awesome-python/website/data/category_intros/data-visualization.md

Pick your Python data visualization library by where the chart goes. Papers take Matplotlib figures, web pages take plotly charts, and data apps use Streamlit.

How to choose:

• Figures for papers and reports: Matplotlib
• Interactive charts on a web page: plotly
• Data dashboards and apps: Streamlit
• Statistical charts from a dataframe: seaborn
• Charts declared from dataframe columns: Vega-Altair
• Interactive plots that call back into Python: Bokeh
• Maps in any projection: Cartopy
• Graph diagrams laid out by Graphviz: PyGraphviz
• Knowledge graph of a codebase: graphify
• Demos for machine learning models: Gradio

Matplotlib has two interfaces. For complicated plots and code you reuse, its docs suggest the explicit, object-oriented one: create the figure with fig, ax = plt.subplots(), then call methods on ax. The implicit pyplot style is fine for quick interactive work. When you make the same plot for many datasets, write a function that takes the ax to draw on.

plotly draws interactive charts in the browser with plotly.js. Start with Plotly Express, which its docs call the recommended starting point for most common figures: pass a DataFrame and column names, and one call builds the figure. Drop to go.Figure for figures Plotly Express can't make or makes awkward, like subplots of different types or dual-axis plots. To share a chart, write_html saves it as an HTML file that stays interactive in any browser.

seaborn builds statistical graphics on Matplotlib and works on whole datasets. Its docs recommend the figure-level functions, like relplot(), for most plots. For one figure that combines different kinds of plots, set it up in Matplotlib and fill it in with axes-level functions. Keep your data in long form, one column per variable and one row per observation, as most of seaborn's examples do.

Vega-Altair is declarative: you link data columns to encoding channels like the x-axis, y-axis, and color, and it handles the rest on top of Vega-Lite. Its docs say the API is more limited than Matplotlib's or Bokeh's, a trade they make to keep exploring data simple. A chart carries its data inside the spec, so for a large dataset, enable the VegaFusion data transformer or pass the data by URL.

Bokeh builds interactive plots for the browser without any JavaScript from you. Start with bokeh.plotting, its primary interface, and output_file() for a standalone HTML file or output_notebook() for Jupyter. What sets it apart is the Bokeh server, which keeps data in sync between Python and the browser. Widgets can then run Python callbacks, and plots can stream data. Write the app as a script and serve it with bokeh serve.

Cartopy draws maps on Matplotlib and suits data over large areas, where Cartesian math breaks down at the poles and the dateline. Set the map's projection on the axes, and always pass transform to say which coordinate system your data is in.

PyGraphviz is a Python interface to Graphviz. Build a graph with AGraph or read a DOT file into one, then lay it out and draw it with one of Graphviz's layout programs.

graphify maps a project's code, docs, PDFs, and images into a knowledge graph your coding agent can query. It draws the graph as a graph.html you can click through in a browser. It parses code locally, so code never leaves your machine; docs and media go through your agent's model. Install the graphifyy package, run graphify install, then type /graphify . in your agent.

Streamlit turns a Python script into a data app, and reruns the whole script from top to bottom every time something on screen changes. Start it with streamlit run. To skip repeated work on those reruns, cache data with st.cache_data, and shared resources like ML models or database connections with st.cache_resource. Keep per-user values in Session State.

Gradio wraps a Python function, often a machine learning model, in a web UI. Use gr.Interface for a demo with inputs and outputs, gr.ChatInterface for a chatbot, and gr.Blocks for custom layouts and data flows. launch(share=True) gives you a public link, and Hugging Face Spaces hosts the app for good. Anyone with the link can call your function, so put a login in front of it or keep sensitive data out. Its security docs also recommend that you set max_file_size and keep allowed_paths as small as possible.

seaborn and Cartopy draw on Matplotlib Axes, so to customize what they draw, use Matplotlib's explicit Axes interface. Streamlit shows Matplotlib, plotly, and Vega-Altair figures, and Gradio's gr.Plot takes those plus Bokeh, so your plotting code carries over into an app.