rest-pandas 0.1.0

Getting Started

pip install rest-pandas

Usage Example

# views.py
from rest_pandas import PandasView
from .models import TimeSeries
class TimeSeriesView(PandasView):
    model = TimeSeries
    def filter_queryset(self, qs):
        # First, filter queryset based on self.request or other settings
        # (useful for limiting memory usage)
        return qs

    def transform_dataframe(self, dataframe):
        # Then (or instead), transform the dataframe based on self.request
        # (useful for pivoting or computing statistics)
        return dataframe

# 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.

Formats

The following output formats are provided by default. These are provided as renderer classes in order to leverage the content 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 above urls.py is followed).

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.