In Python data visualization, how do I choose libraries?

Choosing the right data visualization library in Python depends on several factors such as the complexity of the data, the desired output format, and personal or team preference. Each library has its own strengths and weaknesses. For instance, if you're looking for simplicity and rapid development, libraries like Matplotlib or Seaborn might be your best bet for straightforward plots and visualizations. On the other hand, for interactive visualizations, libraries like Plotly or Bokeh could be more appropriate. Alternatively, if you need high-performance visualizations for large datasets, libraries like Datashader are designed for that purpose.