From 5b718a3e5404efae51b6fb31aae9c62d167f4fca Mon Sep 17 00:00:00 2001
From: Anatoly Myachev <anatoly.myachev@intel.com>
Date: Thu, 4 Feb 2021 13:00:48 +0300
Subject: [PATCH] TEST-#2686: reply to review comments

Signed-off-by: Anatoly Myachev <anatoly.myachev@intel.com>
---
 asv_bench/benchmarks/benchmarks.py | 32 ++++++++++--------------------
 1 file changed, 11 insertions(+), 21 deletions(-)

diff --git a/asv_bench/benchmarks/benchmarks.py b/asv_bench/benchmarks/benchmarks.py
index ce81149a5e0..d7bbf820601 100644
--- a/asv_bench/benchmarks/benchmarks.py
+++ b/asv_bench/benchmarks/benchmarks.py
@@ -49,6 +49,8 @@
     ASV_USE_IMPL = os.environ.get("MODIN_ASV_USE_IMPL", "modin")
     ASV_DATASET_SIZE = os.environ.get("MODIN_TEST_DATASET_SIZE", "Small")
 
+assert ASV_USE_IMPL in ("modin", "pandas")
+
 BINARY_OP_DATA_SIZE = {
     "Big": [
         ((5000, 5000), (5000, 5000)),
@@ -82,6 +84,11 @@
     "Small": 5,
 }
 
+IMPL = {
+    "modin": pd,
+    "pandas": pandas,
+}
+
 
 def execute(df):
     "Make sure the calculations are done."
@@ -217,12 +224,7 @@ def setup(self, shapes, how, axis):
         )
 
     def time_concat(self, shapes, how, axis):
-        if ASV_USE_IMPL == "modin":
-            execute(pd.concat([self.df1, self.df2], axis=axis, join=how))
-        elif ASV_USE_IMPL == "pandas":
-            execute(pandas.concat([self.df1, self.df2], axis=axis, join=how))
-        else:
-            raise NotImplementedError
+        execute(IMPL[ASV_USE_IMPL].concat([self.df1, self.df2], axis=axis, join=how))
 
 
 class TimeBinaryOp:
@@ -366,22 +368,10 @@ class TimeFillna:
     params = [UNARY_OP_DATA_SIZE[ASV_DATASET_SIZE], [None, 0.8]]
 
     def setup(self, shape, limit):
+        pd = IMPL[ASV_USE_IMPL]
         columns = [f"col{x}" for x in range(shape[1])]
-
-        if ASV_USE_IMPL == "modin":
-            self.df = pd.DataFrame(
-                np.nan, index=pd.RangeIndex(shape[0]), columns=columns
-            )
-        elif ASV_USE_IMPL == "pandas":
-            self.df = pandas.DataFrame(
-                np.nan, index=pandas.RangeIndex(shape[0]), columns=columns
-            )
-        else:
-            raise NotImplementedError
-
-        self.limit = None
-        if limit:
-            self.limit = int(limit * shape[0])
+        self.df = pd.DataFrame(np.nan, index=pd.RangeIndex(shape[0]), columns=columns)
+        self.limit = int(limit * shape[0]) if limit else None
 
     def time_fillna(self, shape, limit):
         execute(self.df.fillna(0, limit=self.limit))