col_summary_tbl

col_summary_tbl(data, tbl_name=None)

Generate a column-level summary table of a dataset.

The col_summary_tbl() function generates a summary table of a dataset, focusing on providing column-level information about the dataset. The summary includes the following information:

• the type of the table (e.g., "polars", "pandas", etc.)
• the number of rows and columns in the table
• column-level information, including:
  • the column name
  • the column type
  • measures of missingness and distinctness
  • descriptive stats and quantiles
  • statistics for datetime columns

The summary table is returned as a GT object, which can be displayed in a notebook or saved to an HTML file.

Warning

The col_summary_tbl() function is still experimental. Please report any issues you encounter in the Pointblank issue tracker.

Parameters

data : FrameT | Any

The table to summarize, which could be a DataFrame object or an Ibis table object. Read the Supported Input Table Types section for details on the supported table types.

tbl_name : str | None = None

Optionally, the name of the table could be provided as tbl_name=.

Returns

: GT

A GT object that displays the column-level summaries of the table.

Supported Input Table Types

The data= parameter can be given any of the following table types:

• Polars DataFrame ("polars")
• Pandas DataFrame ("pandas")
• DuckDB table ("duckdb")*
• MySQL table ("mysql")*
• PostgreSQL table ("postgresql")*
• SQLite table ("sqlite")*
• Parquet table ("parquet")*

The table types marked with an asterisk need to be prepared as Ibis tables (with type of ibis.expr.types.relations.Table). Furthermore, using col_summary_tbl() with these types of tables requires the Ibis library (v9.5.0 or above) to be installed. If the input table is a Polars or Pandas DataFrame, the availability of Ibis is not needed.

Examples

It’s easy to get a column-level summary of a table using the col_summary_tbl() function. Here’s an example using the small_table dataset (itself loaded using the load_dataset() function):

import pointblank as pb

small_table = pb.load_dataset(dataset="small_table", tbl_type="polars")

pb.col_summary_tbl(data=small_table)

PolarsRows13Columns8
		Column	NA	UQ	Mean	SD	Min	P5	Q1	Med	Q3	P95	Max	IQR
1		date_time Datetime(time_unit='us', time_zone=None)	0 0.00	12 0.92	—	—	2016-01-04 00:32:00 – 2016-01-30 11:23:00							—
2		date Date	0 0.00	11 0.85	—	—	2016-01-04 – 2016-01-30							—
3		a Int64	0 0.00	7 0.54	3.77	2.09	1.00	1.60	2.00	3.00	4.00	7.40	8.00	2.00
4		b String	0 0.00	12 0.92	9.00 SL	0.00 SL	9 SL	—	—	9 SL	—	—	9 SL	—
5		c Int64	2 0.15	6 0.46	5.73	2.72	2.00	2.50	3.00	7.00	8.00	9.00	9.00	5.00
6		d Float64	0 0.00	12 0.92	2305	2631	108	214	838	1036	3291	6335	10000	2453
7		e Boolean	0 0.00	T 0.61 F 0.39	—	—	—	—	—	—	—	—	—	—
8		f String	0 0.00	3 0.23	3.46 SL	0.52 SL	3 SL	—	—	3 SL	—	—	4 SL	—

This table used above was a Polars DataFrame, but the col_summary_tbl() function works with any table supported by pointblank, including Pandas DataFrames and Ibis backend tables. Here’s an example using a DuckDB table handled by Ibis:

nycflights = pb.load_dataset(dataset="nycflights", tbl_type="duckdb")

pb.col_summary_tbl(data=nycflights, tbl_name="nycflights")

DuckDBnycflightsRows336,776Columns18
		Column	NA	UQ	Mean	SD	Min	P5	Q1	Med	Q3	P95	Max	IQR
1		year int64	0 0.00	1 <0.01	2013	0.00	2013	2013	2013	2013	2013	2013	2013	0
2		month int64	0 0.00	12 <0.01	6.55	3.41	1.00	1.00	4.00	7.00	9.28	12.0	12.0	6.00
3		day int64	0 0.00	31 <0.01	15.7	8.77	1.00	2.00	8.06	16.0	23.0	29.4	31.0	15.0
4		dep_time int64	8255 0.02	1317 <0.01	1349	488	1.00	623	904	1401	1744	2111	2400	837
5		sched_dep_time int64	0 0.00	1021 <0.01	1344	467	106	630	909	1359	1729	2051	2359	823
6		dep_delay int64	8255 0.02	526 <0.01	12.6	40.2	−43.0	−9.00	−5.00	−2.00	10.8	88.3	1301	16.0
7		arr_time int64	8713 0.03	1410 <0.01	1502	533	1.00	734	1096	1535	1941	2248	2400	836
8		sched_arr_time int64	0 0.00	1163 <0.01	1536	497	1.00	815	1124	1556	1945	2247	2359	821
9		arr_delay int64	9430 0.03	576 <0.01	6.90	44.6	−86.0	−32.2	−17.0	−5.00	13.8	91.2	1272	31.0
10		carrier string	0 0.00	16 <0.01	2.00 SL	0.00 SL	2 SL	—	—	2 SL	—	—	2 SL	—
11		flight int64	0 0.00	3844 0.01	1972	1632	1.00	93.7	558	1496	3465	4703	8500	2912
12		tailnum string	2512 <0.01	4042 0.01	6.00 SL	0.07 SL	5 SL	—	—	6 SL	—	—	6 SL	—
13		origin string	0 0.00	3 <0.01	3.00 SL	0.00 SL	3 SL	—	—	3 SL	—	—	3 SL	—
14		dest string	0 0.00	105 <0.01	3.00 SL	0.00 SL	3 SL	—	—	3 SL	—	—	3 SL	—
15		air_time int64	9430 0.03	508 <0.01	151	93.7	20.0	40.0	82.3	129	191	339	695	110
16		distance int64	0 0.00	214 <0.01	1040	733	17.0	198	509	872	1389	2477	4983	887
17		hour int64	0 0.00	20 <0.01	13.2	4.66	1	6	9	13	17	20	23	8
18		minute int64	0 0.00	60 <0.01	26.2	19.3	0.00	0.00	7.94	29.0	43.4	57.8	59.0	36.0