Module ImportFrom alias ImportFrom alias ImportFrom alias Import alias Import alias Assign NameStore Num Assign NameStore Num Assign NameStore Call AttributeLoad NameLoad attr Call AttributeLoad NameLoad attr ListComp Call NameLoad Num comprehension NameStore Call NameLoad NameLoad keyword Str NameLoad Assign NameStore Call NameLoad NameLoad Expr Call AttributeLoad NameLoad attr NameLoad Expr Call AttributeLoad NameLoad attr NameLoad Assign NameStore Call NameLoad Dict Str Str Str NameLoad NameLoad Call AttributeLoad AttributeLoad NameLoad attr attr BinOpMult NameLoad NameLoad Import alias identifier FunctionDef arguments args defaults body Assign NameStore Call AttributeLoad NameLoad attr Call NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index Str Return NameLoad decorator_list FunctionDef arguments args defaults body Assign NameStore Call AttributeLoad NameLoad attr Call NameLoad NameLoad Assign NameStore Call AttributeLoad NameLoad attr SubscriptLoad NameLoad Index Str Return NameLoad decorator_list Assign NameStore Call NameLoad Expr Str
N/A pandas * pandas.util.testing rands pandas.compat range N/A string N/A random N/A k 20000 N/A n 10 N/A foo N/A N/A np tile N/A N/A np array N/A N/A rands 10 N/A _ N/A range k dtype O n N/A foo2 N/A list foo N/A N/A N/A random shuffle foo N/A N/A N/A random shuffle foo2 N/A df N/A DataFrame N/A A B C foo foo2 N/A N/A N/A np random randn N/A n k N/A pandas._sandbox sbx f N/A N/A N/A N/A N/A table N/A N/A sbx StringHashTable N/A len df N/A ret N/A N/A table factorize N/A df N/A A N/A ret N/A g N/A N/A N/A N/A N/A table N/A N/A sbx PyObjectHashTable N/A len df N/A ret N/A N/A table factorize N/A df N/A A N/A ret N/A N/A ret N/A f N/A 
import pandas._tseries as lib

f = np.std


grouped = df.groupby(['A', 'B'])

label_list = [ping.labels for ping in grouped.groupings]
shape = [len(ping.ids) for ping in grouped.groupings]

from pandas.core.groupby import get_group_index


group_index = get_group_index(label_list, shape,
                              sort=True, xnull=True).astype('i4')

ngroups = np.prod(shape)

indexer = lib.groupsort_indexer(group_index, ngroups)

values = df['C'].values.take(indexer)
group_index = group_index.take(indexer)

f = lambda x: x.std(ddof=1)

grouper = lib.Grouper(df['C'], np.ndarray.std, group_index, ngroups)
result = grouper.get_result()

expected = grouped.std()
