A Python data visualization library based on Matplotlib, providing a high-level interface for drawing attractive and informative statistical graphics.
Seaborn
- Python
- 2D-Grafiken, KI und maschinelles Lernen, Bioinformatik, Datenanalyse, Qualität
- Design Denken, Maschinelles Lernen, Statistische Analyse, Statistische Prozesskontrolle (SPC), Benutzererfahrung (UX), Benutzeroberfläche (UI)
Merkmale:
- Statistical plotting (distributions,relationships,categorical data),attractive default styles,high-level functions for common plot types (histograms,scatter plots,bar plots,box plots,violin plots,heatmaps,clustermaps,pair plots,joint plots),integration with pandas DataFrames,color palette management,regression model plotting
Preisgestaltung:
- Kostenlos
- Produces aesthetically pleasing statistical visualizations with minimal code, simplifies complex plot creation, excellent integration with pandas, good for exploratory data analysis, extensive documentation and examples.
- Less customizable for very fine-grained plot details compared to direct Matplotlib (though it builds on it), primarily focused on statistical visualization (not general-purpose plotting like Matplotlib), performance can be an issue with extremely large datasets without pre-aggregation.
Am besten geeignet für:
- Data scientists, statisticians, and analysts for creating informative and visually appealing statistical graphics to explore and understand data.