Minor docstring updates on binning related plot functions

timhoffm · timhoffm · commit 9f225523e483 · 2018-03-18T19:17:50.000+01:00
diff --git a/lib/matplotlib/axes/_axes.py b/lib/matplotlib/axes/_axes.py
@@ -4193,7 +4193,7 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
         If *C* is specified, it specifies values at the coordinate
         (x[i],y[i]). These values are accumulated for each hexagonal
         bin and then reduced according to *reduce_C_function*, which
-        defaults to numpy's mean function (np.mean). (If *C* is
+        defaults to numpy's mean function (`numpy.mean`). (If *C* is
         specified, it must also be a 1-D sequence of the same length
         as *x* and *y*.)
 
@@ -4211,7 +4211,7 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
             tuple with two elements specifying the number of hexagons
             in the *x*-direction and the *y*-direction.
 
-        bins : {'log'} or int or sequence, optional, default is *None*
+        bins : 'log' or int or sequence, optional, default is *None*
             If *None*, no binning is applied; the color of each hexagon
             directly corresponds to its count value.
 
@@ -4287,11 +4287,9 @@ def hexbin(self, x, y, C=None, gridsize=100, bins=None,
 
         Returns
         -------
-        object
-            a :class:`~matplotlib.collections.PolyCollection` instance; use
-            :meth:`~matplotlib.collections.PolyCollection.get_array` on
-            this :class:`~matplotlib.collections.PolyCollection` to get
-            the counts in each hexagon.
+        polycollection
+            A `.PolyCollection` instance; use `.PolyCollection.get_array` on
+            this to get the counts in each hexagon.
 
             If *marginals* is *True*, horizontal
             bar and vertical bar (both PolyCollections) will be attached
@@ -6060,7 +6058,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
             Input values, this takes either a single array or a sequence of
             arrays which are not required to be of the same length
 
-        bins : integer or sequence or 'auto', optional
+        bins : int or sequence or 'auto', optional
             If an integer is given, ``bins + 1`` bin edges are calculated and
             returned, consistent with :func:`numpy.histogram`.
 
@@ -6094,7 +6092,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
 
             Default is ``None``
 
-        density : boolean, optional
+        density : bool, optional
             If ``True``, the first element of the return tuple will
             be the counts normalized to form a probability density, i.e.,
             the area (or integral) under the histogram will sum to 1.
@@ -6118,7 +6116,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
 
             Default is ``None``
 
-        cumulative : boolean, optional
+        cumulative : bool, optional
             If ``True``, then a histogram is computed where each bin gives the
             counts in that bin plus all bins for smaller values. The last bin
             gives the total number of datapoints. If *normed* or *density*
@@ -6178,7 +6176,7 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
 
             Default is ``None``
 
-        log : boolean, optional
+        log : bool, optional
             If ``True``, the histogram axis will be set to a log scale. If
             *log* is ``True`` and *x* is a 1D array, empty bins will be
             filtered out and only the non-empty ``(n, bins, patches)``
@@ -6192,14 +6190,14 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
 
             Default is ``None``
 
-        label : string or None, optional
+        label : str or None, optional
             String, or sequence of strings to match multiple datasets.  Bar
             charts yield multiple patches per dataset, but only the first gets
             the label, so that the legend command will work as expected.
 
             default is ``None``
 
-        stacked : boolean, optional
+        stacked : bool, optional
             If ``True``, multiple data are stacked on top of each other If
             ``False`` multiple data are arranged side by side if histtype is
             'bar' or on top of each other if histtype is 'step'
@@ -6553,10 +6551,10 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
 
         Parameters
         ----------
-        x, y: array_like, shape (n, )
+        x, y : array_like, shape (n, )
             Input values
 
-        bins: [None | int | [int, int] | array_like | [array, array]]
+        bins : None or int or [int, int] or array_like or [array, array]
 
             The bin specification:
 
@@ -6580,7 +6578,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
              xmax], [ymin, ymax]]. All values outside of this range will be
              considered outliers and not tallied in the histogram.
 
-        normed : boolean, optional, default: False
+        normed : bool, optional, default: False
              Normalize histogram.
 
         weights : array_like, shape (n, ), optional, default: None
@@ -6610,7 +6608,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
 
         Other Parameters
         ----------------
-        cmap : {Colormap, string}, optional
+        cmap : Colormap or str, optional
             A :class:`matplotlib.colors.Colormap` instance.  If not set, use rc
             settings.
 
@@ -6619,7 +6617,7 @@ def hist2d(self, x, y, bins=10, range=None, normed=False, weights=None,
             scale luminance data to ``[0, 1]``. If not set, defaults to
             ``Normalize()``.
 
-        vmin/vmax : {None, scalar}, optional
+        vmin/vmax : None or scalar, optional
             Arguments passed to the `Normalize` instance.
 
         alpha : ``0 <= scalar <= 1`` or ``None``, optional
