load_dataset(dataset='small_table', tbl_type='polars')
Load a dataset hosted in the library as specified DataFrame type.
Parameters
dataset : Literal['small_table', 'game_revenue'] = 'small_table'-
The name of the dataset to load. Current options are "small_table" and "game_revenue".
tbl_type : Literal['polars', 'pandas', 'duckdb'] = 'polars'-
The type of DataFrame 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.
Examples
Load the small_table dataset as a Polars DataFrame by calling load_dataset() with its defaults:
import pointblank as pb
small_table = pb.load_dataset()
small_table
shape: (13, 8)| date_time | date | a | b | c | d | e | f |
|---|
| datetime[μs] | date | i64 | str | i64 | f64 | bool | str |
| 2016-01-04 11:00:00 | 2016-01-04 | 2 | "1-bcd-345" | 3 | 3423.29 | true | "high" |
| 2016-01-04 00:32:00 | 2016-01-04 | 3 | "5-egh-163" | 8 | 9999.99 | true | "low" |
| 2016-01-05 13:32:00 | 2016-01-05 | 6 | "8-kdg-938" | 3 | 2343.23 | true | "high" |
| 2016-01-06 17:23:00 | 2016-01-06 | 2 | "5-jdo-903" | null | 3892.4 | false | "mid" |
| 2016-01-09 12:36:00 | 2016-01-09 | 8 | "3-ldm-038" | 7 | 283.94 | true | "low" |
| … | … | … | … | … | … | … | … |
| 2016-01-20 04:30:00 | 2016-01-20 | 3 | "5-bce-642" | 9 | 837.93 | false | "high" |
| 2016-01-20 04:30:00 | 2016-01-20 | 3 | "5-bce-642" | 9 | 837.93 | false | "high" |
| 2016-01-26 20:07:00 | 2016-01-26 | 4 | "2-dmx-010" | 7 | 833.98 | true | "low" |
| 2016-01-28 02:51:00 | 2016-01-28 | 2 | "7-dmx-010" | 8 | 108.34 | false | "low" |
| 2016-01-30 11:23:00 | 2016-01-30 | 1 | "3-dka-303" | null | 2230.09 | true | "high" |
The game_revenue dataset can be loaded as a Pandas DataFrame by specifying the dataset name and setting tbl_type="pandas":
import pointblank as pb
game_revenue = pb.load_dataset(dataset="game_revenue", tbl_type="pandas")
game_revenue
|
player_id |
session_id |
session_start |
time |
item_type |
item_name |
item_revenue |
session_duration |
start_day |
acquisition |
country |
| 0 |
ECPANOIXLZHF896 |
ECPANOIXLZHF896-eol2j8bs |
2015-01-01 01:31:03+00:00 |
2015-01-01 01:31:27+00:00 |
iap |
offer2 |
8.991 |
16.3 |
2015-01-01 |
google |
Germany |
| 1 |
ECPANOIXLZHF896 |
ECPANOIXLZHF896-eol2j8bs |
2015-01-01 01:31:03+00:00 |
2015-01-01 01:36:57+00:00 |
iap |
gems3 |
22.491 |
16.3 |
2015-01-01 |
google |
Germany |
| 2 |
ECPANOIXLZHF896 |
ECPANOIXLZHF896-eol2j8bs |
2015-01-01 01:31:03+00:00 |
2015-01-01 01:37:45+00:00 |
iap |
gold7 |
107.991 |
16.3 |
2015-01-01 |
google |
Germany |
| 3 |
ECPANOIXLZHF896 |
ECPANOIXLZHF896-eol2j8bs |
2015-01-01 01:31:03+00:00 |
2015-01-01 01:42:33+00:00 |
ad |
ad_20sec |
0.760 |
16.3 |
2015-01-01 |
google |
Germany |
| 4 |
ECPANOIXLZHF896 |
ECPANOIXLZHF896-hdu9jkls |
2015-01-01 11:50:02+00:00 |
2015-01-01 11:55:20+00:00 |
ad |
ad_5sec |
0.030 |
35.2 |
2015-01-01 |
google |
Germany |
| ... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
... |
| 1995 |
NAOJRDMCSEBI281 |
NAOJRDMCSEBI281-j2vs9ilp |
2015-01-21 01:57:50+00:00 |
2015-01-21 02:02:50+00:00 |
ad |
ad_survey |
1.332 |
25.8 |
2015-01-11 |
organic |
Norway |
| 1996 |
NAOJRDMCSEBI281 |
NAOJRDMCSEBI281-j2vs9ilp |
2015-01-21 01:57:50+00:00 |
2015-01-21 02:22:14+00:00 |
ad |
ad_survey |
1.350 |
25.8 |
2015-01-11 |
organic |
Norway |
| 1997 |
RMOSWHJGELCI675 |
RMOSWHJGELCI675-vbhcsmtr |
2015-01-21 02:39:48+00:00 |
2015-01-21 02:40:00+00:00 |
ad |
ad_5sec |
0.030 |
8.4 |
2015-01-10 |
other_campaign |
France |
| 1998 |
RMOSWHJGELCI675 |
RMOSWHJGELCI675-vbhcsmtr |
2015-01-21 02:39:48+00:00 |
2015-01-21 02:47:12+00:00 |
iap |
offer5 |
26.091 |
8.4 |
2015-01-10 |
other_campaign |
France |
| 1999 |
GJCXNTWEBIPQ369 |
GJCXNTWEBIPQ369-9elq67md |
2015-01-21 03:59:23+00:00 |
2015-01-21 04:06:29+00:00 |
ad |
ad_5sec |
0.120 |
18.5 |
2015-01-14 |
organic |
United States |
2000 rows × 11 columns