In today’s age, a lot of data is being generated daily. Analyzing these data for certain patterns and trends in the raw format is challenging. Here’s how data visualization comes into play. Data visualization is the process of understanding the data in a more detailed way, using graphical representations such as graphs and plots. Data Visualization with Tableau Certification will equip you with critical skills and enable you to make organized pictorial representations, making them easy to understand, observe and analyze in the future. Let's explore popular Python libraries for data visualization in detail.
How To Use Python For Data Visualization?
Python has now emerged as the go-to language in data science, and it is one of the essential skills required in data science. Python libraries for data visualization are designed with their specifications. Obtaining Business Intelligence Training will assist you in gaining key insight into the data through different graphical representations.
Here are the steps to use Python for data visualization.
Step 1: Importing Packages
Python in data visualization should start by importing the necessary packages. The packages include Pandas for processing, Matplotib for visualization, seaborn for advanced visuals, and Numpy for calculations.
Step 2: Importing And Cleaning Data
This is an important step as a perfect and clean dataset is required for distinct and perfect data visualization.
Step 3: Creating Visualization
You can create different types of visualization, from basic to advanced charts. For instance, a line chart is the most common visualization chart used to observe trends, while a bar chart represents ranking values.
Python Data Visualization Libraries
Let's characterize the data visualization libraries based on:
- Interactivity: Whether the library offers interactive elements.
- Syntax: What level of control does the library offer, and does it follow a specific paradigm?
- Main Strength and Use Case: In which situation is the library best?
The table below summarizes the top Python data visualization libraries based on the above factors:
| Library | Interactive Features | Syntax | Main Strength and Use Case |
| Matplotlib | Limited | Low-level | Highly customized plots |
| seaborn | Limited (via Matplotlib) | High-level | Fast, presentable reports |
| Bokeh | Yes | High- and low-level, influenced by the grammar of graphics | Interactive visualization of big data sets |
| Altair | Yes | High level, declarative, follows the grammar of graphics | Data exploration and interactive reports |
| Plotly | Yes | High- and low-level | Commercial applications and dashboards |
Most Popular Python Libraries For Data Visualization
There are many data visualization libraries in Python that are built to perform numerous functions, contain tools, and have methods to manage and analyze data. Each has a particular objective while managing images, textual data, data mining, data visualization, and more.
Here we have listed the top 10 popular Python libraries for data visualization.
- Matplotlib
- Plotly
- Seaborn
- GGplot
- Altair
- Bokeh
- Pygal
- Geoplotlib
- Folium
- Gleam
1. Matplotlib
Matplotlib is one of the best Python visualization library for generating powerful yet simple visualization. It is a 2-D plotting library that can be used in various ways, including Python, iPython sheets, and Jupyter notebooks.
JavatpointKey Features - It supports various types of graphical representation, including line graphs, bar graphs, and histograms.
- It can work with the NumPy arrays and border SciPy stack.
- It has a huge number of plots for understanding trends and making correlations.
| Pros | Cons |
| Interactive platform | Not ideal for time series data |
| Versatile library | Learning curve for beginners |
| Compatibility with NumPy arrays and border SciPy stack |
|
Installation: Click here to install Matplotlib
2. Plotly
The most popular data visualization library in Python is Plotly, which delivers an interactive plot and is easily readable to beginners. It is widely used for handling financial, geographical, statistical, and scientific data.
mode.comKey Features
- Its robust API functions effectively in both local and web browser modes.
- It is an interactive, open-source, and high-level visualization library.
- It can be viewed in Jupyter notebooks, standalone HTML files, or even hosted online.
| Pros | Cons |
| Offers contour plots, dimension chars, and dendrograms | Difficult to use |
| Allows 40 unique chart and plot types | Limited 3D plotting capabilities |
| Seamless integration with Python | Heavier, resource-intensive library compared to others |
Installation: Click here to install Plotly
3. Seaborn
Seaborn is the best python libraries for data visualization, which offers a variety of visualized patterns. It is designed to work more compatible with Pandas data form and is widely used for statistical visualization.
Heatmap created with seaborn (Source: Seaborn.pydata.org)Key Features - It performs the necessary mapping and aggregation to form information visuals.
- It is integrated to explore and understand data in a better and more detailed way.
- It offers a high level of a crossing point for creating beautiful and informative algebraic graphics.
| Pros | Cons |
| Much more visually appealing representation | Limited customizable options |
| Switch to any other data format | Slow for large datasets |
Installation: Click here to install Seaborn
4. GGplot
GGplot is another popular data visualization library in Python, known as the Python implementation of graphics grammar. It refers to the map of the data, with its aesthetic attributes including color, shape, and geometric objects like points and bars.
javatpointKey Features
- It allows you to build informative visualization substantially with improved representations
- It is integrated with Panda to store data in a data frame.
- It is based on ggplot2, an R programming language plotting system.
| Pros | Cons |
| Documentation is simple and easy to follow | Not suitable for creating highly customized graphics |
| Save method to discuss and exhibit plots | Some features, such as creating maps with theme_map, are not available in ggplot |
Installation: Click here to install GGplot
5. Altair
Altair is regarded as one of the declarative statistical visualization libraries for data visualization in Python. It indicates that we need to define the links in the data columns, which are the x-axis and y-axis, while creating any visuals.
fusionchartsKey Features - It has a user-friendly and consistent API built on Vega-lite JSON specification.
- Its source is present on GitHub
- It is dependent on Python 3.6, entry points, jsonschema, NumPy, Pandas, and Toolz
Pros And Cons
- Create the best visuals with minimal code
- Holds declarative grammar on both visuals and interaction
Installation: Click here to install Altair
6. Bokeh
Bokeh is another interactive Python library for data visualized for modern web browsers. This is best suitable for developing interactive plots and dashboards for complex or streaming data assets.
fusionchartsKey Features - It has a broad range of intuitive graphs which can be leveraged to form solutions.
- It is well-known for creating custom-made visualizations.
- It includes various generation and plot chart methods, including box plots, bar plots, and histograms.
| Pros | Cons |
| Highest level of control for the rapid creation of charts | No pre-set defaults, and users have to define them each time |
| Many graphs with fewer codes and higher resolution | Smaller set of supported chart types |
Installation: Click here to install Boken
7. Pygal
Pygal is one of the most popular and best Python data visualization libraries where the interactive plots are built using the pygal library. It allows you to download your visualization in various formations, including SVG. PNG, Browser, PyQuery, and more.
fusionchartsKey Features - It has three individual map packages to keep the compact module size.
- It offers an interactive experience with data explorations, filtration, and more.
- It is optimized with rich support, allowing users to be more visionary even in multiple complex problems.
| Pros | Cons |
| It can provide output chats of data like SVGs | Become slow with a large number of data points |
| Attractive char in a few code lines | SVGs will suffice as long as you're working with smaller datasets |
Installation: Click here to install Pygal
8. Geoplotlib
Geoplotlib is another data visualization library of Python that allows the user to build maps and plot geographical data. This library is designed to handle the entire dataset, map projection, and tile download of the map automatically.
fusionchartsKey Features - It has the toolbox used to draw various maps, including heatmaps, dot-density maps, and choropleths.
- It has an interface of an object-oriented programming language.
- It also has excellent zooming and panning maps for distinct views.
| Pros |
| Graphics rendering from OpenGL |
| Large datasets can be performed with excellent resolution |
| Enables hardware acceleration |
Installation: Click here to install Geoplotlib
9. Folium
Folium is one of the easy-to-use Python packages for data visualization to visualize and analyze data on an interactive leaflet map. The library utilizes OpenStreetMap, which provides the user with an excellent Google Maps experience with few codings.
MultiPolyline (Folium) Key Features - It has numerous built-in tilesets from various platforms, including Stamen, Mapbox, and OpenStreetMaps.
- It is easy to add locations of other users with markets.
- It also has different plugins and is capable of creating maps similar to plotly, Altari, and broken.
| Pros | Cons |
| Employ various plugins | Complex to handle shapefiles |
| Easy to draw maps with markers | Basemap has the problem of requiring you to get and handle shapefiles, and the result is static images |
Installation: Click here to install Folium
10. Gleam
Gleam is the perfect Python library for data visualization, made from the inspiration of the Shiney package of programming language. The users can develop the basic plot with gleam while building various fields on top, enabling easy filtration and sorting of data.
mode.comKey Features - It is used in the visualization and analysis of data in interactive web applications that take only python scripts.
- It can work with any kind of data visualization of python language.
- It doesn’t require the knowledge of HTML, CSS, or JavaScript.
| Pros |
| Suits all types of library |
| Simple to filter and sort data |
Installation: Click here to install Gleam
Usage of Python Libraries For Data Visualization
Python is a dynamic, portable, interpreted, and object-oriented programming language that has its advantages in Computer vision, data science, machine learning, robotics, and so on. Data visualization in Python libraries gives you many insights throughout the entire process of analysis.
Here are some benefits of Python libraries used for data visualization.
- Due to Python’s popularity, the features of Python libraries inherited simplicity and readability, making it user-friendly for people.
- There are numerous Python visualization libraries where some of which are tailor-made to fit your requirements.
- From importing data from excel sheets to processing datasets for analyzing time series, Python libraries have all. For instance, Matplotlib is equipped with the necessary tools for effective data visualization.
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Career Options In Data Visualization
There are various options for a data visualization career available across multiple industries. If you are interested in the field, it will be helpful to learn the roles and responsibilities of each position to make more informed career choices. Here are some data visualizations of career options.
- Data specialist (Rs. 7.0 Lakhs): They convert data of different forms, including paperwork and physical documents, to digital formations for further data visualization. (Source: Ambitionbox)
- Data visualization engineer (Rs. 12.6 Lakhs): They transform complicated data into easy-to-understand information which is accessible to everyone.
- Data Visualization consultant ( Rs. 6.1 Lakhs): They perform various tasks, including better handling the data, extracting more useful insight, finding solutions to issues, and making better decisions. (Source: Ambitionbox)
Summing Up
Data visualization is one of the critical aspects that needs to be addressed using effective visualization techniques. This article helps you to find the most popular Python libraries for data visualization in a very in-depth way. Are you ready to start your career path in Data visualization? Enrolling in the comprehensive Data Visualization with QlikView Course will be your powerful guide in learning how to use Python to analyze data and create effective and beautiful visualizations. It is designed for both beginners and experienced developers who want to build a promising future in data science.
