Metadata-Version: 2.1
Name: seaborn-analyzer
Version: 0.1.3
Summary: seaborn-analyzer: data visualization of regression, classification and distribution
Home-page: https://github.com/c60evaporator/seaborn-analyzer
Author: Kenta Nakamura
Author-email: c60evaporator@gmail.com
Maintainer: Kenta Nakamura
Maintainer-email: c60evaporator@gmail.com
License: BSD 3-Clause
Download-URL: https://github.com/c60evaporator/seaborn-analyzer
Description: ================
        seaborn-analyzer
        ================
        
        |python| |pypi| |license|
        
        .. |python| image:: https://img.shields.io/pypi/pyversions/seaborn-analyzer
           :target: https://www.python.org/
        
        .. |pypi| image:: https://img.shields.io/pypi/v/seaborn-analyzer?color=blue
           :target: https://pypi.org/project/seaborn-analyzer/
        
        .. |license| image:: https://img.shields.io/pypi/l/seaborn-analyzer?color=blue
           :target: https://github.com/c60evaporator/seaborn-analyzer/blob/master/LICENSE
           
        **A data analysis and visualization tool using Seaborn library.**
        
        .. image:: https://user-images.githubusercontent.com/59557625/126887193-ceba9bdd-3653-4d58-a916-21dcfe9c38a0.png
        
        =====
        Usage
        =====
        An example of using CustomPairPlot class
        
        .. code-block:: python
        
            from seaborn_analyzer import CustomPairPlot
            import seaborn as sns
         
            titanic = sns.load_dataset("titanic")
            cp = CustomPairPlot()
            cp.pairanalyzer(titanic, hue='survived')
           
        If you want to know usage of other classes, see `API Reference
        <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#api-reference>`__ and `Examples
        <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#examples>`__
        
        ============
        Requirements
        ============
        seaborn-analyzer 0.1.2 requires
        
        * Python >=3.6
        * Numpy >=1.20.3
        * Pandas >=1.2.4
        * Matplotlib >=3.3.4
        * Scipy >=1.6.3
        * Scikit-learn >=0.24.2
        
        ===========================
        Installing seaborn-analyzer
        ===========================
        Use pip to install the binary wheels on `PyPI <https://pypi.org/project/seaborn-analyzer/>`__
        
        .. code-block:: console
        
            $ pip install seaborn-analyzer
        
        =======
        Support
        =======
        Bugs may be reported at https://github.com/c60evaporator/seaborn-analyzer/issues
        
        =============
        API Reference
        =============
        The following classes and methods are included in seaborn-analyzer
        
        CustomPairPlot class
        ====================
        
        .. csv-table::
            :header: "Method name", "Summary", "API Documentation", "Example"
            :widths: 30, 50, 15, 15
        
            "**pairanalyzer**", Plotting pair plot including scatter plot and correlation coefficient matrix simultaneously, `CustomPairPlot.pairanalyzer <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#custompairplotpairanalyzer>`__
        
        
        hist class
        ==========
        
        .. csv-table::
            :header: "Method name", "Summary", "API Documentation", "Example"
            :widths: 30, 50, 15, 15
        
            "**plot_normality**", Plotting normality test and QQ plot, `hist.plot_normality <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#histplot_normality>`__
            "**fit_dist**", Fitting distributions and calculating fitting scores, `hist.fit_dist <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#histfit_dist>`__
        
        
        classplot class
        ===============
        
        .. csv-table::
            :header: "Method name", "Summary", "API Documentation", "Example"
            :widths: 30, 50, 15, 15
        
            "**class_separator_plot**", Plotting class separation lines of any scikit-learn classification models, `hist.class_separator_plot <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#classplotclass_separator_plot>`__
            "**class_proba_plot**", Plotting class prediction probability of any scikit-learn classification models, `hist.class_proba_plot <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#classplotclass_proba_plot>`__
        
        
        regplot class
        =============
        
        .. csv-table::
            :header: "Method name", "Summary", "API Documentation", "Example"
            :widths: 30, 50, 15, 15
        
            "**linear_plot**", Plotting linear regression line and calculating Pearson correlation coefficient, `regplot.linear_plot <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#regplotlinear_plot>`__
            "**regression_pred_true**", Plotting prediction value vs. true value scatter plots, `regplot.regression_pred_true <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#regplotregression_pred_true>`__
            "**regression_plot_1d**", Plotting 1d regression lines of any scikit-learn regression models, `regplot.regression_plot_1d <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#regplotregression_plot_1d>`__
            "**regression_heat_plot**", Plotting 2 to 4d regression heat maps of any scikit-learn regression models, `regplot.regression_heat_plot <https://pypi.org/project/seaborn-analyzer/>`__, `example <https://github.com/c60evaporator/seaborn-analyzer/blob/master/README.rst#regplotregression_heat_plot>`__
        
        
        ========
        Examples
        ========
        
        CustomPairPlot.pairanalyzer
        ===========================
        .. code-block:: python
        
            from seaborn_analyzer import CustomPairPlot
            import seaborn as sns
            titanic = sns.load_dataset("titanic")
            cp = CustomPairPlot()
            cp.pairanalyzer(titanic, hue='survived')
        .. image:: https://user-images.githubusercontent.com/59557625/115889860-4e8bde80-a48f-11eb-826a-cd3c79556a42.png
        
        hist.plot_normality
        ===================
        .. code-block:: python
        
            from seaborn_analyzer import hist
            from sklearn.datasets import load_boston
            import pandas as pd
            df = pd.DataFrame(load_boston().data, columns= load_boston().feature_names)
            hist.plot_normality(df, x='LSTAT', norm_hist=False, rounddigit=5)
        .. image:: https://user-images.githubusercontent.com/59557625/117275256-cfd46f80-ae98-11eb-9da7-6f6e133846fa.png
        
        hist.fit_dist
        =============
        .. code-block:: python
        
            from seaborn_analyzer import hist
            from sklearn.datasets import load_boston
            import pandas as pd
            import matplotlib.pyplot as plt
            from scipy import stats
            df = pd.DataFrame(load_boston().data, columns= load_boston().feature_names)
            all_params, all_scores = hist.fit_dist(df, x='LSTAT', dist=['norm', 'gamma', 'lognorm', 'uniform'])
            df_scores = pd.DataFrame(all_scores).T
            df_scores
        .. image:: https://user-images.githubusercontent.com/59557625/115890066-81ce6d80-a48f-11eb-8390-f985d9e2b8b1.png
        .. image:: https://user-images.githubusercontent.com/59557625/115890108-8d219900-a48f-11eb-9896-38f7dedbb6e4.png
        
        classplot.class_separator_plot
        ==============================
        .. code-block:: python
        
            import seaborn as sns
            from sklearn.svm import SVC
            from seaborn_analyzer import classplot
            iris = sns.load_dataset("iris")
            model = SVC()
            classplot.class_separator_plot(model, ['petal_width', 'petal_length'], 'species', iris)
        .. image:: https://user-images.githubusercontent.com/59557625/117274234-d7474900-ae97-11eb-9de2-c8a74dc179a5.png
        
        classplot.class_proba_plot
        ==========================
        .. code-block:: python
        
            import seaborn as sns
            from sklearn.svm import SVC
            from seaborn_analyzer import classplot
            iris = sns.load_dataset("iris")
            model = SVC()
            classplot.class_proba_plot(model, ['petal_width', 'petal_length'], 'species', iris,
                                       proba_type='imshow')
        .. image:: https://user-images.githubusercontent.com/59557625/117276085-a1a35f80-ae99-11eb-8368-cdd1cfa78346.png
        
        regplot.linear_plot
        ===================
        .. code-block:: python
        
            from seaborn_analyzer import regplot
            import seaborn as sns
            iris = sns.load_dataset("iris")
            regplot.linear_plot(x='petal_length', y='sepal_length', data=iris)
        .. image:: https://user-images.githubusercontent.com/59557625/117276994-65243380-ae9a-11eb-8ec8-fa1fb5d60a55.png
        
        regplot.regression_pred_true
        ============================
        .. code-block:: python
        
            import pandas as pd
            from seaborn_analyzer import regplot
            import seaborn as sns
            from sklearn.linear_model import LinearRegression
            df_temp = pd.read_csv(f'./sample_data/temp_pressure.csv')
            regplot.regression_pred_true(LinearRegression(), x=['altitude', 'latitude'], y='temperature', data=df_temp)
        .. image:: https://user-images.githubusercontent.com/59557625/117277036-6fdec880-ae9a-11eb-887a-5f8b2a93b0f9.png
        
        regplot.regression_plot_1d
        ==========================
        .. code-block:: python
        
            from seaborn_analyzer import regplot
            import seaborn as sns
            from sklearn.svm import SVR
            iris = sns.load_dataset("iris")
            regplot.regression_plot_1d(SVR(), x='petal_length', y='sepal_length', data=iris)
        .. image:: https://user-images.githubusercontent.com/59557625/117277075-78cf9a00-ae9a-11eb-835c-01f635754f7b.png
        
        regplot.regression_heat_plot
        ============================
        .. code-block:: python
        
            import pandas as pd
            from sklearn.linear_model import LinearRegression
            from seaborn_analyzer import regplot
            df_temp = pd.read_csv(f'./sample_data/temp_pressure.csv')
            regplot.regression_heat_plot(LinearRegression(), x=['altitude', 'latitude'], y='temperature', data=df_temp)
        .. image:: https://user-images.githubusercontent.com/59557625/115955837-1b4f5b00-a534-11eb-91b0-b913019d26ff.png
        
        
        Contact
        =======
        If you have any questions or comments about seaborn-analyzer,
        please feel free to contact me via
        eMail: c60evaporator@gmail.com
        or Twitter: https://twitter.com/c60evaporator
        This project is hosted at https://github.com/c60evaporator/seaborn-analyzer
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Multimedia :: Graphics
Classifier: Framework :: Matplotlib
Requires-Python: >=3.6
Provides-Extra: tutorial
