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

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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

**Hist plot** stands for histogram plot. It represents the distribution of continuous numerical data. Bar plots display the frequency of different categorical variables, whereas histograms show the frequency of numeric variables in the form of bins.

Let's get started by importing the required libraries. We'll also import the `tips` and `diamonds`  datasets and use them for our visualizations. 

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme()
# import datasets
tips_df = sns.load_dataset("tips")
diamond_df = sns.load_dataset("diamonds")
print(tips_df.head())
print("\n")
print("\n")
print(diamond_df.head())

We plot a histogram for the variable `total_bill` using the `sns.histplot()` function. The x-axis shows the range of values of `total_bill`, which is $10–50, and the y-axis shows the frequency of occurrence. Most of the `total_bill` observations are between $10 and $30.

sns.histplot(x = 'total_bill', data = tips_df)   # histogram
plt.ylabel('frequency')
plt.savefig('output/graph.png')

By default, seaborn determines the number of bins for the histograms based on the dataset. However, we can customize the number of bins in a histogram using the `bins` parameter. Be careful with the number of bins used because a small number may not show the patterns, and a huge number can add too much noise. 


sns.histplot(x = 'total_bill', bins= 30,  data = tips_df)    #customise no. of bins
plt.ylabel('frequency')
plt.savefig('output/graph.png')

We can customize the range and width of the bins using the `binrange` parameter to specify the range and the `binwidth` parameter to determine how wide each bin is, as shown in the code below:

Learn how to plot, design, and interpret hist 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

Hist Plots

Overview

Univariate hist plot