def _grouped_plot_by_column(
plotf,
data,
columns=None,
by=None,
numeric_only=True,
grid=False,
figsize=None,
ax=None,
layout=None,
return_type=None,
**kwargs,
):
grouped = data.groupby(by)
if columns is None:
if not isinstance(by, (list, tuple)):
by = [by]
columns = data._get_numeric_data().columns.difference(by)
naxes = len(columns)
fig, axes = create_subplots(
naxes=naxes,
sharex=kwargs.pop("sharex", True),
sharey=kwargs.pop("sharey", True),
figsize=figsize,
ax=ax,
layout=layout,
)
_axes = flatten_axes(axes)
# GH 45465: move the "by" label based on "vert"
xlabel, ylabel = kwargs.pop("xlabel", None), kwargs.pop("ylabel", None)
if kwargs.get("vert", True):
xlabel = xlabel or by
else:
ylabel = ylabel or by
ax_values = []
for i, col in enumerate(columns):
ax = _axes[i]
gp_col = grouped[col]
keys, values = zip(*gp_col)
re_plotf = plotf(keys, values, ax, xlabel=xlabel, ylabel=ylabel, **kwargs)
ax.set_title(col)
ax_values.append(re_plotf)
ax.grid(grid)
result = pd.Series(ax_values, index=columns)
# Return axes in multiplot case, maybe revisit later # 985
if return_type is None:
result = axes
byline = by[0] if len(by) == 1 else by
fig.suptitle(f"Boxplot grouped by {byline}")
maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
return result
def _grouped_plot_by_column(
plotf,
data,
columns=None,
by=None,
numeric_only=True,
grid=False,
figsize=None,
ax=None,
layout=None,
return_type=None,
**kwargs,
):
grouped = data.groupby(by)
if columns is None:
if not isinstance(by, (list, tuple)):
by = [by]
columns = data._get_numeric_data().columns.difference(by)
naxes = len(columns)
fig, axes = create_subplots(naxes=naxes,
sharex=True,
sharey=True,
figsize=figsize,
ax=ax,
layout=layout)
_axes = flatten_axes(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)
result = pd.Series(ax_values, index=columns)
# Return axes in multiplot case, maybe revisit later # 985
if return_type is None:
result = axes
byline = by[0] if len(by) == 1 else by
fig.suptitle(f"Boxplot grouped by {byline}")
fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
return result
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'
raise ValueError("figsize='default' is no longer supported. "
"Specify figure size by tuple instead")
grouped = data.groupby(by)
if column is not None:
grouped = grouped[column]
naxes = len(grouped)
fig, axes = create_subplots(naxes=naxes,
figsize=figsize,
sharex=sharex,
sharey=sharey,
ax=ax,
layout=layout)
_axes = flatten_axes(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 plot(
plot_func,
ax=None,
figsize=None,
layout=None,
title=None,
xlabel=None,
ylabel=None,
grid: bool = True,
legend: bool = False,
colorbar: bool = False,
):
# TODO: dynamic axes with column = None as default
naxes = 1
fig, axes = pd_mpl_tools.create_subplots(
naxes=naxes,
ax=ax,
figsize=figsize,
layout=layout,
# sharex=sharex,
# sharey=sharey,
squeeze=False,
)
_axes = pd_mpl_tools.flatten_axes(axes)
# Make the target plot
ax = _axes[0]
p = plot_func(ax)
# Other MPL things
ax.set_title(title)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.grid(grid)
if legend:
ax.legend()
if colorbar:
fig.colorbar(p, ax=ax)
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,
):
if subplots is True:
naxes = len(grouped)
fig, axes = create_subplots(
naxes=naxes,
squeeze=False,
ax=ax,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout,
)
axes = flatten_axes(axes)
ret = pd.Series(dtype=object)
for (key, group), ax in zip(grouped, axes):
d = group.boxplot(
ax=ax, column=column, fontsize=fontsize, rot=rot, grid=grid, **kwds
)
ax.set_title(pprint_thing(key))
ret.loc[key] = d
maybe_adjust_figure(fig, bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)
else:
keys, frames = zip(*grouped)
if grouped.axis == 0:
df = pd.concat(frames, keys=keys, axis=1)
else:
if len(frames) > 1:
df = frames[0].join(frames[1::])
else:
df = frames[0]
# GH 16748, DataFrameGroupby fails when subplots=False and `column` argument
# is assigned, and in this case, since `df` here becomes MI after groupby,
# so we need to couple the keys (grouped values) and column (original df
# column) together to search for subset to plot
if column is not None:
column = com.convert_to_list_like(column)
multi_key = pd.MultiIndex.from_product([keys, column])
column = list(multi_key.values)
ret = df.boxplot(
column=column,
fontsize=fontsize,
rot=rot,
grid=grid,
ax=ax,
figsize=figsize,
layout=layout,
**kwds,
)
return ret
def _joyplot(data,
grid=False,
labels=None, sublabels=None,
xlabels=True,
xlabelsize=None, xrot=None,
ylabelsize=None, yrot=None,
ax=None, figsize=None,
hist=False, bins=10,
fade=False,
xlim=None, ylim='max',
fill=True, linecolor=None,
overlap=1, background=None,
range_style='all', x_range=None, tails=0.2,
title=None,
legend=False, loc="upper right",
colormap=None, color=None, last_axis=True,
**kwargs):
"""
Internal method.
Draw a joyplot from an appropriately nested collection of lists
using matplotlib and pandas.
Parameters
----------
data : DataFrame, Series or nested collection
grid : boolean, default True
Whether to show axis grid lines
labels : boolean or list, default True.
If list, must be the same size of the de
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
ax : matplotlib axes object, default None
figsize : tuple
The size of the figure to create in inches by default
hist : boolean, default False
bins : integer, default 10
Number of histogram bins to be used
kwarg : other plotting keyword arguments
To be passed to hist/kde plot function
"""
if fill is True and linecolor is None:
linecolor = "k"
if sublabels is None:
legend = False
def _get_color(i, num_axes, j, num_subgroups):
if isinstance(color, list):
try:
return color[i]
except IndexError:
pass
elif color is not None:
return color
elif isinstance(colormap, list):
return colormap[j](i/num_axes)
elif color is None and colormap is None:
return plt.rcParams['axes.prop_cycle'].by_key()['color'][j]
else:
return colormap(i/num_axes)
ygrid = (grid is True or grid == 'y' or grid == 'both')
xgrid = (grid is True or grid == 'x' or grid == 'both')
num_axes = len(data)
if x_range is None:
global_x_range = _x_range([v for g in data for sg in g for v in sg])
else:
global_x_range = _x_range(x_range, 0.0)
global_x_min, global_x_max = min(global_x_range), max(global_x_range)
# Each plot will have its own axis
fig, axes = create_subplots(naxes=num_axes, ax=ax, squeeze=False,
sharex=False, sharey=False, figsize=figsize,
layout_type='vertical')
_axes = flatten_axes(axes)
# The legend must be drawn in the last axis if we want it at the bottom.
if loc in (3, 4, 8) or 'lower' in str(loc):
legend_axis = num_axes - 1
else:
legend_axis = 0
# A couple of simple checks.
if labels is not None:
assert len(labels) == num_axes
if sublabels is not None:
assert all(len(g) == len(sublabels) for g in data)
#if isinstance(color, list):
# assert all(len(g) == len(color) for g in data)
if isinstance(colormap, list):
assert all(len(g) == len(colormap) for g in data)
for i, group in enumerate(data):
a = _axes[i]
group_zorder = i
if fade:
kwargs['alpha'] = _get_alpha(i, num_axes)
num_subgroups = len(group)
if hist:
# matplotlib hist() already handles multiple subgroups in a histogram
a.hist(group, label=sublabels, bins=bins, color=color,
range=[min(global_x_range), max(global_x_range)],
edgecolor=linecolor, zorder=group_zorder, **kwargs)
else:
for j, subgroup in enumerate(group):
# Compute the x_range of the current plot
if range_style == 'all':
# All plots have the same range
x_range = global_x_range
elif range_style == 'own':
# Each plot has its own range
x_range = _x_range(subgroup, tails)
elif range_style == 'group':
# Each plot has a range that covers the whole group
x_range = _x_range(group, tails)
elif isinstance(range_style, (list, np.ndarray)):
# All plots have exactly the range passed as argument
x_range = _x_range(range_style, 0.0)
else:
raise NotImplementedError("Unrecognized range style.")
if sublabels is None:
sublabel = None
else:
sublabel = sublabels[j]
element_zorder = group_zorder + j/(num_subgroups+1)
element_color = _get_color(i, num_axes, j, num_subgroups)
plot_density(a, x_range, subgroup,
fill=fill, linecolor=linecolor, label=sublabel,
zorder=element_zorder, color=element_color,
bins=bins, **kwargs)
# Setup the current axis: transparency, labels, spines.
col_name = None if labels is None else labels[i]
_setup_axis(a, global_x_range, col_name=col_name, grid=ygrid,
ylabelsize=ylabelsize, yrot=yrot)
# When needed, draw the legend
if legend and i == legend_axis:
a.legend(loc=loc)
# Bypass alpha values, in case
for p in a.get_legend().get_patches():
p.set_facecolor(p.get_facecolor())
p.set_alpha(1.0)
for l in a.get_legend().get_lines():
l.set_alpha(1.0)
# Final adjustments
# Set the y limit for the density plots.
# Since the y range in the subplots can vary significantly,
# different options are available.
if ylim == 'max':
# Set all yaxis limit to the same value (max range among all)
max_ylim = max(a.get_ylim()[1] for a in _axes)
min_ylim = min(a.get_ylim()[0] for a in _axes)
for a in _axes:
a.set_ylim([min_ylim - 0.1*(max_ylim-min_ylim), max_ylim])
elif ylim == 'own':
# Do nothing, each axis keeps its own ylim
pass
else:
# Set all yaxis lim to the argument value ylim
try:
for a in _axes:
a.set_ylim(ylim)
except:
print("Warning: the value of ylim must be either 'max', 'own', or a tuple of length 2. The value you provided has no effect.")
if last_axis is True:
# Compute a final axis, used to apply global settings
last_axis = fig.add_subplot(1, 1, 1)
# Background color
if background is not None:
last_axis.patch.set_facecolor(background)
for side in ['top', 'bottom', 'left', 'right']:
last_axis.spines[side].set_visible(_DEBUG)
# This looks hacky, but all the axes share the x-axis,
# so they have the same lims and ticks
last_axis.set_xlim(_axes[0].get_xlim())
if xlabels is True:
last_axis.set_xticks(np.array(_axes[0].get_xticks()[1:-1]))
for t in last_axis.get_xticklabels():
t.set_visible(True)
t.set_fontsize(xlabelsize)
t.set_rotation(xrot)
# If grid is enabled, do not allow xticks (they are ugly)
if xgrid:
last_axis.tick_params(axis='both', which='both',length=0)
else:
last_axis.xaxis.set_visible(False)
last_axis.yaxis.set_visible(False)
last_axis.grid(xgrid)
# Last axis on the back
last_axis.zorder = min(a.zorder for a in _axes) - 1
_axes = list(_axes) + [last_axis]
if title is not None:
plt.title(title)
# The magic overlap happens here.
#h_pad = 5 + (- 5*(1 + overlap))
#fig.tight_layout(h_pad=h_pad)
return fig, _axes
def scatter_matrix(
frame: DataFrame,
alpha=0.5,
figsize=None,
ax=None,
grid=False,
diagonal="hist",
marker=".",
density_kwds=None,
hist_kwds=None,
range_padding=0.05,
**kwds,
):
df = frame._get_numeric_data()
n = df.columns.size
naxes = n * n
fig, axes = create_subplots(naxes=naxes, figsize=figsize, ax=ax, squeeze=False)
# no gaps between subplots
maybe_adjust_figure(fig, wspace=0, hspace=0)
mask = notna(df)
marker = _get_marker_compat(marker)
hist_kwds = hist_kwds or {}
density_kwds = density_kwds or {}
# GH 14855
kwds.setdefault("edgecolors", "none")
boundaries_list = []
for a in df.columns:
values = df[a].values[mask[a].values]
rmin_, rmax_ = np.min(values), np.max(values)
rdelta_ext = (rmax_ - rmin_) * range_padding / 2
boundaries_list.append((rmin_ - rdelta_ext, rmax_ + rdelta_ext))
for i, a in enumerate(df.columns):
for j, b in enumerate(df.columns):
ax = axes[i, j]
if i == j:
values = df[a].values[mask[a].values]
# Deal with the diagonal by drawing a histogram there.
if diagonal == "hist":
ax.hist(values, **hist_kwds)
elif diagonal in ("kde", "density"):
from scipy.stats import gaussian_kde
y = values
gkde = gaussian_kde(y)
ind = np.linspace(y.min(), y.max(), 1000)
ax.plot(ind, gkde.evaluate(ind), **density_kwds)
ax.set_xlim(boundaries_list[i])
else:
common = (mask[a] & mask[b]).values
ax.scatter(
df[b][common], df[a][common], marker=marker, alpha=alpha, **kwds
)
ax.set_xlim(boundaries_list[j])
ax.set_ylim(boundaries_list[i])
ax.set_xlabel(b)
ax.set_ylabel(a)
if j != 0:
ax.yaxis.set_visible(False)
if i != n - 1:
ax.xaxis.set_visible(False)
if len(df.columns) > 1:
lim1 = boundaries_list[0]
locs = axes[0][1].yaxis.get_majorticklocs()
locs = locs[(lim1[0] <= locs) & (locs <= lim1[1])]
adj = (locs - lim1[0]) / (lim1[1] - lim1[0])
lim0 = axes[0][0].get_ylim()
adj = adj * (lim0[1] - lim0[0]) + lim0[0]
axes[0][0].yaxis.set_ticks(adj)
if np.all(locs == locs.astype(int)):
# if all ticks are int
locs = locs.astype(int)
axes[0][0].yaxis.set_ticklabels(locs)
set_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
return axes
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,
legend: bool = False,
**kwds,
):
if legend and "label" in kwds:
raise ValueError("Cannot use both legend and label")
if by is not None:
axes = _grouped_hist(
data,
column=column,
by=by,
ax=ax,
grid=grid,
figsize=figsize,
sharex=sharex,
sharey=sharey,
layout=layout,
bins=bins,
xlabelsize=xlabelsize,
xrot=xrot,
ylabelsize=ylabelsize,
yrot=yrot,
legend=legend,
**kwds,
)
return axes
if column is not None:
if not isinstance(column, (list, np.ndarray, ABCIndexClass)):
column = [column]
data = data[column]
# GH32590
data = data.select_dtypes(include=(np.number, "datetime64", "datetimetz"),
exclude="timedelta")
naxes = len(data.columns)
if naxes == 0:
raise ValueError(
"hist method requires numerical or datetime columns, nothing to plot."
)
fig, axes = create_subplots(
naxes=naxes,
ax=ax,
squeeze=False,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout,
)
_axes = flatten_axes(axes)
can_set_label = "label" not in kwds
for i, col in enumerate(data.columns):
ax = _axes[i]
if legend and can_set_label:
kwds["label"] = col
ax.hist(data[col].dropna().values, bins=bins, **kwds)
ax.set_title(col)
ax.grid(grid)
if legend:
ax.legend()
set_ticks_props(axes,
xlabelsize=xlabelsize,
xrot=xrot,
ylabelsize=ylabelsize,
yrot=yrot)
fig.subplots_adjust(wspace=0.3, hspace=0.3)
return axes
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,
):
# Start with empty pandas data frame derived from
ed_df_bins, ed_df_weights = data._hist(num_bins=bins)
converter._WARN = False # no warning for pandas plots
if by is not None:
raise NotImplementedError("TODO")
if column is not None:
if not isinstance(column, (list, np.ndarray, ABCIndexClass)):
column = [column]
ed_df_bins = ed_df_bins[column]
ed_df_weights = ed_df_weights[column]
naxes = len(ed_df_bins.columns)
if naxes == 0:
raise ValueError("hist method requires numerical columns, "
"nothing to plot.")
fig, axes = create_subplots(
naxes=naxes,
ax=ax,
squeeze=False,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout,
)
_axes = flatten_axes(axes)
for i, col in enumerate(try_sort(data.columns)):
ax = _axes[i]
ax.hist(
ed_df_bins[col][:-1],
bins=ed_df_bins[col],
weights=ed_df_weights[col],
**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 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,
):
if subplots is True:
naxes = len(grouped)
fig, axes = create_subplots(
naxes=naxes,
squeeze=False,
ax=ax,
sharex=sharex,
sharey=sharey,
figsize=figsize,
layout=layout,
)
axes = flatten_axes(axes)
ret = pd.Series(dtype=object)
for (key, group), ax in zip(grouped, axes):
d = group.boxplot(ax=ax,
column=column,
fontsize=fontsize,
rot=rot,
grid=grid,
**kwds)
ax.set_title(pprint_thing(key))
ret.loc[key] = d
fig.subplots_adjust(bottom=0.15,
top=0.9,
left=0.1,
right=0.9,
wspace=0.2)
else:
keys, frames = zip(*grouped)
if grouped.axis == 0:
df = pd.concat(frames, keys=keys, axis=1)
else:
if len(frames) > 1:
df = frames[0].join(frames[1::])
else:
df = frames[0]
ret = df.boxplot(
column=column,
fontsize=fontsize,
rot=rot,
grid=grid,
ax=ax,
figsize=figsize,
layout=layout,
**kwds,
)
return ret
