Published on Nov 15, 2023
Data has become essential for businesses, used not only to track customers and products but also to predict trends, improve marketing, optimize operations, ensure positive customer experiences, and, ultimately, increase profits.
Every business, regardless of size, relies on data globally to function, compete, and grow. Data is essential, without which many businesses would lag or face failure. However, to make the best from it, the raw data must be processed, transformed, and presented with proper visualization to understand & gather insights from it. This is where a data visualization library becomes crucial.
With the help of data visualization, you can convert data into easily digestible visualizations, simplifying the extraction of necessary insights to make informed decisions quicker. One of the ways to achieve this is to create Python applications to display data in ways that facilitate drawing conclusions and building future business roadmaps.
Python is considered as one of the top choices for programming languages in the realms of data science and visualization, and the right Python data visualization library empowers you to craft interactive plots and other visual representations from your data, ensuring readability and user-friendliness.
How to choose the right Python data visualization library
Selecting the right Python data visualization tool is significant for conveying insights derived from data successfully. Consider the following factors while choosing Python data visualization library.
- Types of visualizations - Consider what kinds of charts and plots are required, and choose data visualization library that is capable of creating the visualization as needed
- Input Formats - Choose a data visualization library that can work with the type of data you have (Structured or unstructured)
- Output Formats - Ensure to choose data visualization library that can output your visualizations in a format you can use, share or embed
- Compatibility - Examine how well the tool combines with your existing data analysis and manipulation tools, such as pandas, NumPy, or Jupyter notebooks
- Learning Curve - Select a tool that is appropriate for the skill level. Choose a library with an easy learning curve for swift adoption
- Customization - Consider your customization requirements. Libraries like Matplotlib and Plotly, allow substantial customization, whereas Seaborn may provide limited room for customization
The top seven Python visualization libraries
Python, being a versatile and widely-used programming language, offers a plethora of visualization libraries that cater to different needs and preferences. The type of data you're working with, the purpose of visualization, and your personal preferences play a major role in choosing the right visualization library. Let’s discover the key features, pros & cons, and use cases of these data visualization libraries.
1. Matplotlib
Released initially in 2003, it has evolved into one of the most widely embraced Python data visualization libraries for plotting.
Key Features
- Effective for swiftly generating high-quality charts suitable for publications
- Open source and free to use
- Allows to display variety of graphical representations, such as line graphs, bar graphs, and histograms
- Supports NumPy arrays and the SciPy stack
Pros & Cons
- Important for users to know Python
- Users should also understand syntax of Matplotlib (based on software, Matlab)
Use Cases
- Basic line plots and scatter plots
- Customized visualizations with precise control over details
2. Plotly
Built on the Plotly JavaScript library, this is another revered Python data visualization library.
Key Features
- High-level, interactive, open-source visualization library
- Simplifies the creation of interactive, high-quality graphs
- Offers various chart styles like line plots, scatter plots, area charts, and bar charts
- Comprehensive API for use in both local and web browser modes
- Allows visualizing scientific data through 3D charts, statistical graphs, geographical maps, etc.
Pros & Cons
- Creates aesthetically pleasing visualizations
- Can create highly interactive visualizations
- More flexible than most data visualization libraries
- Easy to create interactive features within visualization
- Users need to know Python
Use Cases
- Interactive dashboards and web applications
- 3D visualizations and surface plots
3. Seaborn
It is a data package based on Matplotlib, featuring a higher-level interface for creating visually appealing plots with ease.
Key Features
- It does the necessary mapping and aggregation to create informative visualizations
- Helps create appealing statistical graphs with heatmaps, distributions and clusters
- Helps users better explore and comprehend data
Pros & Cons
- Quickly creates simple visualizations
- Resulting visualizations are modern-looking
- Visualizations tend to be more static
- May require simultaneous use of Matplotlib
Use Cases
- Pair plots and correlation matrices
- Categorical plots for exploring relationships in categorical data
4. Altair
It is based on Vega-Lite Python visualization package that enables developers to focus more on insights and requires less coding.
Key Features
- Provides an easy-to-use and consistent API based on the Vega-lite JSON standard
- Helps create faster & interactive Python data visualizations
- Integrates with Apache Arrow and Pandas to allow working with huge datasets
Pros & Cons
- Holds declarative grammar on both visuals and interaction
- Requires number of installation
- Less customizable compared to other libraries
Use Cases
- Declarative visualizations with concise syntax
- Exploratory data analysis with interactive features
5. Bokeh
It is a Python-based interactive data visualization tool offering contemporary charting features designed for web browsers.
Key Features
- Powerful interactive visualization library for web browsers
- Offers a diverse range of intuitive graphs
- Well-known for producing personalized visualizations
- Offers a variety of chart generating and plotting methods, such as box plots, bar plots, and histograms
Pros & Cons
- Facilitates development of highly customized visualizations
- Builds dashboards and charts quickly with interactivity
- Option to embed visualization in the web pages
- Degree of interactivity is limited
Use Cases
- Real-time streaming data visualizations
- Interactive plots for exploratory data analysis
6. Pygal
It is a Python charting library mainly for constructing SVG graphs and visualizing charts for Python data.
Key Features
- Allows to save visualization in different formats, such as SVG, PNG, PyQuery, etc.
- Includes three distinct map packages to keep the module size small
- Offers interactive experience with data exploration, filters, and additional features
- Allows users to be more creative
Pros & Cons
- Generates graphics like SVG
- Attractive char in a few code lines
- Minimal ability to adjust the plot range
- Slows down with a large volume of data points
Use Cases
- Scalable and responsive visualizations
- Animated charts for dynamic data representation
7. Geoplotlib
A Python data visualization library that facilitates the creation of maps and plotting of geographical data.
Key Features
- Comes equipped with a toolkit for crafting heatmaps, dot-density maps, and choropleths
- Features an object-oriented programming language interface
- Facilitates the creation of highly interactive maps with functionalities such as panning and zooming
Pros & Cons
- OpenGL based Graphics rendering
- Processing large datasets with exceptional resolution
- Enables hardware acceleration
- Specializes in geospatial data visualizations
- Provides support for interactive maps
- Constrained to geospatial visualizations
Use Cases
- Visualizing geographical distributions and patterns
- Creating custom maps with overlays for in-depth analysis