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()