Pointblank

https://posit-dev.github.io/pointblank
Admin
Pointblank is a data validation framework for Python that makes data quality checks beautiful and...
Tokens:488,496
Snippets:6,062
Trust Score:8.3
Update:2 months ago
Show doc for...
Tokens:
Raw
### Install and Verify Pointblank CLI

Source: https://posit-dev.github.io/pointblank/examples/advanced-topics/cli-interactive.md

Install the package and verify the installation using the help command.

```bash
pip install pointblank
pb --help  # Verify installation
```

```bash
pip install pointblank
pb --help
```

--------------------------------

### Backend-Specific Installation

Source: https://posit-dev.github.io/pointblank/user-guide/index.md

Install optional dependencies to enable support for specific data backends.

```bash
pip install "pointblank[pl]"       # Polars support
pip install "pointblank[pd]"       # Pandas support
pip install "pointblank[duckdb]"   # DuckDB support
pip install "pointblank[postgres]" # PostgreSQL support
```

--------------------------------

### Install Pointblank and Backends

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Install the core package and optional dependencies for specific data backends.

```bash
pip install pointblank
# or
conda install conda-forge::pointblank
```

```bash
pip install "pointblank[pl]"       # Polars support
pip install "pointblank[pd]"       # Pandas support
pip install "pointblank[duckdb]"   # DuckDB support
pip install "pointblank[postgres]" # PostgreSQL support
```

--------------------------------

### Database connection string examples

Source: https://posit-dev.github.io/pointblank/reference/get_column_count.md

Examples of URI-formatted connection strings used to specify a database table.

```text
"duckdb:///path/to/database.ddb::table_name"
"sqlite:///path/to/database.db::table_name"
"postgresql://user:password@localhost:5432/database::table_name"
"mysql://user:password@localhost:3306/database::table_name"
"bigquery://project/dataset::table_name"
"snowflake://user:password@account/database/schema::table_name"
```

--------------------------------

### Install package and dependencies

Source: https://posit-dev.github.io/pointblank/contributing.md

Commands to install the package in editable mode and include development dependencies.

```bash
pip install -e .
```

```bash
pip install '.[dev]'
```

--------------------------------

### Install Pointblank

Source: https://posit-dev.github.io/pointblank/blog/intro-pointblank

Use pip to install the pointblank package from PyPI.

```bash
pip install pointblank
```

--------------------------------

### Package Installation

Source: https://posit-dev.github.io/pointblank/user-guide/index.md

Install the core Pointblank package using pip or conda.

```bash
pip install pointblank
# or
conda install conda-forge::pointblank
```

--------------------------------

### Pointblank CLI Quick Examples

Source: https://posit-dev.github.io/pointblank/reference/cli/pb.md

Common commands for previewing, scanning, and validating data files.

```bash
pb preview data.csv Preview your data pb scan data.csv Generate data profile pb validate data.csv Run basic validation
```

--------------------------------

### Install Ibis database backends

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Install optional dependencies to enable database connectivity via Ibis.

```bash
pip install pointblank[sqlite]      # SQLite
pip install pointblank[duckdb]      # DuckDB
pip install pointblank[postgres]    # PostgreSQL
pip install pointblank[mysql]       # MySQL
pip install pointblank[mssql]       # Microsoft SQL Server
pip install pointblank[bigquery]    # BigQuery
pip install pointblank[pyspark]     # Apache Spark
pip install pointblank[databricks]  # Databricks
pip install pointblank[snowflake]   # Snowflake

# Example of installing multiple backends
pip install pointblank[duckdb,postgres,sqlite]
```

--------------------------------

### Install CDISC CORE from repository

Source: https://posit-dev.github.io/pointblank/user-guide/metadata-import/cdisc-submission-conformance.md

Clone the source repository and install required dependencies for local execution.

```bash
git clone https://github.com/cdisc-org/cdisc-rules-engine.git
cd cdisc-rules-engine
pip install -r requirements.txt
python core.py --version
```

--------------------------------

### Install Pointblank with uv

Source: https://posit-dev.github.io/pointblank/user-guide/getting-started/installation.md

Installation command for the Pointblank package using the uv package manager.

```bash
uv pip install pointblank
```

--------------------------------

### Install Pointblank with stats support

Source: https://posit-dev.github.io/pointblank/user-guide/metadata-import/statistical-packages.md

Install Pointblank along with all necessary dependencies for statistical file processing.

```bash
pip install pointblank[stats]
```

--------------------------------

### Install Dependencies

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Commands to install the required pyreadstat library or the Pointblank stats extra.

```bash
pip install pyreadstat
```

```bash
pip install pointblank[stats]
```

--------------------------------

### Install Pointblank with pixi

Source: https://posit-dev.github.io/pointblank/user-guide/getting-started/installation.md

Commands to initialize a new project and add Pointblank as a dependency using pixi.

```bash
# add pointblank to project
pixi init name-of-project
cd name-of-project
pixi add pointblank
```

--------------------------------

### Install OTel exporter packages

Source: https://posit-dev.github.io/pointblank/user-guide/integrations/otel-integration.md

Install the necessary exporter packages to send data to an OTel collector via gRPC or HTTP.

```bash
pip install opentelemetry-exporter-otlp-proto-grpc
# or
pip install opentelemetry-exporter-otlp-proto-http
```

--------------------------------

### Install Optional Dependencies

Source: https://posit-dev.github.io/pointblank/user-guide/metadata-import/cdisc-submission-conformance.md

Commands to install optional dependencies for XPT file handling and Excel export functionality.

```bash
pip install pointblank[cdisc]    # adds pyreadstat
pip install pointblank[excel]    # adds openpyxl
```

--------------------------------

### Install Pointblank MCP

Source: https://posit-dev.github.io/pointblank/user-guide/mcp-server/mcp-quick-start.md

Install the necessary package to enable MCP support for Pointblank.

```bash
pip install pointblank[mcp]
```

--------------------------------

### Install Pointblank with CDISC extras

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Alternative installation method to include CDISC dependencies.

```bash
pip install pointblank[cdisc]
```

--------------------------------

### Install Development Version

Source: https://posit-dev.github.io/pointblank/user-guide/getting-started/installation.md

Installs the latest development version of Pointblank directly from the GitHub repository.

```bash
pip install git+https://github.com/posit-dev/pointblank.git
```

--------------------------------

### Example of from_cdisc_null_flavors

Source: https://posit-dev.github.io/pointblank/reference/MissingSpec.md

Demonstrates creating a CDISC-compliant MissingSpec and querying a reason.

```python
import pointblank as pb

cdisc_missing = pb.MissingSpec.from_cdisc_null_flavors()
cdisc_missing.reason_for("NASK")   # "not_asked"
```

--------------------------------

### Install DataFrame library support

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Install Pointblank with specific support for either Polars or Pandas.

```bash
# Using pip
pip install pointblank[pl]

# Or manually
pip install polars>=1.24.0
```

```bash
# Using pip
pip install pointblank[pd]

# Or manually
pip install pandas>=2.2.3
```

--------------------------------

### Check dependencies

Source: https://posit-dev.github.io/pointblank/user-guide/the-pointblank-cli/cli-reference.md

Verify the status of installed dependencies.

```bash
pb requirements [OPTIONS]
```

--------------------------------

### Install Ibis Backend Drivers

Source: https://posit-dev.github.io/pointblank/reference/connect_to_table.md

Install the necessary Ibis framework dependencies to support specific database backends.

```bash
# You can install a set of common backends:
pip install 'ibis-framework[duckdb,postgres,mysql,sqlite]'

# ...or specific backends as needed:
pip install 'ibis-framework[duckdb]'    # for DuckDB
pip install 'ibis-framework[postgres]'  # for PostgreSQL
```

--------------------------------

### Install Pointblank via package managers

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Use these commands to install the core Pointblank package using your preferred environment manager.

```bash
pip install pointblank
```

```bash
uv pip install pointblank
```

```bash
conda install -c conda-forge pointblank
```

```bash
# add pointblank to project
pixi init name-of-project
cd name-of-project
pixi add pointblank
```

--------------------------------

### Define and Create a Transaction Processing Pipeline

Source: https://posit-dev.github.io/pointblank/llms-full.txt

This example demonstrates defining a source contract for raw financial data and a target contract for normalized data, then combining them into a pipeline.

```python
import pointblank as pb
import polars as pl

# Define the source contract (what raw data must look like)
source_contract = pb.Contract(
    name="raw_transactions",
    direction="source",
    schema=pb.Schema(
        txn_id="String",
        amount_cents="Int64",
        currency="String",
        timestamp="String",
    ),
    steps=[
        pb.Step("col_vals_not_null", columns=["txn_id", "amount_cents", "currency"]),
        pb.Step("col_vals_ge", columns="amount_cents", value=0),
        pb.Step("col_vals_in_set", columns="currency", set=["USD", "EUR", "GBP", "JPY"]),
        pb.Step("rows_distinct", columns_subset=["txn_id"]),
    ],
    version="1.0.0",
    owner="ingestion-team",
)

# Define the target contract (what clean data must look like)
target_contract = pb.Contract(
    name="clean_transactions",
    direction="target",
    schema=pb.Schema(
        txn_id="String",
        currency="String",
        timestamp="String",
        amount="Float64",
    ),
    steps=[
        pb.Step("col_vals_not_null", columns=["txn_id", "amount", "currency", "timestamp"]),
        pb.Step("col_vals_gt", columns="amount", value=0),
        pb.Step("col_vals_between", columns="amount", left=0.01, right=100_000.0),
        pb.Step("rows_distinct", columns_subset=["txn_id"]),
    ],
    version="1.0.0",
    consumers=["analytics-team", "fraud-team"],
)

# Create the pipeline
pipeline = pb.Pipeline(
    source=source_contract,
    target=target_contract,
    label="Transaction Processing Pipeline",
)

print(pipeline)
```

--------------------------------

### starts_with() column selection example

Source: https://posit-dev.github.io/pointblank/reference/starts_with.md

Demonstrates selecting columns starting with 'name' from a list of table columns.

```python
columns=starts_with("name")
```

--------------------------------

### Setup Polars DataFrame for validation

Source: https://posit-dev.github.io/pointblank/reference/Validate.col_vals_decreasing.md

Initializes a Polars DataFrame with columns 'a', 'b', and 'c' to be used in subsequent validation examples.

```python
import pointblank as pb
import polars as pl

tbl = pl.DataFrame(
    {
        "a": [6, 5, 4, 3, 2, 1],
        "b": [5, 4, 4, 3, 2, 1],
        "c": [5, 4, 5, 3, 2, 1],
    }
)

pb.preview(tbl)
```

--------------------------------

### View help output

Source: https://posit-dev.github.io/pointblank/reference/cli/make_template.md

Displays the full help documentation for the make-template command.

```text
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.
```

--------------------------------

### Perform environment validation using specially

Source: https://posit-dev.github.io/pointblank/reference/Validate.specially.md

This example defines a custom function to check the installed version of pointblank and integrates it into a validation pipeline using the specially method.

```python
def validate_pointblank_version():
    try:
        import importlib.metadata
        version = importlib.metadata.version("pointblank")
        version_parts = version.split(".")

        # Get major and minor components regardless of how many parts there are
        major = int(version_parts[0])
        minor = int(version_parts[1])

        # Check both major and minor components for version `0.9+`
        return (major > 0) or (major == 0 and minor >= 9)

    except Exception as e:
        # More specific error handling could be added here
        print(f"Version check failed: {e}")
        return False

(
    pb.Validate(data=simple_tbl)
    .specially(
        expr=validate_pointblank_version,
        brief="Check Pointblank version `>=0.9.0`."
    )
    .interrogate()
)
```

--------------------------------

### Build documentation locally

Source: https://posit-dev.github.io/pointblank/contributing.md

Execute this command from the project root to build the documentation site into the docs/_site folder.

```bash
make docs-build
```

--------------------------------

### Example .env file configuration

Source: https://posit-dev.github.io/pointblank/reference/DraftValidation.md

Store API keys in an .env file to allow automatic loading without explicit arguments.

```plaintext
ANTHROPIC_API_KEY="your_anthropic_api_key_here"
OPENAI_API_KEY="your_openai_api_key_here"
```

--------------------------------

### Install Pandas dependency

Source: https://posit-dev.github.io/pointblank/user-guide/getting-started/installation.md

Commands to install Pointblank with Pandas support or install Pandas manually.

```bash
# Using pip
pip install pointblank[pd]

# Or manually
pip install pandas>=2.2.3
```

--------------------------------

### Example MCP chat prompt

Source: https://posit-dev.github.io/pointblank/llms-full.txt

A sample natural language prompt for data validation.

```text
"Load my sales data and check its quality"
```

--------------------------------

### Full help output for pb edit

Source: https://posit-dev.github.io/pointblank/reference/cli/edit.md

Displays the complete help documentation for the command, including all available options.

```bash
Usage: pb edit [OPTIONS] [VALIDATION_FILE]

  Edit an existing validation plan with a natural-language instruction (AI
  Validation Editor).

  VALIDATION_FILE is a Python (.py) or YAML (.yaml/.yml) file containing a
  validation plan. The instruction is sent, along with the current plan, to
  the specified model; the proposed change is shown as a diff for review and
  can optionally be written to a file.

  Examples:

  pb edit plan.py -i "add a not-null check on user_id" -m anthropic:claude-opus-4-8
  pb edit plan.yaml -i "tighten the price range to 0-1000" -m openai:gpt-4o --data sales.csv
  pb edit plan.py -i "drop the email regex check" -m anthropic:claude-opus-4-8 -o plan2.py -y

Options:
  -i, --instruction TEXT  Plain-English description of the change to make to
                          the plan.
  -m, --model TEXT        Model to use, as provider:model (e.g.,
                          anthropic:claude-opus-4-8).
  --data TEXT             Optional data source used for DataScan-informed
                          edits and validation.
  -o, --output PATH       Write the revised plan to this file.
  -y, --yes               Write the output file without asking for
                          confirmation.
  --help                  Show this message and exit.
```

--------------------------------

### Install Polars dependency

Source: https://posit-dev.github.io/pointblank/user-guide/getting-started/installation.md

Commands to install Pointblank with Polars support or install Polars manually.

```bash
# Using pip
pip install pointblank[pl]

# Or manually
pip install polars>=1.24.0
```

--------------------------------

### Perform Column Comparison Validations

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Examples demonstrating basic threshold comparisons, handling null values with na_pass, and comparing columns against other columns.

```python
(
    pb.Validate(data=pb.load_dataset(dataset="small_table", tbl_type="polars"))
    .col_vals_gt(columns="a", value=5)
    .interrogate()
)
```

```python
(
    pb.Validate(data=pb.load_dataset(dataset="small_table", tbl_type="polars"))
    .col_vals_le(columns="c", value=10, na_pass=True)
    .interrogate()
)
```

```python
(
    pb.Validate(data=pb.load_dataset(dataset="small_table", tbl_type="polars"))
    .col_vals_lt(columns="a", value=pb.col("c"))
    .interrogate()
)
```

--------------------------------

### Install lxml for CDISC XML parsing

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Required for parsing Define-XML and Controlled Terminology files.

```bash
pip install lxml
```

--------------------------------

### Install Pointblank with conda

Source: https://posit-dev.github.io/pointblank/user-guide/getting-started/installation.md

Installation command for the Pointblank package from the conda-forge channel.

```bash
conda install -c conda-forge pointblank
```

--------------------------------

### Full help output for pb preview

Source: https://posit-dev.github.io/pointblank/reference/cli/preview.md

Displays the complete list of available options and usage instructions for the preview command.

```text
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 (--columns "name,age,email")
  - --col-range: Select column range (--col-range "1:10", --col-range "5:", --col-range ":15")
  - --col-first: Show first N columns (--col-first 5)
  - --col-last: Show last N columns (--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.
```

--------------------------------

### Install pyreadstat dependency

Source: https://posit-dev.github.io/pointblank/user-guide/metadata-import/statistical-packages.md

Install the required library for reading statistical package metadata.

```bash
pip install pyreadstat
```

--------------------------------

### Examples of pb run commands

Source: https://posit-dev.github.io/pointblank/reference/cli/run.md

Common usage patterns for running validation scripts and configurations with various options.

```bash
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
```

--------------------------------

### Perform a Quick Data Check

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Use this interaction when receiving a new dataset to get an immediate overview of data quality and potential issues.

```text
👤 "I just got netflix_data.csv. Is it clean?"
🤖 "Loading dataset... Found 16,000 movies/shows with 18 columns.
    Quality analysis complete:
    - ✅ No duplicate show IDs
    - ⚠️ 466 missing countries (2.9%)
    - ⚠️ 132 missing directors (0.8%)
    Opening data preview in your browser..."
```

--------------------------------

### Set up development environment with UV

Source: https://posit-dev.github.io/pointblank/contributing.md

Use this command to synchronize the environment when using the UV package manager.

```bash
uv sync
```

--------------------------------

### Install Pointblank with Generation Support

Source: https://posit-dev.github.io/pointblank/reference/DraftValidation.md

Installs the necessary dependencies for using the DraftValidation class.

```bash
pip install pointblank[generate]
```

--------------------------------

### Load and preview dataset

Source: https://posit-dev.github.io/pointblank/reference/Validate.row_count_match.md

Demonstrates how to load the built-in small_table dataset and display a preview.

```python
import pointblank as pb

small_table = pb.load_dataset("small_table")

pb.preview(small_table)
```

--------------------------------

### Verify Pointblank Version

Source: https://posit-dev.github.io/pointblank/user-guide/getting-started/installation.md

Check the installed version of the Pointblank library to confirm successful installation.

```python
import pointblank as pb
print(pb.__version__)
```

--------------------------------

### Basic pb preview usage

Source: https://posit-dev.github.io/pointblank/reference/cli/preview.md

The standard syntax for invoking the preview command with a data source.

```bash
pb preview [OPTIONS] [DATA_SOURCE]
```

--------------------------------

### Install CDISC CORE standalone binary

Source: https://posit-dev.github.io/pointblank/user-guide/metadata-import/cdisc-submission-conformance.md

Make the downloaded binary executable and move it to a system PATH location.

```bash
chmod +x core
sudo mv core /usr/local/bin/
core --version
```

--------------------------------

### Install Pointblank AI Skill

Source: https://posit-dev.github.io/pointblank/skills.html

Methods for installing the Pointblank skill into an AI coding agent.

```shell
npx skills add https://posit-dev.github.io/pointblank/
```

```text
Tell the agent:  
Fetch the skill file at https://posit-dev.github.io/pointblank/skill.md and follow the instructions.
```

```shell
curl -O https://posit-dev.github.io/pointblank/skill.md
```

--------------------------------

### Run Polars expressions and multi-line queries

Source: https://posit-dev.github.io/pointblank/reference/cli/pl.md

Examples demonstrating direct expressions, interactive editing, file-based input, and piping to other commands.

```bash
# 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.
```

--------------------------------

### Example: SAS MissingSpec

Source: https://posit-dev.github.io/pointblank/reference/MissingSpec.md

Example demonstrating the creation of a SAS MissingSpec with custom reason overrides.

```python
import pointblank as pb

sas_missing = pb.MissingSpec.from_sas(
    reasons={".A": "not_applicable", ".B": "below_detection"}
)
sas_missing.reason_for(".A")   # "not_applicable"
sas_missing.reason_for(".C")   # "user_missing_c"
```

--------------------------------

### View pb info help output

Source: https://posit-dev.github.io/pointblank/reference/cli/info.md

Displays the full help documentation for the pb info command.

```text
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.
```

--------------------------------

### View pb requirements help output

Source: https://posit-dev.github.io/pointblank/reference/cli/requirements.md

Displays the help documentation for the requirements command.

```text
Usage: pb requirements [OPTIONS]

  Check installed dependencies and their availability.

Options:
  --help  Show this message and exit.
```

--------------------------------

### Pointblank CLI Usage Overview

Source: https://posit-dev.github.io/pointblank/llms-full.txt

General usage syntax and available commands for the pb CLI tool.

```bash
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.
  edit           Edit an existing validation plan with a natural-language...
```

--------------------------------

### Install Pointblank OTel dependencies

Source: https://posit-dev.github.io/pointblank/user-guide/integrations/otel-integration.md

Install the optional OpenTelemetry dependencies required for exporting validation signals.

```bash
pip install pointblank[otel]
```

--------------------------------

### Format database connection strings

Source: https://posit-dev.github.io/pointblank/llms-full.txt

Examples of connection string formats for various database backends using the required ::table_name suffix.

```text
DuckDB:     "duckdb:///path/to/database.ddb::table_name"
SQLite:     "sqlite:///path/to/database.db::table_name"
PostgreSQL: "postgresql://user:password@localhost:5432/database::table_name"
MySQL:      "mysql://user:password@localhost:3306/database::table_name"
BigQuery:   "bigquery://project/dataset::table_name"
Snowflake:  "snowflake://user:password@account/database/schema::table_name"
```

--------------------------------

### Initialize MetadataPackage

Source: https://posit-dev.github.io/pointblank/reference/MetadataPackage.md

Constructor for creating a new MetadataPackage instance.

```python
MetadataPackage(
    name=None,
    items=dict(),
    description=None,
    version=None,
)
```

--------------------------------

### Example: SPSS MissingSpec

Source: https://posit-dev.github.io/pointblank/reference/MissingSpec.md

Example demonstrating the creation of an SPSS MissingSpec with defined sentinel values and labels.

```python
import pointblank as pb

spss_missing = pb.MissingSpec.from_spss(
    missing_values=[-99, -98],
    labels={-99: "Not asked", -98: "Refused"},
)
spss_missing.reason_for(-98)   # "refused"
```

--------------------------------

### Define and Execute a Pipeline

Source: https://posit-dev.github.io/pointblank/reference/Pipeline.md

Example demonstrating the creation of source and target contracts and their execution within a pipeline.

```python
import pointblank as pb

source_contract = pb.Contract(
    name="raw_data",
    direction="source",
    steps=[pb.Step("col_vals_not_null", columns=["id"])],
)

target_contract = pb.Contract(
    name="clean_data",
    direction="target",
    steps=[pb.Step("col_vals_not_null", columns=pb.everything())],
)

pipeline = pb.Pipeline(
    source=source_contract,
    target=target_contract,
)

# Validate source data
source_result = pipeline.validate_source(raw_data)

# Validate target data
target_result = pipeline.validate_target(clean_data)

# Or do it all in one shot
result = pipeline.run(data=raw_data, transform=my_transform)
```

--------------------------------

### Install CDISC dependencies

Source: https://posit-dev.github.io/pointblank/user-guide/metadata-import/cdisc-validation.md

Install the necessary libraries for CDISC XML parsing and SAS Transport file reading.

```bash
pip install lxml
```

```bash
pip install pointblank[cdisc]
```

```bash
pip install pyreadstat
```
Tokens:
Raw