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506 lines
16 KiB
506 lines
16 KiB
from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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NamedTuple,
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)
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import warnings
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from matplotlib.artist import setp
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import numpy as np
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from pandas.core.dtypes.common import is_dict_like
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from pandas.core.dtypes.missing import remove_na_arraylike
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import pandas as pd
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import pandas.core.common as com
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from pandas.io.formats.printing import pprint_thing
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from pandas.plotting._matplotlib.core import (
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LinePlot,
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MPLPlot,
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)
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from pandas.plotting._matplotlib.groupby import create_iter_data_given_by
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from pandas.plotting._matplotlib.style import get_standard_colors
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from pandas.plotting._matplotlib.tools import (
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create_subplots,
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flatten_axes,
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maybe_adjust_figure,
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)
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if TYPE_CHECKING:
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from matplotlib.axes import Axes
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from matplotlib.lines import Line2D
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class BoxPlot(LinePlot):
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_kind = "box"
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_layout_type = "horizontal"
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_valid_return_types = (None, "axes", "dict", "both")
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class BP(NamedTuple):
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# namedtuple to hold results
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ax: Axes
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lines: dict[str, list[Line2D]]
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def __init__(self, data, return_type="axes", **kwargs):
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# Do not call LinePlot.__init__ which may fill nan
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if return_type not in self._valid_return_types:
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raise ValueError("return_type must be {None, 'axes', 'dict', 'both'}")
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self.return_type = return_type
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MPLPlot.__init__(self, data, **kwargs)
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def _args_adjust(self):
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if self.subplots:
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# Disable label ax sharing. Otherwise, all subplots shows last
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# column label
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if self.orientation == "vertical":
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self.sharex = False
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else:
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self.sharey = False
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@classmethod
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def _plot(cls, ax, y, column_num=None, return_type="axes", **kwds):
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if y.ndim == 2:
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y = [remove_na_arraylike(v) for v in y]
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# Boxplot fails with empty arrays, so need to add a NaN
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# if any cols are empty
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# GH 8181
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y = [v if v.size > 0 else np.array([np.nan]) for v in y]
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else:
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y = remove_na_arraylike(y)
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bp = ax.boxplot(y, **kwds)
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if return_type == "dict":
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return bp, bp
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elif return_type == "both":
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return cls.BP(ax=ax, lines=bp), bp
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else:
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return ax, bp
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def _validate_color_args(self):
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if "color" in self.kwds:
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if self.colormap is not None:
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warnings.warn(
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"'color' and 'colormap' cannot be used "
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"simultaneously. Using 'color'"
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)
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self.color = self.kwds.pop("color")
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if isinstance(self.color, dict):
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valid_keys = ["boxes", "whiskers", "medians", "caps"]
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for key in self.color:
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if key not in valid_keys:
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raise ValueError(
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f"color dict contains invalid key '{key}'. "
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f"The key must be either {valid_keys}"
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)
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else:
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self.color = None
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# get standard colors for default
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colors = get_standard_colors(num_colors=3, colormap=self.colormap, color=None)
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# use 2 colors by default, for box/whisker and median
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# flier colors isn't needed here
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# because it can be specified by ``sym`` kw
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self._boxes_c = colors[0]
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self._whiskers_c = colors[0]
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self._medians_c = colors[2]
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self._caps_c = colors[0]
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def _get_colors(self, num_colors=None, color_kwds="color"):
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pass
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def maybe_color_bp(self, bp):
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if isinstance(self.color, dict):
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boxes = self.color.get("boxes", self._boxes_c)
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whiskers = self.color.get("whiskers", self._whiskers_c)
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medians = self.color.get("medians", self._medians_c)
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caps = self.color.get("caps", self._caps_c)
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else:
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# Other types are forwarded to matplotlib
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# If None, use default colors
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boxes = self.color or self._boxes_c
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whiskers = self.color or self._whiskers_c
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medians = self.color or self._medians_c
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caps = self.color or self._caps_c
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# GH 30346, when users specifying those arguments explicitly, our defaults
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# for these four kwargs should be overridden; if not, use Pandas settings
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if not self.kwds.get("boxprops"):
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setp(bp["boxes"], color=boxes, alpha=1)
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if not self.kwds.get("whiskerprops"):
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setp(bp["whiskers"], color=whiskers, alpha=1)
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if not self.kwds.get("medianprops"):
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setp(bp["medians"], color=medians, alpha=1)
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if not self.kwds.get("capprops"):
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setp(bp["caps"], color=caps, alpha=1)
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def _make_plot(self):
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if self.subplots:
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self._return_obj = pd.Series(dtype=object)
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# Re-create iterated data if `by` is assigned by users
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data = (
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create_iter_data_given_by(self.data, self._kind)
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if self.by is not None
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else self.data
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)
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for i, (label, y) in enumerate(self._iter_data(data=data)):
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ax = self._get_ax(i)
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kwds = self.kwds.copy()
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# When by is applied, show title for subplots to know which group it is
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# just like df.boxplot, and need to apply T on y to provide right input
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if self.by is not None:
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y = y.T
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ax.set_title(pprint_thing(label))
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# When `by` is assigned, the ticklabels will become unique grouped
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# values, instead of label which is used as subtitle in this case.
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ticklabels = [
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pprint_thing(col) for col in self.data.columns.levels[0]
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]
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else:
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ticklabels = [pprint_thing(label)]
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ret, bp = self._plot(
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ax, y, column_num=i, return_type=self.return_type, **kwds
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)
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self.maybe_color_bp(bp)
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self._return_obj[label] = ret
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self._set_ticklabels(ax, ticklabels)
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else:
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y = self.data.values.T
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ax = self._get_ax(0)
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kwds = self.kwds.copy()
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ret, bp = self._plot(
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ax, y, column_num=0, return_type=self.return_type, **kwds
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)
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self.maybe_color_bp(bp)
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self._return_obj = ret
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labels = [left for left, _ in self._iter_data()]
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labels = [pprint_thing(left) for left in labels]
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if not self.use_index:
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labels = [pprint_thing(key) for key in range(len(labels))]
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self._set_ticklabels(ax, labels)
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def _set_ticklabels(self, ax: Axes, labels):
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if self.orientation == "vertical":
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ax.set_xticklabels(labels)
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else:
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ax.set_yticklabels(labels)
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def _make_legend(self):
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pass
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def _post_plot_logic(self, ax, data):
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pass
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@property
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def orientation(self):
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if self.kwds.get("vert", True):
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return "vertical"
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else:
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return "horizontal"
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@property
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def result(self):
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if self.return_type is None:
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return super().result
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else:
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return self._return_obj
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def _grouped_plot_by_column(
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plotf,
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data,
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columns=None,
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by=None,
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numeric_only=True,
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grid=False,
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figsize=None,
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ax=None,
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layout=None,
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return_type=None,
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**kwargs,
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):
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grouped = data.groupby(by)
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if columns is None:
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if not isinstance(by, (list, tuple)):
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by = [by]
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columns = data._get_numeric_data().columns.difference(by)
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naxes = len(columns)
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fig, axes = create_subplots(
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naxes=naxes, sharex=True, sharey=True, figsize=figsize, ax=ax, layout=layout
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)
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_axes = flatten_axes(axes)
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ax_values = []
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for i, col in enumerate(columns):
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ax = _axes[i]
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gp_col = grouped[col]
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keys, values = zip(*gp_col)
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re_plotf = plotf(keys, values, ax, **kwargs)
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ax.set_title(col)
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ax.set_xlabel(pprint_thing(by))
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ax_values.append(re_plotf)
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ax.grid(grid)
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result = pd.Series(ax_values, index=columns)
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# Return axes in multiplot case, maybe revisit later # 985
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if return_type is None:
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result = axes
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byline = by[0] if len(by) == 1 else by
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fig.suptitle(f"Boxplot grouped by {byline}")
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maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
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return result
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def boxplot(
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data,
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column=None,
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by=None,
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ax=None,
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fontsize=None,
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rot=0,
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grid=True,
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figsize=None,
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layout=None,
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return_type=None,
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**kwds,
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):
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import matplotlib.pyplot as plt
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# validate return_type:
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if return_type not in BoxPlot._valid_return_types:
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raise ValueError("return_type must be {'axes', 'dict', 'both'}")
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if isinstance(data, pd.Series):
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data = data.to_frame("x")
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column = "x"
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def _get_colors():
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# num_colors=3 is required as method maybe_color_bp takes the colors
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# in positions 0 and 2.
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# if colors not provided, use same defaults as DataFrame.plot.box
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result = get_standard_colors(num_colors=3)
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result = np.take(result, [0, 0, 2])
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result = np.append(result, "k")
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colors = kwds.pop("color", None)
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if colors:
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if is_dict_like(colors):
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# replace colors in result array with user-specified colors
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# taken from the colors dict parameter
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# "boxes" value placed in position 0, "whiskers" in 1, etc.
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valid_keys = ["boxes", "whiskers", "medians", "caps"]
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key_to_index = dict(zip(valid_keys, range(4)))
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for key, value in colors.items():
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if key in valid_keys:
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result[key_to_index[key]] = value
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else:
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raise ValueError(
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f"color dict contains invalid key '{key}'. "
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f"The key must be either {valid_keys}"
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)
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else:
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result.fill(colors)
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return result
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def maybe_color_bp(bp, **kwds):
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# GH 30346, when users specifying those arguments explicitly, our defaults
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# for these four kwargs should be overridden; if not, use Pandas settings
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if not kwds.get("boxprops"):
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setp(bp["boxes"], color=colors[0], alpha=1)
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if not kwds.get("whiskerprops"):
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setp(bp["whiskers"], color=colors[1], alpha=1)
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if not kwds.get("medianprops"):
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setp(bp["medians"], color=colors[2], alpha=1)
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if not kwds.get("capprops"):
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setp(bp["caps"], color=colors[3], alpha=1)
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def plot_group(keys, values, ax: Axes):
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keys = [pprint_thing(x) for x in keys]
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values = [np.asarray(remove_na_arraylike(v), dtype=object) for v in values]
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bp = ax.boxplot(values, **kwds)
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if fontsize is not None:
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ax.tick_params(axis="both", labelsize=fontsize)
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if kwds.get("vert", 1):
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ticks = ax.get_xticks()
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if len(ticks) != len(keys):
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i, remainder = divmod(len(ticks), len(keys))
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assert remainder == 0, remainder
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keys *= i
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ax.set_xticklabels(keys, rotation=rot)
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else:
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ax.set_yticklabels(keys, rotation=rot)
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maybe_color_bp(bp, **kwds)
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# Return axes in multiplot case, maybe revisit later # 985
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if return_type == "dict":
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return bp
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elif return_type == "both":
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return BoxPlot.BP(ax=ax, lines=bp)
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else:
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return ax
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colors = _get_colors()
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if column is None:
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columns = None
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else:
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if isinstance(column, (list, tuple)):
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columns = column
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else:
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columns = [column]
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if by is not None:
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# Prefer array return type for 2-D plots to match the subplot layout
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# https://github.com/pandas-dev/pandas/pull/12216#issuecomment-241175580
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result = _grouped_plot_by_column(
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plot_group,
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data,
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columns=columns,
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by=by,
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grid=grid,
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figsize=figsize,
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ax=ax,
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layout=layout,
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return_type=return_type,
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)
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else:
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if return_type is None:
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return_type = "axes"
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if layout is not None:
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raise ValueError("The 'layout' keyword is not supported when 'by' is None")
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if ax is None:
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rc = {"figure.figsize": figsize} if figsize is not None else {}
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with plt.rc_context(rc):
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ax = plt.gca()
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data = data._get_numeric_data()
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naxes = len(data.columns)
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if naxes == 0:
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raise ValueError(
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"boxplot method requires numerical columns, nothing to plot."
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)
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if columns is None:
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columns = data.columns
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else:
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data = data[columns]
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result = plot_group(columns, data.values.T, ax)
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ax.grid(grid)
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return result
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def boxplot_frame(
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self,
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column=None,
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by=None,
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ax=None,
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fontsize=None,
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rot=0,
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grid=True,
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figsize=None,
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layout=None,
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return_type=None,
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**kwds,
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):
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import matplotlib.pyplot as plt
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ax = boxplot(
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self,
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column=column,
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by=by,
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ax=ax,
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fontsize=fontsize,
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grid=grid,
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rot=rot,
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figsize=figsize,
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layout=layout,
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return_type=return_type,
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**kwds,
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)
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plt.draw_if_interactive()
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return ax
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def boxplot_frame_groupby(
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grouped,
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subplots=True,
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column=None,
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fontsize=None,
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rot=0,
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grid=True,
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ax=None,
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figsize=None,
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layout=None,
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sharex=False,
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sharey=True,
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**kwds,
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):
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if subplots is True:
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naxes = len(grouped)
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fig, axes = create_subplots(
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naxes=naxes,
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squeeze=False,
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ax=ax,
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sharex=sharex,
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sharey=sharey,
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figsize=figsize,
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layout=layout,
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)
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axes = flatten_axes(axes)
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ret = pd.Series(dtype=object)
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for (key, group), ax in zip(grouped, axes):
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d = group.boxplot(
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ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, **kwds
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)
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ax.set_title(pprint_thing(key))
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ret.loc[key] = d
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maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
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else:
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keys, frames = zip(*grouped)
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if grouped.axis == 0:
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df = pd.concat(frames, keys=keys, axis=1)
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else:
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if len(frames) > 1:
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df = frames[0].join(frames[1::])
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else:
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df = frames[0]
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# GH 16748, DataFrameGroupby fails when subplots=False and `column` argument
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# is assigned, and in this case, since `df` here becomes MI after groupby,
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# so we need to couple the keys (grouped values) and column (original df
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# column) together to search for subset to plot
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if column is not None:
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column = com.convert_to_list_like(column)
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multi_key = pd.MultiIndex.from_product([keys, column])
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column = list(multi_key.values)
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ret = df.boxplot(
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column=column,
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fontsize=fontsize,
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rot=rot,
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grid=grid,
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ax=ax,
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figsize=figsize,
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layout=layout,
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**kwds,
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)
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return ret
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