Python Tutorial

Introduction Python Features Python Applications Python System requirements Python Installation Python Examples Python Basics Python Indentation Python Variables Python Data Types Python IDE Python Keywords Python Operators Python Comments Python Pass Statement

Python Conditional Statements

Python if Statement Python elif Statement Python If-else statement Python Switch Case

Python Loops

Python for loop Python while loop Python Break Statement Python Continue Statement Python Goto Statement

Python Arrays

Python Array Python Matrix

Python Strings

Python Strings Python Regex

Python Built-in Data Structure

Python Lists Python Tuples Python Lists vs Tuples Python Dictionary Python Sets

Python Functions

Python Function Python min() function Python max() function Python User-define Functions Python Built-in Functions Python Recursion Anonymous/Lambda Function in Python apply() function in python Python lambda() Function

Python File Handling

Python File Handling Python Read CSV Python Write CSV Python Read Excel Python Write Excel Python Read Text File Python Write Text File Read JSON File in Python

Python Exception Handling

Python Exception Handling Python Errors and exceptions Python Assert

Python OOPs Concept

OOPs Concepts in Python Classes & Objects in Python Inheritance in Python Polymorphism in Python Python Encapsulation Python Constructor Python Super function Python Static Method Static Variables in Python Abstraction in Python

Python Iterators

Iterators in Python Yield Statement In Python Python Yield vs Return

Python Generators

Python Generator

Python Decorators

Python Decorator

Python Functions and Methods

Python Built-in Functions Python String Methods Python List Methods Python Dictionary Methods Python Tuple Methods Python Set Methods

Python Modules

Python Modules Python Datetime Module Python Math Module Python Import Module Python Time ModulePython Random Module Python Calendar Module CSV Module in Python Python Subprocess Module

Python MySQL

Python MySQL Python MySQL Client Update Operation Delete Operation Database Connection Creating new Database using Python MySQL Creating Tables Performing Transactions

Python MongoDB

Python MongoDB

Python SQLite

Python SQLite

Python Data Structure Implementation

Python Stack Python Queue Python Linked List Python Hash Table Python Graph

Python Advance Topics

Speech Recognition in Python Face Recognition in Python Python Linear regression Python Rest API Python Command Line Arguments Python JSON Python Subprocess Python Virtual Environment Type Casting in Python Python Collections Python Attributes Python Commands Python Data Visualization Python Debugger Python DefaultDict Python Enumerate

Python 2

What is Python 2

Python 3

Anaconda in Python 3 Anaconda python 3 installation for windows 10 List Comprehension in Python3

How to

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Sorting

Python Sort List Sort Dictionary in Python Python sort() function Python Bubble Sort

Programs

Factorial Program in Python Prime Number Program in Python Fibonacci Series Program in Python Leap Year Program in Python Palindrome Program in Python Check Palindrome In Python Calculator Program in Python Armstrong Number Program in Python Python Program to add two numbers Anagram Program in Python Number Pattern Programs in Python Even Odd Program in Python GCD Program in Python Python Exit Program Python Program to check Leap Year Operator Overloading in Python Pointers in Python Python Not Equal Operator Raise Exception in Python Salary of Python Developers in India

Questions

What is a Script in Python What is the re.sub() function in Python After Python What Should I Learn What Does the Percent Sign (%) Mean in Python What is online python free IDE What is Python online compiler What are the Purposes of Python What is Python compiler GDB What is Ipython shell What does base case mean in recursion What does the if __name__ == "__main__" do in Python What is Sleeping Time in Python What is Collaborative Filtering in ML, Python What is the Python Global Interpreter Lock What is None Literal in Python What is the Output of the bool in Python Is Python Case-sensitive when Dealing with Identifiers Is Python Case Sensitive Is Python Object Oriented Programming language

Differences

Difference between Perl and Python Difference between python list and tuple Difference between Input() and raw_input() functions in Python Difference between Python 2 and Python 3 Difference Between Yield And Return In Python Important Difference between Python 2.x and Python 3.x with Example Difference between Package and Module in Python Difference between Expression and Statement in Python Difference between For Loop and While Loop in Python Difference between Module and Package in Python Difference between Sort and Sorted in Python Difference between Overloading and Overriding in Python Python vs HTML Python vs R Python vs Java Python vs PHP While vs For Loop in Python Python Iterator vs Iterable Set Vs List Python

Python Kivy

Kivy Architecture Kivy Buttons Kivy Layouts Kivy Widgets What is Kivy in Python

Python Tkinter

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Python PyQt5

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Misc

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Python Seaborn

Seaborn is an open-source library in Python that is used for data visualization and plotting graphs. The plots are used for the Visualization of data. It is built on top of Matplotlib and very useful for data analysis.

Matplotlib is used for analyzing the data sets in Python, It is a 2-dimensional Plotting library. Similarly in Python, the Seaborn library is used for data visualization.

Features of Seaborn are:

  1. It provides built-in themes for customizing the graphics of Matplotlib.
  2. Statistical and multivariate data visualization.
  3. Plotting statistical time-series data.
  4. Pandas and Numpy are highly efficient for working with Seaborn.
  5. It is fit for linear regression model visualization.

Seaborn Installation

We can easily install Seaborn using PIP with the below command:

Syntax:

pip install seaborn

For Windows, Linux & Mac

For installing Seaborn in Windows, Linux & Mac, we can do it using conda.

Syntax:

conda install seaborn

Dependencies of Seaborn:

  • Python 2.7 or 3.4+
  • numpy
  • scipy
  • pandas
  • matplotlib

Distplots

Distplot is defined as the distribution plot, it accepts an array as input and plots a curve equivalent to the points allocation in an array.

Importing libraries

Let's begin by importing Pandas. Pandas are mainly used for managing relational datasets.

We can import it with the following command

import pandas as pd

Importing Matplotlib library

Matplotlib is used for customizing the plots.

from matplotlib import pyplot as plt

Importing Seaborn library

We can easily import the Seaborn library with the following command.

import seaborn as sns

Plotting a Distplot

We can plot a histogram using a seaborn dist plot.

Example:

import matplotlib.pyplot as plt

import seaborn as sns


sns.distplot([0, 1, 2, 3, 4, 5])


plt.show()

Output:

Python Seaborn

Example 2:

import numpy as np

import seaborn as sns


sns.set(style="white")

 

rs = np.random.RandomState(10)

d = rs.normal(size=100)


# Plot a simple histogram

sns.distplot(d, kde=True, color="m")

Output:

Python Seaborn

Seaborn Plots Customization

The customized themes in seaborn are used for customizing the feel and look of graphs. The graphs can be customized according to the needs.

Changing Figure Aesthetic

If we want to set the looks of the plot, the set_style() method is used for that purpose.

Seaborn contains five themes.

  • darkgrid
  • whitegrid
  • dark
  • white
  • ticks

Syntax:

set_style(style=None, rc=None)

Example of using the dark theme:

import seaborn as sns 

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

#lineplot drawing

sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

# theme will converted to dark

sns.set_style("dark")

plt.show()

Output:

Python Seaborn

Changing the Size of the Figure

The size of the figure can be changed with the figure() method inside Matplotlib. A new figure of specified size is created with the figure() method.

Example:

import seaborn as sns 

import matplotlib.pyplot as plt

 

# loading dataset

data = sns.load_dataset("iris")

 

# changing the size of figure

plt.figure(figsize = (2, 4))

 

# linepot drawing

sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

# spines removing

sns.despine()

 

plt.show()

Output:

Python Seaborn

Spines Removal

The data boundaries are noted by the spines and axis tick marks are connected. The despite() method is used to remove these.

Syntax:

sns.despine(left = True)

Example:

import seaborn as sns 

import matplotlib.pyplot as plt

 

# loading dataset

data = sns.load_dataset("iris")

 

# lineplot drawing

sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

# spines will remove with this

sns.despine()

plt.show()

Output:

Python Seaborn

Plots Scaling

The plot scaling can be done using the set_context() method,. It enables overriding default parameters. This doesn't affect the overall style but it can modify the size of labels, lines, and other elements of the plot.

Syntax:

set_context(context=None, font_scale=1, rc=None)

Example:

import seaborn as sns 

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

# lineplot drawing

sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

# Setting the scale

sns.set_context("paper")

 

plt.show()

Output:

Python Seaborn

Setting the Style Temporarily

For setting the style temporarily axes_style() method is used. This method is used with the statement with.

Syntax:

axes_style(style=None, rc=None)

Example:

import seaborn as sns 

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

 

def plot():

    sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

with sns.axes_style('darkgrid'):

     

    # Adding the subplot

    plt.subplot(211)

    plot()

     

plt.subplot(212)

plot()

Output:

Python Seaborn

Building Color Palette

For visualizing the plots effectively and easily colormaps are used. Different types of colormap can be used for different kinds of plots. The method which is used for giving colors to the plot is color_palette() method.

We can use palplot() function to deal with the color palettes.

Seaborn includes following palattes -

  • Deep
  • Muted
  • Bright
  • Pastel
  • Dark
  • Colorblind

Example:

import seaborn as sns 

import matplotlib.pyplot as plt

 

# current colot palette

palette = sns.color_palette()

 

# plots the color palette as a

# horizontal array

sns.palplot(palette)

 

plt.show()

Output:

Python Seaborn

Sequential Color Palette

The distribution of data ranges from a lower value to a higher value is defined by the sequential color palette. We can do this by adding characters 's' to the color passed in the color palette.

Example:

from matplotlib import pyplot as plt

import seaborn as sb

current_palette = sb.color_palette()

sb.palplot(sb.color_palette("Greens"))

plt.show()

Output:

Python Seaborn

Diverging Color Palette

The two different colors are used by the Diverging palettes. Every color describes the distinction in the value ranging from a common point in both directions.

consider that we are plotting the data ranges between -1 to 1.

One color is taken by the values ranging from -1 to 0 and another is taken by 0 to +1.

Example:

import seaborn as sns 

import matplotlib.pyplot as plt

 

# current palette

palette = sns.color_palette('PiYG', 11)

 

# diverging palette

sns.palplot(palette)

 

plt.show()

Output:

Python Seaborn

Setting the Default Color Palette

The set_palette() function is the mate of the function color_palette(). For both set_palette() and color_palette() the arguments are same.

The default Matplotlib parameter is changed by the set_palette() method.

Example:

import seaborn as sns 

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

def plot():

    sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

# default color palette setting

sns.set_palette('vlag')

plt.subplot(211)

 

# color palette plotting

# as vlag

plot()

 

# setting another default color palette

sns.set_palette('Accent')

plt.subplot(212)

plot()

 

plt.show()

Output:

Python Seaborn

Multiple Plots with Seaborn

In seaborn, by using the Matplotlib library the multiple plots can be created. Some functions are also provided for the same.

Using Matplotlib

There are various functions in Matplotlib for plotting subplots. These are ass_axes(), subplot(), and subplot2grid().

Example - Using add_axes() method

import seaborn as sns

import matplotlib.pyplot as plt

 

 

# dataset loading

data = sns.load_dataset("iris")

 

def graph():

    sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

# defining width = 5 inches

# and height = 4 inches

fig = plt.figure(figsize =(5, 4))

 

# first axes is creating

ax1 = fig.add_axes([0.1, 0.1, 0.8, 0.8])

 

# plotting the graph

graph()

 

# second axes is creating

ax2 = fig.add_axes([0.5, 0.5, 0.3, 0.3])

 

# plotting the graph

graph()

 

plt.show()

Output:

Python Seaborn

Example - Using subplot() method

import seaborn as sns 

import matplotlib.pyplot as plt

 

#dataset loading

data = sns.load_dataset("iris")

 

def graph():

    sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

# sublot to be added at the grid position

plt.subplot(121)

graph()

 

# subplot to be added at the grid position

plt.subplot(122)

graph()

 

plt.show()

Output:

Python Seaborn

Example - Using subplot2grid() method

import seaborn as sns

import matplotlib.pyplot as plt

 

# loading dataset

data = sns.load_dataset("iris")

 

def graph():

    sns.lineplot(x="sepal_length", y="sepal_width", data=data)

 

# adding the subplots

axes1 = plt.subplot2grid (

  (7, 1), (0, 0), rowspan = 2, colspan = 1)

graph()

 

axes2 = plt.subplot2grid (

  (7, 1), (2, 0), rowspan = 2, colspan = 1)

graph()

   

axes3 = plt.subplot2grid (

  (7, 1), (4, 0), rowspan = 2, colspan = 1)

graph()

Output:

Python Seaborn

Facetgrid() Method

The Facegrid() method is used for visualizing the arrangement of one variable and association between multiple variables individually in the subsets of the dataset utilizing multiple panels.

A FacetGrid can be described with up to three dimensions - row, col, and hue.

The first two have precise conformity with the resulting array of axes; think of the hue variable as the third dimension with a depth axis, where various levels are plotted with various colors.

As an input, The Facegrid object accepts a dataframe and the variable names that will form the row or grid hue dimensions.

The variables should be definite and the data at every level of the variable will be used for a facet with that axis.

Syntax:

seaborn.FacetGrid( data, \*\*kwargs)

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

plot = sns.FacetGrid(data, col="species")

plot.map(plt.plot, "sepal_width")

 

plt.show()

Output:

Python Seaborn

 

PairGrid() Method

A subplot grid() is drawn with PairGrid() method by using a similar type of plot for the data visualization.

The pairgrid is the same as facetgrid. Firstly, the initialization is done and then the plotting function is passed.

It is depicted as the supplementary level of conventionalization with the hue parameter. Then, the different subsets of data are plotted in different colors. To resolve the elements on a third dimension it uses color but only subsets are drawn on top of each other.

Syntax:

seaborn.PairGrid( data, \*\*kwargs)

Example:

import pandas as pd

import seaborn as sb

from matplotlib import pyplot as plt

df = sb.load_dataset('iris')

g = sb.PairGrid(df)

g.map(plt.scatter);

plt.show()

Output:

Python Seaborn

Different Types of Plots

We can create different types of plots in Seaborn. Some of them are listed below.

Categorial plots

stripplot()

By using striplot() method, a Scatter plot is created which is based on the category. The data is represented in sorted order with one of the axis.

Syntax:

stripplot([x, y, hue, data, order, ...])

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

sns.stripplot(x='species', y='sepal_width', data=data)

plt.show()

Output:

Python Seaborn

Swarmplot()

The swarmplot is quite the same as striplot instead of the fact that points are arranged so that they do not overlay. Sometimes, the very large numbers do not well scaled by the swarmplot, this is the disadvantage of using swarmplot() method.

Syntax:

swarmplot([x, y, hue, data, order, ...])

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

sns.swarmplot(x='species', y='sepal_width', data=data)

plt.show()

Output:

Python Seaborn

Distribution Plots

Distribution Plots are used for analyzing univariate and bivariate patterns. Below are the important distribution plots.

Histogram

The data presented in a form of some groups are represented with the histogram. This method is very accurate for graphical representation of the distribution of numerical data. This can be done with histplot() function.

Syntax:

histplot(data=None, *, x=None, y=None, hue=None, **kwargs)

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

sns.histplot(x='species', y='sepal_width', data=data)

plt.show()

Output:

Python Seaborn

Jointplot

If we want to draw a two variables plot with bivariate and univariate graphs then the joinplot method is used for this purpose.

It combines two distinct plots. It is plotted using the jointplot() method.

Syntax:

jointplot(x, y[, data, kind, stat_func, ...])

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("iris")

 

sns.jointplot(x='species', y='sepal_width', data=data)

plt.show()

Output:

Python Seaborn

Regression Plots

The regression plots are fundamentally dedicated to adding a visual model that helps to maintain patterns in a dataset while exploratory analysis of the data.

As the name implies Regressions plot creates a line of regression among two parameters and it helps to visualize the linear relations.

lmplot

A linear model plot can be created using the function implot().A scatter plot is created with the linear fit on top of it.

Syntax:

seaborn.lmplot(x, y, data, hue=None, col=None, row=None, **kwargs)

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("dips")

 

sns.lmplot(x='total_bill', y='dip', data=data)

plt.show()

Output:

Python Seaborn

Regplot

The regplot() method and implot() method are similar by which the linear regression model is created.

Syntax:

seaborn.regplot( x, y, data=None, x_estimator=None, **kwargs)

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# loading dataset

data = sns.load_dataset("dips")

 

sns.regplot(x='total_bill', y='dip', data=data)

plt.show()

Output:

Python Seaborn

Matrix Plots

Matrix plot is used for plotting matrix data where the rows data, column data and, values showed by color-coded diagrams. The heatmap and clustermap are used for showing that.

Heatmap

The heatmap is used to represent the graphical data by utilizing the colors for visualizing the matrix value. Reddish colors are used for representing the more general values or higher activities and for representing activity values or less common values, dark colors are used. The heatmap() function is used for plotting.

Syntax:

seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, **kwargs)

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("tips")

 

# correlation between the different parameters

tc = data.corr()

 

sns.heatmap(tc)

plt.show()

Output:

Python Seaborn

Clustermap

The Seaborn clustermap() function plots the hierarchically clustered heatmap of the specified matrix dataset.

Grouping of data based on relations between the variables of data is known as clustering.

Syntax:

seaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, **kwargs)

Example:

import seaborn as sns

import matplotlib.pyplot as plt

 

# dataset loading

data = sns.load_dataset("tips")

 

# correlation between the different parameters

tc = data.corr()



sns.heatmap(tc)

plt.show()

Output:

Python Seaborn

Conclusion

In the above article, we have studied the Seaborn library in Python. Also, we have understood the different types of methods and functions for creating plots.