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

**Lmplot** stands for the linear model plot. We use it to visualize the trends between variables. The reg plot and lmplot are closely related. However, the former is an axes-level function while the latter is a figure-level function that combines a reg plot and a `FacetGrid` property. The `FacetGrid` property allows us to plot multiple plots in parallel.

We've already imported the `mpg` dataset and stored the DataFrame in a variable named `mpg_df` (after removing the null records). We can draw a lmplot using the `sns.lmplot()` function and pass the `displacement` and `horsepower` variables.

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

The plot looks exactly like a reg plot. We can see an upward trend; as the `displacement` increases, so does the `horsepower`. The reg plot and lmplot offer similar kinds of functionalities. Let's explore some additional properties that the lmplot offers. 

We can use the `hue` property with t lmplot. We pass `hue='origin'` in the `sns.lmplot()` function. We can see the relationship between the `displacement` and `horsepower` variables for different origin cars. 

Learn how to plot, design, and interpret lmplots 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

Visualizing the Lmplots

Overview

Plotting and styling