Data Analytics has been on a steady rise since the start of the 21st century. Developers and businesses alike have come to know the value of data and the fruitful offerings it can bear if it is analyzed and put to good use. Almost all of the businesses today make use of a data visualization tool in one way or the other.
The most common ones among these tools are Seaborn and Matplotlib. These are two of the popular data visualization libraries that one can make use of when working with a programming language such as Python.
In this Seaborn in Python blog, we will discuss the following aspects:
Let’s begin!
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Introduction to Seaborn
Seaborn is one of the world’s most regarded Python libraries that is purpose-built to create beautiful-looking visualizations. It can be considered as an extension of another library called Matplotlib as it is built on top of that.
Data visualization is easily performed in Seaborn, and this is how the workflow looks like:
Data from various sources: The data that is needed to perform visualizations and analytics can come into the architecture from a variety of sources, such as a local storage unit, server, cloud structure, etc.
Data visualization: This is where the data is transformed from its number-state into an aesthetically pleasing visual counterpart. Seaborn plays the main role here.
Data Analytics: The result of data visualization is to take a look at the data in a way you have not done before. Analysis helps doing just this to reveal insights and trends that could not have been spotted otherwise.
This workflow is very important as it is the chain of events that helps in driving a variety of businesses and their requirements to their goals.
The Seaborn library is no doubt popular, but I am sure that you are wondering about why people make use of Python for this. Next up in this Seaborn Tutorial for beginners, let’s take a look at just that.
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Python for Data Visualization
Python has been one of the top programming languages of the previous decade, and it has been forecasted to be on the top in the next few years as well. I am sure that you have already heard of Python in one way or another.
What makes Python so popular for data visualization? Well, it provides numerous advantages over many other programming languages out there, especially for data extraction and analytics.
Python: Easy to Use
Python is purpose-built, offering easy development and convenience. This means that it provides a high-level syntax, which is easily understood. Also, it is highly readable without requiring to go through complex training to understand the basic code.
Python: A Powerful Programming Language
Python has been put to use in many of the fields today. Even though the language itself is simple in its working, it definitely packs a big punch. It is actively being used to solve the world’s most complex problems in the fields of medicine, Artificial Intelligence, and computing in general.
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Python: Big Developer User Base
Another advantage with Python is that it has millions of developers across the globe working on it and its libraries. This allows for a large community to work hand in hand to put the language to good use.
Python: Superiority in Data Science
Data visualization is a key part of Data Science and Data Analytics. Python has been the go-to tool for anything related to data processing as it has a plethora of libraries and tools that allow for easy handling and processing of large amounts of data.
Next up in this Seaborn in Python blog, you have to know about some of the top features of Seaborn.
Features of Seaborn
Before we talk about the features of Seaborn, it is important to know one thing: Since Seaborn is an extension of Matplotlib, it is meant to complement the latter rather than replacing it completely.
With this said, here are the top features of Seaborn:
- Lots of themes to work with Matplotlib-style graphics
- Ability to visualize both univariate and multivariate data
- Support for visualizing varieties of regression model data
- Easy plotting of statistical data for time-series analytics
- Seamless performance with Pandas, NumPy, and other Python libraries
Next up in this Seaborn Python Tutorial, you have to understand the differences that lie in between Seaborn and Matplotlib to get a clear understanding of why you should choose Seaborn.
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Seaborn vs Matplotlib
Coming to Seaborn, its creator Michael Waskom says that Seaborn tries to make hard things very easy to do! Now, this is something that is the need of the hour when it comes to working with data visualization and analytics.
Things can get complex really quick with the Matplotlib, but Seaborn is purpose-built to keep these things simple and easy, and this point definitely deserves to be on the top of this list just for that! Each of these tools bring about their own advantages and disadvantages to help cater to providing a good means for data visualization.
Seaborn vs Matplotlib: Interface
Seaborn has a high-level interface that you can use without having to put much effort into figuring out syntaxes as they have high readability as well.
Matplotlib, on the other hand, comes with a low-level interface that might make it difficult for beginners to get started with the library to go on to produce good looking visualizations quickly.
Seaborn vs Matplotlib: Themes
Seaborn has the upper hand in the case of availability of themes as it comes with a large number of customized themes and offerings that developers can use for their graphs, plots, and charts.
With Matplotlib, it takes a considerable amount of time and effort to make the plots look attractive, and this time could very well be put to productive things if Seaborn is used instead.
Seaborn vs Matplotlib: DataFrames
Handling DataFrames in Python is extremely important as most of the datasets or the data that comes into the organization are stored or segregated into DataFrames.
Seaborn can directly handle and work with the Pandas’ DataFrame structure in Python without any hassle.
Matplotlib does not work well with DataFrames, and this can be a huge turn off to the population who use only DataFrames for their input data.
Next up in this Seaborn Tutorial blog, let’s take a look at the things that you need to know when you begin your journey with Seaborn Python.
Getting Started with Seaborn
So, how do you begin to learn and work with Seaborn?
The following are four simple steps that you can use to get started with a library like Seaborn:
- Installing all of the dependencies needed to work with Seaborn
- Understanding the basics and the paradigms of Seaborn
- Learning in context by making use of a real dataset
- Looking around for communities to get projects and ideas for customization
Now, there are some dependencies that you have to know about when working with Seaborn. These are needed for Seaborn to work and are usually used hand in hand to achieve the desired results.
Seaborn Dependencies
Four other libraries are used together with Seaborn most of the time. They are as follows:
Pandas: Pandas is one of the most used data structures and data analysis tools in Python.
NumPy: NumPy is used to work with n-dimensional arrays and other domains of linear algebra.
SciPy: Pronounced as ‘sigh-pie,’ SciPy is the main library used to work with complex mathematical operations, technical computing, engineering, and scientific computing.
Matplotlib: Since Seaborn is built on top of Matplotlib, this being a dependency speaks for itself, doesn’t it?
So, it becomes vital that you have all of them installed before you begin working with Seaborn.
Installing Seaborn
Installing Seaborn in a Python runtime is very simple and straightforward. It can be done in two ways, depending on what package manager you use either pip in python or anaconda:
PIP:
pip install seaborn
Anaconda:
conda install seaborn
And of course, if running it on a cloud-based Jupyter environment, such as Google Colab, then there is a good chance that all of these are already installed, and you can start working by importing them.
Now, be it a local Python runtime or a cloud-based setup, importing these into the program before usage is vital. This is exactly what you will learn next.
Use the following commands and run them to verify that you have all the dependencies installed and that they are working as expected:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy import stats
Next up in this Seaborn in Python blog, let’s dive right into code to see how easy it is to get started and work with Seaborn.
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Hands-on Demo with Seaborn in Python
Working with the following types of plots in Seaborn is very simple:
- Seaborn Heatmap
- Seaborn Barplot
- Seaborn Boxplot
- Seaborn Pairplot
Let’s use the Iris dataset in this hands-on session. This will quickly get you started working with Seaborn without having to put much effort.
To begin with, you always have to import all of the dependencies as shown below:
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
Loading the dataset is very simple in Google Colab. It is a mouse-click away on the left panel.
After importing it, run the following command to load in the dataset:
iris = sns.load_dataset("iris")
You can take a detailed look at the elements present in the dataset using the following:
iris.head
Other statistical descriptions and a summarization of the dataset can be printed using this:
iris.describe()
Printing a swarmplot to separate and visualize the three types of petals (Setosa, Versicolor, and Virginica) is easily done by using the simple command:
sns.set()
%matplotlib inline
sns. swarmplot(x="species", y="petal_length", data=iris)
The output of the above code is this beautiful swarmplot!
Along with swarmplots, there’s another plot type called pairplot. It is an important plot type when working with bi-variate or multivariate analytics.
All it takes is one simple line of code to display a pairplot using Seaborn:
sns.pairplot(iris, hue='species', size=2.5);
Now, let’s visualize a simple difference between Seaborn and Matplotlib.
Importing dependencies:
import matplotlib.pyplot as plt
plt.style.use('classic')
%matplotlib inline
import numpy as np
import pandas as pd
Creation of random data for comparison:
# Create some data
rng = np.random.RandomState(0)
x = np.linspace(0, 10, 500)
y = np.cumsum(rng.randn(500, 6), 0)
Plotting using Matplotlib:
plt.plot(x, y)
plt.legend('ABCDEF', ncol=2, loc='upper left');
The output of the above code is this: A plain visualization of the random data entries.
Now, let’s make use of Seaborn to see what it adds in terms of ease of use and aesthetics.
Plotting the same data using Seaborn:
import seaborn as sns
sns.set()
plt.plot(x, y)
plt.legend('ABCDEF', ncol=2, loc='upper left');
Looking at the above two plots, it can be seen that Seaborn offers better aesthetics for a lot less code as compared to Matplotlib. This is the same case for complex visualization as well, and it is one of the reasons that made Seaborn so popular!
Conclusion
I hope you now have a complete idea about Seaborn and its simplicity. Both Matplotlib and Seaborn have been the go-to data visualization tools when it comes to working with Python.
They have been very effective in providing thousands and thousands of solutions to problems across a variety of domains.
Having Seaborn as a skill is beneficial as well if you are looking to build a career in Data Analytics and Data Science.
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So, what do you think about Seaborn? Head on to the comments section below and let us know!