PyArrow Tables

In-Memory Arrow Table

tbl_preview() also accepts a pyarrow.Table directly — no file needed.

import pyarrow as pa
from great_docs import tbl_preview

tbl = pa.table({
    'city': ['Tokyo', 'Delhi', 'Shanghai', 'São Paulo', 'Mexico City',
             'Cairo', 'Mumbai', 'Beijing', 'Dhaka', 'Osaka'],
    'country': ['Japan', 'India', 'China', 'Brazil', 'Mexico',
                'Egypt', 'India', 'China', 'Bangladesh', 'Japan'],
    'population_m': [13.96, 11.03, 24.87, 12.33, 9.21,
                     9.54, 12.44, 21.54, 8.91, 2.75],
    'area_km2': [2194, 1484, 6341, 1521, 1485,
                 3085, 603, 16411, 306, 225],
})

tbl_preview(tbl, show_all=True)

ArrowRows10Columns4
	city str	country str	population_m f64	area_km2 i64
0	Tokyo	Japan	13.96	2194
1	Delhi	India	11.03	1484
2	Shanghai	China	24.87	6341
3	São Paulo	Brazil	12.33	1521
4	Mexico City	Mexico	9.21	1485
5	Cairo	Egypt	9.54	3085
6	Mumbai	India	12.44	603
7	Beijing	China	21.54	16411
8	Dhaka	Bangladesh	8.91	306
9	Osaka	Japan	2.75	225

Arrow Table with Typed Columns

PyArrow preserves rich type information — booleans, dates, decimals — which tbl_preview() maps to short dtype labels.

import pyarrow as pa
from datetime import date

tbl = pa.table({
    'event': ['Launch', 'Update', 'Hotfix', 'Deprecation'],
    'date': [date(2025, 1, 15), date(2025, 3, 1), date(2025, 3, 12), date(2025, 6, 30)],
    'critical': [True, False, True, False],
    'affected_users': [50000, 12000, 8500, 2000],
})

tbl_preview(tbl, show_all=True)

ArrowRows4Columns4
	event str	date date	critical bool	affected_users i64
0	Launch	2025-01-15	True	50000
1	Update	2025-03-01	False	12000
2	Hotfix	2025-03-12	True	8500
3	Deprecation	2025-06-30	False	2000