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 **heatmap** is a visual approach to displaying a data table. It represents the rectangular data as a color-encoded matrix. The color maps use color variation to represent different details by varying hue, saturation, or brightness. 

We begin by importing the required libraries (`seaborn`, `pandas`, `matplotlib`) and set the default seaborn theme using the `sns.set_theme()` function. Next, we import the `penguins` dataset and view the data with the pandas `head()` function, as shown below:

import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
sns.set_theme()
penguins_df = sns.load_dataset('penguins')
print(penguins_df.head())

Heatmaps are popular for representing correlation among variables. We plot a correlation matrix of the `penguins` dataset on a heatmap by passing `penguins_df.corr()` to the `sns.heatmap()` function. We've customized the font size for the plot by specifying `sns.set(font_scale=0.7)` so that the complete column names are visible in the plot. The heatmap is shown below:

sns.set(font_scale=0.7)
sns.heatmap(penguins_df.corr())
plt.savefig('output/graph.png')

The `corr()` function computes Pearson's correlation for numeric variables present in the data. The values range from –1 to +1. The negative values represent a negative correlation, and the positive values represent a positive correlation. 

In a heatmap, as shown through the color bar, low correlation values are represented with darker colors and high correlation values are represented with lighter colors. The correlation of a variable with itself is always 1, occupying the heatmap's main diagonals.  


Learn how to plot, interpret, and style heatmaps for data analysis.

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 Heatmaps

Overview

Correlation with heatmaps