dmf.io.save

dmf.io.save(data: Any, file_path: str | Path, saver: str | None = None, **kwargs)

Save data to a file using the appropriate saver.

This function saves data to various file formats by automatically determining the appropriate saver based on the file extension. You can also specify the saver explicitly if desired.

Supported Savers

• “pickle”: For .pkl files.

• “joblib”: For .joblib files.

• “pandas”: For .csv, .parquet, .xlsx, .xls, .feather files.

• “json”: For .json files.

• “str”: For .txt, .html, .log, .md, .rst files.

• “hdf5”: For .h5, .hdf5, .hdf files.

• “numpy”: For .npz, .npy files.

• “pillow”: For image files (.jpg, .jpeg, .png, .bmp, .gif, .tiff, .tif, .webp).

• “pytorch”: For PyTorch model files (.pt, .pth).

• “yaml”: For .yaml, .yml files.

• “ini”: For .ini, .cfg files.

• “matlab”: For .mat files.

• “audio”: For audio files (.wav, .mp3, .flac, .ogg).

• “video”: For video files (.mp4, .avi, .mov, .mkv).

param data:

The data to be saved. The type of data should match the saver being used.

type data:

Any

param file_path:

The path to the file where the data should be saved. The file extension will be used to determine the appropriate saver if not specified.

type file_path:

Union[str, Path]

param saver:

The saver type to use. If not provided, it will be inferred from the file extension.

type saver:

Optional[str], default=None

param kwargs:

Additional keyword arguments to pass to the saver function.

type kwargs:

dict

rtype:

None

raises ValueError:

If the file extension or specified saver is not supported, or if the data type does not match the expected type.

raises ImportError:

If the required library for the specified saver is not installed.

Examples

Saving a DataFrame to a CSV file:

import pandas as pd
from dmf.io import save

df = pd.DataFrame({"a": [1, 2, 3]})
save(df, "data.csv")

Saving an image using Pillow:

from PIL import Image
from dmf.io import save

img = Image.new("RGB", (100, 100), color="red")
save(img, "image.png")

Saving a NumPy array to an NPZ file:

import numpy as np
from dmf.io import save

arr = np.array([1, 2, 3])
save(arr, "data.npz")