Pipe Operations in Python: A Guide to Writing Clean Codes

As a serious programmer, it is essential to write clean and readable programs. To do this, one must adhere to proper naming conventions and include comments when necessary. Additionally, writing as few lines of code as possible is beneficial for readability. This article will explore the usefulness of the Python pipe (|) operator and its role in creating more readable source code.

One of the major objectives when designing any application is to ensure the code is written in an easily understandable format. To facilitate this, coders should utilise coding conventions that include integration of comments and indentation, although these are not mandatory for Python programming. Adopting these conventions will enable the code to be more easily debugged and modified, resulting in a better user experience and the satisfaction of all organisational requirements.

Can you explain the piping procedure?

The Python module, Pipe, facilitates the combining of the results of two distinct methods, making the code more legible and straightforward. This library simplifies the process of creating more comprehensible code.

Pipe should be set up in the order stated below before work begins.

“pip” means “pipe installation.”

Incorporates a variety of pipe functions

The pipe library offers operations that bear a resemblance to SQL statements. The following figures offer insight into the use of the where and choose functions. To gain a more comprehensive understanding, let us delve further into these operations.


Similar to the where clause in SQL, the where technique may be used to philtre out (or partition) unwanted data.


As with Python’s map function, the choose method is quite useful. Additionally, it serves the same purpose.

It appears that the use of the ‘where‘ and ‘choose’ techniques are superfluous. While Figure 1 requires a substantial amount of code to achieve the same result, Figures 2 and 3 accomplish the same goal with a single line. Of the two, Figure 3 stands out for its improved readability due to having fewer nested parentheses. This makes Figure 3 the preferred choice when compared to Figure 2.


Normally, dealing with nested iterables is a pain, but the chain method serialises them and makes everything much simpler.


Given that the list in Figure 4 is not fully flattened, we may choose to use the traverse technique as an alternative. This approach can be employed to systematically unfold iterables and flatten deeply nested lists.

Combining parts into groups

The groupby method can be used to great advantage when organising the objects of a list into distinct categories. As an example, consider a list of integers that need to be divided into two sets: odd and even. By utilising the groupby method, it is possible to structure the results into an orderly layout. To further refine the data set, the choose method can be employed to turn the organised results into a dictionary.

The ‘groupby‘ procedure divides a list into two tuples. The second procedure, ‘choose’, then transforms the tuples into a dictionary collection. Each tuple consists of two parts; the key, which is the first element, and the values, which are the second element.


This practice of removing duplicates from a list of items is a common occurence. As an example, consider the list of integers, arr=[1,2,2,3,3,4,4]. This list has many repeated instances of the number four. By using the dedup procedure, we can obtain the sequence [1,2,3,4], which consists of only distinct elements. The Python set function can be used to achieve a similar result, with the added advantage that it allows the programmer to extract only the unique items by using a key.

Standards for writing understandable Python code

In order to make Python code more readable, it’s a good idea to follow these guidelines.

First, give your variables and functions meaningful names. It is essential that all functions and variables have names that are meaningful and clearly communicate their purpose to the reader. This will help the reader to more easily understand the code, as opposed to names that are vague and generic. For example, instead of simply naming the function “calculate,” it may be more appropriate to use a name that provides more context, such as “calculate percentage from cgpa.

Put in a docstring: One good practice to follow when creating a function is to provide docstrings. While not really comments, docstrings provide a similar purpose.

It is important to note that single triple quotes and the hash sign (#) serve two distinct purposes in programming with Python. Single triple quotes are typically used for docstrings, which are strings of text that provide a description of the purpose of a code module, function, class, or method, but do not provide any information about its implementation. On the other hand, the hash sign is used for comments, which are typically disregarded by the interpreter. Docstrings, however, are taken seriously and serve as an extension that can be used in any Python package.

This code snippet demonstrates the declaration of the docStringDemo function, along with a print statement. Documentation for a specified function or class can be retrieved by typing the function name followed by ‘.doc’. It is important to note that docstrings must be properly indented; otherwise, an IndentationError will be raised. In comparison, comments do not have this requirement.

Third, try to always use classes: Python’s classes provide all the features associated with Object Oriented Programming languages. These are an essential tool as they allow all related information to be grouped together and encapsulated in a single object. Invoking this object will then create a new instance, meaning that there is no need to declare functions or variables that will not be subsequently used.

4 Avoid overloading functions with too many responsibilities and keep their lengths short. In order to ensure clarity and readability for other developers, it is advisable to restrict a function to a single purpose. Additionally, functions should be kept concise and should not exceed 30 lines of code. Furthermore, employing operations such as piping could be beneficial in achieving the desired result with fewer lines of code.

5. indent properly: In comparison to programming languages such as C++ and Java, Python requires fewer considerations with respect to indentation. It is generally recommended that indentation levels should not exceed two.

Make sure there aren’t many return statements within the same function body: Keeping this in mind is crucial for the continued readability and understandability of your code.

The utilisation of pipe and other best practices makes it straightforward to author efficient and well-structured code. These techniques are not only simpler to modify and debug, but they also help to reduce the workload of programmers and enhance the speed of computations.

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