Generalized table aggregation example using astropy Table

table_aggregation.py

from __future__ import division, print_function
from astropy import table
from astropy.table import Table
from collections import OrderedDict


t = Table([['a', 'a', 'a', 'b', 'b', 'c'],
           [1, 2, 3, 4, 5, 6],
           [2, 2, 1, 2, 1, 1]],
          names=('name', 'value', 'weight'))

grouped = t.group_by('name')


def transform_table(tbl):
    """
    Generalized function that takes table ``tbl`` as input
    and returns a new Table ``out``.  Note that ``out`` does not
    necessarily need to have the same types or columns.

    The example is just the identity transform.  Be aware that
    in-place operations will affect the input table.
    """
    out = tbl
    return out

out_tables = []
for group in grouped.groups:
    out_tables.append(transform_table(group))
result = table.vstack(out_tables)
print('transform_table')
print(result)
print()


def average_weighted(tbl, name):
    col = tbl[name]
    if name == 'weight':
        value = col.sum()
    else:
        weight = tbl['weight']
        value = (col * weight).sum() / weight.sum()

    return value


def transform_table_to_row(tbl, func):
    """
    Generalized function that takes table ``tbl`` as input
    and returns a new table row as an OrderedDict.  It applies
    function ``func`` to each column.

    The example computes the weighted average of each field (where
    possible) assuming the weights are in column ``weight``.
    """
    out = OrderedDict()
    for name in t.colnames:
        try:
            value = func(tbl, name)
        except:
            # If something went wrong just ignore (could not perform
            # operation on this column).
            pass
        else:
            out[name] = value
    return out


out_rows = []
for group in grouped.groups:
    out_rows.append(transform_table_to_row(group, average_weighted))
result = Table(rows=out_rows)

print('transform_table_to_row')
print(result)