ENH: Expose symlog scaling in plotting API

doc/source/whatsnew/v0.25.0.rst

@@ -18,7 +18,7 @@ including other versions of pandas.
 
 Other Enhancements
 ^^^^^^^^^^^^^^^^^^
-
+- :func:`DataFrame.plot` keywords ``logy``, ``logx`` and ``loglog`` can now accept the value ``'sym'`` for symlog scaling. (:issue:`24867`)
 - Indexing of ``DataFrame`` and ``Series`` now accepts zerodim ``np.ndarray`` (:issue:`24919`)
 - :meth:`Timestamp.replace` now supports the ``fold`` argument to disambiguate DST transition times (:issue:`25017`)
 - :meth:`DataFrame.at_time` and :meth:`Series.at_time` now support :meth:`datetime.time` objects with timezones (:issue:`24043`)

pandas/plotting/_core.py

@@ -308,10 +308,21 @@ def _setup_subplots(self):
 
         axes = _flatten(axes)
 
-        if self.logx or self.loglog:
+        valid_log = {False, True, 'sym', None}
+        input_log = {self.logx, self.logy, self.loglog}
+        if input_log - valid_log:
+            raise ValueError(f"Valid inputs are boolean, None and 'sym'"
+                             f", {i} is given.")
+
+        if self.logx is True or self.loglog is True:
             [a.set_xscale('log') for a in axes]
-        if self.logy or self.loglog:
+        elif self.logx == 'sym' or self.loglog == 'sym':
+            [a.set_xscale('symlog') for a in axes]
+
+        if self.logy is True or self.loglog is True:
             [a.set_yscale('log') for a in axes]
+        elif self.logy == 'sym' or self.loglog == 'sym':
+            [a.set_yscale('symlog') for a in axes]
 
         self.fig = fig
         self.axes = axes

pandas/tests/plotting/test_frame.py

@@ -231,14 +231,42 @@ def test_plot_xy(self):
     @pytest.mark.slow
     def test_logscales(self):
         df = DataFrame({'a': np.arange(100)}, index=np.arange(100))
+
         ax = df.plot(logy=True)
         self._check_ax_scales(ax, yaxis='log')
+        assert ax.get_yscale() == 'log'
+
+        ax = df.plot(logy='sym')
+        self._check_ax_scales(ax, yaxis='symlog')
+        assert ax.get_yscale() == 'symlog'
 
         ax = df.plot(logx=True)
         self._check_ax_scales(ax, xaxis='log')
+        assert ax.get_xscale() == 'log'
+
+        ax = df.plot(logx='sym')
+        self._check_ax_scales(ax, xaxis='symlog')
+        assert ax.get_xscale() == 'symlog'
 
         ax = df.plot(loglog=True)
         self._check_ax_scales(ax, xaxis='log', yaxis='log')
+        assert ax.get_xscale() == 'log'
+        assert ax.get_yscale() == 'log'
+
+        ax = df.plot(loglog='sym')
+        self._check_ax_scales(ax, xaxis='symlog', yaxis='symlog')
+        assert ax.get_xscale() == 'symlog'
+        assert ax.get_yscale() == 'symlog'
+
+    @pytest.mark.parametrize("wrong_input", ["sm", "symlog"])
+    @pytest.mark.parametrize("input_param", ["logx", "logy", "loglog"])
+    def test_invalid_logscale(self, wrong_input, input_param):
+        # GH: 24867
+        df = DataFrame({'a': np.arange(100)}, index=np.arange(100))
+
+        msg = "Valid inputs are boolean, None and 'sym'"
+        with pytest.raises(ValueError, match=msg):
+            df.plot(**{input_param: wrong_input})
 
     @pytest.mark.slow
     def test_xcompat(self):