pandas has a number of essential features like DataFrames, robust and well-optimized data structures built upon the NumPy library, etc. pandas: Technically pandas is not a data visualization package so much as a fully featured data science library.ggplot: ggplot is a port of the R library ggplot2 that like seaborn leverages the Grammar of Graphics theoretical approach to visualization (see more in Dataviz Best Practices).like seaborn, plotly is higher-level and has additional interactivity features. plotly: "plotly.py is an interactive, open-source, and browser-based graphing library for Python".It provides a high-level interface for drawing attractive and informative statistical graphics." seaborn is an excellent choice for high-level visualization that produces an aesthetically pleasing result in relatively few lines of code ( seaborn website) seaborn: "Seaborn is a Python data visualization library based on matplotlib.List of widely used Python visualization packages: Consider what languages and packages folks in your discipline tend to use, but there's really not a wrong choice! R has historically been used more in statistical and quantitative analyses, and Python is a general-purpose programming language used in everything from text analysis to astronomy. If you're new to programming, keep in mind that each language has a very similar set of capacities. Furthermore, the new trend to share code and research as a narrative (such as in a "Notebook") relies upon these programming languages.Ĭhoosing a language: If you already use Python or R, consider sticking to the language and ecosystem of packages you already use. Each programming language has a distinct ecosystem of data science tools that integrates well with data visualization. If so, Python and R are both excellent fits. Are you currently using Python or R to manage, clean, and/or analyze your data? Would you craft a narrative of your research process that includes a mixture of text, interactive code, and dynamic visualizaitons?
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