We can use the style functions built into the pandas library in Python to colour a DataFrame. It helps you easily understand different types of values without even visualizing them. In this article, I’ll walk you through how to colour a Pandas DataFrame in Python to understand the values in your dataset.
Colour Pandas DataFrame in Python
We typically use the Pandas library in Python to read, understand, and prepare data for machine learning algorithms. Sometimes we need to analyze the dataset using the visualization tools in Python before preparing the dataset. But by filling colours in a pandas DataFrame, we can understand most of the values in the dataset without visualizing them.
We can easily understand colours better than just numbers while identifying patterns in a dataset. So let’s see how to colour a pandas DataFrame in Python. I will start this task by importing the dataset:
import pandas as pd
data = pd.read_csv("class_grades.csv")
data.head()
So below is how we can style this DataFrame with colours by using the styling functions provided by the pandas library in Python:
data.style.background_gradient()
The DataFrame above represents dark colours on values that are relatively higher and it represents light colours on values that are relatively lower. We can improve the appearance of the colours used in this DataFrame by using the cmap parameter:
data.style.background_gradient(cmap="Spectral")
Also, Read – Python Projects with Source Code.
Summary
So this is how you can style a pandas DataFrame in Python using colours. It helps you easily understand different types of values without even visualizing them. I hope you liked this article on how to style a pandas DataFrame using colours. Feel free to ask your valuable questions in the comments section below.
