Visualizing the Marketing Landscape: A Two-Part Journey (Part 1: Matplotlib and Seaborn)

As a data analyst, it is vital to choose the appropriate Python package to properly visualize your data. MatPlotlib and Seaborn are widely known to novice data analysts and advanced data analysts apart to be the most popular packages of choice.

In order to best illustrate the difference in usage of Matplotlib and Seaborn, I will analyze the Superstore Dataset from Kaggle.

Data import and Data Processing

Matplotlib

History: Originally released in 2003, by John Hunter, it was designed to emulate MatLab abilities in Python. MatPlotLib is the most widely-used and flexible package for data visualization in for the programming language.

Use cases: Static Visualizations (line plots, scatter plots, bar charts , histograms, etc…)

Pros:

1. Extensive customization options: Matplotlib provides a high level of control and flexibility, allowing users to customize every aspect of their visualizations.
2. Established and widely-used library: Matplotlib is a foundational library for data visualization in Python, with a large and active community.

Cons:

1. Steeper learning curve: Matplotlib has a more complex API compared to higher-level libraries, which can make it more challenging for beginners.
2. Performance limitations for large datasets: Matplotlib may struggle with performance when dealing with very large datasets.

Example 1: Stacked Bar Chart

Example 2: Line Plot

Seaborn

History: Originally released in 2012, by Michael Waskom, to address the limitations and complexities of Matplotlib, which required significant code to to generate visually appealing graphs. The goal of Seaborn is to simplify the process of creating statistical graphics.

Use cases:

Pros:

1. Higher-level abstractions: Seaborn provides a higher-level interface on top of Matplotlib, making it easier to create attractive and informative statistical graphics.
2. Integrated statistical visualizations: Seaborn includes built-in functions for visualizing statistical relationships, such as scatter plots and regression models.

Cons:

1. Limited customization: Seaborn may offer less flexibility for customizing the appearance of your visualizations compared to Matplotlib.
2. Performance limitations for large datasets: Seaborn may struggle with performance when dealing with very large datasets.

Example 1: Heatmap

Example 2: Bar Plot with Confidence Interval Lines