aoc-2022/venv/Lib/site-packages/pandas/plotting/_matplotlib/boxplot.py

540 lines
17 KiB
Python
Raw Normal View History

from __future__ import annotations
from typing import (
TYPE_CHECKING,
Literal,
NamedTuple,
)
import warnings
from matplotlib.artist import setp
import numpy as np
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import is_dict_like
from pandas.core.dtypes.missing import remove_na_arraylike
import pandas as pd
import pandas.core.common as com
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib.core import (
LinePlot,
MPLPlot,
)
from pandas.plotting._matplotlib.groupby import create_iter_data_given_by
from pandas.plotting._matplotlib.style import get_standard_colors
from pandas.plotting._matplotlib.tools import (
create_subplots,
flatten_axes,
maybe_adjust_figure,
)
if TYPE_CHECKING:
from matplotlib.axes import Axes
from matplotlib.lines import Line2D
class BoxPlot(LinePlot):
@property
def _kind(self) -> Literal["box"]:
return "box"
_layout_type = "horizontal"
_valid_return_types = (None, "axes", "dict", "both")
class BP(NamedTuple):
# namedtuple to hold results
ax: Axes
lines: dict[str, list[Line2D]]
def __init__(self, data, return_type: str = "axes", **kwargs) -> None:
# Do not call LinePlot.__init__ which may fill nan
if return_type not in self._valid_return_types:
raise ValueError("return_type must be {None, 'axes', 'dict', 'both'}")
self.return_type = return_type
MPLPlot.__init__(self, data, **kwargs)
def _args_adjust(self):
if self.subplots:
# Disable label ax sharing. Otherwise, all subplots shows last
# column label
if self.orientation == "vertical":
self.sharex = False
else:
self.sharey = False
@classmethod
def _plot(cls, ax, y, column_num=None, return_type="axes", **kwds):
if y.ndim == 2:
y = [remove_na_arraylike(v) for v in y]
# Boxplot fails with empty arrays, so need to add a NaN
# if any cols are empty
# GH 8181
y = [v if v.size > 0 else np.array([np.nan]) for v in y]
else:
y = remove_na_arraylike(y)
bp = ax.boxplot(y, **kwds)
if return_type == "dict":
return bp, bp
elif return_type == "both":
return cls.BP(ax=ax, lines=bp), bp
else:
return ax, bp
def _validate_color_args(self):
if "color" in self.kwds:
if self.colormap is not None:
warnings.warn(
"'color' and 'colormap' cannot be used "
"simultaneously. Using 'color'",
stacklevel=find_stack_level(),
)
self.color = self.kwds.pop("color")
if isinstance(self.color, dict):
valid_keys = ["boxes", "whiskers", "medians", "caps"]
for key in self.color:
if key not in valid_keys:
raise ValueError(
f"color dict contains invalid key '{key}'. "
f"The key must be either {valid_keys}"
)
else:
self.color = None
# get standard colors for default
colors = get_standard_colors(num_colors=3, colormap=self.colormap, color=None)
# use 2 colors by default, for box/whisker and median
# flier colors isn't needed here
# because it can be specified by ``sym`` kw
self._boxes_c = colors[0]
self._whiskers_c = colors[0]
self._medians_c = colors[2]
self._caps_c = colors[0]
def _get_colors(self, num_colors=None, color_kwds="color"):
pass
def maybe_color_bp(self, bp) -> None:
if isinstance(self.color, dict):
boxes = self.color.get("boxes", self._boxes_c)
whiskers = self.color.get("whiskers", self._whiskers_c)
medians = self.color.get("medians", self._medians_c)
caps = self.color.get("caps", self._caps_c)
else:
# Other types are forwarded to matplotlib
# If None, use default colors
boxes = self.color or self._boxes_c
whiskers = self.color or self._whiskers_c
medians = self.color or self._medians_c
caps = self.color or self._caps_c
# GH 30346, when users specifying those arguments explicitly, our defaults
# for these four kwargs should be overridden; if not, use Pandas settings
if not self.kwds.get("boxprops"):
setp(bp["boxes"], color=boxes, alpha=1)
if not self.kwds.get("whiskerprops"):
setp(bp["whiskers"], color=whiskers, alpha=1)
if not self.kwds.get("medianprops"):
setp(bp["medians"], color=medians, alpha=1)
if not self.kwds.get("capprops"):
setp(bp["caps"], color=caps, alpha=1)
def _make_plot(self):
if self.subplots:
self._return_obj = pd.Series(dtype=object)
# Re-create iterated data if `by` is assigned by users
data = (
create_iter_data_given_by(self.data, self._kind)
if self.by is not None
else self.data
)
for i, (label, y) in enumerate(self._iter_data(data=data)):
ax = self._get_ax(i)
kwds = self.kwds.copy()
# When by is applied, show title for subplots to know which group it is
# just like df.boxplot, and need to apply T on y to provide right input
if self.by is not None:
y = y.T
ax.set_title(pprint_thing(label))
# When `by` is assigned, the ticklabels will become unique grouped
# values, instead of label which is used as subtitle in this case.
ticklabels = [
pprint_thing(col) for col in self.data.columns.levels[0]
]
else:
ticklabels = [pprint_thing(label)]
ret, bp = self._plot(
ax, y, column_num=i, return_type=self.return_type, **kwds
)
self.maybe_color_bp(bp)
self._return_obj[label] = ret
self._set_ticklabels(ax, ticklabels)
else:
y = self.data.values.T
ax = self._get_ax(0)
kwds = self.kwds.copy()
ret, bp = self._plot(
ax, y, column_num=0, return_type=self.return_type, **kwds
)
self.maybe_color_bp(bp)
self._return_obj = ret
labels = [left for left, _ in self._iter_data()]
labels = [pprint_thing(left) for left in labels]
if not self.use_index:
labels = [pprint_thing(key) for key in range(len(labels))]
self._set_ticklabels(ax, labels)
def _set_ticklabels(self, ax: Axes, labels):
if self.orientation == "vertical":
ax.set_xticklabels(labels)
else:
ax.set_yticklabels(labels)
def _make_legend(self):
pass
def _post_plot_logic(self, ax, data):
# GH 45465: make sure that the boxplot doesn't ignore xlabel/ylabel
if self.xlabel:
ax.set_xlabel(pprint_thing(self.xlabel))
if self.ylabel:
ax.set_ylabel(pprint_thing(self.ylabel))
@property
def orientation(self):
if self.kwds.get("vert", True):
return "vertical"
else:
return "horizontal"
@property
def result(self):
if self.return_type is None:
return super().result
else:
return self._return_obj
def _grouped_plot_by_column(
plotf,
data,
columns=None,
by=None,
numeric_only=True,
grid=False,
figsize=None,
ax=None,
layout=None,
return_type=None,
**kwargs,
):
grouped = data.groupby(by)
if columns is None:
if not isinstance(by, (list, tuple)):
by = [by]
columns = data._get_numeric_data().columns.difference(by)
naxes = len(columns)
fig, axes = create_subplots(
naxes=naxes,
sharex=kwargs.pop("sharex", True),
sharey=kwargs.pop("sharey", True),
figsize=figsize,
ax=ax,
layout=layout,
)
_axes = flatten_axes(axes)
# GH 45465: move the "by" label based on "vert"
xlabel, ylabel = kwargs.pop("xlabel", None), kwargs.pop("ylabel", None)
if kwargs.get("vert", True):
xlabel = xlabel or by
else:
ylabel = ylabel or by
ax_values = []
for i, col in enumerate(columns):
ax = _axes[i]
gp_col = grouped[col]
keys, values = zip(*gp_col)
re_plotf = plotf(keys, values, ax, xlabel=xlabel, ylabel=ylabel, **kwargs)
ax.set_title(col)
ax_values.append(re_plotf)
ax.grid(grid)
result = pd.Series(ax_values, index=columns)
# Return axes in multiplot case, maybe revisit later # 985
if return_type is None:
result = axes
byline = by[0] if len(by) == 1 else by
fig.suptitle(f"Boxplot grouped by {byline}")
maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
return result
def boxplot(
data,
column=None,
by=None,
ax=None,
fontsize=None,
rot: int = 0,
grid: bool = True,
figsize=None,
layout=None,
return_type=None,
**kwds,
):
import matplotlib.pyplot as plt
# validate return_type:
if return_type not in BoxPlot._valid_return_types:
raise ValueError("return_type must be {'axes', 'dict', 'both'}")
if isinstance(data, pd.Series):
data = data.to_frame("x")
column = "x"
def _get_colors():
# num_colors=3 is required as method maybe_color_bp takes the colors
# in positions 0 and 2.
# if colors not provided, use same defaults as DataFrame.plot.box
result = get_standard_colors(num_colors=3)
result = np.take(result, [0, 0, 2])
result = np.append(result, "k")
colors = kwds.pop("color", None)
if colors:
if is_dict_like(colors):
# replace colors in result array with user-specified colors
# taken from the colors dict parameter
# "boxes" value placed in position 0, "whiskers" in 1, etc.
valid_keys = ["boxes", "whiskers", "medians", "caps"]
key_to_index = dict(zip(valid_keys, range(4)))
for key, value in colors.items():
if key in valid_keys:
result[key_to_index[key]] = value
else:
raise ValueError(
f"color dict contains invalid key '{key}'. "
f"The key must be either {valid_keys}"
)
else:
result.fill(colors)
return result
def maybe_color_bp(bp, **kwds):
# GH 30346, when users specifying those arguments explicitly, our defaults
# for these four kwargs should be overridden; if not, use Pandas settings
if not kwds.get("boxprops"):
setp(bp["boxes"], color=colors[0], alpha=1)
if not kwds.get("whiskerprops"):
setp(bp["whiskers"], color=colors[1], alpha=1)
if not kwds.get("medianprops"):
setp(bp["medians"], color=colors[2], alpha=1)
if not kwds.get("capprops"):
setp(bp["caps"], color=colors[3], alpha=1)
def plot_group(keys, values, ax: Axes, **kwds):
# GH 45465: xlabel/ylabel need to be popped out before plotting happens
xlabel, ylabel = kwds.pop("xlabel", None), kwds.pop("ylabel", None)
if xlabel:
ax.set_xlabel(pprint_thing(xlabel))
if ylabel:
ax.set_ylabel(pprint_thing(ylabel))
keys = [pprint_thing(x) for x in keys]
values = [np.asarray(remove_na_arraylike(v), dtype=object) for v in values]
bp = ax.boxplot(values, **kwds)
if fontsize is not None:
ax.tick_params(axis="both", labelsize=fontsize)
# GH 45465: x/y are flipped when "vert" changes
is_vertical = kwds.get("vert", True)
ticks = ax.get_xticks() if is_vertical else ax.get_yticks()
if len(ticks) != len(keys):
i, remainder = divmod(len(ticks), len(keys))
assert remainder == 0, remainder
keys *= i
if is_vertical:
ax.set_xticklabels(keys, rotation=rot)
else:
ax.set_yticklabels(keys, rotation=rot)
maybe_color_bp(bp, **kwds)
# Return axes in multiplot case, maybe revisit later # 985
if return_type == "dict":
return bp
elif return_type == "both":
return BoxPlot.BP(ax=ax, lines=bp)
else:
return ax
colors = _get_colors()
if column is None:
columns = None
else:
if isinstance(column, (list, tuple)):
columns = column
else:
columns = [column]
if by is not None:
# Prefer array return type for 2-D plots to match the subplot layout
# https://github.com/pandas-dev/pandas/pull/12216#issuecomment-241175580
result = _grouped_plot_by_column(
plot_group,
data,
columns=columns,
by=by,
grid=grid,
figsize=figsize,
ax=ax,
layout=layout,
return_type=return_type,
**kwds,
)
else:
if return_type is None:
return_type = "axes"
if layout is not None:
raise ValueError("The 'layout' keyword is not supported when 'by' is None")
if ax is None:
rc = {"figure.figsize": figsize} if figsize is not None else {}
with plt.rc_context(rc):
ax = plt.gca()
data = data._get_numeric_data()
naxes = len(data.columns)
if naxes == 0:
raise ValueError(
"boxplot method requires numerical columns, nothing to plot."
)
if columns is None:
columns = data.columns
else:
data = data[columns]
result = plot_group(columns, data.values.T, ax, **kwds)
ax.grid(grid)
return result
def boxplot_frame(
self,
column=None,
by=None,
ax=None,
fontsize=None,
rot: int = 0,
grid: bool = True,
figsize=None,
layout=None,
return_type=None,
**kwds,
):
import matplotlib.pyplot as plt
ax = boxplot(
self,
column=column,
by=by,
ax=ax,
fontsize=fontsize,
grid=grid,
rot=rot,
figsize=figsize,
layout=layout,
return_type=return_type,
**kwds,
)
plt.draw_if_interactive()
return ax
def boxplot_frame_groupby(
grouped,
subplots: bool = True,
column=None,
fontsize=None,
rot: int = 0,
grid: bool = True,
ax=None,
figsize=None,
layout=None,
sharex: bool = False,
sharey: bool = True,
**kwds,
):
if subplots is True:
naxes = len(grouped)
fig, axes = create_subplots(
naxes=naxes,
squeeze=False,
ax=ax,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout,
)
axes = flatten_axes(axes)
ret = pd.Series(dtype=object)
for (key, group), ax in zip(grouped, axes):
d = group.boxplot(
ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, **kwds
)
ax.set_title(pprint_thing(key))
ret.loc[key] = d
maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
else:
keys, frames = zip(*grouped)
if grouped.axis == 0:
df = pd.concat(frames, keys=keys, axis=1)
else:
if len(frames) > 1:
df = frames[0].join(frames[1::])
else:
df = frames[0]
# GH 16748, DataFrameGroupby fails when subplots=False and `column` argument
# is assigned, and in this case, since `df` here becomes MI after groupby,
# so we need to couple the keys (grouped values) and column (original df
# column) together to search for subset to plot
if column is not None:
column = com.convert_to_list_like(column)
multi_key = pd.MultiIndex.from_product([keys, column])
column = list(multi_key.values)
ret = df.boxplot(
column=column,
fontsize=fontsize,
rot=rot,
grid=grid,
ax=ax,
figsize=figsize,
layout=layout,
**kwds,
)
return ret