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 **KDE plot** is a kernel density estimation plot that allows us to estimate the probability density function of the data. It uses a nonparametric probability density function, meaning that we don't assume any prior distribution within our data.



We have already imported the required libraries and saved the `penguins` dataset in the DataFrame `penguins_df` (after removing the null values). We use the `sns.kdeplot()` function and pass the variable `'body_mass_g'` to see its distribution as a curve. We can observe from the plot below that most of the penguins have a `body_mass_g` between 3000–4000 since this is where the KDE plot peaks, and very few are in the 6000–7000 range. 


sns.kdeplot(x='body_mass_g' , data = penguins_df)
plt.savefig('output/graph.png')

The bandwidth of a KDE plot is customizable by using the `bw_adjust` parameter, the default value of which is `1`. In the KDE plot, bandwidth acts as a smoothing factor. Lower numbers correspond to spiky curves, whereas larger values reflect smoother curves.

sns.kdeplot(x='body_mass_g' , data = penguins_df, bw_adjust = .08)  #low bandwidth
plt.savefig('output/graph.png')

It's important to be careful about the bandwidth parameter. More smoothing will miss the essential patterns in the data, and less smoothing will result in unnecessary noise—neither of which is desired. For example, in the code below, we pass a high value to the `bw_adjust` parameter, resulting in a highly smooth curve.    

Learn how to plot, design, and interpret KDE plots for data visualizations. 

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

KDE Plots

Overview

Univariate KDE plot