Photo by: tai-bui.
As a Project Manager deeply fascinated in the tech world, I’ve always been on the lookout for tools to optimize my workflows but also bring a bit of joy into the often mundane. My latest try was Polars, a Python library that’s been a game changer for managing and manipulating data in my projects.
Main question, Why Polars?
Transitioning from the familiar arms of Pandas, I was initially skeptical about trying out a new data manipulation tool. However, Polars offered enticing performance improvements and functionalities that were too good to pass up for my project’s needs, especially when dealing with large datasets for testing and development.
And don’t get me wrong, I’m not changing Pandas over Polars, the key difference is that Pandas is more suitable for smaller datasets or tasks requiring extensive library support, while Polars shines in environments where performance and efficiency with larger datasets are crucial.
One of the perennial challenges in project management, especially when overseeing software testing and development, is the generation of realistic test data. Ensuring that data is diverse enough to mimic real-world scenarios without compromising privacy or operational standards is crucial.
Here’s how I integrated Polars to address this challenge:
import polars as pl
from faker import Faker
import random
fake = Faker()
def generate_fake_data(num_entries=10):
data = []
for _ in range(num_entries):
entry = {
"Name": fake.name(),
"Address": fake.address(),
"Email": fake.email(),
"Phone number": fake.phone_number(),
"Date of birth": fake.date_of_birth(minimum_age=18, maximum_age=70).strftime("%Y-%m-%d"),
"Random number": random.randint(1, 100),
"Job title": fake.job(),
"Company": fake.company(),
"Dummy text": fake.text(),
}
data.append(entry)
return pl.DataFrame(data)Here’s the complete gist with some additional annotations.
This simple yet powerful script leverages Faker to generate realistic individual entries, which Polars turns into a fast, efficient DataFrame. The flexibility of Polars allows you to simulate varied datasets that are crucial for robust testing phases.
Practical application
A while ago I utilized this script to generate test cases to fill-up user’s information for a bunch of “dummy” companies. The realistic data helped me uncover several bugs and user experience issues in a staging environment, which was a blessing before the deployment to a Production environment.
While this script primarily serves to create dummy data, Polars’ capabilities extend far beyond. Its efficient handling of large datasets, coupled with its speed, makes it an excellent tool for data analysis and manipulation in real time.
Conclusion
Embracing new technologies and tools like as Polars and Python has not only improved my PM skills, but it has helped with my testing process.
As a manager, I enjoy automating every frequent process I perform, thus incorporating Python into my routine has given me a great deal of enjoyment.
Thanks for reading me, until next time!