serializor 1.0.4

Serialization for common Python objects.

Serialization for common Python objects.

Created to be used in a project, this package is published to github for ease of management and installation across different modules.

Installation

Install from PyPi

pip install serializor

Install from github

pip install git+https://github.com/AresJef/Serializor.git

Compatibility

Supports Python 3.10 and above.

Features

This package is designed to serialize Python object, and deserializes it back to the original (or compatiable) Python object.

Supported data types:

• string: str -> deserialize to str
• float: float & numpy.float_ -> deserialize to float
• integer: int & numpy.int & numpy.uint -> deserialize to int
• boolean: bool & numpy.bool_ -> deserialize to bool
• None: None & numpy.nan -> deserialize to None
• datetime: datetime.datetime, pandas.Timestamp & time.struct_time -> deserialize to datetime.datetime
• datetime64: numpy.datetime64 -> deserialize to numpy.datetime64
• date: datetime.date -> deserialize to datetime.date
• time: datetime.time -> deserialize to datetime.time
• timedelta: datetime.timedelta & pandas.Timedelta -> deserialize to datetime.timedelta
• timedelta64: numpy.timedelta64 -> deserialize to numpy.timedelta64
• decimal: decimal.Decimal -> deserialize to decimal.Decimal
• complex: complex & numpy.complex_ -> deserialize to complex
• bytes: bytes, bytearray & numpy.bytes_ -> deserialize to bytes
• list: list of above supported data types -> deserialize to list
• tuple: tuple of above supported data types -> deserialize to tuple
• set: set & frozenset of above supported data types -> deserialize to set
• dict: dict of above supported data types -> deserialize to dict
• numpy.ndarray: numpy.ndarray of above supported data types -> deserialize to np.ndarray
• pandas.Series: pandas.Series of above supported data types -> deserialize to pandas.Series
• pandas.DataFrame: pandas.DataFrame of above supported data types -> deserialize to pandas.DataFrame

Usage (Serialization & Deserialization)

from pandas import Series
from serializor import serialize, deserialize

obj = Series([1, 2, 3, 4, 5], name="test")
en = serialize(obj)
de = deserialize(en)
print(de)

0    1
1    2
2    3
3    4
4    5
Name: test, dtype: int64

Acknowledgements

serializor is based on several open-source repositories.

• numpy
• orjson
• pandas