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Comments, Docstrings, and Documentation Standards in Python

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Table of Contents

  • Introduction
  • Importance of Code Documentation
  • Writing Effective Comments
  • Best Practices for Comments
  • Understanding Docstrings
  • Docstring Conventions (PEP 257)
  • Single-line vs Multi-line Docstrings
  • Tools for Generating Documentation from Docstrings
  • Documentation Standards for Python Projects
  • Conclusion

Introduction

Writing functional code is only one part of software development. Equally important is writing code that others can easily understand, maintain, and extend. One of the best ways to achieve this is through proper commenting and documentation.

In this article, we will explore the role of comments, docstrings, and documentation standards in Python programming. You will learn how to write documentation that meets professional standards and understand the conventions endorsed by the Python community.


Importance of Code Documentation

Good documentation is essential for:

  • Maintaining large codebases: Future developers (or even your future self) can easily understand the code.
  • Onboarding new team members: Well-documented code reduces the learning curve.
  • Open-source contributions: Proper documentation invites more collaboration and contributions.
  • Reducing bugs and misunderstandings: Clear documentation can prevent wrong assumptions about how code should behave.

Failing to document code properly often results in increased maintenance costs, poor code readability, and a higher likelihood of bugs.


Writing Effective Comments

Comments are explanatory notes within the source code that are ignored by the Python interpreter.
They are useful for explaining the ‘why’ behind code decisions, not the ‘what’—because well-written code should be self-explanatory for the ‘what’.

In Python, comments are written using the hash symbol #:

# Calculate the area of a rectangle
area = length * width

Good comments explain why something is done a certain way:

# Using try-except to handle missing file error gracefully
try:
with open('data.csv') as file:
process(file)
except FileNotFoundError:
handle_error()

Best Practices for Comments

  • Be concise but meaningful: Avoid unnecessary verbosity.
  • Keep comments up-to-date: Outdated comments are worse than no comments.
  • Avoid stating the obvious: Do not comment on self-explanatory lines.
  • Focus on intent and reasoning: Explain why something is done, not just what.

Bad example:

i = 0  # Set i to zero

Good example:

# Initialize counter for tracking the number of processed files
i = 0

Understanding Docstrings

Docstrings are special kinds of multi-line comments that describe the purpose of a function, class, or module.
They are different from regular comments because they are stored as metadata and can be accessed at runtime via the .__doc__ attribute.

Example:

def add(a, b):
"""
Add two numbers and return the result.

Parameters:
a (int or float): First number
b (int or float): Second number

Returns:
int or float: Sum of the two numbers
"""
return a + b

Docstrings are enclosed in triple quotes (""" or ''').


Docstring Conventions (PEP 257)

PEP 257 is the style guide for writing docstrings.
Here are some key points:

  • Use triple double quotes (""") even for one-line docstrings.
  • The first line should be a short, concise summary of the object’s purpose.
  • Add a blank line after the summary if there are more details.
  • End the summary line with a period.

Example of a simple one-line docstring:

def greet(name):
"""Return a greeting string for the given name."""
return f"Hello, {name}!"

Example of a multi-line docstring:

def factorial(n):
"""
Calculate the factorial of a non-negative integer n.

Parameters:
n (int): Non-negative integer

Returns:
int: Factorial of n
"""
if n == 0:
return 1
return n * factorial(n - 1)

Single-line vs Multi-line Docstrings

  • Single-line docstrings: Use for simple functions or methods where a brief description suffices.
  • Multi-line docstrings: Use for more complex functions, classes, or modules that require detailed explanations.

Choose wisely depending on the complexity and expected use of the object being documented.


Tools for Generating Documentation from Docstrings

There are several tools that can automatically generate external documentation based on docstrings:

  • Sphinx: Most widely used for Python projects; generates HTML, PDF, and other formats.
  • pdoc: Lightweight and easy-to-use tool for auto-generating web-based documentation.
  • MkDocs: Static site generator that works well for project documentation.

These tools can parse your Python files, read the docstrings, and create beautifully structured documentation with minimal effort.

For example, to set up Sphinx:

pip install sphinx
sphinx-quickstart

You can then configure it to auto-generate documentation directly from your docstrings.


Documentation Standards for Python Projects

Beyond writing comments and docstrings, large Python projects should adhere to standardized documentation practices:

  • Project-level documentation: Include README.md, CONTRIBUTING.md, LICENSE, CHANGELOG.md, etc.
  • API documentation: Auto-generated from source code (docstrings) using tools like Sphinx.
  • Inline documentation: Rich, meaningful comments and docstrings within code files.
  • Developer guides and installation instructions: Written in markdown or reStructuredText.
  • Versioning and changelogs: Track changes clearly for each release.

Maintaining high standards of documentation boosts the professionalism, usability, and success of your project.


Conclusion

In Python programming, well-written comments, proper docstrings, and robust documentation standards are not optional luxuries—they are essential components of professional software development.

By following best practices outlined in this article and adhering to PEP 257 and PEP 8 guidelines, you ensure that your code is not only functional but also maintainable, scalable, and accessible for other developers.

Good documentation is the bridge between an idea and its implementation — invest the time and effort into doing it right.

Python Code Style (PEP8) and Formatters (black, isort)

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Table of Contents

  • Introduction
  • What is PEP8 and Why Does It Matter?
  • Key Guidelines from PEP8
  • Common Violations and Mistakes
  • Introduction to Code Formatters
  • Overview of black Formatter
  • Overview of isort Tool
  • How to Integrate black and isort into Your Workflow
  • Best Practices for Maintaining Code Quality
  • Conclusion

Introduction

As Python developers, we often focus on writing functional code, but writing readable, consistent, and maintainable code is equally important.
Python embraces the philosophy that “code is read more often than it is written”, and to enforce this, the community follows a standard code style guide known as PEP8.

In this article, you will learn not only about PEP8 and its key rules but also how modern code formatters like black and isort can automate code styling, saving time and improving codebase quality.


What is PEP8 and Why Does It Matter?

PEP8 stands for Python Enhancement Proposal 8.
It is the official style guide for Python code, originally authored by Guido van Rossum (Python’s creator) and others.

The goals of PEP8 are:

  • To improve the readability of Python code.
  • To establish a consistent coding style across the Python community.
  • To encourage best practices that lead to better software maintainability.

Ignoring coding style in collaborative projects can result in messy codebases, endless debates over formatting, and ultimately wasted development hours.

Following PEP8 allows developers to:

  • Easily understand each other’s code.
  • Reduce cognitive load.
  • Catch bugs more easily through uniform formatting.

Key Guidelines from PEP8

Here are some of the essential guidelines outlined in PEP8:

1. Indentation

Use 4 spaces per indentation level. Never use tabs.

def my_function():
print("Hello, world!")

2. Line Length

Limit all lines to a maximum of 79 characters. For docstrings or comments, aim for 72 characters.

3. Blank Lines

Separate top-level functions and class definitions with two blank lines.

def function_one():
pass


def function_two():
pass

4. Imports

  • Imports should usually be on separate lines.
  • Group imports in the following order:
    1. Standard library imports
    2. Related third-party imports
    3. Local application/library-specific imports
  • Use absolute imports where possible.

Example:

import os
import sys

import requests

from mypackage import mymodule

5. Spaces in Expressions and Statements

Avoid extraneous spaces.

Bad:

x = 1 * ( 2 + 3 )

Good:

x = 1 * (2 + 3)

6. Naming Conventions

  • Variables, functions: lowercase_with_underscores
  • Classes: CapWords
  • Constants: UPPERCASE_WITH_UNDERSCORES

Example:

def calculate_area():
pass

class Circle:
pass

MAX_SIZE = 100

Common Violations and Mistakes

  • Mixing tabs and spaces for indentation
  • Not following proper import order
  • Writing excessively long lines
  • Using inconsistent naming conventions
  • Adding too many or too few blank lines
  • Forgetting to add whitespace around operators

Recognizing these early can prevent problems later, especially in larger, more complex projects.


Introduction to Code Formatters

Manually fixing formatting issues can be tedious and error-prone.
Automated code formatters solve this problem by automatically adjusting code to conform to standards like PEP8.

Benefits of using code formatters:

  • Save time and reduce manual work.
  • Maintain consistent style across teams.
  • Focus more on business logic rather than formatting disputes.

The two most popular tools for Python code formatting are black and isort.


Overview of black Formatter

black is a powerful opinionated code formatter for Python.
It automatically reformats Python code to meet PEP8 guidelines with minimal configuration.

Key features of black:

  • Opinionated: You have few configuration options, ensuring everyone’s code looks the same.
  • Deterministic: Given the same input, black always produces the same output.
  • Focus on readability: It optimizes code formatting for maximum clarity.

Installation

pip install black

Basic Usage

black your_script.py

It will reformat your file in place.
You can also format entire directories:

black your_project/

Example

Before black:

def add(a,b):return a+b

After black:

def add(a, b):
return a + b

As you can see, it applies standard spacing, line breaks, and more.


Overview of isort Tool

isort focuses specifically on sorting and organizing imports automatically.

It groups imports correctly (standard libraries first, then third-party, then local imports) and ensures they are ordered alphabetically within their groups.

Installation

pip install isort

Basic Usage

isort your_script.py

You can configure how strict or loose you want the sorting to be through a configuration file (like .isort.cfg or pyproject.toml).

Example

Before isort:

import requests
import os
import sys
from mypackage import utils

After isort:

import os
import sys

import requests

from mypackage import utils

How to Integrate black and isort into Your Workflow

You can integrate black and isort into:

  • Git hooks: Automatically format code before every commit.
  • CI/CD pipelines: Ensure style compliance during automated tests.
  • VSCode / PyCharm extensions: Auto-format code on save.

For Git hooks, you can use pre-commit:

pip install pre-commit
pre-commit install

.pre-commit-config.yaml:

repos:
- repo: https://github.com/psf/black
rev: stable
hooks:
- id: black
- repo: https://github.com/PyCQA/isort
rev: stable
hooks:
- id: isort

This ensures your code is always styled properly without manual intervention.


Best Practices for Maintaining Code Quality

  • Use black and isort together: Ensure full formatting and import sorting.
  • Run formatters early and often: Do not wait until the project is huge.
  • Standardize across the team: Everyone should use the same settings.
  • Include style checks in CI: Automate enforcement of code style rules.
  • Prefer automatic over manual fixes: Let tools do the boring work.

Consistency, not personal preference, should drive formatting decisions.


Conclusion

Adhering to PEP8 and using automated tools like black and isort will help you write professional-grade Python code.
They ensure that your codebase remains clean, readable, and maintainable — a must-have for modern Python development, especially in collaborative environments.

Investing a little time to master these tools will pay significant dividends in improved project quality and developer productivity over time.

Custom Exceptions and Best Practices in Python

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Table of Contents

  • Introduction
  • What Are Custom Exceptions?
  • Why Create Custom Exceptions?
  • How to Define Custom Exceptions in Python
  • Adding Custom Behavior to Exceptions
  • Inheriting from Built-in Exception Classes
  • Using __str__ and __repr__ Methods
  • Best Practices for Writing Custom Exceptions
  • Common Mistakes to Avoid
  • Real-World Use Cases
  • Final Thoughts

Introduction

While Python provides a rich set of built-in exceptions like ValueError, TypeError, and FileNotFoundError, in complex applications these are often not enough.
You might encounter scenarios where defining your own exceptions provides more clarity, better error handling, and improved maintainability.

In this article, we will explore how to create custom exceptions, how to use them effectively, and the best practices that industry professionals follow.


What Are Custom Exceptions?

Custom exceptions are user-defined exception classes that extend Python’s built-in Exception hierarchy.
They allow developers to create meaningful error types that represent domain-specific problems within their applications.

Rather than using generic errors like Exception or ValueError, custom exceptions help in creating more precise, readable, and maintainable codebases.


Why Create Custom Exceptions?

  • Clarity: They convey clear meaning about the type of error that has occurred.
  • Better Debugging: When a custom exception is raised, it is easier to trace and fix the problem.
  • Specific Error Handling: You can catch different types of errors separately and handle them differently.
  • Encapsulation: Abstract error details inside your own exception classes.

In short, custom exceptions are a powerful tool to build more robust and scalable applications.


How to Define Custom Exceptions in Python

Defining a custom exception is straightforward. It simply involves creating a new class that inherits from Python’s built-in Exception class.

class CustomError(Exception):
"""A basic custom exception"""
pass

You can then raise it like any other exception:

raise CustomError("Something went wrong!")

Example: A Custom Validation Exception

class InvalidAgeError(Exception):
"""Raised when the input age is not valid."""
def __init__(self, age, message="Age must be between 0 and 120."):
self.age = age
self.message = message
super().__init__(self.message)

def validate_age(age):
if age < 0 or age > 120:
raise InvalidAgeError(age)
else:
print(f"Age {age} is valid.")

try:
validate_age(150)
except InvalidAgeError as e:
print(f"Error: {e}")

Output:

Error: Age must be between 0 and 120.

Adding Custom Behavior to Exceptions

You can customize your exception classes with additional methods or attributes if needed.

class DatabaseConnectionError(Exception):
"""Raised when a database connection fails."""
def __init__(self, db_url):
self.db_url = db_url
super().__init__(f"Cannot connect to database at {self.db_url}")

try:
raise DatabaseConnectionError("localhost:5432")
except DatabaseConnectionError as e:
print(e)

This approach is useful when your exceptions need to carry additional contextual information.


Inheriting from Built-in Exception Classes

Sometimes, it makes sense to inherit from more specific built-in exceptions rather than directly from Exception.

Example:

class NegativeValueError(ValueError):
"""Raised when a value is negative where it's not allowed."""
pass

Since ValueError already conveys some idea of invalid value usage, extending it improves clarity and makes the exception more logical within Python’s ecosystem.


Using __str__ and __repr__ Methods

Overriding the __str__ or __repr__ methods in your custom exception allows you to control how the exception is displayed when printed or logged.

class AuthorizationError(Exception):
"""Raised when authorization fails."""
def __init__(self, user):
self.user = user

def __str__(self):
return f"User '{self.user}' is not authorized to perform this action."

Example usage:

raise AuthorizationError("john_doe")

Output:

User 'john_doe' is not authorized to perform this action.

Best Practices for Writing Custom Exceptions

  1. Inherit from the Correct Base Class:
    Use Exception for general cases, or a more specific built-in exception when appropriate.
  2. Use Clear and Descriptive Names:
    Make sure the exception class name clearly describes the problem (e.g., PaymentDeclinedError).
  3. Document Exception Classes:
    Always add a docstring explaining what the exception represents.
  4. Keep Exception Hierarchies Simple:
    Avoid overly deep inheritance trees unless absolutely necessary.
  5. Avoid Empty Exception Classes:
    Even a simple custom message or context attribute adds significant value.
  6. Use Custom Exceptions Sparingly:
    Do not create custom exceptions unless you have a clear reason; overuse leads to bloated code.
  7. Group Related Exceptions:
    Grouping related custom exceptions into a module improves organization.

Example:

# errors.py
class ApplicationError(Exception):
"""Base class for all application errors."""
pass

class DataValidationError(ApplicationError):
"""Raised when input data is invalid."""
pass

class ResourceNotFoundError(ApplicationError):
"""Raised when a resource cannot be found."""
pass

Common Mistakes to Avoid

  • Catching base Exception unnecessarily: Use more specific exceptions where possible.
  • Creating exceptions without additional value: If your custom exception doesn’t add meaningful information, consider using a built-in one.
  • Overcomplicating exception hierarchies: Flat, simple structures are usually more maintainable.
  • Silent error swallowing: Never ignore exceptions completely unless you have a strong reason.

Bad Practice:

try:
risky_operation()
except CustomError:
pass # silently ignoring errors

Good Practice:

try:
risky_operation()
except CustomError as e:
log_error(e)
notify_user(e)

Real-World Use Cases

  • Web Applications: Custom exceptions for handling authentication errors, validation errors, and permission denials.
  • Financial Systems: Payment processing errors, insufficient balance errors.
  • Machine Learning Pipelines: Data ingestion errors, model loading errors.
  • API Development: API-specific error codes mapped to custom exceptions for better client communication.

Custom exceptions are not just about error handling; they are about building a robust, predictable communication mechanism between different parts of a system.


Final Thoughts

Custom exceptions are a key part of writing clean, maintainable, and professional Python code, especially for large-scale or production-grade applications.
By following best practices and structuring your exceptions thoughtfully, you make your programs more resilient, your logs more useful, and your debugging process faster.

Always remember:

  • Be descriptive.
  • Be specific.
  • Keep it simple when possible.

With a strong exception handling strategy, you will elevate the reliability and professionalism of your Python projects significantly.

Exception Handling in Python: Mastering try, except, and finally

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Table of Contents

  • Introduction
  • What are Exceptions in Python?
  • Why Handle Exceptions?
  • Basic Exception Handling Using try and except
  • Catching Specific Exceptions
  • Using else and finally Blocks
  • Nested Try-Except Blocks
  • Raising Exceptions Manually with raise
  • Creating Custom Exceptions
  • Best Practices for Exception Handling
  • Common Mistakes to Avoid
  • Final Thoughts

Introduction

In the world of software development, things rarely go perfectly. Errors can occur for countless reasons: invalid user input, unavailable resources, network issues, and more.
Python provides a structured and clean way to manage these errors through exception handling.

This guide will give you a complete understanding of how Python’s try, except, and finally blocks work, with best practices to write more robust and professional code.


What are Exceptions in Python?

An exception is an event that disrupts the normal flow of a program’s instructions.
In Python, exceptions are raised when an error occurs during the execution of a program.

Common examples of exceptions:

  • ZeroDivisionError
  • ValueError
  • TypeError
  • IndexError
  • KeyError
  • FileNotFoundError

If an exception is not handled, the program will crash with an error message (known as a traceback).


Why Handle Exceptions?

  • Prevent Program Crashes: Handle errors gracefully without crashing.
  • Provide Better User Experience: Show meaningful error messages to users.
  • Control Flow Management: Take alternative actions when something goes wrong.
  • Logging and Debugging: Capture error details for later diagnosis.
  • Security and Stability: Avoid leaving the program or server in an unstable state.

Ignoring exceptions is not an option in production-quality software. Proactively managing them is critical.


Basic Exception Handling Using try and except

The simplest structure:

try:
# risky code that may throw an error
result = 10 / 0
except:
print("Something went wrong!")

Output:

Something went wrong!

Here, the try block attempts to divide by zero, causing an exception.
The except block catches it and prints a friendly message instead of letting the program crash.


Catching Specific Exceptions

Catching specific exceptions is a good practice. It allows you to respond differently based on the error.

try:
number = int(input("Enter a number: "))
result = 10 / number
except ValueError:
print("Invalid input! Please enter a number.")
except ZeroDivisionError:
print("Cannot divide by zero!")

This code handles two different exceptions separately:

  • If the user enters non-numeric input (ValueError).
  • If the user enters 0 (ZeroDivisionError).

Catching Multiple Exceptions Together

You can catch multiple exceptions in a single except block:

try:
number = int(input("Enter a number: "))
result = 10 / number
except (ValueError, ZeroDivisionError) as e:
print(f"Error occurred: {e}")

This is useful when the handling logic is the same for multiple exceptions.


Using else and finally Blocks

The else block

The else block executes only if no exception occurs:

try:
number = int(input("Enter a number: "))
result = 10 / number
except ZeroDivisionError:
print("Cannot divide by zero!")
else:
print(f"The result is {result}")

The finally block

The finally block always executes, whether an exception occurred or not.
Use it to release resources like closing a file, closing a network connection, etc.

try:
file = open("example.txt", "r")
content = file.read()
except FileNotFoundError:
print("File not found!")
finally:
print("Closing the file if it was opened.")
if 'file' in locals():
file.close()

Nested Try-Except Blocks

You can nest try-except blocks inside one another. This helps when you need finer control in complex operations.

try:
x = int(input("Enter a number: "))
try:
result = 10 / x
except ZeroDivisionError:
print("Cannot divide by zero!")
except ValueError:
print("Invalid input!")

However, overusing nesting can make the code harder to read. Prefer flat structures whenever possible.


Raising Exceptions Manually with raise

Sometimes you want to force an exception to occur based on a condition:

age = int(input("Enter your age: "))
if age < 0:
raise ValueError("Age cannot be negative")
else:
print(f"Your age is {age}")

Use raise to improve validation and error signaling inside your code.


Creating Custom Exceptions

You can define your own exception classes for very specific error types:

class NegativeAgeError(Exception):
"""Raised when the input age is negative."""
pass

age = int(input("Enter your age: "))
if age < 0:
raise NegativeAgeError("Age must be positive!")

Custom exceptions help make large applications more readable and organized.


Best Practices for Exception Handling

  • Catch Specific Exceptions: Avoid except: without specifying the exception type.
  • Avoid Bare Excepts: Catching everything may hide bugs.
  • Use finally for Cleanup: Always close files, release locks, or clean up resources.
  • Log Errors: Use Python’s logging module to log exceptions for production applications.
  • Fail Fast: Raise exceptions early to catch bugs before they cause larger issues.
  • Keep Try Blocks Small: Only wrap the code that may fail, not large blocks unnecessarily.

Example of logging an error:

import logging

logging.basicConfig(level=logging.ERROR)

try:
result = 10 / 0
except ZeroDivisionError as e:
logging.error("Exception occurred", exc_info=True)

Common Mistakes to Avoid

  • Catching too many exceptions at once.
  • Silencing exceptions without handling them.
  • Relying on exceptions for flow control instead of proper checks.
  • Not using finally to clean up.

Incorrect:

try:
process_important_data()
except:
pass # Bad practice: Swallowing all errors silently

Correct:

try:
process_important_data()
except SpecificError as e:
handle_error(e)

Final Thoughts

Mastering exception handling is essential for becoming a professional Python developer.
A good developer anticipates potential failures and writes resilient code that can recover gracefully or fail clearly when necessary.

Remember:

  • Handle exceptions thoughtfully.
  • Don’t hide bugs under silent except blocks.
  • Keep your code predictable and maintainable.

In the real world, proper exception handling can mean the difference between a graceful recovery and catastrophic system failure.

Python Virtual Environments (venv and pipenv): A Complete Beginner-to-Advanced Guide

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Table of Contents

  • Introduction
  • Why Use a Virtual Environment?
  • What is venv?
    • Creating a Virtual Environment with venv
    • Activating and Deactivating venv
    • Installing Packages Inside venv
    • Deleting a Virtual Environment
  • What is pipenv?
    • Why Use pipenv Over venv?
    • Installing pipenv
    • Creating and Managing a pipenv Environment
    • Pipfile and Pipfile.lock Explained
  • Differences Between venv and pipenv
  • Best Practices for Managing Python Environments
  • Common Errors and Troubleshooting
  • Final Thoughts

Introduction

As Python developers, we often work on multiple projects at the same time. Each project might require different versions of libraries or even different versions of Python itself. This can lead to dependency conflicts and major headaches.

The solution? Virtual Environments.
Using virtual environments like venv and pipenv ensures that each project has its own isolated workspace, free from interference with other projects or the system Python installation.

In this guide, we’ll explore venv and pipenv in detail, with step-by-step examples and best practices.


Why Use a Virtual Environment?

  • Avoid Dependency Conflicts: Different projects can require different versions of libraries.
  • Isolate Environments: Keep your global Python environment clean.
  • Reproducibility: Others can replicate your environment easily.
  • Ease of Deployment: Deployments often expect isolated environments.
  • Security: Minimize risks by installing only the needed packages for each project.

Imagine working on two projects:

  • Project A needs Django 3.2.
  • Project B needs Django 4.1.
    Without virtual environments, you’d face version conflicts that could break both projects.

What is venv?

Python 3.3+ comes with a built-in module called venv for creating virtual environments.

It allows you to create lightweight, isolated environments containing their own Python binaries and pip packages.


Creating a Virtual Environment with venv

Navigate to your project directory and run:

python3 -m venv myenv

Here, myenv is the name of your virtual environment folder. You can name it anything.

A new folder myenv/ will be created with a copy of the Python interpreter and a fresh pip.


Activating and Deactivating venv

On Windows:

myenv\Scripts\activate

On macOS/Linux:

source myenv/bin/activate

You’ll notice your terminal prompt changes, showing the environment name like this:

(myenv) $

To deactivate:

deactivate

This brings you back to the system’s Python environment.


Installing Packages Inside venv

After activation, use pip normally:

pip install requests

Packages are installed only inside the virtual environment, not globally.


Deleting a Virtual Environment

Simply delete the folder:

rm -rf myenv

or on Windows:

rmdir /s myenv

What is pipenv?

pipenv is a packaging tool that automatically creates and manages a virtual environment for your project, as well as adding/removing packages to a Pipfile as you install or uninstall packages.

It combines pip and venv in one tool.


Why Use pipenv Over venv?

  • Simplified dependency management: No need to manually manage requirements.txt.
  • Automatic environment creation: No need to manually activate/deactivate environments.
  • Pipfile and Pipfile.lock: Provides clear tracking of dependencies and exact versions.
  • Better security: Lock files make environments more reproducible.

Installing pipenv

Install via pip globally:

pip install pipenv

Confirm installation:

pipenv --version

Creating and Managing a pipenv Environment

Navigate to your project directory and install a package:

pipenv install requests
  • A virtual environment is automatically created.
  • A Pipfile is generated to track project dependencies.
  • A Pipfile.lock is created to ensure reproducibility.

To activate the environment shell:

pipenv shell

Now you can install more packages:

pipenv install flask

If you exit the shell:

exit

Pipfile and Pipfile.lock Explained

  • Pipfile: Human-readable list of top-level packages you requested.
  • Pipfile.lock: Machine-generated, records exact versions of all installed packages (including subdependencies).

Example of a Pipfile:

[[source]]
name = "pypi"
url = "https://pypi.org/simple"
verify_ssl = true

[packages]
requests = "*"
flask = "*"

[dev-packages]
pytest = "*"

Differences Between venv and pipenv

Featurevenvpipenv
Built-in?Yes (Python 3.3+)No (Needs separate install)
Manages dependencies?NoYes
Handles Pipfile.lock?NoYes
Ease of useManualMore automated
Popular for large projects?LessMore

Short Answer:

  • If you need simple isolation: use venv.
  • If you need isolation + dependency management: use pipenv.

Best Practices for Managing Python Environments

  • Always create a virtual environment for every project.
  • Never install project dependencies globally.
  • Use version control (git) to track your Pipfile, but not the virtual environment folder itself.
  • Regenerate environments on new machines using Pipfile.lock for exact dependency versions.
  • Regularly update dependencies using:
pipenv update

Common Errors and Troubleshooting

ErrorCauseSolution
command not found: pipenvpipenv not installed globallypip install pipenv
ModuleNotFoundError after installationEnvironment not activatedActivate the environment or install in the right one
Permission errors on LinuxInstalling without sudo when neededUse virtual environments; avoid sudo if possible

Tip: Always double-check which Python interpreter is active by:

which python

or

where python

inside the environment.


Final Thoughts

Understanding and using virtual environments effectively is one of the most critical skills for any Python developer.
Whether you choose the built-in simplicity of venv or the automated, dependency-tracking capabilities of pipenv, using a virtual environment will make your projects more manageable, reproducible, and professional.

Master this, and you’ll not only avoid common pitfalls but also be able to confidently work across multiple projects without worrying about conflicting dependencies.