High- and low-level, influenced by grammar of graphics The following table summarizes the top Python visualization libraries according to these factors: Library Main Strength and Use Case: In what situation is the library the best choice?.Syntax: What level of control the library offers, and whether it follows a specific paradigm.Interactivity: Whether the library offers interactive elements.We can characterize data visualization libraries using the following factors: To help you with that, the rest of our article will give you an overview of the top five Python visualization libraries. However, Python’s popularity and rich ecosystem might be intimidating for newcomers, as it is hard to understand which visualization library to use for which use case. This makes Python especially useful in domains where you need to complement your work with analytics, like marketing or sales. Python has a thriving data science ecosystem, including data visualization libraries that surpass Excel’s capabilities. Many big companies use Python to run critical operations within their business. It is easy to learn, helps with automation, and provides access to data and analytics. Python is excellent to learn for your career and is a great language to introduce to your organization. Python is currently one of the most popular programming languages and the primary one when it comes to data science, making it a safe learning choice. What if there was a tool that would be versatile enough to use with a wide range of problems, data sources, and use cases? And had little infrastructural requirements?įortunately, there is such a tool: Python! Why Python is a Great Language for Data Visualization However, some of the tools require too much overhead to set up or are limited in their capabilities. You can also use different tools for that. There are many situations where you can benefit from visualizing data – like doing a sales presentation, conducting market research, or setting up a KPI dashboard. For these reasons, data visualization is a central activity in any organization that wants to make complex data-based decisions.Īn example of a sales data visualization ( source ) A good visualization often reveals insights faster than hours of data munging (aka data wrangling) and is more intuitive for non-technical audiences. The main challenge of data analysis is understanding relationships within a dataset and their relevance to the use case. Why Data Visualization Is Importantĭata visualization is a powerful way to gain and communicate insights from data. It goes on to showcase the top five Python data visualization libraries, their main features, and when it is a good idea to use them. It lays out why data visualization is important and why Python is one of the best visualization tools. Python’s visualization landscape in 2018 ( source ). Python has a vast ecosystem of visualization tools it can be hard to pick the right one. You might wonder which Python data visualization library you should learn or use for a given project. It offers over 140 exercises to improve your Python skills and practice loading, transforming, and visualizing data. If you want to use Python to get insights from data, check out our Introduction to Python for Data Science course. It is versatile, needs little maintenance, and can access almost any available data source. Python, the number one language in data science, offers a better way. Many require setting up, maintaining, and using elaborate BI tools with limited capabilities. There are many paths to learning and practicing data visualization. You can benefit from it in your career and use it to pivot toward a data-focused role. It helps you to find insight into data and to communicate your findings to less technical audiences. Which Python library should you pick for data visualization? Read on to see our assessment!ĭata visualization is an increasingly valuable skill, one that’s sought after in many organizations.
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