# Reference

## Validate

When performing data validation, use the [Validate](../reference/Validate.md#pointblank.Validate) class to get the process started. It takes the target table and options for metadata and failure thresholds (using the [Thresholds](../reference/Thresholds.md#pointblank.Thresholds) class or shorthands). The [Validate](../reference/Validate.md#pointblank.Validate) class has numerous methods for defining validation steps and for obtaining post-interrogation metrics and data.

[Validate](Validate.md#pointblank.Validate)  
Workflow for defining a set of validations on a table and interrogating for results.

[Thresholds](Thresholds.md#pointblank.Thresholds)  
Definition of threshold values.

[Actions](Actions.md#pointblank.Actions)  
Definition of action values.

[FinalActions](FinalActions.md#pointblank.FinalActions)  
Define actions to be taken after validation is complete.

[Schema](Schema.md#pointblank.Schema)  
Definition of a schema object.

[DraftValidation](DraftValidation.md#pointblank.DraftValidation)  
Draft a validation plan for a given table using an LLM.

[EditValidation](EditValidation.md#pointblank.EditValidation)  
Edit an existing validation plan with a plain-English instruction using an LLM.

[MissingSpec](MissingSpec.md#pointblank.MissingSpec)  
Specification for structured missing values in a column.

## Contracts and Pipelines

Use [Contract](../reference/Contract.md#pointblank.Contract) and [Step](../reference/Step.md#pointblank.Step) to define declarative data quality contracts that specify what valid data looks like. Use [Steps](../reference/Steps.md#pointblank.Steps) to build reusable step libraries with a fluent API and compose them into [Validate](../reference/Validate.md#pointblank.Validate) plans via [add_steps()](../reference/Validate.add_steps.md#pointblank.Validate.add_steps). Use [Pipeline](../reference/Pipeline.md#pointblank.Pipeline) to enforce contracts at both boundaries of a data transformation (source and target), producing a [PipelineResult](../reference/PipelineResult.md#pointblank.PipelineResult) with full introspection into what passed and what failed.

[Contract](Contract.md#pointblank.Contract)  
A declarative boundary contract for pipeline data.

[Step](Step.md#pointblank.Step)  
A single validation step, stored as data.

[Steps](Steps.md#pointblank.Steps)  
A reusable collection of validation step definitions.

[Validate.add_steps()](Validate.add_steps.md#pointblank.Validate.add_steps)  
Add the steps from one or more [Steps](../reference/Steps.md#pointblank.Steps) collections to the validation.

[Pipeline](Pipeline.md#pointblank.Pipeline)  
Enforce data contracts at both ends of a data transformation.

[PipelineResult](PipelineResult.md#pointblank.PipelineResult)  
The outcome of running a data pipeline.

[ContractViolationError](ContractViolationError.md#pointblank.ContractViolationError)  
The error raised when data fails a contract with `on_violation="raise"`.

## Contract Import/Export

Import external schema definitions (JSON Schema, Frictionless Table Schema, and more) into Pointblank validation workflows, or export Pointblank contracts to those formats. Use `import_contract()` as the entry point, `export_contract()` for the reverse, and [register_adapter()](../reference/register_adapter.md#pointblank.register_adapter) to add support for custom formats.

[import_contract()](import_contract.md#pointblank.import_contract)  
Import a contract or schema written in another format.

[export_contract()](export_contract.md#pointblank.export_contract)  
Export a Pointblank validation or contract to another format.

[list_adapters()](list_adapters.md#pointblank.list_adapters)  
List the formats available for contract import and export.

[register_adapter()](register_adapter.md#pointblank.register_adapter)  
Register a contract adapter so that it can be used for import and export.

[ContractImport](ContractImport.md#pointblank.ContractImport)  
The result of importing a contract or schema from another format.

[ContractAdapter](ContractAdapter.md#pointblank.ContractAdapter)  
The base class for adapters that translate contracts to and from other formats.

[adapters.MappedConstraint](adapters.MappedConstraint.md#pointblank.adapters.MappedConstraint)  
A rule from an external format, translated into a Pointblank validation step.

## Validation Steps

Validation steps are sequential validations on the target data. Call [Validate](../reference/Validate.md#pointblank.Validate)'s validation methods to build up a validation plan: a collection of steps that provides good validation coverage.

[Validate.col_vals_gt()](Validate.col_vals_gt.md#pointblank.Validate.col_vals_gt)  
Are column data greater than a fixed value or data in another column?

[Validate.col_vals_lt()](Validate.col_vals_lt.md#pointblank.Validate.col_vals_lt)  
Are column data less than a fixed value or data in another column?

[Validate.col_vals_ge()](Validate.col_vals_ge.md#pointblank.Validate.col_vals_ge)  
Are column data greater than or equal to a fixed value or data in another column?

[Validate.col_vals_le()](Validate.col_vals_le.md#pointblank.Validate.col_vals_le)  
Are column data less than or equal to a fixed value or data in another column?

[Validate.col_vals_eq()](Validate.col_vals_eq.md#pointblank.Validate.col_vals_eq)  
Are column data equal to a fixed value or data in another column?

[Validate.col_vals_ne()](Validate.col_vals_ne.md#pointblank.Validate.col_vals_ne)  
Are column data not equal to a fixed value or data in another column?

[Validate.col_vals_between()](Validate.col_vals_between.md#pointblank.Validate.col_vals_between)  
Do column data lie between two specified values or data in other columns?

[Validate.col_vals_outside()](Validate.col_vals_outside.md#pointblank.Validate.col_vals_outside)  
Do column data lie outside of two specified values or data in other columns?

[Validate.col_vals_in_set()](Validate.col_vals_in_set.md#pointblank.Validate.col_vals_in_set)  
Validate whether column values are in a set of values.

[Validate.col_vals_not_in_set()](Validate.col_vals_not_in_set.md#pointblank.Validate.col_vals_not_in_set)  
Validate whether column values are not in a set of values.

[Validate.col_vals_increasing()](Validate.col_vals_increasing.md#pointblank.Validate.col_vals_increasing)  
Are column data increasing by row?

[Validate.col_vals_decreasing()](Validate.col_vals_decreasing.md#pointblank.Validate.col_vals_decreasing)  
Are column data decreasing by row?

[Validate.col_vals_null()](Validate.col_vals_null.md#pointblank.Validate.col_vals_null)  
Validate whether values in a column are Null.

[Validate.col_vals_not_null()](Validate.col_vals_not_null.md#pointblank.Validate.col_vals_not_null)  
Validate whether values in a column are not Null.

[Validate.col_vals_regex()](Validate.col_vals_regex.md#pointblank.Validate.col_vals_regex)  
Validate whether column values match a regular expression pattern.

[Validate.col_vals_within_spec()](Validate.col_vals_within_spec.md#pointblank.Validate.col_vals_within_spec)  
Validate whether column values fit within a specification.

[Validate.col_vals_str_len()](Validate.col_vals_str_len.md#pointblank.Validate.col_vals_str_len)  
Validate whether the length of string values falls within specified bounds.

[Validate.col_vals_in_table()](Validate.col_vals_in_table.md#pointblank.Validate.col_vals_in_table)  
Validate that column values exist in a reference table (referential integrity).

[Validate.col_vals_expr()](Validate.col_vals_expr.md#pointblank.Validate.col_vals_expr)  
Validate column values using a custom expression.

[Validate.col_exists()](Validate.col_exists.md#pointblank.Validate.col_exists)  
Validate whether one or more columns exist in the table.

[Validate.col_pct_null()](Validate.col_pct_null.md#pointblank.Validate.col_pct_null)  
Validate whether a column has a specific percentage of Null values.

[Validate.col_pct_missing()](Validate.col_pct_missing.md#pointblank.Validate.col_pct_missing)  
Validate that the percentage of *structured* missing values stays within a limit.

[Validate.col_missing_coded()](Validate.col_missing_coded.md#pointblank.Validate.col_missing_coded)  
Validate that all missing values in a column are *coded* (no uncoded nulls).

[Validate.col_missing_only_coded()](Validate.col_missing_only_coded.md#pointblank.Validate.col_missing_only_coded)  
Validate that a column contains only documented codes and legitimate values.

[Validate.col_missing_consistent()](Validate.col_missing_consistent.md#pointblank.Validate.col_missing_consistent)  
Validate that related columns share a consistent missingness pattern for a given reason.

[Validate.rows_distinct()](Validate.rows_distinct.md#pointblank.Validate.rows_distinct)  
Validate whether rows in the table are distinct.

[Validate.rows_complete()](Validate.rows_complete.md#pointblank.Validate.rows_complete)  
Validate whether row data are complete by having no missing values.

[Validate.col_schema_match()](Validate.col_schema_match.md#pointblank.Validate.col_schema_match)  
Do columns in the table (and their types) match a predefined schema?

[Validate.row_count_match()](Validate.row_count_match.md#pointblank.Validate.row_count_match)  
Validate whether the row count of the table matches a specified count.

[Validate.col_count_match()](Validate.col_count_match.md#pointblank.Validate.col_count_match)  
Validate whether the column count of the table matches a specified count.

[Validate.data_freshness()](Validate.data_freshness.md#pointblank.Validate.data_freshness)  
Validate that data in a datetime column is not older than a specified maximum age.

[Validate.tbl_match()](Validate.tbl_match.md#pointblank.Validate.tbl_match)  
Validate whether the target table matches a comparison table.

[Validate.tbl_reconcile()](Validate.tbl_reconcile.md#pointblank.Validate.tbl_reconcile)  
Validate that aggregates of the target table reconcile with a comparison table.

[Validate.conjointly()](Validate.conjointly.md#pointblank.Validate.conjointly)  
Perform multiple row-wise validations for joint validity.

[Validate.specially()](Validate.specially.md#pointblank.Validate.specially)  
Perform a specialized validation with customized logic.

[Validate.prompt()](Validate.prompt.md#pointblank.Validate.prompt)  
Validate rows using AI/LLM-powered analysis.

## Aggregation Steps

These validation methods check aggregated column values (sums, averages, standard deviations) against fixed values or column references.

[Validate.col_sum_gt()](Validate.col_sum_gt.md#pointblank.Validate.col_sum_gt)  
Does the column sum satisfy a greater than comparison?

[Validate.col_sum_lt()](Validate.col_sum_lt.md#pointblank.Validate.col_sum_lt)  
Does the column sum satisfy a less than comparison?

[Validate.col_sum_ge()](Validate.col_sum_ge.md#pointblank.Validate.col_sum_ge)  
Does the column sum satisfy a greater than or equal to comparison?

[Validate.col_sum_le()](Validate.col_sum_le.md#pointblank.Validate.col_sum_le)  
Does the column sum satisfy a less than or equal to comparison?

[Validate.col_sum_eq()](Validate.col_sum_eq.md#pointblank.Validate.col_sum_eq)  
Does the column sum satisfy an equal to comparison?

[Validate.col_avg_gt()](Validate.col_avg_gt.md#pointblank.Validate.col_avg_gt)  
Does the column average satisfy a greater than comparison?

[Validate.col_avg_lt()](Validate.col_avg_lt.md#pointblank.Validate.col_avg_lt)  
Does the column average satisfy a less than comparison?

[Validate.col_avg_ge()](Validate.col_avg_ge.md#pointblank.Validate.col_avg_ge)  
Does the column average satisfy a greater than or equal to comparison?

[Validate.col_avg_le()](Validate.col_avg_le.md#pointblank.Validate.col_avg_le)  
Does the column average satisfy a less than or equal to comparison?

[Validate.col_avg_eq()](Validate.col_avg_eq.md#pointblank.Validate.col_avg_eq)  
Does the column average satisfy an equal to comparison?

[Validate.col_sd_gt()](Validate.col_sd_gt.md#pointblank.Validate.col_sd_gt)  
Does the column standard deviation satisfy a greater than comparison?

[Validate.col_sd_lt()](Validate.col_sd_lt.md#pointblank.Validate.col_sd_lt)  
Does the column standard deviation satisfy a less than comparison?

[Validate.col_sd_ge()](Validate.col_sd_ge.md#pointblank.Validate.col_sd_ge)  
Does the column standard deviation satisfy a greater than or equal to comparison?

[Validate.col_sd_le()](Validate.col_sd_le.md#pointblank.Validate.col_sd_le)  
Does the column standard deviation satisfy a less than or equal to comparison?

[Validate.col_sd_eq()](Validate.col_sd_eq.md#pointblank.Validate.col_sd_eq)  
Does the column standard deviation satisfy an equal to comparison?

## Column Selection

Use the [col()](../reference/col.md#pointblank.col) function along with column selection helpers to flexibly select columns for validation. Combine [col()](../reference/col.md#pointblank.col) with [starts_with()](../reference/starts_with.md#pointblank.starts_with), `matches()`, etc. for selecting multiple target columns.

[col()](col.md#pointblank.col)  
Helper function for referencing a column in the input table.

[starts_with()](starts_with.md#pointblank.starts_with)  
Select columns that start with specified text.

[ends_with()](ends_with.md#pointblank.ends_with)  
Select columns that end with specified text.

[contains()](contains.md#pointblank.contains)  
Select columns that contain specified text.

[matches()](matches.md#pointblank.matches)  
Select columns that match a specified regular expression pattern.

[everything()](everything.md#pointblank.everything)  
Select all columns.

[first_n()](first_n.md#pointblank.first_n)  
Select the first `n` columns in the column list.

[last_n()](last_n.md#pointblank.last_n)  
Select the last `n` columns in the column list.

[expr_col()](expr_col.md#pointblank.expr_col)  
Create a column expression for use in `conjointly()` validation.

## Segment Groups

Combine multiple values into a single segment using `seg_*()` helper functions.

[seg_group()](seg_group.md#pointblank.seg_group)  
Group together values for segmentation.

## Interrogation and Reporting

The validation plan is executed when [interrogate()](../reference/Validate.interrogate.md#pointblank.Validate.interrogate) is called. After interrogation, view validation reports, extract metrics, or split data based on results.

[Validate.interrogate()](Validate.interrogate.md#pointblank.Validate.interrogate)  
Execute each validation step against the table and store the results.

[Validate.set_tbl()](Validate.set_tbl.md#pointblank.Validate.set_tbl)  
Set or replace the table associated with the Validate object.

[Validate.get_tabular_report()](Validate.get_tabular_report.md#pointblank.Validate.get_tabular_report)  
Validation report as a GT table.

[Validate.get_step_report()](Validate.get_step_report.md#pointblank.Validate.get_step_report)  
Get a detailed report for a single validation step.

[Validate.get_json_report()](Validate.get_json_report.md#pointblank.Validate.get_json_report)  
Get a report of the validation results as a JSON-formatted string.

[Validate.get_dataframe_report()](Validate.get_dataframe_report.md#pointblank.Validate.get_dataframe_report)  
Get a report of the validation results as a DataFrame.

[Validate.to_code()](Validate.to_code.md#pointblank.Validate.to_code)  
Render this validation plan as canonical Pointblank Python code.

[Validate.to_yaml()](Validate.to_yaml.md#pointblank.Validate.to_yaml)  
Serialize this validation plan to a [yaml_interrogate()](../reference/yaml_interrogate.md#pointblank.yaml_interrogate)-compatible YAML config.

[Validate.to_json_schema()](Validate.to_json_schema.md#pointblank.Validate.to_json_schema)  
Export this validation plan as a JSON Schema document.

[Validate.suggest_improvements()](Validate.suggest_improvements.md#pointblank.Validate.suggest_improvements)  
Propose AI-generated improvements to this validation plan.

[Validate.from_prompt()](Validate.from_prompt.md#pointblank.Validate.from_prompt)  
Build a validation plan for this table from a natural-language prompt.

[Validate.get_sundered_data()](Validate.get_sundered_data.md#pointblank.Validate.get_sundered_data)  
Get the data that passed or failed the validation steps.

[Validate.get_row_flags()](Validate.get_row_flags.md#pointblank.Validate.get_row_flags)  
Get the target table with columns flagging the validation steps each row failed.

[Validate.get_data_extracts()](Validate.get_data_extracts.md#pointblank.Validate.get_data_extracts)  
Get the rows that failed for each validation step.

[Validate.all_passed()](Validate.all_passed.md#pointblank.Validate.all_passed)  
Determine if every validation step passed perfectly, with no failing test units.

[Validate.assert_passing()](Validate.assert_passing.md#pointblank.Validate.assert_passing)  
Raise an `AssertionError` if all tests are not passing.

[Validate.assert_below_threshold()](Validate.assert_below_threshold.md#pointblank.Validate.assert_below_threshold)  
Raise an `AssertionError` if validation steps exceed a specified threshold level.

[Validate.assert_dimension_scores()](Validate.assert_dimension_scores.md#pointblank.Validate.assert_dimension_scores)  
Raise an `AssertionError` if any dimension's health score falls below a minimum.

[Validate.above_threshold()](Validate.above_threshold.md#pointblank.Validate.above_threshold)  
Check if any validation steps exceed a specified threshold level.

[Validate.get_dimension_scores()](Validate.get_dimension_scores.md#pointblank.Validate.get_dimension_scores)  
Get per-dimension health scores from the validation results.

[Validate.get_health_score()](Validate.get_health_score.md#pointblank.Validate.get_health_score)  
Get the overall data quality health score from the validation results.

[Validate.get_scorecard()](Validate.get_scorecard.md#pointblank.Validate.get_scorecard)  
Get a data quality scorecard as a GT table.

[Validate.n()](Validate.n.md#pointblank.Validate.n)  
Provides a dictionary of the number of test units for each validation step.

[Validate.n_passed()](Validate.n_passed.md#pointblank.Validate.n_passed)  
Provides a dictionary of the number of test units that passed for each validation step.

[Validate.n_failed()](Validate.n_failed.md#pointblank.Validate.n_failed)  
Provides a dictionary of the number of test units that failed for each validation step.

[Validate.f_passed()](Validate.f_passed.md#pointblank.Validate.f_passed)  
Provides a dictionary of the fraction of test units that passed for each validation step.

[Validate.f_failed()](Validate.f_failed.md#pointblank.Validate.f_failed)  
Provides a dictionary of the fraction of test units that failed for each validation step.

[Validate.warning()](Validate.warning.md#pointblank.Validate.warning)  
Get the 'warning' level status for each validation step.

[Validate.error()](Validate.error.md#pointblank.Validate.error)  
Get the 'error' level status for each validation step.

[Validate.critical()](Validate.critical.md#pointblank.Validate.critical)  
Get the 'critical' level status for each validation step.

## Inspection and Assistance

Functions for getting to grips with a new data table. Use [DataScan](../reference/DataScan.md#pointblank.DataScan) for a quick overview, [preview()](../reference/preview.md#pointblank.preview) for first/last rows, [col_summary_tbl()](../reference/col_summary_tbl.md#pointblank.col_summary_tbl) for column summaries, and [missing_vals_tbl()](../reference/missing_vals_tbl.md#pointblank.missing_vals_tbl) for missing value analysis.

[DataScan](DataScan.md#pointblank.DataScan)  
Get a summary of a dataset.

[DataScanDiff](DataScanDiff.md#pointblank.DataScanDiff)  
The result of comparing two [DataScan](../reference/DataScan.md#pointblank.DataScan) profiles.

[preview()](preview.md#pointblank.preview)  
Display a table preview that shows some rows from the top, some from the bottom.

[col_summary_tbl()](col_summary_tbl.md#pointblank.col_summary_tbl)  
Generate a column-level summary table of a dataset.

[missing_vals_tbl()](missing_vals_tbl.md#pointblank.missing_vals_tbl)  
Display a table that shows the missing values in the input table.

[load_dataset()](load_dataset.md#pointblank.load_dataset)  
Load a dataset hosted in the library as specified table type.

[get_data_path()](get_data_path.md#pointblank.get_data_path)  
Get the file path to a dataset included with the Pointblank package.

[connect_to_table()](connect_to_table.md#pointblank.connect_to_table)  
Connect to a database table using a connection string.

[print_database_tables()](print_database_tables.md#pointblank.print_database_tables)  
List all tables in a database from a connection string.

## DataScan Methods

Methods for the DataScan class

[DataScan.get_tabular_report()](DataScan.get_tabular_report.md#pointblank.DataScan.get_tabular_report)  
Get a tabular report of the scan.

[DataScan.summary_data](DataScan.summary_data.md#pointblank.DataScan.summary_data)  
The column statistics as a DataFrame.

[DataScan.compare()](DataScan.compare.md#pointblank.DataScan.compare)  
Compare this scan against a baseline and return the differences.

[DataScan.save_to_json()](DataScan.save_to_json.md#pointblank.DataScan.save_to_json)  
Save the profile to a JSON file.

[DataScan.load_from_json()](DataScan.load_from_json.md#pointblank.DataScan.load_from_json)  
Load a [DataScan](../reference/DataScan.md#pointblank.DataScan) from a JSON file produced by [save_to_json()](../reference/DataScan.save_to_json.md#pointblank.DataScan.save_to_json).

[DataScan.to_json()](DataScan.to_json.md#pointblank.DataScan.to_json)  
Export the profile as a JSON string.

[DataScan.from_json()](DataScan.from_json.md#pointblank.DataScan.from_json)  
Restore a [DataScan](../reference/DataScan.md#pointblank.DataScan) from a JSON string.

[DataScan.to_dict()](DataScan.to_dict.md#pointblank.DataScan.to_dict)  
Export the profile as a structured dictionary.

[DataScan.from_dict()](DataScan.from_dict.md#pointblank.DataScan.from_dict)  
Restore a [DataScan](../reference/DataScan.md#pointblank.DataScan) from a dictionary.

## Table Comparison

Compare two tables row by row. `compare()` aligns the rows of a source and a target table (by key, by position, or as multisets) and reports missing, extra, and changed rows, plus schema differences. The result is a [Comparison](../reference/Comparison.md#pointblank.Comparison) object with counts, row samples, and a tabular report. The same comparison is available as a validation step through [Validate.tbl_match()](../reference/Validate.tbl_match.md#pointblank.Validate.tbl_match), and [Validate.tbl_reconcile()](../reference/Validate.tbl_reconcile.md#pointblank.Validate.tbl_reconcile) compares aggregates (e.g., totals per group) instead of rows.

[compare()](compare.md#pointblank.compare)  
Compare two tables row by row and report how they differ.

[Comparison](Comparison.md#pointblank.Comparison)  
The result of comparing a source table with a target table.

[SchemaDiff](SchemaDiff.md#pointblank.SchemaDiff)  
Differences between the schemas of the source and target tables.

## Comparison Methods

Methods for the Comparison class

[Comparison.identical](Comparison.identical.md#pointblank.Comparison.identical)  
Whether the tables match under the comparison settings.

[Comparison.schema_ok](Comparison.schema_ok.md#pointblank.Comparison.schema_ok)  
Whether the schema check passed.

[Comparison.status_counts](Comparison.status_counts.md#pointblank.Comparison.status_counts)  
The number of test units with each row status.

[Comparison.n](Comparison.n.md#pointblank.Comparison.n)  
The number of test units (aligned rows).

[Comparison.n_passed](Comparison.n_passed.md#pointblank.Comparison.n_passed)  
The number of test units that passed.

[Comparison.n_failed](Comparison.n_failed.md#pointblank.Comparison.n_failed)  
The number of test units that failed.

[Comparison.rows()](Comparison.rows.md#pointblank.Comparison.rows)  
Get a sample of rows with a given status.

[Comparison.column_summary()](Comparison.column_summary.md#pointblank.Comparison.column_summary)  
Get a per-column summary of value mismatches.

[Comparison.get_tabular_report()](Comparison.get_tabular_report.md#pointblank.Comparison.get_tabular_report)  
Get a tabular report of the comparison.

[Comparison.to_dict()](Comparison.to_dict.md#pointblank.Comparison.to_dict)  
Get the comparison results as a JSON-serializable dict.

[Comparison.to_json()](Comparison.to_json.md#pointblank.Comparison.to_json)  
Get the comparison results as a JSON string.

## Table Pre-checks

Helper functions for use with the `active=` parameter of validation methods. These inspect the target table before a step runs and conditionally skip the step when preconditions are not met.

[has_columns()](has_columns.md#pointblank.has_columns)  
Check whether one or more columns exist in a table.

[has_rows()](has_rows.md#pointblank.has_rows)  
Check whether a table has a certain number of rows.

## YAML

Functions for using YAML to orchestrate validation workflows.

[yaml_interrogate()](yaml_interrogate.md#pointblank.yaml_interrogate)  
Execute a YAML-based validation workflow.

[validate_yaml()](validate_yaml.md#pointblank.validate_yaml)  
Validate YAML configuration against the expected structure.

[yaml_to_python()](yaml_to_python.md#pointblank.yaml_to_python)  
Convert YAML validation configuration to equivalent Python code.

## Utility Functions

Functions for accessing metadata about the target data and managing configuration.

[get_column_count()](get_column_count.md#pointblank.get_column_count)  
Get the number of columns in a table.

[get_row_count()](get_row_count.md#pointblank.get_row_count)  
Get the number of rows in a table.

[get_action_metadata()](get_action_metadata.md#pointblank.get_action_metadata)  
Access step-level metadata when authoring custom actions.

[get_validation_summary()](get_validation_summary.md#pointblank.get_validation_summary)  
Access validation summary information when authoring final actions.

[write_file()](write_file.md#pointblank.write_file)  
Write a Validate object to disk as a serialized file.

[read_file()](read_file.md#pointblank.read_file)  
Read a Validate object from disk that was previously saved with [write_file()](../reference/write_file.md#pointblank.write_file).

[ref()](ref.md#pointblank.ref)  
Reference a column from the reference data for aggregate comparisons.

## Test Data Generation

Generate synthetic test data based on schema definitions. Use [generate_dataset()](../reference/generate_dataset.md#pointblank.generate_dataset) to create data from a Schema object, or [schema_from_tbl()](../reference/schema_from_tbl.md#pointblank.schema_from_tbl) to infer a generation-ready schema from an existing table (Polars, Pandas, or Ibis/DuckDB).

[generate_dataset()](generate_dataset.md#pointblank.generate_dataset)  
Generate synthetic test data from a schema.

[schema_from_tbl()](schema_from_tbl.md#pointblank.schema_from_tbl)  
Create a Schema from an existing table with inferred Field constraints.

[int_field()](int_field.md#pointblank.int_field)  
Create an integer column specification for use in a schema.

[float_field()](float_field.md#pointblank.float_field)  
Create a floating-point column specification for use in a schema.

[string_field()](string_field.md#pointblank.string_field)  
Create a string column specification for use in a schema.

[bool_field()](bool_field.md#pointblank.bool_field)  
Create a boolean column specification for use in a schema.

[date_field()](date_field.md#pointblank.date_field)  
Create a date column specification for use in a schema.

[datetime_field()](datetime_field.md#pointblank.datetime_field)  
Create a datetime column specification for use in a schema.

[time_field()](time_field.md#pointblank.time_field)  
Create a time column specification for use in a schema.

[duration_field()](duration_field.md#pointblank.duration_field)  
Create a duration column specification for use in a schema.

[profile_fields()](profile_fields.md#pointblank.profile_fields)  
Create a dict of string field specifications representing a person profile.

## Prebuilt Actions

Prebuilt action functions for common notification patterns.

[send_slack_notification()](send_slack_notification.md#pointblank.send_slack_notification)  
Create an action that sends a Slack notification about a validation.

[emit_otel()](emit_otel.md#pointblank.emit_otel)  
Create a final action that sends a validation's results to OpenTelemetry.

## Metadata Import/Export

Import variable-level metadata from external data standards files (CDISC Define-XML, Controlled Terminology, SPSS `.sav`, SAS XPORT, Stata `.dta`, and more) and export metadata to various formats. Use [import_metadata()](../reference/import_metadata.md#pointblank.import_metadata) as the entry point and [export_metadata()](../reference/export_metadata.md#pointblank.export_metadata) for the reverse.

[import_metadata()](import_metadata.md#pointblank.import_metadata)  
Import metadata from a statistical-package file or a metadata standard.

[export_metadata()](export_metadata.md#pointblank.export_metadata)  
Export metadata as a Frictionless Table Schema.

[load_metadata_example()](load_metadata_example.md#pointblank.load_metadata_example)  
Get the path to a bundled metadata example file.

[MetadataImport](MetadataImport.md#pointblank.MetadataImport)  
The metadata for a single dataset.

[MetadataPackage](MetadataPackage.md#pointblank.MetadataPackage)  
The metadata for several datasets, read from one source.

[VariableMetadata](VariableMetadata.md#pointblank.VariableMetadata)  
The metadata for a single variable (column).

[Codelist](Codelist.md#pointblank.Codelist)  
A set of permitted values, each with a label.

[CodelistEntry](CodelistEntry.md#pointblank.CodelistEntry)  
A single permitted value in a codelist.

[MissingValueCode](MissingValueCode.md#pointblank.MissingValueCode)  
A declared missing-value code, as read from a metadata file.

## SDTM Validation

Validate clinical datasets against CDISC SDTM domain templates. Use [validate_sdtm()](../reference/validate_sdtm.md#pointblank.validate_sdtm) to generate a full [Validate](../reference/Validate.md#pointblank.Validate) workflow, or [validate_sdtm_structure()](../reference/validate_sdtm_structure.md#pointblank.validate_sdtm_structure) for a quick structural conformance check. Retrieve domain templates with [get_sdtm_domain()](../reference/get_sdtm_domain.md#pointblank.get_sdtm_domain) and [list_sdtm_domains()](../reference/list_sdtm_domains.md#pointblank.list_sdtm_domains).

[validate_sdtm()](validate_sdtm.md#pointblank.validate_sdtm)  
Build a validation plan for a dataset from an SDTM domain template.

[validate_sdtm_structure()](validate_sdtm_structure.md#pointblank.validate_sdtm_structure)  
Check a dataset's variables against an SDTM domain template.

[sdtm_to_metadata()](sdtm_to_metadata.md#pointblank.sdtm_to_metadata)  
Convert an SDTM domain template to a [MetadataImport](../reference/MetadataImport.md#pointblank.MetadataImport) object.

[get_sdtm_domain()](get_sdtm_domain.md#pointblank.get_sdtm_domain)  
Get the template for an SDTM domain.

[list_sdtm_domains()](list_sdtm_domains.md#pointblank.list_sdtm_domains)  
List the SDTM domains that have a template.

[SDTMDomainTemplate](SDTMDomainTemplate.md#pointblank.SDTMDomainTemplate)  
The structural template for an SDTM domain.

[SDTMVariableSpec](SDTMVariableSpec.md#pointblank.SDTMVariableSpec)  
The specification of one variable in an SDTM domain template.

## ADaM Validation

Validate analysis datasets against CDISC ADaM templates. Use [validate_adam()](../reference/validate_adam.md#pointblank.validate_adam) to generate a full [Validate](../reference/Validate.md#pointblank.Validate) workflow, or [validate_adam_structure()](../reference/validate_adam_structure.md#pointblank.validate_adam_structure) for a quick structural conformance check. Retrieve dataset templates with [get_adam_dataset()](../reference/get_adam_dataset.md#pointblank.get_adam_dataset) and [list_adam_datasets()](../reference/list_adam_datasets.md#pointblank.list_adam_datasets).

[validate_adam()](validate_adam.md#pointblank.validate_adam)  
Build a validation plan for a dataset from an ADaM dataset template.

[validate_adam_structure()](validate_adam_structure.md#pointblank.validate_adam_structure)  
Check a dataset's variables against an ADaM dataset template.

[adam_to_metadata()](adam_to_metadata.md#pointblank.adam_to_metadata)  
Convert an ADaM dataset template to a [MetadataImport](../reference/MetadataImport.md#pointblank.MetadataImport) object.

[get_adam_dataset()](get_adam_dataset.md#pointblank.get_adam_dataset)  
Get the template for a type of ADaM dataset.

[list_adam_datasets()](list_adam_datasets.md#pointblank.list_adam_datasets)  
List the ADaM dataset templates.

[ADaMDatasetTemplate](ADaMDatasetTemplate.md#pointblank.ADaMDatasetTemplate)  
The structural template for an ADaM dataset.

[ADaMVariableSpec](ADaMVariableSpec.md#pointblank.ADaMVariableSpec)  
The specification of one variable in an ADaM dataset template.

## CDISC Submission Conformance

Validate SDTM datasets for CDISC conformance. [validate_sdtmig()](../reference/validate_sdtmig.md#pointblank.validate_sdtmig) is the primary entry point: pass a dictionary of domain DataFrames and receive a [ConformanceReport](../reference/ConformanceReport.md#pointblank.ConformanceReport) with 362 SDTMIG 3.4 rules evaluated in-process. To work with a whole submission (including cross-dataset referential integrity and SUPP- linkage, with `validate_conformance(engine="validate")`), use [SubmissionPackage](../reference/SubmissionPackage.md#pointblank.SubmissionPackage). For the authoritative CDISC-certified rule set, use [validate_cdisc_submission()](../reference/validate_cdisc_submission.md#pointblank.validate_cdisc_submission) (requires the CORE CLI) or `SubmissionPackage.validate_conformance(engine="core")`. All paths return a [ConformanceReport](../reference/ConformanceReport.md#pointblank.ConformanceReport).

[validate_sdtmig()](validate_sdtmig.md#pointblank.validate_sdtmig)  
Check SDTM datasets with Pointblank's bundled SDTMIG conformance rules.

[validate_cdisc_submission()](validate_cdisc_submission.md#pointblank.validate_cdisc_submission)  
Check a CDISC submission with the CDISC CORE engine.

[SubmissionPackage](SubmissionPackage.md#pointblank.SubmissionPackage)  
A study's datasets, gathered for CDISC conformance validation.

[ConformanceReport](ConformanceReport.md#pointblank.ConformanceReport)  
The results of a CDISC conformance check.

## Integrations

Classes for integrating Pointblank with external observability and monitoring systems. Use [OTelExporter](../reference/integrations.otel.OTelExporter.md#pointblank.integrations.otel.OTelExporter) to export validation results as OpenTelemetry metrics, traces, and logs.

[integrations.otel.OTelExporter](integrations.otel.OTelExporter.md#pointblank.integrations.otel.OTelExporter)  
Export Pointblank validation results as OpenTelemetry signals.