zipdist 0.1.4

Tidy saving and loading of Numpy and Pandas object attributes

zipdist

Keeping NumPy and Pandas attributes of python classes nice and tidy

The Zipdist2 class provides methods for saving and reloading NumPy arrays and Pandas DataFrame object attributes.

Complex attributes are saved to a single .tar.gz file.

The contents of the .tar.gz provides a tidy human-readable record of Pandas and Numpy Python class attributes as .csv and binary files.

Install

pip install zipdist==0.1.3

Basic Example

from zipdist.zip2 import Zipdist2
import pandas as pd
import numpy as np
import os

class X():
	def __init__(self, name):
		self.name = name

x = X(name = 'example_target_object')
x.example_simple_attr = [1989, 2020]
x.example_np_attr = np.array([[0, 1, 2, 3], [4, 5, 6, 7]])
x.example_pd_attr  = pd.DataFrame({"A":[1,2,3], "B":[2,4,6]})

z = Zipdist2(name = "zipper", target = x)
z._save(dest = "archive", dest_tar = "archive.tar.gz")
assert os.path.isfile("archive.tar.gz")

Saving example_np_attr to .csv : archive/example_np_attr.csv
Saving example_np_attr to .npy : archive/example_np_attr.npy
Saving example_pd_attr to .csv : archive/example_pd_attr.csv
Saving example_pd_attr to .feather : archive/example_pd_attr.feather
Saving JSON with complex attribute definitions : archive/complex_attributes.json
Saving JSON with simple attribute definitions : archive/simple_attributes.json
Combining saved files in : [archive.tar.gz].

Contents of archive.tar.gz

archive
├── complex_attributes.json
├── example_np_attr.csv
├── example_np_attr.npy
├── example_pd_attr.csv
├── example_pd_attr.feather
└── simple_attributes.json

Reload All Attributes of a Target Object with ._build()

x_new = X(name = 'example_target_object')
z = Zipdist2(name = "zipper", target = x_new)
# You can use the z._build() and reload all object attributes
z._build(target = x_new, dest = "archive", dest_tar = "archive.tar.gz")

Setting simple attribute name to example_target_object
Setting simple attribute example_simple_attr to [1989, 2020]
Setting [npy] to [np.ndarray] for attribute example_np_attr from: example_np_attr.npy
Setting [feather] to [pd.DataFrame] for attribute example_pd_attr from: example_pd_attr.feather

For instance:

>>>	x_new.example_simple_attr:
[1989, 2020]
>>>	x_new.example_np_attr:
[[0 1 2 3]
 [4 5 6 7]]
>>>	x_new.example_pd_attr:
   A  B
0  1  2
1  2  4
2  3  6

Reload Simple and Complex Object Attributes One by One with ._ready(), _reload_simple(), and _reload_complex().

# You can alternatively use the z._ready() and reload object attributes one by one
x_new = X(name = 'example_target_object')
z = Zipdist2(name = "zipper", target = x_new)
z._ready(target = x_new, dest = "archive", dest_tar = "archive.tar.gz")
z._reload_complex(k='example_np_attr')
z._reload_simple(k='example_simple_attr')