The document provides an overview of Seaborn, a Python statistical graphical library that enhances Matplotlib by integrating better with pandas for data visualization. It discusses the characteristics of Seaborn compared to Matplotlib, highlighting its simpler syntax and automation features. Various types of plots within Seaborn, particularly focusing on distribution plots, are also detailed, along with examples of their implementation.

1. SUBMITTED BY : SUBMITTED
TO:
MANISH SINGH DR. PRAGYA TEWARI
MA’AM
MASTERS OF COMPUTER APPLICATION
ASSISTANT PROFESSOR

2. CONTENTS
• Introduction
• Characteristics of Seaborn vs Matplotlib
• Various plots in Seaborn
• Seaborn plots
• Displots

3. INTRODUCTION
• Seaborn is a Python Statistical graphical library,
• It Builds on top of matplotlib and incorporates data structures
closely with pandas.
• Seaborn allows you to explore and understand your data.
• Its plotting functions work on data frames and arrays
containing entire datasets and internally perform the necessary
semantic mapping and statistical aggregation for the
Development of information plots.

4. • It makes the charts more appealing and promotes some of the
common needs for data visualization (like mapping a color to a
variable).
• Importing seaborn:
import seaborn as sns
• To check the version of seaborn on your system:
print(sns.version)

5. CHARACTERISTICS OF SEABORN
VS
CHARACTERISTICS OF MATPLOTLIB
Charecteristic Matplotlib Seaborn
Use Cases
Matplotlib plots various graphs
using Pandas & Numpy
Seaborn is the extendec version
of Matplotlib which uses
Matplotlib along with Numpy &
Pandas for plotting graphs
Complexity of
Syntax
It uses comparatively complex
& lengthy syntax
It uses Comparatively simple
syntax which is easier to learn
and understand
Multiple Figures
Matplotlib has multiple figures
can be opened
Seaborn automates the creation
of multiple figures which
sometimes leads to out of
memory loss
Flexibility
Matplotlib is highly customized
& powerful
Seaborn avoids a ton of boiler
plates by providing default

6. VARIOUS PLOTS IN SEABORN
• Wide range of plots can be plotted using seaborn for
visualization.
• Here are some of them:
• Univariate Data: Plotting a graph for single variable
• Distplot
• Rug Plot
• Bivariate Data : Plotting a graph for multiple variables
• Scatter Plot
• Hexabin Plot
• Kde Plot

7. SEABORN PLOTS

8. DISTPLOT
• DISTPLOT stands for Distribution Plot, It takes as input an array
and plots a curve corresponding to the distribution of points in
the array.
• We will be using distplot() function for this purpose.
• The output plot will have basically 2 graphs.
• Histogram
• Kdeurve – kernel density

9. EXAMPLE
• Import matplotlib.pyplot as plt
• Import seaborn as sns
• Sns.distplot([0,1,2,3,4.5])
• Plt.show()

10. OUTPUT

11. • We can also separately visualize both the plots.
• sns.distplot([0,1,2,3,4,5],kde = False)
only histogram curve will be observed.
• sns.distplot([0,1,2,3,4,5],hist=False)
only kde curve will be observed