def __call__(
self,
data: DataFrame,
groupby: GroupBy,
orient: str,
scales: dict[str, Scale],
) -> DataFrame:
boot_kws = {"n_boot": self.n_boot, "seed": self.seed}
engine = EstimateAggregator(self.func, self.errorbar, **boot_kws)
var = {"x": "y", "y": "x"}.get(orient)
res = (groupby.apply(
data, self._process, var,
engine).dropna(subset=["x", "y"]).reset_index(drop=True))
res = res.fillna({f"{var}min": res[var], f"{var}max": res[var]})
return res
def __call__(self, data, groupby, orient, scales):
# TODO better to do this as an isinstance check?
# We are only asking about Nominal scales now,
# but presumably would apply to Ordinal too?
scale_type = scales[orient].__class__.__name__.lower()
grouping_vars = [v for v in data if v in groupby.order]
if not grouping_vars or self.common_bins is True:
bin_kws = self._define_bin_params(data, orient, scale_type)
data = groupby.apply(data, self._eval, orient, bin_kws)
else:
if self.common_bins is False:
bin_groupby = GroupBy(grouping_vars)
else:
bin_groupby = GroupBy(self.common_bins)
data = bin_groupby.apply(
data,
self._get_bins_and_eval,
orient,
groupby,
scale_type,
)
# TODO Make this an option?
# (This needs to be tested if enabled, and maybe should be in _eval)
# other = {"x": "y", "y": "x"}[orient]
# data = data[data[other] > 0]
if not grouping_vars or self.common_norm is True:
data = self._normalize(data, orient)
else:
if self.common_norm is False:
norm_grouper = grouping_vars
else:
norm_grouper = self.common_norm
normalize = partial(self._normalize, orient=orient)
data = GroupBy(norm_grouper).apply(data, normalize)
return data
def __call__(self, data, groupby, orient, scales):
scale_type = scales[orient].scale_type
grouping_vars = [v for v in data if v in groupby.order]
if not grouping_vars or self.common_bins is True:
bin_kws = self._define_bin_params(data, orient, scale_type)
data = groupby.apply(data, self._eval, orient, bin_kws)
else:
if self.common_bins is False:
bin_groupby = GroupBy(grouping_vars)
else:
bin_groupby = GroupBy(self.common_bins)
data = bin_groupby.apply(
data,
self._get_bins_and_eval,
orient,
groupby,
scale_type,
)
# TODO Make this an option?
# (This needs to be tested if enabled, and maybe should be in _eval)
# other = {"x": "y", "y": "x"}[orient]
# data = data[data[other] > 0]
if not grouping_vars or self.common_norm is True:
data = self._normalize(data, orient)
else:
if self.common_norm is False:
norm_grouper = grouping_vars
else:
norm_grouper = self.common_norm
normalize = partial(self._normalize, orient=orient)
data = GroupBy(norm_grouper).apply(data, normalize)
return data
def __call__(self, data: DataFrame, groupby: GroupBy,
orient: str) -> DataFrame:
other = {"x": "y", "y": "x"}[orient]
return groupby.apply(data, self._norm, other)