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

**Conditional multiple plots** imply the construction of multiple plots in parallel, where the plotting is based on one or more conditions. We can draw conditional multiple plots using the seaborn library's `FacetGrid` class. It allows us to compare and visualize relationships among data variables along different subsets of the data. There are four seaborn plots, namely `relplot()`, `displot()`, `catplot()`, and `lmplot`. These plots, along with their individual visualization properties, also provide faceting options because they're built on top of seaborn's `FacetGrid` class.  



We can use the `FacetGrid` class in the plots by initializing a `FacetGrid` object. We pass the variables and DataFrame to the `FacetGrid` constructor. One important thing to remember is that the variable we pass to the `FacetGrid` constructor must be categorical or discrete (finite choices).
A `FacetGrid` can be constructed with the three dimensions `col`, `row`, and `hue`. 

For example, let's take a look at seaborn's `iris` dataset, which we have already imported and stored in the variable `iris_df`. To initialize the `FacetGrid` class, we use the `sns.FacetGrid()` function and pass both our DataFrame `iris_df` and `col=species`, as shown in **line 5** of the code below, which means we want to examine the `species` of the `iris` data alongside the column dimensions. The `species` column has three types, so `FacetGrid` creates three column grids, but no data points are currently plotted.



Learn how to use and customize conditional multiple plots in seaborn.

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

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

Conditional Multiple Plots

Overview