python data science libray seaborn.pptx

Visualizing Data with
Python: Seaborn Library
by-
 E YUGANDHAR SAI
CHOWDARY (CSE dept)
 GRITAEYU P NISVAN(CSE
dept)

Topics:
• Introduction to Data Visualization
• Why Data Visualization?
• Library Functions and Types
• Data Visualization in Python
• Matplotlib and its Disadvantages
• Seaborn and its Advantages
• Comparison of Seaborn and Matplotlib
• Pandas Overview
• Using Pandas with Seaborn
• Instantiation and Importing Seaborn
• Seaborn Functions
• Types of Graphs and Real-Time Applications
• Bar Graphs
• Line Graphs
• Scatter Plots
• Heat Maps
• Pie Charts
• Histograms
• Box Plots
• Violin Plots
• Swarm Plots
• Pair Plots
• Joint Plots
• Conclusion
• References

Introduction to Data
Visualization
Data visualization is the graphical
representation of information and data. It
allows us to see patterns, trends, and
relationships that might not be apparent from
looking at raw data alone.
In today's world, where we are inundated with
data from various sources, data visualization
has become an essential tool for decision-
making. It helps us understand complex data
sets quickly and make informed decisions
based on the insights derived from them.

Why Data Visualization?
Data visualization is an essential tool for
understanding complex data sets. By presenting
data in a visual format, patterns and trends
become much easier to identify. This can lead to
better decision-making and improved outcomes.
For example, imagine you are trying to understand
the sales performance of a particular product over
time. A simple line graph can quickly show
whether sales are increasing or decreasing, and at
what rate. Without this visual representation, it
would be much more difficult to make sense of the
data.

Data Visualization through Python
Python is a powerful language for data analysis
and visualization. With libraries like Matplotlib,
Seaborn, and Plotly, creating beautiful and
informative visualizations has never been easier.
From simple line charts to complex heatmaps,
Python can handle it all.
One of the key benefits of using Python for data
visualization is the ability to automate the
process. By writing scripts to generate
visualizations, you can easily update them as new
data becomes available. This saves time and
ensures that your visualizations are always up-to-
date.

6
Library Functions in Python
Library functions are pre-written code that can be
used to perform specific tasks. These functions are
created by developers and made available for
others to use in their own programs.
There are several types of library functions,
including built-in functions, external functions, and
user-defined functions. Built-in functions are part
of the programming language and can be used
without any additional setup. External functions
are provided by third-party libraries and must be
imported before they can be used. User-defined
functions are created by the programmer and can
be customized to fit specific needs.

Matplotlib and its Disadvantages
Matplotlib is a popular library for data
visualization in Python, but it has some
disadvantages that should be considered. One of
the main drawbacks is its complexity. Matplotlib
requires a lot of code to create even simple
visualizations, and this can make it difficult to use
for beginners.
While it offers a wide range of chart types,
customizing them can be challenging. This can be
frustrating for users who want to create unique
visualizations that stand out from the crowd.

What is Seaborn?
Seaborn is a Python data visualization library
based on Matplotlib. It provides a high-level
interface for creating informative and attractive
statistical graphics. Seaborn includes several built-
in themes and color palettes that can be easily
applied to create aesthetically pleasing plots.
One of the key features of Seaborn is its ability to
create complex visualizations with minimal code.
For example, Seaborn's `FacetGrid` function
allows you to create multiple plots with different
subsets of your data in just a few lines of code.
Seaborn also includes functions for creating
regression plots, heatmaps, and categorical plots,
among others.
Y-

Seaborn VS Matplotlib
Seaborn and Matplotlib are two popular libraries used for data visualization in Python. While
they share many similarities, there are also some key differences between the two.
Matplotlib is a more basic library that provides a wide range of plotting functions. It is highly
customizable but requires more coding to achieve advanced visualizations. On the other hand,
Seaborn is a higher-level library that offers more complex visualizations with less code. It also has
built-in themes and color palettes, making it easier to create aesthetically pleasing graphs.

Pandas Overview
Pandas is a powerful open-source data analysis and
manipulation tool that provides fast, flexible, and
expressive data structures designed to work with
relational or labeled data both easily and intuitively. It
allows you to perform data cleaning, merging,
reshaping, slicing, and filtering operations on your
datasets in a simple yet effective way.
With Pandas, you can easily import data from various
file formats such as CSV, Excel, SQL databases, and
JSON. You can also handle missing data, group data,
and perform statistical functions on your data.
Moreover, Pandas offers a wide range of visualization
tools that allow you to create beautiful and
informative graphs and charts to better understand
your data.

Using Pandas with Seaborn
Pandas is a powerful library for data manipulation and analysis in Python. It
provides easy-to-use data structures and data analysis tools that make it an ideal
choice for working with large datasets. When used in combination with Seaborn,
Pandas can be used to create stunning visualizations that help to reveal insights in
the data.
One of the key advantages of using Pandas with Seaborn is the ability to easily
manipulate and filter the data before creating visualizations. This allows for more
targeted and meaningful visualizations that can uncover hidden patterns and
relationships within the data. Additionally, Pandas provides a wide range of
statistical functions that can be used to further analyze the data and create even
more insightful visualizations.

Instantiation and Importing Seaborn
To instantiate and import Seaborn into a Python project,
you first need to install the library. This can be done using
pip, a package installer for Python. Once installed, you can
import Seaborn by using the command 'import seaborn as
sns'.
To instantiate Seaborn, you must first create a figure with
the desired size and style. You can then use the various
functions available in Seaborn to create your desired plot.
For example, if you want to create a scatter plot, you can
use the 'sns.scatterplot()' function.

Seaborn Functions
Seaborn is a Python data visualization library based on Matplotlib.
It provides a high-level interface for drawing attractive and
informative statistical graphics. Seaborn functions are built on top
of Matplotlib, which means that they can be used in combination
with Matplotlib to create complex visualizations.
Seaborn provides several functions for visualizing univariate and
bivariate distributions, regression models, categorical data, and
timeseries data. Some of the most commonly used Seaborn
functions include distplot, jointplot, pairplot, lmplot, catplot,
boxplot, swarmplot, and tsplot. Each function has its own unique
set of parameters that can be used to customize the plot according
to your needs.

Types of Graphs and Real-Time
Applications
Graphs are a powerful tool for visualizing data and
conveying complex information in an easy-to-
understand way. There are many different types of
graphs, each with its own strengths and weaknesses.
Some common types of graphs include bar graphs, line
graphs, scatter plots, heat maps, pie charts, histograms,
box plots, violin plots, swarm plots, pair plots, and joint
plots.
Real-time applications of graphs include tracking stock
prices over time, analyzing customer behavior patterns,
monitoring website traffic, and predicting weather
patterns. By using the right type of graph for your data,
you can gain valuable insights that can help you make
informed decisions and improve your business
operations.

python data science libray seaborn.pptx

python data science libray seaborn.pptx

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