BENCH: Better usage of np.random

Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>

2 files changed, 4 insertions, 3 deletions

diff --git a/benchmarks/benchmarks/bench_lib.py b/benchmarks/benchmarks/bench_lib.py
index 72d988bdf..f792116a6 100644
--- a/benchmarks/benchmarks/bench_lib.py
+++ b/benchmarks/benchmarks/bench_lib.py
@@ -133,7 +133,7 @@ class Unique(Benchmark):
         # approximate desired percentage np.nan content
         base_array = np.random.uniform(size=array_size)
         n_nan = int(percent_nans * array_size)
-        nan_indices = np.random.randint(array_size, size=n_nan)
+        nan_indices = np.random.choice(np.arange(array_size), size=n_nan)
         base_array[nan_indices] = np.nan
         self.arr = base_array
 
diff --git a/benchmarks/benchmarks/bench_manipulate.py b/benchmarks/benchmarks/bench_manipulate.py
index 070ea9d27..5923a8cac 100644
--- a/benchmarks/benchmarks/bench_manipulate.py
+++ b/benchmarks/benchmarks/bench_manipulate.py
@@ -27,10 +27,11 @@ class BroadcastArraysTo(Benchmark):
     timeout = 10
 
     def setup(self, size, ndtype):
-        self.xarg = np.random.ranf(size)
+        self.rng = np.random.default_rng()
+        self.xarg = self.rng.random(size)
         self.xarg = self.xarg.astype(ndtype)
         if ndtype.startswith('complex'):
-            self.xarg += np.random.ranf(1)*1j
+            self.xarg += self.rng.random(1)*1j
 
     def time_broadcast_to(self, size, ndtype):
         np.broadcast_to(self.xarg, (size, size))