Results for “purple-cow”

Oracle PL/SQL procedural language. Covers stored procedures, functions, packages, triggers, cursors, collections, and exception handling. Use for Oracle database server-side programming. USE WHEN: user mentions "plsql", "Oracle procedures", "Oracle packages", "Oracle triggers", "BULK COLLECT", "FORALL", "DBMS_OUTPUT", "Oracle functions" DO NOT USE FOR: basic Oracle SQL - use `oracle` instead, PostgreSQL - use `plpgsql` instead, T-SQL - use `tsql` instead

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Process data with high-performance DataFrames using Polars' expression-based API, lazy evaluation, and parallel execution for ETL, analytics, and pandas migration.

Perform high-performance genomic interval operations and bioinformatics file I/O on Polars DataFrames, including overlap, nearest, merge, coverage, complement, subtract, and reading/writing BED, VCF, BAM, GFF, FASTA, and FASTQ formats with streaming and cloud-native support.

Fast DataFrame library (Apache Arrow). Select, filter, group_by, joins, lazy evaluation, CSV/Parquet I/O, expression API, for high-performance data analysis workflows.

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

High-performance DataFrame library for Python ETL, analytics, and pandas migration. Use for expression-based data manipulation with lazy query optimization, parallel execution, streaming out-of-core processing, Arrow interoperability, and optional GPU execution.

Analyzes uploaded data files with full Python access, producing cleaned datasets, statistical reports, charts, and dashboards via the CellCog coding agent.