A set of tools to make Pandas easy to use with Django REST Framework projects
Latest commit ec754ac Oct 10, 2016 @abarto abarto Merge branch 'development'




pandas-drf-tools is a set of viewsets, serializers and mixins to allow using Pandas DataFrames with Django REST Framework sites.


The package can be installed using pip from PyPI:

$ pip install pandas-drf-tools

An you can also install it from source cloning the project's GitHub repository:

$ git clone git://github.com/abarto/pandas-drf-tools.git
$ cd pandas-drf-tools
$ python setup.py install


How you use pandas-drf-tools depends on the level of integration you need. The simplest use-case are regular DRF views that expose a DataFrame. pandas-drf-tools provides several Serializers that turn a DataFrame into its JSON representation using to_* methods in the DataFrame API and a little bit of data processing. You can also parse (and validate) data sent to the view into a DataFrame using the provided Serializers. For example:

class DataFrameIndexSerializerTestView(views.APIView):
    def get_serializer_class(self):
        return DataFrameIndexSerializer

    def get(self, request, *args, **kwargs):
        sample = get_some_dataframe().sample(20)
        serializer = self.get_serializer_class()(sample)
        return response.Response(serializer.data)

    def post(self, request, *args, **kwargs):
        serializer = self.get_serializer_class()(data=request.data)
        data_frame = serializer.validated_data
        data = {
            'columns': list(data_frame.columns),
            'len': len(data_frame)
        return response.Response(data)

The APIView above uses DataFrameIndexSerializer to serialize the DataFrame sample on the get method, and to de-serialize the request payload on the post method. It also provide basic validation. Here's the code for DataFrameIndexSerializer:

class DataFrameIndexSerializer(Serializer):
    def to_internal_value(self, data):
            data_frame = pd.DataFrame.from_dict(data, orient='index').rename(index=int)
            return data_frame
        except ValueError as e:
            raise ValidationError({api_settings.NON_FIELD_ERRORS_KEY: [str(e)]})

    def to_representation(self, instance):
        instance = instance.rename(index=str)
        return instance.to_dict(orient='index')

As you can see, the brunt of the work is done by DataFrame.to_dict. These are all the Serializers available:

  • DataFrameReadOnlyToDictRecordsSerializer: A read-only (it doesn't implement to_internal_value) serializer that uses DataFrame.to_dict with records orientation.
  • DataFrameListSerializer: A serializer that uses DataFrame.to_dict with list orientation for serialization and columns for de-serialization.
  • DataFrameIndexSerializer: A serializer that uses DataFrame.to_dict with index orientation for serialization and de-serialization. Due to the restrictions imposed on keys by the JSON format, the index is converted to str on serialization and to int on deseralization.
  • DataFrameRecordsSerializer: A serializer that uses DataFrame.to_records for serialization and DataFrame.from_records de-serialization.

Besides serializers, pandas-drf-tools also provides a GenericDataFrameAPIView to expose a DataFrame using a view, the same way DRF's GenericAPIView does it with Django's querysets. This class will rarely be used directly. Same as with DRF, pandas-drf-tools also provides a GenericDataFrameViewSet class that, combined with custom list, retrieve, create, and update mixins turn into DataFrameViewSet (and ReadOnlyDataFrameViewSet) which mimics the behaviour of ModelViewSet.

Instead of setting a queryset field of overriding get_queryset, users of DataFrameViewSet need to set a dataframe field or override the get_dataframe method. Another difference is that, by default, write operations do not change the original dataframe. The create, update, and destroy methods defined in the mixins return a new DataFrame based on the one set by get_dataframe. In order to give the users the chance of doing something with the new DataFrame, we provide an update_dataframe callback that is called whenever a write operation is called. Take a look at the CreateDataFrameMixin class:

class CreateDataFrameMixin(object):
    Adds a row to the dataframe.
    def create(self, request, *args, **kwargs):
        serializer = self.get_serializer(data=request.data)
        headers = self.get_success_headers(serializer.data)
        return Response(serializer.data, status=status.HTTP_201_CREATED, headers=headers)

    def perform_create(self, serializer):
        dataframe = self.get_dataframe()
        return self.update_dataframe(dataframe.append(serializer.validated_data))

    def get_success_headers(self, data):
            return {'Location': data[api_settings.URL_FIELD_NAME]}
        except (TypeError, KeyError):
            return {}

We call append on the original dataframe and we pass the result onto update_dataframe. The default behaviour of update_dataframe is just returning whatever was passed onto it, so all operations are basically read-only. Here's an example of how to integrate all the components:

import pandas as pd

class TestDataFrameViewSet(DataFrameViewSet):
    serializer_class = DataFrameRecordsSerializer

    def get_dataframe(self):
        return pd.read_pickle('test.pkl')

    def update_dataframe(self, dataframe):
        return dataframe

This viewset can then be used the same way as regular DRF viewset. For instance, we could use a router:

from rest_framework.routers import DefaultRouter

router = DefaultRouter()
router.register(r'test', TestDataFrameViewSet, base_name='test')

The only caveat is that, since there's no queryset (nor model) associated with the viewset, DRF cannot guess the base name, so it has to be set explicitly.

That's everything you need. Now you API is ready to receive regular REST calls (POST for create, PUT for update, etc.) that will read or change the DataFrame.

Whenever possible, I followed DRF's existing architecture so most things should feel natural if you already have experience with the framework.


A complete example that uses the US Census Data is available on GitHub.

What's missing?

  • No unit tests. Although the package is fully functional, I wouldn't use it in any production environment yet as I haven't had time to fully test it just.
  • No validation. The serializers just use pandas' methods without checking payload thoroughly. I'm still looking for ways on improving this, probably using the columns dtypes to validate each serialized cell.
  • No filtering backends. If you need filtering, you can override the filter_dataframe method, which is does the same as the filter_queryset method. I'm planning on implementing some filters (like the SearchFilter) to provide guidance if you want to build your own.
  • No page pagination. Only LimitOffsetPagination is provided.
  • Proper documentation.


Comments, tickets and pull requests are welcomed. You can also reach me at abarto@machinalis.com if you have specific questions.