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get_data_path()

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

Usage

Source

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():

  • "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:

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
Pointblank Validation
2026-11-05|01:35:36
Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_exists
col_exists()
 a — ✓ 1 1
1.00		 0
0.00		 — — — —
#4CA64C 2
col_exists
col_exists()
 b — ✓ 1 1
1.00		 0
0.00		 — — — —
#4CA64C 3
col_exists
col_exists()
 c — ✓ 1 1
1.00		 0
0.00		 — — — —
#4CA64C 4
col_vals_gt
col_vals_gt()
 d 0 ✓ 13 13
1.00		 0
0.00		 — — — —
2026-11-05 01:35:36 UTC< 1 s2026-11-05 01:35:36 UTC

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:

# 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
Pointblank Validation
Game Revenue Data Validation
Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_not_null
col_vals_not_null()
 player_id — ✓ 2000 2000
1.00		 0
0.00		 — — — —
#4CA64C 2
col_vals_not_null
col_vals_not_null()
 session_id — ✓ 2000 2000
1.00		 0
0.00		 — — — —
#4CA64C 3
col_vals_gt
col_vals_gt()
 item_revenue 0 ✓ 2000 2000
1.00		 0
0.00		 — — — —
2026-11-05 01:35:36 UTC< 1 s2026-11-05 01:35:36 UTC

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:

# 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
Pointblank Validation
Sales Data Validation
Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_exists
col_exists()
 customer_id — ✓ 1 1
1.00		 0
0.00		 — — — —
#4CA64C 2
col_exists
col_exists()
 product_id — ✓ 1 1
1.00		 0
0.00		 — — — —
#4CA64C 3
col_exists
col_exists()
 revenue — ✓ 1 1
1.00		 0
0.00		 — — — —
#4CA64C66 4
col_vals_regex
col_vals_regex()
 customer_id [0-9a-f]{8} ✓ 50.0K 49.7K
0.99		 334
0.01		 — — —
2026-11-05 01:35:36 UTC< 1 s2026-11-05 01:35:36 UTC

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()

Load a dataset directly as a table object.