| import pandas as pd |
| from sklearn import datasets |
| |
| from hamilton.function_modifiers import dataloader, datasaver |
| from hamilton.io import utils as io_utils |
| |
| |
| @dataloader() |
| def raw_data() -> tuple[pd.DataFrame, dict]: |
| data = datasets.load_digits() |
| df = pd.DataFrame(data.data, columns=[f"feature_{i}" for i in range(data.data.shape[1])]) |
| metadata = io_utils.get_dataframe_metadata(df) |
| return df, metadata |
| |
| |
| def transformed_data(raw_data: pd.DataFrame) -> pd.DataFrame: |
| return raw_data |
| |
| |
| @datasaver() |
| def saved_data(transformed_data: pd.DataFrame, filepath: str) -> dict: |
| transformed_data.to_csv(filepath) |
| metadata = io_utils.get_file_and_dataframe_metadata(filepath, transformed_data) |
| return metadata |
