PandasScatterSerializer

Django REST Pandas’ PandasUnstackedSerializer serializer class extends PandasSerializer to unstack the dataframe and combine 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).

The example below assumes the following dataset:

# serializers.py
from rest_framework import serializers
from .models import TimeSeries

class TimeSeriesSerializer(serializers.ModelSerializer):
    class Meta:
        model = MultiTimeSeries
        fields = ['date', 'location', 'measurement', 'value']
        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:

This could then be processed by @wq/pandas into the following structure:

[
    {
        "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:

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);
});

});