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In programming languages, we have a library called Seaborn, which mainly uses' Matplotlib' to plot the displayed graphs while coding. This library is also used for the visualisation of random distribution graphs. In this article, we will talk about the python seaborn library along with its example and how it is used for data visualisation, different categories of seaborn for data visualisation, its dependencies, and programs to demonstrate the plots using seaborn in python.
Python Seaborn is actually a data visualisation library that mostly comes into play when there is a need for statistical plotting of graphics in Python. It consists of a lot of styles present by default along with beautiful colour palettes which makes the statistical plots look very attractive. It uses the matplotlib library which is then integrated with the data structures present in pandas.
The main aim of the Seaborn library is to visualise the inside part while exploring the data and understanding it. It majorly comprises APIs which are dataset-oriented and lets the user switch between various types of visual representations within the same given variables. It is for a better understanding of the graphics or plots present as data.
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There are plots present under the seaborn library in python. They are used to visualise the relationship between different variables which are totally numerical or can be a group, a class, or else a division. The categories of plots are:
These types of plots deal with categorical variables. They also help to visualise how graphs can be plotted.
These plots are useful for understanding the relationship between two different variables.
These types of plots in seaborn library are used to examine the bivariate as well as univariate distributions.
The plot is mainly used to add a visual to the data. It also helps in emphasising the patterns of the dataset while exploring it in the process of data analyses.
This plot is actually an array present in scatterplots.
These grids are useful for drawing multiple instances within the same plot of the dataset present on non-identical subsets.
Open the command prompt on your device and follow the syntax below for installing seaborn in python:
Pip install seaborn
There are some dependencies on which seaborn is dependent. Let us have a look at them:
Now, let us see the plots that come under the seaborn library and have a better understanding of them using python programming:
This plot under Seaborn is useful for plotting a histogram. It can also plot the histogram with some variations such as kdeplot and rugplot.
Let us see the example below and understand the histogram as an output:
import matplotlib.pyplot as plt1
import seaborn as sns
sns.distplot([10, 11, 12, 13, 14, 15])
plt1.show()
Output:
This plot in the seaborn library is the most used plot. Its main use is to plot and visualise the data in a series form which means in a continuous manner.
Below is an example in python to demonstrate the line plot in seaborn.
import seaborn as sns
sns.set(style="light")
data1 = sns.load_dataset("x")
sns.lineplot(x="timepoint",
y="signal",
hue="region",
style="event",
data=data1)
Output:
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This plot in the seaborn library is very common for representing a line using linear regression. IT showcases the data points also in 2-D and displays them both horizontally as well as vertically.
Below is an example in python to demonstrate the lmplot in seaborn.
import seaborn as sns
sns.set(style="ticks")
df = sns.load_dataset("HKR")
sns.lmplot(x="size", y="total_bill", data=data1)
Output:
This type of plot works on the iris dataset. The Iris dataset is a well-known dataset for its floral representations such as the flower’s size of the petal, its length, and its width with the sepal size such as length, width, or height. The features present in the iris dataset help in classifying it into setosa, virginica, etc. These plots mostly defined the correlation between the data of a dataset.
This plot in the Seaborn library helps in plotting more than one scatter plot. This helps in providing a quick overview of the plotted data. It aims at pairing all the data and plotting the correlation between them. Below is the syntax to follow while using pair plots in python:
sns.pairplot(df)
The heatmap in the seaborn library is useful for visualising the confusion as well as correlation in the matrices. It is possible to customise the values, colour schemes, and annotations in heatmaps.
Below is the syntax to follow while using the heatmap plots in python:
sns.pairplot(df)
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The Seaborn library in python is actually used for data visualisation. Seaborn works as a very powerful tool for the same. The great thing about using it is that it is built on matplotlib, so the user is able to customise his graphs and plots as per his need. In this article, we have understood what a seaborn library is, along with the types that come under it. We have also understood its dependencies, and programs which demonstrate the plots using seaborn in python.
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