Harness the Power of Custom Python Libraries: Streamline Your Workflow and Amplify Your Productivity

Introduction

In the dynamic world of data science and coding, efficiency is paramount. Whether it’s for professional projects or personal endeavors, the ability to streamline processes is invaluable. Data Scientists and Analysts alike have leveraged OpenAI to enhance their workflow, but there is still something very useful that is missed or not as attentive too. This is where the creation of custom Python libraries comes into play, offering a path to optimize repetitive tasks, ensure consistency, and elevate overall productivity. Custom Python libraries can significantly reduce code redundancy, enhance code readability, and make maintenance easier.

Whether you’ve taken an introduction to python course or live the life of a Python Engineer, you probably have seen this:

What exactly does this mean? A library called pandas is being imported into your coding environment and being assigned the alias of pd (to make it easier to remember by 4 letters). The pandas library has a multitude of capabilities including but not limited to, efficient data manipulation and analysis through its DataFrame and Series data structures.

Let’s walk through how to build your own libraries:

TLDR;

This guide demonstrates how to create custom libraries using Jupyter Notebook, Python 3, and terminal commands. Creating personal libraries allows reusing functions for tasks like connecting to databases or setting up visualization configurations.

The Tech Stack

This article uses

• Python 3

• Jupyter Notebook

• Terminal

Start

1. Create a new folder anywhere on the computer.

For this example, it’s named multiply_function. (this will also be the name of the libary!)

2. In this folder, open a new Jupyter Notebook

For this example, it’s named multiply.ipynb.

Building the Function:

1. In multiply.ipynb, a basic multiplication function is written and tested.

2. The aim is to build a library that performs this task, eliminating the need to repeatedly write the entire function.

3. Upon confirming the function’s efficacy, delete the output cell, retaining only the function.

Creating the Necessary Files:

1. This step may seem strange, but since the function is tested and works, the function will be copy and pasted into a py file. This py file will be named multiply.py.

2. The original multiply.ipynb can now be deleted.

3. Next, create a __init__.py file in the multiply_function folder. This file designates the directory as a Python package.

4. Inside __init__.py, add a line to import the multiplication function from multiply.py.

5. Create a setup.py file in the multiply function folder. Why? This holds information about the package.

6. Here is what the inside of the setup.py file looks like:

from setuptools import setup, find_packages

setup(
    #Name this the same as the folder, in this case multiply_function
    name='multiply_function',
    version='0.1',
    packages=find_packages(),
    description='A simple multiplication package',
    author='Author Name',
    author_email='youremail@email.com',
    url='URL to your package, if available'
)

7. Here is what the multiply_function folder looks like:

Installing Locally:

1. Open up the terminal and access the folder that was created earlier:

2. Install the library:

3. This process automatically generates two new folders within multiply_function.

Using The New Library

1. Open a new Jupyter notebook in any directory.

2. Test the new library:

Use Cases for Your Job

1. Connecting to Company’s Database: Streamlines the process of connecting to your company’s various databases. Instead of copying and pasting connection strings and credentials every time, the package can include functions that encapsulate these details. You can also import getpass to securly input your password!

2. Experiment Analysis Results: Assists in analyzing and visualizing the results of various experiments or tests conducted within the company.

Final Thoughts

As we venture through the intricacies of Python, it’s common to find the concept of custom libraries somewhat intimidating, especially for those at the early stages of their coding journey. Initially, these libraries might appear as complex enigmas reserved for the more experienced programmers. However, the reality is far more encouraging. With practice and persistence, the creation of custom libraries becomes not just feasible but a vital part of one’s technical repertoire.

The journey from understanding pre-built libraries to constructing your own is both enlightening and empowering. Starting with simpler tasks, such as connecting to a database or performing basic data analysis, you gradually build up your skills. This progression leads to the ability to craft libraries that are tailored to your specific needs, whether in a professional setting or for personal projects.

Building custom libraries is more than just a technical skill. It’s a form of creative expression in programming, allowing you to encapsulate your unique approach to problem-solving into reusable code. This process not only enhances your technical skillset but also contributes to a deeper understanding of Python and programming concepts in general.

Moreover, the creation of these libraries is an aspect of Python programming that is not often talked about enough. It’s a powerful tool that goes beyond the immediate task at hand, offering a way to contribute to the broader Python community. Sharing your custom libraries can help others who might be facing similar challenges, fostering a culture of collaboration and continuous learning.

In conclusion, whether you’re taking your first steps in Python or looking to expand your expertise, the development of custom libraries is a journey worth embarking on. It’s a pathway to transforming from a consumer of code to a creator, enhancing not just your projects but also your overall approach to programming. Embrace the challenge, and you’ll find that building your own libraries is an immensely rewarding part of your coding adventure.