Matplotlib vs seaborn

Verdict

This is less a rivalry than a layering, and most Python users end up using both in the same notebook. Matplotlib is the foundation: an object-oriented Figure/Axes API that gives fine-grained control over every element of a chart — ticks, spines, annotations, layout — which is why it remains the default for publication-quality static figures. seaborn is built directly on top of it, adding high-level functions like scatterplot, boxplot, and heatmap that take a DataFrame and column names directly and handle grouping, color mapping, and confidence intervals automatically, with more polished default styling out of the box. Because seaborn returns Matplotlib Axes objects, any seaborn chart can still be fine-tuned with Matplotlib's lower-level API when seaborn's defaults fall short.

The practical pattern: start in seaborn for fast exploratory statistical charts, and drop to raw Matplotlib when you need a specific element — a custom annotation, a non-standard layout, precise control over a publication figure — that seaborn's higher-level functions don't expose.

Choose Matplotlib if

• You need precise, element-by-element control over a static figure, especially for publication or print.
• Your chart type isn't one of seaborn's statistical plot functions and you're building it from primitives.
• You're already deep in Matplotlib's API and don't need seaborn's statistical conveniences.

Choose seaborn if

• You're doing fast, exploratory statistical analysis on a DataFrame and want good-looking defaults with minimal code.
• You want built-in grouping, color mapping, and confidence intervals without computing them yourself first.
• Faceted grids (FacetGrid, pairplot) across categorical subsets are central to what you're building.

What they share

Both are free, open source, and produce static output — neither is interactive, and neither is built for dashboards; for that, look at Plotly or Bokeh instead. Both integrate tightly with Pandas DataFrames and render inline in Jupyter notebooks. Because seaborn is a layer over Matplotlib rather than an independent renderer, there's no real cost to using both — the question is only which one gets you to the chart you want with less code on a given day. See data visualization and exploratory data analysis.