bamboo-pandas 0.0.2

`bamboo` is Python package that attempts to add more structure and validation to pandas data transformations.

Bamboo

Current version: "0.0.2"

Bamboo is a small library that adds structure and validation to pandas DataFrame row-transformations. It provides a lightweight way to declare the expected input and output shapes for a row-wise transformation using simple Bamboo data objects, and a decorator that converts between pd.Series rows and your typed objects while validating inputs and outputs.

Why use bamboo

• Safer transforms: validate that each row can be converted into the declared input type and the transformation returns the expected output type.
• Documented data contracts: row types live next to your transform code, making the expected inputs/outputs explicit and easy to read.
• Plays nicely with tooling: works with tqdm, swifter, and runtime type checkers like beartype (see examples).

Quick example

This example demonstrates the default, type-hinted usage with @bamboo_transform.

from dataclasses import dataclass
import pandas as pd

from bamboo import BambooObject, bamboo_transform


@dataclass
class Row(BambooObject):
    a: int
    b: int


@dataclass
class Out(Row):
    pass


@bamboo_transform
def add_and_mul(row: Row) -> Out:
    return Out(a=row.a + 1, b=row.b * 2)


df = pd.DataFrame({"a": [1, 2, 3], "b": [10, 20, 30]})
validate(df, Row)
print(df.apply(add_and_mul, axis=1))

More examples

See the examples folder for:

• default_typed.py — inferred typed example (default use-case).
• default_untyped.py — parameterized decorator for un-annotated functions.
• vectorized_validate.py — write fast vectorized operations, then validate output with validate().
• tqdm_swifter.py — shows compatibility with tqdm and swifter.
• beartyped_columns.py — example using beartype for runtime type checking.

Patterns

Row-wise with @bamboo_transform: Use when you need per-row type validation as you transform. Good for smaller datasets or when type contracts are critical. Works with tqdm, but may be slower with swifter (only row-wise path).

Vectorized + validate: Write fast vectorized operations (using pandas, swifter, or NumPy) and call validate() on the result as a sanity check. Good for large datasets where raw speed matters; validation happens after the fast operation completes.

Running examples

Install dev dependencies:

poetry install

Run an individual example:

python examples/default_typed.py
python examples/vectorized_validate.py
python examples/tqdm_swifter.py
python examples/beartyped_columns.py

Development

Install dev dependencies:

poetry install

See the Makefile for common commands.

License

MIT