Metadata-Version: 1.1
Name: rest-pandas
Version: 0.2.1
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 + pandas = 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)
        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.
        
        |Build Status| |PyPI Package|
        
        Tested on Python 2.7 and 3.4, with Django 1.6 and 1.7.
        
        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
        ``Accepts: text/csv`` or by appending ``.csv`` to the URL (if the URL
        configuration below is used).
        
        .. csv-table::
          :header: "Format", "Content Type", "pandas Dataframe Function", "Notes"
          :widths: 50, 150, 70, 500
        
          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"
        
        See the implementation notes below for more details.
        
        Usage
        -----
        
        Getting Started
        ~~~~~~~~~~~~~~~
        
        .. code:: bash
        
            pip3 install rest-pandas
        
        Usage Example
        ~~~~~~~~~~~~~
        
        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
            class TimeSeriesView(PandasView):
                model = TimeSeries
                
                # In response to get(), the underlying Django REST Framework ListAPIView
                # will load the default queryset (self.model.objects.all()) 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)
                    return qs
                    
                # Then, the included PandasSerializer will serialize the queryset into a
                # simple list of dicts (using the DRF ModelSerializer).  To customize
                # which fields to include, subclass PandasSerializer and set the
                # appropriate ModelSerializer options.  Then, set the serializer_class
                # property on the view to your PandasSerializer subclass.
                
                # Next, the PandasSerializer will load the ModelSerializer result into a
                # DataFrame and pass it to the following function on the view.
        
                def transform_dataframe(self, dataframe):
                    # Here you can transform the dataframe based on self.request
                    # (useful for pivoting or computing statistics)
                    return dataframe
                    
                # Finally, the included Renderers will process the dataframe into one of
                # the output formats below.
        
        .. code:: python
        
            # urls.py
            from django.conf.urls import patterns, include, url
            from rest_framework.urlpatterns import format_suffix_patterns
        
            from .views import TimeSeriesView
            urlpatterns = patterns('',
                url(r'^data', TimeSeriesView.as_view()),
            )
            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>`__, or
        a ``PandasSimpleView`` if you would just like to serve up some data
        without a Django model as the source.
        
        Implementation Notes
        ~~~~~~~~~~~~~~~~~~~~
        
        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.
        
        Perhaps counterintuitively, the CSV renderer is the default in Django
        REST Pandas, as it is the most stable and useful for API building. 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. This is particularly valuable for pandas
        dataframes, in which:
        
        -  each record has the same keys, and
        -  there are (usually) no nested objects
        
        While a normal CSV file only has a single row of column headers, pandas
        can produce files with nested columns. This is a useful way to provide
        metadata about time series that is difficult to represent in a plain CSV
        file. However, it also makes the resulting CSV more difficult to parse.
        For this reason, you may be interested in
        `wq/pandas.js <http://wq.io/docs/pandas-js>`__, a d3 extension for
        loading the complex CSV generated by pandas Dataframes.
        
        .. code:: javascript
        
            // mychart.js
            define(['d3', 'wq/pandas'], function(d3, pandas) {
        
            d3.csv("/data.csv", render);
            // Or
            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.
        
        .. |Build Status| image:: https://travis-ci.org/wq/django-rest-pandas.png?branch=master
           :target: https://travis-ci.org/wq/django-rest-pandas
        .. |PyPI Package| image:: https://pypip.in/version/rest-pandas/badge.png
           :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
