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 **box plot** (also called a **whisker plot**) shows the distribution of data, which allows us to compare and visualize the data across different categories. The box plot mainly consists of three parts: the median of data, the whiskers representing the inner quartile range of the data, and outliers. **Outliers** in the dataset imply those data points whose distribution is highly different from the rest of the data. 
 


We'll use the `titanic` dataset in this lesson—it's loaded in the DataFrame named `titanic_df`. Let’s check the distribution of the `age` column by drawing a box plot using the `sns.boxplot()` function. 


sns.boxplot(x='age', data = titanic_df)   
plt.savefig('output/graph.png')

The box plot has several different components, as illustrated in the figure below:



Seaborn first computes the median of our data to plot a box plot. Once the median is located, a line is drawn there. Next, it computes the 25th and 75th percentile and draws a line. In the end, it joins these lines to form a box. Finally, the whiskers are calculated using the **Inner Quartile Range (IQR)**; the points beyond the IQR are the outliers.

We can verify the values plotted on a box plot using the pandas `describe()` function, as shown in the code below. We can see that the same data is reflected in the box plot; the 25th percentile is around 20, and the 75th percentile is approximately 38.



Learn how to plot, interpret, and style box 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

Visualization with Box Plots

Overview

Plotting box plots