10 Steps to Set Up Your Python Project for Success

10 Steps to Set Up Your Python Project for Success

In this guide we’ll walk through adding tests and integrations to speed development and improve code quality and consistency. If don’t have a basic working Python package, check out my guide to building one and then meet right back here.

In this guide we’ll walk through adding tests and integrations to speed development and improve code quality and consistency. If don’t have a basic working Python package, check out my guide to building one and then meet right back here.

Cool. Here’s our ten-step plan for this article:

  1. Install Black
  2. Create .pycache
  3. Install pytest
  4. Create Tests
  5. Sign up for Travis CI and Configure
  6. Create .travis.yaml
  7. Test Travis CI
  8. Add Code Coverage
  9. Add Coveralls
  10. Add PyUp

This guide is for macOS with Python 3.7. Everything works as of early 2019, but things change fast.

We’ve got work to do. Let’s hop to it! 🐸

Step 1: Install Black

Your package code should follows common style conventions. Black is a Python package that automatically formats your code for you so that it meet PEP 8. Black is relatively new and already has over a million downloads. Using it has quickly become a best practice in Python coding. Here’s a good guide to Black.

I’m using Atom for my editor, so I added the Python-Black package to Atom _— _install info is here. Now Atom will reformat your code when you save your file.

While we’re at it, let’s add Black to the development environment for our collaborators. Eventually, anyone who works on the project will adhere to the same style guide, or else their pull request won’t be accepted. 😦

Add black==18.9b0 to the next empty line of requirements_dev.txt and run pip install -r requirements_dev.txt.

Black makes 88 characters the default max line length. Some guides and programs require 79 characters, e.g. Sphinx style guide. In the Black Atom, package you can set the max length.

Now that we’re set up to save time writing code, let’s save time pushing our app to PyPI.

Step 2: Create .pypirc

When we use twine to push our builds to TestPyPI and PyPI we need to enter our login info manually. See my previous article if you aren’t familiar with twine. Let’s automate that process.

Twine will look or a file named .pypirc in our home directory. It will grab our url, login, and password when uploading our file.

Create your .pypirc file in your home directory with:

touch ~/.pypirc

Add the following contents to your .pypirc file:

[distutils]
index-servers =
    pypi
    testpypi

[testpypi]
repository: https://test.pypi.org/legacy
username = your_username
password = your_pypitest_password

[pypi]
username = your_username
password = your_pypi_password

Replace with your username and passwords. Make sure to save this file in your home directory and not your current working directory. If you want to make sure other users are on your machine can’t access this file, you can change its permissions from the command line:

chmod 600 ~/.pypirc

Now you can upload your package to TestPyPI with the following command:

twine upload -r testpypi dist/*

Upload to the real PyPI with this command:

twine upload dist/*

No more usernames and passwords to enter. Isn’t that nice? 😄

Now let’s add some tests to make sure our package works.

Step 3: Install and Configure pytest

Pytest is the most popular easy-to-use library for testing your Python code. In this example, we’ll add simple tests to our project. Here’s a nice pytest intro tutorial, if you want to go deeper.

Add pytest to your requirements_dev.txt file with

pytest==4.3.0

Run pip install requirements_dev.txt

Then run the following so that pytest can find your package:

pip install -e .

If you deactivate your virtual environment, you’ll need to run both pip commands again to run your tests.

Step 4: Create Tests

Add a tests folder in the top level of your project. Add a file inside it calledtest_your_package_name.py. My file is named test_notebookc.py. Starting the file with test_ makes it automatically discoverable by pytest.

In test_notebookc.py I added the following test to check whether the correct name prints as part of the function output. Modify to fit your own file and function names.

"""Tests for `notebookc` package."""
import pytest
from notebookc import notebookc


def test_convert(capsys):
    """Correct my_name argument prints"""
    notebookc.convert("Jill")
    captured = capsys.readouterr()
    assert "Jall" in captured.out

What’s going on here?

We first import our module. Then we create a function with test_my_function_name. This naming convention is helpful for other people and the code coverage package we’ll add soon.

Then we call our function, convert, with “Jill” as the argument. Then we capture the output. As a reminder, our convert function is extremely basic — it takes the parameter my_name and outputs a line:

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

Pytest checks to see if the string “Jall” is in the output. It shouldn’t be present, because we passed in “Jill”. See the pytest documentation on capturing output here.

Run your test by entering pytest on the command line. Your test should fail with red text.

It’s good practice to make sure your tests fail when they should. Don’t just write them so they are green right away. Otherwise, your tests might not be testing what you think they are. 😉

After we have a failing test we can change our expected output from Jall to Jill, and our tests should pass in green.

Yep, all good. Now we have a test that ensures that when someone passes a string value to our function, that string is printed.

Let’s add a test to check that only a string has been passed to our function. If anything other than a string is passed, then a TypeError should be raised. Here’s a good guide on exceptions and error handling in Python.

When we write the test before we write the code that makes the test pass, we’re doing test-driven development (TDD). TDD is a proven method to write code with fewer errors. Here’s a nice article on TDD.

Let’s try something different this time. As an exercise, add your own test and code to ensure only a string can be passed as the argument to convert(). Hint: integers, lists, and dicts get type-converted to strings. Follow me on Twitter @discdiver and I’ll post the solution there.

After we have passing tests we are ready to integrate our package with a CI service.

Step 5: Sign up for Travis CI and Configure

Travis CI is a “hosted, distributed continuous integration service used to build and test software projects”. It was recently acquired by Idera. There are other CI options, but Travis CI is popular, free for open-source, and well-documented.

Travis CI makes it easier to ensure that only code that passes your tests and standards is integrated into your project. Learn more about Travis CI here and more about continuous integration here.

Sign up for an account at https://travis-ci.org/. Click on the Review and add your authorized organizations link from your Travis CI profile page. You’ll be prompted for your GitHub password. Click Grant next to your organization access.

I had to sync my account for notebooktoall to show as an organization and for the notebookc repository to appear. It often takes a minute or more for data to start flowing. Then toggle your repo to on.

Click on settings. You can choose whether you want Travis to build on pushed pull requests and/or on pushed branches.

Now we need to configure a file locally so that Travis will build for each pull request.

Step 6: Create .travis.yml

In the top level of your project folder, add a .travis.yml file with these contents:

dist: xenial
language: python
python: 3.7.2
install:
  - pip install -r requirements_dev.txt
  - pip install -e .
script:
  - pytest

dist: xenial is needed to specify that Travis should use Ubuntu Xenial 16.04 for its virtual environment. Xenial must be specified for testing Python 3.7 code. More info here.

Different versions of Python can be specified for testing. We’ll get into that topic in a future article. Follow me to make sure you don’t miss it!

The install section ensures our packages for development are installed. pip install -e . installs your package as a wheel into Travis’s virtual environment. Then Travis will find your package when it runs pytest.

Step 7: Test Travis CI

Commit your changes, push to GitHub, make a PR. Travis should start to run automatically within a few seconds.

Here’s what Travis is doing.

Travis will tell you if your PR fails.

Note that if a pull request fails, you can push to the same branch and Travis automatically reruns.

Go to your repo’s page on Travis and have a look around. There’s lots of info on Travis about your builds. You’ll probably be visiting this site a good bit in the future trying to figure out why your build didn’t pass. 😄

Assuming everything is green, you’re good to go!

If you don’t see any red or green, click on the More options menu and select Requests from the dropdown. If you see red, have a look at the error messages. If you see the error Build config file is required, then Travis isn’t finding your .travis.yml file on GitHub. Make sure it’s in your GitHub repo. 😉

Travis sends you emails to let you know when a build fails and when a failed build has been fixed.

Remember that you can keep pushing your commits to an open PR and Travis will rerun automatically.

Let’s see just how much of our code has test coverage.

Step 8: Add Code Coverage

A code coverage report shows you what percentage of your code has at least some test coverage. We’ll add the pytest-cov package to create a report.

Add the following line to requirements_dev.txt:

pytest-cov==2.6.1

Run with pytest --cov=my_project_name

My output of pytest --cov=notebookc looks like this:

Sweet, all our code is covered! When you only have a few lines that’s not a high bar. 😄 But we don’t need to tell the world that — let’s show them that we’ve got coverage!

Step 9: Add Coveralls

Coveralls provides a history of your code coverage for all the world to see.

Head over to https://coveralls.io/ and signup for an account using your GitHub credentials. Add your organization and toggle on your repo when it appears.

In requirements_dev.txt add coveralls==1.6.0. Your requirements_dev.txt should now look like this:

pip==19.0.3
wheel==0.33.0
twine==1.13.0
pytest==4.3.0
pytest-cov==2.6.1
coveralls==1.6.0

Alter your .travis.yml file so it looks like the following (substituting your package name):

dist: xenial
language: python
python: 3.7.2
install:
  — pip install -r requirements_dev.txt
  — pip install -e .
script:
  — pytest --cov=my_package_name
after_success:
  — coveralls

Now when Travis builds your project, it will install the necessary packages, run your tests, and create a coverage report. Then it sends the coverage report to coveralls.

Commit, push to GitHub, and watch the magic happen. It can take a few minutes for your coverage report to flow, so be patient.

Now coveralls shows in your PR checks. Cool!

Over on the Coveralls webpage, we should show 100% coverage.

Alright, let’s add one more tool to our belt.

Step 10: Add PyUp

PyUp.io lets you know when package dependencies are out of date or have security vulnerabilities. It automatically makes a pull request to update the package on GitHub.

Go to https://pyup.io/, register through GitHub, and connect your organization.

When you add your repo, I suggest you toggle your update schedule to every week. Then you won’t get lots of pull requests if you have a bunch of package dependencies.

Here’s an example of a repository on PyUp that shows some out of date packages.

Now you’ll know when a package is updated — and knowing is half the battle. Automated pull requests must be the other half, right? 😏

Wrap

In this article you’ve learned how to add and configure Black, pytest, Travis CI, Coveralls, and PyUp. We’ve set the stage for more secure code with more consistent style. That’s pretty sweet!

In a future article we’ll look at how to configure and build your docs with Read The Docs, add badges, manage releases, and more. Follow me to make sure you don’t miss it.

I hope you found this guide useful. If you did, please share it on your favorite social media channels so others can find it too. 👏

Learn More

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Thanks for reading!

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 myFile.py, 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.

Breakpoints

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.

Fonts/Tabs

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.

Highlights

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!

Keys

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.

General

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.

Extensions

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!

Conclusion

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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Original video source: https://www.youtube.com/watch?v=VMP1oQOxfM0