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Using Machine Learning to learn how to Compress

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pip install shrynk

Then in Python:

from shrynk.pandas import save, load
file_path = save(my_df, "mypath")
# e.g. mypath.csv.bz2
loaded_df = load(file_path)

Add your own data

If you want more control you can do the following:

import pandas as pd
from shrynk.pandas import PandasCompressor

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

pdc = PandasCompressor("default")
pdc.run_benchmarks([df], save=False) # adds data to the default

pdc.train_model(size=3, write=1, read=1)


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shrynk-0.0.14-py2.py3-none-any.whl (1.7 MB view hashes)

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