The issue appears to be related to the inverse relationship between price and marker size. The way the data is calculated in legend_elements doesn't account for this, and the calculation doesn't quite work. I've submitted a pull request.
The problem is in np.interp that expects increasing input for the second argument. Here is a work around for now that sorts the input first:
legend2 = ax.legend(*legend_elements(scatter, **kw),
loc="lower right", title="Price")
Run this after defining legend_elements as:
def legend_elements(self, prop="colors", num="auto",
fmt=None, func=lambda x: x, **kwargs):
"""
Creates legend handles and labels for a PathCollection. This is useful
for obtaining a legend for a :meth:`~.Axes.scatter` plot. E.g.::
scatter = plt.scatter([1, 2, 3], [4, 5, 6], c=[7, 2, 3])
plt.legend(*scatter.legend_elements())
Also see the :ref:`automatedlegendcreation` example.
Parameters
----------
prop : string, optional, default *"colors"*
Can be *"colors"* or *"sizes"*. In case of *"colors"*, the legend
handles will show the different colors of the collection. In case
of "sizes", the legend will show the different sizes.
num : int, None, "auto" (default), array-like, or `~.ticker.Locator`,
optional
Target number of elements to create.
If None, use all unique elements of the mappable array. If an
integer, target to use *num* elements in the normed range.
If *"auto"*, try to determine which option better suits the nature
of the data.
The number of created elements may slightly deviate from *num* due
to a `~.ticker.Locator` being used to find useful locations.
If a list or array, use exactly those elements for the legend.
Finally, a `~.ticker.Locator` can be provided.
fmt : str, `~matplotlib.ticker.Formatter`, or None (default)
The format or formatter to use for the labels. If a string must be
a valid input for a `~.StrMethodFormatter`. If None (the default),
use a `~.ScalarFormatter`.
func : function, default *lambda x: x*
Function to calculate the labels. Often the size (or color)
argument to :meth:`~.Axes.scatter` will have been pre-processed
by the user using a function *s = f(x)* to make the markers
visible; e.g. *size = np.log10(x)*. Providing the inverse of this
function here allows that pre-processing to be inverted, so that
the legend labels have the correct values;
e.g. *func = np.exp(x, 10)*.
kwargs : further parameters
Allowed keyword arguments are *color* and *size*. E.g. it may be
useful to set the color of the markers if *prop="sizes"* is used;
similarly to set the size of the markers if *prop="colors"* is
used. Any further parameters are passed onto the `.Line2D`
instance. This may be useful to e.g. specify a different
*markeredgecolor* or *alpha* for the legend handles.
Returns
-------
tuple (handles, labels)
with *handles* being a list of `.Line2D` objects
and *labels* a matching list of strings.
"""
handles = []
labels = []
hasarray = self.get_array() is not None
if fmt is None:
fmt = mpl.ticker.ScalarFormatter(useOffset=False, useMathText=True)
elif isinstance(fmt, str):
fmt = mpl.ticker.StrMethodFormatter(fmt)
fmt.create_dummy_axis()
if prop == "colors":
if not hasarray:
warnings.warn("Collection without array used. Make sure to "
"specify the values to be colormapped via the "
"`c` argument.")
return handles, labels
u = np.unique(self.get_array())
size = kwargs.pop("size", mpl.rcParams["lines.markersize"])
elif prop == "sizes":
u = np.unique(self.get_sizes())
color = kwargs.pop("color", "k")
else:
raise ValueError("Valid values for `prop` are 'colors' or "
f"'sizes'. You supplied '{prop}' instead.")
fmt.set_bounds(func(u).min(), func(u).max())
if num == "auto":
num = 9
if len(u) <= num:
num = None
if num is None:
values = u
label_values = func(values)
else:
if prop == "colors":
arr = self.get_array()
elif prop == "sizes":
arr = self.get_sizes()
if isinstance(num, mpl.ticker.Locator):
loc = num
elif np.iterable(num):
loc = mpl.ticker.FixedLocator(num)
else:
num = int(num)
loc = mpl.ticker.MaxNLocator(nbins=num, min_n_ticks=num-1,
steps=[1, 2, 2.5, 3, 5, 6, 8, 10])
label_values = loc.tick_values(func(arr).min(), func(arr).max())
cond = ((label_values >= func(arr).min()) &
(label_values <= func(arr).max()))
label_values = label_values[cond]
yarr = np.linspace(arr.min(), arr.max(), 256)
xarr = func(yarr)
ix = np.argsort(xarr)
values = np.interp(label_values, xarr[ix], yarr[ix])
kw = dict(markeredgewidth=self.get_linewidths()[0],
alpha=self.get_alpha())
kw.update(kwargs)
for val, lab in zip(values, label_values):
if prop == "colors":
color = self.cmap(self.norm(val))
elif prop == "sizes":
size = np.sqrt(val)
if np.isclose(size, 0.0):
continue
h = mlines.Line2D([0], [0], ls="", color=color, ms=size,
marker=self.get_paths()[0], **kw)
handles.append(h)
if hasattr(fmt, "set_locs"):
fmt.set_locs(label_values)
l = fmt(lab)
labels.append(l)
return handles, labels
