merge_asof() has type specializations and can take multiple 'by' parameters (pandas-dev#13936)

Author: Christopher C. Aycock

asv_bench/benchmarks/join_merge.py

@@ -310,6 +310,25 @@ def time_merge_asof_noby(self):
  merge_asof(self.df1, self.df2, on='time')
 
 
+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):
@@ -318,10 +337,10 @@ def setup(self):
  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.uppercase), 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.uppercase), 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')
@@ -349,6 +368,28 @@ def time_merge_asof_by_int(self):
  merge_asof(self.df1, self.df2, on='time', by='key')
 
 
+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