# PyArrow Tables
# In-Memory Arrow Table
`tbl_preview()` also accepts a `pyarrow.Table` directly -- no file needed.
``` python
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.
``` python
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 |