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What is Pointblank?

Pointblank is a powerful, yet elegant data validation framework for Python that transforms how you ensure data quality. With its intuitive, chainable API, you can quickly validate your data against comprehensive quality checks and visualize results through stunning, interactive reports that make data issues immediately actionable.

Whether you're a data scientist, data engineer, or analyst, Pointblank helps you catch data quality issues before they impact your analyses or downstream systems.

Getting Started in 30 Seconds

```python import pointblank as pb

validation = ( pb.Validate(data=pb.loaddataset(dataset="smalltable")) .colvalsgt(columns="d", value=100) # Validate values > 100 .colvalsle(columns="c", value=5) # Validate values <= 5 .colexists(columns=["date", "datetime"]) # Check columns exist .interrogate() # Execute and collect results )

Get the validation report from the REPL with:

validation.gettabularreport().show()

From a notebook simply use:

validation ```

Why Choose Pointblank?

• Works with your existing stack: Seamlessly integrates with Polars, Pandas, DuckDB, MySQL, PostgreSQL, SQLite, Parquet, PySpark, Snowflake, and more!
• Beautiful, interactive reports: Crystal-clear validation results that highlight issues and help communicate data quality
• Composable validation pipeline: Chain validation steps into a complete data quality workflow
• Threshold-based alerts: Set 'warning', 'error', and 'critical' thresholds with custom actions
• Practical outputs: Use validation results to filter tables, extract problematic data, or trigger downstream processes

Real-World Example

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

Load your data

salesdata = pl.readcsv("sales_data.csv")

Create a comprehensive validation

validation = ( pb.Validate( data=salesdata, tblname="salesdata", # Name of the table for reporting label="Real-world example.", # Label for the validation, appears in reports thresholds=(0.01, 0.02, 0.05), # Set thresholds for warnings, errors, and critical issues actions=pb.Actions( # Define actions for any threshold exceedance critical="Major data quality issue found in step {step} ({time})." ), finalactions=pb.FinalActions( # Define final actions for the entire validation pb.sendslacknotification( webhookurl="https://hooks.slack.com/services/your/webhook/url" ) ), brief=True, # Add automatically-generated briefs for each step ) .colvalsbetween( # Check numeric ranges with precision columns=["price", "quantity"], left=0, right=1000 ) .colvalsnotnull( # Ensure that columns ending with 'id' don't have null values columns=pb.endswith("id") ) .colvalsregex( # Validate patterns with regex columns="email", pattern="^[a-zA-Z0-9.%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$" ) .colvalsinset( # Check categorical values columns="status", set=["pending", "shipped", "delivered", "returned"] ) .conjointly( # Combine multiple conditions lambda df: pb.exprcol("revenue") == pb.exprcol("price") * pb.exprcol("quantity"), lambda df: pb.exprcol("tax") >= pb.exprcol("revenue") * 0.05 ) .interrogate() ) ```

Major data quality issue found in step 7 (2025-04-16 15:03:04.685612+00:00).

```python

Get an HTML report you can share with your team

validation.gettabularreport().show("browser") ```

```python

Get a report of failing records from a specific step

validation.getstepreport(i=3).show("browser") # Get failing records from step 3 ```

YAML Configuration

For teams that need portable, version-controlled validation workflows, Pointblank supports YAML configuration files. This makes it easy to share validation logic across different environments and team members, ensuring everyone is on the same page.

validation.yaml

```yaml validate: data: smalltable tblname: "small_table" label: "Getting started validation"

steps: - colvalsgt: columns: "d" value: 100 - colvalsle: columns: "c" value: 5 - colexists: columns: ["date", "datetime"] ```

Execute the YAML validation

```python import pointblank as pb

Run validation from YAML configuration

validation = pb.yaml_interrogate("validation.yaml")

Get the results just like any other validation

validation.gettabularreport().show() ```

This approach is perfect for:

• CI/CD pipelines: Store validation rules alongside your code
• Team collaboration: Share validation logic in a readable format
• Environment consistency: Use the same validation across dev, staging, and production
• Documentation: YAML files serve as living documentation of your data quality requirements

Command Line Interface (CLI)

Pointblank includes a powerful CLI utility called pb that lets you run data validation workflows directly from the command line. Perfect for CI/CD pipelines, scheduled data quality checks, or quick validation tasks.

Explore Your Data

```bash

Get a quick preview of your data

pb preview small_table

Preview data from GitHub URLs

pb preview "https://github.com/user/repo/blob/main/data.csv"

Check for missing values in Parquet files

pb missing data.parquet

Generate column summaries from database connections

pb scan "duckdb:///data/sales.ddb::customers" ```

Run Essential Validations

```bash

Run validation from YAML configuration file

pb run validation.yaml

Run validation from Python file

pb run validation.py

Check for duplicate rows

pb validate small_table --check rows-distinct

Validate data directly from GitHub

pb validate "https://github.com/user/repo/blob/main/sales.csv" --check col-vals-not-null --column customer_id

Verify no null values in Parquet datasets

pb validate "data/*.parquet" --check col-vals-not-null --column a

Extract failing data for debugging

pb validate small_table --check col-vals-gt --column a --value 5 --show-extract ```

Integrate with CI/CD

```bash

Use exit codes for automation in one-liner validations (0 = pass, 1 = fail)

pb validate small_table --check rows-distinct --exit-code

Run validation workflows with exit codes

pb run validation.yaml --exit-code pb run validation.py --exit-code ```

Click the following headings to see some video demonstrations of the CLI:

Getting Started with the Pointblank CLI

Doing Some Data Exploration

Validating Data with the CLI

Using Polars in the CLI

Integrating Pointblank with CI/CD

Features That Set Pointblank Apart

• Complete validation workflow: From data access to validation to reporting in a single pipeline
• Built for collaboration: Share results with colleagues through beautiful interactive reports
• Practical outputs: Get exactly what you need: counts, extracts, summaries, or full reports
• Flexible deployment: Use in notebooks, scripts, or data pipelines
• Customizable: Tailor validation steps and reporting to your specific needs
• Internationalization: Reports can be generated in over 20 languages, including English, Spanish, French, and German

Documentation and Examples

Visit our documentation site for:

• The User Guide
• API reference
• Example gallery
• The Pointblog

Join the Community

We'd love to hear from you! Connect with us:

• GitHub Issues for bug reports and feature requests
• Discord server for discussions and help
• Contributing guidelines if you'd like to help improve Pointblank

Installation

You can install Pointblank using pip:

bash pip install pointblank

You can also install Pointblank from Conda-Forge by using:

bash conda install conda-forge::pointblank

If you don't have Polars or Pandas installed, you'll need to install one of them to use Pointblank.

bash pip install "pointblank[pl]" # Install Pointblank with Polars pip install "pointblank[pd]" # Install Pointblank with Pandas

To use Pointblank with DuckDB, MySQL, PostgreSQL, or SQLite, install Ibis with the appropriate backend:

bash pip install "pointblank[duckdb]" # Install Pointblank with Ibis + DuckDB pip install "pointblank[mysql]" # Install Pointblank with Ibis + MySQL pip install "pointblank[postgres]" # Install Pointblank with Ibis + PostgreSQL pip install "pointblank[sqlite]" # Install Pointblank with Ibis + SQLite

Technical Details

Pointblank uses Narwhals to work with Polars and Pandas DataFrames, and integrates with Ibis for database and file format support. This architecture provides a consistent API for validating tabular data from various sources.

Contributing to Pointblank

There are many ways to contribute to the ongoing development of Pointblank. Some contributions can be simple (like fixing typos, improving documentation, filing issues for feature requests or problems, etc.) and others might take more time and care (like answering questions and submitting PRs with code changes). Just know that anything you can do to help would be very much appreciated!

Please read over the contributing guidelines for information on how to get started.

Pointblank for R

There's also a version of Pointblank for R, which has been around since 2017 and is widely used in the R community. You can find it at https://github.com/rstudio/pointblank.

Roadmap

We're actively working on enhancing Pointblank with:

1. Additional validation methods for comprehensive data quality checks
2. Advanced logging capabilities
3. Messaging actions (Slack, email) for threshold exceedances
4. LLM-powered validation suggestions and data dictionary generation
5. JSON/YAML configuration for pipeline portability
6. CLI utility for validation from the command line
7. Expanded backend support and certification
8. High-quality documentation and examples

If you have any ideas for features or improvements, don't hesitate to share them with us! We are always looking for ways to make Pointblank better.

Code of Conduct

Please note that the Pointblank project is released with a contributor code of conduct.
By participating in this project you agree to abide by its terms.

License

Pointblank is licensed under the MIT license.

Posit Software, PBC.

Governance

This project is primarily maintained by Rich Iannone. Other authors may occasionally assist with some of these duties.