CLN: ASV remove uncessary selfs and add setups (pandas-dev#18575)

Author: mroeschke

asv_bench/benchmarks/algorithms.py

@@ -11,6 +11,8 @@
     except:
         pass
 
+from .pandas_vb_common import setup # noqa
+
 
 class Factorize(object):
 
@@ -21,7 +23,6 @@ class Factorize(object):
 
     def setup(self, sort):
         N = 10**5
-        np.random.seed(1234)
         self.int_idx = pd.Int64Index(np.arange(N).repeat(5))
         self.float_idx = pd.Float64Index(np.random.randn(N).repeat(5))
         self.string_idx = tm.makeStringIndex(N)
@@ -45,7 +46,6 @@ class Duplicated(object):
 
     def setup(self, keep):
         N = 10**5
-        np.random.seed(1234)
         self.int_idx = pd.Int64Index(np.arange(N).repeat(5))
         self.float_idx = pd.Float64Index(np.random.randn(N).repeat(5))
         self.string_idx = tm.makeStringIndex(N)
@@ -79,7 +79,6 @@ class Match(object):
     goal_time = 0.2
 
     def setup(self):
-        np.random.seed(1234)
         self.uniques = tm.makeStringIndex(1000).values
         self.all = self.uniques.repeat(10)
 
@@ -92,7 +91,6 @@ class Hashing(object):
     goal_time = 0.2
 
     def setup_cache(self):
-        np.random.seed(1234)
         N = 10**5
 
         df = pd.DataFrame(

asv_bench/benchmarks/binary_ops.py

@@ -6,6 +6,8 @@
 except ImportError:
     import pandas.computation.expressions as expr
 
+from .pandas_vb_common import setup # noqa
+
 
 class Ops(object):
 
@@ -15,7 +17,6 @@ class Ops(object):
     param_names = ['use_numexpr', 'threads']
 
     def setup(self, use_numexpr, threads):
-        np.random.seed(1234)
         self.df = DataFrame(np.random.randn(20000, 100))
         self.df2 = DataFrame(np.random.randn(20000, 100))
 
@@ -47,7 +48,6 @@ class Ops2(object):
 
     def setup(self):
         N = 10**3
-        np.random.seed(1234)
         self.df = DataFrame(np.random.randn(N, N))
         self.df2 = DataFrame(np.random.randn(N, N))
 
@@ -89,14 +89,12 @@ class Timeseries(object):
     param_names = ['tz']
 
     def setup(self, tz):
-        self.N = 10**6
-        self.halfway = ((self.N // 2) - 1)
-        self.s = Series(date_range('20010101', periods=self.N, freq='T',
-                                   tz=tz))
-        self.ts = self.s[self.halfway]
+        N = 10**6
+        halfway = (N // 2) - 1
+        self.s = Series(date_range('20010101', periods=N, freq='T', tz=tz))
+        self.ts = self.s[halfway]
 
-        self.s2 = Series(date_range('20010101', periods=self.N, freq='s',
-                                    tz=tz))
+        self.s2 = Series(date_range('20010101', periods=N, freq='s', tz=tz))
 
     def time_series_timestamp_compare(self, tz):
         self.s <= self.ts
@@ -131,7 +129,6 @@ class AddOverflowArray(object):
     goal_time = 0.2
 
     def setup(self):
-        np.random.seed(1234)
         N = 10**6
         self.arr = np.arange(N)
         self.arr_rev = np.arange(-N, 0)

asv_bench/benchmarks/categoricals.py

@@ -9,6 +9,8 @@
     except ImportError:
         pass
 
+from .pandas_vb_common import setup # noqa
+
 
 class Concat(object):
 
@@ -76,7 +78,6 @@ class ValueCounts(object):
 
     def setup(self, dropna):
         n = 5 * 10**5
-        np.random.seed(2718281)
         arr = ['s%04d' % i for i in np.random.randint(0, n // 10, size=n)]
         self.ts = pd.Series(arr).astype('category')
 
@@ -101,7 +102,6 @@ class SetCategories(object):
 
     def setup(self):
         n = 5 * 10**5
-        np.random.seed(2718281)
         arr = ['s%04d' % i for i in np.random.randint(0, n // 10, size=n)]
         self.ts = pd.Series(arr).astype('category')
 
@@ -116,7 +116,6 @@ class Rank(object):
     def setup(self):
         N = 10**5
         ncats = 100
-        np.random.seed(1234)
 
         self.s_str = pd.Series(tm.makeCategoricalIndex(N, ncats)).astype(str)
         self.s_str_cat = self.s_str.astype('category')

asv_bench/benchmarks/ctors.py

@@ -1,14 +1,15 @@
 import numpy as np
 from pandas import DataFrame, Series, Index, DatetimeIndex, Timestamp
 
+from .pandas_vb_common import setup # noqa
+
 
 class Constructors(object):
 
     goal_time = 0.2
 
     def setup(self):
         N = 10**2
-        np.random.seed(1234)
         self.arr = np.random.randn(N, N)
         self.arr_str = np.array(['foo', 'bar', 'baz'], dtype=object)
 

asv_bench/benchmarks/eval.py

@@ -5,6 +5,8 @@
 except ImportError:
     import pandas.computation.expressions as expr
 
+from .pandas_vb_common import setup # noqa
+
 
 class Eval(object):
 
@@ -14,7 +16,6 @@ class Eval(object):
     param_names = ['engine', 'threads']
 
     def setup(self, engine, threads):
-        np.random.seed(1234)
         self.df = pd.DataFrame(np.random.randn(20000, 100))
         self.df2 = pd.DataFrame(np.random.randn(20000, 100))
         self.df3 = pd.DataFrame(np.random.randn(20000, 100))
@@ -45,17 +46,16 @@ class Query(object):
     goal_time = 0.2
 
     def setup(self):
-        np.random.seed(1234)
-        self.N = 10**6
-        self.halfway = (self.N // 2) - 1
-        self.index = pd.date_range('20010101', periods=self.N, freq='T')
-        self.s = pd.Series(self.index)
-        self.ts = self.s.iloc[self.halfway]
-        self.df = pd.DataFrame({'a': np.random.randn(self.N), 'dates': self.s},
-                               index=self.index)
-        self.data = np.random.randn(self.N)
-        self.min_val = self.data.min()
-        self.max_val = self.data.max()
+        N = 10**6
+        halfway = (N // 2) - 1
+        index = pd.date_range('20010101', periods=N, freq='T')
+        s = pd.Series(index)
+        self.ts = s.iloc[halfway]
+        self.df = pd.DataFrame({'a': np.random.randn(N), 'dates': s},
+                               index=index)
+        data = np.random.randn(N)
+        self.min_val = data.min()
+        self.max_val = data.max()
 
     def time_query_datetime_index(self):
         self.df.query('index < @self.ts')

asv_bench/benchmarks/frame_ctor.py

@@ -4,27 +4,23 @@
 try:
     from pandas.tseries import offsets
 except:
-    from pandas.core.datetools import *
+    from pandas.core.datetools import * # noqa
 
+from .pandas_vb_common import setup # noqa
 
-# ----------------------------------------------------------------------
-# Creation from nested dict
 
 class FromDicts(object):
 
     goal_time = 0.2
 
     def setup(self):
-        np.random.seed(1234)
         N, K = 5000, 50
-        self.index = tm.makeStringIndex(N)
-        self.columns = tm.makeStringIndex(K)
-        self.frame = DataFrame(np.random.randn(N, K),
-                               index=self.index,
-                               columns=self.columns)
-        self.data = self.frame.to_dict()
+        index = tm.makeStringIndex(N)
+        columns = tm.makeStringIndex(K)
+        frame = DataFrame(np.random.randn(N, K), index=index, columns=columns)
+        self.data = frame.to_dict()
         self.some_dict = list(self.data.values())[0]
-        self.dict_list = self.frame.to_dict(orient='records')
+        self.dict_list = frame.to_dict(orient='records')
         self.data2 = {i: {j: float(j) for j in range(100)}
                       for i in range(2000)}
 
@@ -42,14 +38,13 @@ def time_frame_ctor_nested_dict_int64(self):
         DataFrame(self.data2)
 
 
-# from a mi-series
-
 class FromSeries(object):
+
     goal_time = 0.2
 
     def setup(self):
-        self.mi = MultiIndex.from_product([range(100), range(100)])
-        self.s = Series(np.random.randn(10000), index=self.mi)
+        mi = MultiIndex.from_product([range(100), range(100)])
+        self.s = Series(np.random.randn(10000), index=mi)
 
     def time_frame_from_mi_series(self):
         DataFrame(self.s)

asv_bench/benchmarks/frame_methods.py

@@ -3,7 +3,8 @@
 import pandas.util.testing as tm
 from pandas import (DataFrame, Series, MultiIndex, date_range, period_range,
                     isnull, NaT)
-from .pandas_vb_common import setup
+
+from .pandas_vb_common import setup # noqa
 
 
 class GetNumericData(object):