## get_data_path() Get the file path to a dataset included with the Pointblank package. Usage ``` 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. ## 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](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") 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/tmpc_6h0xzy.csv
Pointblank Validation
2026-07-13|17:55:01 Polars
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_exists()
a -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_exists()
b -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 3
col_exists()
c -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_vals_gt()
d 0 ✓ 13 13
1.00		 0
0.00		 -- -- -- --
2026-07-13 17:55:01 UTC< 1 s2026-07-13 17:55:01 UTC
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 ``` UxNzE4MDcsMTMuNDkyNDc3OCA3LjQyOTI4MDk1LDE0LjgxNDkxNSA1LjgwMzc1MDQ2LDE0LjgxNDkxNSBaIiBpZD0iU2hhcGUiIGZpbGw9IiMwMDAwMDAiIGZpbGwtcnVsZT0ibm9uemVybyIgLz4KICAgICAgICAgICAgPHBhdGggZD0iTTEzLjk2MzgxODksOC42OTkzMzUgQzEzLjkzNjQ2MjEsOC43MDQzMDkyNSAxMy45MDkxMDU5LDguNzExNzY5NjggMTMuODg0MjM1OSw4LjcxOTIzMDc0IEMxMy43ODIyNzA0LDguNzM2NjM5NjcgMTMuNjg3NzY1NCw4Ljc3NjQzMTE1IDEzLjYwNTY5NTYsOC44Mzg2MDUxOCBMMTAuMjQzMzE1NiwxMS4zODUyNTk4IEMxMC4wNzY2ODg2LDExLjUwNDYzNDMgOS45NzcyMDk5MywxMS42OTg2MTgxIDkuOTc3MjA5OTMsMTEuOTAyNTQ5MSBDOS45NzcyMDk5MywxMi4xMDY0ODA3IDEwLjA3NjY4ODYsMTIuMzAwNDYzOSAxMC4yNDMzMTU2LDEyLjQxOTgzODMgTDEzLjYwNTY5NTYsMTQuOTY2NDkzIEMxMy44OTE2OTcsMTUuMTgwMzcyNSAxNC4yOTcwNzI5LDE1LjEyMzE3MjEgMTQuNTEwOTUxNywxNC44MzcxNzA3IEMxNC43MjQ4MzEzLDE0LjU1MTE2OTIgMTQuNjY3NjMwOSwxNC4xNDU3OTQgMTQuMzgxNjI5NCwxMy45MzE5MTQ1IEwxMi41MzEzMjU3LDEyLjUzOTIxMjcgTDIxLjg4MTI0OTUsMTIuNTM5MjEyNyBMMjEuODgxMjQ5NSwxMS4yNjU4ODU0IEwxMi41MzEzMjU3LDExLjI2NTg4NTQgTDE0LjM4MTYyOTQsOS44NzMxODM2NCBDMTQuNjM3Nzg3Miw5LjcxNjUwNDUzIDE0Ljc0OTcwMDYsOS40MDA2NjAxNCAxNC42NDc3MzUxLDkuMTE3MTQ1NTMgQzE0LjU0ODI1NjQsOC44MzM2MzE1NiAxNC4yNjIyNTUsOC42NTk1NDM1MiAxMy45NjM4MTg5LDguNjk5MzM1IFoiIGlkPSJhcnJvdyIgZmlsbD0iIzAwMDAwMCIgdHJhbnNmb3JtPSJ0cmFuc2xhdGUoMTUuOTI5MjMwLCAxMS44OTQ3MzcpIHJvdGF0ZSgtMTgwLjAwMDAwMCkgdHJhbnNsYXRlKC0xNS45MjkyMzAsIC0xMS44OTQ3MzcpICIgLz4KICAgICAgICA8L2c+CiAgICA8L2c+Cjwvc3ZnPg==" />
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()
player_id -- ✓ 2000 2000
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_vals_not_null()
session_id -- ✓ 2000 2000
1.00		 0
0.00		 -- -- -- --
#4CA64C 3
col_vals_gt()
item_revenue 0 ✓ 2000 2000
1.00		 0
0.00		 -- -- -- --
2026-07-13 17:55:01 UTC< 1 s2026-07-13 17:55:01 UTC
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", "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.