FormatFlex

FormatFlex is a flexible Python library designed to seamlessly handle various text and data formats.

(This repository welcomes as many pull requests (PRs) as possible. Your active contributions are greatly appreciated!)

Supported Data Formats

Currently, it is largely divided into three categories: hierarchical, tabular, and general types. If there are other classification methods or missing parts in each type support, it would be better if you could leave it as an issue or send a PR.

API USAGE

Hierarchical Data (HData)

• XML
• JSON
• YAML
• TOML
• CBOR2
• MSGPACK
• ~~BSON~~ 🆘
• UBJSON

Tabular Data (TData)

• CSV
• Excel
• Parquet
• ORC
• Feather
• ~~HDF5~~ 🆘

Serialization (GData)

• Pickle
• Dill
• Cloudpickle
• Joblib

Installation

pip install format_flex

Usage

Hierarchical Data Example (HData)

from format_flex import HData

with open("sample/sample.json", "rt", encoding="utf-8") as f:
    data = f.read()

hdata = HData(data)
save_path = "./output/hdata_example"

# Convert and save in various formats
print(hdata.to.xml(save_path).summary)
print(hdata.to.json(save_path).summary)
print(hdata.to.yaml(save_path).summary)
print(hdata.to.toml(save_path).summary)
print(hdata.to.bson(save_path).summary)
print(hdata.to.cbor2(save_path).summary)
print(hdata.to.msgpack(save_path).summary)
print(hdata.to.ubjson(save_path).summary)

Tabular Data Example (TData)

from format_flex import TData

tdata = TData("sample/music_dataset.csv")
save_path = "./output/tdata_example"

print(tdata.to.csv(save_path).summary)
print(tdata.to.excel(save_path).summary)
print(tdata.to.parquet(save_path).summary)
print(tdata.to.orc(save_path).summary)
print(tdata.to.feather(save_path).summary)
print(tdata.to.hdf5(save_path).summary)

Serialization Example (GData)

import numpy as np
from format_flex import GData

data = {
    "name": "Alice",
    "age": 30,
    "scores": [95, 87, 78],
    "numbers": np.random.random((10, 10)),
    "details": {
        "hobbies": ["reading", "cycling", "hiking"],
        "active": True,
        "balance": 1234.56
    }
}

save_path = "./output/gdata_example"
gdata = GData(data)

print(gdata.to.pickle(save_path).summary)
print(gdata.to.dill(save_path).summary)
print(gdata.to.cloudpickle(save_path).summary)
print(gdata.to.joblib(save_path).summary)

License

This project is licensed under the MIT License.