pd-helper 0.1.4

A helpful script to optimize a Pandas DataFrame.

pd-helper

A helpful package to streamline Pandas DataFrame optimization.

Save 50-75% on DataFrame memory usage by running the optimizer.

Autoconfigure dtypes for appropriate data types in each column with helper.

Generate a random DataFrame of controlled random variables for testing with maker.

Install

pip install pd-helper

Basic Usage to Iterate over DataFrame

from pd_helper.maker import MakeData 
from pd_helper.helper import optimize
faker = MakeData()

if __name__ == "__main__":
   # MakeData() generates a fake dataframe, convenient for testing
   df = faker.make_df()
   df = optimize(df)

Better Usage With Multiprocessing

from pd_helper.maker import MakeData 
from pd_helper.helper import optimize
faker = MakeData()

if __name__ == "__main__":
   # MakeData() generates a fake dataframe, convenient for testing
   df = faker.make_df()
   df = optimize(df, enable_mp=True)

Specify Special Mappings

from pd_helper.maker import MakeData 
from pd_helper.helper import optimize
faker = MakeData()

if __name__ == "__main__":
   # MakeData() generates a fake dataframe, convenient for testing
   df = faker.make_df()
   special_mappings = {'string': ['object_id'],
                       'category': ['item_name']}

   # special mappings will be applied instead of by optimize ruleset, they will be returned.
   df = optimize(df
                 , enable_mp=True,
                 special_mappings=special_mappings
                 )

Sample Results

Starting with 175.63 MB memory.

After optmization. 

Ending with 65.33 MB memory.

TODO

• Improve efficiency of iterating on DataFrame.

• Allow user to toggle logging.

• Provide tools for imputing missing data.