Create Simple & Complex Python Package with Examples


Python

This guide will lead you on how to create a Python package which includes setup, configuration, building and publishing. You will learn how to include more files, test locally and version control for successful publication in PyPI.

A Python package is just a folder containing some python modules and __init__.py file. An empty file with the name __init__.py should be there for python recognize that as a directory that holds a package. This arrangement helps in organizing Python code hierarchically hence easily manageable and reusable. With Python 3.3, you no longer need __init__.py to have packages as long as you have namespace packages. These allow you to split a package across directories or distributions. However, for traditional package layouts __init__.py is still a crucial file and it’s very common in the python packaging conventions used today.

 

Pre-requisites: Setting up Environment

Before you can successfully create Python package, there are some fundamental prerequisites you'll need to have in place.

 

Install Python3

Your first step is to make sure you have Python installed on your system. If you don't already have it, download it from the official Python website. To check if Python is installed, open your command line (cmd on Windows, Terminal on macOS or Linux) and type the following command:

python3 --version

You should see the Python version displayed. If not, it means Python isn't installed, and you'll need to download and install it using package managers or from GitHub.

# For Debian-based systems like Ubuntu:
sudo apt update
sudo apt install python3

# For Red Hat-based systems like CentOS:
sudo dnf install python3

 

Install pip3

pip is Python's package installer, and you'll need it to install various Python packages from the Python Package Index (PyPI) to create python package. If you've installed Python 3 from the official website or using a package manager, pip3 is likely already installed.

To check if pip3 is installed:

pip3 --version

If pip3 is not installed, you can install it using the following command:

# For Ubuntu and other Debian-based distributions
sudo apt update                  
sudo apt install python3-pip

# For Red Hat-based systems like CentOS:
sudo dnf install python3-pip

 

Install dependent packages required for package development

We will be using setuptools and wheel package to create a python package so make sure these are also installed on your setup.

pip3 install setuptools
pip3 install wheel

 

1. Create Simple Python Package from Scratch - For Beginners

Follow these steps to create, build and publish a Python package.

We will make everything simpler with steps starting from the initial setup to publishing on PyPI (Python Package Index) which is an official online repository for packages. This section aims to help beginners and explain how you can design a basic text analyzer package using Python. Among the functions that our package will have are counting how many words, sentences, and characters there are in a given text.

 

1.1 Setup Your Package Directory

Create the root directory of your package and name it something related to your package's functionality, such as textanalyzer_package. Inside this directory, create a subdirectory with the name of your package (textanalyzer).

textanalyzer_package/
└── textanalyzer/
    └── __init__.py
Create Simple & Complex Python Package with Examples

 

1.2 Write Your Module Code

Inside the textanalyzer directory, create a Python file named analysis.py. In this file, you will define functions for analyzing text.

# analysis.py

def count_words(text):
    return len(text.split())

def count_sentences(text):
    # Simplistic sentence counting
    return text.count('.') + text.count('!') + text.count('?')

def count_characters(text):
    return len(text)

Make sure to create an __init__.py file within the textanalyzer directory to include your module's functionality. This file should import the functions you want to expose.

# __init__.py

from .analysis import count_words, count_sentences, count_characters

 

1.3 Create a setup.py File

In the textanalyzer_package root directory, create a setup.py file with the necessary metadata about your package.

from setuptools import setup, find_packages

setup(
    name="textanalyzer",
    version="1.0",
    author="Your Name",
    author_email="your.email@example.com",
    description="A simple text analysis package",
    packages=find_packages(),
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
        "Operating System :: OS Independent",
    ],
)

 

1.4 Building the Package

Ensure you have setuptools and wheel installed, then navigate to your package's root directory and run:

python3 setup.py sdist bdist_wheel
Create Simple & Complex Python Package with Examples

 

1.5 Publish Your Package to PyPI

First, if you haven't already, install twine:

pip install twine

Next, use twine to upload your package to PyPI:

twine upload dist/*

Remember, you need a PyPI account to do this step.

 

1.6 Install and Use Your Package

Now, you or anyone can install your package using pip:

pip3 install textanalyzer

And use it in Python:

from textanalyzer import count_words, count_sentences, count_characters

text = "Hello world! Python packaging is fun. Let's share our package with the world."
print(f"Words: {count_words(text)}")
print(f"Sentences: {count_sentences(text)}")
print(f"Characters: {count_characters(text)}")

Congratulations, You've made a textanalyzer package which can count words, sentences, and characters in some given text. This guide helped you get an understanding of the basics of Python package creation and distribution. It also got you started with a practical example. However, keep in mind that more complex projects will require additional steps such as including a more complicated set of dependencies, automated testing being done, or comprehensive documentation to be written which we will cover in next section.

 

2. Create Complex Python Package from Scratch - For Advanced Users

If you’re itching to level up your Python skills, creating a complex Python package will force you to figure out packaging, organization, dependencies, tests and documentation. We’re only going to cover these topics in the context of experienced users looking to create robust packages. COME!

 

2.1 Planning Your Package

When planning to create python package, deciding whether it will be a single module or consist of multiple modules is crucial. A single module is generally easier to manage but might become cluttered as the package grows, while multiple modules are more organized but might make the package more complex.

For our DataValidator package, we decide to go with multiple modules to separate different validation functionalities, such as:

  • string_validator.py: For validating string data
  • email_validator.py: For validating email addresses
  • phone_validator.py: For validating phone numbers

 

2.2 Creating Your Package Directory Structure

Creating a well-organized directory structure is an essential step to create Python package processes that are both maintainable and scalable. It's important to group related files together and to give files and folders names that reflect their purpose.

This is the structure of DataValidator package from my setup:

DataValidator/
├── data_validator/
│   ├── __init__.py
│   ├── string_validator.py
│   ├── email_validator.py
│   └── phone_validator.py
├── tests/
│   ├── __init__.py
│   └── test_validators.py
├── setup.py
└── README.md

Here:

  • The data_validator folder contains the actual Python modules.
  • The tests folder will contain all your unit tests.
  • setup.py will help in packaging your Python code.
  • README.md for documenting your package.

 

2.3 Setting Up Development Environment

The virtual environment is important for cleaning the slate. It ensures that you have an isolated package so you can start developing. Open your command line and find the folder for your DataValidator project. If you haven’t made a directory yet, do it now:

mkdir DataValidator
cd DataValidator

Now, let's create the virtual environment before we create python package. The command to create a virtual environment may differ depending on your operating system.

# On Windows
python -m venv venv

# On macOS and Linux
python3 -m venv venv

This will create a new folder named venv inside your DataValidator project folder, containing the virtual environment.

Before you can start installing packages or running Python code, you need to activate the virtual environment.

# On Windows:
.\venv\Scripts\Activate

# On macOS and Linux:
source venv/bin/activate

Once activated, your command line should show the name of the activated environment, in this case, venv.

(venv) deepak@deepak-VirtualBox:~/DataValidator$ 

You can verify that you're using the Python interpreter from within the virtual environment by checking its location:

which python3  # On macOS and Linux
where python3  # On Windows

It should point to the Python executable inside the venv folder.

/home/deepak/DataValidator/venv/bin/python3

Now your environment is ready to create python package.

 

2.4 Let's get to code writing

In this section, we will discuss Python best practices when we create Python package, the importance of docstrings, and how to manage version control with Git while creating your DataValidator package.

string_validator.py: This module validates strings based on length or content.

# string_validator.py

def is_string_empty(s):
    """
    Checks if the given string is empty.

    Parameters:
        s (str): String to check.

    Returns:
        bool: True if empty, False otherwise.
    """
    return len(s.strip()) == 0

def has_special_characters(s):
    """
    Checks if the given string has special characters.

    Parameters:
        s (str): String to check.

    Returns:
        bool: True if has special characters, False otherwise.
    """
    return any(not c.isalnum() for c in s)

email_validator.py: This module validates email addresses.

# email_validator.py

def validate_email(email):
    """
    Validates if the input is an email.

    Parameters:
        email (str): The email address to validate.

    Returns:
        bool: True if valid email, False otherwise.
    """
    return "@" in email and "." in email

phone_validator.py: This module validates phone numbers based on a simple rule: they must be numeric and 10 digits long.

# phone_validator.py

def validate_phone(phone):
    """
    Validates if the input is a phone number.

    Parameters:
        phone (str): The phone number to validate.

    Returns:
        bool: True if valid phone number, False otherwise.
    """
    return phone.isdigit() and len(phone) == 10

 

2.5 Dependency Management

To cook up a mean Python package, managing dependencies is key. Dependencies, ya know. The third-party libraries or modules that your package relies on to function correctly. Now that you know what they are we can get into how to manage them effectively. Especially for your DataValidator package.

Using requirements.txt or Pipfile - Which on to use?

We have two popular ways to manage package dependencies when we create Python package: either through a requirements.txt file or a Pipfile.
The former, requirements.txt, is an easy way to write all of your project’s third-party packages in a plain text file.
While the latter, Pipfile, works with Pipenv and has more advanced features compared to requirements.txt.

Let's assume that our DataValidator package will require the following third-party packages:

  1. validators for advanced validation.
  2. pytest for running tests.
  3. requests for some future features that will require HTTP requests.

 

2.5.1 Using requirements.txt

In this example, create a requirements.txt file in the root directory of your project where we create python package. Populate this file as follows:

validators==0.18.2
pytest==6.2.5
requests==2.26.0

This explicitly defines which versions of the dependencies your package relies on.

End-users can install all the dependencies at once using:

pip3 install -r requirements.txt

 

2.5.2 Using Pipfile

If you use Pipenv to create python packages, a Pipfile is generated when you install packages using “pipenv install.”
Head over to your terminal and locate your project directory.
Run these commands:

pip3 install pipenv
pipenv install validators
pipenv install pytest
pipenv install requests

A Pipfile will be generated in your project directory, and it will look something like:

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

[packages]
validators = "==0.18.2"
pytest = "==6.2.5"
requests = "==2.26.0"

[dev-packages]

[requires]
python_version = "3.9"

 

2.5.3 Managing Third-party Libraries

There are times when you have to use outside libraries that already have dependencies. It’s very important to make sure that the embedded dependencies do not clash with each other.
For example, if the validators package requires a certain version of another package called six. You’ll need to make sure that if your package also needs six, both dependencies require the exact same version. If they don’t, you’ll have to decide which version is crucial and possibly find other packages that won’t interfere with each other.

To see what dependencies your third-party libraries have for the python packge you’re using, just run:

pip3 show validators

This will list all the dependencies that validators relies on, allowing you to manage them effectively.

 

2.6 Writing Unit Tests using unittest

Python’s built-in unittest framework is commonly used for writing robust tests.

Let's assume we're writing unit tests for our DataValidator package. Create a test_validators.py file in the tests directory of your package, and add the following example code:

import unittest
from data_validator import validate_email, validate_phone, is_string_empty

class TestDataValidator(unittest.TestCase):

    def test_email_validator(self):
        self.assertTrue(validate_email("test@example.com"))
        self.assertFalse(validate_email("testexample.com"))

    def test_phone_validator(self):
        self.assertTrue(validate_phone("1234567890"))
        self.assertFalse(validate_phone("123-456-7890"))
        
    def test_string_empty(self):
        self.assertTrue(is_string_empty("   "))
        self.assertFalse(is_string_empty("Not empty"))

if __name__ == "__main__":
    unittest.main()

In this test suite, we have three tests:

  1. test_email_validator: Validates the validate_email function from our package.
  2. test_phone_validator: Validates the validate_phone function from our package.
  3. test_string_empty: Validates the is_string_empty function from our package.

To run these tests, navigate to the root directory of your project where you create python package and run:

python3 -m unittest tests/test_validators.py

 

2.7 Create Documentation (README)

The README. It’s the file people see first when they encounter your package. It’s an overview of what your package does, how to install it and basic usage examples. So you should include it

# DataValidator

## Overview

DataValidator is a Python package that provides various validation utilities for strings, emails, and phone numbers.

## Installation

To install DataValidator, run the following command:

```bash
pip install DataValidator
```

## Usage

Here are some quick examples:

```python
from data_validator import validate_email, validate_phone

print(validate_email("test@example.com"))  # True
print(validate_phone("1234567890"))  # True
```

## Contributing

Feel free to open an issue or submit a pull request if you find a bug or have a feature request.

## License

MIT

 

2.8 Packaging Your Code

While you're looking to create your Python package, there are several ways to go about packaging code. This is where the setuptools library comes in and it’s one of the most common methods people choose to use when getting things done. In this write up we’ll show you how to put together a setup.py file which will generate distribution packages.
Essentially, the setup.py file is a build script for setuptools. It holds metadata concerning your package and contains instructions for everything related to packaging, distributing, and installing modules.

Below is the simple setup.py file that we’ve used on our DataValidator package:

from setuptools import setup, find_packages

setup(
    name='DataValidator',
    version='0.1',
    packages=find_packages(),
    install_requires=[
        'validators==0.18.2',
        'pytest==6.2.5',
        'requests==2.26.0',
    ],
    author='Your Name',
    author_email='your.email@example.com',
    description='A utility package for validating data',
)

This setup.py file specifies the package name, version, dependencies, and other information. When you run this script, setuptools will use this information to create a distributable package.

Once you have a setup.py file, you can generate different types of distribution packages:

  • Source Distribution (sdist)
  • Built Distribution (bdist_wheel)

Run the following commands:

python3 setup.py sdist
python3 setup.py bdist_wheel

This will generate a dist directory where you create python package containing your distribution packages, for example:

  • DataValidator-0.1.tar.gz (sdist)
  • DataValidator-0.1-py3-none-any.whl (bdist_wheel)

 

install_requires Vs requirements.txt - Are they dependent?

When constructing your python package, the install_requires parameter of the setup.py file is used to specify which dependencies need installing for it to function properly. These are automatically installed when a user installs your package.
The list of dependencies within this parameter often looks like the list of packages in your requirements.txt file, another way that people commonly manage project dependencies. To install them you need to run pip install -r requirements.txt.

  • Common Source: The same packages are usually found in both install_requires and requirements.txt; although there might be additional ones in the text file which help with development but aren't necessary for running the package.
  • Version Pinning: You can pinpoint versions in both places if you want. If for example your code depends on version 0.18.2 of a package called validators, you would include 'validators==0.18.2' in both install_requires and requirements.txt.
  • Flexibility: Using operators such as >=, <= etc., helps make version specification more flexible within install_requires, a trait particularly useful when packaging libraries. This can also be done with requirements.txt but it's more common that dependencies are pinned to particular versions when developing applications.
  • Automatic Installation: Dependencies listed in install_requires will get installed automatically once someone installs your package. With requirements.txt things work differently; first you need to manually run pip install -r requirements.txt to actually do anything with its contents

Let's say your requirements.txt file looks like this:

validators==0.18.2
pytest==6.2.5
requests==2.26.0

And your setup.py contains:

install_requires=[
    'validators==0.18.2',
    'pytest==6.2.5',
    'requests==2.26.0',
],

As soon as users download your DataValidator package, the linked packages in install_requires will be automatically downloaded too. Developers of your DataValidator package can use requirements.txt to create a virtual environment with all the necessary dependencies.
Now, even though they both specify dependencies, while one is for the user and the other is for developers, probably requiring more technical knowledge.

 

2.9 Publishing Your Package

After we create python package, the Python Package Index (PyPI) is the go-to repository for Python packages, and we'll be using it as our example.

Uploading Your Package to PyPI

Before you can upload your package, you need to have an account on PyPI.

  • Navigate to the PyPI website.
  • Click on "Register" and follow the steps to create your account.

To upload your package to PyPI, you'll first need to install a tool called twine.

pip3 install twine

Once twine is installed, navigate to your project directory (where the dist directory resides) and execute the following:

twine upload dist/*

Example for DataValidator

Let's say your dist directory contains:

  • DataValidator-0.1.tar.gz (sdist)
  • DataValidator-0.1-py3-none-any.whl (bdist_wheel)

After running twine upload dist/*, these files will be uploaded to PyPI, making your DataValidator package publicly available.

Versioning Your Package

When you create a Python package, it’s crucial to keep in mind that you will probably update it later. That's why you should have a versioning strategy for your package. As we know, semantic versions are common for Python packages. What does this mean? Well the version number is usually specified in the setup.py file.

setup(
    name='DataValidator',
    version='0.1',
    ...
)

And don’t forget! When you update your package don’t just do the changes and stop there. Remember to also update the version number from setup.py to reflect the changes according to your versioning strategy (e.g., major, minor, or patch updates).

For instance: If you make a small change which is still backward-compatible with previous code versions then we could change the version number from '0.1' to '0.1.1'. But if there’s a breaking change, I’d recommend updating it to '0.2' or even '1.0'. Just think about how much impact these new changes could have!

 

2.10 Testing and Verifying your Python Package

You can install the package locally either directly from the source code or by using the distribution files you created earlier (sdist or bdist_wheel).

Direct Installation from Source Code

Navigate to the project directory and run:

pip3 install .

Installation Using Distribution Files

If you've created a dist directory that contains your package files (DataValidator-0.1.tar.gz, DataValidator-0.1-py3-none-any.whl), you can install from them as follows:

pip3 install dist/DataValidator-0.1-py3-none-any.whl

or

pip3 install dist/DataValidator-0.1.tar.gz

After installing your package locally, you should verify its functionality to ensure it behaves as expected.

Import the Package:

Open a Python interpreter and try importing your package.

import DataValidator

Run Some Basic Tests

Use the functions and classes in your package to make sure they are working as intended.

from DataValidator import validate_email, validate_phone

print(validate_email("test@example.com"))  # Should return True
print(validate_phone("1234567890"))       # Should return True

Check Dependencies

Ensure that all dependencies listed in install_requires or requirements.txt are installed correctly. You can list installed packages with pip freeze.

Uninstall and Reinstall

It may also be useful to uninstall the package and reinstall it to make sure the installation process is seamless.

pip3 uninstall DataValidator
pip3 install .

Check README and Documentation

Ensure that your README file and any other documentation you may have are included in the package and are accessible to the users.

 

Let's also cover some more advanced topics

Creating Executable Scripts

Python packages often come with executable scripts that users can run from the command line. These scripts can simplify complex tasks or serve as utilities related to your package.

Suppose you want to add a script that validates an email and a phone number from the command line. You would:

Create a new Python script named validate_data.py in your package directory.

from DataValidator import validate_email, validate_phone
import argparse

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--email', help='Email to validate')
    parser.add_argument('--phone', help='Phone number to validate')
    args = parser.parse_args()

    if args.email:
        print("Email validation:", validate_email(args.email))
    
    if args.phone:
        print("Phone validation:", validate_phone(args.phone))

if __name__ == "__main__":
    main()

In your setup.py, add an entry_points section.

setup(
    ...
    entry_points = {
        'console_scripts': ['validate-data=DataValidator.validate_data:main'],
    }
    ...
)

After installing your package, users can run validate-data --email test@example.com --phone 1234567890 directly from the command line.

 

Multi-Python Version Support

If you want your package to be used, it’s best to support as many Python versions as possible. People like having options after all! And of course you need to make sure the code is compatible with each version and then specify which ones your package supports in the metadata.

Testing: Use tools like tox to test your package against different Python versions.

pip3 install tox

Create a tox.ini file with the Python versions you want to support.

[tox]
envlist = py36, py37, py38, py39

[testenv]
deps = pytest
commands = pytest

Specify Versions in setup.py: In your setup.py file, specify which Python versions your package is compatible with.

setup(
    ...
    classifiers=[
        ...
        'Programming Language :: Python :: 3.6',
        'Programming Language :: Python :: 3.7',
        'Programming Language :: Python :: 3.8',
        'Programming Language :: Python :: 3.9',
        ...
    ],
    python_requires='>=3.6, <4',
    ...
)

This ensures that your package will only be installable on compatible versions of Python.

 

Summary and Conclusion

Creating a Python package from nothing can seem super intimidating. But hopefully, you have found this comprehensive guide to be helpful and easy to follow. We have covered so many different elements of Python package creation, starting with basic prerequisites to more advanced topics. All in all, these steps make up a guide on how to create an effective Python package.
From creating a development environment to coding, testing, documenting, and publishing your new package. Going through each of these steps will lead you towards making a robust and user-friendly package that’s also easily distributable.

If you’re someone who is brand new at coding or even experienced developer looking for tips on how to refine your packaging skills, we’ve got you covered. These guidelines are meant to provide you with a step-by-step roadmap on how to build the best Python package possible.

By following all of these steps properly and ensuring that each task is completed thoroughly, there’s no doubt that your new Python package will meet your needs and be beneficial to the larger community as well

 

Further Additional Resources

 

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