Merge pull request numpy#19714 from shubham11941140/patch1

MAINT: Remove redundant Python2 float/int conversions

numpy/core/tests/test_datetime.py

@@ -152,7 +152,7 @@ def test_datetime_nat_argsort_stability(self, size):
         expected = np.arange(size)
         arr = np.tile(np.datetime64('NaT'), size)
         assert_equal(np.argsort(arr, kind='mergesort'), expected)
-    
+
     @pytest.mark.parametrize("size", [
         3, 21, 217, 1000])
     def test_timedelta_nat_argsort_stability(self, size):
@@ -1373,13 +1373,13 @@ def test_datetime_divide(self):
             assert_equal(tda / 0.5, tdc)
             assert_equal((tda / 0.5).dtype, np.dtype('m8[h]'))
             # m8 / m8
-            assert_equal(tda / tdb, 6.0 / 9.0)
-            assert_equal(np.divide(tda, tdb), 6.0 / 9.0)
-            assert_equal(np.true_divide(tda, tdb), 6.0 / 9.0)
-            assert_equal(tdb / tda, 9.0 / 6.0)
+            assert_equal(tda / tdb, 6 / 9)
+            assert_equal(np.divide(tda, tdb), 6 / 9)
+            assert_equal(np.true_divide(tda, tdb), 6 / 9)
+            assert_equal(tdb / tda, 9 / 6)
             assert_equal((tda / tdb).dtype, np.dtype('f8'))
-            assert_equal(tda / tdd, 60.0)
-            assert_equal(tdd / tda, 1.0 / 60.0)
+            assert_equal(tda / tdd, 60)
+            assert_equal(tdd / tda, 1 / 60)
 
             # int / m8
             assert_raises(TypeError, np.divide, 2, tdb)

numpy/core/tests/test_numeric.py

@@ -2381,7 +2381,7 @@ def test_clip_property(self, data, arr):
             shape=in_shapes[1], elements={"allow_nan": False}))
 
         # Then calculate our result and expected result and check that they're
-        # equal!  See gh-12519 and gh-19457 for discussion deciding on this 
+        # equal!  See gh-12519 and gh-19457 for discussion deciding on this
         # property and the result_type argument.
         result = np.clip(arr, amin, amax)
         t = np.result_type(arr, amin, amax)
@@ -2637,15 +2637,15 @@ def test_scalars(self):
 
     def test_ddof1(self):
         assert_almost_equal(np.var(self.A, ddof=1),
-                            self.real_var*len(self.A)/float(len(self.A)-1))
+                            self.real_var * len(self.A) / (len(self.A) - 1))
         assert_almost_equal(np.std(self.A, ddof=1)**2,
-                            self.real_var*len(self.A)/float(len(self.A)-1))
+                            self.real_var*len(self.A) / (len(self.A) - 1))
 
     def test_ddof2(self):
         assert_almost_equal(np.var(self.A, ddof=2),
-                            self.real_var*len(self.A)/float(len(self.A)-2))
+                            self.real_var * len(self.A) / (len(self.A) - 2))
         assert_almost_equal(np.std(self.A, ddof=2)**2,
-                            self.real_var*len(self.A)/float(len(self.A)-2))
+                            self.real_var * len(self.A) / (len(self.A) - 2))
 
     def test_out_scalar(self):
         d = np.arange(10)

numpy/fft/tests/test_pocketfft.py

@@ -10,7 +10,7 @@
 
 def fft1(x):
     L = len(x)
-    phase = -2j*np.pi*(np.arange(L)/float(L))
+    phase = -2j * np.pi * (np.arange(L) / L)
     phase = np.arange(L).reshape(-1, 1) * phase
     return np.sum(x*np.exp(phase), axis=1)
 

numpy/random/tests/test_generator_mt19937_regressions.py

@@ -32,11 +32,11 @@ def test_logseries_convergence(self):
         # these two frequency counts should be close to theoretical
         # numbers with this large sample
         # theoretical large N result is 0.49706795
-        freq = np.sum(rvsn == 1) / float(N)
+        freq = np.sum(rvsn == 1) / N
         msg = f'Frequency was {freq:f}, should be > 0.45'
         assert_(freq > 0.45, msg)
         # theoretical large N result is 0.19882718
-        freq = np.sum(rvsn == 2) / float(N)
+        freq = np.sum(rvsn == 2) / N
         msg = f'Frequency was {freq:f}, should be < 0.23'
         assert_(freq < 0.23, msg)
 

numpy/random/tests/test_randomstate_regression.py

@@ -43,11 +43,11 @@ def test_logseries_convergence(self):
         # these two frequency counts should be close to theoretical
         # numbers with this large sample
         # theoretical large N result is 0.49706795
-        freq = np.sum(rvsn == 1) / float(N)
+        freq = np.sum(rvsn == 1) / N
         msg = f'Frequency was {freq:f}, should be > 0.45'
         assert_(freq > 0.45, msg)
         # theoretical large N result is 0.19882718
-        freq = np.sum(rvsn == 2) / float(N)
+        freq = np.sum(rvsn == 2) / N
         msg = f'Frequency was {freq:f}, should be < 0.23'
         assert_(freq < 0.23, msg)
 

numpy/random/tests/test_regression.py

@@ -39,11 +39,11 @@ def test_logseries_convergence(self):
         # these two frequency counts should be close to theoretical
         # numbers with this large sample
         # theoretical large N result is 0.49706795
-        freq = np.sum(rvsn == 1) / float(N)
+        freq = np.sum(rvsn == 1) / N
         msg = f'Frequency was {freq:f}, should be > 0.45'
         assert_(freq > 0.45, msg)
         # theoretical large N result is 0.19882718
-        freq = np.sum(rvsn == 2) / float(N)
+        freq = np.sum(rvsn == 2) / N
         msg = f'Frequency was {freq:f}, should be < 0.23'
         assert_(freq < 0.23, msg)