Pointblank

6 min read Original article ↗

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):

Show the code 
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")

Game Revenue Validation Report

Comprehensive validation of game revenue data

Polarsgame_revenueWARNING0.1ERROR0.25CRITICAL0.35
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1

col_vals_regex

col_vals_regex()

Expect that values in player_id should match the regular expression: ^[A-Z]{12}[0-9]{3}$.

 player_id ^[A-Z]{12}[0-9]{3}$ ✓ 2000 2000
1.00		 0
0.00		 ○ ○ ○ —
#EBBC14 2

col_vals_gt

col_vals_gt()

Expect that values in session_duration should be > 20.

 session_duration 20 ✓ 2000 1418
0.71		 582
0.29		 ● ● ○
#FF3300 3

col_vals_ge

col_vals_ge()

Expect that values in item_revenue should be >= 0.2.

 item_revenue 0.2 ✓ 2000 1192
0.60		 808
0.40		 ● ● ●
#4CA64C 4

col_vals_in_set

col_vals_in_set()

Expect that values in item_type should be in the set of iap, ad.

 item_type iap, ad ✓ 2000 2000
1.00		 0
0.00		 ○ ○ ○ —
#4CA64C66 5

col_vals_in_set

col_vals_in_set()

Expect that values in acquisition should be in the set of google, facebook, organic, and 2 more.

 acquisition google, facebook, organic, crosspromo, other_campaign ✓ 2000 1975
0.99		 25
0.01		 ○ ○ ○
#AAAAAA 6

col_vals_not_in_set

col_vals_not_in_set()

Expect that values in country should not be in the set of Mongolia, Germany.

 country Mongolia, Germany ✓ 2000 1775
0.89		 225
0.11		 ● ○ ○
#4CA64C 7

col_vals_between

col_vals_between()

Expect that the median of session_duration should be between 10 and 50.

 session_duration [10, 50] ✓ 1 1
1.00		 0
0.00		 ○ ○ ○ —
#4CA64C66 8

rows_distinct

rows_distinct()

Expect entirely distinct rows across player_id, session_id, time.

 player_id, session_id, time — ✓ 2000 1978
0.99		 22
0.01		 ○ ○ ○
#4CA64C 9

row_count_match

row_count_match()

Expect that the row count is exactly 2000.

 — 2000 ✓ 1 1
1.00		 0
0.00		 ○ ○ ○ —
#4CA64C 10

col_count_match

col_count_match()

Expect that the column count is exactly 11.

 — 11 ✓ 1 1
1.00		 0
0.00		 ○ ○ ○ —
#4CA64C 11

col_vals_not_null

col_vals_not_null()

Expect that all values in item_type should not be Null.

 item_type — ✓ 2000 2000
1.00		 0
0.00		 ○ ○ ○ —
#4CA64C 12

col_exists

col_exists()

Expect that column start_day exists.

 start_day — ✓ 1 1
1.00		 0
0.00		 ○ ○ ○ —

2026-01-01 21:30:25 UTC< 1 s2026-01-01 21:30:25 UTC
Notes

Step 7 (pre_applied) Precondition applied: table dimensions [2,000 rows, 11 columns] → [1 row, 1 column].

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 to quickly generate a validation plan from your existing data (great for getting started fast).

Ready to validate? Start with our Installation guide or jump straight to the User Guide.

By the way, Pointblank is made with 💙 by Posit.

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
  • 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

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):

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:

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

Command Line Power

Run validations without writing code:

# 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:

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

For specific backends:

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 for more details.

Text Formats

The docs are also available in llms.txt format: