Seaborn

TL;DR

High-level Python library for data visualization built on top of Matplotlib.
Simplifies creation of statistical plots (distributions, boxplots, scatter, bar, line).
Offers sensible defaults and customization options for informative, attractive plots.

Definition

Seaborn is a library in Python that is used for data visualization. It is built on top of the popular data visualization library Matplotlib and provides a higher-level interface for creating beautiful and informative visualizations.

Explanation

Seaborn makes it easy to create statistical visualizations with fewer lines of code than lower-level libraries. It supports plots that show distributions (e.g., histograms with kernel density estimates) and plots that compare multiple variables (e.g., boxplots showing medians, quartiles, and outliers). Seaborn also provides functions for scatter plots, bar plots, line plots, and other common plot types, and includes customization options for color, font size, and other appearance settings.

Examples

Distribution plot (distplot)

import seaborn as sns

sns.distplot(student_scores)

In this plot, the x-axis represents the range of test scores, and the y-axis represents the frequency of scores within that range. The histogram shows the distribution of scores as a series of bars, and the kernel density estimate is a smooth curve that represents the probability density function of the data. This plot gives a good understanding of the distribution of scores, including the range of scores, the mean and median scores, and any outliers.

Boxplot

import seaborn as sns

sns.boxplot(x=“job_title”, y=“salary”, data=employee_data)

In this plot, the x-axis represents the different job titles, and the y-axis represents the salary of employees in each job. The boxplot shows the distribution of salaries for each job, including the median salary, the first and third quartiles, and any outliers. This plot allows quick comparison of the salaries of different job titles and identification of any significant differences.

Use cases

Creating statistical visualizations for exploratory data analysis.
Comparing distributions and summary statistics across categories.
Used by data scientists, data analysts, and others who need clear visualizations of data.

Matplotlib