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Serves up your Pandas dataframes via the Django REST Framework for use in client-side (i.e. d3.js) visualizations and offline analysis
Python
branch: master

README.md

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 DataFrames via the Django REST Framework. The resulting API can serve up CSV (and a number of other formats) for consumption by a client-side visualization tool like d3.js.

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 library, which provides wq/chart.js and wq/pandas.js, a collection of chart functions and data loaders that work well with CSV served by DRP and wq.db's chart module.

Build Status PyPI Package

Tested on Python 2.7 & 3.4, with Django 1.7 & 1.8, and Django REST Framework 2.4 & 3.1.

Live Demo

The climata-viewer project uses Django REST Pandas and wq/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 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 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 provides a direct bridge from matplotlib to d3.js, complete with seamless IPython integration. It is restricted to the (large) matplotlib chart vocabularly but should be sufficient for many use cases.
  • Bokeh 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 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 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).

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

See the implementation notes below for more details.

Usage

Getting Started

pip3 install rest-pandas

Usage Example

The example below assumes you already have a Django project set up with a single TimeSeries model.

# views.py
from rest_pandas import PandasView
from .models import TimeSeries
from .serializers import TimeSeriesSerializer

class TimeSeriesView(PandasView):
    # Django REST Framework 2.4
    model = TimeSeries

    # Django REST Framework 3+
    queryset = TimeSeries.objects.all()
    serializer_class = TimeSeriesSerializer  # extends ModelSerializer
    # pandas_serializer_class = PandasSerializer  # extends ListSerializer

    # In response to get(), the underlying Django REST Framework ListAPIView
    # 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)
        return qs

    # Then, the default serializer (typically a DRF ModelSerializer) should
    # serialize each row in the queryset into a simple dict format.  To
    # customize which fields to include, create a subclass of ModelSerializer
    # and assign it to serializer_class on your view.

    # Next, the included 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

    # For more control over dataframe creation, subclass PandasSerializer and
    # set pandas_serializer_class on the view.  (Or set list_serializer_class
    # on your ModelSerializer subclass' Meta class if you're using DRF 3).

    # Finally, the included Renderers will process the dataframe into one of
    # the output formats below.
# 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 and 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 that take the normal serializer result and put it into a dataframe. Then, the included renderers 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, a d3 extension for loading the complex CSV generated by pandas Dataframes.

// 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.

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