posit-dev/pointblank

draft-validation

Use LLMs to draft and edit Pointblank validation plans. Covers DraftValidation for generating plans from data, EditValidation for modifying existing plans with natural language, and the interactive assistant() chat interface. Supports Anthropic, OpenAI,…

원문 언어: 영어

pointblank

Validate DataFrames and database tables with Pointblank. Covers the Validate workflow (plan, interrogate, report), column selectors, data backends (Polars, Pandas, DuckDB, databases via Ibis), threshold levels, actions, and result extraction. Use when…

원문 언어: 영어

write-validation

Write data-validation plans with Pointblank. Covers choosing the right validation methods for each data quality concern, composing multi-step plans, setting thresholds and actions, using segments, conditional steps, handling nulls and missing values, and…

원문 언어: 영어

validate-yaml

Define Pointblank validation plans in YAML instead of Python code. Covers the YAML schema, validate_yaml() for syntax checking, yaml_interrogate() for execution, yaml_to_python() for code generation, and data source configuration. Use when defining validation…

원문 언어: 영어

scan-and-profile

Profile and scan datasets with Pointblank before writing validation rules. Covers DataScan for column-level statistics, Schema inference with schema_from_tbl(), missing values analysis with missing_vals_tbl(), and table previewing. Use when exploring a new…

원문 언어: 영어

generate-data

Generate synthetic datasets with Pointblank using Schema and field classes. Covers IntField, FloatField, StringField, BoolField, DateField, DatetimeField, and more. Supports presets (name, email, address, etc.), country-specific data, nullable columns, unique…

원문 언어: 영어

define-contracts

Define data contracts and pipeline validation with Pointblank. Covers Contract, Step, Schema, Pipeline, and PipelineResult for enforcing structural and semantic expectations at data boundaries. Use when setting up source/target contracts, pipeline validation,…

원문 언어: 영어