data-science-utilities 0.2.3

Data Science utilities in python.

Data Science Utilities

Data Science utilities in python.

• Free software: MIT license

• Documentation: http://data-science-utilities-python.readthedocs.io.

Features

• Missing Data Statistic:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> # make statistic
  >>> missing_data = data_science_utilities.missing_data_stats(df)
  >>> # display statistic
  >>> missing_data

• Read CSV files from path:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> train_path = '../data/raw/train.csv'
  >>> test_path = '../data/raw/test.csv'
  >>>
  >>> X_train, X_test = data_science_utilities.read_csv_files(train_path, test_path)

• Plotting distribution normal:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> data_science_utilities.plot_dist_norm(dist, 'distribution normal')

• Plotting correlation matrix:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> data_science_utilities.plot_corelation_matrix(data)

• Plotting top attributes correlation matrix:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> data_science_utilities.plot_top_corelation_matrix(data, target, k=10, cmap='YlGnBu')

• Plotting attributes by scatter chart:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> data_science_utilities.plot_scatter(data, column_name, target)

• Plotting attributes by box bar:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> data_science_utilities.plot_box(data, column_name, target)

• Plotting category by box bar:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> data_science_utilities.plot_category_columns(data, limit_bars=10)

• Generate a simple plot of the test and traning learning curve:

  >>> from data_science_utilities import data_science_utilities
  >>>
  >>> data_science_utilities.plot_learning_curve(estimator, title, X, y, ylim=None,
  >>>                     cv=None, train_sizes=np.linspace(.1, 1.0, 5))

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.2.3 (2018-05-21)

• Fixed render docs on https://pypi.org/.

0.2.2 (2018-05-21)

• Fix render docs con’t.

0.2.1 (2018-05-21)

• Fix render docs.

0.2.0 (2018-05-14)

• Adds utils about visualization.

0.1.0 (2018-05-11)

• First release on PyPI.