diff --git a/lib/matplotlib/scale.py b/lib/matplotlib/scale.py
index 449368d8206c..f0182347f7e8 100644
--- a/lib/matplotlib/scale.py
+++ b/lib/matplotlib/scale.py
@@ -485,18 +485,14 @@ def __init__(self, base, linthresh, linscale):
         self._log_base = np.log(base)
 
     def transform_non_affine(self, a):
-        sign = np.sign(a)
-        masked = ma.masked_inside(a,
-                                  -self.linthresh,
-                                  self.linthresh,
-                                  copy=False)
-        log = sign * self.linthresh * (
-            self._linscale_adj +
-            ma.log(np.abs(masked) / self.linthresh) / self._log_base)
-        if masked.mask.any():
-            return ma.where(masked.mask, a * self._linscale_adj, log)
-        else:
-            return log
+        abs_a = np.abs(a)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            out = np.sign(a) * self.linthresh * (
+                self._linscale_adj +
+                np.log(abs_a / self.linthresh) / self._log_base)
+            inside = abs_a <= self.linthresh
+        out[inside] = a[inside] * self._linscale_adj
+        return out
 
     def inverted(self):
         return InvertedSymmetricalLogTransform(self.base, self.linthresh,
@@ -519,16 +515,14 @@ def __init__(self, base, linthresh, linscale):
         self._linscale_adj = (linscale / (1.0 - self.base ** -1))
 
     def transform_non_affine(self, a):
-        sign = np.sign(a)
-        masked = ma.masked_inside(a, -self.invlinthresh,
-                                  self.invlinthresh, copy=False)
-        exp = sign * self.linthresh * (
-            ma.power(self.base, (sign * (masked / self.linthresh))
-            - self._linscale_adj))
-        if masked.mask.any():
-            return ma.where(masked.mask, a / self._linscale_adj, exp)
-        else:
-            return exp
+        abs_a = np.abs(a)
+        with np.errstate(divide="ignore", invalid="ignore"):
+            out = np.sign(a) * self.linthresh * (
+                np.power(self.base,
+                         abs_a / self.linthresh - self._linscale_adj))
+            inside = abs_a <= self.invlinthresh
+        out[inside] = a[inside] / self._linscale_adj
+        return out
 
     def inverted(self):
         return SymmetricalLogTransform(self.base,
diff --git a/lib/matplotlib/tests/test_scale.py b/lib/matplotlib/tests/test_scale.py
index c0ca6230ed33..3eee976a3e7f 100644
--- a/lib/matplotlib/tests/test_scale.py
+++ b/lib/matplotlib/tests/test_scale.py
@@ -1,9 +1,11 @@
 from matplotlib.cbook import MatplotlibDeprecationWarning
 import matplotlib.pyplot as plt
-from matplotlib.scale import Log10Transform, InvertedLog10Transform
+from matplotlib.scale import (Log10Transform, InvertedLog10Transform,
+                              SymmetricalLogTransform)
 from matplotlib.testing.decorators import check_figures_equal, image_comparison
 
 import numpy as np
+from numpy.testing import assert_allclose
 import io
 import platform
 import pytest
@@ -22,6 +24,33 @@ def test_log_scales(fig_test, fig_ref):
     ax_ref.plot(xlim, [24.1, 24.1], 'b')
 
 
+def test_symlog_mask_nan():
+    # Use a transform round-trip to verify that the forward and inverse
+    # transforms work, and that they respect nans and/or masking.
+    slt = SymmetricalLogTransform(10, 2, 1)
+    slti = slt.inverted()
+
+    x = np.arange(-1.5, 5, 0.5)
+    out = slti.transform_non_affine(slt.transform_non_affine(x))
+    assert_allclose(out, x)
+    assert type(out) == type(x)
+
+    x[4] = np.nan
+    out = slti.transform_non_affine(slt.transform_non_affine(x))
+    assert_allclose(out, x)
+    assert type(out) == type(x)
+
+    x = np.ma.array(x)
+    out = slti.transform_non_affine(slt.transform_non_affine(x))
+    assert_allclose(out, x)
+    assert type(out) == type(x)
+
+    x[3] = np.ma.masked
+    out = slti.transform_non_affine(slt.transform_non_affine(x))
+    assert_allclose(out, x)
+    assert type(out) == type(x)
+
+
 @image_comparison(['logit_scales.png'], remove_text=True)
 def test_logit_scales():
     fig, ax = plt.subplots()