532 lines
14 KiB
Python
532 lines
14 KiB
Python
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from __future__ import annotations
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from typing import (
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TYPE_CHECKING,
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Literal,
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)
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import numpy as np
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from pandas._typing import PlottingOrientation
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from pandas.core.dtypes.common import (
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is_integer,
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is_list_like,
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)
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from pandas.core.dtypes.generic import (
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ABCDataFrame,
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ABCIndex,
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)
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from pandas.core.dtypes.missing import (
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isna,
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remove_na_arraylike,
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)
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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 (
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create_iter_data_given_by,
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reformat_hist_y_given_by,
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)
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from pandas.plotting._matplotlib.misc import unpack_single_str_list
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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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set_ticks_props,
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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 pandas import DataFrame
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class HistPlot(LinePlot):
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@property
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def _kind(self) -> Literal["hist", "kde"]:
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return "hist"
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def __init__(
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self,
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data,
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bins: int | np.ndarray | list[np.ndarray] = 10,
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bottom: int | np.ndarray = 0,
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**kwargs,
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) -> None:
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self.bins = bins # use mpl default
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self.bottom = bottom
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# Do not call LinePlot.__init__ which may fill nan
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MPLPlot.__init__(self, data, **kwargs)
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def _args_adjust(self):
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# calculate bin number separately in different subplots
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# where subplots are created based on by argument
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if is_integer(self.bins):
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if self.by is not None:
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by_modified = unpack_single_str_list(self.by)
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grouped = self.data.groupby(by_modified)[self.columns]
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self.bins = [self._calculate_bins(group) for key, group in grouped]
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else:
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self.bins = self._calculate_bins(self.data)
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if is_list_like(self.bottom):
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self.bottom = np.array(self.bottom)
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def _calculate_bins(self, data: DataFrame) -> np.ndarray:
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"""Calculate bins given data"""
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nd_values = data._convert(datetime=True)._get_numeric_data()
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values = np.ravel(nd_values)
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values = values[~isna(values)]
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hist, bins = np.histogram(
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values, bins=self.bins, range=self.kwds.get("range", None)
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)
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return bins
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@classmethod
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def _plot(
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cls,
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ax,
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y,
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style=None,
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bins=None,
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bottom=0,
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column_num=0,
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stacking_id=None,
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**kwds,
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):
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if column_num == 0:
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cls._initialize_stacker(ax, stacking_id, len(bins) - 1)
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base = np.zeros(len(bins) - 1)
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bottom = bottom + cls._get_stacked_values(ax, stacking_id, base, kwds["label"])
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# ignore style
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n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)
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cls._update_stacker(ax, stacking_id, n)
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return patches
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def _make_plot(self):
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colors = self._get_colors()
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stacking_id = self._get_stacking_id()
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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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label = pprint_thing(label)
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label = self._mark_right_label(label, index=i)
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kwds["label"] = label
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style, kwds = self._apply_style_colors(colors, kwds, i, label)
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if style is not None:
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kwds["style"] = style
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kwds = self._make_plot_keywords(kwds, y)
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# the bins is multi-dimension array now and each plot need only 1-d and
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# when by is applied, label should be columns that are grouped
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if self.by is not None:
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kwds["bins"] = kwds["bins"][i]
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kwds["label"] = self.columns
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kwds.pop("color")
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y = reformat_hist_y_given_by(y, self.by)
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# We allow weights to be a multi-dimensional array, e.g. a (10, 2) array,
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# and each sub-array (10,) will be called in each iteration. If users only
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# provide 1D array, we assume the same weights is used for all iterations
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weights = kwds.get("weights", None)
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if weights is not None and np.ndim(weights) != 1:
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kwds["weights"] = weights[:, i]
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artists = self._plot(ax, y, column_num=i, stacking_id=stacking_id, **kwds)
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# when by is applied, show title for subplots to know which group it is
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if self.by is not None:
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ax.set_title(pprint_thing(label))
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self._append_legend_handles_labels(artists[0], label)
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def _make_plot_keywords(self, kwds, y):
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"""merge BoxPlot/KdePlot properties to passed kwds"""
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# y is required for KdePlot
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kwds["bottom"] = self.bottom
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kwds["bins"] = self.bins
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return kwds
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def _post_plot_logic(self, ax: Axes, data):
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if self.orientation == "horizontal":
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ax.set_xlabel("Frequency")
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else:
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ax.set_ylabel("Frequency")
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@property
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def orientation(self) -> PlottingOrientation:
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if self.kwds.get("orientation", None) == "horizontal":
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return "horizontal"
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else:
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return "vertical"
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class KdePlot(HistPlot):
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@property
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def _kind(self) -> Literal["kde"]:
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return "kde"
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@property
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def orientation(self) -> Literal["vertical"]:
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return "vertical"
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def __init__(self, data, bw_method=None, ind=None, **kwargs) -> None:
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MPLPlot.__init__(self, data, **kwargs)
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self.bw_method = bw_method
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self.ind = ind
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def _args_adjust(self):
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pass
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def _get_ind(self, y):
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if self.ind is None:
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# np.nanmax() and np.nanmin() ignores the missing values
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sample_range = np.nanmax(y) - np.nanmin(y)
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ind = np.linspace(
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np.nanmin(y) - 0.5 * sample_range,
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np.nanmax(y) + 0.5 * sample_range,
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1000,
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)
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elif is_integer(self.ind):
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sample_range = np.nanmax(y) - np.nanmin(y)
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ind = np.linspace(
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np.nanmin(y) - 0.5 * sample_range,
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np.nanmax(y) + 0.5 * sample_range,
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self.ind,
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)
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else:
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ind = self.ind
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return ind
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@classmethod
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def _plot(
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cls,
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ax,
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y,
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style=None,
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bw_method=None,
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ind=None,
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column_num=None,
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stacking_id=None,
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**kwds,
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):
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from scipy.stats import gaussian_kde
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y = remove_na_arraylike(y)
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gkde = gaussian_kde(y, bw_method=bw_method)
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y = gkde.evaluate(ind)
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lines = MPLPlot._plot(ax, ind, y, style=style, **kwds)
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return lines
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def _make_plot_keywords(self, kwds, y):
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kwds["bw_method"] = self.bw_method
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kwds["ind"] = self._get_ind(y)
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return kwds
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def _post_plot_logic(self, ax, data):
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ax.set_ylabel("Density")
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def _grouped_plot(
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plotf,
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data,
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column=None,
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by=None,
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numeric_only=True,
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figsize=None,
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sharex=True,
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sharey=True,
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layout=None,
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rot=0,
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ax=None,
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**kwargs,
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):
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if figsize == "default":
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# allowed to specify mpl default with 'default'
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raise ValueError(
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"figsize='default' is no longer supported. "
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"Specify figure size by tuple instead"
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)
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grouped = data.groupby(by)
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if column is not None:
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grouped = grouped[column]
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naxes = len(grouped)
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fig, axes = create_subplots(
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naxes=naxes, figsize=figsize, sharex=sharex, sharey=sharey, ax=ax, layout=layout
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)
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_axes = flatten_axes(axes)
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for i, (key, group) in enumerate(grouped):
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ax = _axes[i]
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if numeric_only and isinstance(group, ABCDataFrame):
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group = group._get_numeric_data()
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plotf(group, ax, **kwargs)
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ax.set_title(pprint_thing(key))
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return fig, axes
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def _grouped_hist(
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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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bins=50,
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figsize=None,
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layout=None,
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sharex=False,
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sharey=False,
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rot=90,
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grid=True,
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xlabelsize=None,
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xrot=None,
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ylabelsize=None,
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yrot=None,
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legend=False,
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**kwargs,
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):
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"""
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Grouped histogram
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Parameters
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----------
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data : Series/DataFrame
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column : object, optional
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by : object, optional
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ax : axes, optional
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bins : int, default 50
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figsize : tuple, optional
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layout : optional
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sharex : bool, default False
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sharey : bool, default False
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rot : int, default 90
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grid : bool, default True
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legend: : bool, default False
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kwargs : dict, keyword arguments passed to matplotlib.Axes.hist
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Returns
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-------
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collection of Matplotlib Axes
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"""
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if legend:
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assert "label" not in kwargs
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if data.ndim == 1:
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kwargs["label"] = data.name
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elif column is None:
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kwargs["label"] = data.columns
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else:
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kwargs["label"] = column
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def plot_group(group, ax):
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ax.hist(group.dropna().values, bins=bins, **kwargs)
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if legend:
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ax.legend()
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if xrot is None:
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xrot = rot
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fig, axes = _grouped_plot(
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plot_group,
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data,
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column=column,
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by=by,
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sharex=sharex,
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sharey=sharey,
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ax=ax,
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figsize=figsize,
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layout=layout,
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rot=rot,
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)
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set_ticks_props(
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axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
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)
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maybe_adjust_figure(
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fig, bottom=0.15, top=0.9, left=0.1, right=0.9, hspace=0.5, wspace=0.3
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)
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return axes
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def hist_series(
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self,
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by=None,
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ax=None,
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grid: bool = True,
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xlabelsize=None,
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xrot=None,
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ylabelsize=None,
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yrot=None,
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figsize=None,
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bins: int = 10,
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legend: bool = False,
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**kwds,
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):
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import matplotlib.pyplot as plt
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if legend and "label" in kwds:
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raise ValueError("Cannot use both legend and label")
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if by is None:
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if kwds.get("layout", None) is not None:
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raise ValueError("The 'layout' keyword is not supported when 'by' is None")
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# hack until the plotting interface is a bit more unified
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fig = kwds.pop(
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"figure", plt.gcf() if plt.get_fignums() else plt.figure(figsize=figsize)
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)
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|
|
if figsize is not None and tuple(figsize) != tuple(fig.get_size_inches()):
|
||
|
|
fig.set_size_inches(*figsize, forward=True)
|
||
|
|
if ax is None:
|
||
|
|
ax = fig.gca()
|
||
|
|
elif ax.get_figure() != fig:
|
||
|
|
raise AssertionError("passed axis not bound to passed figure")
|
||
|
|
values = self.dropna().values
|
||
|
|
if legend:
|
||
|
|
kwds["label"] = self.name
|
||
|
|
ax.hist(values, bins=bins, **kwds)
|
||
|
|
if legend:
|
||
|
|
ax.legend()
|
||
|
|
ax.grid(grid)
|
||
|
|
axes = np.array([ax])
|
||
|
|
|
||
|
|
set_ticks_props(
|
||
|
|
axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
|
||
|
|
)
|
||
|
|
|
||
|
|
else:
|
||
|
|
if "figure" in kwds:
|
||
|
|
raise ValueError(
|
||
|
|
"Cannot pass 'figure' when using the "
|
||
|
|
"'by' argument, since a new 'Figure' instance will be created"
|
||
|
|
)
|
||
|
|
axes = _grouped_hist(
|
||
|
|
self,
|
||
|
|
by=by,
|
||
|
|
ax=ax,
|
||
|
|
grid=grid,
|
||
|
|
figsize=figsize,
|
||
|
|
bins=bins,
|
||
|
|
xlabelsize=xlabelsize,
|
||
|
|
xrot=xrot,
|
||
|
|
ylabelsize=ylabelsize,
|
||
|
|
yrot=yrot,
|
||
|
|
legend=legend,
|
||
|
|
**kwds,
|
||
|
|
)
|
||
|
|
|
||
|
|
if hasattr(axes, "ndim"):
|
||
|
|
if axes.ndim == 1 and len(axes) == 1:
|
||
|
|
return axes[0]
|
||
|
|
return axes
|
||
|
|
|
||
|
|
|
||
|
|
def hist_frame(
|
||
|
|
data,
|
||
|
|
column=None,
|
||
|
|
by=None,
|
||
|
|
grid: bool = True,
|
||
|
|
xlabelsize=None,
|
||
|
|
xrot=None,
|
||
|
|
ylabelsize=None,
|
||
|
|
yrot=None,
|
||
|
|
ax=None,
|
||
|
|
sharex: bool = False,
|
||
|
|
sharey: bool = False,
|
||
|
|
figsize=None,
|
||
|
|
layout=None,
|
||
|
|
bins: int = 10,
|
||
|
|
legend: bool = False,
|
||
|
|
**kwds,
|
||
|
|
):
|
||
|
|
if legend and "label" in kwds:
|
||
|
|
raise ValueError("Cannot use both legend and label")
|
||
|
|
if by is not None:
|
||
|
|
axes = _grouped_hist(
|
||
|
|
data,
|
||
|
|
column=column,
|
||
|
|
by=by,
|
||
|
|
ax=ax,
|
||
|
|
grid=grid,
|
||
|
|
figsize=figsize,
|
||
|
|
sharex=sharex,
|
||
|
|
sharey=sharey,
|
||
|
|
layout=layout,
|
||
|
|
bins=bins,
|
||
|
|
xlabelsize=xlabelsize,
|
||
|
|
xrot=xrot,
|
||
|
|
ylabelsize=ylabelsize,
|
||
|
|
yrot=yrot,
|
||
|
|
legend=legend,
|
||
|
|
**kwds,
|
||
|
|
)
|
||
|
|
return axes
|
||
|
|
|
||
|
|
if column is not None:
|
||
|
|
if not isinstance(column, (list, np.ndarray, ABCIndex)):
|
||
|
|
column = [column]
|
||
|
|
data = data[column]
|
||
|
|
# GH32590
|
||
|
|
data = data.select_dtypes(
|
||
|
|
include=(np.number, "datetime64", "datetimetz"), exclude="timedelta"
|
||
|
|
)
|
||
|
|
naxes = len(data.columns)
|
||
|
|
|
||
|
|
if naxes == 0:
|
||
|
|
raise ValueError(
|
||
|
|
"hist method requires numerical or datetime columns, nothing to plot."
|
||
|
|
)
|
||
|
|
|
||
|
|
fig, axes = create_subplots(
|
||
|
|
naxes=naxes,
|
||
|
|
ax=ax,
|
||
|
|
squeeze=False,
|
||
|
|
sharex=sharex,
|
||
|
|
sharey=sharey,
|
||
|
|
figsize=figsize,
|
||
|
|
layout=layout,
|
||
|
|
)
|
||
|
|
_axes = flatten_axes(axes)
|
||
|
|
|
||
|
|
can_set_label = "label" not in kwds
|
||
|
|
|
||
|
|
for i, col in enumerate(data.columns):
|
||
|
|
ax = _axes[i]
|
||
|
|
if legend and can_set_label:
|
||
|
|
kwds["label"] = col
|
||
|
|
ax.hist(data[col].dropna().values, bins=bins, **kwds)
|
||
|
|
ax.set_title(col)
|
||
|
|
ax.grid(grid)
|
||
|
|
if legend:
|
||
|
|
ax.legend()
|
||
|
|
|
||
|
|
set_ticks_props(
|
||
|
|
axes, xlabelsize=xlabelsize, xrot=xrot, ylabelsize=ylabelsize, yrot=yrot
|
||
|
|
)
|
||
|
|
maybe_adjust_figure(fig, wspace=0.3, hspace=0.3)
|
||
|
|
|
||
|
|
return axes
|