Learning programming languages such as Python can give you the power and flexibility to create incredible apps and websites. You just have to be ready to work hard and learn from the best.
The Complete 2020 Python Programming Certification Bundle is the only resource you need to learn these coding skills from scratch, and it’s down from almost $2,400 to just $49.99 on Tech Deals right now.
This hands-on learning kit packs enough content to take you from zero to hero in one of the world’s leading languages. You can use it to script webpages, build game elements, and much more. If you want to learn the language from top to bottom, you can explore over 60 hours of content.
Not only can you learn about the functions of Python, but you can also build ten real-world apps in the very first module. You can explore Python for data mining and visualization and even work with Keras. Better yet, if you’ve already learned one skill or another, you can just skip that module and keep going.
The 12 modules in the Complete 2020 Python Programming Certification Bundle have a combined retail value of nearly $2,400. You can save 97% right now and start learning for only $49.99. Ready to start your coding odyssey? You don’t have long to sign up at this price, so hit the widget below to find out more. $49 .99 The Complete 2020 Python Programming Certification Bundle Save $2335 .01 Buy it Now The Complete 2020 Python Programming Certification Bundle Buy it Now Save $2335 .01 $49 .99 Is this deal not quite right for you? To see all our hottest deals, head over to the DEALS HUB.
#) Python "preeminent" in O'Reilly learning platform usage analysis
At left, 2019 O'Reilly learning platform popularity, by usage, for programming languages, with growth rate relative to 2018, at right. Credit: O'Reilly Media In a report released by O'Reilly Media today, Python is the top search term on the company's learning platform for the full 2019 calendar year, and it's the #1 programming language, based on content usage, for the same period. In its report, O'Reilly describes Python as "preeminent" and, while data on its platform may not be perfectly reflective of the wider world of developers and data professionals, the company is well-respected in those arenas, with an array of popular books and tier-1 events, including Strata Data & AI Conference and Oscon. The more you know Anyone doing much work with data these days knows Python is hot, and that it's become the programming language of choice for data science. So while the news of Python's popularity may not be earth-shattering, there's lots of interesting data that goes beyond that headline. For example, it's not just that Python is popular; it's that it's still growing, in comparison to a similar analysis last year of search and usage data from 2018. And in a one-two punch, interest in Python's "rivals" is decreasing. One place Python code shows up frequently is in notebooks, and another language that shows up frequently in notebooks, especially for code running on Apache Spark, is Scala. But based on search frequency rating, 2019 interest in Scala was down over 20% from 2018 levels, indicating that Python may be making inroads in the data engineering world, where Scala has been popular. And the R programming language, which arguably had been Python's chief competitor in the Machine Learning (ML) realm, is down from 2018 too, almost as much as is Scala's. Even beyond the data world, Python's increased popularity is noteworthy, given that interest in Java, JavaScript, C# and C++ are all on the decline in O'Reilly's study. Other categories Other growing categories on the upswing include data engineering, data science, and AI + ML, generally, and Kafka, specifically. Even interest in SQL showed growth. Meanwhile, interest in data management and Spark is down, and that of Hadoop has dropped even more precipitously. According to O'Reilly's report on the data, "Hadoop and its ecosystem of related projects (such as Hive) are in the midst of a protracted, years-long decline." In fact, year-over-year, Hadoop and Hive are each down 34%, and Spark is down by 21%. Cloud's growing in general. Among the major public cloud providers, AWS, Azure and Google Cloud Platform (GCP) come in first, second and third in platform content usage, respectively, and all three are growing. But the growth in their numbers ranks in the opposite order, with usage of GCP-related content growing at almost 40%, Azure at 30% and AWS at 15%. And in the different, but related, world of container technology, content usage on Docker is down slightly while that of Kubernetes is up almost 40%. Speaking more broadly, 2019 seems to have been a year for maturity, and rigor. Usage on O'Reilly's security-related content was up 26%. Enterprise architecture, though small in absolute terms, was up roughly 50%. Architectural patterns, and serverless architecture were up significantly as well. Interestingly, DevOps, while still the top infrastructure + ops topic, was down 5% year-over-year. Extensibility and popularity Meanwhile, back to Python. The core language is a versatile one, but the real fuel for its momentum may be its extensibility and huge ecosystem support, resulting in a dizzying array of packages that extend the language. That's certainly true in the machine learning arena, where Python packages like NumPy, Pandas, Scikit-learn and PyTorch seem to make up much of the de facto ML stack. As a result, Python sample code is everywhere. And while I'm not much of a Python developer, I find Python code easy to read, and even to modify. That's both good and bad -- it means even novices may find the language approachable, but it may also mean that a chunk of Python code out there isn't the best, in terms of quality, performance or efficient use of the language and its packages. All the more reason, then, for the industry to be focusing on architecture, design and good development practices. Let's all hope interest in those topics of technological robustness stay strong and grow in 2020.
#) How to use Poetry to manage Python projects
“There should be one — and preferably only one — obvious way to do it.”
While that line comes from Tim Peters’s Zen of Python, Python doesn’t always adhere to that principle. One area where Python has fallen short of that ideal is project management. For too long, managing Python projects has involved a mishmash of tools and methodologies. However, a de facto standard may be emerging—Poetry. Poetry brings to Python the kind of all-in-one project management tool that Go and Rust have long enjoyed. Poetry allows projects to have deterministic dependencies with specific package versions, so they build consistently in different places. Poetry also makes it easier to build, package, and publish projects and libraries to PyPI, so others can share the fruits of your Python labors. In this article, we’ll walk through the use of Poetry for Python development projects — how to set up Poetry, how to use Poetry to configure a project’s dependencies and virtual environment, and how to avoid some of the pitfalls that come with Poetry’s unique way of doing things.
Set up Poetry in Python Poetry is deliberately unlike other Python dependency and project management tools, beginning with setup. Instead of using pip, Poetry uses a custom installer. The installer adds the Poetry app to your user’s profile directory, so it can be used with any Python installation in your system, present or future. Although you can use pip install poetry to install Poetry in a specific Python installation, this isn’t recommended because a) it might conflict with other system files, and b) it makes it difficult to use Poetry consistently with different versions of Python and different virtual environments. Create a Poetry-managed Python project Once you have Poetry installed, you can create a new Poetry-managed project directory simply by typing poetry new. This command creates a subdirectory named and populates it with a project scaffold.
The Poetry project scaffold includes the following:
pyproject.toml — the definition file for the project. Poetry manages this definition for you. If you know what you’re doing, you can edit this file directly, but most of the time you won’t need to.
README.rst — an empty README file in ReStructuredText format, the file format used for Python documentation. (There is no rule that says you must use .rst format for your docs; you can use Markdown for simpler cases.)
tests — a subdirectory with scaffolding for unit tests. If you aren’t in the habit of writing tests for your new projects, you should be!
And finally, a subdirectory with the project name that contains the code for your project.
Manage Python virtual environments in Poetry
Probably the first thing you’ll want with a new Poetry project is a Python virtual environment. True to form, Poetry has its own distinct way of handling virtual environments. Instead of placing virtual environments inside the project directory, Poetry puts them in a centralized cache directory that varies according to the operating system:
Unix: ~/.cache/pypoetry/virtualenvs
MacOS: ~/Library/Caches/pypoetry/virtualenvs
Windows: C:\Users\\AppData\Local\pypoetry\Cache\virtualenvs
or %LOCALAPPDATA%\pypoetry\Cache\virtualenvs
The advantage to Poetry’s approach is the ability to share virtual environments across projects, whenever it makes sense. But it does require altering your work habits.
To set up a virtual environment in Poetry, go to the directory for the project and type poetry env use python. Poetry will create a new virtual environment, store it in the cache directory, and display a randomly generated name for the virtual environment (note this for later use).
For added convenience, Poetry will also install any dependencies listed in the project’s pyproject.toml file. You will find this super useful should you ever want to copy a Poetry project from somewhere else and get it set up on your system.
Note that if you run poetry env use python in a project directory that already has a Poetry-assigned virtual environment, Poetry will activate that virtual environment in the context of that CLI session.
Next comes the hard part, which is getting your Poetry-managed virtual environments to work with your IDE.
Visual Studio Code, for instance, auto-detects the presence of a virtual environment inside a project directory, but doesn’t (yet) detect the presence of virtual environments managed with Poetry. The (near-term) solution is to add a line to the project’s settings.json file that indicates where Poetry keeps its virtual environments:
"python.venvPath": "C:\\Users\\username\\AppData\\Local\\pypoetry\\Cache\\virtualenvs"
Be sure to restart Visual Studio Code after making this change.
If you don’t want Poetry to manage your virtual environments, you can disable that behavior with this command:
poetry config virtualenvs.create false
Add dependencies to a Python project in Poetry
Poetry tracks two kinds of project dependencies: packages required for the project to run (production dependencies), and packages required only during the development process (development dependencies). Production dependencies would include any third-party libraries you use for the app’s functionality; development dependencies would include coding tools like black, mypy, or docutils.
To add production dependencies to the project, use poetry add .
To add development dependencies, use poetry add -D.
Note that you also use the -D switch when removing development dependencies (i.e., those added using the -D switch) using the command poetry remove .
Note that the poetry add command works much like pip install in that you can specify either a package name or
Git path (e.g., git+https://github.com/developer/project.git#branchname).
You can also configure Poetry to use private repos.
Once dependencies are resolved and installed, Poetry creates a file named poetry.lock in the project directory. This file is a manifest of all of the downloaded dependencies, and should be saved along with the rest of your project. Then anyone who pulls a copy of the project from source control will get the same versions of all required packages.
Now you’re ready to begin the project in earnest. All you have to remember from this point forward is to use Poetry — and only Poetry — to manage all dependencies and virtual environments for the project.
This story, "How to use Poetry to manage Python projects" was originally published by InfoWorld.
The 12 modules in the Complete 2020 Python Programming Certification Bundle have a combined retail value of nearly $2,400. You can save 97% right now and start learning for only $49.99. Ready to start your coding odyssey? You don’t have long to sign up at this price, so hit the widget below to find out more. $49 .99 The Complete 2020 Python Programming Certification Bundle Save $2335 .01 Buy it Now The Complete 2020 Python Programming Certification Bundle Buy it Now Save $2335 .01 $49 .99 Is this deal not quite right for you? To see all our hottest deals, head over to the DEALS HUB.
#) Python "preeminent" in O'Reilly learning platform usage analysis
At left, 2019 O'Reilly learning platform popularity, by usage, for programming languages, with growth rate relative to 2018, at right. Credit: O'Reilly Media In a report released by O'Reilly Media today, Python is the top search term on the company's learning platform for the full 2019 calendar year, and it's the #1 programming language, based on content usage, for the same period. In its report, O'Reilly describes Python as "preeminent" and, while data on its platform may not be perfectly reflective of the wider world of developers and data professionals, the company is well-respected in those arenas, with an array of popular books and tier-1 events, including Strata Data & AI Conference and Oscon. The more you know Anyone doing much work with data these days knows Python is hot, and that it's become the programming language of choice for data science. So while the news of Python's popularity may not be earth-shattering, there's lots of interesting data that goes beyond that headline. For example, it's not just that Python is popular; it's that it's still growing, in comparison to a similar analysis last year of search and usage data from 2018. And in a one-two punch, interest in Python's "rivals" is decreasing. One place Python code shows up frequently is in notebooks, and another language that shows up frequently in notebooks, especially for code running on Apache Spark, is Scala. But based on search frequency rating, 2019 interest in Scala was down over 20% from 2018 levels, indicating that Python may be making inroads in the data engineering world, where Scala has been popular. And the R programming language, which arguably had been Python's chief competitor in the Machine Learning (ML) realm, is down from 2018 too, almost as much as is Scala's. Even beyond the data world, Python's increased popularity is noteworthy, given that interest in Java, JavaScript, C# and C++ are all on the decline in O'Reilly's study. Other categories Other growing categories on the upswing include data engineering, data science, and AI + ML, generally, and Kafka, specifically. Even interest in SQL showed growth. Meanwhile, interest in data management and Spark is down, and that of Hadoop has dropped even more precipitously. According to O'Reilly's report on the data, "Hadoop and its ecosystem of related projects (such as Hive) are in the midst of a protracted, years-long decline." In fact, year-over-year, Hadoop and Hive are each down 34%, and Spark is down by 21%. Cloud's growing in general. Among the major public cloud providers, AWS, Azure and Google Cloud Platform (GCP) come in first, second and third in platform content usage, respectively, and all three are growing. But the growth in their numbers ranks in the opposite order, with usage of GCP-related content growing at almost 40%, Azure at 30% and AWS at 15%. And in the different, but related, world of container technology, content usage on Docker is down slightly while that of Kubernetes is up almost 40%. Speaking more broadly, 2019 seems to have been a year for maturity, and rigor. Usage on O'Reilly's security-related content was up 26%. Enterprise architecture, though small in absolute terms, was up roughly 50%. Architectural patterns, and serverless architecture were up significantly as well. Interestingly, DevOps, while still the top infrastructure + ops topic, was down 5% year-over-year. Extensibility and popularity Meanwhile, back to Python. The core language is a versatile one, but the real fuel for its momentum may be its extensibility and huge ecosystem support, resulting in a dizzying array of packages that extend the language. That's certainly true in the machine learning arena, where Python packages like NumPy, Pandas, Scikit-learn and PyTorch seem to make up much of the de facto ML stack. As a result, Python sample code is everywhere. And while I'm not much of a Python developer, I find Python code easy to read, and even to modify. That's both good and bad -- it means even novices may find the language approachable, but it may also mean that a chunk of Python code out there isn't the best, in terms of quality, performance or efficient use of the language and its packages. All the more reason, then, for the industry to be focusing on architecture, design and good development practices. Let's all hope interest in those topics of technological robustness stay strong and grow in 2020.
#) How to use Poetry to manage Python projects
“There should be one — and preferably only one — obvious way to do it.”
While that line comes from Tim Peters’s Zen of Python, Python doesn’t always adhere to that principle. One area where Python has fallen short of that ideal is project management. For too long, managing Python projects has involved a mishmash of tools and methodologies. However, a de facto standard may be emerging—Poetry. Poetry brings to Python the kind of all-in-one project management tool that Go and Rust have long enjoyed. Poetry allows projects to have deterministic dependencies with specific package versions, so they build consistently in different places. Poetry also makes it easier to build, package, and publish projects and libraries to PyPI, so others can share the fruits of your Python labors. In this article, we’ll walk through the use of Poetry for Python development projects — how to set up Poetry, how to use Poetry to configure a project’s dependencies and virtual environment, and how to avoid some of the pitfalls that come with Poetry’s unique way of doing things.
Set up Poetry in Python Poetry is deliberately unlike other Python dependency and project management tools, beginning with setup. Instead of using pip, Poetry uses a custom installer. The installer adds the Poetry app to your user’s profile directory, so it can be used with any Python installation in your system, present or future. Although you can use pip install poetry to install Poetry in a specific Python installation, this isn’t recommended because a) it might conflict with other system files, and b) it makes it difficult to use Poetry consistently with different versions of Python and different virtual environments. Create a Poetry-managed Python project Once you have Poetry installed, you can create a new Poetry-managed project directory simply by typing poetry new
Comments
Post a Comment