PandasUnstackedSerializer

Django REST Pandas’ PandasUnstackedSerializer serializer class extends PandasSerializer to pivot repeating series metadata to the top of the dataframe.

To do this, PandasUnstackedSerializer unstacks the dataframe so a few key attributes are listed in a multi-row column header. This makes it easier to include metadata about e.g. a time series without repeating the same values on every data row.

To specify which attributes to use in column headers, define the attribute pandas_unstacked_header on your ModelSerializer subclass. 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_unstacked_header = ['location', 'measurement']

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

class TimeSeriesView(PandasView):
    queryset = TimeSeries.objects.all()
    serializer_class = TimeSeriesSerializer
    pandas_serializer_class = PandasUnstackedSerializer

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",
        "measurement": "temperature",
        "data": [
            {"date": "2016-01-01", "value": 3}
        ]
    },
    {
        "location": "site1",
        "measurement": "humidity",
        "data": [
            {"date": "2016-01-01", "value": 30}
        ]
    },
    {
        "location": "site2",
        "measurement": "temperature",
        "data": [
            {"date": "2016-01-01", "value": 4},
            {"date": "2016-01-02", "value": 5}
        ]
    }
]

The output of PandasUnstackedSerializer can be used with the timeSeries() chart provided by @wq/chart:

define(['d3', 'wq/pandas', 'wq/chart'], function(d3, pandas, chart) {

var svg = d3.select('svg');
var plot = chart.timeSeries();
pandas.get('/data/timeseries.csv', function(data) {
    svg.datum(data).call(plot);
});

});