zipdist 0.1.2

Tidy saving and loading of Numpy and Pandas object attributes

zipdist

Keeping NumPy and Pandas attributes of python classes nice and tidy

The Zipdist parent 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 or files.

Install

pip install zipdist

Basic Example

from zipdist.zip import Zipdist
import pandas as pd
import numpy as np
import sys

class Y(Zipdist):
	def __init__(self, name):
		self.name = name
		self.year = None

y = Y(name ='Simpsons')
y.years = [1989, 2020]
y.bart = np.zeros(10)
y.lisa = pd.DataFrame([{"Pi":3.1415,"e":2.7182}])
y._save(dest="Simpsons", dest_tar = "Simpsons.tar.gz")


ynew = Y(name = "Simpsons")
ynew._build(dest="Simpsons", dest_tar = "Simpsons.tar.gz")
sys.stdout.write(f"ynew.years: {ynew.years}\n")
sys.stdout.write(f"ynew.lisa:\n{ynew.lisa}\n")
sys.stdout.write(f"ynew.bart: {ynew.bart}\n")

Saving bart to .csv : Simpsons/bart.cs
Saving lisa to .csv : Simpsons/lisa.csv
Saving JSON with complex attribute definitions : Simpsons/complex_attributes.json
Saving JSON with simple attribute definitions : Simpsons/simple_attributes.json
Combining saved files in : [Simpsons.tar.gz].
Contents of Simpsons.tar.gz :
	Simpsons
	Simpsons/bart.cs
	Simpsons/complex_attributes.json
	Simpsons/lisa.csv
	Simpsons/simple_attributes.json
setting simple attribute name to Simpsons
setting simple attribute years to [2020, 2019]
setting [csv] to [np.ndarray] for attribute bart from: Simpsons/bart.cs
setting [csv] to [pd.DataFrame] for attribute lisa from: Simpsons/lisa.csv

ynew.years: [1989, 2020]
ynew.lisa:
       Pi       e
0  3.1415  2.7182
ynew.bart: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]

Explanation

from zipdist.zip import Zipdist
import pandas as pd
import numpy as np
import sys
import os

Suppose you have some class Y. Let it inherit methods from Zipdist

class Y(Zipdist):
	def __init__(self, name):
		self.name = name
		self.year = None

Say you instantiate an instance of Y, assigning it two new complex attributes:

• bart a basic numpy array of zeros, and
• lisa a smart pandas DataFrame

y = Y(name ='Simpsons')
y.years = [1989, 2020]
y.bart = np.zeros(10)
y.lisa = pd.DataFrame([{"Pi":3.1415,"e":2.7182}])

_save()

Because Y inherits methods from the parent Zipdist (in particular _save(), and _build(), you can archive the non-json serializable attributes of your class, and rebuild the attributes from the original instance directly from Simpsons.tar.gz.

# ._save creates the file Simpsons.tar.gz
y._save(dest="Simpsons", dest_tar = "Simpsons.tar.gz")
assert os.path.isfile("Simpsons.tar.gz")

_build()

Suppose you want to rebuild the instance. (Note, it has no attributes bart or lisa)

ynew = Y("Simpsons")
assert 'bart' not in ynew.__dict__.keys()
assert 'years' not in ynew.__dict__.keys()

But you can rebuilt the attributes from Willy directly from a archived .tar.gz file

ynew._build(dest="Simpsons", dest_tar = "Simpsons.tar.gz")
assert isinstance(ynew.bart, np.ndarray)
assert isinstance(ynex.lisa, pd.DataFrame)

_ready()

You can also add simple (i.e., json serializable) or complex (numpy and panadas attributes) one by one as desired with _ready() and _reload_complex() and _reload_simple('years')

ynew = Y("Simpsons")
assert 'lisa' not in ynew.__dict__.keys()
assert 'bart' not in ynew.__dict__.keys()
assert 'years' not in ynew.__dict__.keys()
ynew._ready(dest="Simpsons", dest_tar = "Simpsons.tar.gz")
ynew._reload_complex('lisa')
assert isinstance(ynew.lisa, pd.DataFrame)
ynew._reload_simple('years')
assert isinstance(ynew.years, list)