Top 15 Data Visualization Libraries in Python for Business

Introduction

Data visualisation plays a vital role in data analysis, especially in business. The Python data science community is active and offers data visualisation tools beyond Excel. The complex graphical interface of Python may be intimidating to newbies. Therefore, it is essential to grasp the visualisation package that best suits the specific use case.

Along with exploring a wide range of data visualisation libraries in Python, aspirants can leverage the benefits of the Graduate Certificate Programme in Data Science from upGrad to upskill themselves on the path to becoming better data experts. 

So, let’s embark on this journey to explore the best Python data visualization libraries for business and unleash the potential of visual storytelling with data!

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Why is Data Visualisation Important for Business?

Data visualization libraries in Python serve a vital role for businesses since it:

• Allows business users to acquire insight into their massive volumes of data, highlights patterns and trends, and helps viewers to receive rapid information
• Simplifies the detection of patterns, trends, and outliers in massive data sets
• Empowers viewers to grasp and act on insights rapidly
• Helps organisations analyse which variables drive client behaviour, discover areas that require improvement, and estimate sales quantities
• Efficiently communicates information globally using visual representations
• Increases efficiency in business intelligence
• Guides companies in strategic product placement decisions

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Criteria for Selection of the Libraries

Consider the following factors while choosing data visualization using Python:

Type of data: Depending on the kind of data you’re dealing with, different visualisation libraries suit various data types. Use a library like Folium, for instance, when working with geographical locations helpful in working with time series data.

Interactivity: Different visualisation software offers varying degrees of interactivity. Use a library like Bokeh or Plotly to create interactive visualisations that let users explore the data.

Usage simplicity: Some visualisation libraries are more user-friendly than others. If you are new to Python or data visualisation, you can start with a tool like Matplotlib, which has a comparatively simple syntax.

Customizability: Different visualisation libraries provide different levels of customizability. Use a library like Seaborn, which allows for much customisation, to create highly customised visualisations.

Performance: Python visualization libraries can also be chosen depending on their response times. If you work with massive datasets, you can use a library like Plotly, designed to handle massive datasets well.

Here are the top 15 Python libraries for data visualization:

1: Matplotlib

Matplotlib is one of the most common Python visualization libraries that allows users to create static, animated, and interactive visualisations. It is built on top of NumPy arrays and is designed to work with the broader SciPy stack. With Matplotlib, users can generate line charts, bar charts, scatter plots, and histograms. 

To use Matplotlib, it needs to be installed first, which can be done via pip using the following command in the terminal:

pip install matplotlib

Matplotlib may be imported into a Python script once it has been installed with the following command:

import matplotlib.pyplot as plt

2: Seaborn

Seaborn is a Python data visualisation library built on Matplotlib and integrates closely with Pandas data structures. It provides a high-level interface for creating attractive and informative statistical graphics, including scatter plots, line plots, bar plots, histograms, heat maps, regression plots, joint plots, and pair plots. 

Seaborn is a robust and versatile data visualization in Python framework that can assist users in acquiring insights into their data fast and simply. It is connected with Pandas and Matplotlib, making it an excellent tool for data scientists and analysts working in Python.

3: Plotly

Plotly is an open-source data visualisation library in Python that provides various chart types and interactive tools. It supports over 40 unique chart types and allows users to make line plots, scatter plots, area charts, bar charts, box plots, histograms, heat maps, 3D surface plots, and more. It also provides tools to create dynamic dashboards and interactive web-based visualisations with Jupyter Notebook.

4: Bokeh

Bokeh is a Python framework for creating live maps for modern web browsers. It generates high-performance dynamic charts and graphs in several formats, including HTML and JavaScript. Bokeh offers two rendering interfaces: bokeh.models and bokeh.plots and supports various presentation options like scatter plots, line charts, bar charts, histograms, and box plots.

To install Bokeh, you can use pip by running the following command in the terminal:

pip install bokeh

5: Altair

Altair is a declarative statistical visualisation toolkit for Python based on the Vega and Vega-Lite visualisation grammars. It is simple to deal with data on the Pandas DataFrame. It enables data aggregation, saving some operations.

6: Plotnine

Plotnine is a data visualisation toolkit that uses Python and is inspired by the powerful R package ggplot2. It allows users to create visually appealing and customisable graphics with ease. Plotnine provides a wide range of geometric objects, themes, coordinate systems, and sizing options to help users modify the appearance of their visualisations. 

7: Geopandas

GeoPandas, an open-source Python program, expands Pandas’ capabilities by enabling Pandas to quickly analyse and report on geographic data. It is built on existing Python libraries like Shapely, Fiona, and Matplotlib, which provide the necessary features for working with geographic data. 

GeoPandas lets users read, edit, and update geographic data in several formats and perform geospatial operations, including spatial joins, geographical searches, and spatial aggregations. 

Being one of the most significant libraries, certified programs like upGrad’s Executive PG Program in Data Science & Machine Learning from University of Maryland also incorporate this visualisation tool in their learning curriculum to equip learners with in-demand skillsets.

8: Basemap

A deprecated toolkit in Matplotlib, Basemap enables users to make maps and transform data into different map projections. Basemap can convert coordinates into one of 25 alternative map projections. It may be challenging when creating personalised maps and visualisation of geographic data.

9: D3.js

D3.js is a JavaScript framework that creates customisable interactive data visualisations using SVG, HTML5, and CSS standards. D3Blocks can be integrated into Python, which allows you to create dynamic and aesthetically appealing charts based on D3.js visuals but can be customised in Python. It offers a straightforward Python API for building D3.js charts that are completely customisable using Python.

10: Pygal

Users can create dynamic, adaptable charts and graphs in various styles using Pygal, an open-source data visualisation tool built on Python. Users must first install it using pip, the Python package manager, before they can use it. Users may import Pygal after installing it, choose the type of chart they aim to create, and then choose the data they want to show to create a chart object. Afterwards, the graph may be edited, saved to a file, or displayed on a screen.

11: Vincent

Initially created to fill the gap between Python and Vega, this framework enables users to create interactive representations by specifying them in a JSON-based specification language. Vincent acts as a Python to Vega translator, making it simple for users to produce complex visualisations. 

12: ggplot

While ggplot is a potent data visualisation package in R that gives a more concise and straightforward language for creating a wide range of visualisations, including scatter plots, line plots and more. Inspired by its flexible functionality to create visually appealing plots, Python has a different library called the plotnine, which supports a large number of features as offered in ggplot. 

13: Cairo

Python programmers may utilise Cairo, a 2D graphics toolkit, to visualise data. Constructing a Cairo context to draw on a surface, such as an image, PDF file, or window, is possible using Pycairo, which offers Python bindings for the library.

14: Folium

A Python program called Folium produces interactive maps and shows geographical data. It gives a rapid technique to build maps of any location in the world and is designed on top of the leaflet.js JavaScript framework. It allows users to communicate sophisticated vector, raster, and HTML visualisations as markers and attach data to a map for choropleth presentations.

15: NetworkX

A Python module called NetworkX offers tools for creating, altering, and researching extensive networks. It offers various data structures, algorithms, and analytical measures for graphs, including degree and betweenness centrality, clustering, and path-finding.

Future Developments in Data Visualization Libraries in Python

Python’s data visualisation packages are designed with specific requirements and goals. The most widely used libraries for data visualisation are Matplotlib and Seaborn, while Plotly and Bokeh are renowned for making interactive graphs. Python’s popularity has led to the development of user-friendly libraries, making data visualisation accessible to all. Given their significance and efficiency, the future appears to be pretty promising for these tools to continue being in use for a long time.

Conclusion

Python has a thriving ecosystem of data visualisation libraries that cater to a wide range of business requirements. The top Python data visualization libraries mentioned above offer various options for creating visually compelling and interactive graphs.

On the other hand, if you’re hoping to further advance your skills in data visualisation, enrol in upGrad Advanced Certificate Programme in Data Science to develop the practical implementation of these tools in the expanding field of data science!