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Data Science Library

Project description


polar is a Python module that contains simple to use data science functions. It is built on top of SciPy, scikit-learn, seaborn and pandas.


If you already have a working installation of numpy and scipy, the easiest way to install parkitny is using pip:

pip install -U polar


polar requires:

  • Python (>= 3.5)
  • NumPy (>= 1.11.0)
  • SciPy (>= 0.17.0)
  • Seaborn (>= 0.9.0)
  • scikit-learn (>= 0.21.3)

ACA (Automated Cohort Analysis) Example

ACLTVA (Automated Cohort Life Time Value Analysis) Example

EDA Example

import pandas as pd
import openml
import polar as pl

dataset = openml.datasets.get_dataset(31)
X, y, categorical_indicator, attribute_names = \

openml_df = pd.DataFrame(X)
openml_df['target'] = y

data_df = pl.analyze_correlation(openml_df,'target')

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data_df = pl.analyze_association(openml_df,'target',verbose=0)

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print(pl.analyze_df(openml_df, 'target',10))


data_df = pl.get_important_features(openml_df,'target')

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Project details

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Files for polar, version 0.0.9
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