VerticaPy

1.1.1 verified Fri May 01 auth: no python

VerticaPy is a Python library for data exploration, data cleaning, and machine learning in Vertica. It simplifies the integration between Python and Vertica databases, providing a pandas-like interface and ML capabilities that run directly in-database. Current version is 1.1.1, released May 2025. The project follows a monthly release cadence.

pip install verticapy

Common errors

error No module named 'verticapy.core' ↓

cause Importing from the old submodule path that was removed in 1.0.0.

fix

Use from verticapy import vDataFrame instead of from verticapy.core import vDataFrame.

error AttributeError: 'vDataFrame' object has no attribute 'select' ↓

cause The `select` method was renamed to `select_` (with trailing underscore) to avoid Python keyword conflict.

fix

Use vdf.select_('col1', 'col2') instead of vdf.select('col1', 'col2').

Warnings

breaking Import paths changed significantly in 1.0.0. Many functions moved from submodules to top-level or were renamed. ↓

fix Run `verticapy.upgrade()` or consult the migration guide. For example, `from verticapy import vDataFrame` instead of `from verticapy.core import vDataFrame`.

gotcha The database connection must be passed explicitly when creating a vDataFrame. It is easy to forget and assume an implicit connection. ↓

fix Always provide a cursor or connection object: `vDataFrame('table', cursor)`.

gotcha vDataFrame methods mutate the object in-place by default, unlike pandas which returns a new object. ↓

fix Be aware that operations like `drop()` modify the current vDataFrame; use `.copy()` if you need to preserve the original.

Imports

vDataFrame
wrong
```
from verticapy.core import vDataFrame
```
correct
```
from verticapy import vDataFrame
```
Since 1.0.0, the API was restructured; vDataFrame is directly importable from the top-level package.

set_option

wrong

from verticapy.options import set_option

correct

from verticapy import set_option

set_option is now at the top level, not in a submodule.

Quickstart

Quickstart: connect to Vertica, load a table into vDataFrame, explore.

from verticapy import vDataFrame, set_option

# Optional: configure display
set_option('max_cellwidth', 50)

# Connect using parameters (replace with actual credentials)
from vertica_python import connect
conn_info = {
    'host': 'localhost',
    'port': 5433,
    'user': 'dbadmin',
    'password': '',
    'database': 'vmart',
    'ssl': False
}
cur = connect(**conn_info).cursor()

# Create vDataFrame from a table
vdf = vDataFrame('public.my_table', cur)

# Quick exploration
print(vdf.shape())
print(vdf.describe())