Matplotlib is the fundamental library for visualizing data in Python. While there are more sophisticated packages and libraries available for visualization, Matplotlib continues to be a solid entry point. Several other major data visualization libraries for Python either use Matplotlib as a foundation, or have similar syntax in terms of how plots are programmed.
There are several tutorials available on Matplotlib's page that provide an introduction to the module's capabilities.
Seaborn is essentially an expanded version of Matplotlib. It provides several more themes, more plot types, better integration with pandas, and support for combining multiple types of plots into the same visualization. However, it can require the use of more parameters than Matplotlib.
Plotnine is a Python implementation of the popular R module ggplot2, which is itself based on the principles for visualization outlined in The Grammar of Graphics by Leland Wilkinson.
Plotly and Bokeh are both widely-used libraries for creating interactive visualizations in Python. Both Plotly and Bokeh are built on top of a JavaScript framework, but neither requires users to know how to program in JavaScript (in most cases.)
Both libraries allow users to mouse over graphs and see a tooltip that gives more information about the data under the cursor. Plotly provides better support for 3-D visualizations.
Other such libraries include Altair, mpld3, streamlit, pygal, and bqplot, described in the article linked below.
Each of these libraries has its own capabilities and syntax.
