diff --git a/asv_bench/benchmarks/algos/isin.py b/asv_bench/benchmarks/algos/isin.py
index 4b58981694014..296101c9f9800 100644
--- a/asv_bench/benchmarks/algos/isin.py
+++ b/asv_bench/benchmarks/algos/isin.py
@@ -50,8 +50,6 @@ def setup(self, dtype):
 
         elif dtype in ["category[object]", "category[int]"]:
             # Note: sizes are different in this case than others
-            np.random.seed(1234)
-
             n = 5 * 10 ** 5
             sample_size = 100
 
@@ -99,7 +97,6 @@ class IsinAlmostFullWithRandomInt:
     def setup(self, dtype, exponent, title):
         M = 3 * 2 ** (exponent - 2)
         # 0.77-the maximal share of occupied buckets
-        np.random.seed(42)
         self.series = Series(np.random.randint(0, M, M)).astype(dtype)
 
         values = np.random.randint(0, M, M).astype(dtype)
@@ -132,7 +129,6 @@ class IsinWithRandomFloat:
     param_names = ["dtype", "size", "title"]
 
     def setup(self, dtype, size, title):
-        np.random.seed(42)
         self.values = np.random.rand(size)
         self.series = Series(self.values).astype(dtype)
         np.random.shuffle(self.values)
@@ -179,7 +175,6 @@ class IsinWithArange:
 
     def setup(self, dtype, M, offset_factor):
         offset = int(M * offset_factor)
-        np.random.seed(42)
         tmp = Series(np.random.randint(offset, M + offset, 10 ** 6))
         self.series = tmp.astype(dtype)
         self.values = np.arange(M).astype(dtype)
@@ -290,10 +285,8 @@ def setup(self, dtype, MaxNumber, series_type):
             raise NotImplementedError
 
         if series_type == "random_hits":
-            np.random.seed(42)
             array = np.random.randint(0, MaxNumber, N)
         if series_type == "random_misses":
-            np.random.seed(42)
             array = np.random.randint(0, MaxNumber, N) + MaxNumber
         if series_type == "monotone_hits":
             array = np.repeat(np.arange(MaxNumber), N // MaxNumber)
@@ -322,7 +315,6 @@ def setup(self, dtype, series_type):
             raise NotImplementedError
 
         if series_type == "random":
-            np.random.seed(42)
             vals = np.random.randint(0, 10 * N, N)
         if series_type == "monotone":
             vals = np.arange(N)
diff --git a/asv_bench/benchmarks/frame_ctor.py b/asv_bench/benchmarks/frame_ctor.py
index 3367898101528..7fbe249788a98 100644
--- a/asv_bench/benchmarks/frame_ctor.py
+++ b/asv_bench/benchmarks/frame_ctor.py
@@ -67,7 +67,6 @@ class FromDictwithTimestamp:
 
     def setup(self, offset):
         N = 10 ** 3
-        np.random.seed(1234)
         idx = date_range(Timestamp("1/1/1900"), freq=offset, periods=N)
         df = DataFrame(np.random.randn(N, 10), index=idx)
         self.d = df.to_dict()
diff --git a/asv_bench/benchmarks/hash_functions.py b/asv_bench/benchmarks/hash_functions.py
index 394433f7c8f99..6703cc791493a 100644
--- a/asv_bench/benchmarks/hash_functions.py
+++ b/asv_bench/benchmarks/hash_functions.py
@@ -67,7 +67,6 @@ class NumericSeriesIndexingShuffled:
 
     def setup(self, index, N):
         vals = np.array(list(range(55)) + [54] + list(range(55, N - 1)))
-        np.random.seed(42)
         np.random.shuffle(vals)
         indices = index(vals)
         self.data = pd.Series(np.arange(N), index=indices)
diff --git a/asv_bench/benchmarks/indexing.py b/asv_bench/benchmarks/indexing.py
index 86790063c5056..10fb926ee4d03 100644
--- a/asv_bench/benchmarks/indexing.py
+++ b/asv_bench/benchmarks/indexing.py
@@ -368,17 +368,14 @@ def setup(self):
         self.df = DataFrame(index=range(self.N))
 
     def time_insert(self):
-        np.random.seed(1234)
         for i in range(100):
             self.df.insert(0, i, np.random.randn(self.N), allow_duplicates=True)
 
     def time_assign_with_setitem(self):
-        np.random.seed(1234)
         for i in range(100):
             self.df[i] = np.random.randn(self.N)
 
     def time_assign_list_like_with_setitem(self):
-        np.random.seed(1234)
         self.df[list(range(100))] = np.random.randn(self.N, 100)
 
     def time_assign_list_of_columns_concat(self):
diff --git a/asv_bench/benchmarks/series_methods.py b/asv_bench/benchmarks/series_methods.py
index d05a28e0873d0..7592ce54e3712 100644
--- a/asv_bench/benchmarks/series_methods.py
+++ b/asv_bench/benchmarks/series_methods.py
@@ -145,7 +145,6 @@ class Mode:
     param_names = ["N", "dtype"]
 
     def setup(self, N, dtype):
-        np.random.seed(42)
         self.s = Series(np.random.randint(0, N, size=10 * N)).astype(dtype)
 
     def time_mode(self, N, dtype):
diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 7cc171330e01a..1844cb863c183 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -77,6 +77,10 @@ if [[ -z "$CHECK" || "$CHECK" == "patterns" ]]; then
     invgrep -R --include="*.rst" -E "[a-zA-Z0-9]\`\`?[a-zA-Z0-9]" doc/source/
     RET=$(($RET + $?)) ; echo $MSG "DONE"
 
+    MSG='Check for unnecessary random seeds in asv benchmarks' ; echo $MSG
+    invgrep -R --exclude pandas_vb_common.py -E 'np.random.seed' asv_bench/benchmarks/
+    RET=$(($RET + $?)) ; echo $MSG "DONE"
+
 fi
 
 ### CODE ###