Python Applications For Mac

Release Date: June 27, 2018

• Python For Mac Download
• Python For Mac
• Python On Mac
• Install Python For Mac
• Python Application For Mac
• Python App For Mac

Python 3.7.0 was the initial feature release of Python 3.7.

There are now newer bugfix releases of Python 3.7 that supersede 3.7.0 and Python 3.8 is now the latest feature release of Python 3. Get the latest releases of 3.7.x and 3.8.x here. We plan to continue to provide bugfix releasesfor 3.7.x until mid 2020 and security fixes until mid 2023.

The official home of the Python Programming Language. Applications; Quotes; Getting Started; Help; Python Brochure; Downloads. All releases; Source code; Windows; Mac OS X. Mac OS X; Python Releases for Mac OS X. Latest Python 3 Release - Python 3.9.0; Latest Python 2 Release - Python 2.7.18.

Python is installed by default on a Mac (YES!), but IDLE is not. IDLE makes it easier to interact with Python, so we will install and use it within this tutorial. The link on python.org will. Rhino for Mac users who are familiar with the Windows version may suffer from scripting envy not being aware that there are ways to write and execute Python scripts in Rhino for Mac. Atom is an open source text editor t. Getting and Installing MacPython¶ Mac OS X 10.8 comes with Python 2.7 pre-installed by Apple. While the Popen answers are reasonable for the general case, I would recommend win32api for this specific case, if you want to do something useful with it. It goes something like this: from win32com.client import Dispatch xl = Dispatch('Excel.Application') wb = xl.Workbooks.Open('C:Documents and SettingsGradeBook.xls') xl.Visible = True # optional. Note: Mac users may get the following message: This program needs access to the screen. Please run with a Framework build of python, and only when you are logged in on the main display of your Mac. If you see this message and you are not running in a virtualenv, then you need to run your application with pythonw instead of python.

Among the major new features in Python 3.7 are:

• PEP 539, new C API for thread-local storage
• PEP 545, Python documentation translations
• New documentation translations: Japanese,French, andKorean.
• PEP 552, Deterministic pyc files
• PEP 553, Built-in breakpoint()
• PEP 557, Data Classes
• PEP 560, Core support for typing module and generic types
• PEP 562, Customization of access to module attributes
• PEP 563, Postponed evaluation of annotations
• PEP 564, Time functions with nanosecond resolution
• PEP 565, Improved DeprecationWarning handling
• PEP 567, Context Variables
• Avoiding the use of ASCII as a default text encoding (PEP 538, legacy C locale coercionand PEP 540, forced UTF-8 runtime mode)
• The insertion-order preservation nature of dict objects is now an official part of the Python language spec.
• Notable performance improvements in many areas.

Please see What's New In Python 3.7 for more information.

• PEP 537, 3.7 Release Schedule
• Report bugs at https://bugs.python.org.
• Help fund Python and its community.

• The binaries for AMD64 will also work on processors that implement the Intel 64 architecture. (Also known as the 'x64' architecture, and formerly known as both 'EM64T' and 'x86-64'.)
• There are now 'web-based' installers for Windows platforms; the installer will download the needed software components at installation time.
• There are redistributable zip files containing the Windows builds, making it easy to redistribute Python as part of another software package. Please see the documentation regarding Embedded Distribution for more information.

• For 3.7.0, we provide two binary installer options for download. The default variant is 64-bit-only and works on macOS 10.9 (Mavericks) and later systems. We also continue to provide a 64-bit/32-bit variant that works on all versions of macOS from 10.6 (Snow Leopard) on. Both variants now come with batteries-included versions oF Tcl/Tk 8.6 for users of IDLE and other tkinter-based GUI applications; third-party and system versions of Tcl/Tk are no longer used. Consider using the new 10.9 64-bit-only installer variant, unless you are building Python applications that also need to work on older macOS systems.
• Both python.org installer variants include private copies of OpenSSL 1.1.0. Please carefully read the Important Information displayed during installation for information about SSL/TLS certificate validation and the Install Certificates.command.

As a general-purpose programming language, Python is designed to beused in many ways. You can build web sites or industrial robots or agame for your friends to play, and much more, all using the samecore technology.

Python's flexibility is why the first step in every Python projectmust be to think about the project's audience and the correspondingenvironment where the project will run. It might seem strange to thinkabout packaging before writing code, but this process does wonders foravoiding future headaches.

This overview provides a general-purpose decision tree for reasoningabout Python's plethora of packaging options. Read on to choose the besttechnology for your next project.

Contents

Packages exist to be installed (or deployed), so before you packageanything, you'll want to have some answers to the deployment questionsbelow:

• Who are your software's users? Will your software be installed byother developers doing software development, operations people in adatacenter, or a less software-savvy group?

• Is your software intended to run on servers, desktops, mobileclients (phones, tablets, etc.), or embedded in dedicated devices?

• Is your software installed individually, or in large deployment batches?

Packaging is all about target environment and deploymentexperience. There are many answers to the questions above and eachcombination of circumstances has its own solutions. With thisinformation, the following overview will guide you to the packagingtechnologies best suited to your project.

You may have heard about PyPI, setup.py, and wheelfiles. These are just a few of the tools Python's ecosystem providesfor distributing Python code to developers, which you can read about inPackaging and distributing projects.

The following approaches to packaging are meant for libraries andtools used by technical audience in a development setting. If you'relooking for ways to package Python for a non-technical audience and/ora production setting, skip ahead to Packaging Python applications.

A Python file, provided it only relies on the standard library, can beredistributed and reused. You will also need to ensure it's writtenfor the right version of Python, and only relies on the standardlibrary.

This is great for sharing simple scripts and snippets between peoplewho both have compatible Python versions (such as via email,StackOverflow, or GitHub gists). There are even some entire Pythonlibraries that offer this as an option, such as bottle.py and boltons.

However, this pattern won't scale for projects that consist ofmultiple files, need additional libraries, or need a specific versionof Python, hence the options below.

If your code consists of multiple Python files, it's usually organizedinto a directory structure. Any directory containing Python files cancomprise an Import Package.

Because packages consist of multiple files, they are harder todistribute. Most protocols support transferring only one file at atime (when was the last time you clicked a link and it downloadedmultiple files?). It's easier to get incomplete transfers, and harderto guarantee code integrity at the destination.

So long as your code contains nothing but pure Python code, and youknow your deployment environment supports your version of Python, thenyou can use Python's native packaging tools to create a sourceDistribution Package, or sdist for short.

Python's sdists are compressed archives (.tar.gz files)containing one or more packages or modules. If your code ispure-Python, and you only depend on other Python packages, you can gohere to learn more.

If you rely on any non-Python code, or non-Python packages (such aslibxml2 in the case oflxml, or BLAS libraries in thecase of numpy), you will need touse the format detailed in the next section, which also has manyadvantages for pure-Python libraries.

Note

Python For Mac Download

Python and PyPI support multiple distributions providingdifferent implementations of the same package. For instance theunmaintained-but-seminal PIL distribution provides the PIL package, and sodoes Pillow, anactively-maintained fork of PIL!

This Python packaging superpower makes it possible for Pillow to bea drop-in replacement for PIL, just by changing your project'sinstall_requires or requirements.txt.

So much of Python's practical power comes from its ability tointegrate with the software ecosystem, in particular libraries writtenin C, C++, Fortran, Rust, and other languages.

Not all developers have the right tools or experiences to build thesecomponents written in these compiled languages, so Python created theWheel, a package format designed to ship libraries withcompiled artifacts. In fact, Python's package installer, pip,always prefers wheels because installation is always faster, so evenpure-Python packages work better with wheels.

Binary distributions are best when they come with source distributionsto match. Even if you don't upload wheels of your code for everyoperating system, by uploading the sdist, you're enabling users ofother platforms to still build it for themselves. Default topublishing both sdist and wheel archives together, unless you'recreating artifacts for a very specific use case where you know therecipient only needs one or the other.

Python and PyPI make it easy to upload both wheels and sdiststogether. Just follow the Packaging Python Projectstutorial.

Python's recommended built-in library and tool packagingtechnologies. Excerpted from The Packaging Gradient (2017).¶

So far we've only discussed Python's native distribution tools. Basedon our introduction, you would be correct to infer these built-inapproaches only target environments which have Python, and anaudience who knows how to install Python packages.

With the variety of operating systems, configurations, and people outthere, this assumption is only safe when targeting a developeraudience.

Python's native packaging is mostly built for distributing reusablecode, called libraries, between developers. You can piggybacktools, or basic applications for developers, on top of Python'slibrary packaging, using technologies like setuptools entry_points.

Libraries are building blocks, not complete applications. Fordistributing applications, there's a whole new world of technologiesout there.

The next few sections organize these application packaging optionsaccording to their dependencies on the target environment,so you can choose the right one for your project.

Some types of Python applications, like web site backends and othernetwork services, are common enough that they have frameworks toenable their development and packaging. Other types of applications,like dynamic web frontends and mobile clients, are complex enough totarget that a framework becomes more than a convenience.

In all these cases, it makes sense to work backwards, from theframework's packaging and deployment story. Some frameworks include adeployment system which wraps the technologies outlined in the rest ofthe guide. In these cases, you'll want to defer to your framework'spackaging guide for the easiest and most reliable production experience.

If you ever wonder how these platforms and frameworks work under thehood, you can always read the sections beyond.

If you're developing for a 'Platform-as-a-Service' or 'PaaS' likeHeroku or Google App Engine, you are going to want to follow theirrespective packaging guides.

• 'Serverless' frameworks like Zappa

In all these setups, the platform takes care of packaging anddeployment, as long as you follow their patterns. Most software doesnot fit one of these templates, hence the existence of all the otheroptions below.

If you're developing software that will be deployed to machines youown, users' personal computers, or any other arrangement, read on.

Python's steady advances are leading it into new spaces. These daysyou can write a mobile app or web application frontend inPython. While the language may be familiar, the packaging anddeployment practices are brand new.

If you're planning on releasing to these new frontiers, you'll want tocheck out the following frameworks, and refer to their packagingguides:

If you are not interested in using a framework or platform, or justwonder about some of the technologies and techniques utilized by theframeworks above, continue reading below.

Pick an arbitrary computer, and depending on the context, there's a verygood chance Python is already installed. Included by default in mostLinux and Mac operating systems for many years now, you can reasonablydepend on Python preexisting in your data centers or on the personalmachines of developers and data scientists.

Technologies which support this model:

• PEX (Python EXecutable)

• zipapp (does not help manage dependencies, requires Python 3.5+)

• shiv (requires Python 3)

Note

Of all the approaches here, depending on a pre-installedPython relies the most on the target environment. Of course,this also makes for the smallest package, as small assingle-digit megabytes, or even kilobytes.

In general, decreasing the dependency on the target systemincreases the size of our package, so the solutions hereare roughly arranged by increasing size of output.

Python For Mac

For a long time many operating systems, including Mac and Windows,lacked built-in package management. Only recently did these OSes gainso-called 'app stores', but even those focus on consumer applicationsand offer little for developers.

Developers long sought remedies, and in this struggle, emerged withtheir own package management solutions, such as Homebrew. The most relevant alternative for Pythondevelopers is a package ecosystem called Anaconda. Anacondais built around Python and is increasingly common in academic,analytical, and other data-oriented environments, even making its wayinto server-oriented environments.

Instructions on building and publishing for the Anaconda ecosystem:

A similar model involves installing an alternative Pythondistribution, but does not support arbitrary operating system-levelpackages:

In all these setups, the platform takes care of packaging anddeployment, as long as you follow their patterns. Most software doesnot fit one of these templates, hence the existence of all the otheroptions below.

If you're developing software that will be deployed to machines youown, users' personal computers, or any other arrangement, read on.

Python's steady advances are leading it into new spaces. These daysyou can write a mobile app or web application frontend inPython. While the language may be familiar, the packaging anddeployment practices are brand new.

If you're planning on releasing to these new frontiers, you'll want tocheck out the following frameworks, and refer to their packagingguides:

If you are not interested in using a framework or platform, or justwonder about some of the technologies and techniques utilized by theframeworks above, continue reading below.

Pick an arbitrary computer, and depending on the context, there's a verygood chance Python is already installed. Included by default in mostLinux and Mac operating systems for many years now, you can reasonablydepend on Python preexisting in your data centers or on the personalmachines of developers and data scientists.

Technologies which support this model:

• PEX (Python EXecutable)

• zipapp (does not help manage dependencies, requires Python 3.5+)

• shiv (requires Python 3)

Note

Of all the approaches here, depending on a pre-installedPython relies the most on the target environment. Of course,this also makes for the smallest package, as small assingle-digit megabytes, or even kilobytes.

In general, decreasing the dependency on the target systemincreases the size of our package, so the solutions hereare roughly arranged by increasing size of output.

Python For Mac

For a long time many operating systems, including Mac and Windows,lacked built-in package management. Only recently did these OSes gainso-called 'app stores', but even those focus on consumer applicationsand offer little for developers.

Developers long sought remedies, and in this struggle, emerged withtheir own package management solutions, such as Homebrew. The most relevant alternative for Pythondevelopers is a package ecosystem called Anaconda. Anacondais built around Python and is increasingly common in academic,analytical, and other data-oriented environments, even making its wayinto server-oriented environments.

Instructions on building and publishing for the Anaconda ecosystem:

A similar model involves installing an alternative Pythondistribution, but does not support arbitrary operating system-levelpackages:

Computing as we know it is defined by the ability to executeprograms. Every operating system natively supports one or more formatsof program they can natively execute.

There are many techniques and technologies which turn your Pythonprogram into one of these formats, most of which involve embedding thePython interpreter and any other dependencies into a single executablefile.

This approach, called freezing, offers wide compatiblity andseamless user experience, though often requires multiple technologies,and a good amount of effort.

A selection of Python freezers:

• pyInstaller - Cross-platform

• cx_Freeze - Cross-platform

• constructor - For command-line installers

• py2exe - Windows only

• py2app - Mac only

• bbFreeze - Windows, Linux, Python 2 only

  From Adobe Systems: Shockwave Player is the web standard for powerful multimedia playback. The Shockwave Player allows you to view interactive web content like games, business presentations. Adobe Shockwave Player can deliver 3D effects, realistic simulations due to the advanced physics support, surround sound multimedia, bitmap filters, parallax/cube/normal mapping, cloth simulations. Adobe Shockwave allows you to play Flash content, created using Adobe Director, on the Web.ProsContent works well: No problems or errors were encountered when. Adobe shockwave player 11 free download for mac. Shockwave Player is also a plugin for several browsers, including Safari and Firefox, which lets you view multimedia content developed use with this technology, which is seen especially in web applications and games (for example, Habbo). Generally, online resources that use Shockwave indicate so before displaying the content. Adobe Shockwave Player is compatible with all major web browsers including Firefox, Chrome and Internet Explorer. Installation is very quick and you should be seeing content within a few seconds of restarting your browser. Compared to Flash for example however, Shockwave is quite resource heavy.

• osnap - Windows and Mac

• pynsist - Windows only

Most of the above imply single-user deployments. For multi-componentserver applications, see Chef Omnibus.

An increasing number of operating systems – including Linux, Mac OS,and Windows – can be set up to run applications packaged aslightweight images, using a relatively modern arrangement oftenreferred to as operating-system-level virtualization,or containerization.

These techniques are mostly Python agnostic, because they packagewhole OS filesystems, not just Python or Python packages.

Python On Mac

Adoption is most extensive among Linux servers, where the technologyoriginated and where the technologies below work best:

Most operating systems support some form of classical virtualization,running applications packaged as images containing a full operatingsystem of their own. Running these virtual machines, or VMs, is amature approach, widespread in data center environments.

These techniques are mostly reserved for larger scale deployments indata centers, though certain complex applications can benefit fromthis packaging. Technologies are Python agnostic, and include:

• VHD, AMI, and other formats

• OpenStack - A cloud management system in Python, with extensive VM support

The most all-encompassing way to ship your software would be to shipit already-installed on some hardware. This way, your software's userwould require only electricity.

Whereas the virtual machines described above are primarily reservedfor the tech-savvy, you can find hardware appliances being used byeveryone from the most advanced data centers to the youngest children.

Embed your code on an Adafruit,MicroPython, or more-powerful hardwarerunning Python, then ship it to the datacenter or your users'homes. They plug and play, and you can call it a day.

The simplified gamut of technologies used to package Python applications.¶

The sections above can only summarize so much, and you might bewondering about some of the more conspicuous gaps.

As mentioned in Depending on a separate software distribution ecosystem above, some operatingsystems have package managers of their own. If you're very sure of theoperating system you're targeting, you can depend directly on a formatlike deb (forDebian, Ubuntu, etc.) or RPM (for Red Hat,Fedora, etc.), and use that built-in package manager to take care ofinstallation, and even deployment. You can even use FPM togenerate both deb and RPMs from the same source.

In most deployment pipelines, the OS package manager is just one pieceof the puzzle.

Virtualenvs havebeen an indispensible tool for multiple generations of Pythondeveloper, but are slowly fading from view, as they are being wrappedby higher-level tools. With packaging in particular, virtualenvs areused as a primitive in the dh-virtualenv tool andosnap, both of which wrapvirtualenvs in a self-contained way.

For production deployments, do not rely on running pipinstallfrom the Internet into a virtualenv, as one might do in a developmentenvironment. The overview above is full of much better solutions.

Install Python For Mac

The further down the gradient you come, the harder it gets to updatecomponents of your package. Everything is more tightly bound together.

Python Application For Mac

For example, if a kernel security issue emerges, and you're deployingcontainers, the host system's kernel can be updated without requiringa new build on behalf of the application. If you deploy VM images,you'll need a new build. Whether or not this dynamic makes one optionmore secure is still a bit of an old debate, going back to thestill-unsettled matter of static versus dynamic linking.

Python App For Mac

Packaging in Python has a bit of a reputation for being a bumpyride. This impression is mostly a byproduct of Python'sversatility. Once you understand the natural boundaries between eachpackaging solution, you begin to realize that the varied landscape isa small price Python programmers pay for using one of the mostbalanced, flexible language available.