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

The term **ECDF** stands for the empirical cumulative distribution function. An ECDF plot represents the percentage or number of observations in a dataset that fall below each distinct value. Although the measures of central tendency are less evident from the plot, an ECDF plot allows us to compare the distributions of many variables.

Let's get started by importing the required libraries and the `mpg` dataset. We plot an ECDF plot by passing `x='displacement'` in the `sns.ecdfplot()` function. The ECDF plot shows the distribution of the `displacement` variable of the `mpg` dataset. We can observe that around 70% of the cars have displacement below `300`.


sns.ecdfplot(x='displacement', data= mpg_df)   #uni modal
plt.savefig('output/graph.png')

By default, the ECDF plot shows the proportion of each observation. However, we can switch it to display the actual count by passing `stat='count'` in the `sns.ecdfplot()` function. As we can see from the plot below, precisely 300 cars have displacement below `300` from the whole dataset.

Learn how to plot, design, and interpret ECDF 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

ECDF Plots

Overview

Plotting an ECDF plot