## col_summary_tbl() Generate a column-level summary table of a dataset. Usage ``` python col_summary_tbl( data, tbl_name=None, ) ``` The [col_summary_tbl()](col_summary_tbl.md#pointblank.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: Warning** > > The [col_summary_tbl()](col_summary_tbl.md#pointblank.col_summary_tbl) function is still experimental. Please report any issues you encounter in the [Pointblank issue tracker](https://github.com/posit-dev/pointblank/issues). ## Parameters `data: 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()](col_summary_tbl.md#pointblank.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()](col_summary_tbl.md#pointblank.col_summary_tbl) function. Here's an example using the `small_table` dataset (itself loaded using the load_dataset() function): ``` python 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()](col_summary_tbl.md#pointblank.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: ``` python nycflights = pb.load_dataset(dataset="nycflights", tbl_type="duckdb") pb.col_summary_tbl(data=nycflights, tbl_name="nycflights") ``` /opt/hostedtoolcache/Python/3.11.15/x64/lib/python3.11/site-packages/great_tables/_tbl_data.py:825: 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.