get_data_path()function

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

USAGE

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.

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 file path to the requested dataset file.

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")
print(csv_path)

# 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
/tmp/tmp22p14vgo.csv
Pointblank Validation
2025-06-22|01:24:06
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		 — — — —
2025-06-22 01:24:06 UTC< 1 s2025-06-22 01:24:06 UTC

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		 — — — —
2025-06-22 01:24:06 UTC< 1 s2025-06-22 01:24:06 UTC

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", "amount"])
    .col_vals_regex(columns="customer_id", pattern=r"CUST_[0-9]{6}")
    .interrogate()
)

See Also

load_dataset() for loading datasets directly as table objects.

⦾