{"categories":[{"label":"Database","url":"https://skillfed.io/packages/category/database/2"}],"enrichment":{"capability":"pandasql lets you query pandas DataFrames using SQL syntax instead of pandas methods, bridging SQL familiarity with DataFrame manipulation.","skillfed_tags":["sql-interface","abandoned"],"use_cases":["Migrate SQL-based data workflows to Python without rewriting queries as pandas method chains","Join multiple DataFrames using familiar SQL INNER/LEFT/RIGHT JOIN syntax","Perform GROUP BY aggregations and window functions on DataFrames using SQL","Prototype data transformations quickly if you think in SQL rather than pandas idioms"],"what_it_does":"pandasql is a thin wrapper that translates SQL queries into pandas operations, using SQLite syntax as the query language. It automatically detects pandas DataFrames in the calling scope and treats them as SQL tables, allowing you to write SELECT, JOIN, GROUP BY, and other SQL statements against them. The main entry point is the sqldf function, which takes a SQL query string and a namespace dict (typically globals() or locals()) to resolve DataFrame names.\n\nThe package was designed to lower the barrier for people familiar with SQL but new to pandas, providing a more intuitive interface for data manipulation and cleaning. However, it has been abandoned since 2016 with no active maintenance, creating significant compatibility concerns for modern Python and pandas environments.","worth_installing":"No. The package is abandoned (last release 2016, no commits since July 2024 in archived repo) and has high install friction. Modern alternatives like DuckDB or Polars provide SQL-on-DataFrames with active maintenance, better performance, and compatibility with current Python versions. Use pandasql only if you have legacy code already depending on it; do not start new projects with it."},"id":"pandasql","links":{"html":"https://skillfed.io/packages/pandasql","md":"https://skillfed.io/packages/pandasql.md","pypi":"https://pypi.org/project/pandasql/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2016-04-20","license_spdx":null,"license_treatment":"unclear","name":"pandasql","python_support":"unspecified","summary":"sqldf for pandas"},"popularity":{"monthly_downloads":1963671,"position":3405,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.7.3"}