def _apply_basic_agg(self, agg_type, sort_results=False):
"""
Parameters
----------
agg_type : str
The aggregation function to run.
"""
result = DataFrame()
add_col_values = True
ctx = ffi.new('gdf_context*')
ctx.flag_sorted = 0
ctx.flag_method = self._method
ctx.flag_distinct = 0
val_columns = self._val_columns
val_columns_out = self._val_columns
result = self._apply_agg(agg_type,
result,
add_col_values,
ctx,
val_columns,
val_columns_out,
sort_result=sort_results)
# If a Groupby has one index column and one value column
# and as_index is set, return a Series instead of a df
if isinstance(val_columns, (str, Number)) and self._as_index:
result_series = result[val_columns]
idx = index.as_index(result[self._by[0]])
if self.level == 0:
idx.name = self._original_index_name
else:
idx.name = self._by[0]
result_series = result_series.set_index(idx)
return result_series
# TODO: Do MultiIndex here
if (self._as_index):
idx = index.as_index(result[self._by[0]])
idx.name = self._by[0]
result.drop_column(idx.name)
if self.level == 0:
idx.name = self._original_index_name
else:
idx.name = self._by[0]
result = result.set_index(idx)
nvtx_range_pop()
return result
def agg(self, args):
""" Invoke aggregation functions on the groups.
Parameters
----------
args : dict, list, str, callable
- str
The aggregate function name.
- list
List of *str* of the aggregate function.
- dict
key-value pairs of source column name and list of
aggregate functions as *str*.
Returns
-------
result : DataFrame
Notes
-----
Since multi-indexes aren't supported aggregation results are returned
in columns using the naming scheme of `aggregation_columnname`.
"""
result = DataFrame()
add_col_values = True
ctx = ffi.new('gdf_context*')
ctx.flag_sorted = 0
ctx.flag_method = self._method
ctx.flag_distinct = 0
sort_result = True
# TODO: Use MultiColumn here instead of use_prefix
# use_prefix enables old functionality - prefixing column
# groupby names since we don't support MultiColumn quite yet
use_prefix = 1 < len(self._val_columns) or 1 < len(args)
if not isinstance(args, str) and isinstance(args,
collections.abc.Sequence):
for agg_type in args:
val_columns_out = [
agg_type + '_' + val for val in self._val_columns
]
if not use_prefix:
val_columns_out = self._val_columns
result = self._apply_agg(agg_type,
result,
add_col_values,
ctx,
self._val_columns,
val_columns_out,
sort_result=sort_result)
add_col_values = False # we only want to add them once
# TODO: Do multindex here
if (self._as_index) and 1 == len(self._by):
idx = index.as_index(result[self._by[0]])
idx.name = self._by[0]
result = result.set_index(idx)
result.drop_column(idx.name)
elif isinstance(args, collections.abc.Mapping):
if (len(args.keys()) == 1):
if (len(list(args.values())[0]) == 1):
sort_result = False
for val, agg_type in args.items():
if not isinstance(agg_type, str) and \
isinstance(agg_type, collections.abc.Sequence):
for sub_agg_type in agg_type:
val_columns_out = [sub_agg_type + '_' + val]
if not use_prefix:
val_columns_out = self._val_columns
result = self._apply_agg(sub_agg_type,
result,
add_col_values,
ctx, [val],
val_columns_out,
sort_result=sort_result)
elif isinstance(agg_type, str):
val_columns_out = [agg_type + '_' + val]
if not use_prefix:
val_columns_out = self._val_columns
result = self._apply_agg(agg_type,
result,
add_col_values,
ctx, [val],
val_columns_out,
sort_result=sort_result)
add_col_values = False # we only want to add them once
# TODO: Do multindex here
if (self._as_index) and 1 == len(self._by):
idx = index.as_index(result[self._by[0]])
idx.name = self._by[0]
result = result.set_index(idx)
result.drop_column(idx.name)
else:
result = self.agg([args])
nvtx_range_pop()
return result
