> ## Documentation Index > Fetch the complete documentation index at: https://docs.mantlebio.com/llms.txt > Use this file to discover all available pages before exploring further. # Beyond Matplotlib -- Declarative plotting in Python Plotting is a crucial part of bioinformatics and computational biology -- it serves as both a starting place, through exploratory data analysis, and the endpoint of a data workflow. One of the most well-known plotting libraries in Python is **Matplotlib**. It was one of the first widely-adopted plotting libraries in Python and is used as the plotting package within many domain-specific analysis packages, such as Scanpy. Matplotlib supports a wide variety of plot types and allows for extremely fine-grained control over plot styling, which makes it a great choice for producing publication-ready plots. However, this level of control is a double-edged sword. Its syntax can be quite verbose. Because Matplotlib uses the imperative programming paradigm, you need to explicitly define the construction of each visualization, with step-by-step commands, from the axes to the marks to the labels and legends. Many bioinformaticians and computational biologists who are familiar with the ggplot package in R, which uses a declarative programming paradigm, find the procedural nature of plotting with Matplotlib to be quite onerous. In this notebook, we briefly introduce a few declarative plotting packages that may allow you to more quickly jump into exploratory data analysis or modify figures as needed for presentations or reports. We use the [Palmer Penguins](https://allisonhorst.github.io/palmerpenguins/) dataset as our dataset. > Data were collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER, a member of the Long Term Ecological Research Network. We demonstrate how to create a scatter plot of flipper length vs. body mass, colored by penguin species, in Matplotlib, Seaborn, Plotnine, Vega-Altair, and Holoviews.