# get_data_path()

Get the file path to a dataset included with the Pointblank package.

Usage

[Source](https://github.com/posit-dev/pointblank/blob/main/pointblank/validate.py#L1559-L1724)

``` python
get_data_path(
    dataset="small_table",
    file_type="csv",
)
```

This function provides direct access to the file paths of datasets included with Pointblank. These paths can be used in examples and documentation to demonstrate file-based data loading without requiring the actual data files. The returned paths can be used with `Validate(data=path)` to demonstrate CSV and Parquet file loading capabilities.

The first call for a dataset and file type writes the dataset to a temporary file and returns its path. Later calls with the same arguments return the same path, unless the file has been changed or deleted since, in which case a fresh copy is written. All of these files are deleted when the Python session ends, so copy a file elsewhere if you need to keep it.

## Parameters

`dataset: Literal["small_table", "game_revenue", "nycflights", "global_sales"] = "small_table"`  
The name of the dataset to get the path for. Current options are `"small_table"`, `"game_revenue"`, `"nycflights"`, and `"global_sales"`.

`file_type: Literal["csv", "parquet", "duckdb"] = "csv"`  
The file format to get the path for. Options are `"csv"`, `"parquet"`, or `"duckdb"`.

## Returns

`str`  
The path to a temporary file holding the requested dataset in the requested format. The file is deleted when the Python session ends.

## Included Datasets

The available datasets are the same as those in [`load_dataset()`](../reference/load_dataset.md#pointblank.load_dataset):

- `"small_table"`: A small dataset with 13 rows and 8 columns. Ideal for testing and examples.
- `"game_revenue"`: A dataset with 2000 rows and 11 columns. Revenue data for a game company.
- `"nycflights"`: A dataset with 336,776 rows and 18 columns. Flight data from NYC airports.
- `"global_sales"`: A dataset with 50,000 rows and 20 columns. Global sales data across regions.

## File Types

Each dataset is available in multiple formats:

- `"csv"`: Comma-separated values file (`.csv`)
- `"parquet"`: Parquet file (`.parquet`)
- `"duckdb"`: DuckDB database file (`.ddb`)

## Examples

Get the path to a CSV file and use it with [Validate](../reference/Validate.md#pointblank.Validate):

``` python
import pointblank as pb

# Get path to the small_table CSV file
csv_path = pb.get_data_path("small_table", "csv")

# Use the path directly with Validate
validation = (
    pb.Validate(data=csv_path)
    .col_exists(["a", "b", "c"])
    .col_vals_gt(columns="d", value=0)
    .interrogate()
)

validation
```

[TABLE]

The CSV file is read into a table during interrogation. The three `col_exists()` steps and the `col_vals_gt()` step all pass.

We can also get a Parquet file path for validation examples:

``` python
# Get path to the game_revenue Parquet file
parquet_path = pb.get_data_path(dataset="game_revenue", file_type="parquet")

# Validate the Parquet file directly
validation = (
    pb.Validate(data=parquet_path, label="Game Revenue Data Validation")
    .col_vals_not_null(columns=["player_id", "session_id"])
    .col_vals_gt(columns="item_revenue", value=0)
    .interrogate()
)

validation
```

[TABLE]

All 2,000 rows have player and session IDs and positive revenue values, so every step passes.

This is particularly useful for documentation examples where you want to demonstrate file-based workflows without requiring users to have specific data files:

``` python
# Example showing CSV file validation
sales_csv = pb.get_data_path(dataset="global_sales", file_type="csv")

validation = (
    pb.Validate(data=sales_csv, label="Sales Data Validation")
    .col_exists(["customer_id", "product_id", "revenue"])
    .col_vals_regex(columns="customer_id", pattern=r"[0-9a-f]{8}")
    .interrogate()
)

validation
```

[TABLE]

All three columns exist, and every customer ID that's present is an eight-character hexadecimal string. The 334 failing test units in the last step are rows where `customer_id` is missing; setting `na_pass=True` would count those rows as passing.

## See Also

[load_dataset()](../reference/load_dataset.md#pointblank.load_dataset)  
Load a dataset directly as a table object.