1.
Introduction
2.
Main Data Visualisation Libraries:
2.1.
Matplotlib:
2.2.
Seaborn:
2.3.
Geoplotlib:
2.4.
ggplot:
2.5.
ggplot2:
2.6.
D3.js:
2.7.
Chart.js:
2.8.
Plotly:
2.9.
Echarts:
2.10.
Leaflet:
Last Updated: Mar 27, 2024
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# Top 10 Libraries for Data Visualisation

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Basics of Machine Learning
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## Introduction

Data Visualisation is a tool used by Data Scientists to convey important information represented by it to other people who are not experts in the domain.

Data is an extremely required part of this new age era with the continuous growth of companies and industries which needs to be handled very carefully. These data can help in making many decisions that may lead to the growth of a business through Data Visualisation.

A data scientist may be able to understand all the important features and statistical model over a particular data but it can be very difficult to convey all those features to someone, not from the data science domain data visualization provide an ultimate scope to data scientist which allow them to create the graphical representation of provided data that may help understand all concepts and interlinking of data more thoroughly. There are many data visualization libraries across various programming languages that are used to perform this operation, the most common ones are Python, JavaScript, and R.

Data Visualization will allow the Data Scientist to create bar charts, pie charts, histograms, counterplots, flow trees, etc. using data visualization libraries.

Also Read, clustering in machine learning

## Main Data Visualisation Libraries:

### Matplotlib:

It is a Python library used for data visualization, this library helps create many different kinds of graphs and charts such as pie charts, histograms, error charts, etc. All these representations can be done by importing Matplotlib in our working module. Generally, we import matplotlib.pyplot which provides MATHLAB like plotting framework, pyplot combines the plotting sequences with NumPy which work as.

import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 5, 0.1);
y = np.sin(x)
plt.plot(x, y)

Matplotlib can be used in Python script, IPython shell, Jupyter notebook, web application servers, etc.

### Seaborn:

It is a python library integrated with Numpy and Pandas (which are other libraries for data representation), Seaborn is closely related to Matplotlib and allow the data scientist to create beautiful and informative statistical graphs and charts which provide a clear idea and follow of pieces of information within modules. Seaborn takes Data frames (when dealing with pandas) and a complete array of the dataset (when dealing with a Numpy array). Most of the work is done internally with required mapping within the information that users desire to have in their data. The Seaborn data graphics can include bar charts, pie charts, histograms, scatterplots, error charts, etc.

Example:
import seaborn as sns

sns.set(style=”white”)

sns.lineplot(x=”timepoint”, y=”signal”, hue=”region”, style=”event”, data=fmri)

### Geoplotlib:

Matplotlib is a complete data visualisation library which helps create charts and graphs and it still can’t deal with many geographical maps related data. Geoplotlib is an important Python library which deals with geographical data. Geoplotlib helps create many different types of geographical maps such as dot-density maps, choropleths, symbol maps, weather maps, etc. Geoplotlib requires Numpy and pyglet to be installed before using any map function of Geoplotlib, this library is an excellent option to deal with any geographically related data.

### ggplot:

It is said to be fun, powerful, and easy to learn. It’s a python data visualisation library that is based on ggplot2 which is created for the R programming language and implementation of the Grammar of Graphics, It is built for making professional-looking plots quickly with minimal code. To produce a plot with ggplot we need to decide on three main things:

• A data frame (within pandas) containing our data.
• How the columns of the data frame can be translated into positions, colours, sizes, etc. of graphical elements.
• The actual graphical elements to display.

ggplot in Python can create data visualisations such as bar charts, pie charts, histograms, scatterplots, error charts, etc.

### ggplot2:

It is the data visualisation library of R programming language, it is a system for creating graphs and charts based on “The Grammar of Graphics”. ggplot2 also follows the same steps as that of ggplot, we provide the data for which we want to create our plots, tell how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details. To work with ggplot2 we need to install the tidyverse. ggplot2 allows you to add different types of data visualisation components or layers in a single visualisation which ultimately creates histograms, scatterplots, error charts, bar charts, pie charts, scatterplots, etc.

### D3.js:

It is a JavaScript based data visualisation library which works to manipulate the data with HTML, CSS, and SVG to obtain better data visualisation that can be understood by the regular users of the websites. D3 is framework independent it does not care about what framework the developer is using, it better work with selected jquery to manipulate DOM elements, it applies events to the DOM elements, D3 provides different chart types for data analysis, hierarchies, networks, as well as common charts like scatter plots, line charts, bar charts, pie charts, etc. D3 also provides animation options like an animated treemap, zoomable bar charts, etc.

### Chart.js:

It is a JavaScript data visualisation library and open source, it is a lightweight library and has fully responsive charts that include bar charts, pie charts, histograms, scatterplots, error charts, etc. we can also create mixed charts from all the common charts provided within the library that will eventually give an option to customise our charts, we can also add animations in our charts. Charts.js is being widely used because of its extremely small size of around 11kb zipped which plays an important role when we are dealing with web applications. Chart.js also uses native API’s so the learning curve is tiny to none.

### Plotly:

It is a javascript(Plotly.js) stand-alone library that can also work well with Python and R having module names of Plotly.py and Plotly. R respectively. Plotly can be used to create many types of charts and visualisations, including business charts, statistical charts, 3D graphs, scientific charts, SVG and tilemaps, financial charts, and more. Plotly can be used without any internet connection in an offline mode. Plotly.js is a high-level, declarative charting library. plotly.js ships with over 40 chart types. Plotly provides an option of Transforms, Animations and Custom Controls.

### Echarts:

It is a JavScript library, echarts is a Declarative Framework for Rapid Construction of Web-based Visualisation, it is free, powerful, and provides an easy way to add customised, intuitive, interactive charts to any product. ECharts is an incubator project of the Apache Software Foundation. Echarts provides Multidimensional data analysis (Analysis of the relationship between multiple charts), and Charts for all sized devices. Echarts is used to create many types of charts like line charts, Bar charts, Pie charts, Scatter charts, Geo/maps, Graphs, etc.

### Leaflet:

The leaflet is a very popular open-source javascript library for interactive maps. It provides many interactive options to the user such as Interactive panning/zooming, the options to combine Polygons, Lines, Popups, etc. leaflet creates maps using arbitrary combinations of Map tiles, Markers, Polygons, Lines, Popups, GeoJSON. All maps are created from the R console or RStudio, it displays maps in non-spherical Mercator projections, also augment map features using chosen plugins from leaflet plugins repository.

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