Package: data-science-utilities
======================Data Science Utilities======================.. image:: https://img.shields.io/pypi/v/data_science_utilities.svg:target: https://pypi.python.org/pypi/data_science_utilities.. image:: https://img.shields.io/travis/truocphamkhac/data-science-utilities.svg:target: https://travis-ci.org/truocphamkhac/data-science-utilities.. image:: https://readthedocs.org/projects/data-science-utilities/badge/?version=latest:target: http://data-science-utilities-python.readthedocs.io/en/latest/?badge=latest:alt: Documentation StatusData Science utilities in python.* Free software: MIT license* Documentation: http://data-science-utilities-python.readthedocs.io.Features========Missing Data Statistic----------------------.. code:: pythonfrom data_science_utilities import data_science_utilities# make statisticmissing_data = data_science_utilities.missing_data_stats(df)# display statisticmissing_dataRead CSV files from path------------------------.. code:: pythonfrom data_science_utilities import data_science_utilitiestrain_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----------------------------.. code:: pythonfrom data_science_utilities import data_science_utilitiesdata_science_utilities.plot_dist_norm(dist, 'distribution normal')Plotting correlation matrix---------------------------.. code:: pythonfrom data_science_utilities import data_science_utilitiesdata_science_utilities.plot_corelation_matrix(data)Plotting top attributes correlation matrix------------------------------------------.. code:: pythonfrom data_science_utilities import data_science_utilitiesdata_science_utilities.plot_top_corelation_matrix(data, target, k=10, cmap='YlGnBu')Plotting attributes by scatter chart------------------------------------.. code:: pythonfrom data_science_utilities import data_science_utilitiesdata_science_utilities.plot_scatter(data, column_name, target)Plotting attributes by box bar------------------------------.. code:: pythonfrom data_science_utilities import data_science_utilitiesdata_science_utilities.plot_box(data, column_name, target)Plotting category by box bar----------------------------.. code:: pythonfrom data_science_utilities import data_science_utilitiesdata_science_utilities.plot_category_columns(data, limit_bars=10)Generate a simple plot of the test and traning learning curve-------------------------------------------------------------.. code:: pythonfrom data_science_utilities import data_science_utilitiesdata_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... _Cookiecutter: https://github.com/audreyr/cookiecutter.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage=======History=======0.2.4 (2018-05-21)------------------* Fixed render docs on README.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.