# CLI Reference This page provides a complete reference for all Pointblank CLI commands. Each section shows the full help text as it appears in the terminal, giving you quick access to all available options and examples. For practical usage examples and workflows, see the [CLI Data Validation](../../user-guide/the-pointblank-cli/cli-data-validation.md) and [CLI Data Inspection](../../user-guide/the-pointblank-cli/cli-data-inspection.md) guides. # `pb` - Main Command The main entry point for all Pointblank CLI operations: > Usage: pb [OPTIONS] COMMAND [ARGS]... > > Pointblank CLI: Data validation and quality tools for data engineers. > > Use this CLI to validate data quality, explore datasets, and generate > comprehensive reports for CSV, Parquet, and database sources. Suitable for > data pipelines, ETL validation, and exploratory data analysis from the > command line. > > Quick Examples: > > pb preview data.csv Preview your data > pb scan data.csv Generate data profile > pb validate data.csv Run basic validation > > Use pb COMMAND --help for detailed help on any command. > > Options: > -v, --version Show the version and exit. > -h, --help Show this message and exit. > > Commands: > info Display information about a data source. > preview Preview a data table showing head and tail rows. > scan Generate a data scan profile report. > missing Generate a missing values report for a data table. > validate Perform single or multiple data validations. > run Run a Pointblank validation script or YAML configuration. > make-template Create a validation script or YAML configuration template. > pl Execute Polars expressions and display results. > datasets List available built-in datasets. > requirements Check installed dependencies and their availability. # `pb info` - Data Source Information Display basic information about a data source: > Usage: pb info [OPTIONS] [DATA_SOURCE] > > Display information about a data source. > > Shows table type, dimensions, column names, and data types. > > DATA_SOURCE can be: > > - CSV file path (e.g., data.csv) > - Parquet file path or pattern (e.g., data.parquet, data/*.parquet) > - GitHub URL to CSV/Parquet (e.g., https://github.com/user/repo/blob/main/data.csv) > - Database connection string (e.g., duckdb:///path/to/db.ddb::table_name) > - Dataset name from pointblank (small_table, game_revenue, nycflights, global_sales) > > Options: > --help Show this message and exit. # `pb preview` - Data Table Preview Preview data showing head and tail rows: > Usage: pb preview [OPTIONS] [DATA_SOURCE] > > Preview a data table showing head and tail rows. > > DATA_SOURCE can be: > > - CSV file path (e.g., data.csv) > - Parquet file path or pattern (e.g., data.parquet, data/*.parquet) > - GitHub URL to CSV/Parquet (e.g., https://github.com/user/repo/blob/main/data.csv) > - Database connection string (e.g., duckdb:///path/to/db.ddb::table_name) > - Dataset name from pointblank (small_table, game_revenue, nycflights, global_sales) > - Piped data from pb pl command > > COLUMN SELECTION OPTIONS: > > For tables with many columns, use these options to control which columns are > displayed: > > - --columns: Specify exact columns (e.g., --columns "name,age,email") > - --col-range: Select column range (e.g., --col-range "1:10", --col-range "5:", --col-range ":15") > - --col-first: Show first N columns (e.g., --col-first 5) > - --col-last: Show last N columns (e.g., --col-last 3) > > Tables with >15 columns automatically show first 7 and last 7 columns with > indicators. > > Options: > --columns TEXT Comma-separated list of columns to display > --col-range TEXT Column range like '1:10' or '5:' or ':15' > (1-based indexing) > --col-first INTEGER Show first N columns > --col-last INTEGER Show last N columns > --head INTEGER Number of rows from the top (default: 5) > --tail INTEGER Number of rows from the bottom (default: 5) > --limit INTEGER Maximum total rows to display (default: 50) > --no-row-numbers Hide row numbers > --max-col-width INTEGER Maximum column width in pixels (default: 250) > --min-table-width INTEGER Minimum table width in pixels (default: 500) > --no-header Hide table header > --output-html PATH Save HTML output to file > --help Show this message and exit. # `pb scan` - Data Profile Reports Generate comprehensive data profiles: > Usage: pb scan [OPTIONS] [DATA_SOURCE] > > Generate a data scan profile report. > > Produces a comprehensive data profile including: > > - Column types and distributions > - Missing value patterns > - Basic statistics > - Data quality indicators > > DATA_SOURCE can be: > > - CSV file path (e.g., data.csv) > - Parquet file path or pattern (e.g., data.parquet, data/*.parquet) > - GitHub URL to CSV/Parquet (e.g., https://github.com/user/repo/blob/main/data.csv) > - Database connection string (e.g., duckdb:///path/to/db.ddb::table_name) > - Dataset name from pointblank (small_table, game_revenue, nycflights, global_sales) > - Piped data from pb pl command > > Options: > --output-html PATH Save HTML scan report to file > -c, --columns TEXT Comma-separated list of columns to scan > --help Show this message and exit. # `pb missing` - Missing Values Reports Generate reports focused on missing values: > Usage: pb missing [OPTIONS] [DATA_SOURCE] > > Generate a missing values report for a data table. > > DATA_SOURCE can be: > > - CSV file path (e.g., data.csv) > - Parquet file path or pattern (e.g., data.parquet, data/*.parquet) > - GitHub URL to CSV/Parquet (e.g., https://github.com/user/repo/blob/main/data.csv) > - Database connection string (e.g., duckdb:///path/to/db.ddb::table_name) > - Dataset name from pointblank (small_table, game_revenue, nycflights, global_sales) > - Piped data from pb pl command > > Options: > --output-html PATH Save HTML output to file > --help Show this message and exit. # `pb validate` - Quick Data Validations Perform single or multiple data validations: > Usage: pb validate [OPTIONS] [DATA_SOURCE] > > Perform single or multiple data validations. > > Run one or more validation checks on your data in a single command. Use > multiple --check options to perform multiple validations. > > DATA_SOURCE can be: > > - CSV file path (e.g., data.csv) > - Parquet file path or pattern (e.g., data.parquet, data/*.parquet) > - GitHub URL to CSV/Parquet (e.g., https://github.com/user/repo/blob/main/data.csv) > - Database connection string (e.g., duckdb:///path/to/db.ddb::table_name) > - Dataset name from pointblank (small_table, game_revenue, nycflights, global_sales) > > AVAILABLE CHECK_TYPES: > > Require no additional options: > > - rows-distinct: Check if all rows in the dataset are unique (no duplicates) > - rows-complete: Check if all rows are complete (no missing values in any column) > > Require --column: > > - col-exists: Check if a specific column exists in the dataset > - col-vals-not-null: Check if all values in a column are not null/missing > > Require --column and --value: > > - col-vals-gt: Check if column values are greater than a fixed value > - col-vals-ge: Check if column values are greater than or equal to a fixed value > - col-vals-lt: Check if column values are less than a fixed value > - col-vals-le: Check if column values are less than or equal to a fixed value > > Require --column and --set: > > - col-vals-in-set: Check if column values are in an allowed set > > Use --list-checks to see all available validation methods with examples. The > default CHECK_TYPE is 'rows-distinct' which checks for duplicate rows. > > Examples: > > pb validate data.csv # Uses default validation (rows-distinct) > pb validate data.csv --list-checks # Show all available checks > pb validate data.csv --check rows-distinct > pb validate data.csv --check rows-distinct --show-extract > pb validate data.csv --check rows-distinct --write-extract failing_rows_folder > pb validate data.csv --check rows-distinct --exit-code > pb validate data.csv --check col-exists --column price > pb validate data.csv --check col-vals-not-null --column email > pb validate data.csv --check col-vals-gt --column score --value 50 > pb validate data.csv --check col-vals-in-set --column status --set "active,inactive,pending" > > Multiple validations in one command: pb validate data.csv --check rows- > distinct --check rows-complete > > Options: > --list-checks List available validation checks and exit > --check CHECK_TYPE Type of validation check to perform. Can be used > multiple times for multiple checks. > --column TEXT Column name or integer position as #N (1-based index) > for validation. > --set TEXT Comma-separated allowed values for col-vals-in-set > checks. > --value FLOAT Numeric value for comparison checks. > --show-extract Show extract of failing rows if validation fails > --write-extract TEXT Save failing rows to folder. Provide base name for > folder. > --limit INTEGER Maximum number of failing rows to save to CSV > (default: 500) > --exit-code Exit with non-zero code if validation fails > --help Show this message and exit. # `pb run` - Validation Scripts and YAML Run Python validation scripts or YAML configurations: > Usage: pb run [OPTIONS] [VALIDATION_FILE] > > Run a Pointblank validation script or YAML configuration. > > VALIDATION_FILE can be: - A Python file (.py) that defines validation logic > - A YAML configuration file (.yaml, .yml) that defines validation steps > > Python scripts should load their own data and create validation objects. > YAML configurations define data sources and validation steps declaratively. > > If --data is provided, it will automatically replace the data source in your > validation objects (Python scripts) or override the 'tbl' field (YAML > configs). > > To get started quickly, use 'pb make-template' to create templates. > > DATA can be: > > - CSV file path (e.g., data.csv) > - Parquet file path or pattern (e.g., data.parquet, data/*.parquet) > - GitHub URL to CSV/Parquet (e.g., https://github.com/user/repo/blob/main/data.csv) > - Database connection string (e.g., duckdb:///path/to/db.ddb::table_name) > - Dataset name from pointblank (small_table, game_revenue, nycflights, global_sales) > > Examples: > > pb make-template my_validation.py # Create a Python template > pb run validation_script.py > pb run validation_config.yaml > pb run validation_script.py --data data.csv > pb run validation_config.yaml --data small_table --output-html report.html > pb run validation_script.py --show-extract --fail-on error > pb run validation_config.yaml --write-extract extracts_folder --fail-on critical > > Options: > --data TEXT Data source to replace in validation objects > (Python scripts and YAML configs) > --output-html PATH Save HTML validation report to file > --output-json PATH Save JSON validation summary to file > --show-extract Show extract of failing rows if validation > fails > --write-extract TEXT Save failing rows to folders (one CSV per > step). Provide base name for folder. > --limit INTEGER Maximum number of failing rows to save to > CSV (default: 500) > --fail-on [critical|error|warning|any] > Exit with non-zero code when validation > reaches this threshold level > --help Show this message and exit. # `pb make-template` - Template Generation Create validation script or YAML configuration templates: > Usage: pb make-template [OPTIONS] [OUTPUT_FILE] > > Create a validation script or YAML configuration template. > > Creates a sample Python script or YAML configuration with examples showing > how to use Pointblank for data validation. The template type is determined > by the file extension: - .py files create Python script templates - > .yaml/.yml files create YAML configuration templates > > Edit the template to add your own data loading and validation rules, then > run it with 'pb run'. > > OUTPUT_FILE is the path where the template will be created. > > Examples: > > pb make-template my_validation.py # Creates Python script template > pb make-template my_validation.yaml # Creates YAML config template > pb make-template validation_template.yml # Creates YAML config template > > Options: > --help Show this message and exit. # `pb pl` - Polars Expression Execution Execute Polars expressions and display results: > Usage: pb pl [OPTIONS] [POLARS_EXPRESSION] > > Execute Polars expressions and display results. > > Execute Polars DataFrame operations from the command line and display the > results using Pointblank's visualization tools. > > POLARS_EXPRESSION should be a valid Polars expression that returns a > DataFrame. The 'pl' module is automatically imported and available. > > Examples: > > # Direct expression > pb pl "pl.read_csv('data.csv')" > pb pl "pl.read_csv('data.csv').select(['name', 'age'])" > pb pl "pl.read_csv('data.csv').filter(pl.col('age') > 25)" > > # Multi-line with editor (supports multiple statements) > pb pl --edit > > # Multi-statement code example in editor: > # csv = pl.read_csv('data.csv') > # result = csv.select(['name', 'age']).filter(pl.col('age') > 25) > > # Multi-line with a specific editor > pb pl --edit --editor nano > pb pl --edit --editor code > pb pl --edit --editor micro > > # From file > pb pl --file query.py > > Piping to other pb commands > pb pl "pl.read_csv('data.csv').head(20)" --pipe | pb validate --check rows-distinct > pb pl --edit --pipe | pb preview --head 10 > pb pl --edit --pipe | pb scan --output-html report.html > pb pl --edit --pipe | pb missing --output-html missing_report.html > > Use --output-format to change how results are displayed: > pb pl "pl.read_csv('data.csv')" --output-format scan > pb pl "pl.read_csv('data.csv')" --output-format missing > pb pl "pl.read_csv('data.csv')" --output-format info > > Note: For multi-statement code, assign your final result to a variable like > 'result', 'df', 'data', or ensure it's the last expression. > > Options: > -e, --edit Open editor for multi-line input > -f, --file PATH Read query from file > --editor TEXT Editor to use for --edit mode (overrides > $EDITOR and auto-detection) > -o, --output-format [preview|scan|missing|info] > Output format for the result > --preview-head INTEGER Number of head rows for preview > --preview-tail INTEGER Number of tail rows for preview > --output-html PATH Save HTML output to file > --pipe Output data in a format suitable for piping > to other pb commands > --pipe-format [parquet|csv] Format for piped output (default: parquet) > --help Show this message and exit. # `pb datasets` - Built-in Datasets List available built-in datasets: > Usage: pb datasets [OPTIONS] > > List available built-in datasets. > > Options: > --help Show this message and exit. # `pb requirements` - Dependency Check Check installed dependencies and their availability: > Usage: pb requirements [OPTIONS] > > Check installed dependencies and their availability. > > Options: > --help Show this message and exit. # Common Data Source Types All commands that accept a `DATA_SOURCE` parameter support these formats: - **CSV files**: `data.csv`, `path/to/data.csv` - **Parquet files**: `data.parquet`, `data/*.parquet` (patterns supported) - **GitHub URLs**: `https://github.com/user/repo/blob/main/data.csv` - **Database connections**: `duckdb:///path/to/db.ddb::table_name` - **Built-in datasets**: `small_table`, `game_revenue`, `nycflights`, `global_sales` - **Piped data**: Output from `pb pl` command (where supported) # Exit Codes and Automation Many commands support options useful for automation and CI/CD: - `--exit-code`: Exit with non-zero code on validation failure - `--fail-on [critical|error|warning|any]`: Control failure thresholds - `--output-html`, `--output-json`: Save reports for external consumption - `--write-extract`: Save failing rows for investigation These features make Pointblank CLI commands suitable for integration into data pipelines, quality gates, and automated workflows.