Skill UI

GeoPandas extends pandas for geospatial vector data, enabling reading/writing shapefiles, GeoJSON, GeoPackage and PostGIS, performing CRS management, spatial joins, overlays, buffering, area/distance calculations, and producing static or interactive choropleth maps.

Guidance and local audit tools for Python workflows using GeoPandas GeoSeries, GeoDataFrame, spatial operations, CRS handling, and vector-data I/O. Includes safety gates for provenance, geometry validity, and privacy-aware geodata sharing.

This comprehensive guide provides a structured framework for building and auditing empirical data and estimation pipelines in finance and banking research. It covers data construction from various sources (e.g., CRSP, Call Reports), advanced econometric techniques like fixed effects and winsorization, conducting stress tests (e.g., crisis periods, large banks), and ensuring full reproducibility. It emphasizes reporting results with meaningful economic magnitudes.

This comprehensive guide outlines the best practices for preparing a complete and reproducible research package for submission to the Journal of Banking & Finance (JBF) following Elsevier Option C guidelines. It details requirements for data availability statements, proper structuring of code and data access materials, and best methods for managing proprietary or restricted datasets (such as CRSP or Compustat), ensuring research transparency and verifiability.

A comprehensive guide for conducting advanced empirical analysis required for top-tier finance journals. It covers assembling multi-source firm panels (Compustat, CRSP, etc.), constructing complex corporate finance variables, implementing advanced econometric techniques (high-dimensional fixed effects, clustered errors), and systematically planning robustness checks and replication strategies.

A comprehensive guide for authors submitting to the Journal of Corporate Finance (JCF). It details how to comply with Elsevier's Option C data availability requirements, focusing on drafting the data availability statement and structuring the research archive. It provides best practices for handling licensed, proprietary data (e.g., Compustat, CRSP) by recommending the sharing of construction code and access instructions, rather than raw data deposits.

This comprehensive guide assists in executing and documenting rigorous empirical finance research, tailored for top-tier journals like JFQA. It covers the entire pipeline, including complex financial data construction (CRSP, Compustat), advanced data cleaning (winsorizing, outlier handling), and sophisticated econometrics (fixed effects, clustered standard errors, Fama-MacBeth). The core focus is achieving absolute reproducibility, ensuring that all filters, robustness checks, and analytical steps are meticulously documented for archival and peer review.

Astropy is the essential Python package for astronomical research and data analysis. It provides a comprehensive suite of tools for handling celestial coordinate transformations (e.g., ICRS, Galactic), managing physical units and quantities, processing FITS files, performing complex cosmological calculations, and ensuring precise time handling. It is indispensable for any project involving scientific data in the field of astronomy and astrophysics.