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Code Issues Projects Releases Wiki Activity Software Blog

Updated benchmarks to reflect new ASV setup

This commit is contained in:
Christopher C. Aycock 2016-12-12 11:26:45 -05:00
parent 5eeb7d98b2
commit fafbb02265
1 changed files with 13 additions and 63 deletions
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76
asv_bench/benchmarks/join_merge.py
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@ -239,42 +239,6 @@ class Merge(object):
merge(self.df, self.df2, on='key1')
class merge_asof_int32_noby(object):
def setup(self):
np.random.seed(0)
one_count = 200000
two_count = 1000000
self.df1 = pd.DataFrame({'time': np.random.randint(0, one_count/20, one_count),
'value1': np.random.randn(one_count)})
self.df1.time = np.int32(self.df1.time)
self.df2 = pd.DataFrame({'time': np.random.randint(0, two_count/20, two_count),
'value2': np.random.randn(two_count)})
self.df2.time = np.int32(self.df2.time)
self.df1 = self.df1.sort_values('time')
self.df2 = self.df2.sort_values('time')
def time_merge_asof_int32_noby(self):
merge_asof(self.df1, self.df2, on='time')
class merge_asof_by_object(object):
def setup(self):
import string
np.random.seed(0)
one_count = 200000
two_count = 1000000
self.df1 = pd.DataFrame({'time': np.random.randint(0, one_count/20, one_count),
'key': np.random.choice(list(string.ascii_uppercase), one_count),
'value1': np.random.randn(one_count)})
self.df2 = pd.DataFrame({'time': np.random.randint(0, two_count/20, two_count),
'key': np.random.choice(list(string.ascii_uppercase), two_count),
'value2': np.random.randn(two_count)})
self.df1 = self.df1.sort_values('time')
self.df2 = self.df2.sort_values('time')
class i8merge(object):
goal_time = 0.2
@ -306,35 +270,8 @@ class MergeOrdered(object):
'key' : np.tile(np.arange(0, 10000, 2), 10),
'lvalue': np.random.randn(50000)})
<<<<<<< HEAD
class merge_asof_multiby(object):
def setup(self):
import string
np.random.seed(0)
one_count = 200000
two_count = 1000000
self.df1 = pd.DataFrame({'time': np.random.randint(0, one_count/20, one_count),
'key1': np.random.choice(list(string.ascii_uppercase), one_count),
'key2': np.random.choice(list(string.ascii_uppercase), one_count),
'value1': np.random.randn(one_count)})
self.df2 = pd.DataFrame({'time': np.random.randint(0, two_count/20, two_count),
'key1': np.random.choice(list(string.ascii_uppercase), two_count),
'key2': np.random.choice(list(string.ascii_uppercase), two_count),
'value2': np.random.randn(two_count)})
self.df1 = self.df1.sort_values('time')
self.df2 = self.df2.sort_values('time')
def time_merge_asof_multiby(self):
merge_asof(self.df1, self.df2, on='time', by=['key1', 'key2'])
class join_non_unique_equal(object):
goal_time = 0.2
=======
self.right = pd.DataFrame({'key' : np.arange(10000),
'rvalue' : np.random.randn(10000)})
>>>>>>> master
def time_merge_ordered(self):
merge_ordered(self.left, self.right, on='key', left_by='group')
@ -365,12 +302,19 @@ class MergeAsof(object):
self.df1 = self.df1.sort_values('time')
self.df2 = self.df2.sort_values('time')
self.df1['time32'] = np.int32(self.df1.time)
self.df2['time32'] = np.int32(self.df2.time)
self.df1a = self.df1[['time', 'value1']]
self.df2a = self.df2[['time', 'value2']]
self.df1b = self.df1[['time', 'key', 'value1']]
self.df2b = self.df2[['time', 'key', 'value2']]
self.df1c = self.df1[['time', 'key2', 'value1']]
self.df2c = self.df2[['time', 'key2', 'value2']]
self.df1d = self.df1[['time32', 'value1']]
self.df2d = self.df2[['time32', 'value2']]
self.df1e = self.df1[['time', 'key', 'key2', 'value1']]
self.df2e = self.df2[['time', 'key', 'key2', 'value2']]
def time_noby(self):
merge_asof(self.df1a, self.df2a, on='time')
@ -381,6 +325,12 @@ class MergeAsof(object):
def time_by_int(self):
merge_asof(self.df1c, self.df2c, on='time', by='key2')
def time_on_int32(self):
merge_asof(self.df1d, self.df2d, on='time32')
def time_multiby(self):
merge_asof(self.df1e, self.df2e, on='time', by=['key', 'key2'])
#----------------------------------------------------------------------
# data alignment