Data validation made beautiful and powerful

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

import pointblank as pb

validation = (
   pb.Validate(data=pb.load_dataset(dataset="small_table"))
   .col_vals_gt(columns="d", value=100)             # Validate values > 100
   .col_vals_le(columns="c", value=5)               # Validate values <= 5
   .col_exists(columns=["date", "date_time"])       # Check columns exist
   .interrogate()                                   # Execute and collect results
)

# Get the validation report from the REPL with:
validation.get_tabular_report().show()

# From a notebook simply use:
validation

Real-World Example

import pointblank as pb
import polars as pl

# Load your data
sales_data = pl.read_csv("sales_data.csv")

# Create a comprehensive validation
validation = (
   pb.Validate(
      data=sales_data,
      tbl_name="sales_data",           # 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})."
      ),
      final_actions=pb.FinalActions(   # Define final actions for the entire validation
         pb.send_slack_notification(
            webhook_url="https://hooks.slack.com/services/your/webhook/url"
         )
      ),
      brief=True,                      # Add automatically-generated briefs for each step
   )
   .col_vals_between(            # Check numeric ranges with precision
      columns=["price", "quantity"],
      left=0, right=1000
   )
   .col_vals_not_null(           # Ensure that columns ending with '_id' don't have null values
      columns=pb.ends_with("_id")
   )
   .col_vals_regex(              # Validate patterns with regex
      columns="email",
      pattern="^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\\.[a-zA-Z]{2,}$"
   )
   .col_vals_in_set(             # Check categorical values
      columns="status",
      set=["pending", "shipped", "delivered", "returned"]
   )
   .conjointly(                  # Combine multiple conditions
      lambda df: pb.expr_col("revenue") == pb.expr_col("price") * pb.expr_col("quantity"),
      lambda df: pb.expr_col("tax") >= pb.expr_col("revenue") * 0.05
   )
   .interrogate()
)

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

# Get an HTML report you can share with your team
validation.get_tabular_report().show("browser")

# Get a report of failing records from a specific step
validation.get_step_report(i=3).show("browser")  # Get failing records from step 3

Command Line Interface

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

# Get a quick preview of your data
pb preview small_table

# Check for missing values
pb missing small_table

# Generate column summaries
pb scan small_table

Run Essential Validations

# Check for duplicate rows
pb validate-simple small_table --check rows-distinct

# Verify no null values
pb validate-simple small_table --check col-vals-not-null --column a

# Extract failing data for debugging
pb validate-simple small_table --check col-vals-gt --column a --value 5 --show-extract

Integrate with CI/CD

# Use exit codes for automation (0 = pass, 1 = fail)
pb validate-simple small_table --check rows-distinct && echo "✅ Quality checks passed"

Learn more in our CLI documentation.

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:

pip install pointblank

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

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.

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:

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.

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.