Understanding the Menu System

Ptpython’s menu system enhances the interactive Python programming experience by providing a structured and navigable interface within the terminal. It offers an organized menu containing various options, including code history, command manipulation, and configurable settings.

The menu allows you to efficiently explore and access these features, improving your workflow and facilitating quick access to essential functionalities while working within the ptpython environment:

When you open the menu by typingF2, a variety of options will appear on the right side of your terminal, allowing you to manipulate how the REPL will behave.

The menu system simplifies interaction by presenting options in a clear format, making it easier to navigate and execute different commands or actions. For the most part, you’ll turn functionalities on or off. To exit the menu, press theEnter key, and you’ll be back to the interactive shell.

Another feature to note is that the menu is divided into sub-menus that distinguish the types of available functionalities. You’ll find three sub-menus in total:

1. Input is where you can configure settings related to how the shell processes and handles your keyboard input. This includes options for enabling or disabling features like auto-suggestions and auto-indentation, as well as adjusting the behavior of the tab key. Fine-tuning these settings can significantly affect how you interact with the shell as you type and edit code.

2. Display allows you to configure various aspects of how the interactive shell presents output and results. This includes options related to the formatting of data, the verbosity of output, and the handling of long lines. You can customize settings to control how the shell displays information as you interact with Python code.

3. Colors provides options for customizing the syntax highlighting colors that the interface uses. You can select different color schemes to tailor the appearance of various code elements, making the code more visually distinct and easier to read. This feature contributes to a more personalized and comfortable coding environment.

In the following sections, you’ll have the opportunity to use the menu to access advanced features within the three sub-menus.

Declaring Variables in Ptpython

In the Ptpython shell, declaring variables is similar to in the native Python shell. You can simply type the variable name followed by an equal sign (=) and the value that you want to assign. With this step, there are no significant differences. Type in the following lines of code, which you’ll use to explore ptpython:

>>>title="Jurassic Park">>>release_year=1993>>>director="Steven Spielberg">>>lead_actor="Sam Neill">>>budget=63_000_000# In USD>>>box_office_revenue=1_029_442_529# In USD>>>genres=["Action","Adventure","Sci-Fi"]>>>rating=8.1

The above variables are statistics on the movieJurassic Park. You’ll use this data to explore what the shell has in store. What’s exciting is that even in this step, the magic is already happening in the background.

Completing Code While Typing

The first piece of magic is a powerful completion feature that can lighten the load in accessing previously defined variables and objects. This capability also works for built-in Python functions.

As you start typing a variable, function, module, or any other valid Python identifier, ptpython will present a list of possible completions that match what you’ve typed so far. The feature calledComplete while typing works right out of the box:

With the above example, the variabletitle is available in the dropdown, and you can access it using theUp,Down,Left, andRight keys. You can also use theTab key to toggle between the dropdown choices. You can use theEnter key to insert your selected variable into the interactive shell.

You can turn this feature off in the menu system under theComplete while typing option in theInput sub-menu.

Using the Auto-Suggestion Feature

There’s a second way is to access earlier code using the auto-suggestion feature. Once you run any line of Python code in the ptpython shell, it’s recorded in ahistory file.

Once you start typing, the auto-suggestion functionality will give you a hint of a matching line of code that you ran previously. You can then complete the entry using theRight key. To turn on this functionality, open up themenu. Navigate to theAuto suggestion option and use theRight key to turn it on. Then, pressEnter to save the change:

In the above example, once theAuto suggestion feature is turned on, you can see the hint of thetitle variable. You use theRight key to complete the variable, and you enter it into the shell using theEnter key.

You can quickly insert code that you ran earlier in this session—or even a previous session—by using auto-suggestions. Now imagine you need to recreate a boatload of variables from an earlier session. You can’t remember them all, but ptpython can. These provisions can save you a lot of time and remove errors.

In addition, there’s another way to access earlier codes and commands from yourhistory file.

Checking Code History and Reinserting Earlier Code

Accessing command history in ptpython is intuitive. You can utilize theUp andDown arrow keys to navigate through previously entered commands, making it easy to review, modify, and re-execute past code.

Additionally, you can get a glimpse of all the code that you’ve punched into the interactive shell by pressing a menu shortcut: theF3 key. A new column listing all of your previous code will show up on the left, including code from earlier sessions:

As seen in the above screenshot, the menu lists your earlier code and commands in order of execution. This view gives a useful bird’s-eye view of your workflow within the shell.

Utilize theUp andDown keys to navigate to the desired line of code for copying. Press theSpace key to select the code for insertion. You can repeat that process, and all of your selected lines will show up in the right-hand column. Once you’ve finished selecting all the code that you want, you can press theEnter key to insert these lines into your interactive shell:

As you can see above, all the variables that you selected have been inserted into the interactive space. As a developer, you now have code history at your fingertips, and you have all the tools to avoid re-entering the same code or code blocks that you wrote earlier.

Utilizing Auto-Indentation

The requirement forcode indentation in Python when working with functions and classes can present a difficulty for developers working in the standard Python shell. For such code, a manual entry of four spaces can slow down the process of creating an object or function. This enhanced Python shell offers automatic indentation for objects such as functions and classes.

To showcase this modality,create a function that calculates the budget and box office earnings of the first threeJurassic Park movies. In the ptpython shell, type in these two lists:

>>>jurassic_budgets=[63_000_000,73_000_000,93_000_000]>>>jurassic_revenues=[1_029_442_529,618_600_000,368_800_000]

Bothjurassic_budgets andjurassic_revenues include data for the first threeJurassic Park movies (Jurassic Park,The Lost World: Jurassic Park, andJurassic Park III) in order. You’ll use the following function in the interactive shell:

defcalculate_total_budget(budgets):returnsum(budgets)

The above function takes in a list of integers and returns a sum total of all the items. The resulting number is the total budget for all the threeJurassic Park movies.

Type thecalculate_total_budget() function into the ptpython shell and see automatic indentation at work:

The function works as expected and returns the total budget. As you can see, when creating multiline code such as functions, ptpython automatically uses the indentation-based syntax required in Python code. You can practice using thejurassic_revenues list:

defcalculate_total_revenue(revenues):returnsum(revenues)calculate_total_revenue(jurassic_revenues)

The Python indentation requirement in a shell environment can be time-consuming and prone to errors. Having auto-indentation takes away this headache, allowing you to focus on creating functions and objects effectively.

Clearing Code From the Shell

Have you ever gotten deep into a coding session and found yourself struggling with a cluttered screen? You want a clean slate, but you don’t want to lose any of your variables or functions. With ptpython, you can declutter your workspace while holding onto everything that you’ve defined.

To clear the interactive shell and remove all the previous input and output, just press and hold theCtrl key and then press theL key while you’re in the interactive shell. This will clear the screen and give you a fresh, empty shell prompt:

Now you have a clean slate to continue with your coding session without closing the REPL. All your variables are still defined, so theComplete while typing feature works like before. You can also still access previously written lines of code using theUp andDown keys like before. TheCtrl+L command only visually tidies up your workspace.

You now have an understanding of the basic use of ptpython and these features. You can now dive in and fully utilize this tool in your workflow. You can now define variables, make use of type completion capabilities, and recap previous code through auto-suggestion and code history features. You can also define functions with the aid of auto-indentation, making your coding experience seamless.

But there’s more. There are advanced functionalities that can add more productivity to your work in the interactive shell. You’ll discover them next.

Viewing Methods and Parameters in Objects

Ptpython makes it convenient to view all arguments required in any objects. This can work for both built-in Python objects and objects from external libraries. Within the shell, you can view and meet all the requirements as you type in your code. This can be helpful, as you might not recall all the requirements in a given object.

To showcase this powerful feature, you’ll use the familiarprint() function, which take multiple arguments and concatenate them for output. It can also support various formatting options. In the following example, the output ofprint() will be separated by a hyphen using thesep argument:

As you type in the text thatprint() should print, ptpython can access all the optional parameters. In the above example, you select thesep parameter from the dropdown. The argument can accept any separator character to use in the concatenation of two strings.

You’ve used a hyphen in this example. The function prints the output with the hyphen between the two words as expected. Without thesep argument, a space would’ve separated the concatenated words.

You can practice with more Python functions likerange(),sum(), andlen() to see if you can peek into some of the parameters that they require. You can also gain more understanding of Python objects by accessing the documentation, which you’ll learn how to do next.

Reading Documentation

You can viewdocstrings directly within the interactive shell. To enable this functionality, go to the menu by pressingF2 and scrolling down to theShow docstring option in the menu. Use theLeft andRight keys to turn it on or off. Now you can view the docstrings of the code object in your interactive session:

For example, you can access the documentation ofprint() right within your interactive shell. You can access valuable documentation in thedocstring section at the bottom of the shell window.

These functionalities can give you an edge in learning and understanding libraries and objects that you’re interested in, without jumping out of your shell and perusing external documentation. However, they’re not a full replacement for official documentation for Python libraries.

Applying Input Validation

In the standard REPL, when you enter code that contains incorrect syntax in Python, the code will execute and return aSyntaxError. You then have to re-enter all the code correctly to get the desired output. In ptpython, the shell will issue a syntax error warning through theInput validation feature and won’t allow you to execute the code.

You can go back and fix the erroneous syntax, and only then can you execute your code. This option is automatically on, and you can access it in the menu under the sub-menuInput with the titleInput validation.

Input validation can be helpful when you accidentally miss a piece of necessary syntax when creatingfor loops,Python classes, or in function definitions.

Consider a small example, where you try to callprint(), but you forgot to add the closing parenthesis. With input validation, the code won’t execute, and you’ll see a syntax error warning:

The code won’t execute in the shell with incorrect Python syntax, such as a missing closing parenthesis at the end of a function call. The shell already knows that this is a requirement and issues a warning at the bottom of the shell window. But once you provide the closing parenthesis, the code executes as expected.

These notifications are quite helpful to eradicate syntax errors in your workflow, so you should probably keep this feature enabled.

Understanding the Paste Mode in Ptpython

Imagine that you have a block of code in a separate Python file that you’d like to use in your ptpython shell. The code block has hundreds of lines, and it’d take you some time to type them in. Wouldn’t it be advantageous to copy and paste them in?

You’re in luck. The ptpython paste mode makes it convenient to paste multiple lines of code into the terminal at once. This is particularly useful for copying and testing larger code snippets without the need for line-by-line input. Below is the code snippet from the firstJurassic Park movie:

title="Jurassic Park"release_year=1993director="Steven Spielberg"lead_actor="Sam Neill"budget=63_000_000# In USDbox_office_revenue=1_029_442_529# In USDgenres=["Action","Adventure","Sci-Fi"]rating=8.1

Copy the above code chunk in readiness to paste it into the ptpython shell. But first, you have to turn on paste mode. You can access paste mode through the menu:

Scroll down toPaste mode and use theRight key to turn it on. Once you exit the menu, you can see a notification at the bottom confirming that paste mode is on. Now you can paste code into the interactive shell by right-clicking in Windows or clicking withCtrl in macOS. Give it a try by copying the code above and pasting it into the interactive shell using paste mode:

Once in paste mode, you can seePaste mode (on) in your status bar. This indicates that you can paste code into the shell. Once you’re done, pressF6 to turn off paste mode. Press theEnter key to execute the code you pasted.

Consequently, you can quickly turn paste mode on and off using the keyboard shortcut,F6.

Highlighting Code Syntax

Code syntax highlighting enhances the readability of code by applying different colors and styles to different elements of the code, such as keywords, strings, comments, and variables. This visual distinction helps you quickly identify and differentiate various components of the code, making it intuitive to read, understand, and debug.

To enter this mode, go to the menu and scroll to the sub-menuColors. Under the sub-menu, select theSyntax highlighting option and use the arrow buttons to turn it on. Then scroll down to theCode option, where you’ll find up to thirty-two themes to choose from:

When you turn on these two menu options, your code will respond according to the theme that you have set. You’ll have to experiment until you get a theme that’s suitable for your shell. In this tutorial, the theme that you’re seeing ismonokai in case you fancy it.

Highlighting Matching Parentheses

In Python, you’ll often use parentheses. In some cases, you might have several sets of parentheses nested within a code block, which means that you might end up missing a closing parenthesis. The interactive shell can help you overcome this pitfall if you turn on theHighlight parenthesis option under theDisplay sub-menu:

You can access the menu item responsible for highlighting parenthesis in the menus system. After you’ve turned the option for parenthesis on, the shell will highlight the matching parenthesis in all your code:

The automatic highlight of matching parenthesis gives you a visual way to know that your code has every corresponding parenthesis. So now, when you have a huge number of parenthesis pairs, you’ll know off the bat which ones belong together. Hasn’t this small tweak made your life easier?

Changing the Prompt and Cursor Style

When you’re coding, you spend a lot of time with your REPL’s prompt and cursor. The prompt is the indicator character that appears in the shell to demonstrate that the shell is ready to accept your input. It’s where you type your Python code and shell commands.

On the other hand, the cursor lets you find where you’re currently located on a line, showing precisely where your next action or modification will take place. Because you spend a lot of time with these indicators, you probably want to make sure that you enjoy looking at them.

You can quickly change the prompt style that you’d like in the interactive shell. Currently, you can choose from theclassic prompt or theipython prompt. The IPython option offers numbered prompts, helping you to visually track the code inputs and outputs:

The prompt menu item is under theDisplay sub-menu, and you can use the arrow keys to choose which prompt best suits your case.

For the cursor, navigate to theInput sub-menu and access theCursor options. Here you can choose from a variety of cursor styles.

You can experiment with different cursor styles to choose what works for you. You can always change to another one using the menu item.

Changing the Configurations Permanently

There’s one problem that you’ll face with all the customizations that you’ve done so far. Once you exit the shell, you’ll lose all the custom configurations. There’s a way to ensure that your configurations are saved permanently. All menu options are stored in aconfig.py file. To change them permanently, you’ll have to alter this file.

Before you access theconfig.py file, it’s a good idea to note what preferences you’d like to change before applying the alterations. The file includes descriptions of all the configurations, giving guidance on the functionalities, so it can be a lot to wade through. Once you’ve decided which settings you want to adjust, you can access the file on your operating system as follows:

• Windows
• Linux
• macOS

Once you’ve successfully accessed the file, open it up in your code editor.

Then, go to the official ptython GitHub page, copy the providedconfig.py example file, and paste it into yourconfig.py file in the above directory.

Now you can use your power to turn configurations on and off by usingTrue andFalse options to match your preferences. This will permanently save your settings. As you learned, there are a lot of configurations that you could adjust! For a handy guide, download the cheat sheet below: