![](../assets/pointblank_logo.svg)

**Data validation toolkit for assessing and monitoring data quality.**

Pointblank is a data validation framework for Python that makes data quality checks beautiful, powerful, and stakeholder-friendly. Instead of cryptic error messages, get stunning interactive reports that turn data issues into conversations.

Here's what a validation looks like (click "Show the code" to see how it's done):

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

validation = (
    pb.Validate(
        data=pb.load_dataset(dataset="game_revenue", tbl_type="polars"),
        tbl_name="game_revenue",
        label="Comprehensive validation of game revenue data",
        thresholds=pb.Thresholds(warning=0.10, error=0.25, critical=0.35),
        brief=True
    )
    .col_vals_regex(columns="player_id", pattern=r"^[A-Z]{12}[0-9]{3}$")        # STEP 1
    .col_vals_gt(columns="session_duration", value=20)                          # STEP 2
    .col_vals_ge(columns="item_revenue", value=0.20)                            # STEP 3
    .col_vals_in_set(columns="item_type", set=["iap", "ad"])                    # STEP 4
    .col_vals_in_set(                                                           # STEP 5
        columns="acquisition",
        set=["google", "facebook", "organic", "crosspromo", "other_campaign"]
    )
    .col_vals_not_in_set(columns="country", set=["Mongolia", "Germany"])        # STEP 6
    .col_vals_between(                                                          # STEP 7
        columns="session_duration",
        left=10, right=50,
        pre = lambda df: df.select(pl.median("session_duration")),
        brief="Expect that the median of `session_duration` should be between `10` and `50`."
    )
    .rows_distinct(columns_subset=["player_id", "session_id", "time"])          # STEP 8
    .row_count_match(count=2000)                                                # STEP 9
    .col_count_match(count=11)                                                  # STEP 10
    .col_vals_not_null(columns="item_type")                                     # STEP 11
    .col_exists(columns="start_day")                                            # STEP 12
    .interrogate()
)

validation.get_tabular_report(title="Game Revenue Validation Report")
```

[TABLE]

That's the kind of report you get from Pointblank: clear, interactive, and designed for everyone on your team. And if you need help getting started or want to work faster, Pointblank has built-in AI support through the `assistant()` function to guide you along the way. You can also use [`DraftValidation`](advanced-validation/draft-validation.md) to quickly generate a validation plan from your existing data (great for getting started fast), or the [AI Validation Editor](advanced-validation/ai-validation-editor.md) to refine an existing plan with plain-English instructions.

Ready to validate? Start with our [Installation](getting-started/installation.md) guide or jump straight to the [Quickstart](getting-started/quickstart.md).

By the way, Pointblank is made with 💙 by [Posit](https://posit.co/).

## What is Data Validation?

Data validation ensures your data meets quality standards before it's used in analysis, reports, or downstream systems. Pointblank provides a structured way to define validation rules, execute them, and communicate results to both technical and non-technical stakeholders.

With Pointblank you can:

- **Validate data** through a fluent, chainable API with [25+ validation methods](../reference/index.md#validation-steps)
- **Set thresholds** to define acceptable levels of data quality (warning, error, critical)
- **Take actions** when thresholds are exceeded (notifications, logging, custom functions)
- **Generate reports** that make data quality issues immediately understandable
- **Inspect data** with built-in tools for previewing, summarizing, and finding missing values

## Why Pointblank?

Pointblank is designed for the entire data team, not just engineers:

- 🎨 **Beautiful Reports**: Interactive validation reports that stakeholders actually want to read
- 📊 **Threshold Management**: Define quality standards with warning, error, and critical levels
- 🔍 **Error Drill-Down**: Inspect failing data to get to root causes quickly
- 🔗 **Universal Compatibility**: Works with Polars, Pandas, DuckDB, MySQL, PostgreSQL, SQLite, and more
- 🌍 **Multilingual Support**: Reports available in 40 languages for global teams
- 📝 **YAML Support**: Write validations in YAML for version control and team collaboration
- ⚡ **CLI Tools**: Run validations from the command line for CI/CD pipelines or as quick checks
- 📋 **Rich Inspection**: Preview data, analyze columns, and visualize missing values

## Quick Examples

### Threshold-Based Quality

Set expectations and react when data quality degrades (with alerts, logging, or custom functions):

``` python
validation = (
    pb.Validate(data=sales_data, thresholds=(0.01, 0.02, 0.05)) # Three threhold levels set
    .col_vals_not_null(columns="customer_id")
    .col_vals_in_set(columns="status", set=["pending", "shipped", "delivered"])
    .interrogate()
)
```

### YAML Workflows

Works wonderfully for CI/CD pipelines and team collaboration:

``` yaml
validate:
  data: sales_data
  tbl_name: "sales_data"
  thresholds: [0.01, 0.02, 0.05]

steps:
  - col_vals_not_null:
      columns: "customer_id"
  - col_vals_in_set:
      columns: "status"
      set: ["pending", "shipped", "delivered"]
```

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

### Command Line Power

Run validations without writing code:

``` bash
# Quick validation
pb validate sales_data.csv --check col-vals-not-null --column customer_id

# Run YAML workflows
pb run validation.yaml --exit-code  # <- Great for CI/CD!

# Explore your data
pb scan sales_data.csv
pb missing sales_data.csv
```

## Installation

Install Pointblank using pip or conda:

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

For specific 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
```

See the [Installation guide](getting-started/installation.md) for more details.

## Join the Community

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

- [GitHub Issues](https://github.com/posit-dev/pointblank/issues) for bug reports and feature requests
- [Discord server](https://discord.com/invite/YH7CybCNCQ) for discussions and help
- [Contributing guidelines](https://github.com/posit-dev/pointblank/blob/main/CONTRIBUTING.md) if you'd like to contribute

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**License**: MIT \| **© 2024-2026 Posit Software, PBC**