### Install Pointblank MCP

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

Installs the pointblank library with MCP support using pip. This is the initial step to enable conversational data validation features.

```shell
pip install pointblank[mcp]
```

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

### Verifying Pointblank Installation and Version

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

Imports the Pointblank library and prints its version number to confirm a successful installation.

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

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

### Basic Pointblank Installation with Pixi

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

Installs Pointblank within a project managed by pixi. This ensures reproducible environments.

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

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

### Pointblank MCP - Load Data Commands

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

Examples of natural language commands to load data into Pointblank for validation. These commands specify file paths, dataset names, and file types.

```text
"Load the file /path/to/data.csv"
"Load my Netflix dataset from the working directory"
"Load the CSV file with sales metrics"
"Load customer_data.csv as my main dataset"
```

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

### Conversational Data Validation Prompt

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

An example prompt to initiate data validation using Pointblank MCP. This demonstrates how to interact with the system conversationally to load and check data quality.

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

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

### Pointblank Development Version Installation from GitHub

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

Installs the latest development version of Pointblank directly from its GitHub repository using pip. This is for users who want the newest features and are comfortable with potential instability.

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

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

### Pointblank MCP - Explore Data Commands

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

Examples of natural language commands to explore and profile data using Pointblank MCP. These commands generate data previews, column summaries, and missing value analyses.

```text
"Analyze the data for netflix_data"
"Show me a preview of the loaded data"
"Create a column summary table"
"Generate a missing values analysis"
```

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

### Pointblank Ibis Backends Installation with uv

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

Installs optional Pointblank Ibis backends for various database systems using uv. This provides fast installation for different database connectors.

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

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

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

### Basic Pointblank Installation with uv

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

Installs the core Pointblank validation functionality using the uv package manager. uv is known for its speed.

```shell
uv pip install pointblank
```

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

### Pointblank MCP - Quick Data Check Use Case

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

Example conversational flow for a quick data check upon receiving a new dataset. It demonstrates loading, quality analysis, and user feedback.

```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..."
```

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

### Pointblank MCP - Run and Export Commands

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

Examples of natural language commands to execute data validation checks and export results using Pointblank MCP. These commands trigger validation runs and save failure reports.

```text
"Run the validation and show results"
"Export validation failures to CSV"
"Get failed rows for step 2"
"Save the validation report"
```

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

### Pointblank AI-Assisted Validation Installation with Pip

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

Installs Pointblank with experimental support for AI-assisted validation plan generation using pip. This includes dependencies for LLM provider integration.

```shell
pip install pointblank[generate]
```

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

### Pointblank MCP - Check Quality Commands

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

Examples of natural language commands to initiate data quality checks using Pointblank MCP. These commands can apply predefined templates or specify custom validation rules.

```text
"Create a validator for netflix_data"
"Add validation that ratings are between 0 and 10"
"Check that all release years are reasonable"
"Apply the basic_quality template"
```

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

### Pointblank Ibis Backends Installation with Pixi

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

Installs optional Pointblank Ibis backends for various database systems using pixi. This integrates database connectivity into your pixi-managed project.

```shell
pixi add pointblank-sqlite      # SQLite
pixi add pointblank-duckdb      # DuckDB
pixi add pointblank-postgres    # PostgreSQL
pixi add pointblank-mysql       # MySQL
pixi add pointblank-mssql       # Microsoft SQL Server
pixi add pointblank-bigquery    # BigQuery
pixi add pointblank-pyspark     # Apache Spark
pixi add pointblank-databricks  # Databricks
pixi add pointblank-snowflake   # Snowflake

# Example of installing multiple backends
pixi add pointblank-duckdb pointblank-postgres pointblank-sqlite
```

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

### Pointblank Ibis Backends Installation with Pip

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

Installs optional Pointblank Ibis backends for various database systems using pip. Each backend enables connectivity to a specific database.

```shell
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]
```

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

### Load and Preview Dataset (Python)

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

Loads the 'small_table' dataset using Pointblank and displays a preview of its structure and first few rows. This is a common setup for demonstrating validation examples.

```python
import pointblank as pb

small_table = pb.load_dataset("small_table")

pb.preview(small_table)
```

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

### VS Code MCP Server Configuration

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

Configures the Pointblank MCP server for VS Code. This JSON file specifies the command and arguments to launch the server, allowing for conversational data validation within the IDE.

```json
{
  "servers": {
    "pointblank": {
      "command": "python",
      "args": ["-m", "pointblank_mcp_server.pointblank_server"]
    }
  }
}
```

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

### Database Connection String Examples

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

Examples of database connection strings for various database types. These strings specify the database dialect, connection details, and the table name using a `::table_name` suffix. Ibis library with the appropriate backend driver is required.

```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"
```

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

### Preview and Scan Data Sources with Pointblank CLI

Source: https://posit-dev.github.io/pointblank/demos/cli-interactive

Demonstrates how to preview and scan various data sources using the Pointblank CLI. It covers built-in datasets, local files (including patterns for multiple files), remote files from GitHub, and database tables via connection strings. This allows for quick inspection and assessment of data.

```shell
# Try previewing a built-in dataset
pb preview small_table

# Access local files (even use patterns to combine multiple Parquet files)
pb preview sales_data.csv
pb scan "data/*.parquet"

# Inspect datasets in GitHub repositories (no need to download the data!)
pb preview "https://github.com/user/repo/blob/main/data.csv"
pb missing "https://raw.githubusercontent.com/user/repo/main/sales.parquet"

# Work with DB tables through connection strings
pb info "duckdb:///warehouse/analytics.ddb::customers"
```

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

### Install and Verify Pointblank CLI

Source: https://posit-dev.github.io/pointblank/demos/cli-interactive

Installs the Pointblank Python package using pip and verifies the installation by running the 'pb --help' command. This is the initial step to set up and confirm the CLI tool is ready for use.

```shell
pip install pointblank
pb --help
```

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

### Pointblank MCP - Issue Investigation Use Case

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

Example conversational flow for investigating specific data issues. It demonstrates querying for data meeting certain criteria and exporting the relevant rows.

```text
👤 "Show me movies with ratings above 9.5"
🤖 "Creating validator for high ratings...
    Found 47 entries with ratings > 9.5
    Exporting suspicious rows to CSV
    Most are documentaries - could be valid!"
```

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

### Preview Columns Matching a Pattern using Pointblank

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

This example shows how to preview columns that match a regular expression pattern using the `pb.matches()` selector within the `columns_subset=` argument of `pb.preview()`. This is useful for dynamically selecting columns based on naming conventions, such as those containing 'dep_' or 'arr_'.

```r
pb.preview(nycflights, columns_subset=pb.matches("dep_|arr_"))
```

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

### Pointblank MCP - Daily Validation Use Case

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

Example conversational flow for performing daily data quality checks using a predefined validation template. It shows applying a template and receiving a success report.

```text
👤 "Apply the basic_quality template to my data"
🤖 "Creating validator with basic quality checks...
    ✅ Data types validated
    ✅ Missing value thresholds checked
    ✅ Duplicate detection passed
    Validation complete! All checks passed."
```

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

### Install Ibis Framework with Backend Drivers (Bash)

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

Installs the Ibis framework along with common database backend drivers, or specific drivers as needed. This is a prerequisite for using the `connect_to_table` function with various database systems.

```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
```

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

### Get Row Count from Database Connection String

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

This example shows how to retrieve the row count from a database table by providing a connection string. The function supports various database backends via Ibis, requiring the backend driver to be installed.

```python
# Example connection string for DuckDB
db_connection_string = "duckdb:///path/to/database.ddb::table_name"
pb.get_row_count(db_connection_string)

# Example connection string for PostgreSQL
# postgres_connection_string = "postgresql://user:password@localhost:5432/database::table_name"
# pb.get_row_count(postgres_connection_string)
```

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

### Preview Data with pb preview

Source: https://posit-dev.github.io/pointblank/user-guide/cli-data-inspection

The `pb preview` command displays the first and last few rows of a dataset in a table format within the console. It provides information about the data source, DataFrame library, and simplified data types. Options include `--no-row-numbers` to hide row indicators and `--columns` or `--col-range` for selecting specific columns. It can also save the preview as an HTML file using `--output-html`.

```bash
pb preview worldcities.csv
pb preview worldcities.csv --no-row-numbers
pb preview game_revenue
pb preview game_revenue -columns "player_id, item_type, item_name, start_day"
pb preview game_revenue —-col-range "3:6"
pb preview --help
```

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

### List Built-in Datasets with `pb datasets`

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

Lists all available built-in datasets that can be used with Pointblank commands. This command does not accept any arguments.

```bash
pb datasets

```

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

### Configuring prompt() with Advanced Parameters

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

Illustrates how to use advanced parameters of the prompt() method, such as 'columns_subset', 'batch_size', and 'max_concurrent', to fine-tune the validation process for efficiency and cost management.

```python
from pointblank import Validate

# Example validation prompt
validation_prompt = "Values should indicate positive sentiment."

# Specify the LLM model to use
model_name = "openai:gpt-4o"

# Assuming 'df' is your pandas DataFrame
validation_results = Validate(df).prompt(
    prompt=validation_prompt,
    model=model_name,
    columns_subset=["review_text", "rating"],
    batch_size=500,
    max_concurrent=5,
    brief="Sentiment check on reviews"
).validate()

print(validation_results)
```

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

### Preview Database Table using DuckDB Connection String with Pointblank

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

This example shows how to preview data directly from a database table using a connection string. It first retrieves the path to a DuckDB database file and then constructs a connection string specifying the database file and the table name using the `::table_name` suffix.

```python
# Get path to a DuckDB database file from package data
duckdb_path = pb.get_data_path("game_revenue", "duckdb")

pb.preview(f"duckdb:///{duckdb_path}::game_revenue")
```

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

### Basic Pointblank Installation with Conda

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

Installs the Pointblank package from the conda-forge channel using the conda package manager.

```shell
conda install -c conda-forge pointblank
```

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

### Pointblank MCP - Create Data Validations Commands

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

Examples of natural language commands to add specific data validation rules within Pointblank MCP. These rules enforce uniqueness, check for non-empty fields, and validate formats.

```text
"Add validation that show_id values are unique"
"Check that cast field is not empty for movies"
"Ensure vote_count is greater than 0"
"Validate that country field follows ISO format"
```

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

### Pointblank with Pandas DataFrame Library Installation

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

Installs Pointblank along with the Pandas DataFrame library. This is useful if you prefer Pandas for data manipulation.

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

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

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

### Using prompt() with Preprocessing and Segmentation

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

Shows how to incorporate custom preprocessing functions ('pre') and data segmentation ('segments') with the prompt() method to handle complex data structures or perform targeted validations.

```python
from pointblank import Validate

# Example preprocessing function
def clean_text(table):
    table['text_column'] = table['text_column'].str.lower().str.strip()
    return table

# Example validation prompt
validation_prompt = "The description should mention a country name."

# Specify the LLM model to use
model_name = "ollama:llama3"

# Assuming 'df' is your pandas DataFrame
validation_results = Validate(df).prompt(
    prompt=validation_prompt,
    model=model_name,
    pre=clean_text,
    segments=("category_column", ["electronics", "books"]),
    brief="Country mention validation"
).validate()

print(validation_results)
```

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

### Pointblank with Polars DataFrame Library Installation

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

Installs Pointblank along with the Polars DataFrame library. This is useful if you prefer Polars for data manipulation.

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

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

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

### Main Command: pb

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

The main entry point for all Pointblank CLI operations, including data validation, exploration, and report generation.

```APIDOC
## pb - Main Command

### Description
The main entry point for all Pointblank CLI operations.

### Method
CLI Command

### Endpoint
`pb`

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Request Example
```bash
pb preview data.csv
pb scan data.csv
pb validate data.csv
```

### Response
#### Success Response (Output to console)
- **Version** (`-v`, `--version`): Shows the version of the Pointblank CLI.
- **Help** (`-h`, `--help`): Shows the help message for the main command.

#### Response Example
```
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.
```
```

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

### Create and Preview Sample Sales Data with Pandas

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

This code snippet generates a sample sales dataset with columns for region, product type, units sold, revenue, cost, profit, and profit margin. It uses pandas for data manipulation and numpy for random data generation. The 'pb.preview()' function is used to display the first few rows of the dataset.

```python
import pandas as pd
import numpy as np

# Create a sample sales dataset
np.random.seed(123)

# Create a simple sales dataset
sales_data = pd.DataFrame({
    "region": np.random.choice(["North", "South", "East", "West"], 100),
    "product_type": np.random.choice(["Electronics", "Clothing", "Food"], 100),
    "units_sold": np.random.randint(5, 100, 100),
    "revenue": np.random.uniform(100, 10000, 100),
    "cost": np.random.uniform(50, 5000, 100)
})

# Calculate profit
sales_data["profit"] = sales_data["revenue"] - sales_data["cost"]
sales_data["profit_margin"] = sales_data["profit"] / sales_data["revenue"]

# Preview the dataset
pb.preview(sales_data)
```

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

### Preview Clean Data with Pointblank

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

This snippet demonstrates how to use the `pb.preview()` function from the Pointblank library to display a preview of clean data. It assumes that `clean_data` has already been processed and is available. This is a basic step in understanding the structure and content of your validated dataset.

```python
pb.preview(clean_data)
```

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

### Simple Threshold Example in Python

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

Demonstrates a basic Pointblank validation using `col_vals_not_null` with warning and error thresholds defined using the `Thresholds` class. It shows how to set tolerance levels for null values in a column.

```python
import pointblank as pb

(
    pb.Validate(data=pb.load_dataset(dataset="small_table", tbl_type="polars"))
    .col_vals_not_null(
        columns="c",

        # Set thresholds for the validation step ---
        thresholds=pb.Thresholds(warning=1, error=0.2)
    )
    .interrogate()
)
```

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

### Pointblank Ibis Backends Installation with Conda

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

Installs optional Pointblank Ibis backends from conda-forge for various database systems. This method is suitable for users managing environments with conda.

```shell
conda install -c conda-forge pointblank-sqlite      # SQLite
conda install -c conda-forge pointblank-duckdb      # DuckDB
conda install -c conda-forge pointblank-postgres    # PostgreSQL
conda install -c conda-forge pointblank-mysql       # MySQL
conda install -c conda-forge pointblank-mssql       # Microsoft SQL Server
conda install -c conda-forge pointblank-bigquery    # BigQuery
conda install -c conda-forge pointblank-pyspark     # Apache Spark
conda install -c conda-forge pointblank-databricks  # Databricks
conda install -c conda-forge pointblank-snowflake   # Snowflake

# Example of installing multiple backends
conda install -c conda-forge pointblank-duckdb pointblank-postgres pointblank-sqlite
```

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

### Command Line Data Validation with Pointblank

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

These command-line examples demonstrate how to use Pointblank's CLI tools for quick data validation, running YAML workflows, and exploring data. They include examples for checking non-null values in a specific column, executing a YAML validation file with an exit code for CI/CD, and scanning/inspecting data files.

```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
```

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

### Setup: Create a Polars DataFrame for Validation Examples

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

Initializes a sample Polars DataFrame named 'tbl' with three numeric columns ('a', 'b', 'c') to be used in subsequent validation examples. This setup is a prerequisite for demonstrating Pointblank's validation capabilities.

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

tbl = pl.DataFrame(
    {
        "a": [5, 6, 5, 7, 5, 5],
        "b": [2, 3, 6, 4, 3, 6],
        "c": [9, 8, 8, 9, 9, 7],
    }
)

pb.preview(tbl)
```

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

### Preview Multiple Parquet Files with Glob Patterns and Directories using Pointblank

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

This code illustrates how to use `pb.preview()` with glob patterns to select multiple Parquet files or to preview all Parquet files within a directory. It also shows how to preview a partitioned Parquet dataset, where partition columns are automatically discovered.

```python
# Multiple Parquet files with glob patterns
pb.preview("data/sales_*.parquet")

# Directory containing Parquet files
pb.preview("parquet_data/")

# Partitioned Parquet dataset
pb.preview("sales_data/")  # Auto-discovers partition columns
```

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

### Python: Load API Key from Environment Variable

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

Demonstrates loading an API key from an environment variable using the 'os' module. This is a more secure method for managing API keys compared to hardcoding them directly. The environment variable should be set in shell configuration files like .bashrc or .zshrc.

```python
import os

api_key = os.getenv("PROVIDER_API_KEY")
```

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

### Get Row Count from Single Parquet File Path

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

This example demonstrates how to get the row count from a single Parquet file using its path. The get_row_count() function handles Parquet file inputs directly.

```python
# Single Parquet file from package data
parquet_path = pb.get_data_path("nycflights", "parquet")

pb.get_row_count(parquet_path)
```

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

### Get Column Count from DuckDB Table (Ibis)

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

Demonstrates getting the column count from a DuckDB table managed by Ibis using the get_column_count() function. This usage requires the Ibis library (v9.5.0+) and its corresponding backend driver to be installed.

```python
import pointblank as pb

small_table_duckdb = pb.load_dataset("small_table", tbl_type="duckdb")

pb.get_column_count(small_table_duckdb)
```

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

### Initializing Example Tables for `tbl_match()`

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

Sets up two simple Polars DataFrames, `tbl_1` and `tbl_2`, which are used in subsequent examples to demonstrate the `tbl_match()` validation functionality. This includes defining columns with different data types.

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

# Create the first table
tbl_1 = pl.DataFrame({
    "a": [1, 2, 3, 4],
    "b": ["w", "x", "y", "z"],
    "c": [4.0, 5.0, 6.0, 7.0]
})

# Create the second table
tbl_2 = pl.DataFrame({
    "a": [1, 2, 3, 4],
    "b": ["w", "x", "y", "z"],
    "c": [4.0, 5.0, 6.0, 7.0]
})
```

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

### Pointblank Slack Notification: Basic Setup with Webhook URL

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

Illustrates the basic setup for sending Slack notifications using Pointblank's `send_slack_notification` function. This example requires a valid `webhook_url` to send notifications to a Slack channel.

```python
import pointblank as pb

# Create a Slack notification function
notify_slack = pb.send_slack_notification(
    webhook_url="https://hooks.slack.com/services/your/webhook/url"
)
```

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

### Validate Columns Starting with 'paid' and Matching Years '23' or '24'

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

This example demonstrates validating columns that start with 'paid' and also match the years '2023' or '2024'. It combines `starts_with()` and `matches()` column selectors using the '&' operator within `pb.col()`. Input is a polars DataFrame, and output is a pointblank validation object.

```python
tbl = pl.DataFrame(
    {
        "name": ["Alice", "Bob", "Charlie"],
        "hours_2022": [160, 180, 160],
        "hours_2023": [182, 168, 175],
        "hours_2024": [200, 165, 190],
        "paid_2022": [18.62, 16.95, 18.25],
        "paid_2023": [19.29, 17.75, 18.35],
        "paid_2024": [20.73, 18.35, 20.10],
    }
)

validation = (
    pb.Validate(data=tbl)
    .col_vals_gt(
        columns=pb.col(pb.starts_with("paid") & pb.matches("23|24")),
        value=10
    )
    .interrogate()
)

validation
```