Hi, guys, welcome back. In this article, I will be discussing the top 5 Python IDE for Windows, Linux & Mac. The finest Python IDE and code editors for developers are designed for easy coding and debugging. Let’s begin to know about it, now.

Top 5 Python IDE for Windows, Linux & Mac

Manually Locate Where Python is Installed. Alternatively, you can manually locate where Python is installed by typing ‘Python’ in the Windows Search Bar: Right-click on the Python App, and then select “Open file location” as captured below: Right-click on the Python shortcut, and then select Properties: Click on “Open File Location“. Run a Python script under Windows with the Command Prompt. Windows users must pass the path of the program as an argument to the Python interpreter. Such as follows: 1. C: Python27 python.exe C: Users Username Desktop mypythonscript.py. Note that you must use the full path of the Python interpreter. Ability to download, install and set up Python packages from actions/python-versions that do not come preinstalled on runners. Allows for pinning to a specific patch version of Python without the worry of it ever being removed or changed. Automatic setup and download of Python packages if. Jul 14, 2020 Instead: 1. Press the Windows key and R to open the Run dialog. Enter 'sysdm.cpl'. Click the 'Advanced' tab of the System Properties Window. Click the 'Environmental variables'. Most everything else would work as described even on Windows 95 (if there's a version of Python for Windows 95).

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A Python IDE (Integrated Development Environment) is a platform used by python developers to develop the software or program. It provides various features such as debugging tools, syntax highlighting, automatic code completion, and many other features, which help the developers to write the program more quickly and debug easily. To build a desktop or web application, developers can use such editors.

I have discussed the top 5 python IDEs (Integrated Development Environment) in this article. Let’s see each of them.

PyCharm

• It is a Python IDE used by most python developers.
• It was created by the Czech company JetBrains.
• It is a cross-platform Python IDE that can run on Windows, Linux & macOS.
• It includes debugging features, code testing, quick & safe code refactoring, and completion of auto code, highlighting the syntax, standard database access tools for accessing databases like MySQL and PostgreSQL, IDE Oracle, etc.
• It supports web development technologies and frameworks such as Django, Flask, CSS (Cascading Style Sheet), TypeScript, CoffeeScript and JavaScript, and many more.
  One of the disadvantages of PyCharm is it has a slow loading time.

Spyder

• Pierre Raybaut developed Spyder in 2008.
• It is a free & open-source Python IDE (Integrated Development Environment).
• It is also referred to as Scientific Python Development IDE.
• It is mostly used by data scientists who can integrate with various scientific python libraries such as Matplotlib, Pandas, Numpy, SciPy, Cython, IPython, etc.
• Spyder contains fundamental features like syntax highlight and automatic code completion, data exploration and it is easy to install.
• It runs on Windows, Linux & macOS and is provided by Anaconda Package Manager Distribution.

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Jupyter

• It is one of the top Python IDE that enables the visualization of data, data cleansing, numerical computation, and statistical modeling of data.
• In the field of data science, Jupyter is widely used.
• It is free to use and provides python libraries such as Matplotlib, Pandas, Numpy, etc., required in data science & machine learning algorithms.
• It is user-friendly, interactive, and allows sharing and visualization of live code.
• It combines code, text, and images to provide a user experience.

Python IDLE (Integrated Development Learning Environment)

• It is free & easy to use for python programming.
• This python IDE is suitable for developers at the beginning level.
• IDLE is a Python-based open-source IDE, so you don’t have to bother about installation or set-up.
• It is a cross-platform that can work in Windows, macOS, and Linux.
• This Python IDE is not suited for larger projects, therefore, try other IDEs when you are clear with the basics of python programming.
• It contains an interactive interpreter, different colors for input, output, and error messages, highlights syntax, and correct indentation.

Sublime text 3

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• Sublime Text 3 is a popular code editor which supports a range of programming languages, including Python.
• It is written in C++ and Python by a Google engineer.
• It is a free, cross-platform Python IDE that has basic built-in support for Python.
• It is compatible with Windows, Linux & macOS X.
• It offers features such as syntax highlighting and allows simultaneous editing (multiple selections) of python code, a command palette that accepts text input from users, and efficient handling of the project directory.
• You can easily customize the sublime text as per your requirement and also supports additional web development and scientific development packages for Python.

When should you use which Python IDE?

There are many other popular Python IDEs such as Visual Studio Code (VS Code), PyDev, Thonny, Wing and Vim, Atom, etc.

If you are a beginner-level python programmer, then it is good to go with IDLE and Thonny IDEs.

If you are an intermediate python programmer, then you should use PyCharm, Atom, Sublime Text, Visual Studio (VS) code are some of the good options.

I hope this module has helped you to know about some of the Python IDE. Based on your requirements and level of knowledge for python, you can use different python IDEs. You can visit our website for more useful modules like this.

Note

This document assumes you are working from anin-development checkout of Python. If youare not then some things presented here may not work as they may dependon new features not available in earlier versions of Python.

Running¶

The shortest, simplest way of running the test suite is the following commandfrom the root directory of your checkout (after you havebuilt Python):

You may need to change this command as follows throughout this section.On most Mac OS X systems, replace ./pythonwith ./python.exe. On Windows, use python.bat. If usingPython 2.7, replace test with test.regrtest.

If you don’t have easy access to a command line, you can run the test suite froma Python or IDLE shell:

This will run the majority of tests, but exclude a small portion of them; theseexcluded tests use special kinds of resources: for example, accessing theInternet, or trying to play a sound or to display a graphical interface onyour desktop. They are disabled by default so that running the test suiteis not too intrusive. To enable some of these additional tests (and forother flags which can help debug various issues such as reference leaks), readthe help text:

If you want to run a single test file, simply specify the test file name(without the extension) as an argument. You also probably want to enableverbose mode (using -v), so that individual failures are detailed:

To run a single test case, use the unittest module, providing the importpath to the test case:

If you have a multi-core or multi-CPU machine, you can enable parallel testingusing several Python processes so as to speed up things:

If you are running a version of Python prior to 3.3 you must specify the numberof processes to run simultaneously (e.g. -j2).

Finally, if you want to run tests under a more strenuous set of settings, youcan run test as:

The various extra flags passed to Python cause it to be much stricter aboutvarious things (the -Wd flag should be -Werror at some point, but thetest suite has not reached a point where all warnings have been dealt with andso we cannot guarantee that a bug-free Python will properly complete a test runwith -Werror). The -r flag to the test runner causes it to run tests ina more random order which helps to check that the various tests do not interferewith each other. The -w flag causes failing tests to be run again to seeif the failures are transient or consistent.The -uall flag allows the use of all availableresources so as to not skip tests requiring, e.g., Internet access.

To check for reference leaks (only needed if you modified C code), use the-R flag. For example, -R3:2 will first run the test 3 times to settledown the reference count, and then run it 2 more times to verify if there areany leaks.

You can also execute the Tools/scripts/run_tests.py script as found in aCPython checkout. The script tries to balance speed with thoroughness. But ifyou want the most thorough tests you should use the strenuous approach shownabove.

Unexpected Skips¶

Sometimes when running the test suite, you will see “unexpected skips”reported. These represent cases where an entire test module has beenskipped, but the test suite normally expects the tests in that module tobe executed on that platform.

Often, the cause is that an optional module hasn’t been built due to missingbuild dependencies. In these cases, the missing module reported when the testis skipped should match one of the modules reported as failing to build whenCompile and build.

In other cases, the skip message should provide enough detail to help figureout and resolve the cause of the problem (for example, the default securitysettings on some platforms will disallow some tests)

Writing¶

Writing tests for Python is much like writing tests for your own code. Testsneed to be thorough, fast, isolated, consistently repeatable, and as simple aspossible. We try to have tests both for normal behaviour and for errorconditions. Tests live in the Lib/test directory, where every file thatincludes tests has a test_ prefix.

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One difference with ordinary testing is that you are encouraged to rely on thetest.support module. It contains various helpers that are tailored toPython’s test suite and help smooth out common problems such as platformdifferences, resource consumption and cleanup, or warnings management.That module is not suitable for use outside of the standard library.

When you are adding tests to an existing test file, it is also recommendedthat you study the other tests in that file; it will teach you which precautionsyou have to take to make your tests robust and portable.

Benchmarks¶

Benchmarking is useful to test that a change does not degrade performance.

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The Python Benchmark Suitehas a collection of benchmarks for all Python implementations. Documentationabout running the benchmarks is in the README.txt of the repo.