Metadata-Version: 2.1
Name: pandas-visual-analysis
Version: 0.0.4
Summary: A package for interactive visual analysis in Jupyter notebooks.
Home-page: https://github.com/meffmadd/pandas-visual-analysis
Author: Matthias Matt
Author-email: matthias.matt@student.tuwien.ac.at
License: MIT License
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        Code
        
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        |
        |
        
        .. intro-start
        
        Generates an `interactive visual analysis <https://en.wikipedia.org/wiki/Interactive_visual_analysis>`_ widget to analyze a pandas ``DataFrame`` in Jupyter notebooks.
        It can display various different types of graphs with support for linked-brushing in interactive widgets.
        This allows data exploration and cognition to be simple, even with complex multivariate datasets.
        
        There is no need to create and style plots or interactivity - its all ready without any configuration.
        
        |
        
        .. image:: https://github.com/meffmadd/pandas-visual-analysis/blob/master/docs/source/_static/assets/default_layout.gif?raw=true
           :width: 70%
           :alt: interactivity
           :align: center
        
        |
        
        .. intro-end
        
        ==================
        Installation
        ==================
        
        .. installation-start
        
        Using pip
        ##########
        
        To install this package with pip run:
        
        .. code-block::
        
            pip install pandas-visual-analysis
        
        
        Using conda
        ###########
        
        To install this package with conda run:
        
        .. code-block::
        
            conda install -c meffmadd pandas-visual-analysis
        
        From Source
        ###########
        
        To install this package from source, clone into the repository or download the `zip file <https://github.com/meffmadd/pandas-visual-analysis/archive/master.zip>`_
        and run:
        
        .. code-block::
        
            python setup.py install
        
        
        .. installation-end
        
        ==================
        Usage
        ==================
        
        .. usage-start
        
        Basic Usage
        ###############
        
        
        Having a ``DataFrame``, for example:
        
        .. code-block:: python
        
            import pandas as pd
            import ssl
            ssl._create_default_https_context = ssl._create_unverified_context
            url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/mpg.csv"
        
            df = pd.read_csv(url)
        
        you can just pass it to ``VisualAnalysis`` to display the default layout:
        
        .. code-block:: python
        
            from pandas_visual_analysis import VisualAnalysis
            VisualAnalysis(df)
        
        If you want to specify which columns of the ``DataFrame`` are categorical, just pass the ``categorical_columns`` option:
        
        .. code-block:: python
        
            from pandas_visual_analysis import VisualAnalysis
            categorical = ["name", "origin", "model_year", "cylinders"]
            VisualAnalysis(df, categorical_columns=categorical)
        
        
        Selection Types
        ###############
        
        |
        
        .. image:: https://github.com/meffmadd/pandas-visual-analysis/blob/master/docs/source/_static/assets/selection_types.gif?raw=true
           :width: 70%
           :alt: selection types
           :align: center
        
        |
        
        By default a new selection replaces the old selection, however, it is also possible to add data points to the existing
        selection by selecting the `Additive` selection type. By choosing the `Subtractive` selection newly selected
        data points are removed from the selection.
        
        
        Using DataSource
        ################
        
        Instead of passing the ``DataFrame`` object directly to ``VisualAnalysis`` it is possible to use a ``DataSource`` object.
        This enables linked-brushing across multiple notebook cells if the object is used across cells.
        
        .. code-block:: python
        
            from pandas_visual_analysis import VisualAnalysis, DataSource
        
            data = DataSource(df)
            VisualAnalysis(data)
        
        Later you can create a new analysis with the brushing still preserved
        simply by using the same ``data`` object created earlier.
        
        .. code-block:: python
        
            VisualAnalysis(data)
        
        Using Layouts
        #############
        
        If you want to specify your own layout, you can do that by passing the ``layout`` parameter.
        The parameter is a list of rows, where each row is in turn a list specifying the Widgets in that row.
        
        .. code-block:: python
        
            from pandas_visual_analysis import VisualAnalysis
        
            VisualAnalysis(df,
                layout=[["Scatter", "Scatter"],
                        ["ParallelCoordinates"]]
            )
        
        Here, two scatter plots will share the first row while the second row only contains a parallel coordinates plot.
        In order to see all the possible options you can call the ``widgets`` class-method of ``VisualAnalysis``.
        
        .. code-block:: python
        
            VisualAnalysis.widgets()
        
        This outputs the following list of possible plots:
        
        .. code-block:: python
        
            ['Scatter',
             'ParallelCoordinates',
             'BrushSummary',
             'Histogram',
             'ParallelCategories',
             'BoxPlot']
        
        Any of those can be part of the layout specification.
        See also: `widgets Documentation <https://pandas-visual-analysis.readthedocs.io/en/latest/api/widgets.html>`_.
        
        For more advanced features of the ``VisualAnalysis`` class see:
        `VisualAnalysis Documentation <https://pandas-visual-analysis.readthedocs.io/en/latest/api/visual_analysis.html#advanced-usage>`_
        
        .. usage-end
        
        
        ====================
        Documentation
        ====================
        
        For more details see the `Official Documentation <https://pandas-visual-analysis.readthedocs.io/>`_.
Keywords: pandas data-science visualization data-analysis brushing linked-brushing python jupyter ipython
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: License :: OSI Approved :: MIT License
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Framework :: IPython
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/x-rst
Provides-Extra: notebook
