## Live training with Teddy Petrou starts on December 1st and 8th at 12 PM (EST)

Training duration: 4 hours (Hands-on)

## Price with 30% discount

Regular Price: \$210.00

## Instructor Bio:

### Python Data Science Expert Instructor - Author of Multiple Books and Python LIbraries | Founder | Dunder Data

Teddy Petrou

Teddy Petrou is the author of Pandas Cookbook, a highly rated text on performing real-world data analysis with Pandas. He is also the author of the books Exercise Python and Master Data Analysis with Python. He is the founder of Dunder Data, a company that teaches the fundamentals of data science and machine learning. He really enjoys discovering best practices on how to use and teach data analysis with Python.

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## Learning Objectives

• Learn the fundamentals of using the Seaborn data visualization library and how it integrates with Pandas DataFrames

• Understand the different categories of Seaborn plotting functions and the return type (Grid or Axes) of each

• Learn how tidy data provides the best structure to take advantage of the Seaborn plotting functions

• Learn how to choose grouping and aggregating variables, and how to customize plots with all the other available parameters

• Learn how to create grids of plots

## Course Outline

Day 1

Module 1: An overview of the

Matplotlib Figure and Axes

Training Overview

Matplotlib Figure and Axes objects

An object-oriented approach to Matplotlib

Seaborn as a “wrapper” for Matplotlib

Module 2: Introduction to Seaborn

Seaborn documentation and API

Integration with Pandas DataFrames

Main plotting parameters

Grid vs Axes plots

A different categorization for Seaborn plots

Module 3: Distribution Plots

Univariate distribution plots

Box plots

Changing the orientation of plots

Histograms and KDEs

Other distribution plots

Module 4: Automatic Grouping by Category

Plotting a continuous by a categorical variable

Grouping with two continuous variables

Splitting groups by setting hue

Day 2

Module 5: Grouping and Aggregating Plots

Univariate grouping and aggregating

Choosing the aggregation function

Bar, Point, Count, and Line plots

Module 6: Tidy Data

Definition of tidy data

Long data vs wide data

Seaborn automatically groups and aggregates

Manually grouping and aggregating with pandas

Module 7: Raw Data Plots

Scatterplots

Regression plots

Heatmaps

Module 8: Grid Plots

Relationship with Matplotlib Figures

Discovering the Grid plotting functions

Setting the plot with the kind parameter

Creating multiple Axes with row/col parameters

Bivariate distribution plots

Hierarchical cluster maps

Module 9: Seaborn Styles and Palettes

Run configuration parameters

Specific Seaborn styles

Color palettes and widgets

## Course Abstract

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. This course provides comprehensive coverage of how to use all of the Seaborn plotting functions with real-life data.

## What background knowledge you should have?

• Elementary knowledge of the Python programming language is necessary

• Some experience with Matplotlib and Pandas libraries are helpful, but not strictly necessary

• Prior work with Jupyter Notebooks would be helpful as all material is delivered with them