Pipe Operations in Python: A Guide to Writing Clean Codes

For any diligent programmer, it is crucial to produce neat and easy-to-follow codes. This necessitates following appropriate naming conventions and making comments as required. Furthermore, minimal coding is preferable to enhance readability. This post delves into the Python pip (|) operator’s usefulness and its part in crafting more comprehensible source codes.

When designing any application, a key aim is to ensure the code is composed in an easily comprehensible format. This requires coders to follow coding conventions, including comments and indentation, though these are not compulsory for Python programming. Adhering to such customs facilitates easier debugging and modification of the code, resulting in an improved user experience, as well as fulfilling all organizational requirements.

Can you elaborate on the piping process?

With the Python module, Pipe, it is now easier to combine the results of two separate methods, resulting in more readable and straightforward code. This library streamlines the process of producing more comprehensible code.

The following is the setup order for Pipe before commencing work:

The term “pip” refers to “pipe installation.”

Includes an array of pipe functions

The Pipe library presents operations that are akin to SQL statements. The succeeding examples illustrate the application of the where and choose functions. For a more in-depth understanding, let us explore these operations further.


Just like the where clause in SQL, the where technique can be employed to filter out (or split) undesired data.


The choose method, akin to Python’s map function, is highly beneficial and serves the same purpose.

It may seem that the ‘where‘ and ‘choose’ techniques are redundant. however, while Figure 1 necessitates a significant amount of code to produce the same outcome, Figures 2 and 3 accomplish the same task with a single line of code. Figure 3 is preferred over Figure 2 due to its enhanced legibility resulting from the absence of nested parentheses.


Working with nested iterables is often cumbersome, but the chain method simplifies this by serializing them.


In case the list in Figure 4 is not completely flattened, the traverse approach may be utilized as an alternative. This method can be used to methodically expand iterables and flatten deeply nested lists.

Merging components into groups

When categorizing the elements of a list into distinct groups, the groupby method is incredibly useful. Suppose a set of integers needs to be separated into two categories: even and odd. With the groupby method, the final results can be arranged in an organized format. To further refine the data set, the choose method can be used to convert the structured results into a dictionary.

The ‘groupby‘ method splits a list into two tuples. Thereafter, the ‘choose’ method converts these tuples into a collection of dictionaries. Each tuple is composed of two elements; the first element being the key, and the second element being the values.


Eliminating duplicates from a list of items is a routine occurrence. For instance, suppose we have a list of integers as follows: arr=[1,2,2,3,3,4,4]. This list includes several instances of the number four. By utilising the dedup procedure, the resulting sequence [1,2,3,4] will only contain unique items. The Python set function can also produce a similar outcome, providing the added advantage of extracting only the unique items via a key.

Principles for Writing Understandable Python Code

To enhance the readability of Python code, it is recommended that these instructions be followed.

Begin by assigning meaningful names to your variables and functions. It is critical that all functions and variables are given names that clearly convey their purpose to the reader. This helps the reader to better comprehend the code, unlike names that are ambiguous and generic. For instance, instead of using the straightforward name “calculate” for a function, it may be more fitting to use a name that provides context such as “calculate percentage from cgpa.”

Include a docstring: A good practice to incorporate in function creation is the use of docstrings. Although not formally classified as comments, docstrings serve a similar function.

It’s worth noting that within Python programming, single triple quotes and the hash sign (#) have two distinct functions. Single triple quotes are generally used for docstrings, which are textual strings that describe the intent of a code module, function, class or method, but do not provide information on its execution. Conversely, the hash sign is used for comments, which are commonly ignored by the interpreter. Docstrings, however, are treated seriously and may be utilised as an extension in any Python package.

The following code snippet showcases how the docStringDemo function and a print statement are defined. To obtain documentation for a specific function or class, the function name should be written followed by .doc. It’s worth keeping in mind that docstrings must be appropriately indented; otherwise, an IndentationError will be generated. Conversely, comments do not need to follow this rule.

Thirdly, endeavour to frequently utilise classes: Python’s classes provide all the components found in Object Oriented Programming languages. They are an essential tool, since they enable related information to be assembled and encapsulated within a single object. When this object is called, a new instance is created, eliminating the need to declare functions or variables that will not be utilised later on.

4. Refrain from overloading functions with excessive responsibilities and maintain their lengths to a minimum. To ensure that other developers can easily understand and interpret the code, it is advised that a function be used for a single specific purpose. Moreover, functions should be succinct and not surpass 30 lines of code. Employing operations such as piping could also be advantageous in reducing the number of lines of code necessary to achieve the sought outcome.

5. Properly indent: In contrast to languages such as C++ and Java, Python necessitates fewer concerns about indentation. It is commonly advised that indentation levels should not surpass two.

Avoid having too many return statements within the same function body: Adhering to this practice is vital to maintaining the readability and comprehension of your code.

Using pipes and other best practices simplifies the task of composing efficient and well-organized code. These approaches are not only easier to modify and debug, but they also reduce the workload of developers and improve the processing speed of computations.

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