diff --git a/asv_bench/benchmarks/groupby.py b/asv_bench/benchmarks/groupby.py
index 3e7e5c821b14c..7777322071957 100644
--- a/asv_bench/benchmarks/groupby.py
+++ b/asv_bench/benchmarks/groupby.py
@@ -14,7 +14,10 @@
method_blacklist = {
'object': {'median', 'prod', 'sem', 'cumsum', 'sum', 'cummin', 'mean',
'max', 'skew', 'cumprod', 'cummax', 'rank', 'pct_change', 'min',
- 'var', 'mad', 'describe', 'std'}
+ 'var', 'mad', 'describe', 'std'},
+ 'datetime': {'median', 'prod', 'sem', 'cumsum', 'sum', 'mean', 'skew',
+ 'cumprod', 'cummax', 'pct_change', 'var', 'mad', 'describe',
+ 'std'}
}
@@ -90,45 +93,6 @@ def time_series_groups(self, data, key):
self.ser.groupby(self.ser).groups
-class FirstLast(object):
-
- goal_time = 0.2
-
- param_names = ['dtype']
- params = ['float32', 'float64', 'datetime', 'object']
-
- def setup(self, dtype):
- N = 10**5
- # with datetimes (GH7555)
- if dtype == 'datetime':
- self.df = DataFrame({'values': date_range('1/1/2011',
- periods=N,
- freq='s'),
- 'key': range(N)})
- elif dtype == 'object':
- self.df = DataFrame({'values': ['foo'] * N,
- 'key': range(N)})
- else:
- labels = np.arange(N / 10).repeat(10)
- data = Series(np.random.randn(len(labels)), dtype=dtype)
- data[::3] = np.nan
- data[1::3] = np.nan
- labels = labels.take(np.random.permutation(len(labels)))
- self.df = DataFrame({'values': data, 'key': labels})
-
- def time_groupby_first(self, dtype):
- self.df.groupby('key').first()
-
- def time_groupby_last(self, dtype):
- self.df.groupby('key').last()
-
- def time_groupby_nth_all(self, dtype):
- self.df.groupby('key').nth(0, dropna='all')
-
- def time_groupby_nth_none(self, dtype):
- self.df.groupby('key').nth(0)
-
-
class GroupManyLabels(object):
goal_time = 0.2
@@ -149,39 +113,40 @@ class Nth(object):
goal_time = 0.2
- def setup_cache(self):
- df = DataFrame(np.random.randint(1, 100, (10000, 2)))
- df.iloc[1, 1] = np.nan
- return df
-
- def time_frame_nth_any(self, df):
- df.groupby(0).nth(0, dropna='any')
-
- def time_frame_nth(self, df):
- df.groupby(0).nth(0)
-
+ param_names = ['dtype']
+ params = ['float32', 'float64', 'datetime', 'object']
- def time_series_nth_any(self, df):
- df[1].groupby(df[0]).nth(0, dropna='any')
+ def setup(self, dtype):
+ N = 10**5
+ # with datetimes (GH7555)
+ if dtype == 'datetime':
+ values = date_range('1/1/2011', periods=N, freq='s')
+ elif dtype == 'object':
+ values = ['foo'] * N
+ else:
+ values = np.arange(N).astype(dtype)
- def time_series_nth(self, df):
- df[1].groupby(df[0]).nth(0)
+ key = np.arange(N)
+ self.df = DataFrame({'key': key, 'values': values})
+ self.df.iloc[1, 1] = np.nan # insert missing data
+ def time_frame_nth_any(self, dtype):
+ self.df.groupby('key').nth(0, dropna='any')
-class NthObject(object):
+ def time_groupby_nth_all(self, dtype):
+ self.df.groupby('key').nth(0, dropna='all')
- goal_time = 0.2
+ def time_frame_nth(self, dtype):
+ self.df.groupby('key').nth(0)
- def setup_cache(self):
- df = DataFrame(np.random.randint(1, 100, (10000,)), columns=['g'])
- df['obj'] = ['a'] * 5000 + ['b'] * 5000
- return df
+ def time_series_nth_any(self, dtype):
+ self.df['values'].groupby(self.df['key']).nth(0, dropna='any')
- def time_nth(self, df):
- df.groupby('g').nth(5)
+ def time_groupby_nth_all(self, dtype):
+ self.df['values'].groupby(self.df['key']).nth(0, dropna='all')
- def time_nth_last(self, df):
- df.groupby('g').last()
+ def time_series_nth(self, dtype):
+ self.df['values'].groupby(self.df['key']).nth(0)
class DateAttributes(object):
@@ -243,7 +208,7 @@ def time_multi_count(self, df):
df.groupby(['key1', 'key2']).count()
-class CountInt(object):
+class CountMultiInt(object):
goal_time = 0.2
@@ -255,10 +220,10 @@ def setup_cache(self):
'ints2': np.random.randint(0, 1000, size=n)})
return df
- def time_int_count(self, df):
+ def time_multi_int_count(self, df):
df.groupby(['key1', 'key2']).count()
- def time_int_nunique(self, df):
+ def time_multi_int_nunique(self, df):
df.groupby(['key1', 'key2']).nunique()
@@ -266,7 +231,7 @@ class AggFunctions(object):
goal_time = 0.2
- def setup_cache(self):
+ def setup_cache():
N = 10**5
fac1 = np.array(['A', 'B', 'C'], dtype='O')
fac2 = np.array(['one', 'two'], dtype='O')
@@ -361,9 +326,6 @@ def setup(self):
def time_multi_size(self):
self.df.groupby(['key1', 'key2']).size()
- def time_dt_size(self):
- self.df.groupby(['dates']).size()
-
def time_dt_timegrouper_size(self):
with warnings.catch_warnings(record=True):
self.df.groupby(TimeGrouper(key='dates', freq='M')).size()
@@ -376,15 +338,16 @@ class GroupByMethods(object):
goal_time = 0.2
- param_names = ['dtype', 'method']
- params = [['int', 'float', 'object'],
+ param_names = ['dtype', 'method', 'application']
+ params = [['int', 'float', 'object', 'datetime'],
['all', 'any', 'bfill', 'count', 'cumcount', 'cummax', 'cummin',
'cumprod', 'cumsum', 'describe', 'ffill', 'first', 'head',
'last', 'mad', 'max', 'min', 'median', 'mean', 'nunique',
'pct_change', 'prod', 'rank', 'sem', 'shift', 'size', 'skew',
- 'std', 'sum', 'tail', 'unique', 'value_counts', 'var']]
+ 'std', 'sum', 'tail', 'unique', 'value_counts', 'var'],
+ ['direct', 'transformation']]
- def setup(self, dtype, method):
+ def setup(self, dtype, method, application):
if method in method_blacklist.get(dtype, {}):
raise NotImplementedError # skip benchmark
ngroups = 1000
@@ -398,12 +361,28 @@ def setup(self, dtype, method):
np.random.random(ngroups) * 10.0])
elif dtype == 'object':
key = ['foo'] * size
+ elif dtype == 'datetime':
+ key = date_range('1/1/2011', periods=size, freq='s')
df = DataFrame({'values': values, 'key': key})
- self.df_groupby_method = getattr(df.groupby('key')['values'], method)
- def time_method(self, dtype, method):
- self.df_groupby_method()
+ if application == 'transform':
+ if method == 'describe':
+ raise NotImplementedError
+
+ self.as_group_method = lambda: df.groupby(
+ 'key')['values'].transform(method)
+ self.as_field_method = lambda: df.groupby(
+ 'values')['key'].transform(method)
+ else:
+ self.as_group_method = getattr(df.groupby('key')['values'], method)
+ self.as_field_method = getattr(df.groupby('values')['key'], method)
+
+ def time_dtype_as_group(self, dtype, method, application):
+ self.as_group_method()
+
+ def time_dtype_as_field(self, dtype, method, application):
+ self.as_field_method()
class Float32(object):