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/ plotting / _core.py
# being a bit too dynamic
# pylint: disable=E1101
from __future__ import division
from collections import namedtuple
from distutils.version import LooseVersion
import re
import warnings
import numpy as np
import pandas.compat as compat
from pandas.compat import lrange, map, range, string_types, zip
from pandas.errors import AbstractMethodError
from pandas.util._decorators import Appender, cache_readonly
from pandas.core.dtypes.common import (
is_hashable, is_integer, is_iterator, is_list_like, is_number)
from pandas.core.dtypes.generic import (
ABCDataFrame, ABCIndexClass, ABCMultiIndex, ABCPeriodIndex, ABCSeries)
from pandas.core.dtypes.missing import isna, notna, remove_na_arraylike
from pandas.core.base import PandasObject
import pandas.core.common as com
from pandas.core.config import get_option
from pandas.core.generic import _shared_doc_kwargs, _shared_docs
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._compat import _mpl_ge_3_0_0
from pandas.plotting._style import _get_standard_colors, plot_params
from pandas.plotting._tools import (
_flatten, _get_all_lines, _get_xlim, _handle_shared_axes, _set_ticks_props,
_subplots, format_date_labels, table)
try:
from pandas.plotting import _converter
except ImportError:
_HAS_MPL = False
else:
_HAS_MPL = True
if get_option('plotting.matplotlib.register_converters'):
_converter.register(explicit=False)
def _raise_if_no_mpl():
# TODO(mpl_converter): remove once converter is explicit
if not _HAS_MPL:
raise ImportError("matplotlib is required for plotting.")
def _get_standard_kind(kind):
return {'density': 'kde'}.get(kind, kind)
def _gca(rc=None):
import matplotlib.pyplot as plt
with plt.rc_context(rc):
return plt.gca()
def _gcf():
import matplotlib.pyplot as plt
return plt.gcf()
class MPLPlot(object):
"""
Base class for assembling a pandas plot using matplotlib
Parameters
----------
data :
"""
@property
def _kind(self):
"""Specify kind str. Must be overridden in child class"""
raise NotImplementedError
_layout_type = 'vertical'
_default_rot = 0
orientation = None
_pop_attributes = ['label', 'style', 'logy', 'logx', 'loglog',
'mark_right', 'stacked']
_attr_defaults = {'logy': False, 'logx': False, 'loglog': False,
'mark_right': True, 'stacked': False}
def __init__(self, data, kind=None, by=None, subplots=False, sharex=None,
sharey=False, use_index=True,
figsize=None, grid=None, legend=True, rot=None,
ax=None, fig=None, title=None, xlim=None, ylim=None,
xticks=None, yticks=None,
sort_columns=False, fontsize=None,
secondary_y=False, colormap=None,
table=False, layout=None, **kwds):
_raise_if_no_mpl()
_converter._WARN = False
self.data = data
self.by = by
self.kind = kind
self.sort_columns = sort_columns
self.subplots = subplots
if sharex is None:
if ax is None:
self.sharex = True
else:
# if we get an axis, the users should do the visibility
# setting...
self.sharex = False
else:
self.sharex = sharex
self.sharey = sharey
self.figsize = figsize
self.layout = layout
self.xticks = xticks
self.yticks = yticks
self.xlim = xlim
self.ylim = ylim
self.title = title
self.use_index = use_index
self.fontsize = fontsize
if rot is not None:
self.rot = rot
# need to know for format_date_labels since it's rotated to 30 by
# default
self._rot_set = True
else:
self._rot_set = False
self.rot = self._default_rot
if grid is None:
grid = False if secondary_y else self.plt.rcParams['axes.grid']
self.grid = grid
self.legend = legend
self.legend_handles = []
self.legend_labels = []
for attr in self._pop_attributes:
value = kwds.pop(attr, self._attr_defaults.get(attr, None))
setattr(self, attr, value)
self.ax = ax
self.fig = fig
self.axes = None
# parse errorbar input if given
xerr = kwds.pop('xerr', None)
yerr = kwds.pop('yerr', None)
self.errors = {kw: self._parse_errorbars(kw, err)
for kw, err in zip(['xerr', 'yerr'], [xerr, yerr])}
if not isinstance(secondary_y, (bool, tuple, list,
np.ndarray, ABCIndexClass)):
secondary_y = [secondary_y]
self.secondary_y = secondary_y
# ugly TypeError if user passes matplotlib's `cmap` name.
# Probably better to accept either.
if 'cmap' in kwds and colormap:
raise TypeError("Only specify one of `cmap` and `colormap`.")
elif 'cmap' in kwds:
self.colormap = kwds.pop('cmap')
else:
self.colormap = colormap
self.table = table
self.kwds = kwds
self._validate_color_args()
def _validate_color_args(self):
if 'color' not in self.kwds and 'colors' in self.kwds:
warnings.warn(("'colors' is being deprecated. Please use 'color'"
"instead of 'colors'"))
colors = self.kwds.pop('colors')
self.kwds['color'] = colors
if ('color' in self.kwds and self.nseries == 1 and
not is_list_like(self.kwds['color'])):
# support series.plot(color='green')
self.kwds['color'] = [self.kwds['color']]
if ('color' in self.kwds and isinstance(self.kwds['color'], tuple) and
self.nseries == 1 and len(self.kwds['color']) in (3, 4)):
# support RGB and RGBA tuples in series plot
self.kwds['color'] = [self.kwds['color']]
if ('color' in self.kwds or 'colors' in self.kwds) and \
self.colormap is not None:
warnings.warn("'color' and 'colormap' cannot be used "
"simultaneously. Using 'color'")
if 'color' in self.kwds and self.style is not None:
if is_list_like(self.style):
styles = self.style
else:
styles = [self.style]
# need only a single match
for s in styles:
if re.match('^[a-z]+?', s) is not None:
raise ValueError(
"Cannot pass 'style' string with a color "
"symbol and 'color' keyword argument. Please"
" use one or the other or pass 'style' "
"without a color symbol")
def _iter_data(self, data=None, keep_index=False, fillna=None):
if data is None:
data = self.data
if fillna is not None:
data = data.fillna(fillna)
# TODO: unused?
# if self.sort_columns:
# columns = com.try_sort(data.columns)
# else:
# columns = data.columns
for col, values in data.iteritems():
if keep_index is True:
yield col, values
else:
yield col, values.values
@property
def nseries(self):
if self.data.ndim == 1:
return 1
else:
return self.data.shape[1]
def draw(self):
self.plt.draw_if_interactive()
def generate(self):
self._args_adjust()
self._compute_plot_data()
self._setup_subplots()
self._make_plot()
self._add_table()
self._make_legend()
self._adorn_subplots()
for ax in self.axes:
self._post_plot_logic_common(ax, self.data)
self._post_plot_logic(ax, self.data)
def _args_adjust(self):
pass
def _has_plotted_object(self, ax):
"""check whether ax has data"""
return (len(ax.lines) != 0 or
len(ax.artists) != 0 or
len(ax.containers) != 0)
def _maybe_right_yaxis(self, ax, axes_num):
if not self.on_right(axes_num):
# secondary axes may be passed via ax kw
return self._get_ax_layer(ax)
if hasattr(ax, 'right_ax'):
# if it has right_ax proparty, ``ax`` must be left axes
return ax.right_ax
elif hasattr(ax, 'left_ax'):
# if it has left_ax proparty, ``ax`` must be right axes
return ax
else:
# otherwise, create twin axes
orig_ax, new_ax = ax, ax.twinx()
# TODO: use Matplotlib public API when available
new_ax._get_lines = orig_ax._get_lines
new_ax._get_patches_for_fill = orig_ax._get_patches_for_fill
orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax
if not self._has_plotted_object(orig_ax): # no data on left y
orig_ax.get_yaxis().set_visible(False)
if self.logy or self.loglog:
new_ax.set_yscale('log')
return new_ax
def _setup_subplots(self):
if self.subplots:
fig, axes = _subplots(naxes=self.nseries,
sharex=self.sharex, sharey=self.sharey,
figsize=self.figsize, ax=self.ax,
layout=self.layout,
layout_type=self._layout_type)
else:
if self.ax is None:
fig = self.plt.figure(figsize=self.figsize)
axes = fig.add_subplot(111)
else:
fig = self.ax.get_figure()
if self.figsize is not None:
fig.set_size_inches(self.figsize)
axes = self.ax
axes = _flatten(axes)
if self.logx or self.loglog:
[a.set_xscale('log') for a in axes]
if self.logy or self.loglog:
[a.set_yscale('log') for a in axes]
self.fig = fig
self.axes = axes
@property
def result(self):
"""
Return result axes
"""
if self.subplots:
if self.layout is not None and not is_list_like(self.ax):
return self.axes.reshape(*self.layout)
else:
return self.axes
else:
sec_true = isinstance(self.secondary_y, bool) and self.secondary_y
all_sec = (is_list_like(self.secondary_y) and
len(self.secondary_y) == self.nseries)
if (sec_true or all_sec):
# if all data is plotted on secondary, return right axes
return self._get_ax_layer(self.axes[0], primary=False)
else:
return self.axes[0]
def _compute_plot_data(self):
data = self.data
if isinstance(data, ABCSeries):
label = self.label
if label is None and data.name is None:
label = 'None'
data = data.to_frame(name=label)
# GH16953, _convert is needed as fallback, for ``Series``
# with ``dtype == object``
data = data._convert(datetime=True, timedelta=True)
numeric_data = data.select_dtypes(include=[np.number,
"datetime",
"datetimetz",
"timedelta"])
try:
is_empty = numeric_data.empty
except AttributeError:
is_empty = not len(numeric_data)
# no empty frames or series allowed
if is_empty:
raise TypeError('Empty {0!r}: no numeric data to '
'plot'.format(numeric_data.__class__.__name__))
self.data = numeric_data
def _make_plot(self):
raise AbstractMethodError(self)
def _add_table(self):
if self.table is False:
return
elif self.table is True:
data = self.data.transpose()
else:
data = self.table
ax = self._get_ax(0)
table(ax, data)
def _post_plot_logic_common(self, ax, data):
"""Common post process for each axes"""
def get_label(i):
try:
return pprint_thing(data.index[i])
except Exception:
return ''
if self.orientation == 'vertical' or self.orientation is None:
if self._need_to_set_index:
xticklabels = [get_label(x) for x in ax.get_xticks()]
ax.set_xticklabels(xticklabels)
self._apply_axis_properties(ax.xaxis, rot=self.rot,
fontsize=self.fontsize)
self._apply_axis_properties(ax.yaxis, fontsize=self.fontsize)
if hasattr(ax, 'right_ax'):
self._apply_axis_properties(ax.right_ax.yaxis,
fontsize=self.fontsize)
elif self.orientation == 'horizontal':
if self._need_to_set_index:
yticklabels = [get_label(y) for y in ax.get_yticks()]
ax.set_yticklabels(yticklabels)
self._apply_axis_properties(ax.yaxis, rot=self.rot,
fontsize=self.fontsize)
self._apply_axis_properties(ax.xaxis, fontsize=self.fontsize)
if hasattr(ax, 'right_ax'):
self._apply_axis_properties(ax.right_ax.yaxis,
fontsize=self.fontsize)
else: # pragma no cover
raise ValueError
def _post_plot_logic(self, ax, data):
"""Post process for each axes. Overridden in child classes"""
pass
def _adorn_subplots(self):
"""Common post process unrelated to data"""
if len(self.axes) > 0:
all_axes = self._get_subplots()
nrows, ncols = self._get_axes_layout()
_handle_shared_axes(axarr=all_axes, nplots=len(all_axes),
naxes=nrows * ncols, nrows=nrows,
ncols=ncols, sharex=self.sharex,
sharey=self.sharey)
for ax in self.axes:
if self.yticks is not None:
ax.set_yticks(self.yticks)
if self.xticks is not None:
ax.set_xticks(self.xticks)
if self.ylim is not None:
ax.set_ylim(self.ylim)
if self.xlim is not None:
ax.set_xlim(self.xlim)
ax.grid(self.grid)
if self.title:
if self.subplots:
if is_list_like(self.title):
if len(self.title) != self.nseries:
msg = ('The length of `title` must equal the number '
'of columns if using `title` of type `list` '
'and `subplots=True`.\n'
'length of title = {}\n'
'number of columns = {}').format(
len(self.title), self.nseries)
raise ValueError(msg)
for (ax, title) in zip(self.axes, self.title):
ax.set_title(title)
else:
self.fig.suptitle(self.title)
else:
if is_list_like(self.title):
msg = ('Using `title` of type `list` is not supported '
'unless `subplots=True` is passed')
raise ValueError(msg)
self.axes[0].set_title(self.title)
def _apply_axis_properties(self, axis, rot=None, fontsize=None):
labels = axis.get_majorticklabels() + axis.get_minorticklabels()
for label in labels:
if rot is not None:
label.set_rotation(rot)
if fontsize is not None:
label.set_fontsize(fontsize)
@property
def legend_title(self):
if not isinstance(self.data.columns, ABCMultiIndex):
name = self.data.columns.name
if name is not None:
name = pprint_thing(name)
return name
else:
stringified = map(pprint_thing,
self.data.columns.names)
return ','.join(stringified)
def _add_legend_handle(self, handle, label, index=None):
if label is not None:
if self.mark_right and index is not None:
if self.on_right(index):
label = label + ' (right)'
self.legend_handles.append(handle)
self.legend_labels.append(label)
def _make_legend(self):
ax, leg = self._get_ax_legend(self.axes[0])
handles = []
labels = []
title = ''
if not self.subplots:
if leg is not None:
title = leg.get_title().get_text()
handles = leg.legendHandles
labels = [x.get_text() for x in leg.get_texts()]
if self.legend:
if self.legend == 'reverse':
self.legend_handles = reversed(self.legend_handles)
self.legend_labels = reversed(self.legend_labels)
handles += self.legend_handles
labels += self.legend_labels
if self.legend_title is not None:
title = self.legend_title
if len(handles) > 0:
ax.legend(handles, labels, loc='best', title=title)
elif self.subplots and self.legend:
for ax in self.axes:
if ax.get_visible():
ax.legend(loc='best')
def _get_ax_legend(self, ax):
leg = ax.get_legend()
other_ax = (getattr(ax, 'left_ax', None) or
getattr(ax, 'right_ax', None))
other_leg = None
if other_ax is not None:
other_leg = other_ax.get_legend()
if leg is None and other_leg is not None:
leg = other_leg
ax = other_ax
return ax, leg
@cache_readonly
def plt(self):
import matplotlib.pyplot as plt
return plt
_need_to_set_index = False
def _get_xticks(self, convert_period=False):
index = self.data.index
is_datetype = index.inferred_type in ('datetime', 'date',
'datetime64', 'time')
if self.use_index:
if convert_period and isinstance(index, ABCPeriodIndex):
self.data = self.data.reindex(index=index.sort_values())
x = self.data.index.to_timestamp()._mpl_repr()
elif index.is_numeric():
"""
Matplotlib supports numeric values or datetime objects as
xaxis values. Taking LBYL approach here, by the time
matplotlib raises exception when using non numeric/datetime
values for xaxis, several actions are already taken by plt.
"""
x = index._mpl_repr()
elif is_datetype:
self.data = self.data[notna(self.data.index)]
self.data = self.data.sort_index()
x = self.data.index._mpl_repr()
else:
self._need_to_set_index = True
x = lrange(len(index))
else:
x = lrange(len(index))
return x
@classmethod
def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds):
mask = isna(y)
if mask.any():
y = np.ma.array(y)
y = np.ma.masked_where(mask, y)
if isinstance(x, ABCIndexClass):
x = x._mpl_repr()
if is_errorbar:
if 'xerr' in kwds:
kwds['xerr'] = np.array(kwds.get('xerr'))
if 'yerr' in kwds:
kwds['yerr'] = np.array(kwds.get('yerr'))
return ax.errorbar(x, y, **kwds)
else:
# prevent style kwarg from going to errorbar, where it is
# unsupported
if style is not None:
args = (x, y, style)
else:
args = (x, y)
return ax.plot(*args, **kwds)
def _get_index_name(self):
if isinstance(self.data.index, ABCMultiIndex):
name = self.data.index.names
if com._any_not_none(*name):
name = ','.join(pprint_thing(x) for x in name)
else:
name = None
else:
name = self.data.index.name
if name is not None:
name = pprint_thing(name)
return name
@classmethod
def _get_ax_layer(cls, ax, primary=True):
"""get left (primary) or right (secondary) axes"""
if primary:
return getattr(ax, 'left_ax', ax)
else:
return getattr(ax, 'right_ax', ax)
def _get_ax(self, i):
# get the twinx ax if appropriate
if self.subplots:
ax = self.axes[i]
ax = self._maybe_right_yaxis(ax, i)
self.axes[i] = ax
else:
ax = self.axes[0]
ax = self._maybe_right_yaxis(ax, i)
ax.get_yaxis().set_visible(True)
return ax
def on_right(self, i):
if isinstance(self.secondary_y, bool):
return self.secondary_y
if isinstance(self.secondary_y, (tuple, list,
np.ndarray, ABCIndexClass)):
return self.data.columns[i] in self.secondary_y
def _apply_style_colors(self, colors, kwds, col_num, label):
"""
Manage style and color based on column number and its label.
Returns tuple of appropriate style and kwds which "color" may be added.
"""
style = None
if self.style is not None:
if isinstance(self.style, list):
try:
style = self.style[col_num]
except IndexError:
pass
elif isinstance(self.style, dict):
style = self.style.get(label, style)
else:
style = self.style
has_color = 'color' in kwds or self.colormap is not None
nocolor_style = style is None or re.match('[a-z]+', style) is None
if (has_color or self.subplots) and nocolor_style:
kwds['color'] = colors[col_num % len(colors)]
return style, kwds
def _get_colors(self, num_colors=None, color_kwds='color'):
if num_colors is None:
num_colors = self.nseries
return _get_standard_colors(num_colors=num_colors,
colormap=self.colormap,
color=self.kwds.get(color_kwds))
def _parse_errorbars(self, label, err):
"""
Look for error keyword arguments and return the actual errorbar data
or return the error DataFrame/dict
Error bars can be specified in several ways:
Series: the user provides a pandas.Series object of the same
length as the data
ndarray: provides a np.ndarray of the same length as the data
DataFrame/dict: error values are paired with keys matching the
key in the plotted DataFrame
str: the name of the column within the plotted DataFrame
"""
if err is None:
return None
def match_labels(data, e):
e = e.reindex(data.index)
return e
# key-matched DataFrame
if isinstance(err, ABCDataFrame):
err = match_labels(self.data, err)
# key-matched dict
elif isinstance(err, dict):
pass
# Series of error values
elif isinstance(err, ABCSeries):
# broadcast error series across data
err = match_labels(self.data, err)
err = np.atleast_2d(err)
err = np.tile(err, (self.nseries, 1))
# errors are a column in the dataframe
elif isinstance(err, string_types):
evalues = self.data[err].values
self.data = self.data[self.data.columns.drop(err)]
err = np.atleast_2d(evalues)
err = np.tile(err, (self.nseries, 1))
elif is_list_like(err):
if is_iterator(err):
err = np.atleast_2d(list(err))
else:
# raw error values
err = np.atleast_2d(err)
err_shape = err.shape
# asymmetrical error bars
if err.ndim == 3:
if (err_shape[0] != self.nseries) or \
(err_shape[1] != 2) or \
(err_shape[2] != len(self.data)):
msg = "Asymmetrical error bars should be provided " + \
"with the shape (%u, 2, %u)" % \
(self.nseries, len(self.data))
raise ValueError(msg)
# broadcast errors to each data series
if len(err) == 1:
err = np.tile(err, (self.nseries, 1))
elif is_number(err):
err = np.tile([err], (self.nseries, len(self.data)))
else:
msg = "No valid {label} detected".format(label=label)
raise ValueError(msg)
return err
def _get_errorbars(self, label=None, index=None, xerr=True, yerr=True):
errors = {}
for kw, flag in zip(['xerr', 'yerr'], [xerr, yerr]):
if flag:
err = self.errors[kw]
# user provided label-matched dataframe of errors
if isinstance(err, (ABCDataFrame, dict)):
if label is not None and label in err.keys():
err = err[label]
else:
err = None
elif index is not None and err is not None:
err = err[index]
if err is not None:
errors[kw] = err
return errors
def _get_subplots(self):
from matplotlib.axes import Subplot
return [ax for ax in self.axes[0].get_figure().get_axes()
if isinstance(ax, Subplot)]
def _get_axes_layout(self):
axes = self._get_subplots()
x_set = set()
y_set = set()
for ax in axes:
# check axes coordinates to estimate layout
points = ax.get_position().get_points()
x_set.add(points[0][0])
y_set.add(points[0][1])
return (len(y_set), len(x_set))
class PlanePlot(MPLPlot):
"""
Abstract class for plotting on plane, currently scatter and hexbin.
"""
_layout_type = 'single'
def __init__(self, data, x, y, **kwargs):
MPLPlot.__init__(self, data, **kwargs)
if x is None or y is None:
raise ValueError(self._kind + ' requires an x and y column')
if is_integer(x) and not self.data.columns.holds_integer():
x = self.data.columns[x]
if is_integer(y) and not self.data.columns.holds_integer():
y = self.data.columns[y]
if len(self.data[x]._get_numeric_data()) == 0:
raise ValueError(self._kind + ' requires x column to be numeric')
if len(self.data[y]._get_numeric_data()) == 0:
raise ValueError(self._kind + ' requires y column to be numeric')
self.x = x
self.y = y
@property
def nseries(self):
return 1
def _post_plot_logic(self, ax, data):
x, y = self.x, self.y
ax.set_ylabel(pprint_thing(y))
ax.set_xlabel(pprint_thing(x))
def _plot_colorbar(self, ax, **kwds):
# Addresses issues #10611 and #10678:
# When plotting scatterplots and hexbinplots in IPython
# inline backend the colorbar axis height tends not to
# exactly match the parent axis height.
# The difference is due to small fractional differences
# in floating points with similar representation.
# To deal with this, this method forces the colorbar
# height to take the height of the parent axes.
# For a more detailed description of the issue
# see the following link:
# https://github.com/ipython/ipython/issues/11215
img = ax.collections[0]
cbar = self.fig.colorbar(img, ax=ax, **kwds)
if _mpl_ge_3_0_0():
# The workaround below is no longer necessary.
return
points = ax.get_position().get_points()
cbar_points = cbar.ax.get_position().get_points()
cbar.ax.set_position([cbar_points[0, 0],
points[0, 1],
cbar_points[1, 0] - cbar_points[0, 0],
points[1, 1] - points[0, 1]])
# To see the discrepancy in axis heights uncomment
# the following two lines:
# print(points[1, 1] - points[0, 1])
# print(cbar_points[1, 1] - cbar_points[0, 1])
class ScatterPlot(PlanePlot):
_kind = 'scatter'
def __init__(self, data, x, y, s=None, c=None, **kwargs):
if s is None:
# hide the matplotlib default for size, in case we want to change
# the handling of this argument later
s = 20
super(ScatterPlot, self).__init__(data, x, y, s=s, **kwargs)
if is_integer(c) and not self.data.columns.holds_integer():
c = self.data.columns[c]
self.c = c
def _make_plot(self):
x, y, c, data = self.x, self.y, self.c, self.data
ax = self.axes[0]
c_is_column = is_hashable(c) and c in self.data.columns
# plot a colorbar only if a colormap is provided or necessary
cb = self.kwds.pop('colorbar', self.colormap or c_is_column)
# pandas uses colormap, matplotlib uses cmap.
cmap = self.colormap or 'Greys'
cmap = self.plt.cm.get_cmap(cmap)
color = self.kwds.pop("color", None)
if c is not None and color is not None:
raise TypeError('Specify exactly one of `c` and `color`')
elif c is None and color is None:
c_values = self.plt.rcParams['patch.facecolor']
elif color is not None:
c_values = color
elif c_is_column:
c_values = self.data[c].values
else:
c_values = c
if self.legend and hasattr(self, 'label'):
label = self.label
else:
label = None
scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
label=label, cmap=cmap, **self.kwds)
if cb:
cbar_label = c if c_is_column else ''
self._plot_colorbar(ax, label=cbar_label)
if label is not None:
self._add_legend_handle(scatter, label)
else:
self.legend = False
errors_x = self._get_errorbars(label=x, index=0, yerr=False)
errors_y = self._get_errorbars(label=y, index=0, xerr=False)
if len(errors_x) > 0 or len(errors_y) > 0:
err_kwds = dict(errors_x, **errors_y)
err_kwds['ecolor'] = scatter.get_facecolor()[0]
ax.errorbar(data[x].values, data[y].values,
linestyle='none', **err_kwds)
class HexBinPlot(PlanePlot):
_kind = 'hexbin'
def __init__(self, data, x, y, C=None, **kwargs):
super(HexBinPlot, self).__init__(data, x, y, **kwargs)
if is_integer(C) and not self.data.columns.holds_integer():
C = self.data.columns[C]
self.C = C
def _make_plot(self):
x, y, data, C = self.x, self.y, self.data, self.C
ax = self.axes[0]
# pandas uses colormap, matplotlib uses cmap.
cmap = self.colormap or 'BuGn'
cmap = self.plt.cm.get_cmap(cmap)
cb = self.kwds.pop('colorbar', True)
if C is None:
c_values = None
else:
c_values = data[C].values
ax.hexbin(data[x].values, data[y].values, C=c_values, cmap=cmap,
**self.kwds)
if cb:
self._plot_colorbar(ax)
def _make_legend(self):
pass
class LinePlot(MPLPlot):
_kind = 'line'
_default_rot = 0
orientation = 'vertical'
def __init__(self, data, **kwargs):
MPLPlot.__init__(self, data, **kwargs)
if self.stacked:
self.data = self.data.fillna(value=0)
self.x_compat = plot_params['x_compat']
if 'x_compat' in self.kwds:
self.x_compat = bool(self.kwds.pop('x_compat'))
def _is_ts_plot(self):
# this is slightly deceptive
return not self.x_compat and self.use_index and self._use_dynamic_x()
def _use_dynamic_x(self):
from pandas.plotting._timeseries import _use_dynamic_x
return _use_dynamic_x(self._get_ax(0), self.data)
def _make_plot(self):
if self._is_ts_plot():
from pandas.plotting._timeseries import _maybe_convert_index
data = _maybe_convert_index(self._get_ax(0), self.data)
x = data.index # dummy, not used
plotf = self._ts_plot
it = self._iter_data(data=data, keep_index=True)
else:
x = self._get_xticks(convert_period=True)
plotf = self._plot
it = self._iter_data()
stacking_id = self._get_stacking_id()
is_errorbar = com._any_not_none(*self.errors.values())
colors = self._get_colors()
for i, (label, y) in enumerate(it):
ax = self._get_ax(i)
kwds = self.kwds.copy()
style, kwds = self._apply_style_colors(colors, kwds, i, label)
errors = self._get_errorbars(label=label, index=i)
kwds = dict(kwds, **errors)
label = pprint_thing(label) # .encode('utf-8')
kwds['label'] = label
newlines = plotf(ax, x, y, style=style, column_num=i,
stacking_id=stacking_id,
is_errorbar=is_errorbar,
**kwds)
self._add_legend_handle(newlines[0], label, index=i)
lines = _get_all_lines(ax)
left, right = _get_xlim(lines)
ax.set_xlim(left, right)
@classmethod
def _plot(cls, ax, x, y, style=None, column_num=None,
stacking_id=None, **kwds):
# column_num is used to get the target column from protf in line and
# area plots
if column_num == 0:
cls._initialize_stacker(ax, stacking_id, len(y))
y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])
lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
cls._update_stacker(ax, stacking_id, y)
return lines
@classmethod
def _ts_plot(cls, ax, x, data, style=None, **kwds):
from pandas.plotting._timeseries import (_maybe_resample,
_decorate_axes,
format_dateaxis)
# accept x to be consistent with normal plot func,
# x is not passed to tsplot as it uses data.index as x coordinate
# column_num must be in kwds for stacking purpose
freq, data = _maybe_resample(data, ax, kwds)
# Set ax with freq info
_decorate_axes(ax, freq, kwds)
# digging deeper
if hasattr(ax, 'left_ax'):
_decorate_axes(ax.left_ax, freq, kwds)
if hasattr(ax, 'right_ax'):
_decorate_axes(ax.right_ax, freq, kwds)
ax._plot_data.append((data, cls._kind, kwds))
lines = cls._plot(ax, data.index, data.values, style=style, **kwds)
# set date formatter, locators and rescale limits
format_dateaxis(ax, ax.freq, data.index)
return lines
def _get_stacking_id(self):
if self.stacked:
return id(self.data)
else:
return None
@classmethod
def _initialize_stacker(cls, ax, stacking_id, n):
if stacking_id is None:
return
if not hasattr(ax, '_stacker_pos_prior'):
ax._stacker_pos_prior = {}
if not hasattr(ax, '_stacker_neg_prior'):
ax._stacker_neg_prior = {}
ax._stacker_pos_prior[stacking_id] = np.zeros(n)
ax._stacker_neg_prior[stacking_id] = np.zeros(n)
@classmethod
def _get_stacked_values(cls, ax, stacking_id, values, label):
if stacking_id is None:
return values
if not hasattr(ax, '_stacker_pos_prior'):
# stacker may not be initialized for subplots
cls._initialize_stacker(ax, stacking_id, len(values))
if (values >= 0).all():
return ax._stacker_pos_prior[stacking_id] + values
elif (values <= 0).all():
return ax._stacker_neg_prior[stacking_id] + values
raise ValueError('When stacked is True, each column must be either '
'all positive or negative.'
'{0} contains both positive and negative values'
.format(label))
@classmethod
def _update_stacker(cls, ax, stacking_id, values):
if stacking_id is None:
return
if (values >= 0).all():
ax._stacker_pos_prior[stacking_id] += values
elif (values <= 0).all():
ax._stacker_neg_prior[stacking_id] += values
def _post_plot_logic(self, ax, data):
condition = (not self._use_dynamic_x() and
data.index.is_all_dates and
not self.subplots or
(self.subplots and self.sharex))
index_name = self._get_index_name()
if condition:
# irregular TS rotated 30 deg. by default
# probably a better place to check / set this.
if not self._rot_set:
self.rot = 30
format_date_labels(ax, rot=self.rot)
if index_name is not None and self.use_index:
ax.set_xlabel(index_name)
class AreaPlot(LinePlot):
_kind = 'area'
def __init__(self, data, **kwargs):
kwargs.setdefault('stacked', True)
data = data.fillna(value=0)
LinePlot.__init__(self, data, **kwargs)
if not self.stacked:
# use smaller alpha to distinguish overlap
self.kwds.setdefault('alpha', 0.5)
if self.logy or self.loglog:
raise ValueError("Log-y scales are not supported in area plot")
@classmethod
def _plot(cls, ax, x, y, style=None, column_num=None,
stacking_id=None, is_errorbar=False, **kwds):
if column_num == 0:
cls._initialize_stacker(ax, stacking_id, len(y))
y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])
# need to remove label, because subplots uses mpl legend as it is
line_kwds = kwds.copy()
line_kwds.pop('label')
lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds)
# get data from the line to get coordinates for fill_between
xdata, y_values = lines[0].get_data(orig=False)
# unable to use ``_get_stacked_values`` here to get starting point
if stacking_id is None:
start = np.zeros(len(y))
elif (y >= 0).all():
start = ax._stacker_pos_prior[stacking_id]
elif (y <= 0).all():
start = ax._stacker_neg_prior[stacking_id]
else:
start = np.zeros(len(y))
if 'color' not in kwds:
kwds['color'] = lines[0].get_color()
rect = ax.fill_between(xdata, start, y_values, **kwds)
cls._update_stacker(ax, stacking_id, y)
# LinePlot expects list of artists
res = [rect]
return res
def _post_plot_logic(self, ax, data):
LinePlot._post_plot_logic(self, ax, data)
if self.ylim is None:
if (data >= 0).all().all():
ax.set_ylim(0, None)
elif (data <= 0).all().all():
ax.set_ylim(None, 0)
class BarPlot(MPLPlot):
_kind = 'bar'
_default_rot = 90
orientation = 'vertical'
def __init__(self, data, **kwargs):
# we have to treat a series differently than a
# 1-column DataFrame w.r.t. color handling
self._is_series = isinstance(data, ABCSeries)
self.bar_width = kwargs.pop('width', 0.5)
pos = kwargs.pop('position', 0.5)
kwargs.setdefault('align', 'center')
self.tick_pos = np.arange(len(data))
self.bottom = kwargs.pop('bottom', 0)
self.left = kwargs.pop('left', 0)
self.log = kwargs.pop('log', False)
MPLPlot.__init__(self, data, **kwargs)
if self.stacked or self.subplots:
self.tickoffset = self.bar_width * pos
if kwargs['align'] == 'edge':
self.lim_offset = self.bar_width / 2
else:
self.lim_offset = 0
else:
if kwargs['align'] == 'edge':
w = self.bar_width / self.nseries
self.tickoffset = self.bar_width * (pos - 0.5) + w * 0.5
self.lim_offset = w * 0.5
else:
self.tickoffset = self.bar_width * pos
self.lim_offset = 0
self.ax_pos = self.tick_pos - self.tickoffset
def _args_adjust(self):
if is_list_like(self.bottom):
self.bottom = np.array(self.bottom)
if is_list_like(self.left):
self.left = np.array(self.left)
@classmethod
def _plot(cls, ax, x, y, w, start=0, log=False, **kwds):
return ax.bar(x, y, w, bottom=start, log=log, **kwds)
@property
def _start_base(self):
return self.bottom
def _make_plot(self):
import matplotlib as mpl
colors = self._get_colors()
ncolors = len(colors)
pos_prior = neg_prior = np.zeros(len(self.data))
K = self.nseries
for i, (label, y) in enumerate(self._iter_data(fillna=0)):
ax = self._get_ax(i)
kwds = self.kwds.copy()
if self._is_series:
kwds['color'] = colors
else:
kwds['color'] = colors[i % ncolors]
errors = self._get_errorbars(label=label, index=i)
kwds = dict(kwds, **errors)
label = pprint_thing(label)
if (('yerr' in kwds) or ('xerr' in kwds)) \
and (kwds.get('ecolor') is None):
kwds['ecolor'] = mpl.rcParams['xtick.color']
start = 0
if self.log and (y >= 1).all():
start = 1
start = start + self._start_base
if self.subplots:
w = self.bar_width / 2
rect = self._plot(ax, self.ax_pos + w, y, self.bar_width,
start=start, label=label,
log=self.log, **kwds)
ax.set_title(label)
elif self.stacked:
mask = y > 0
start = np.where(mask, pos_prior, neg_prior) + self._start_base
w = self.bar_width / 2
rect = self._plot(ax, self.ax_pos + w, y, self.bar_width,
start=start, label=label,
log=self.log, **kwds)
pos_prior = pos_prior + np.where(mask, y, 0)
neg_prior = neg_prior + np.where(mask, 0, y)
else:
w = self.bar_width / K
rect = self._plot(ax, self.ax_pos + (i + 0.5) * w, y, w,
start=start, label=label,
log=self.log, **kwds)
self._add_legend_handle(rect, label, index=i)
def _post_plot_logic(self, ax, data):
if self.use_index:
str_index = [pprint_thing(key) for key in data.index]
else:
str_index = [pprint_thing(key) for key in range(data.shape[0])]
name = self._get_index_name()
s_edge = self.ax_pos[0] - 0.25 + self.lim_offset
e_edge = self.ax_pos[-1] + 0.25 + self.bar_width + self.lim_offset
self._decorate_ticks(ax, name, str_index, s_edge, e_edge)
def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge):
ax.set_xlim((start_edge, end_edge))
ax.set_xticks(self.tick_pos)
ax.set_xticklabels(ticklabels)
if name is not None and self.use_index:
ax.set_xlabel(name)
class BarhPlot(BarPlot):
_kind = 'barh'
_default_rot = 0
orientation = 'horizontal'
@property
def _start_base(self):
return self.left
@classmethod
def _plot(cls, ax, x, y, w, start=0, log=False, **kwds):
return ax.barh(x, y, w, left=start, log=log, **kwds)
def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge):
# horizontal bars
ax.set_ylim((start_edge, end_edge))
ax.set_yticks(self.tick_pos)
ax.set_yticklabels(ticklabels)
if name is not None and self.use_index:
ax.set_ylabel(name)
class HistPlot(LinePlot):
_kind = 'hist'
def __init__(self, data, bins=10, bottom=0, **kwargs):
self.bins = bins # use mpl default
self.bottom = bottom
# Do not call LinePlot.__init__ which may fill nan
MPLPlot.__init__(self, data, **kwargs)
def _args_adjust(self):
if is_integer(self.bins):
# create common bin edge
values = (self.data._convert(datetime=True)._get_numeric_data())
values = np.ravel(values)
values = values[~isna(values)]
hist, self.bins = np.histogram(
values, bins=self.bins,
range=self.kwds.get('range', None),
weights=self.kwds.get('weights', None))
if is_list_like(self.bottom):
self.bottom = np.array(self.bottom)
@classmethod
def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0,
stacking_id=None, **kwds):
if column_num == 0:
cls._initialize_stacker(ax, stacking_id, len(bins) - 1)
y = y[~isna(y)]
base = np.zeros(len(bins) - 1)
bottom = bottom + \
cls._get_stacked_values(ax, stacking_id, base, kwds['label'])
# ignore style
n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)
cls._update_stacker(ax, stacking_id, n)
return patches
def _make_plot(self):
colors = self._get_colors()
stacking_id = self._get_stacking_id()
for i, (label, y) in enumerate(self._iter_data()):
ax = self._get_ax(i)
kwds = self.kwds.copy()
label = pprint_thing(label)
kwds['label'] = label
style, kwds = self._apply_style_colors(colors, kwds, i, label)
if style is not None:
kwds['style'] = style
kwds = self._make_plot_keywords(kwds, y)
artists = self._plot(ax, y, column_num=i,
stacking_id=stacking_id, **kwds)
self._add_legend_handle(artists[0], label, index=i)
def _make_plot_keywords(self, kwds, y):
"""merge BoxPlot/KdePlot properties to passed kwds"""
# y is required for KdePlot
kwds['bottom'] = self.bottom
kwds['bins'] = self.bins
return kwds
def _post_plot_logic(self, ax, data):
if self.orientation == 'horizontal':
ax.set_xlabel('Frequency')
else:
ax.set_ylabel('Frequency')
@property
def orientation(self):
if self.kwds.get('orientation', None) == 'horizontal':
return 'horizontal'
else:
return 'vertical'
_kde_docstring = """
Generate Kernel Density Estimate plot using Gaussian kernels.
In statistics, `kernel density estimation`_ (KDE) is a non-parametric
way to estimate the probability density function (PDF) of a random
variable. This function uses Gaussian kernels and includes automatic
bandwidth determination.
.. _kernel density estimation:
https://en.wikipedia.org/wiki/Kernel_density_estimation
Parameters
----------
bw_method : str, scalar or callable, optional
The method used to calculate the estimator bandwidth. This can be
'scott', 'silverman', a scalar constant or a callable.
If None (default), 'scott' is used.
See :class:`scipy.stats.gaussian_kde` for more information.
ind : NumPy array or integer, optional
Evaluation points for the estimated PDF. If None (default),
1000 equally spaced points are used. If `ind` is a NumPy array, the
KDE is evaluated at the points passed. If `ind` is an integer,
`ind` number of equally spaced points are used.
**kwds : optional
Additional keyword arguments are documented in
:meth:`pandas.%(this-datatype)s.plot`.
Returns
-------
axes : matplotlib.axes.Axes or numpy.ndarray of them
See Also
--------
scipy.stats.gaussian_kde : Representation of a kernel-density
estimate using Gaussian kernels. This is the function used
internally to estimate the PDF.
%(sibling-datatype)s.plot.kde : Generate a KDE plot for a
%(sibling-datatype)s.
Examples
--------
%(examples)s
"""
class KdePlot(HistPlot):
_kind = 'kde'
orientation = 'vertical'
def __init__(self, data, bw_method=None, ind=None, **kwargs):
MPLPlot.__init__(self, data, **kwargs)
self.bw_method = bw_method
self.ind = ind
def _args_adjust(self):
pass
def _get_ind(self, y):
if self.ind is None:
# np.nanmax() and np.nanmin() ignores the missing values
sample_range = np.nanmax(y) - np.nanmin(y)
ind = np.linspace(np.nanmin(y) - 0.5 * sample_range,
np.nanmax(y) + 0.5 * sample_range, 1000)
elif is_integer(self.ind):
sample_range = np.nanmax(y) - np.nanmin(y)
ind = np.linspace(np.nanmin(y) - 0.5 * sample_range,
np.nanmax(y) + 0.5 * sample_range, self.ind)
else:
ind = self.ind
return ind
@classmethod
def _plot(cls, ax, y, style=None, bw_method=None, ind=None,
column_num=None, stacking_id=None, **kwds):
from scipy.stats import gaussian_kde
from scipy import __version__ as spv
y = remove_na_arraylike(y)
if LooseVersion(spv) >= '0.11.0':
gkde = gaussian_kde(y, bw_method=bw_method)
else:
gkde = gaussian_kde(y)
if bw_method is not None:
msg = ('bw_method was added in Scipy 0.11.0.' +
' Scipy version in use is {spv}.'.format(spv=spv))
warnings.warn(msg)
y = gkde.evaluate(ind)
lines = MPLPlot._plot(ax, ind, y, style=style, **kwds)
return lines
def _make_plot_keywords(self, kwds, y):
kwds['bw_method'] = self.bw_method
kwds['ind'] = self._get_ind(y)
return kwds
def _post_plot_logic(self, ax, data):
ax.set_ylabel('Density')
class PiePlot(MPLPlot):
_kind = 'pie'
_layout_type = 'horizontal'
def __init__(self, data, kind=None, **kwargs):
data = data.fillna(value=0)
if (data < 0).any().any():
raise ValueError("{0} doesn't allow negative values".format(kind))
MPLPlot.__init__(self, data, kind=kind, **kwargs)
def _args_adjust(self):
self.grid = False
self.logy = False
self.logx = False
self.loglog = False
def _validate_color_args(self):
pass
def _make_plot(self):
colors = self._get_colors(
num_colors=len(self.data), color_kwds='colors')
self.kwds.setdefault('colors', colors)
for i, (label, y) in enumerate(self._iter_data()):
ax = self._get_ax(i)
if label is not None:
label = pprint_thing(label)
ax.set_ylabel(label)
kwds = self.kwds.copy()
def blank_labeler(label, value):
if value == 0:
return ''
else:
return label
idx = [pprint_thing(v) for v in self.data.index]
labels = kwds.pop('labels', idx)
# labels is used for each wedge's labels
# Blank out labels for values of 0 so they don't overlap
# with nonzero wedges
if labels is not None:
blabels = [blank_labeler(l, value) for
l, value in zip(labels, y)]
else:
blabels = None
results = ax.pie(y, labels=blabels, **kwds)
if kwds.get('autopct', None) is not None:
patches, texts, autotexts = results
else:
patches, texts = results
autotexts = []
if self.fontsize is not None:
for t in texts + autotexts:
t.set_fontsize(self.fontsize)
# leglabels is used for legend labels
leglabels = labels if labels is not None else idx
for p, l in zip(patches, leglabels):
self._add_legend_handle(p, l)
class BoxPlot(LinePlot):
_kind = 'box'
_layout_type = 'horizontal'
_valid_return_types = (None, 'axes', 'dict', 'both')
# namedtuple to hold results
BP = namedtuple("Boxplot", ['ax', 'lines'])
def __init__(self, data, return_type='axes', **kwargs):
# 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'")
self.color = self.kwds.pop('color')
if isinstance(self.color, dict):
valid_keys = ['boxes', 'whiskers', 'medians', 'caps']
for key, values in compat.iteritems(self.color):
if key not in valid_keys:
raise ValueError("color dict contains invalid "
"key '{0}' "
"The key must be either {1}"
.format(key, 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 = 'k' # mpl default
def _get_colors(self, num_colors=None, color_kwds='color'):
pass
def maybe_color_bp(self, bp):
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
from matplotlib.artist import setp
setp(bp['boxes'], color=boxes, alpha=1)
setp(bp['whiskers'], color=whiskers, alpha=1)
setp(bp['medians'], color=medians, alpha=1)
setp(bp['caps'], color=caps, alpha=1)
def _make_plot(self):
if self.subplots:
from pandas.core.series import Series
self._return_obj = Series()
for i, (label, y) in enumerate(self._iter_data()):
ax = self._get_ax(i)
kwds = self.kwds.copy()
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
label = [pprint_thing(label)]
self._set_ticklabels(ax, label)
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 = [l for l, _ in self._iter_data()]
labels = [pprint_thing(l) for l 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, 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):
pass
@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(BoxPlot, self).result
else:
return self._return_obj
# kinds supported by both dataframe and series
_common_kinds = ['line', 'bar', 'barh',
'kde', 'density', 'area', 'hist', 'box']
# kinds supported by dataframe
_dataframe_kinds = ['scatter', 'hexbin']
# kinds supported only by series or dataframe single column
_series_kinds = ['pie']
_all_kinds = _common_kinds + _dataframe_kinds + _series_kinds
_klasses = [LinePlot, BarPlot, BarhPlot, KdePlot, HistPlot, BoxPlot,
ScatterPlot, HexBinPlot, AreaPlot, PiePlot]
_plot_klass = {klass._kind: klass for klass in _klasses}
def _plot(data, x=None, y=None, subplots=False,
ax=None, kind='line', **kwds):
kind = _get_standard_kind(kind.lower().strip())
if kind in _all_kinds:
klass = _plot_klass[kind]
else:
raise ValueError("%r is not a valid plot kind" % kind)
if kind in _dataframe_kinds:
if isinstance(data, ABCDataFrame):
plot_obj = klass(data, x=x, y=y, subplots=subplots, ax=ax,
kind=kind, **kwds)
else:
raise ValueError("plot kind %r can only be used for data frames"
% kind)
elif kind in _series_kinds:
if isinstance(data, ABCDataFrame):
if y is None and subplots is False:
msg = "{0} requires either y column or 'subplots=True'"
raise ValueError(msg.format(kind))
elif y is not None:
if is_integer(y) and not data.columns.holds_integer():
y = data.columns[y]
# converted to series actually. copy to not modify
data = data[y].copy()
data.index.name = y
plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
else:
if isinstance(data, ABCDataFrame):
data_cols = data.columns
if x is not None:
if is_integer(x) and not data.columns.holds_integer():
x = data_cols[x]
elif not isinstance(data[x], ABCSeries):
raise ValueError("x must be a label or position")
data = data.set_index(x)
if y is not None:
# check if we have y as int or list of ints
int_ylist = is_list_like(y) and all(is_integer(c) for c in y)
int_y_arg = is_integer(y) or int_ylist
if int_y_arg and not data.columns.holds_integer():
y = data_cols[y]
label_kw = kwds['label'] if 'label' in kwds else False
for kw in ['xerr', 'yerr']:
if (kw in kwds) and \
(isinstance(kwds[kw], string_types) or
is_integer(kwds[kw])):
try:
kwds[kw] = data[kwds[kw]]
except (IndexError, KeyError, TypeError):
pass
# don't overwrite
data = data[y].copy()
if isinstance(data, ABCSeries):
label_name = label_kw or y
data.name = label_name
else:
match = is_list_like(label_kw) and len(label_kw) == len(y)
if label_kw and not match:
raise ValueError(
"label should be list-like and same length as y"
)
label_name = label_kw or data.columns
data.columns = label_name
plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
plot_obj.generate()
plot_obj.draw()
return plot_obj.result
df_kind = """- 'scatter' : scatter plot
- 'hexbin' : hexbin plot"""
series_kind = ""
df_coord = """x : label or position, default None
y : label, position or list of label, positions, default None
Allows plotting of one column versus another"""
series_coord = ""
df_unique = """stacked : boolean, default False in line and
bar plots, and True in area plot. If True, create stacked plot.
sort_columns : boolean, default False
Sort column names to determine plot ordering
secondary_y : boolean or sequence, default False
Whether to plot on the secondary y-axis
If a list/tuple, which columns to plot on secondary y-axis"""
series_unique = """label : label argument to provide to plot
secondary_y : boolean or sequence of ints, default False
If True then y-axis will be on the right"""
df_ax = """ax : matplotlib axes object, default None
subplots : boolean, default False
Make separate subplots for each column
sharex : boolean, default True if ax is None else False
In case subplots=True, share x axis and set some x axis labels to
invisible; defaults to True if ax is None otherwise False if an ax
is passed in; Be aware, that passing in both an ax and sharex=True
will alter all x axis labels for all axis in a figure!
sharey : boolean, default False
In case subplots=True, share y axis and set some y axis labels to
invisible
layout : tuple (optional)
(rows, columns) for the layout of subplots"""
series_ax = """ax : matplotlib axes object
If not passed, uses gca()"""
df_note = """- If `kind` = 'scatter' and the argument `c` is the name of a dataframe
column, the values of that column are used to color each point.
- If `kind` = 'hexbin', you can control the size of the bins with the
`gridsize` argument. By default, a histogram of the counts around each
`(x, y)` point is computed. You can specify alternative aggregations
by passing values to the `C` and `reduce_C_function` arguments.
`C` specifies the value at each `(x, y)` point and `reduce_C_function`
is a function of one argument that reduces all the values in a bin to
a single number (e.g. `mean`, `max`, `sum`, `std`)."""
series_note = ""
_shared_doc_df_kwargs = dict(klass='DataFrame', klass_obj='df',
klass_kind=df_kind, klass_coord=df_coord,
klass_ax=df_ax, klass_unique=df_unique,
klass_note=df_note)
_shared_doc_series_kwargs = dict(klass='Series', klass_obj='s',
klass_kind=series_kind,
klass_coord=series_coord, klass_ax=series_ax,
klass_unique=series_unique,
klass_note=series_note)
_shared_docs['plot'] = """
Make plots of %(klass)s using matplotlib / pylab.
*New in version 0.17.0:* Each plot kind has a corresponding method on the
``%(klass)s.plot`` accessor:
``%(klass_obj)s.plot(kind='line')`` is equivalent to
``%(klass_obj)s.plot.line()``.
Parameters
----------
data : %(klass)s
%(klass_coord)s
kind : str
- 'line' : line plot (default)
- 'bar' : vertical bar plot
- 'barh' : horizontal bar plot
- 'hist' : histogram
- 'box' : boxplot
- 'kde' : Kernel Density Estimation plot
- 'density' : same as 'kde'
- 'area' : area plot
- 'pie' : pie plot
%(klass_kind)s
%(klass_ax)s
figsize : a tuple (width, height) in inches
use_index : boolean, default True
Use index as ticks for x axis
title : string or list
Title to use for the plot. If a string is passed, print the string at
the top of the figure. If a list is passed and `subplots` is True,
print each item in the list above the corresponding subplot.
grid : boolean, default None (matlab style default)
Axis grid lines
legend : False/True/'reverse'
Place legend on axis subplots
style : list or dict
matplotlib line style per column
logx : boolean, default False
Use log scaling on x axis
logy : boolean, default False
Use log scaling on y axis
loglog : boolean, default False
Use log scaling on both x and y axes
xticks : sequence
Values to use for the xticks
yticks : sequence
Values to use for the yticks
xlim : 2-tuple/list
ylim : 2-tuple/list
rot : int, default None
Rotation for ticks (xticks for vertical, yticks for horizontal plots)
fontsize : int, default None
Font size for xticks and yticks
colormap : str or matplotlib colormap object, default None
Colormap to select colors from. If string, load colormap with that name
from matplotlib.
colorbar : boolean, optional
If True, plot colorbar (only relevant for 'scatter' and 'hexbin' plots)
position : float
Specify relative alignments for bar plot layout.
From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)
table : boolean, Series or DataFrame, default False
If True, draw a table using the data in the DataFrame and the data will
be transposed to meet matplotlib's default layout.
If a Series or DataFrame is passed, use passed data to draw a table.
yerr : DataFrame, Series, array-like, dict and str
See :ref:`Plotting with Error Bars <visualization.errorbars>` for
detail.
xerr : same types as yerr.
%(klass_unique)s
mark_right : boolean, default True
When using a secondary_y axis, automatically mark the column
labels with "(right)" in the legend
`**kwds` : keywords
Options to pass to matplotlib plotting method
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
Notes
-----
- See matplotlib documentation online for more on this subject
- If `kind` = 'bar' or 'barh', you can specify relative alignments
for bar plot layout by `position` keyword.
From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)
%(klass_note)s
"""
@Appender(_shared_docs['plot'] % _shared_doc_df_kwargs)
def plot_frame(data, x=None, y=None, kind='line', ax=None,
subplots=False, sharex=None, sharey=False, layout=None,
figsize=None, use_index=True, title=None, grid=None,
legend=True, style=None, logx=False, logy=False, loglog=False,
xticks=None, yticks=None, xlim=None, ylim=None,
rot=None, fontsize=None, colormap=None, table=False,
yerr=None, xerr=None,
secondary_y=False, sort_columns=False,
**kwds):
return _plot(data, kind=kind, x=x, y=y, ax=ax,
subplots=subplots, sharex=sharex, sharey=sharey,
layout=layout, figsize=figsize, use_index=use_index,
title=title, grid=grid, legend=legend,
style=style, logx=logx, logy=logy, loglog=loglog,
xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim,
rot=rot, fontsize=fontsize, colormap=colormap, table=table,
yerr=yerr, xerr=xerr,
secondary_y=secondary_y, sort_columns=sort_columns,
**kwds)
@Appender(_shared_docs['plot'] % _shared_doc_series_kwargs)
def plot_series(data, kind='line', ax=None, # Series unique
figsize=None, use_index=True, title=None, grid=None,
legend=False, style=None, logx=False, logy=False, loglog=False,
xticks=None, yticks=None, xlim=None, ylim=None,
rot=None, fontsize=None, colormap=None, table=False,
yerr=None, xerr=None,
label=None, secondary_y=False, # Series unique
**kwds):
import matplotlib.pyplot as plt
if ax is None and len(plt.get_fignums()) > 0:
ax = _gca()
ax = MPLPlot._get_ax_layer(ax)
return _plot(data, kind=kind, ax=ax,
figsize=figsize, use_index=use_index, title=title,
grid=grid, legend=legend,
style=style, logx=logx, logy=logy, loglog=loglog,
xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim,
rot=rot, fontsize=fontsize, colormap=colormap, table=table,
yerr=yerr, xerr=xerr,
label=label, secondary_y=secondary_y,
**kwds)
_shared_docs['boxplot'] = """
Make a box plot from DataFrame columns.
Make a box-and-whisker plot from DataFrame columns, optionally grouped
by some other columns. A box plot is a method for graphically depicting
groups of numerical data through their quartiles.
The box extends from the Q1 to Q3 quartile values of the data,
with a line at the median (Q2). The whiskers extend from the edges
of box to show the range of the data. The position of the whiskers
is set by default to `1.5 * IQR (IQR = Q3 - Q1)` from the edges of the box.
Outlier points are those past the end of the whiskers.
For further details see
Wikipedia's entry for `boxplot <https://en.wikipedia.org/wiki/Box_plot>`_.
Parameters
----------
column : str or list of str, optional
Column name or list of names, or vector.
Can be any valid input to :meth:`pandas.DataFrame.groupby`.
by : str or array-like, optional
Column in the DataFrame to :meth:`pandas.DataFrame.groupby`.
One box-plot will be done per value of columns in `by`.
ax : object of class matplotlib.axes.Axes, optional
The matplotlib axes to be used by boxplot.
fontsize : float or str
Tick label font size in points or as a string (e.g., `large`).
rot : int or float, default 0
The rotation angle of labels (in degrees)
with respect to the screen coordinate system.
grid : boolean, default True
Setting this to True will show the grid.
figsize : A tuple (width, height) in inches
The size of the figure to create in matplotlib.
layout : tuple (rows, columns), optional
For example, (3, 5) will display the subplots
using 3 columns and 5 rows, starting from the top-left.
return_type : {'axes', 'dict', 'both'} or None, default 'axes'
The kind of object to return. The default is ``axes``.
* 'axes' returns the matplotlib axes the boxplot is drawn on.
* 'dict' returns a dictionary whose values are the matplotlib
Lines of the boxplot.
* 'both' returns a namedtuple with the axes and dict.
* when grouping with ``by``, a Series mapping columns to
``return_type`` is returned.
If ``return_type`` is `None`, a NumPy array
of axes with the same shape as ``layout`` is returned.
**kwds
All other plotting keyword arguments to be passed to
:func:`matplotlib.pyplot.boxplot`.
Returns
-------
result :
The return type depends on the `return_type` parameter:
* 'axes' : object of class matplotlib.axes.Axes
* 'dict' : dict of matplotlib.lines.Line2D objects
* 'both' : a namedtuple with structure (ax, lines)
For data grouped with ``by``:
* :class:`~pandas.Series`
* :class:`~numpy.array` (for ``return_type = None``)
See Also
--------
Series.plot.hist: Make a histogram.
matplotlib.pyplot.boxplot : Matplotlib equivalent plot.
Notes
-----
Use ``return_type='dict'`` when you want to tweak the appearance
of the lines after plotting. In this case a dict containing the Lines
making up the boxes, caps, fliers, medians, and whiskers is returned.
Examples
--------
Boxplots can be created for every column in the dataframe
by ``df.boxplot()`` or indicating the columns to be used:
.. plot::
:context: close-figs
>>> np.random.seed(1234)
>>> df = pd.DataFrame(np.random.randn(10,4),
... columns=['Col1', 'Col2', 'Col3', 'Col4'])
>>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3'])
Boxplots of variables distributions grouped by the values of a third
variable can be created using the option ``by``. For instance:
.. plot::
:context: close-figs
>>> df = pd.DataFrame(np.random.randn(10, 2),
... columns=['Col1', 'Col2'])
>>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
... 'B', 'B', 'B', 'B', 'B'])
>>> boxplot = df.boxplot(by='X')
A list of strings (i.e. ``['X', 'Y']``) can be passed to boxplot
in order to group the data by combination of the variables in the x-axis:
.. plot::
:context: close-figs
>>> df = pd.DataFrame(np.random.randn(10,3),
... columns=['Col1', 'Col2', 'Col3'])
>>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
... 'B', 'B', 'B', 'B', 'B'])
>>> df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A',
... 'B', 'A', 'B', 'A', 'B'])
>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])
The layout of boxplot can be adjusted giving a tuple to ``layout``:
.. plot::
:context: close-figs
>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
... layout=(2, 1))
Additional formatting can be done to the boxplot, like suppressing the grid
(``grid=False``), rotating the labels in the x-axis (i.e. ``rot=45``)
or changing the fontsize (i.e. ``fontsize=15``):
.. plot::
:context: close-figs
>>> boxplot = df.boxplot(grid=False, rot=45, fontsize=15)
The parameter ``return_type`` can be used to select the type of element
returned by `boxplot`. When ``return_type='axes'`` is selected,
the matplotlib axes on which the boxplot is drawn are returned:
>>> boxplot = df.boxplot(column=['Col1','Col2'], return_type='axes')
>>> type(boxplot)
<class 'matplotlib.axes._subplots.AxesSubplot'>
When grouping with ``by``, a Series mapping columns to ``return_type``
is returned:
>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
... return_type='axes')
>>> type(boxplot)
<class 'pandas.core.series.Series'>
If ``return_type`` is `None`, a NumPy array of axes with the same shape
as ``layout`` is returned:
>>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
... return_type=None)
>>> type(boxplot)
<class 'numpy.ndarray'>
"""
@Appender(_shared_docs['boxplot'] % _shared_doc_kwargs)
def boxplot(data, column=None, by=None, ax=None, fontsize=None,
rot=0, grid=True, figsize=None, layout=None, return_type=None,
**kwds):
# validate return_type:
if return_type not in BoxPlot._valid_return_types:
raise ValueError("return_type must be {'axes', 'dict', 'both'}")
if isinstance(data, ABCSeries):
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.
return _get_standard_colors(color=kwds.get('color'), num_colors=3)
def maybe_color_bp(bp):
if 'color' not in kwds:
from matplotlib.artist import setp
setp(bp['boxes'], color=colors[0], alpha=1)
setp(bp['whiskers'], color=colors[0], alpha=1)
setp(bp['medians'], color=colors[2], alpha=1)
def plot_group(keys, values, ax):
keys = [pprint_thing(x) for x in keys]
values = [np.asarray(remove_na_arraylike(v)) for v in values]
bp = ax.boxplot(values, **kwds)
if fontsize is not None:
ax.tick_params(axis='both', labelsize=fontsize)
if kwds.get('vert', 1):
ax.set_xticklabels(keys, rotation=rot)
else:
ax.set_yticklabels(keys, rotation=rot)
maybe_color_bp(bp)
# 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)
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 {}
ax = _gca(rc)
data = data._get_numeric_data()
if columns is None:
columns = data.columns
else:
data = data[columns]
result = plot_group(columns, data.values.T, ax)
ax.grid(grid)
return result
@Appender(_shared_docs['boxplot'] % _shared_doc_kwargs)
def boxplot_frame(self, column=None, by=None, ax=None, fontsize=None, rot=0,
grid=True, figsize=None, layout=None,
return_type=None, **kwds):
import matplotlib.pyplot as plt
_converter._WARN = False
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 scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False,
**kwargs):
"""
Make a scatter plot from two DataFrame columns
Parameters
----------
data : DataFrame
x : Column name for the x-axis values
y : Column name for the y-axis values
ax : Matplotlib axis object
figsize : A tuple (width, height) in inches
grid : Setting this to True will show the grid
kwargs : other plotting keyword arguments
To be passed to scatter function
Returns
-------
fig : matplotlib.Figure
"""
import matplotlib.pyplot as plt
kwargs.setdefault('edgecolors', 'none')
def plot_group(group, ax):
xvals = group[x].values
yvals = group[y].values
ax.scatter(xvals, yvals, **kwargs)
ax.grid(grid)
if by is not None:
fig = _grouped_plot(plot_group, data, by=by, figsize=figsize, ax=ax)
else:
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
else:
fig = ax.get_figure()
plot_group(data, ax)
ax.set_ylabel(pprint_thing(y))
ax.set_xlabel(pprint_thing(x))
ax.grid(grid)
return fig
def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False,
sharey=False, figsize=None, layout=None, bins=10, **kwds):
"""
Make a histogram of the DataFrame's.
A `histogram`_ is a representation of the distribution of data.
This function calls :meth:`matplotlib.pyplot.hist`, on each series in
the DataFrame, resulting in one histogram per column.
.. _histogram: https://en.wikipedia.org/wiki/Histogram
Parameters
----------
data : DataFrame
The pandas object holding the data.
column : string or sequence
If passed, will be used to limit data to a subset of columns.
by : object, optional
If passed, then used to form histograms for separate groups.
grid : boolean, default True
Whether to show axis grid lines.
xlabelsize : int, default None
If specified changes the x-axis label size.
xrot : float, default None
Rotation of x axis labels. For example, a value of 90 displays the
x labels rotated 90 degrees clockwise.
ylabelsize : int, default None
If specified changes the y-axis label size.
yrot : float, default None
Rotation of y axis labels. For example, a value of 90 displays the
y labels rotated 90 degrees clockwise.
ax : Matplotlib axes object, default None
The axes to plot the histogram on.
sharex : boolean, default True if ax is None else False
In case subplots=True, share x axis and set some x axis labels to
invisible; defaults to True if ax is None otherwise False if an ax
is passed in.
Note that passing in both an ax and sharex=True will alter all x axis
labels for all subplots in a figure.
sharey : boolean, default False
In case subplots=True, share y axis and set some y axis labels to
invisible.
figsize : tuple
The size in inches of the figure to create. Uses the value in
`matplotlib.rcParams` by default.
layout : tuple, optional
Tuple of (rows, columns) for the layout of the histograms.
bins : integer or sequence, default 10
Number of histogram bins to be used. If an integer is given, bins + 1
bin edges are calculated and returned. If bins is a sequence, gives
bin edges, including left edge of first bin and right edge of last
bin. In this case, bins is returned unmodified.
**kwds
All other plotting keyword arguments to be passed to
:meth:`matplotlib.pyplot.hist`.
Returns
-------
axes : matplotlib.AxesSubplot or numpy.ndarray of them
See Also
--------
matplotlib.pyplot.hist : Plot a histogram using matplotlib.
Examples
--------
.. plot::
:context: close-figs
This example draws a histogram based on the length and width of
some animals, displayed in three bins
>>> df = pd.DataFrame({
... 'length': [1.5, 0.5, 1.2, 0.9, 3],
... 'width': [0.7, 0.2, 0.15, 0.2, 1.1]
... }, index= ['pig', 'rabbit', 'duck', 'chicken', 'horse'])
>>> hist = df.hist(bins=3)
"""
_raise_if_no_mpl()
_converter._WARN = False
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, **kwds)
return axes
if column is not None:
if not isinstance(column, (list, np.ndarray, ABCIndexClass)):
column = [column]
data = data[column]
data = data._get_numeric_data()
naxes = len(data.columns)
fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False,
sharex=sharex, sharey=sharey, figsize=figsize,
layout=layout)
_axes = _flatten(axes)
for i, col in enumerate(com.try_sort(data.columns)):
ax = _axes[i]
ax.hist(data[col].dropna().values, bins=bins, **kwds)
ax.set_title(col)
ax.grid(grid)
_set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot,
ylabelsize=ylabelsize, yrot=yrot)
fig.subplots_adjust(wspace=0.3, hspace=0.3)
return axes
def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None,
xrot=None, ylabelsize=None, yrot=None, figsize=None,
bins=10, **kwds):
"""
Draw histogram of the input series using matplotlib.
Parameters
----------
by : object, optional
If passed, then used to form histograms for separate groups
ax : matplotlib axis object
If not passed, uses gca()
grid : boolean, default True
Whether to show axis grid lines
xlabelsize : int, default None
If specified changes the x-axis label size
xrot : float, default None
rotation of x axis labels
ylabelsize : int, default None
If specified changes the y-axis label size
yrot : float, default None
rotation of y axis labels
figsize : tuple, default None
figure size in inches by default
bins : integer or sequence, default 10
Number of histogram bins to be used. If an integer is given, bins + 1
bin edges are calculated and returned. If bins is a sequence, gives
bin edges, including left edge of first bin and right edge of last
bin. In this case, bins is returned unmodified.
bins : integer, default 10
Number of histogram bins to be used
`**kwds` : keywords
To be passed to the actual plotting function
See Also
--------
matplotlib.axes.Axes.hist : Plot a histogram using matplotlib.
"""
import matplotlib.pyplot as plt
if by is None:
if kwds.get('layout', None) is not None:
raise ValueError("The 'layout' keyword is not supported when "
"'by' is None")
# hack until the plotting interface is a bit more unified
fig = kwds.pop('figure', plt.gcf() if plt.get_fignums() else
plt.figure(figsize=figsize))
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
ax.hist(values, bins=bins, **kwds)
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, **kwds)
if hasattr(axes, 'ndim'):
if axes.ndim == 1 and len(axes) == 1:
return axes[0]
return axes
def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None,
layout=None, sharex=False, sharey=False, rot=90, grid=True,
xlabelsize=None, xrot=None, ylabelsize=None, yrot=None,
**kwargs):
"""
Grouped histogram
Parameters
----------
data : Series/DataFrame
column : object, optional
by : object, optional
ax : axes, optional
bins : int, default 50
figsize : tuple, optional
layout : optional
sharex : boolean, default False
sharey : boolean, default False
rot : int, default 90
grid : bool, default True
kwargs : dict, keyword arguments passed to matplotlib.Axes.hist
Returns
-------
axes : collection of Matplotlib Axes
"""
_raise_if_no_mpl()
_converter._WARN = False
def plot_group(group, ax):
ax.hist(group.dropna().values, bins=bins, **kwargs)
xrot = xrot or rot
fig, axes = _grouped_plot(plot_group, data, column=column,
by=by, sharex=sharex, sharey=sharey, ax=ax,
figsize=figsize, layout=layout, rot=rot)
_set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot,
ylabelsize=ylabelsize, yrot=yrot)
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9,
hspace=0.5, wspace=0.3)
return axes
def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
rot=0, grid=True, ax=None, figsize=None,
layout=None, sharex=False, sharey=True, **kwds):
"""
Make box plots from DataFrameGroupBy data.
Parameters
----------
grouped : Grouped DataFrame
subplots :
* ``False`` - no subplots will be used
* ``True`` - create a subplot for each group
column : column name or list of names, or vector
Can be any valid input to groupby
fontsize : int or string
rot : label rotation angle
grid : Setting this to True will show the grid
ax : Matplotlib axis object, default None
figsize : A tuple (width, height) in inches
layout : tuple (optional)
(rows, columns) for the layout of the plot
sharex : bool, default False
Whether x-axes will be shared among subplots
.. versionadded:: 0.23.1
sharey : bool, default True
Whether y-axes will be shared among subplots
.. versionadded:: 0.23.1
`**kwds` : Keyword Arguments
All other plotting keyword arguments to be passed to
matplotlib's boxplot function
Returns
-------
dict of key/value = group key/DataFrame.boxplot return value
or DataFrame.boxplot return value in case subplots=figures=False
Examples
--------
>>> import itertools
>>> tuples = [t for t in itertools.product(range(1000), range(4))]
>>> index = pd.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
>>> data = np.random.randn(len(index),4)
>>> df = pd.DataFrame(data, columns=list('ABCD'), index=index)
>>>
>>> grouped = df.groupby(level='lvl1')
>>> boxplot_frame_groupby(grouped)
>>>
>>> grouped = df.unstack(level='lvl1').groupby(level=0, axis=1)
>>> boxplot_frame_groupby(grouped, subplots=False)
"""
_raise_if_no_mpl()
_converter._WARN = False
if subplots is True:
naxes = len(grouped)
fig, axes = _subplots(naxes=naxes, squeeze=False,
ax=ax, sharex=sharex, sharey=sharey,
figsize=figsize, layout=layout)
axes = _flatten(axes)
from pandas.core.series import Series
ret = Series()
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
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1,
right=0.9, wspace=0.2)
else:
from pandas.core.reshape.concat import concat
keys, frames = zip(*grouped)
if grouped.axis == 0:
df = concat(frames, keys=keys, axis=1)
else:
if len(frames) > 1:
df = frames[0].join(frames[1::])
else:
df = frames[0]
ret = df.boxplot(column=column, fontsize=fontsize, rot=rot,
grid=grid, ax=ax, figsize=figsize,
layout=layout, **kwds)
return ret
def _grouped_plot(plotf, data, column=None, by=None, numeric_only=True,
figsize=None, sharex=True, sharey=True, layout=None,
rot=0, ax=None, **kwargs):
if figsize == 'default':
# allowed to specify mpl default with 'default'
warnings.warn("figsize='default' is deprecated. Specify figure"
"size by tuple instead", FutureWarning, stacklevel=4)
figsize = None
grouped = data.groupby(by)
if column is not None:
grouped = grouped[column]
naxes = len(grouped)
fig, axes = _subplots(naxes=naxes, figsize=figsize,
sharex=sharex, sharey=sharey, ax=ax,
layout=layout)
_axes = _flatten(axes)
for i, (key, group) in enumerate(grouped):
ax = _axes[i]
if numeric_only and isinstance(group, ABCDataFrame):
group = group._get_numeric_data()
plotf(group, ax, **kwargs)
ax.set_title(pprint_thing(key))
return fig, axes
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 = _subplots(naxes=naxes, sharex=True, sharey=True,
figsize=figsize, ax=ax, layout=layout)
_axes = _flatten(axes)
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, **kwargs)
ax.set_title(col)
ax.set_xlabel(pprint_thing(by))
ax_values.append(re_plotf)
ax.grid(grid)
from pandas.core.series import Series
result = 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('Boxplot grouped by {byline}'.format(byline=byline))
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
return result
class BasePlotMethods(PandasObject):
def __init__(self, data):
self._parent = data # can be Series or DataFrame
def __call__(self, *args, **kwargs):
raise NotImplementedError
class SeriesPlotMethods(BasePlotMethods):
"""
Series plotting accessor and method.
Examples
--------
>>> s.plot.line()
>>> s.plot.bar()
>>> s.plot.hist()
Plotting methods can also be accessed by calling the accessor as a method
with the ``kind`` argument:
``s.plot(kind='line')`` is equivalent to ``s.plot.line()``
"""
def __call__(self, kind='line', ax=None,
figsize=None, use_index=True, title=None, grid=None,
legend=False, style=None, logx=False, logy=False,
loglog=False, xticks=None, yticks=None,
xlim=None, ylim=None,
rot=None, fontsize=None, colormap=None, table=False,
yerr=None, xerr=None,
label=None, secondary_y=False, **kwds):
return plot_series(self._parent, kind=kind, ax=ax, figsize=figsize,
use_index=use_index, title=title, grid=grid,
legend=legend, style=style, logx=logx, logy=logy,
loglog=loglog, xticks=xticks, yticks=yticks,
xlim=xlim, ylim=ylim, rot=rot, fontsize=fontsize,
colormap=colormap, table=table, yerr=yerr,
xerr=xerr, label=label, secondary_y=secondary_y,
**kwds)
__call__.__doc__ = plot_series.__doc__
def line(self, **kwds):
"""
Line plot.
Parameters
----------
`**kwds` : optional
Additional keyword arguments are documented in
:meth:`pandas.Series.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
Examples
--------
.. plot::
:context: close-figs
>>> s = pd.Series([1, 3, 2])
>>> s.plot.line()
"""
return self(kind='line', **kwds)
def bar(self, **kwds):
"""
Vertical bar plot.
Parameters
----------
`**kwds` : optional
Additional keyword arguments are documented in
:meth:`pandas.Series.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
"""
return self(kind='bar', **kwds)
def barh(self, **kwds):
"""
Horizontal bar plot.
Parameters
----------
`**kwds` : optional
Additional keyword arguments are documented in
:meth:`pandas.Series.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
"""
return self(kind='barh', **kwds)
def box(self, **kwds):
"""
Boxplot.
Parameters
----------
`**kwds` : optional
Additional keyword arguments are documented in
:meth:`pandas.Series.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
"""
return self(kind='box', **kwds)
def hist(self, bins=10, **kwds):
"""
Histogram.
Parameters
----------
bins : integer, default 10
Number of histogram bins to be used
`**kwds` : optional
Additional keyword arguments are documented in
:meth:`pandas.Series.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
"""
return self(kind='hist', bins=bins, **kwds)
@Appender(_kde_docstring % {
'this-datatype': 'Series',
'sibling-datatype': 'DataFrame',
'examples': """
Given a Series of points randomly sampled from an unknown
distribution, estimate its PDF using KDE with automatic
bandwidth determination and plot the results, evaluating them at
1000 equally spaced points (default):
.. plot::
:context: close-figs
>>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])
>>> ax = s.plot.kde()
A scalar bandwidth can be specified. Using a small bandwidth value can
lead to over-fitting, while using a large bandwidth value may result
in under-fitting:
.. plot::
:context: close-figs
>>> ax = s.plot.kde(bw_method=0.3)
.. plot::
:context: close-figs
>>> ax = s.plot.kde(bw_method=3)
Finally, the `ind` parameter determines the evaluation points for the
plot of the estimated PDF:
.. plot::
:context: close-figs
>>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])
""".strip()
})
def kde(self, bw_method=None, ind=None, **kwds):
return self(kind='kde', bw_method=bw_method, ind=ind, **kwds)
density = kde
def area(self, **kwds):
"""
Area plot.
Parameters
----------
`**kwds` : optional
Additional keyword arguments are documented in
:meth:`pandas.Series.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
"""
return self(kind='area', **kwds)
def pie(self, **kwds):
"""
Pie chart.
Parameters
----------
`**kwds` : optional
Additional keyword arguments are documented in
:meth:`pandas.Series.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
"""
return self(kind='pie', **kwds)
class FramePlotMethods(BasePlotMethods):
"""DataFrame plotting accessor and method
Examples
--------
>>> df.plot.line()
>>> df.plot.scatter('x', 'y')
>>> df.plot.hexbin()
These plotting methods can also be accessed by calling the accessor as a
method with the ``kind`` argument:
``df.plot(kind='line')`` is equivalent to ``df.plot.line()``
"""
def __call__(self, x=None, y=None, kind='line', ax=None,
subplots=False, sharex=None, sharey=False, layout=None,
figsize=None, use_index=True, title=None, grid=None,
legend=True, style=None, logx=False, logy=False, loglog=False,
xticks=None, yticks=None, xlim=None, ylim=None,
rot=None, fontsize=None, colormap=None, table=False,
yerr=None, xerr=None,
secondary_y=False, sort_columns=False, **kwds):
return plot_frame(self._parent, kind=kind, x=x, y=y, ax=ax,
subplots=subplots, sharex=sharex, sharey=sharey,
layout=layout, figsize=figsize, use_index=use_index,
title=title, grid=grid, legend=legend, style=style,
logx=logx, logy=logy, loglog=loglog, xticks=xticks,
yticks=yticks, xlim=xlim, ylim=ylim, rot=rot,
fontsize=fontsize, colormap=colormap, table=table,
yerr=yerr, xerr=xerr, secondary_y=secondary_y,
sort_columns=sort_columns, **kwds)
__call__.__doc__ = plot_frame.__doc__
def line(self, x=None, y=None, **kwds):
"""
Plot DataFrame columns as lines.
This function is useful to plot lines using DataFrame's values
as coordinates.
Parameters
----------
x : int or str, optional
Columns to use for the horizontal axis.
Either the location or the label of the columns to be used.
By default, it will use the DataFrame indices.
y : int, str, or list of them, optional
The values to be plotted.
Either the location or the label of the columns to be used.
By default, it will use the remaining DataFrame numeric columns.
**kwds
Keyword arguments to pass on to :meth:`pandas.DataFrame.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or :class:`numpy.ndarray`
Returns an ndarray when ``subplots=True``.
See Also
--------
matplotlib.pyplot.plot : Plot y versus x as lines and/or markers.
Examples
--------
.. plot::
:context: close-figs
The following example shows the populations for some animals
over the years.
>>> df = pd.DataFrame({
... 'pig': [20, 18, 489, 675, 1776],
... 'horse': [4, 25, 281, 600, 1900]
... }, index=[1990, 1997, 2003, 2009, 2014])
>>> lines = df.plot.line()
.. plot::
:context: close-figs
An example with subplots, so an array of axes is returned.
>>> axes = df.plot.line(subplots=True)
>>> type(axes)
<class 'numpy.ndarray'>
.. plot::
:context: close-figs
The following example shows the relationship between both
populations.
>>> lines = df.plot.line(x='pig', y='horse')
"""
return self(kind='line', x=x, y=y, **kwds)
def bar(self, x=None, y=None, **kwds):
"""
Vertical bar plot.
A bar plot is a plot that presents categorical data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
axis of the plot shows the specific categories being compared, and the
other axis represents a measured value.
Parameters
----------
x : label or position, optional
Allows plotting of one column versus another. If not specified,
the index of the DataFrame is used.
y : label or position, optional
Allows plotting of one column versus another. If not specified,
all numerical columns are used.
**kwds
Additional keyword arguments are documented in
:meth:`pandas.DataFrame.plot`.
Returns
-------
axes : matplotlib.axes.Axes or np.ndarray of them
An ndarray is returned with one :class:`matplotlib.axes.Axes`
per column when ``subplots=True``.
See Also
--------
pandas.DataFrame.plot.barh : Horizontal bar plot.
pandas.DataFrame.plot : Make plots of a DataFrame.
matplotlib.pyplot.bar : Make a bar plot with matplotlib.
Examples
--------
Basic plot.
.. plot::
:context: close-figs
>>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
>>> ax = df.plot.bar(x='lab', y='val', rot=0)
Plot a whole dataframe to a bar plot. Each column is assigned a
distinct color, and each row is nested in a group along the
horizontal axis.
.. plot::
:context: close-figs
>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
... 'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = pd.DataFrame({'speed': speed,
... 'lifespan': lifespan}, index=index)
>>> ax = df.plot.bar(rot=0)
Instead of nesting, the figure can be split by column with
``subplots=True``. In this case, a :class:`numpy.ndarray` of
:class:`matplotlib.axes.Axes` are returned.
.. plot::
:context: close-figs
>>> axes = df.plot.bar(rot=0, subplots=True)
>>> axes[1].legend(loc=2) # doctest: +SKIP
Plot a single column.
.. plot::
:context: close-figs
>>> ax = df.plot.bar(y='speed', rot=0)
Plot only selected categories for the DataFrame.
.. plot::
:context: close-figs
>>> ax = df.plot.bar(x='lifespan', rot=0)
"""
return self(kind='bar', x=x, y=y, **kwds)
def barh(self, x=None, y=None, **kwds):
"""
Make a horizontal bar plot.
A horizontal bar plot is a plot that presents quantitative data with
rectangular bars with lengths proportional to the values that they
represent. A bar plot shows comparisons among discrete categories. One
axis of the plot shows the specific categories being compared, and the
other axis represents a measured value.
Parameters
----------
x : label or position, default DataFrame.index
Column to be used for categories.
y : label or position, default All numeric columns in dataframe
Columns to be plotted from the DataFrame.
**kwds
Keyword arguments to pass on to :meth:`pandas.DataFrame.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them.
See Also
--------
pandas.DataFrame.plot.bar: Vertical bar plot.
pandas.DataFrame.plot : Make plots of DataFrame using matplotlib.
matplotlib.axes.Axes.bar : Plot a vertical bar plot using matplotlib.
Examples
--------
Basic example
.. plot::
:context: close-figs
>>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
>>> ax = df.plot.barh(x='lab', y='val')
Plot a whole DataFrame to a horizontal bar plot
.. plot::
:context: close-figs
>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
... 'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = pd.DataFrame({'speed': speed,
... 'lifespan': lifespan}, index=index)
>>> ax = df.plot.barh()
Plot a column of the DataFrame to a horizontal bar plot
.. plot::
:context: close-figs
>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
... 'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = pd.DataFrame({'speed': speed,
... 'lifespan': lifespan}, index=index)
>>> ax = df.plot.barh(y='speed')
Plot DataFrame versus the desired column
.. plot::
:context: close-figs
>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
... 'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = pd.DataFrame({'speed': speed,
... 'lifespan': lifespan}, index=index)
>>> ax = df.plot.barh(x='lifespan')
"""
return self(kind='barh', x=x, y=y, **kwds)
def box(self, by=None, **kwds):
r"""
Make a box plot of the DataFrame columns.
A box plot is a method for graphically depicting groups of numerical
data through their quartiles.
The box extends from the Q1 to Q3 quartile values of the data,
with a line at the median (Q2). The whiskers extend from the edges
of box to show the range of the data. The position of the whiskers
is set by default to 1.5*IQR (IQR = Q3 - Q1) from the edges of the
box. Outlier points are those past the end of the whiskers.
For further details see Wikipedia's
entry for `boxplot <https://en.wikipedia.org/wiki/Box_plot>`__.
A consideration when using this chart is that the box and the whiskers
can overlap, which is very common when plotting small sets of data.
Parameters
----------
by : string or sequence
Column in the DataFrame to group by.
**kwds : optional
Additional keywords are documented in
:meth:`pandas.DataFrame.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
See Also
--------
pandas.DataFrame.boxplot: Another method to draw a box plot.
pandas.Series.plot.box: Draw a box plot from a Series object.
matplotlib.pyplot.boxplot: Draw a box plot in matplotlib.
Examples
--------
Draw a box plot from a DataFrame with four columns of randomly
generated data.
.. plot::
:context: close-figs
>>> data = np.random.randn(25, 4)
>>> df = pd.DataFrame(data, columns=list('ABCD'))
>>> ax = df.plot.box()
"""
return self(kind='box', by=by, **kwds)
def hist(self, by=None, bins=10, **kwds):
"""
Draw one histogram of the DataFrame's columns.
A histogram is a representation of the distribution of data.
This function groups the values of all given Series in the DataFrame
into bins and draws all bins in one :class:`matplotlib.axes.Axes`.
This is useful when the DataFrame's Series are in a similar scale.
Parameters
----------
by : str or sequence, optional
Column in the DataFrame to group by.
bins : int, default 10
Number of histogram bins to be used.
**kwds
Additional keyword arguments are documented in
:meth:`pandas.DataFrame.plot`.
Returns
-------
axes : matplotlib.AxesSubplot histogram.
See Also
--------
DataFrame.hist : Draw histograms per DataFrame's Series.
Series.hist : Draw a histogram with Series' data.
Examples
--------
When we draw a dice 6000 times, we expect to get each value around 1000
times. But when we draw two dices and sum the result, the distribution
is going to be quite different. A histogram illustrates those
distributions.
.. plot::
:context: close-figs
>>> df = pd.DataFrame(
... np.random.randint(1, 7, 6000),
... columns = ['one'])
>>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
>>> ax = df.plot.hist(bins=12, alpha=0.5)
"""
return self(kind='hist', by=by, bins=bins, **kwds)
@Appender(_kde_docstring % {
'this-datatype': 'DataFrame',
'sibling-datatype': 'Series',
'examples': """
Given several Series of points randomly sampled from unknown
distributions, estimate their PDFs using KDE with automatic
bandwidth determination and plot the results, evaluating them at
1000 equally spaced points (default):
.. plot::
:context: close-figs
>>> df = pd.DataFrame({
... 'x': [1, 2, 2.5, 3, 3.5, 4, 5],
... 'y': [4, 4, 4.5, 5, 5.5, 6, 6],
... })
>>> ax = df.plot.kde()
A scalar bandwidth can be specified. Using a small bandwidth value can
lead to over-fitting, while using a large bandwidth value may result
in under-fitting:
.. plot::
:context: close-figs
>>> ax = df.plot.kde(bw_method=0.3)
.. plot::
:context: close-figs
>>> ax = df.plot.kde(bw_method=3)
Finally, the `ind` parameter determines the evaluation points for the
plot of the estimated PDF:
.. plot::
:context: close-figs
>>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])
""".strip()
})
def kde(self, bw_method=None, ind=None, **kwds):
return self(kind='kde', bw_method=bw_method, ind=ind, **kwds)
density = kde
def area(self, x=None, y=None, **kwds):
"""
Draw a stacked area plot.
An area plot displays quantitative data visually.
This function wraps the matplotlib area function.
Parameters
----------
x : label or position, optional
Coordinates for the X axis. By default uses the index.
y : label or position, optional
Column to plot. By default uses all columns.
stacked : bool, default True
Area plots are stacked by default. Set to False to create a
unstacked plot.
**kwds : optional
Additional keyword arguments are documented in
:meth:`pandas.DataFrame.plot`.
Returns
-------
matplotlib.axes.Axes or numpy.ndarray
Area plot, or array of area plots if subplots is True
See Also
--------
DataFrame.plot : Make plots of DataFrame using matplotlib / pylab.
Examples
--------
Draw an area plot based on basic business metrics:
.. plot::
:context: close-figs
>>> df = pd.DataFrame({
... 'sales': [3, 2, 3, 9, 10, 6],
... 'signups': [5, 5, 6, 12, 14, 13],
... 'visits': [20, 42, 28, 62, 81, 50],
... }, index=pd.date_range(start='2018/01/01', end='2018/07/01',
... freq='M'))
>>> ax = df.plot.area()
Area plots are stacked by default. To produce an unstacked plot,
pass ``stacked=False``:
.. plot::
:context: close-figs
>>> ax = df.plot.area(stacked=False)
Draw an area plot for a single column:
.. plot::
:context: close-figs
>>> ax = df.plot.area(y='sales')
Draw with a different `x`:
.. plot::
:context: close-figs
>>> df = pd.DataFrame({
... 'sales': [3, 2, 3],
... 'visits': [20, 42, 28],
... 'day': [1, 2, 3],
... })
>>> ax = df.plot.area(x='day')
"""
return self(kind='area', x=x, y=y, **kwds)
def pie(self, y=None, **kwds):
"""
Generate a pie plot.
A pie plot is a proportional representation of the numerical data in a
column. This function wraps :meth:`matplotlib.pyplot.pie` for the
specified column. If no column reference is passed and
``subplots=True`` a pie plot is drawn for each numerical column
independently.
Parameters
----------
y : int or label, optional
Label or position of the column to plot.
If not provided, ``subplots=True`` argument must be passed.
**kwds
Keyword arguments to pass on to :meth:`pandas.DataFrame.plot`.
Returns
-------
axes : matplotlib.axes.Axes or np.ndarray of them.
A NumPy array is returned when `subplots` is True.
See Also
--------
Series.plot.pie : Generate a pie plot for a Series.
DataFrame.plot : Make plots of a DataFrame.
Examples
--------
In the example below we have a DataFrame with the information about
planet's mass and radius. We pass the the 'mass' column to the
pie function to get a pie plot.
.. plot::
:context: close-figs
>>> df = pd.DataFrame({'mass': [0.330, 4.87 , 5.97],
... 'radius': [2439.7, 6051.8, 6378.1]},
... index=['Mercury', 'Venus', 'Earth'])
>>> plot = df.plot.pie(y='mass', figsize=(5, 5))
.. plot::
:context: close-figs
>>> plot = df.plot.pie(subplots=True, figsize=(6, 3))
"""
return self(kind='pie', y=y, **kwds)
def scatter(self, x, y, s=None, c=None, **kwds):
"""
Create a scatter plot with varying marker point size and color.
The coordinates of each point are defined by two dataframe columns and
filled circles are used to represent each point. This kind of plot is
useful to see complex correlations between two variables. Points could
be for instance natural 2D coordinates like longitude and latitude in
a map or, in general, any pair of metrics that can be plotted against
each other.
Parameters
----------
x : int or str
The column name or column position to be used as horizontal
coordinates for each point.
y : int or str
The column name or column position to be used as vertical
coordinates for each point.
s : scalar or array_like, optional
The size of each point. Possible values are:
- A single scalar so all points have the same size.
- A sequence of scalars, which will be used for each point's size
recursively. For instance, when passing [2,14] all points size
will be either 2 or 14, alternatively.
c : str, int or array_like, optional
The color of each point. Possible values are:
- A single color string referred to by name, RGB or RGBA code,
for instance 'red' or '#a98d19'.
- A sequence of color strings referred to by name, RGB or RGBA
code, which will be used for each point's color recursively. For
instance ['green','yellow'] all points will be filled in green or
yellow, alternatively.
- A column name or position whose values will be used to color the
marker points according to a colormap.
**kwds
Keyword arguments to pass on to :meth:`pandas.DataFrame.plot`.
Returns
-------
axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
See Also
--------
matplotlib.pyplot.scatter : Scatter plot using multiple input data
formats.
Examples
--------
Let's see how to draw a scatter plot using coordinates from the values
in a DataFrame's columns.
.. plot::
:context: close-figs
>>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
... [6.4, 3.2, 1], [5.9, 3.0, 2]],
... columns=['length', 'width', 'species'])
>>> ax1 = df.plot.scatter(x='length',
... y='width',
... c='DarkBlue')
And now with the color determined by a column as well.
.. plot::
:context: close-figs
>>> ax2 = df.plot.scatter(x='length',
... y='width',
... c='species',
... colormap='viridis')
"""
return self(kind='scatter', x=x, y=y, c=c, s=s, **kwds)
def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
**kwds):
"""
Generate a hexagonal binning plot.
Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
(the default), this is a histogram of the number of occurrences
of the observations at ``(x[i], y[i])``.
If `C` is specified, specifies values at given coordinates
``(x[i], y[i])``. These values are accumulated for each hexagonal
bin and then reduced according to `reduce_C_function`,
having as default the NumPy's mean function (:meth:`numpy.mean`).
(If `C` is specified, it must also be a 1-D sequence
of the same length as `x` and `y`, or a column label.)
Parameters
----------
x : int or str
The column label or position for x points.
y : int or str
The column label or position for y points.
C : int or str, optional
The column label or position for the value of `(x, y)` point.
reduce_C_function : callable, default `np.mean`
Function of one argument that reduces all the values in a bin to
a single number (e.g. `np.mean`, `np.max`, `np.sum`, `np.std`).
gridsize : int or tuple of (int, int), default 100
The number of hexagons in the x-direction.
The corresponding number of hexagons in the y-direction is
chosen in a way that the hexagons are approximately regular.
Alternatively, gridsize can be a tuple with two elements
specifying the number of hexagons in the x-direction and the
y-direction.
**kwds
Additional keyword arguments are documented in
:meth:`pandas.DataFrame.plot`.
Returns
-------
matplotlib.AxesSubplot
The matplotlib ``Axes`` on which the hexbin is plotted.
See Also
--------
DataFrame.plot : Make plots of a DataFrame.
matplotlib.pyplot.hexbin : Hexagonal binning plot using matplotlib,
the matplotlib function that is used under the hood.
Examples
--------
The following examples are generated with random data from
a normal distribution.
.. plot::
:context: close-figs
>>> n = 10000
>>> df = pd.DataFrame({'x': np.random.randn(n),
... 'y': np.random.randn(n)})
>>> ax = df.plot.hexbin(x='x', y='y', gridsize=20)
The next example uses `C` and `np.sum` as `reduce_C_function`.
Note that `'observations'` values ranges from 1 to 5 but the result
plot shows values up to more than 25. This is because of the
`reduce_C_function`.
.. plot::
:context: close-figs
>>> n = 500
>>> df = pd.DataFrame({
... 'coord_x': np.random.uniform(-3, 3, size=n),
... 'coord_y': np.random.uniform(30, 50, size=n),
... 'observations': np.random.randint(1,5, size=n)
... })
>>> ax = df.plot.hexbin(x='coord_x',
... y='coord_y',
... C='observations',
... reduce_C_function=np.sum,
... gridsize=10,
... cmap="viridis")
"""
if reduce_C_function is not None:
kwds['reduce_C_function'] = reduce_C_function
if gridsize is not None:
kwds['gridsize'] = gridsize
return self(kind='hexbin', x=x, y=y, C=C, **kwds)