Metadata-Version: 1.1
Name: rest-pandas
Version: 0.4.0
Summary: Serves up pandas dataframes via the Django REST Framework for client-side (i.e. d3.js) visualizations
Home-page: https://github.com/wq/django-rest-pandas
Author: S. Andrew Sheppard
Author-email: andrew@wq.io
License: MIT
Description: Django REST Pandas
        ==================
        
        `Django REST Framework <http://django-rest-framework.org>`__ + `pandas <http://pandas.pydata.org>`__ = A Model-driven Visualization API
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        **Django REST Pandas** (DRP) provides a simple way to generate and serve
        `pandas <http://pandas.pydata.org>`__ DataFrames via the `Django REST
        Framework <http://django-rest-framework.org>`__. The resulting API can
        serve up CSV (and a number of `other formats <#supported-formats>`__)
        for consumption by a client-side visualization tool like
        `d3.js <http://d3js.org>`__.
        
        The design philosophy of DRP enforces a strict separation between data
        and presentation. This keeps the implementation simple, but also has the
        nice side effect of making it trivial to provide the source data for
        your visualizations. This capability can often be leveraged by sending
        users to the same URL that your visualization code uses internally to
        load the data.
        
        DRP does not include any JavaScript code, leaving the implementation of
        interactive visualizations as an exercise for the implementer. That
        said, DRP is commonly used in conjunction with the
        `wq.app <http://wq.io/wq.app>`__ library, which provides
        `wq/chart.js <http://wq.io/docs/chart-js>`__ and
        `wq/pandas.js <http://wq.io/docs/pandas-js>`__, a collection of chart
        functions and data loaders that work well with CSV served by DRP and
        `wq.db <http://wq.io/wq.db>`__'s `chart <http://wq.io/docs/chart>`__
        module.
        
        |Latest PyPI Release| |Release Notes| |License| |GitHub Stars| |GitHub
        Forks| |GitHub Issues|
        
        |Travis Build Status| |Python Support| |Django Support| |Django REST
        Framework Support|
        
        **Note:** Support for Django REST Framework 2.4 will be dropped in DRP
        0.5.
        
        Live Demo
        ---------
        
        The `climata-viewer <http://climata.houstoneng.net>`__ project uses
        Django REST Pandas and `wq/chart.js <http://wq.io/docs/chart-js>`__ to
        provide interactive visualizations and spreadsheet downloads.
        
        Related Work
        ------------
        
        The field of Python-powered data analysis and visualization is growing,
        and there are a number of similar solutions that may fit your needs
        better.
        
        -  `Django Pandas <https://github.com/chrisdev/django-pandas/>`__
           provides a custom ORM model manager with pandas support. By contrast,
           Django REST Pandas works at the *view* level, by integrating pandas
           via custom Django REST Framework serializers and renderers.
        -  `DRF-CSV <https://github.com/mjumbewu/django-rest-framework-csv>`__
           provides straightforward CSV renderers for use with Django REST
           Framework. It may be useful if you just want a CSV API and don't have
           a need for the pandas DataFrame functionality.
        -  `mpld3 <http://mpld3.github.io/>`__ provides a direct bridge from
           `matplotlib <http://matplotlib.org/>`__ to
           `d3.js <http://d3js.org>`__, complete with seamless
           `IPython <http://ipython.org/>`__ integration. It is restricted to
           the (large) matplotlib chart vocabularly but should be sufficient for
           many use cases.
        -  `Bokeh <http://bokeh.pydata.org/>`__ is a complete client-server
           visualization platform. It does not leverage d3 or Django, but is
           notable as a comprehensive, forward-looking approach to addressing
           similar use cases.
        
        The goal of Django REST Pandas is to provide a generic REST API for
        serving up pandas dataframes. In this sense, it is similar to the Plot
        Server in Bokeh, but more generic in that it does not assume any
        particular visualization format or technology. Further, DRP is optimized
        for integration with public-facing Django-powered websites (unlike mpld3
        which is primarily intended for use within IPython).
        
        In summary, DRP is designed for use cases where:
        
        -  You want to support live spreadsheet downloads as well as interactive
           visualizations, and/or
        -  You want full control over the client visualization stack in order to
           integrate it with the rest of your website and/or build process. This
           usually means writing JavaScript code by hand.
           `mpld3 <http://mpld3.github.io/>`__ may be a better choice for data
           exploration if you are more comfortable with (I)Python and need
           something that can generate interactive visualizations out of the
           box.
        
        Supported Formats
        -----------------
        
        The following output formats are provided by default. These are provided
        as `renderer
        classes <http://www.django-rest-framework.org/api-guide/renderers>`__ in
        order to leverage the content type negotiation built into Django REST
        Framework. This means clients can specify a format via:
        
        -  an HTTP "Accepts" header (``Accepts: text/csv``),
        -  a format parameter (``/path?format=csv``), or
        -  a format extension (``/path.csv``)
        
        The HTTP header and format parameter are enabled by default on every
        pandas view. Using the extension requires a custom URL configuration
        (see below).
        
        +----------+---------------------------------------+-----------------------------+------------------------------------------------------------------------------------------+
        | Format   | Content Type                          | pandas DataFrame Function   | Notes                                                                                    |
        +==========+=======================================+=============================+==========================================================================================+
        | CSV      | ``text/csv``                          | ``to_csv()``                |
        +----------+---------------------------------------+-----------------------------+------------------------------------------------------------------------------------------+
        | TXT      | ``text/plain``                        | ``to_csv()``                | Useful for testing, as most browsers will download a CSV file instead of displaying it   |
        +----------+---------------------------------------+-----------------------------+------------------------------------------------------------------------------------------+
        | JSON     | ``application/json``                  | ``to_json()``               |
        +----------+---------------------------------------+-----------------------------+------------------------------------------------------------------------------------------+
        | XLSX     | ``application/vnd.openxml...sheet``   | ``to_excel()``              |
        +----------+---------------------------------------+-----------------------------+------------------------------------------------------------------------------------------+
        | XLS      | ``application/vnd.ms-excel``          | ``to_excel()``              |
        +----------+---------------------------------------+-----------------------------+------------------------------------------------------------------------------------------+
        | PNG      | ``image/png``                         | ``plot()``                  | Currently not very customizable, but a simple way to view the data as an image.          |
        +----------+---------------------------------------+-----------------------------+------------------------------------------------------------------------------------------+
        | SVG      | ``image/svg``                         | ``plot()``                  | Eventually these could become a fallback for clients that can't handle d3.js             |
        +----------+---------------------------------------+-----------------------------+------------------------------------------------------------------------------------------+
        
        The underlying implementation is a set of
        `serializers <https://github.com/wq/django-rest-pandas/blob/master/rest_pandas/serializers.py>`__
        that take the normal serializer result and put it into a dataframe.
        Then, the included
        `renderers <https://github.com/wq/django-rest-pandas/blob/master/rest_pandas/renderers.py>`__
        generate the output using the built in pandas functionality.
        
        Usage
        -----
        
        Getting Started
        ~~~~~~~~~~~~~~~
        
        .. code:: bash
        
            pip3 install rest-pandas
        
        **NOTE:** Django REST Pandas relies on pandas, which itself relies on
        NumPy and other scientific Python libraries. If you are having trouble
        installing DRP due to dependency issues, you may want to pre-install
        pandas using another tool. For example, on Ubuntu 14.04 LTS you can
        pre-install pandas using this command:
        
        .. code:: bash
        
            sudo apt-get install python3-pandas
            sudo pip3 install rest-pandas
        
        The `pandas documentation <http://pandas.pydata.org>`__ recommends using
        conda to install pandas for similar reasons. We've found the apt-get
        approach to be the fastest route to getting DRP running with the default
        Apache WSGI implementation on Ubuntu.
        
        Usage Example
        ~~~~~~~~~~~~~
        
        No Model
        ^^^^^^^^
        
        The example below allows you to create a simple API for an existing
        Pandas DataFrame, e.g. generated from an existing file.
        
        .. code:: python
        
            # views.py
            from rest_pandas import PandasSimpleView
            import pandas as pd
        
            class TimeSeriesView(PandasSimpleView):
                def get_data(self):
                    return pd.read_csv('data.csv')
        
        Model-Backed
        ^^^^^^^^^^^^
        
        The example below assumes you already have a Django project set up with
        a single ``TimeSeries`` model.
        
        .. code:: python
        
            # views.py
            from rest_pandas import PandasView
            from .models import TimeSeries
            from .serializers import TimeSeriesSerializer
        
            # Short version (leverages default DRP settings):
            class TimeSeriesView(PandasView):
                queryset = TimeSeries.objects.all()
                serializer_class = TimeSeriesSerializer
                # That's it!  The view will be able to export the model dataset to any of
                # the included formats listed above.  No further customization is needed to
                # leverage the defaults.
        
            # Long Version and step-by-step explanation
            class TimeSeriesView(PandasView):
                # Assign a default model queryset to the view
                queryset = TimeSeries.objects.all()
        
                # Step 1. In response to get(), the underlying Django REST Framework view
                # will load the queryset and then pass it to the following function.
                def filter_queryset(self, qs): 
                    # At this point, you can filter queryset based on self.request or other
                    # settings (useful for limiting memory usage).  This function can be
                    # omitted if you are using a filter backend or do not need filtering.
                    return qs
                    
                # Step 2. A Django REST Framework serializer class should serialize each
                # row in the queryset into a simple dict format.  A simple ModelSerializer
                # should be sufficient for most cases.
                serializer_class = TimeSeriesSerializer  # extends ModelSerializer
        
                # Step 3.  The included PandasSerializer will load all of the row dicts
                # into array and convert the array into a pandas DataFrame.  The DataFrame
                # is essentially an intermediate format between Step 2 (dict) and Step 4
                # (output format).  The default DataFrame simply maps each model field to a
                # column heading, and will be sufficient in many cases.  If you do not need
                # to transform the dataframe, you can skip to step 4.
                
                # If you would like to transform the dataframe (e.g. to pivot or add
                # columns), you can do so in one of two ways:
        
                # A. Create a subclass of PandasSerializer, define a function called
                # transform_dataframe(self, dataframe) on the subclass, and assign it to
                # pandas_serializer_class on the view.  You can also use one of the three
                # provided pivoting serializers (see Advanced Usage below).
                #
                # class MyCustomPandasSerializer(PandasSerializer):
                #     def transform_dataframe(self, dataframe):
                #         dataframe.some_pivot_function(in_place=True)
                #         return dataframe
                #
                pandas_serializer_class = MyCustomPandasSerializer
        
                # B. Alternatively, you can create a custom transform_dataframe function
                # directly on the view.  Again, if no custom transformations are needed,
                # this function does not need to be defined.
                def transform_dataframe(self, dataframe):
                    dataframe.some_pivot_function(in_place=True)
                    return dataframe
                
                # NOTE: As the name implies, the primary purpose of transform_dataframe()
                # is to apply a transformation to an existing dataframe.  In PandasView,
                # this dataframe is created by serializing data queried from a Django
                # model.  If you would like to supply your own custom DataFrame from the
                # start (without using a Django model), you can do so with PandasSimpleView
                # as shown in the first example.
        
                # Step 4. Finally, the provided renderer classes will convert the DataFrame
                # to any of the supported output formats (see above).  By default, all of
                # the formats above are enabled.  To restrict output to only the formats
                # you are interested in, you can define renderer_classes on the view:
                renderer_classes = [PandasCSVRenderer, PandasExcelRenderer]
                # You can also set the default renderers for all of your pandas views by
                # defining the PANDAS_RENDERERS in your settings.py.
        
        Registering URLs
        ^^^^^^^^^^^^^^^^
        
        .. code:: python
        
            # urls.py
            from django.conf.urls import patterns, include, url
        
            from .views import TimeSeriesView
            urlpatterns = patterns('',
                url(r'^data', TimeSeriesView.as_view()),
            )
        
            # This is only required to support extension-style formats (e.g. /data.csv)
            from rest_framework.urlpatterns import format_suffix_patterns
            urlpatterns = format_suffix_patterns(urlpatterns)
        
        The default ``PandasView`` will serve up all of the available data from
        the provided model in a simple tabular form. You can also use a
        ``PandasViewSet`` if you are using Django REST Framework's
        `ViewSets <http://www.django-rest-framework.org/api-guide/viewsets>`__
        and
        `Routers <http://www.django-rest-framework.org/api-guide/routers>`__.
        
        Building Interactive Charts
        ---------------------------
        
        In addition to use as a data export tool, DRP is well-suited for
        creating data API backends for interactive charts. In particular, DRP
        can be used with `d3.js <http://d3js.org>`__,
        `wq/pandas.js <http://wq.io/docs/pandas-js>`__, and
        `wq/chart.js <http://wq.io/docs/chart-js>`__, to create interactive time
        series, scatter, and box plot charts - as well as any of the infinite
        other charting possibilities d3.js provides.
        
        To facilitate data API building, the CSV renderer is the default in
        Django REST Pandas. While the pandas JSON serializer is improving, the
        primary reason for making CSV the default is the compactness it provides
        over JSON when serializing time series data. The default CSV output from
        DRP will have single row of column headers, making it suitable as-is for
        use with e.g. ``d3.csv()``. However, DRP is often used with the custom
        serializers below to produce a dataframe with nested multi-row column
        headers. This is harder to parse with ``d3.csv()`` but can be easily
        processed by `wq/pandas.js <http://wq.io/docs/pandas-js>`__, an
        extension to d3.js.
        
        .. code:: javascript
        
            // mychart.js
            define(['d3', 'wq/pandas', 'wq/chart'], function(d3, pandas, chart) {
        
            // Unpivoted data (single-row header)
            d3.csv("/data.csv", render);
        
            // Pivoted data (multi-row header)
            pandas.get('/data.csv', render);
        
            function render(error, data) {
                d3.select('svg')
                   .selectAll('rect')
                   .data(data)
                   // ...
            }
        
            });
        
        You can override the default renderers by setting ``PANDAS_RENDERERS``
        in your ``settings.py``, or by overriding ``renderer_classes`` in your
        ``PandasView`` subclass. ``PANDAS_RENDERERS`` is intentionally set
        separately from Django REST Framework's own ``DEFAULT_RENDERER_CLASSES``
        setting, as it is likely that you will be mixing DRP views with regular
        DRF views.
        
        As of version 0.4, DRP includes three custom serializers with
        ``transform_dataframe()`` functions that address common use cases. These
        serializer classes can be leveraged by assigning them to
        ``pandas_serializer_class`` on your view.
        
        For documentation purposes, the examples below assume the following
        dataset:
        
        +------------+---------------+--------------+---------+
        | Location   | Measurement   | Date         | Value   |
        +============+===============+==============+=========+
        | site1      | temperature   | 2016-01-01   | 3       |
        +------------+---------------+--------------+---------+
        | site1      | humidity      | 2016-01-01   | 30      |
        +------------+---------------+--------------+---------+
        | site2      | temperature   | 2016-01-01   | 4       |
        +------------+---------------+--------------+---------+
        | site2      | temperature   | 2016-01-02   | 5       |
        +------------+---------------+--------------+---------+
        
        PandasUnstackedSerializer
        ~~~~~~~~~~~~~~~~~~~~~~~~~
        
        ``PandasUnstackedSerializer``
        `unstacks <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.unstack.html>`__
        the dataframe so a few key attributes are listed in a multi-row column
        header. This makes it easier to include metadata about e.g. a time
        series without repeating the same values on every data row.
        
        To specify which attributes to use in column headers, define the
        attribute ``pandas_unstacked_header`` on your ``ModelSerializer``
        subclass. You will generally also want to define ``pandas_index``, which
        is a list of metadata fields unique to each row (e.g. the timestamp).
        
        .. code:: python
        
            # serializers.py
            from rest_framework import serializers
            from .models import TimeSeries
        
            class TimeSeriesSerializer(serializers.ModelSerializer):
                class Meta:
                    model = MultiTimeSeries
                    pandas_index = ['date']
                    pandas_unstacked_header = ['location', 'measurement']
        
            # views.py
            from rest_pandas import PandasView, PandasUnstackedSerializer
            from .models import TimeSeries
            from .serializers import TimeSeriesSerializer
        
            class TimeSeriesView(PandasView):
                queryset = TimeSeries.objects.all()
                serializer_class = TimeSeriesSerializer
                pandas_serializer_class = PandasUnstackedSerializer
        
        With the above example data, this configuration would output a CSV file
        with the following layout:
        
        +-------------------+-----------------+--------------+-----------------+
        |                   | Value           | Value        | Value           |
        +===================+=================+==============+=================+
        | **Location**      | *site1*         | *site1*      | *site2*         |
        +-------------------+-----------------+--------------+-----------------+
        | **Measurement**   | *temperature*   | *humidity*   | *temperature*   |
        +-------------------+-----------------+--------------+-----------------+
        | **Date**          |                 |              |
        +-------------------+-----------------+--------------+-----------------+
        | 2016-01-01        | 3               | 30           | 4               |
        +-------------------+-----------------+--------------+-----------------+
        | 2016-01-02        |                 |              | 5               |
        +-------------------+-----------------+--------------+-----------------+
        
        This could then be processed by
        `wq/pandas.js <http://wq.io/docs/pandas-js>`__ into the following
        structure:
        
        .. code:: javascript
        
            [
                {
                    "location": "site1",
                    "measurement": "temperature",
                    "data": [
                        {"date": "2016-01-01", "value": 3}
                    ]
                },
                {
                    "location": "site1",
                    "measurement": "humidity",
                    "data": [
                        {"date": "2016-01-01", "value": 30}
                    ]
                },
                {
                    "location": "site2",
                    "measurement": "temperature",
                    "data": [
                        {"date": "2016-01-01", "value": 4},
                        {"date": "2016-01-02", "value": 5}
                    ]
                }
            ]
        
        The output of ``PandasUnstackedSerializer`` can be used with the
        ``timeSeries()`` chart provided by
        `wq/chart.js <http://wq.io/docs/chart-js>`__:
        
        .. code:: javascript
        
            define(['d3', 'wq/pandas', 'wq/chart'], function(d3, pandas, chart) {
        
            var svg = d3.select('svg');
            var plot = chart.timeSeries();
            pandas.get('/data/timeseries.csv', function(data) {
                svg.datum(data).call(plot);
            });
        
            });
        
        PandasScatterSerializer
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        ``PandasScatterSerializer`` unstacks the dataframe and also combines
        selected attributes to make it easier to plot two measurements against
        each other in an x-y scatterplot.
        
        To specify which attributes to use for the coordinate names, define the
        attribute ``pandas_scatter_coord`` on your ``ModelSerializer`` subclass.
        You can also specify additional metadata attributes to include in the
        header with ``pandas_scatter_header``. You will generally also want to
        define ``pandas_index``, which is a list of metadata fields unique to
        each row (e.g. the timestamp).
        
        .. code:: python
        
            # serializers.py
            from rest_framework import serializers
            from .models import TimeSeries
        
            class TimeSeriesSerializer(serializers.ModelSerializer):
                class Meta:
                    model = MultiTimeSeries
                    pandas_index = ['date']
                    pandas_scatter_coord = ['measurement']
                    pandas_scatter_header = ['location']
        
            # views.py
            from rest_pandas import PandasView, PandasScatterSerializer
            from .models import TimeSeries
            from .serializers import TimeSeriesSerializer
        
            class TimeSeriesView(PandasView):
                queryset = TimeSeries.objects.all()
                serializer_class = TimeSeriesSerializer
                pandas_serializer_class = PandasScatterSerializer
        
        With the above example data, this configuration would output a CSV file
        with the following layout:
        
        +----------------+---------------------+------------------+---------------------+
        |                | temperature-value   | humidity-value   | temperature-value   |
        +================+=====================+==================+=====================+
        | **Location**   | *site1*             | *site1*          | *site2*             |
        +----------------+---------------------+------------------+---------------------+
        | **Date**       |                     |                  |
        +----------------+---------------------+------------------+---------------------+
        | 2014-01-01     | 3                   | 30               | 4                   |
        +----------------+---------------------+------------------+---------------------+
        | 2014-01-02     |                     |                  | 5                   |
        +----------------+---------------------+------------------+---------------------+
        
        This could then be processed by
        `wq/pandas.js <http://wq.io/docs/pandas-js>`__ into the following
        structure:
        
        .. code:: javascript
        
            [
                {
                    "location": "site1",
                    "data": [
                        {
                            "date": "2016-01-01",
                            "temperature-value": 3,
                            "humidity-value": 30
                        }
                    ]
                },
                {
                    "location": "site2",
                    "data": [
                        {
                            "date": "2016-01-01",
                            "temperature-value": 4
                        },
                        {
                            "date": "2016-01-02",
                            "temperature-value": 5
                        }
                    ]
                }
            ]
        
        The output of ``PandasScatterSerializer`` can be used with the
        ``scatter()`` chart provided by
        `wq/chart.js <http://wq.io/docs/chart-js>`__:
        
        .. code:: javascript
        
            define(['d3', 'wq/pandas', 'wq/chart'], function(d3, pandas, chart) {
        
            var svg = d3.select('svg');
            var plot = chart.scatter()
                .xvalue(function(d) {
                    return d['temperature-value'];
                })
                .yvalue(function(d) {
                    return d['humidity-value'];
                });
        
            pandas.get('/data/scatter.csv', function(data) {
                svg.datum(data).call(plot);
            });
        
            });
        
        PandasBoxplotSerializer
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        ``PandasBoxplotSerializer`` computes boxplot statistics (via
        matplotlib's
        `boxplot\_stats <http://matplotlib.org/api/cbook_api.html#matplotlib.cbook.boxplot_stats>`__)
        and pushes the results out via an unstacked dataframe. The statistics
        can be aggregated for a specified group column as well as by date.
        
        To specify which attribute to use for the group column, define the
        attribute ``pandas_boxplot_group`` on your ``ModelSerializer`` subclass.
        To specify an attribute to use for date-based grouping, define
        ``pandas_boxplot_date``. You will generally also want to define
        ``pandas_boxplot_header``, which will unstack any metadata columns and
        exclude them from statistics.
        
        .. code:: python
        
            # serializers.py
            from rest_framework import serializers
            from .models import TimeSeries
        
            class TimeSeriesSerializer(serializers.ModelSerializer):
                class Meta:
                    model = MultiTimeSeries
                    pandas_boxplot_group = 'site'
                    pandas_boxplot_date = 'date'
                    pandas_boxplot_header = ['measurement']
        
            # views.py
            from rest_pandas import PandasView, PandasBoxplotSerializer
            from .models import TimeSeries
            from .serializers import TimeSeriesSerializer
        
            class TimeSeriesView(PandasView):
                queryset = TimeSeries.objects.all()
                serializer_class = TimeSeriesSerializer
                pandas_serializer_class = PandasBoxplotSerializer
        
        With the above example data, this configuration will output a CSV file
        with the same general structure as ``PandasUnstackedSerializer``, but
        with the ``value`` spread across multiple boxplot statistics columns
        (``value-mean``,
        ``value-q1``,value-whishi\ ``, etc.).  An optional``\ group\` parameter
        can be added to the query string to switch between various groupings:
        
        +---------------------------+----------------------------------------------+
        | name                      | purpose                                      |
        +===========================+==============================================+
        | ``?group=series``         | Group by series (``pandas_boxplot_group``)   |
        +---------------------------+----------------------------------------------+
        | ``?group=series-year``    | Group by series, then by year                |
        +---------------------------+----------------------------------------------+
        | ``?group=series-month``   | Group by series, then by month               |
        +---------------------------+----------------------------------------------+
        | ``?group=year``           | Summarize all data by year                   |
        +---------------------------+----------------------------------------------+
        | ``?group=month``          | Summarize all data by month                  |
        +---------------------------+----------------------------------------------+
        
        The output of ``PandasBoxplotSerializer`` can be used with the
        ``boxplot()`` chart provided by
        `wq/chart.js <http://wq.io/docs/chart-js>`__:
        
        .. code:: javascript
        
            define(['d3', 'wq/pandas', 'wq/chart'], function(d3, pandas, chart) {
        
            var svg = d3.select('svg');
            var plot = chart.boxplot();
            pandas.get('/data/boxplot.csv?group=year', function(data) {
                svg.datum(data).call(plot);
            });
        
            });
        
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        .. |GitHub Forks| image:: https://img.shields.io/github/forks/wq/django-rest-pandas.svg
           :target: https://github.com/wq/django-rest-pandas/network
        .. |GitHub Issues| image:: https://img.shields.io/github/issues/wq/django-rest-pandas.svg
           :target: https://github.com/wq/django-rest-pandas/issues
        .. |Travis Build Status| image:: https://img.shields.io/travis/wq/django-rest-pandas.svg
           :target: https://travis-ci.org/wq/django-rest-pandas
        .. |Python Support| image:: https://img.shields.io/pypi/pyversions/rest-pandas.svg
           :target: https://pypi.python.org/pypi/rest-pandas
        .. |Django Support| image:: https://img.shields.io/badge/Django-1.7%2C%201.8%2C%201.9-blue.svg
           :target: https://pypi.python.org/pypi/rest-pandas
        .. |Django REST Framework Support| image:: https://img.shields.io/badge/DRF-2.4%2C%203.3-blue.svg
           :target: https://pypi.python.org/pypi/rest-pandas
        
Platform: UNKNOWN
Classifier: Framework :: Django
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Web Environment
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
