df-types 0.0.2

A tool for generating dataclass type files for pandas DataFrame rows

df-types

A Python tool for automatically generating dataclass definitions from pandas DataFrames.

Getting Started

pip install df-types

from df_types import DFTypes
import pandas as pd
import random

# Load your data
df = pd.DataFrame({
    "id": list(range(1, 301)),
    "name": ["Alice", "Bob", "Charlie"] * 100,
    "age": [random.randint(18, 100) for _ in range(300)],
    "prefers-pizza": [random.choice([True, False]) for _ in range(300)]  # Not a valid Python identifier, will be normalized
})

# Generate type definitions
dft = DFTypes(df)
dft.write_types()  # Creates typed_df.py

# Creates the following dataclass:
#
# @dataclass(slots=True)
# class TypedRowData:
#     id: int
#     name: Literal['Alice', 'Bob', 'Charlie']
#     age: int
#     prefers_pizza: bool

# Use the generated types
from typed_df import convert, iter_dataclasses

df_typed = convert(df)  # Converts NaNs to None, normalizes column names to Python identifiers
for row_data in iter_dataclasses(df_typed):
    # Each row_data is now a typed dataclass
    print(f"ID: {row_data.id}, Name: {row_data.name}, Age: {row_data.age}, Prefers Pizza: {row_data.prefers_pizza}")

Features

Supported Types

Feature	Example	Description
Literal Types	Literal["A", "B", "C"]	For categorical data with known values
Union Types	int | float	For columns with mixed numeric types
Optional Types	str | None	For columns with missing values
Custom Types	pd.Timestamp, Decimal	Import and use external types
Primitive Types	int, str, bool, float	Standard Python types

Configuration

from df_types.config import DFTypesConfig

# Basic options
config = DFTypesConfig(
    filename="my_types.py",
    class_name="MyRow",  # Default is "TypedRowData"
    max_literal_values=10  # Increase if you have more categories you want to infer as Literal types
)

dft = DFTypes(df, config=config)
dft.write_types()

from my_types import convert, iter_dataclasses, MyRow

# Use the generated types

Considerations

If a type cannot be imported from the generated file, it will be given the type hint object and a warning will be printed. Most often this occurs because the type is contained in the calling module (e.g., main.py which imports df_types and provides CustomType that is contained in the DataFrame). You can manually move the type definition to another file to avoid this warning.

Due to sampling, if you have a column with a large number of rows and a disproportionate distribution of values, the inferred literals may not include all possible values. You can increase sample_middle_rows, sample_head_rows, or sample_tail_rows if you want to sample more rows.

Future Features

• Support for typed containers (e.g., List[int], Dict[str, int])
• Support for nested dataclasses
• More advanced configuration options

License

This project is licensed under the MIT License - see the LICENSE file for details.