1. An overview of charting with
Seaborn and Plotly using Python
Seaborn & Plotly Intro
2. Introduction
This presentation introduces Seaborn and Plotly, two powerful Python
libraries for data visualization. It covers fundamental chart types
available in these libraries, with practical examples and code snippets.
4. Introduction to Seaborn
Seaborn is a Python data visualization library based on Matplotlib. It
provides a high-level interface for drawing attractive statistical
graphics. Seaborn simplifies the process of creating complex
visualizations while making them aesthetically pleasing and
informative.
5. Creating Scatter and Bubble Charts
Scatter plots are used to display values for
typically two variables for a set of data.
Bubble charts extend scatter plots by using a
third variable to dictate the size of markers,
representing an additional layer of
quantitative data.
6. Heatmaps and Contour Plots
Heatmaps are data visualizations that use color to represent different
values, allowing for easy identification of patterns or correlations in
larger datasets. Contour plots display the density of points and can
illustrate the topography of data by connecting points of equal value.
Both are essential for visualizing complex multi-dimensional data.
8. Overview of Plotly
Plotly is a graphing library for Python that allows for the creation of
interactive charts and graphs. It supports a wide variety of chart types
and offers highly customizable visualizations. Plotly is especially
useful for creating web-based interactive plots that can enhance data
exploration and presentation.
9. Constructing Pie and Sunburst Charts
Pie charts display proportions of a whole
using slices of a circle, while sunburst charts
visualize hierarchical data, allowing users to
drill down into layers of information. Both
chart types are effective for representing
categorical data and demonstrating parts-to-
whole relationships.
10. Gantt and Polar Charts
Gantt charts are used in project management to illustrate project
schedules, showing tasks over time. Polar charts, on the other hand,
display data in a circular grid, ideal for representing data with a
direction or cycle, such as wind speeds or seasonal data patterns.
11. Conclusions
In summary, Seaborn and Plotly are powerful
tools for creating a diverse array of
visualizations in Python, from simple scatter
plots to complex interactive charts.
Understanding how to leverage these libraries
can significantly enhance data presentation
and interpretation.
12. CREDITS: This presentation template was created by
Slidesgo, and includes icons by Flaticon, and
infographics & images by Freepik
Thank you!
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