## load_dataset() Load a dataset hosted in the library as specified table type. Usage ``` python load_dataset( dataset="small_table", tbl_type="polars", ) ``` The Pointblank library includes several datasets that can be loaded using the [load_dataset()](load_dataset.md#pointblank.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. ## Parameters `dataset: Literal[``"small_table", ``"game_revenue"``, ``"nycflights"``, ``"global_sales"``]`` = ``"small_table"` The name of the dataset to load. Current options are `"small_table"`, `"game_revenue"`, `"nycflights"`, and `"global_sales"`. `tbl_type: Literal[``"polars", ``"pandas"``, ``"duckdb"``]`` = ``"polars"` The type of table to generate from the dataset. The named options are `"polars"`, `"pandas"`, and `"duckdb"`. ## Returns `Any` The dataset for the [Validate](Validate.md#pointblank.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()](load_dataset.md#pointblank.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()](load_dataset.md#pointblank.load_dataset) with `dataset="small_table"` and `tbl_type="polars"`: ``` python import pointblank as pb small_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"`: ``` python game_revenue = pb.load_dataset(dataset="game_revenue", tbl_type="pandas") pb.preview(game_revenue) ```
PandasRows2,000Columns11
player_id object
session_id object
session_start datetime64[ns, UTC]
time datetime64[ns, UTC]
item_type object
item_name object
item_revenue float64
session_duration float64
start_day datetime64[ns]
acquisition object
country object
1 ECPANOIXLZHF896 ECPANOIXLZHF896-eol2j8bs 2015-01-01 01:31:03+00:00 2015-01-01 01:31:27+00:00 iap offer2 8.99 16.3 2015-01-01 00:00:00 google Germany
2 ECPANOIXLZHF896 ECPANOIXLZHF896-eol2j8bs 2015-01-01 01:31:03+00:00 2015-01-01 01:36:57+00:00 iap gems3 22.49 16.3 2015-01-01 00:00:00 google Germany
3 ECPANOIXLZHF896 ECPANOIXLZHF896-eol2j8bs 2015-01-01 01:31:03+00:00 2015-01-01 01:37:45+00:00 iap gold7 107.99 16.3 2015-01-01 00:00:00 google Germany
4 ECPANOIXLZHF896 ECPANOIXLZHF896-eol2j8bs 2015-01-01 01:31:03+00:00 2015-01-01 01:42:33+00:00 ad ad_20sec 0.76 16.3 2015-01-01 00:00:00 google Germany
5 ECPANOIXLZHF896 ECPANOIXLZHF896-hdu9jkls 2015-01-01 11:50:02+00:00 2015-01-01 11:55:20+00:00 ad ad_5sec 0.03 35.2 2015-01-01 00:00:00 google Germany
1996 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 00:00:00 organic Norway
1997 NAOJRDMCSEBI281 NAOJRDMCSEBI281-j2vs9ilp 2015-01-21 01:57:50+00:00 2015-01-21 02:22:14+00:00 ad ad_survey 1.35 25.8 2015-01-11 00:00:00 organic Norway
1998 RMOSWHJGELCI675 RMOSWHJGELCI675-vbhcsmtr 2015-01-21 02:39:48+00:00 2015-01-21 02:40:00+00:00 ad ad_5sec 0.03 8.4 2015-01-10 00:00:00 other_campaign France
1999 RMOSWHJGELCI675 RMOSWHJGELCI675-vbhcsmtr 2015-01-21 02:39:48+00:00 2015-01-21 02:47:12+00:00 iap offer5 26.09 8.4 2015-01-10 00:00:00 other_campaign France
2000 GJCXNTWEBIPQ369 GJCXNTWEBIPQ369-9elq67md 2015-01-21 03:59:23+00:00 2015-01-21 04:06:29+00:00 ad ad_5sec 0.12 18.5 2015-01-14 00:00:00 organic United States
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"`: ``` python nycflights = pb.load_dataset(dataset="nycflights", tbl_type="duckdb") pb.preview(nycflights) ```
DuckDBRows336,776Columns18
year int64
month int64
day int64
dep_time int64
sched_dep_time int64
dep_delay int64
arr_time int64
sched_arr_time int64
arr_delay int64
carrier string
flight int64
tailnum string
origin string
dest string
air_time int64
distance int64
hour int64
minute int64
1 2013 1 1 517 515 2 830 819 11 UA 1545 N14228 EWR IAH 227 1400 5 15
2 2013 1 1 533 529 4 850 830 20 UA 1714 N24211 LGA IAH 227 1416 5 29
3 2013 1 1 542 540 2 923 850 33 AA 1141 N619AA JFK MIA 160 1089 5 40
4 2013 1 1 544 545 -1 1004 1022 -18 B6 725 N804JB JFK BQN 183 1576 5 45
5 2013 1 1 554 600 -6 812 837 -25 DL 461 N668DN LGA ATL 116 762 6 0
336772 2013 9 30 NULL 1455 NULL NULL 1634 NULL 9E 3393 NULL JFK DCA NULL 213 14 55
336773 2013 9 30 NULL 2200 NULL NULL 2312 NULL 9E 3525 NULL LGA SYR NULL 198 22 0
336774 2013 9 30 NULL 1210 NULL NULL 1330 NULL MQ 3461 N535MQ LGA BNA NULL 764 12 10
336775 2013 9 30 NULL 1159 NULL NULL 1344 NULL MQ 3572 N511MQ LGA CLE NULL 419 11 59
336776 2013 9 30 NULL 840 NULL NULL 1020 NULL MQ 3531 N839MQ LGA RDU NULL 431 8 40
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: ``` python global_sales = pb.load_dataset(dataset="global_sales") pb.preview(global_sales) ```
PolarsRows50,000Columns20
product_id String
product_category String
customer_id String
customer_segment String
region String
country String
city String
timestamp Datetime
quarter String
month Int64
year Int64
price Float64
quantity Int64
status String
email String
revenue Float64
tax Float64
total Float64
payment_method String
sales_channel String
1 98b70df0 Manufacturing cf3b13c7 Government Asia Pacific Australia Melbourne 2021-12-25 19:00:00 2021-Q4 12 2021 186.0 7 returned user1651@test.org 1302.0 127.45 1429.45 Apple Pay Partner
2 9d09fef5 Manufacturing 08b5db12 Consumer Europe France Nice 2022-06-12 17:25:00 2022-Q2 6 2022 137.03 8 returned user5200@company.io 1096.24 222.52 1318.76 PayPal Distributor
3 8ac6b077 Retail 41079b2e Consumer Europe France Toulouse 2023-05-06 09:09:00 2023-Q2 5 2023 330.08 4 shipped user9180@mockdata.com 1320.32 260.89 1581.21 PayPal Phone
4 13d2df9d Healthcare b421eece Consumer North America USA Miami 2023-10-11 16:53:00 2023-Q4 10 2023 420.09 3 shipped user1636@example.com 1260.27 103.99 1364.26 Bank Transfer Phone
5 98b70df0 Manufacturing 5906a04f SMB North America Canada Calgary 2022-05-05 01:53:00 2022-Q2 5 2022 187.77 3 delivered user9971@mockdata.com 563.31 75.73 639.04 Credit Card Phone
49996 53a36468 Finance 966a8bbe Government Asia Pacific Australia Melbourne 2023-11-04 14:45:00 2023-Q4 11 2023 198.18 1 pending user8593@test.org 198.18 18.3 216.48 Google Pay Partner
49997 a42fd1ff Healthcare ff8933e4 SMB Asia Pacific Japan Kyoto 2023-04-27 17:27:00 2023-Q2 4 2023 419.72 2 returned user5448@company.io 839.44 90.49 929.93 Google Pay Partner
49998 bbf158d2 Technology f0c0af3f Enterprise North America USA Los Angeles 2021-04-24 23:15:00 2021-Q2 4 2021 302.52 1 pending user1463@test.org 302.52 21.68 324.2 Bank Transfer Online
49999 2a0866de Healthcare 5b27ba59 SMB Europe France Nice 2023-12-30 19:44:00 2023-Q4 12 2023 433.82 5 pending user4167@test.org 2169.1 448.87 2617.97 Credit Card Online
50000 6260f67c Technology 482c1d84 Consumer Asia Pacific Japan Kyoto 2021-12-05 09:49:00 2021-Q4 12 2021 400.31 8 returned user4238@example.com 3202.48 339.84 3542.32 Apple Pay Distributor
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.