Author(s): Gencay I.

Originally published on Towards AI.

Discover five pandas tricks to enhance Python data analysis for Data Science workflows.Created with GPT 4o

“The goal is to turn data into information, and information into insight.” — Carly Fiorina

To do that, one of the best ways in data science is using Python with Pandas. But do we know each function in Pandas? I have been using it for years regularly, yet I still am discovering new functions.

In this one, we’ll discover them and their effects together. Let’s start!

Photo by Davor Denkovski on Unsplash

You don’t always need .loc — sometimes .at is way faster and simpler for single value edits. Let’s create a simple dataset.

import pandas as pddf = pd.DataFrame({ 'Name': ['Alice', 'Bob', 'Charlie'], 'Score': [85, 90, 88]})

Here is our dataframe.

SS of output

.at is the fastest way to update a single value in a DataFrame. It’s optimized for scalar access and avoids overhead from .loc.

Clean, fast, perfect for loops. Here is the code.

df.at[1, 'Score'] = 95print(df)

Here is the output.

SS of output

As you can see, the score at index one has changed to 95. Simple and fast!

Photo by Drew Beamer on Unsplash

Sometimes one row hides many values. .explode() brings them to light. Here is our dataset creation code.

df = pd.DataFrame({ 'Name': ['Alice', 'Bob'], 'Hobbies': [['Reading', 'Hiking'], ['Gaming']]})

Here is our dataset.

SS of output

When your… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI