swev-id: matplotlib__matplotlib-26113 — Align hexbin mincnt behavior with and without C (>= mincnt)

lib/matplotlib/axes/_axes.py

@@ -4847,8 +4847,8 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
         yscale : {'linear', 'log'}, default: 'linear'
             Use a linear or log10 scale on the vertical axis.
 
-        mincnt : int > 0, default: *None*
-            If not *None*, only display cells with more than *mincnt*
+        mincnt : int >= 0, default: *None*
+            If not *None*, only display cells with at least *mincnt*
             number of points in the cell.
 
         marginals : bool, default: *False*
@@ -5012,9 +5012,10 @@ def reduce_C_function(C: array) -> float
                 else:
                     Cs_at_i2[i2[i]].append(C[i])
             if mincnt is None:
-                mincnt = 0
+                mincnt = 1
+            threshold = max(mincnt, 1)
             accum = np.array(
-                [reduce_C_function(acc) if len(acc) > mincnt else np.nan
+                [reduce_C_function(acc) if len(acc) >= threshold else np.nan
                  for Cs_at_i in [Cs_at_i1, Cs_at_i2]
                  for acc in Cs_at_i[1:]],  # [1:] drops out-of-range points.
                 float)

lib/matplotlib/tests/test_axes.py

@@ -999,6 +999,58 @@ def test_hexbin_log_clim():
     assert h.get_clim() == (2, 100)
 
 
+def test_hexbin_mincnt_counts():
+    x = np.array([0.1, 1.6])
+    y = np.array([0.1, 1.6])
+    fig, ax = plt.subplots()
+    coll = ax.hexbin(
+        x, y, gridsize=(2, 2), extent=(0, 2, 0, 2), mincnt=1)
+    assert_array_equal(coll.get_array(), np.array([1.0, 1.0]))
+
+
+def test_hexbin_mincnt_with_C():
+    x = np.array([0.1, 1.6])
+    y = np.array([0.1, 1.6])
+    fig, ax = plt.subplots()
+    coll = ax.hexbin(
+        x, y, C=np.ones_like(x), gridsize=(2, 2), extent=(0, 2, 0, 2),
+        mincnt=1, reduce_C_function=np.sum)
+    assert_array_equal(coll.get_array(), np.array([1.0, 1.0]))
+
+
+def test_hexbin_mincnt_excludes_when_below_threshold():
+    x = np.array([0.1, 1.6])
+    y = np.array([0.1, 1.6])
+    fig, ax = plt.subplots()
+    counts = ax.hexbin(
+        x, y, gridsize=(2, 2), extent=(0, 2, 0, 2), mincnt=2)
+    assert len(counts.get_array()) == 0
+
+    fig, ax = plt.subplots()
+    reduced = ax.hexbin(
+        x, y, C=np.ones_like(x), gridsize=(2, 2), extent=(0, 2, 0, 2),
+        mincnt=2, reduce_C_function=np.sum)
+    assert len(reduced.get_array()) == 0
+
+
+def test_hexbin_mincnt_zero_with_C_matches_default():
+    x = np.array([0.1, 1.6])
+    y = np.array([0.1, 1.6])
+    fig, ax = plt.subplots()
+    hb_zero = ax.hexbin(
+        x, y, C=np.ones_like(x), gridsize=(3, 3), extent=(0, 2, 0, 2),
+        mincnt=0, reduce_C_function=np.sum)
+
+    fig, ax = plt.subplots()
+    hb_default = ax.hexbin(
+        x, y, C=np.ones_like(x), gridsize=(3, 3), extent=(0, 2, 0, 2),
+        reduce_C_function=np.sum)
+
+    assert_array_equal(hb_zero.get_offsets(), hb_default.get_offsets())
+    assert_array_equal(hb_zero.get_array(), hb_default.get_array())
+    assert len(hb_zero.get_array()) == 2
+
+
 def test_inverted_limits():
     # Test gh:1553
     # Calling invert_xaxis prior to plotting should not disable autoscaling