Take your analysis to the next level with Seaborn, a statistical visualization library for Python.
Seaborn has been around for a long time.
It is one of the most well-known and used libraries for data visualization because it is beginner-friendly and allows non-statisticians to build powerful graphics that help extract statistically-backed insights. I’m sure there is.
I’m not a statistician. My interest is in data science. You need to learn statistical concepts to perform your job better. So I like that I can easily access histograms, confidence intervals, and linear regression with very little code.
Seaborn’s syntax is very basic. sns.type_of_plot(data, x, y). You can use that simple template to build a variety of visualizations, including: barplot, histplot, scatterplot, lineplot, boxplotmore.
But this post isn’t about talking about them. Learn about other enhanced types of visualizations that can make a difference in your analysis.
Let’s see what they are.
To create these visualizations and code along with this exercise, simply import seaborn using: import seaborn as sns.
The dataset used here is student performancecreated by Paulo Cortes Contributed to the UCI Repository under a Creative Commons license. You can import it directly into Python using the code below.
# Install UCI Repo
pip install ucimlrepo# Loading a dataset
from ucimlrepo import fetch_ucirepo
# fetch dataset
student_performance = fetch_ucirepo(id=320)
# data (as pandas dataframes)
X = student_performance.data.features
y = student_performance.data.targets
# Gather X and Y for visualizations
df = pd.concat([X,y], axis=1)
df.head(3)
So let’s talk about the five visualizations.
1. Strip plot
The first plot chosen is stripplot. It’s easy to see why this is interesting. Using this simple line of code, you’ll see the following viz:
# Plot
sns.stripplot(data=df);
