Load a dataset hosted in the library as specified table type.
The Pointblank library includes several datasets that can be loaded using the load_dataset() function. The datasets can be loaded as a Polars DataFrame, a Pandas DataFrame, or as a DuckDB table (which uses the Ibis library backend). These datasets are used throughout the documentation’s examples to demonstrate the functionality of the library. They’re also useful for experimenting with the library and trying out different validation scenarios.
The type of table to generate from the dataset. The named options are "polars", "pandas", and "duckdb".
Returns
:FrameT | Any
The dataset for the Validate object. This could be a Polars DataFrame, a Pandas DataFrame, or a DuckDB table as an Ibis table.
Included Datasets
There are three included datasets that can be loaded using the load_dataset() function:
"small_table": A small dataset with 13 rows and 8 columns. This dataset is useful for testing and demonstration purposes.
"game_revenue": A dataset with 2000 rows and 11 columns. Provides revenue data for a game development company. For the particular game, there are records of player sessions, the items they purchased, ads viewed, and the revenue generated.
"nycflights": A dataset with 336,776 rows and 18 columns. This dataset provides information about flights departing from New York City airports (JFK, LGA, or EWR) in 2013.
"global_sales": A dataset with 50,000 rows and 20 columns. Provides information about global sales of products across different regions and countries.
Supported DataFrame Types
The tbl_type= parameter can be set to one of the following:
"polars": A Polars DataFrame.
"pandas": A Pandas DataFrame.
"duckdb": An Ibis table for a DuckDB database.
Examples
Load the "small_table" dataset as a Polars DataFrame by calling load_dataset() with dataset="small_table" and tbl_type="polars":
import pointblank as pbsmall_table = pb.load_dataset(dataset="small_table", tbl_type="polars")pb.preview(small_table)
PolarsRows13Columns8
date_time
Datetime
date
Date
a
Int64
b
String
c
Int64
d
Float64
e
Boolean
f
String
1
2016-01-04 11:00:00
2016-01-04
2
1-bcd-345
3
3423.29
True
high
2
2016-01-04 00:32:00
2016-01-04
3
5-egh-163
8
9999.99
True
low
3
2016-01-05 13:32:00
2016-01-05
6
8-kdg-938
3
2343.23
True
high
4
2016-01-06 17:23:00
2016-01-06
2
5-jdo-903
None
3892.4
False
mid
5
2016-01-09 12:36:00
2016-01-09
8
3-ldm-038
7
283.94
True
low
9
2016-01-20 04:30:00
2016-01-20
3
5-bce-642
9
837.93
False
high
10
2016-01-20 04:30:00
2016-01-20
3
5-bce-642
9
837.93
False
high
11
2016-01-26 20:07:00
2016-01-26
4
2-dmx-010
7
833.98
True
low
12
2016-01-28 02:51:00
2016-01-28
2
7-dmx-010
8
108.34
False
low
13
2016-01-30 11:23:00
2016-01-30
1
3-dka-303
None
2230.09
True
high
Note that the "small_table" dataset is a Polars DataFrame and using the preview() function will display the table in an HTML viewing environment.
The "game_revenue" dataset can be loaded as a Pandas DataFrame by specifying the dataset name and setting tbl_type="pandas":
The "game_revenue" dataset is a more real-world dataset with a mix of data types, and it’s significantly larger than the small_table dataset at 2000 rows and 11 columns.
The "nycflights" dataset can be loaded as a DuckDB table by specifying the dataset name and setting tbl_type="duckdb":
The "nycflights" dataset is a large dataset with 336,776 rows and 18 columns. This dataset is truly a real-world dataset and provides information about flights originating from New York City airports in 2013.
Finally, the "global_sales" dataset can be loaded as a Polars table by specifying the dataset name. Since tbl_type= is set to "polars" by default, we don’t need to specify it:
The "global_sales" dataset is a large dataset with 50,000 rows and 20 columns. Each record describes the sales of a particular product to a customer located in one of three global regions: North America, Europe, or Asia.