Top 5 Reasons to Use Seaborn for Data Visualizations

The Seaborn data visualization library in Python provides a simple and intuitive interface for making beautiful plots directly from a Pandas DataFrame. When users arrange their data in tidy form, the Seaborn plotting functions perform the heavy lifting by grouping, splitting, aggregating, and plotting data, often with a single line of code. In this article, I will provide my top five reasons for using the Seaborn library to create data visualizations with Python.

Reason # 1 — Intuitive API — plotting with a single line of code

The Seaborn library’s API is intuitive, fairly easy to use, and quite uniform. Many visualizations can be created with a single line of code that takes the following form, where sns is the Seaborn library, plotting_func is a specific plotting function, df is the pandas DataFrame where the data is stored, x is the string name of the column holding the horizontal values, and y is the string name of the column holding the vertical values.

sns.plotting_func(data=df, x=x, y=y)

Of course there are many optional parameters to create the exact plot you desire, but nearly all plotting functions follow this format and will produce a basic plot with this line. Below, we read in a simple dataset, which each row containing information about a single employee.

import pandas as pd
import seaborn as sns

emp = pd.read_csv('employee.csv')
emp.head()