Delve into data visualization and analysis with Python's Seaborn library. Learn about variable types, data cleaning, creating detailed visualizations, and applying themes for more appealing results.

Data Visualization and Analysis With Seaborn Library (1).png

jupyter.tar.gz

Adding Hue Parameter for color encoding

Data cleaning with Pandas

Getting Started with Seaborn

Intro to Pandas

Line Plots in Seaborn

Pair Plot

Scatter Plot

libraries

project

solution

This course aims to provide an introduction to data visualization and analysis using Python and the Seaborn library.

The course begins by introducing various variable types and statistical analysis methods. Then, you get to review the foundations of data cleaning and extraction using the pandas library. In the second half of the course, you will go over different plots in Seaborn for numerical, continuous, and categorical data, as well as distribution and regression plots to gain insightful information and identify patterns in the data. Lastly, you get to learn to create complex visualizations that are also aesthetically pleasing and go into great detail about the Seaborn themes, color palettes, styling, and multiplot grids.

By the end of this course, you’ll apply the knowledge you’ve gained with a hands-on project.

Data Visualization and Analysis With Seaborn Library

A variety of seaborn plots (including scatter plots and pair plots) solely plot the numerical variables while excluding the categorical variables. These may be helpful when the only variables in the data are numerical. However, categorical variables frequently convey more insights and patterns in the data.

Seaborn's `hue` parameter, accessible for practically all seaborn plots, solves this problem.
In seaborn, the `hue` parameter is used for color encoding. We can pass any categorical variable to the `hue` parameter, resulting in different color codes for each unique category of the categorical variable. As a result, we can use categorical variables for color encoding, even for plots that are built solely on numerical data.

We begin by importing the `iris` dataset from the seaborn library. The code widget below shows that the `iris` dataset has five features. We further analyze the data types of different columns using the `dtypes` method, which returns the data types of each column. All the variables are numerical, and the `species` variable is categorical, which we can pass to the `hue` parameter. 

 

Learn how to use the hue parameter for categorical variables in different plots.

About the Course

Introduction to Seaborn and Statistical Analysis

Plotting Numerical Data

Assessment on Plotting Numerical Data

Plotting Categorical Data

Plotting the Categorical Data

Assessment on Categorical Plots

Visualizing Distribution of Data

Data Visualization with Seaborn for Walmart Sales Projection

Assessment on Distributions of Data

Visualizing Regression Models

Assessment on Visualization of Regression Models

Styling and Figure Aesthetics

Assessment on Styling and Figure Aesthetics

Multiplot Grids

Assessment on Multi-Plot Grids

Project

Closing Remarks

Adding the hue Parameter for Color Encoding

Why do we need the `hue` parameter in plots?

Use hue parameter in plots