macbook Pro with an i9 and 32 gigs of ram. (It’s utterly mind-blowing how badly Word performs. I want something that doesn’t crash or hang all the time, unlike Word. Yet I recognize that many people that publish things I write need Word format, so I need to do something that converts to Word fairly readily. Subconsiderations: I won’t use it if it can be helped at all. I’m incredibly paranoid about losing work.ī. Here are the considerations, in rough order of priority, that drive me:Ī. This is a work in progress document I’ve promised to share my toolkit with a couple people, so, as those people say to me “hey, this makes no sense,” I’ll probably edit to clarify. I have an unusual and complicated academic writing workflow/toolkit, but one that might be of use to some other people, so I thought I’d share it here. This one is for academics rather than practitioners, though some of the details are applicable to practitioners as well.
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The automated statbook
#THE AUTOMATED STATBOOK FOR FREE#
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(Link to the older 1st edition.) Additional Content
Appendix C – Answers to the Practice Questions.
Appendix A – Installing Third-Party Modules.
Chapter 20 – Controlling the Keyboard and Mouse with GUI Automation.
Chapter 18 – Sending Email and Text Messages.
Chapter 17 – Keeping Time, Scheduling Tasks, and Launching Programs.
Chapter 16 – Working with CSV Files and JSON Data.
Chapter 15 – Working with PDF and Word Documents.
Chapter 14 – Working with Google Spreadsheets.
Chapter 13 – Working with Excel Spreadsheets.
Chapter 7 – Pattern Matching with Regular Expressions.
Chapter 5 – Dictionaries and Structuring Data.
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Or preview the first 15 course videos for free on YouTube. Use this link to apply a 60% discount code. This video course follows much (though not all) of the content of the book. New Book: "The Big Book of Small Python Projects" Learn how in Automate the Boring Stuff with Python. Even if you've never written a line of code, you can make your computer do the grunt work. Step-by-step instructions walk you through each program, and practice projects at the end of each chapter challenge you to improve those programs and use your newfound skills to automate similar tasks.ĭon't spend your time doing work a well-trained monkey could do. Send reminder emails and text notifications.Split, merge, watermark, and encrypt PDFs.Update and format data in Excel spreadsheets of any size.Search the Web and download online content.Create, update, move, and rename files and folders.Search for text in a file or across multiple files.Once you've mastered the basics of programming, you'll create Python programs that effortlessly perform useful and impressive feats of automation to:
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In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand - no prior programming experience required. But what if you could have your computer do them for you? If you've ever spent hours renaming files or updating hundreds of spreadsheet cells, you know how tedious tasks like these can be. Wil Wheaton, Practical Programming for Total Beginners "I'm having a lot of fun breaking things and then putting them back together, and just remembering the joy of turning a set of instructions into something useful and fun, like I did when I was a kid." Hilary Mason, Data Scientist and Founder of Fast Forward Labs Automate the Boring Stuff with Python frames all of programming as these small triumphs it makes the boring fun." "The best part of programming is the triumph of seeing the machine do something useful.
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Introducing Python: Modern Computing in Simple Packages Easy to understand and fun to read, this updated edition of Introducing Python is ideal for beginning programmers as well as those new to the language. Author Bill Lubanovic takes you from the basics to more involved and varied topics, mixing tutorials with cookbook-style code recipes to explain concepts in Python 3. End-of-chapter exercises help you practice what you’ve learned. You’ll gain a strong foundation in the language, including best practices for testing, debugging, code reuse, and other development tips. This book also shows you how to use Python for applications in business, science, and the arts, using various Python tools and open source packages. . . . . . . #pdf #epub #digitalbook #python #pythonlearning #django #programing https://www.instagram.com/p/Ch_6HKzsD8I/?igshid=NGJjMDIxMWI=
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(PDF/ePub) Elements of Programming Interviews in Python: The Insiders' Guide Writen By Adnan Aziz
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[Read] [Kindle] Python Scripting for Arcgis Pro By Paul A. Zandbergen
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