# 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 ` 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 `_. 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) 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) 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) """ @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) .. 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 `__. 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)