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 **joint plot** allows us to see the relationship between two variables and the distribution of the variables together. It combines bivariate and univariate graphs in a single plot.

To get started, we import the required libraries and storing the `mpg` dataset in the DataFrame `mpg_df` (after removing the null values). Let's draw a joint plot for the `horsepower` and `mpg` variables using the `sns.jointplot()` function. A joint plot plots a relational plot as the main plot and a distribution plot along the axis. 

sns.jointplot( x= 'horsepower', y= 'mpg', data = mpg_df)
plt.savefig('output/graph.png')

We can observe from the plot above that as the `horsepower` increases, we see a decrease in `mpg`. This results in a negative correlation between `mpg` and `horsepower`. Similarly, on the x-axis, we see the `horsepower` marginal distribution represented in histograms. We see that most cars have a horsepower between 50–100. Similarly, on the y-axis, we see that most cars have an `mpg` below 40. Very few cars have an `mpg` above 40.

The scatter plot is the default kind of joint plot. We can create six different plots in the joint plot by specifying them in the `kind` argument. For example, let's pass `kind='kde'` in the `sns.jointplot()` function below. Moreover, we can apply all the styling properties to a KDE plot drawn from a joint plot, just as we pass `shaded=True` to shade a KDE plot. We can observe the data distribution in terms of the probability distribution function. The darker areas of the KDE plot represent a denser area with more observations.
 

Learn to plot, design, and interpret joint 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

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

Joint Plots

Overview

Plotting joint plots