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Data Science Library

Project description

polar

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.

Installation

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

pip install -U polar

Dependencies

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 = \
dataset.get_data(target=dataset.default_target_attribute,dataset_format='dataframe')

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

data_df = pl.analyze_correlation(openml_df,'target')
print(data_df)
pl.get_heatmap(data_df,'heat_map_1.jpg',0.5,5,'0.1f')

Image Image

data_df = pl.analyze_association(openml_df,'target',verbose=0)
print(data_df)
pl.get_heatmap(data_df,'heat_map_2.jpg',0.5,5,'0.1f')

Image Image

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

Image

data_df = pl.get_important_features(openml_df,'target')
print(data_df)
pl.get_bar(data_df,'bar.jpg')

Image Image

Project details


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