artifact-analysis

To perform correspondence analysis on ceramic typology data, you encode ceramic attributes into a contingency table and apply multivariate statistical methods to map structural relationships between contexts and ware types, generating interpretable figures via matplotlib.

Yes, you can calculate assemblage diversity indices like Shannon, Simpson, and evenness using Python. This Skill computes these metrics to support comparative grouping and clustering of assemblages across different archaeological sites or contexts.

Analyzing lithic reduction stages involves processing coded lithic attributes to evaluate cortex percentages and quantify statistical associations across lithic categories, revealing reduction sequence patterns within the debitage assemblage.

Yes, you need pandas and scipy, along with numpy, scikit-learn, and matplotlib, to run archaeological artifact analysis. These dependencies are required for data processing, computing statistical tests, and producing interpretable summary plots.

Clustering compares archaeological assemblages by grouping them based on computed diversity indices and statistical summaries, revealing comparative structural patterns and relationships across different sites or contexts.