TST: asv benchmarks for Series constructors

Author: toobaz

asv_bench/benchmarks/ctors.py

@@ -4,7 +4,30 @@
 from .pandas_vb_common import setup # noqa
 
 
-class Constructors(object):
+class SeriesConstructors(object):
+
+    goal_time = 0.2
+
+    param_names = ["data_fmt", "with_index"]
+    params = [[lambda x: x,
+               list,
+               lambda arr: dict(zip(range(len(arr)), arr)),
+               lambda arr: [[i, -i] for i in arr],
+               lambda arr: ([[i, -i] for i in arr][:-1] + [None])],
+              [False, True]]
+
+    def setup(self, data_fmt, with_index):
+        N = 10**2
+        np.random.seed(1234)
+        arr = np.random.randn(N)
+        self.data = data_fmt(arr)
+        self.index = np.arange(N) if with_index else None
+
+    def time_series_constructor(self, data_fmt, with_index):
+        Series(self.data, index=self.index)
+
+
+class SeriesDtypesConstructors(object):
 
     goal_time = 0.2
 
@@ -19,12 +42,6 @@ def setup(self):
         self.s = Series([Timestamp('20110101'), Timestamp('20120101'),
                          Timestamp('20130101')] * N * 10)
 
-    def time_frame_from_ndarray(self):
-        DataFrame(self.arr)
-
-    def time_series_from_ndarray(self):
-        Series(self.data, index=self.index)
-
     def time_index_from_array_string(self):
         Index(self.arr_str)
 

asv_bench/benchmarks/frame_ctor.py

@@ -81,3 +81,15 @@ def setup(self, nrows):
     def time_frame_from_records_generator(self, nrows):
         # issue-6700
         self.df = DataFrame.from_records(self.gen, nrows=nrows)
+
+
+class FromNDArray(object):
+
+    goal_time = 0.2
+
+    def setup(self):
+        N = 100000
+        self.data = np.random.randn(N)
+
+    def time_frame_from_ndarray(self):
+        self.df = DataFrame(self.data)