def __call__(
self,
data: DataFrame,
groupby: GroupBy,
orient: str,
scales: dict[str, Scale],
) -> DataFrame:
var = {"x": "y", "y": "x"}.get(orient)
res = (groupby.agg(data, {
var: self.func
}).dropna().reset_index(drop=True))
return res
def __call__(self, data: DataFrame, groupby: GroupBy,
orient: str) -> DataFrame:
grouping_vars = [v for v in groupby.order if v in data]
groups = groupby.agg(data, {"width": "max"})
if self.empty == "fill":
groups = groups.dropna()
def groupby_pos(s):
grouper = [groups[v] for v in [orient, "col", "row"] if v in data]
return s.groupby(grouper, sort=False, observed=True)
def scale_widths(w):
# TODO what value to fill missing widths??? Hard problem...
# TODO short circuit this if outer widths has no variance?
empty = 0 if self.empty == "fill" else w.mean()
filled = w.fillna(empty)
scale = filled.max()
norm = filled.sum()
if self.empty == "keep":
w = filled
return w / norm * scale
def widths_to_offsets(w):
return w.shift(1).fillna(0).cumsum() + (w - w.sum()) / 2
new_widths = groupby_pos(groups["width"]).transform(scale_widths)
offsets = groupby_pos(new_widths).transform(widths_to_offsets)
if self.gap:
new_widths *= 1 - self.gap
groups["_dodged"] = groups[orient] + offsets
groups["width"] = new_widths
out = (data.drop("width", axis=1).merge(
groups, on=grouping_vars,
how="left").drop(orient,
axis=1).rename(columns={"_dodged": orient}))
return out