col_summary_tbl()function

Generate a column-level summary table of a dataset.

USAGE

col_summary_tbl(data, tbl_name=None)

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, an Ibis table object, a CSV file path, a Parquet file path, or a database connection string. 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")*
• CSV files (string path or pathlib.Path object with .csv extension)
• Parquet files (string path, pathlib.Path object, glob pattern, directory with .parquet extension, or partitioned dataset)
• GitHub URLs (direct links to CSV or Parquet files on GitHub)
• Database connection strings (URI format with optional table specification)

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
	date_time Datetime(time_unit='us', time_zone=None)	0 0	12 0.92	-	-	2016 01 04 00:32:00	-	-	-	-	-	2016 01 30 11:23:00	-
	date Date	0 0	11 0.85	-	-	2016 01 04	-	-	-	-	-	2016 01 30	-
	a Int64	0 0	7 0.54	3.77	2.09	1	1.06	2	3	4	7.4	8	2
	b String	0 0	12 0.92	9	0	9	9	9	9	9	9	9	0
	c Int64	2 0.15	7 0.54	5.73	2.72	2	2.05	3	7	8	9	9	5
	d Float64	0 0	12 0.92	2,304.7	2,631.36	108.34	118.88	837.93	1,035.64	3,291.03	6,335.44	9999.99	2,453.1
	e Boolean	0 0	T0.62 F0.38	-	-	-	-	-	-	-	-	-	-
	f String	0 0	3 0.23	3.46	0.52	3	3	3	3	4	4	4	1
String columns statistics regard the string's length.

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")

/opt/hostedtoolcache/Python/3.11.13/x64/lib/python3.11/site-packages/great_tables/_tbl_data.py:817: UserWarning: PyArrow Table support is currently experimental.
  warnings.warn("PyArrow Table support is currently experimental.")

Rows336,776Columns18
	Column	NA	UQ	Mean	SD	Min	P5	Q1	Med	Q3	P95	Max	IQR
	year Int64	0 0	1 <.01	2,013	0	2,013	2,013	2,013	2,013	2,013	2,013	2,013	0
	month Int64	0 0	12 <.01	6.55	3.41	1	1	4	6.77	10	12	12	6
	day Int64	0 0	31 <.01	15.71	8.77	1	1	8	15.64	23	29	31	15
	dep_time Int64	8255 0.02	1319 <.01	1,349.11	488.28	1	514	907	1,381.15	1,744	2,112	2,400	837
	sched_dep_time Int64	0 0	1021 <.01	1,344.25	467.34	106	545	906	1,377.13	1,729	2,050	2,359	823
	dep_delay Int64	8255 0.02	528 <.01	12.64	40.21	−43	−13	−5	−1.58	11	88	1,301	16
	arr_time Int64	8713 0.03	1412 <.01	1,502.05	533.26	1	10	1,104	1,539.25	1,940	2,248	2,400	836
	sched_arr_time Int64	0 0	1163 <.01	1,536.38	497.46	1	15	1,124	1,575.93	1,945	2,246	2,359	821
	arr_delay Int64	9430 0.03	578 <.01	6.9	44.63	−86	−48	−17	−4.92	14	91	1,272	31
	carrier String	0 0	16 <.01	2	0	2	2	2	2	2	2	2	0
	flight Int64	0 0	3844 0.01	1,971.92	1,632.47	1	4	553	1,499.04	3,465	4,695	8,500	2,912
	tailnum String	2512 <.01	4044 0.01	6	0.07	5	6	6	6	6	6	6	0
	origin String	0 0	3 <.01	3	0	3	3	3	3	3	3	3	0
	dest String	0 0	105 <.01	3	0	3	3	3	3	3	3	3	0
	air_time Int64	9430 0.03	510 <.01	150.69	93.69	20	31	82	129.35	192	339	695	110
	distance Int64	0 0	214 <.01	1,039.91	733.23	17	116	502	861.05	1,389	2,475	4,983	887
	hour Int64	0 0	20 <.01	13.18	4.66	1	5	9	13.4	17	20	23	8
	minute Int64	0 0	60 <.01	26.23	19.3	0	0	8	28.3	44	58	59	36
String columns statistics regard the string's length.