Build Your First Open Source Python Project

Build Your First Open Source Python Project

In this article, we’ll go through each step to make a basic Python package. In future articles we’ll set up automated tests and doc builds. We’ll also integrate other helpful apps to improve your package development.

In this article, we’ll go through each step to make a basic Python package. In future articles we’ll set up automated tests and doc builds. We’ll also integrate other helpful apps to improve your package development.

A step-by-step guide to a working package

Every software developer and data scientist should go through the exercise of making a package. You’ll learn so much along the way.

Making an open source Python package may sound daunting, but you don’t need to be a grizzled veteran. You also don’t need an elaborate product idea. You do need persistence and time. Hopefully this guide will help you need less of both. 😃

You can then expand what you’ve built to suit your needs.

This guide is for macOS with Python 3.7. Everything works now, but things change fast, so no guarantees if your reading this in 2030, or even 2020. Replace my_package, my_file, etc. with your own names.

Let’s get to it! 🚀

Step 1: Make a Plan

We’re eventually planning to make a very simple library for use in a Python program. The package will allow the user to easily convert a Jupyter notebook into an HTML file or a Python script.

The first iteration of our package will allow a user to call a function that will print a statement.

Now that we know what we want to make, we need to decide what to call the package.

Step 2: Name it

Naming things is tricky. Names should be unique, short, and memorable. They should also be all lowercase and definitely not have any dashes or other punctuation in them. Underscores are discouraged. When you’re building your package, check that the name is available on GitHub, Google, and PyPI.

If you have high hopes that your package will some day have 10,000 GitHub stars, then you might want to check if the name is available on social networks. In this example, I’m going to name my package notebookc because it’s available, short, and semi-descriptive. 😄

Step 3: Configure Environment

Make sure you have Python 3.7, GitHub, and Homebrew installed and configured. If you need any of those here are the details:


Download Python 3.7 here and install it.


If you don’t have a GitHub account, go here and sign up for a free one. See how to install and configure Git here. You want the command line tool. Follow the links to download it, install it, set your username, and set your commit email address.


Homebrew is a Mac-specific package manager. Install instructions are here.


As of Python 3.6, it is recommended to use venvto create your virtual environment for package development. There are many ways to manage virtual environments with Python and the recommendations have evolved. See discussion here, but beware going down this rabbit hole. 🕳

venv comes installed with Python since Python 3.3. Note that venv installs pip and setuptools into the virtual environment since Python 3.4 .

Create a Python 3.7 virtual environment with the following command:

python3.7 -m venv my_env

Replace my_env with any name you like. Activate your virtual environment like this:

source my_env/bin/activate

You should now see (my_env)— or whatever you named your virtual environment— to the far left in your terminal prompt.

When you’re finished with your development, deactivate your virtual environment with deactivate.

Now let’s take care of setting up things on GitHub.

Step 4: Create Organization on GitHub

GitHub is the market leader in version control registries. GitLab and Bitbucket are other popular options. We’ll be using GitHub in this guide.

  • You’ll use Git and GitHub a bunch, so if you aren’t familiar, check out my article here.
  • Create a new organization on Github. Follow the prompts. I named my organization notebooktoall. You could create the repo under your own personal account, but part of the goal is to learn how to setup an open source project for the larger community.

Step 5: Set up GitHub Repo

Create a new repository. I named my repo notebookc.

Add a .gitignore from the dropdown list. Choose Python for your repository. The contents of your .gitignore file will match folders and file types to exclude from your Git repo. You can alter your .gitignore later to exclude other unnecessary or sensitive files.

I suggest you choose a license from the Add a license dropdown_._ The licensedefines what users of your repository content can do. Some licenses are more permissive than others. Default copyright laws apply if no license is chosen. Learn more about licenses here.

For this project I chose the GNU General Public License v3.0 because it is popular, legit, and “guarantees end users the freedom to run, study, share and modify the software” — source.

Step 6: Clone and Add Directories

Choose where you want to clone your repo locally and run the following:

git clone [[](](]( "](")

Substitute your organization and repo.

Move into your project folder with your desktop GUI or code editor. Or use the command line with cd my-project and then view your files with ls —A. Your initial folders and files should look like this:


Create a subfolder for your primary project files. I suggest you name this primary subfolder the same name as your package. Make sure the name doesn’t have any spaces in it.

Make a file named init*.py* in your primary subfolder. This file will remain empty for now. This file is necessary for the files in this folder to be imported.

Make another file with the same name as your primary subfolder with .py appended to it. My file is named You could name this Python file whatever you like. Your package users will

My notebookc directory contents now look like this:



Step 7: Create and Install requirements_dev.txt

In the top level of your project directory, create a requirements_dev.txt file. Often this file is named requirements.txt. Calling it requirements_dev.txt highlights that these packages are only installed by project developers.

In requirements_dev.txt, specify that pip and wheel should be installed.


Notice that we specify exact versions of these packages with double equals signs and full major.minor.micro version numbers.

A collaborator who forks the project repo and installs the pinned requirements_dev.txt packages with pip will have the same package versions you did. You know they will work for them. Also, Read The Docs will use this file to install packages when it builds your documentation.

In your activated virtual environment, install the packages in requirements_dev.txt with the following command:

pip install -r requirements_dev.txt

You’ll want to keep these packages updated as newer versions are released. For now, you can install whatever versions are newest by searching PyPI.

We’ll install a tool to help with this process in a future article. Follow me to make sure you don’t miss it.

Step 8: Code and Commit

Let’s create a basic function for demonstration purposes. You can create your own awesome function later. 👍

Type the following into your primary file (for me that’s notebookc/notebookc/

def convert(my_name):
    Print a line about converting a notebook.
        my_name (str): person's name

print(f"I'll convert a notebook for you some day, {my_name}.")

That’s our function in all its glory. 😃

The docstrings begin and end with three consecutive double quotes. They’ll be used in later article to automatically create documentation.

Let’s commit our changes. See this article if you’d like a Git workflow refresher.

Step 9: Create

The file is the build script for your package. The setup function from Setuptools will build your package for upload to PyPI. Setuptools includes information about your package, your version number, and which other packages are required for users.

Here’s my example file:

from setuptools import setup, find_packages

with open("", "r") as readme_file:
    readme =

requirements = ["ipython>=6", "nbformat>=4", "nbconvert>=5", "requests>=2"]

    author="Jeff Hale",
    author_email="[email protected]",
    description="A package to convert your Jupyter Notebook",
        "Programming Language :: Python :: 3.7",
        "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",

Notice that long_description is set to the contents of your file.

The requirements list specified in setuptools.setup.install_requires includes all necessary package dependencies for for your package to work.

Unlike the list of packages required for development in requirements_dev.txt, this list of packages should be as permissive as possible. Read more about why here.

Limit this install_requires list of packages to only wants needed — you don’t want to make users install unnecessary packages. Note that you only need to list packages that aren’t part of the Python standard library. Your user will have Python installed if they will be using your package. 😄

Our package doesn’t require any external dependencies, so you can exclude the four packages listed aboveA collaborator who forks the project repo and installs the pinned packages with pip will have the same package versions you did. You know they will work.

Change the other setuptools information to match your package information. There are many other optional keyword arguments and classifiers — see a list here. More in-depth guides to can be found here and here.

Commit your code to your local Git repo. Let’s get ready to build a package!

Step 10: Build First Version

Twine is a collection of utilities for securely publishing Python packages on PyPI. Add the Twine package to the next blank line of requirements_dev.txt like so:


Then install Twine into your virtual environment by reinstalling your requirements_dev.txt packages.

pip install -r requirements_dev.txt

Then run the following command to create your package files:

python sdist bdist_wheel

Multiple hidden folders should be created: dist, build, and — in my case — notebookc.egg-info. Let’s look at the files in the dist folder. The .whl file is the Wheel file — the built distribution. The .tar.gz file is the a source archive.

On a user’s machine, pip will install packages as wheels whenever it can. Wheels are faster to install. When pip can’t install a wheel, it falls back on the source archive.

Let’s get ready to upload our wheel and source archive.

Step 11: Create TestPyPI Account

PyPI stands for Python Package Index. It’s the official Python package manager. pip grabs files from PyPI when they aren’t already installed locally.

TestPyPI is a functioning test version of PyPI. Create a TestPyPI account here and confirm your email address. Note that you’ll have separate passwords for uploading to the test site and official site.

Step 12: Publish to TestPyPI

Use Twine to securely publish your package to TestPyPI. Enter the following command — no modifications are necessary.

twine upload --repository-url [[](](]( "](") dist/*

You’ll be prompted for your username and password. Remember, TestPyPI and PyPI have different passwords!

If needed, fix any errors, make a new version number in, and delete the old build artifacts: the build, dist, and egg folders. Rebuild with python sdist bdist_wheel and re-upload with Twine. Having version numbers on TestPyPI that don’t signify anything isn’t a big deal — you’re the only one who will use those package versions. 😄

Once you’ve successfully uploaded your package, let’s make sure you can install it and use it.

Step 13: Verify Installation and Use

Create another tab in your terminal shell and make another virtual environment.

python3.7 -m venv my_env

Activate it.

source my_env/bin/activate

If you had uploaded your package to the official PyPI site you would then pip install your-package. We can retrieve the package from TestPypPI and install it with a modified command.

Here are the official instructions to install your package from TestPyPI:

You can tell pip to download packages from TestPyPI instead of PyPI by specifying the — index-url flag> You can tell pip to download packages from TestPyPI instead of PyPI by specifying the — index-url flag> You can tell pip to download packages from TestPyPI instead of PyPI by specifying the — index-url flag> You can tell pip to download packages from TestPyPI instead of PyPI by specifying the — index-url flag> You can tell pip to download packages from TestPyPI instead of PyPI by specifying the — index-url flag> You can tell pip to download packages from TestPyPI instead of PyPI by specifying the — index-url flag> You can tell pip to download packages from TestPyPI instead of PyPI by specifying the — index-url flag
If your package has dependencies, use the second command and substitute your package name.

You should see the latest version of your package installed in your virtual environment.

To verify you can use your package, start an IPython session in your terminal with:


Import your function and call your function with a string argument. Here’s what my code looks like:

from notebookc.notebookc import convert


I then see this output:

I’ll convert a notebook for you some day, Jeff.

Sure you will. 😉

Alright, let’s upload our work to GitHub.

Step 14: Push to GitHub

Make sure you have your code committed.

My notebookc project folder looks like this:



Exclude any virtual environments you don’t want to upload. The Python .gitignore file that we chose when we made the repo should keep build artifacts from being indexed. You may need to delete your virtual environment folders.

Push your local branch to GitHub with git push origin my_branch.

Step 15: Create and Merge PR

From your browser, navigate to GitHub. You should see an option to make a pull request. Keep pressing the green buttons to create and merge your PR, and delete your remote feature branch.

Back in your terminal, delete your local feature branch with git branch -d my_feature_branch.

We’ll add many more files and folders in future articles.

Let’s recap our steps.

Recap: 15 Steps to a Working Package

  1. Make a Plan
  2. Name it
  3. Configure Environment
  4. Create Organization on Github
  5. Set up GitHub Repo
  6. Clone and Add Directories
  7. Create and Install requirements_dev.txt
  8. Code and Commit
  9. Create
  10. Build First Version
  11. Create TestPyPI Account
  12. Push to TestPyPI
  13. Verify Installation and Use
  14. Push to GithHub
  15. Create and Merge PR


I hope you found this guide to making and releasing your first Python package useful. If you did, please share it on your favorite social media channels so others can find it too. 👏

Happy building!

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What's Python IDLE? How to use Python IDLE to interact with Python?

What's Python IDLE? How to use Python IDLE to interact with Python?

In this tutorial, you’ll learn all the basics of using **IDLE** to write Python programs. You'll know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

In this tutorial, you'll learn how to use the development environment included with your Python installation. Python IDLE is a small program that packs a big punch! You'll learn how to use Python IDLE to interact with Python directly, work with Python files, and improve your development workflow.

If you’ve recently downloaded Python onto your computer, then you may have noticed a new program on your machine called IDLE. You might be wondering, “What is this program doing on my computer? I didn’t download that!” While you may not have downloaded this program on your own, IDLE comes bundled with every Python installation. It’s there to help you get started with the language right out of the box. In this tutorial, you’ll learn how to work in Python IDLE and a few cool tricks you can use on your Python journey!

In this tutorial, you’ll learn:

  • What Python IDLE is
  • How to interact with Python directly using IDLE
  • How to edit, execute, and debug Python files with IDLE
  • How to customize Python IDLE to your liking

Table of Contents

What Is Python IDLE?

Every Python installation comes with an Integrated Development and Learning Environment, which you’ll see shortened to IDLE or even IDE. These are a class of applications that help you write code more efficiently. While there are many IDEs for you to choose from, Python IDLE is very bare-bones, which makes it the perfect tool for a beginning programmer.

Python IDLE comes included in Python installations on Windows and Mac. If you’re a Linux user, then you should be able to find and download Python IDLE using your package manager. Once you’ve installed it, you can then use Python IDLE as an interactive interpreter or as a file editor.

An Interactive Interpreter

The best place to experiment with Python code is in the interactive interpreter, otherwise known as a shell. The shell is a basic Read-Eval-Print Loop (REPL). It reads a Python statement, evaluates the result of that statement, and then prints the result on the screen. Then, it loops back to read the next statement.

The Python shell is an excellent place to experiment with small code snippets. You can access it through the terminal or command line app on your machine. You can simplify your workflow with Python IDLE, which will immediately start a Python shell when you open it.

A File Editor

Every programmer needs to be able to edit and save text files. Python programs are files with the .py extension that contain lines of Python code. Python IDLE gives you the ability to create and edit these files with ease.

Python IDLE also provides several useful features that you’ll see in professional IDEs, like basic syntax highlighting, code completion, and auto-indentation. Professional IDEs are more robust pieces of software and they have a steep learning curve. If you’re just beginning your Python programming journey, then Python IDLE is a great alternative!

How to Use the Python IDLE Shell

The shell is the default mode of operation for Python IDLE. When you click on the icon to open the program, the shell is the first thing that you see:

This is a blank Python interpreter window. You can use it to start interacting with Python immediately. You can test it out with a short line of code:

Here, you used print() to output the string "Hello, from IDLE!" to your screen. This is the most basic way to interact with Python IDLE. You type in commands one at a time and Python responds with the result of each command.

Next, take a look at the menu bar. You’ll see a few options for using the shell:

You can restart the shell from this menu. If you select that option, then you’ll clear the state of the shell. It will act as though you’ve started a fresh instance of Python IDLE. The shell will forget about everything from its previous state:

In the image above, you first declare a variable, x = 5. When you call print(x), the shell shows the correct output, which is the number 5. However, when you restart the shell and try to call print(x) again, you can see that the shell prints a traceback. This is an error message that says the variable x is not defined. The shell has forgotten about everything that came before it was restarted.

You can also interrupt the execution of the shell from this menu. This will stop any program or statement that’s running in the shell at the time of interruption. Take a look at what happens when you send a keyboard interrupt to the shell:

A KeyboardInterrupt error message is displayed in red text at the bottom of your window. The program received the interrupt and has stopped executing.

How to Work With Python Files

Python IDLE offers a full-fledged file editor, which gives you the ability to write and execute Python programs from within this program. The built-in file editor also includes several features, like code completion and automatic indentation, that will speed up your coding workflow. First, let’s take a look at how to write and execute programs in Python IDLE.

Opening a File

To start a new Python file, select File → New File from the menu bar. This will open a blank file in the editor, like this:

From this window, you can write a brand new Python file. You can also open an existing Python file by selecting File → Open… in the menu bar. This will bring up your operating system’s file browser. Then, you can find the Python file you want to open.

If you’re interested in reading the source code for a Python module, then you can select File → Path Browser. This will let you view the modules that Python IDLE can see. When you double click on one, the file editor will open up and you’ll be able to read it.

The content of this window will be the same as the paths that are returned when you call sys.path. If you know the name of a specific module you want to view, then you can select File → Module Browser and type in the name of the module in the box that appears.

Editing a File

Once you’ve opened a file in Python IDLE, you can then make changes to it. When you’re ready to edit a file, you’ll see something like this:

The contents of your file are displayed in the open window. The bar along the top of the window contains three pieces of important information:

  1. The name of the file that you’re editing
  2. The full path to the folder where you can find this file on your computer
  3. The version of Python that IDLE is using

In the image above, you’re editing the file, which is located in the Documents folder. The Python version is 3.7.1, which you can see in parentheses.

There are also two numbers in the bottom right corner of the window:

  1. Ln: shows the line number that your cursor is on.
  2. Col: shows the column number that your cursor is on.

It’s useful to see these numbers so that you can find errors more quickly. They also help you make sure that you’re staying within a certain line width.

There are a few visual cues in this window that will help you remember to save your work. If you look closely, then you’ll see that Python IDLE uses asterisks to let you know that your file has unsaved changes:

The file name shown in the top of the IDLE window is surrounded by asterisks. This means that there are unsaved changes in your editor. You can save these changes with your system’s standard keyboard shortcut, or you can select File → Save from the menu bar. Make sure that you save your file with the .py extension so that syntax highlighting will be enabled.

Executing a File

When you want to execute a file that you’ve created in IDLE, you should first make sure that it’s saved. Remember, you can see if your file is properly saved by looking for asterisks around the filename at the top of the file editor window. Don’t worry if you forget, though! Python IDLE will remind you to save whenever you attempt to execute an unsaved file.

To execute a file in IDLE, simply press the F5 key on your keyboard. You can also select Run → Run Module from the menu bar. Either option will restart the Python interpreter and then run the code that you’ve written with a fresh interpreter. The process is the same as when you run python3 -i [filename] in your terminal.

When your code is done executing, the interpreter will know everything about your code, including any global variables, functions, and classes. This makes Python IDLE a great place to inspect your data if something goes wrong. If you ever need to interrupt the execution of your program, then you can press Ctrl+C in the interpreter that’s running your code.

How to Improve Your Workflow

Now that you’ve seen how to write, edit, and execute files in Python IDLE, it’s time to speed up your workflow! The Python IDLE editor offers a few features that you’ll see in most professional IDEs to help you code faster. These features include automatic indentation, code completion and call tips, and code context.

Automatic Indentation

IDLE will automatically indent your code when it needs to start a new block. This usually happens after you type a colon (:). When you hit the enter key after the colon, your cursor will automatically move over a certain number of spaces and begin a new code block.

You can configure how many spaces the cursor will move in the settings, but the default is the standard four spaces. The developers of Python agreed on a standard style for well-written Python code, and this includes rules on indentation, whitespace, and more. This standard style was formalized and is now known as PEP 8. To learn more about it, check out How to Write Beautiful Python Code With PEP 8.

Code Completion and Call Tips

When you’re writing code for a large project or a complicated problem, you can spend a lot of time just typing out all of the code you need. Code completion helps you save typing time by trying to finish your code for you. Python IDLE has basic code completion functionality. It can only autocomplete the names of functions and classes. To use autocompletion in the editor, just press the tab key after a sequence of text.

Python IDLE will also provide call tips. A call tip is like a hint for a certain part of your code to help you remember what that element needs. After you type the left parenthesis to begin a function call, a call tip will appear if you don’t type anything for a few seconds. For example, if you can’t quite remember how to append to a list, then you can pause after the opening parenthesis to bring up the call tip:

The call tip will display as a popup note, reminding you how to append to a list. Call tips like these provide useful information as you’re writing code.

Code Context

The code context functionality is a neat feature of the Python IDLE file editor. It will show you the scope of a function, class, loop, or other construct. This is particularly useful when you’re scrolling through a lengthy file and need to keep track of where you are while reviewing code in the editor.

To turn it on, select Options → Code Context in the menu bar. You’ll see a gray bar appear at the top of the editor window:

As you scroll down through your code, the context that contains each line of code will stay inside of this gray bar. This means that the print() functions you see in the image above are a part of a main function. When you reach a line that’s outside the scope of this function, the bar will disappear.

How to Debug in IDLE

A bug is an unexpected problem in your program. They can appear in many forms, and some are more difficult to fix than others. Some bugs are tricky enough that you won’t be able to catch them by just reading through your program. Luckily, Python IDLE provides some basic tools that will help you debug your programs with ease!

Interpreter DEBUG Mode

If you want to run your code with the built-in debugger, then you’ll need to turn this feature on. To do so, select Debug → Debugger from the Python IDLE menu bar. In the interpreter, you should see [DEBUG ON] appear just before the prompt (>>>), which means the interpreter is ready and waiting.

When you execute your Python file, the debugger window will appear:

In this window, you can inspect the values of your local and global variables as your code executes. This gives you insight into how your data is being manipulated as your code runs.

You can also click the following buttons to move through your code:

  • Go: Press this to advance execution to the next breakpoint. You’ll learn about these in the next section.
  • Step: Press this to execute the current line and go to the next one.
  • Over: If the current line of code contains a function call, then press this to step over that function. In other words, execute that function and go to the next line, but don’t pause while executing the function (unless there is a breakpoint).
  • Out: If the current line of code is in a function, then press this to step out of this function. In other words, continue the execution of this function until you return from it.

Be careful, because there is no reverse button! You can only step forward in time through your program’s execution.

You’ll also see four checkboxes in the debug window:

  1. Globals: your program’s global information
  2. Locals: your program’s local information during execution
  3. Stack: the functions that run during execution
  4. Source: your file in the IDLE editor

When you select one of these, you’ll see the relevant information in your debug window.


A breakpoint is a line of code that you’ve identified as a place where the interpreter should pause while running your code. They will only work when DEBUG mode is turned on, so make sure that you’ve done that first.

To set a breakpoint, right-click on the line of code that you wish to pause. This will highlight the line of code in yellow as a visual indication of a set breakpoint. You can set as many breakpoints in your code as you like. To undo a breakpoint, right-click the same line again and select Clear Breakpoint.

Once you’ve set your breakpoints and turned on DEBUG mode, you can run your code as you would normally. The debugger window will pop up, and you can start stepping through your code manually.

Errors and Exceptions

When you see an error reported to you in the interpreter, Python IDLE lets you jump right to the offending file or line from the menu bar. All you have to do is highlight the reported line number or file name with your cursor and select Debug → Go to file/line from the menu bar. This is will open up the offending file and take you to the line that contains the error. This feature works regardless of whether or not DEBUG mode is turned on.

Python IDLE also provides a tool called a stack viewer. You can access it under the Debug option in the menu bar. This tool will show you the traceback of an error as it appears on the stack of the last error or exception that Python IDLE encountered while running your code. When an unexpected or interesting error occurs, you might find it helpful to take a look at the stack. Otherwise, this feature can be difficult to parse and likely won’t be useful to you unless you’re writing very complicated code.

How to Customize Python IDLE

There are many ways that you can give Python IDLE a visual style that suits you. The default look and feel is based on the colors in the Python logo. If you don’t like how anything looks, then you can almost always change it.

To access the customization window, select Options → Configure IDLE from the menu bar. To preview the result of a change you want to make, press Apply. When you’re done customizing Python IDLE, press OK to save all of your changes. If you don’t want to save your changes, then simply press Cancel.

There are 5 areas of Python IDLE that you can customize:

  1. Fonts/Tabs
  2. Highlights
  3. Keys
  4. General
  5. Extensions

Let’s take a look at each of them now.


The first tab allows you to change things like font color, font size, and font style. You can change the font to almost any style you like, depending on what’s available for your operating system. The font settings window looks like this:

You can use the scrolling window to select which font you prefer. (I recommend you select a fixed-width font like Courier New.) Pick a font size that’s large enough for you to see well. You can also click the checkbox next to Bold to toggle whether or not all text appears in bold.

This window will also let you change how many spaces are used for each indentation level. By default, this will be set to the PEP 8 standard of four spaces. You can change this to make the width of your code more or less spread out to your liking.


The second customization tab will let you change highlights. Syntax highlighting is an important feature of any IDE that highlights the syntax of the language that you’re working in. This helps you visually distinguish between the different Python constructs and the data used in your code.

Python IDLE allows you to fully customize the appearance of your Python code. It comes pre-installed with three different highlight themes:

  1. IDLE Day
  2. IDLE Night
  3. IDLE New

You can select from these pre-installed themes or create your own custom theme right in this window:

Unfortunately, IDLE does not allow you to install custom themes from a file. You have to create customs theme from this window. To do so, you can simply start changing the colors for different items. Select an item, and then press Choose color for. You’ll be brought to a color picker, where you can select the exact color that you want to use.

You’ll then be prompted to save this theme as a new custom theme, and you can enter a name of your choosing. You can then continue changing the colors of different items if you’d like. Remember to press Apply to see your changes in action!


The third customization tab lets you map different key presses to actions, also known as keyboard shortcuts. These are a vital component of your productivity whenever you use an IDE. You can either come up with your own keyboard shortcuts, or you can use the ones that come with IDLE. The pre-installed shortcuts are a good place to start:

The keyboard shortcuts are listed in alphabetical order by action. They’re listed in the format Action - Shortcut, where Action is what will happen when you press the key combination in Shortcut. If you want to use a built-in key set, then select a mapping that matches your operating system. Pay close attention to the different keys and make sure your keyboard has them!

Creating Your Own Shortcuts

The customization of the keyboard shortcuts is very similar to the customization of syntax highlighting colors. Unfortunately, IDLE does not allow you to install custom keyboard shortcuts from a file. You must create a custom set of shortcuts from the Keys tab.

Select one pair from the list and press Get New Keys for Selection. A new window will pop up:

Here, you can use the checkboxes and scrolling menu to select the combination of keys that you want to use for this shortcut. You can select Advanced Key Binding Entry >> to manually type in a command. Note that this cannot pick up the keys you press. You have to literally type in the command as you see it displayed to you in the list of shortcuts.


The fourth tab of the customization window is a place for small, general changes. The general settings tab looks like this:

Here, you can customize things like the window size and whether the shell or the file editor opens first when you start Python IDLE. Most of the things in this window are not that exciting to change, so you probably won’t need to fiddle with them much.


The fifth tab of the customization window lets you add extensions to Python IDLE. Extensions allow you to add new, awesome features to the editor and the interpreter window. You can download them from the internet and install them to right into Python IDLE.

To view what extensions are installed, select Options → Configure IDLE -> Extensions. There are many extensions available on the internet for you to read more about. Find the ones you like and add them to Python IDLE!


In this tutorial, you’ve learned all the basics of using IDLE to write Python programs. You know what Python IDLE is and how you can use it to interact with Python directly. You’ve also learned how to work with Python files and customize Python IDLE to your liking.

You’ve learned how to:

  • Work with the Python IDLE shell
  • Use Python IDLE as a file editor
  • Improve your workflow with features to help you code faster
  • Debug your code and view errors and exceptions
  • Customize Python IDLE to your liking

Now you’re armed with a new tool that will let you productively write Pythonic code and save you countless hours down the road. Happy programming!

Importance of Python Programming skills

Importance of Python Programming skills

Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving...

Python is one among the most easiest and user friendly programming languages when it comes to the field of software engineering. The codes and syntaxes of python is so simple and easy to use that it can be deployed in any problem solving challenges. The codes of Python can easily be deployed in Data Science and Machine Learning. Due to this ease of deployment and easier syntaxes, this platform has a lot of real world problem solving applications. According to the sources the companies are eagerly hunting for the professionals with python skills along with SQL. An average python developer in the united states makes around 1 lakh U.S Dollars per annum. In some of the top IT hubs in our country like Bangalore, the demand for professionals in the domains of Data Science and Python Programming has surpassed over the past few years. As a result of which a lot of various python certification courses are available right now.

Array in Python: An array is defined as a data structure that can hold a fixed number of elements that are of the same python data type. The following are some of the basic functions of array in python:

  1. To find the transverse
  2. For insertion of the elements
  3. For deletion of the elements
  4. For searching the elements

Along with this one can easily crack any python interview by means of python interview questions

Tkinter Python Tutorial | Python GUI Programming Using Tkinter Tutorial | Python Training

This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.

This video on Tkinter tutorial covers all the basic aspects of creating and making use of your own simple Graphical User Interface (GUI) using Python. It establishes all of the concepts needed to get started with building your own user interfaces while coding in Python.

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