/usr/lib/python3/dist-packages/seaborn/axisgrid.py is in python3-seaborn 0.4.0-3.
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from itertools import product
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
from six import string_types
from . import utils
from .palettes import color_palette
class Grid(object):
"""Base class for grids of subplots."""
_margin_titles = False
_legend_out = True
def set(self, **kwargs):
"""Set attributes on each subplot Axes."""
for ax in self.axes.flat:
ax.set(**kwargs)
return self
def savefig(self, *args, **kwargs):
"""Save the figure."""
self.fig.savefig(*args, **kwargs)
def add_legend(self, legend_data=None, title=None, label_order=None):
"""Draw a legend, possibly resizing the figure."""
# Find the data for the legend
legend_data = self._legend_data if legend_data is None else legend_data
if label_order is None:
if self.hue_names is None:
label_order = np.sort(list(legend_data.keys()))
else:
label_order = list(map(str, self.hue_names))
handles = [legend_data[l] for l in label_order if l in legend_data]
title = self._hue_var if title is None else title
try:
title_size = mpl.rcParams["axes.labelsize"] * .85
except TypeError: # labelsize is something like "large"
title_size = mpl.rcParams["axes.labelsize"]
if self._legend_out:
# Draw a full-figure legend outside the grid
figlegend = plt.figlegend(handles, label_order, "center right",
scatterpoints=1)
self._legend = figlegend
figlegend.set_title(title)
# Set the title size a roundabout way to maintain
# compatability with matplotlib 1.1
prop = mpl.font_manager.FontProperties(size=title_size)
figlegend._legend_title_box._text.set_font_properties(prop)
# Draw the plot to set the bounding boxes correctly
plt.draw()
# Calculate and set the new width of the figure so the legend fits
legend_width = figlegend.get_window_extent().width / self.fig.dpi
figure_width = self.fig.get_figwidth()
self.fig.set_figwidth(figure_width + legend_width)
# Draw the plot again to get the new transformations
plt.draw()
# Now calculate how much space we need on the right side
legend_width = figlegend.get_window_extent().width / self.fig.dpi
space_needed = legend_width / (figure_width + legend_width)
margin = .04 if self._margin_titles else .01
self._space_needed = margin + space_needed
right = 1 - self._space_needed
# Place the subplot axes to give space for the legend
self.fig.subplots_adjust(right=right)
else:
# Draw a legend in the first axis
leg = self.axes.flat[0].legend(handles, label_order, loc="best")
leg.set_title(title)
# Set the title size a roundabout way to maintain
# compatability with matplotlib 1.1
prop = mpl.font_manager.FontProperties(size=title_size)
leg._legend_title_box._text.set_font_properties(prop)
def _clean_axis(self, ax):
"""Turn off axis labels and legend."""
ax.set_xlabel("")
ax.set_ylabel("")
ax.legend_ = None
return self
def _update_legend_data(self, ax):
"""Extract the legend data from an axes object and save it."""
handles, labels = ax.get_legend_handles_labels()
data = {l: h for h, l in zip(handles, labels)}
self._legend_data.update(data)
def _get_palette(self, data, hue, hue_order, palette, dropna):
"""Get a list of colors for the hue variable."""
if hue is None:
palette = color_palette(n_colors=1)
else:
if hue_order is None:
hue_names = np.unique(np.sort(data[hue]))
else:
hue_names = hue_order
if dropna:
# Filter NA from the list of unique hue names
hue_names = list(filter(pd.notnull, hue_names))
n_colors = len(hue_names)
# By default use either the current color palette or HUSL
if palette is None:
current_palette = mpl.rcParams["axes.color_cycle"]
if n_colors > len(current_palette):
colors = color_palette("husl", n_colors)
else:
colors = color_palette(n_colors=n_colors)
# Allow for palette to map from hue variable names
elif isinstance(palette, dict):
color_names = [palette[h] for h in hue_names]
colors = color_palette(color_names, n_colors)
# Otherwise act as if we just got a list of colors
else:
colors = color_palette(palette, n_colors)
palette = color_palette(colors, n_colors)
return palette
class FacetGrid(Grid):
"""Subplot grid for plotting conditional relationships in a dataset."""
def __init__(self, data, row=None, col=None, hue=None, col_wrap=None,
sharex=True, sharey=True, size=3, aspect=1, palette=None,
row_order=None, col_order=None, hue_order=None,
dropna=True, legend=True, legend_out=True, despine=True,
margin_titles=False, xlim=None, ylim=None):
"""Initialize the plot figure and FacetGrid object.
Parameters
----------
data : DataFrame
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
row, col, hue : strings, optional
Variable (column) names to subset the data for the facets.
col_wrap : int, optional
Wrap the column variable at this width. Incompatible with `row`.
share{x, y}: booleans, optional
Lock the limits of the vertical andn horizontal axes across the
facets.
size : scalar, optional
Height (in inches) of each facet.
aspect : scalar, optional
Aspect * size gives the width (in inches) of each facet.
palette : dict or seaborn color palette
Set of colors for mapping the `hue` variable. If a dict, keys
should be values in the `hue` variable.
{row, col, hue}_order: sequence of strings
Order to plot the values in the faceting variables in, otherwise
sorts the unique values.
dropna : boolean, optional
Drop missing values from the data before plotting.
legend : boolean, optional
Draw a legend for the data when using a `hue` variable.
legend_out: boolean, optional
Draw the legend outside the grid of plots.
despine : boolean, optional
Remove the top and right spines from the plots.
margin_titles : boolean, optional
Write the column and row variable labels on the margins of the
grid rather than above each plot.
{x, y}lim: tuples, optional
Limits for each of the axes on each facet when share{x, y} is True.
Returns
-------
self : FacetGrid
Returns self for plotting onto the grid.
See Also
--------
PairGrid : Subplot grid for plotting pairwise relationships.
lmplot : Combines regplot and a FacetGrid
factorplot : Combines pointplot, barplot, or boxplot and a FacetGrid
"""
# Compute the grid shape
ncol = 1 if col is None else len(data[col].unique())
nrow = 1 if row is None else len(data[row].unique())
self._n_facets = ncol * nrow
self._col_wrap = col_wrap
if col_wrap is not None:
ncol = col_wrap
nrow = int(np.ceil(len(data[col].unique()) / col_wrap))
self._ncol = ncol
self._nrow = nrow
# Calculate the base figure size
# This can get stretched later by a legend
figsize = (ncol * size * aspect, nrow * size)
# Validate some inputs
if col_wrap is not None:
margin_titles = False
# Build the subplot keyword dictionary
subplot_kw = {}
if xlim is not None:
subplot_kw["xlim"] = xlim
if ylim is not None:
subplot_kw["ylim"] = ylim
# Initialize the subplot grid
if col_wrap is None:
fig, axes = plt.subplots(nrow, ncol, figsize=figsize,
squeeze=False,
sharex=sharex, sharey=sharey,
subplot_kw=subplot_kw)
self.axes = axes
else:
# If wrapping the col variable we need to make the grid ourselves
n_axes = len(data[col].unique())
fig = plt.figure(figsize=figsize)
axes = np.empty(n_axes, object)
axes[0] = fig.add_subplot(nrow, ncol, 1, **subplot_kw)
if sharex:
subplot_kw["sharex"] = axes[0]
if sharey:
subplot_kw["sharey"] = axes[0]
for i in range(1, n_axes):
axes[i] = fig.add_subplot(nrow, ncol, i + 1, **subplot_kw)
self.axes = axes
# Now we turn off labels on the inner axes
if sharex:
for ax in self._not_bottom_axes:
for label in ax.get_xticklabels():
label.set_visible(False)
ax.xaxis.offsetText.set_visible(False)
if sharey:
for ax in self._not_left_axes:
for label in ax.get_yticklabels():
label.set_visible(False)
ax.yaxis.offsetText.set_visible(False)
# Determine the hue facet layer information
hue_var = hue
if hue is None:
hue_names = None
else:
if hue_order is None:
hue_names = np.unique(np.sort(data[hue]))
else:
hue_names = hue_order
if dropna:
# Filter NA from the list of unique hue names
hue_names = list(filter(pd.notnull, hue_names))
colors = self._get_palette(data, hue, hue_order, palette, dropna)
# Make a boolean mask that is True anywhere there is an NA
# value in one of the faceting variables, but only if dropna is True
none_na = np.zeros(len(data), np.bool)
if dropna:
row_na = none_na if row is None else data[row].isnull()
col_na = none_na if col is None else data[col].isnull()
hue_na = none_na if hue is None else data[hue].isnull()
not_na = ~(row_na | col_na | hue_na)
else:
not_na = ~none_na
# Set up the lists of names for the row and column facet variables
if row is None:
row_names = []
elif row_order is None:
row_names = np.unique(np.sort(data[row]))
else:
row_names = row_order
if dropna:
row_names = list(filter(pd.notnull, row_names))
if col is None:
col_names = []
elif col_order is None:
col_names = np.unique(np.sort(data[col]))
else:
col_names = col_order
if dropna:
col_names = list(filter(pd.notnull, col_names))
# Set up the class attributes
# ---------------------------
# First the public API
self.data = data
self.fig = fig
self.axes = axes
self.row_names = row_names
self.col_names = col_names
self.hue_names = hue_names
# Next the private variables
self._nrow = nrow
self._row_var = row
self._ncol = ncol
self._col_var = col
self._margin_titles = margin_titles
self._col_wrap = col_wrap
self._hue_var = hue_var
self._colors = colors
self._draw_legend = ((hue is not None and hue not in [col, row])
and legend)
self._legend_out = legend_out
self._legend = None
self._legend_data = {}
self._x_var = None
self._y_var = None
self._dropna = dropna
self._not_na = not_na
# Make the axes look good
fig.tight_layout()
if despine:
self.despine()
def facet_data(self):
"""Generator for name indices and data subsets for each facet.
Yields
------
(i, j, k), data_ijk : tuple of ints, DataFrame
The ints provide an index into the {row, col, hue}_names attribute,
and the dataframe contains a subset of the full data corresponding
to each facet. The generator yields subsets that correspond with
the self.axes.flat iterator, or self.axes[i, j] when `col_wrap`
is None.
"""
data = self.data
# Construct masks for the row variable
if self._nrow == 1 or self._col_wrap is not None:
row_masks = [np.repeat(True, len(self.data))]
else:
row_masks = [data[self._row_var] == n for n in self.row_names]
# Construct masks for the column variable
if self._ncol == 1:
col_masks = [np.repeat(True, len(self.data))]
else:
col_masks = [data[self._col_var] == n for n in self.col_names]
# Construct masks for the hue variable
if len(self._colors) == 1:
hue_masks = [np.repeat(True, len(self.data))]
else:
hue_masks = [data[self._hue_var] == n for n in self.hue_names]
# Here is the main generator loop
for (i, row), (j, col), (k, hue) in product(enumerate(row_masks),
enumerate(col_masks),
enumerate(hue_masks)):
data_ijk = data[row & col & hue & self._not_na]
yield (i, j, k), data_ijk
def map(self, func, *args, **kwargs):
"""Apply a plotting function to each facet's subset of the data.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. It
must plot to the currently active matplotlib Axes and take a
`color` keyword argument. If faceting on the `hue` dimension,
it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
# If color was a keyword argument, grab it here
kw_color = kwargs.pop("color", None)
# Iterate over the data subsets
for (row_i, col_j, hue_k), data_ijk in self.facet_data():
# If this subset is null, move on
if not data_ijk.values.tolist():
continue
# Get the current axis
ax = self.facet_axis(row_i, col_j)
# Decide what color to plot with
kwargs["color"] = self._facet_color(hue_k, kw_color)
# Insert a label in the keyword arguments for the legend
if self._hue_var is not None:
kwargs["label"] = str(self.hue_names[hue_k])
# Get the actual data we are going to plot with
plot_data = data_ijk[list(args)]
if self._dropna:
plot_data = plot_data.dropna()
plot_args = [v for k, v in plot_data.iteritems()]
# Some matplotlib functions don't handle pandas objects correctly
if func.__module__.startswith("matplotlib"):
plot_args = [v.values for v in plot_args]
# Draw the plot
self._facet_plot(func, ax, plot_args, kwargs)
# Finalize the annotations and layout
self._finalize_grid(args[:2])
return self
def map_dataframe(self, func, *args, **kwargs):
"""Like `map` but passes args as strings and inserts data in kwargs.
This method is suitable for plotting with functions that accept a
long-form DataFrame as a `data` keyword argument and access the
data in that DataFrame using string variable names.
Parameters
----------
func : callable
A plotting function that takes data and keyword arguments. Unlike
the `map` method, a function used here must "understand" Pandas
objects. It also must plot to the currently active matplotlib Axes
and take a `color` keyword argument. If faceting on the `hue`
dimension, it must also take a `label` keyword argument.
args : strings
Column names in self.data that identify variables with data to
plot. The data for each variable is passed to `func` in the
order the variables are specified in the call.
kwargs : keyword arguments
All keyword arguments are passed to the plotting function.
Returns
-------
self : object
Returns self.
"""
# If color was a keyword argument, grab it here
kw_color = kwargs.pop("color", None)
# Iterate over the data subsets
for (row_i, col_j, hue_k), data_ijk in self.facet_data():
# If this subset is null, move on
if not data_ijk.values.tolist():
continue
# Get the current axis
ax = self.facet_axis(row_i, col_j)
# Decide what color to plot with
kwargs["color"] = self._facet_color(hue_k, kw_color)
# Insert a label in the keyword arguments for the legend
if self._hue_var is not None:
kwargs["label"] = self.hue_names[hue_k]
# Stick the facet dataframe into the kwargs
if self._dropna:
data_ijk = data_ijk.dropna()
kwargs["data"] = data_ijk
# Draw the plot
self._facet_plot(func, ax, args, kwargs)
# Finalize the annotations and layout
self._finalize_grid(args[:2])
return self
def _facet_color(self, hue_index, kw_color):
color = self._colors[hue_index]
if kw_color is not None:
return kw_color
elif color is not None:
return color
def _facet_plot(self, func, ax, plot_args, plot_kwargs):
# Draw the plot
func(*plot_args, **plot_kwargs)
# Sort out the supporting information
self._update_legend_data(ax)
self._clean_axis(ax)
def _finalize_grid(self, axlabels):
"""Finalize the annotations and layout."""
self.set_axis_labels(*axlabels)
self.set_titles()
self.fig.tight_layout()
def facet_axis(self, row_i, col_j):
"""Make the axis identified by these indices active and return it."""
# Calculate the actual indices of the axes to plot on
if self._col_wrap is not None:
ax = self.axes.flat[col_j]
else:
ax = self.axes[row_i, col_j]
# Get a reference to the axes object we want, and make it active
plt.sca(ax)
return ax
def despine(self, **kwargs):
"""Remove axis spines from the facets."""
utils.despine(self.fig, **kwargs)
return self
def set_axis_labels(self, x_var=None, y_var=None):
"""Set axis labels on the left column and bottom row of the grid."""
if x_var is not None:
self._x_var = x_var
self.set_xlabels(x_var)
if y_var is not None:
self._y_var = y_var
self.set_ylabels(y_var)
return self
def set_xlabels(self, label=None, **kwargs):
"""Label the x axis on the bottom row of the grid."""
if label is None:
label = self._x_var
for ax in self._bottom_axes:
ax.set_xlabel(label, **kwargs)
return self
def set_ylabels(self, label=None, **kwargs):
"""Label the y axis on the left column of the grid."""
if label is None:
label = self._y_var
for ax in self._left_axes:
ax.set_ylabel(label, **kwargs)
return self
def set_xticklabels(self, labels=None, step=None, **kwargs):
"""Set x axis tick labels on the bottom row of the grid."""
for ax in self.axes[-1, :]:
if labels is None:
labels = [l.get_text() for l in ax.get_xticklabels()]
if step is not None:
xticks = ax.get_xticks()[::step]
labels = labels[::step]
ax.set_xticks(xticks)
ax.set_xticklabels(labels, **kwargs)
return self
def set_yticklabels(self, labels=None, **kwargs):
"""Set y axis tick labels on the left column of the grid."""
for ax in self.axes[-1, :]:
if labels is None:
labels = [l.get_text() for l in ax.get_yticklabels()]
ax.set_yticklabels(labels, **kwargs)
return self
def set_titles(self, template=None, row_template=None, col_template=None,
**kwargs):
"""Draw titles either above each facet or on the grid margins.
Parameters
----------
template : string
Template for all titles with the formatting keys {col_var} and
{col_name} (if using a `col` faceting variable) and/or {row_var}
and {row_name} (if using a `row` faceting variable).
row_template:
Template for the row variable when titles are drawn on the grid
margins. Must have {row_var} and {row_name} formatting keys.
col_template:
Template for the row variable when titles are drawn on the grid
margins. Must have {col_var} and {col_name} formatting keys.
Returns
-------
self: object
Returns self.
"""
args = dict(row_var=self._row_var, col_var=self._col_var)
kwargs["size"] = kwargs.pop("size", mpl.rcParams["axes.labelsize"])
# Establish default templates
if row_template is None:
row_template = "{row_var} = {row_name}"
if col_template is None:
col_template = "{col_var} = {col_name}"
if template is None:
if self._row_var is None:
template = col_template
elif self._col_var is None:
template = row_template
else:
template = " | ".join([row_template, col_template])
if self._margin_titles:
if self.row_names is not None:
# Draw the row titles on the right edge of the grid
for i, row_name in enumerate(self.row_names):
ax = self.axes[i, -1]
args.update(dict(row_name=row_name))
title = row_template.format(**args)
trans = self.fig.transFigure.inverted()
bbox = ax.bbox.transformed(trans)
x = bbox.xmax + 0.01
y = bbox.ymax - (bbox.height / 2)
self.fig.text(x, y, title, rotation=270,
ha="left", va="center", **kwargs)
if self.col_names is not None:
# Draw the column titles as normal titles
for j, col_name in enumerate(self.col_names):
args.update(dict(col_name=col_name))
title = col_template.format(**args)
self.axes[0, j].set_title(title, **kwargs)
return self
# Otherwise title each facet with all the necessary information
if (self._row_var is not None) and (self._col_var is not None):
for i, row_name in enumerate(self.row_names):
for j, col_name in enumerate(self.col_names):
args.update(dict(row_name=row_name, col_name=col_name))
title = template.format(**args)
self.axes[i, j].set_title(title, **kwargs)
elif self.row_names is not None and len(self.row_names):
for i, row_name in enumerate(self.row_names):
args.update(dict(row_name=row_name))
title = template.format(**args)
self.axes[i, 0].set_title(title, **kwargs)
elif self.col_names is not None and len(self.col_names):
for i, col_name in enumerate(self.col_names):
args.update(dict(col_name=col_name))
title = template.format(**args)
# Index the flat array so col_wrap works
self.axes.flat[i].set_title(title, **kwargs)
return self
@property
def _inner_axes(self):
"""Return a flat array of the inner axes."""
if self._col_wrap is None:
return self.axes[:-1, 1:].flat
else:
axes = []
n_empty = self._nrow * self._ncol - self._n_facets
for i, ax in enumerate(self.axes):
append = (i % self._ncol and
i < (self._ncol * (self._nrow - 1)) and
i < (self._ncol * (self._nrow - 1) - n_empty))
if append:
axes.append(ax)
return np.array(axes, object).flat
@property
def _left_axes(self):
"""Return a flat array of the left column of axes."""
if self._col_wrap is None:
return self.axes[:, 0].flat
else:
axes = []
for i, ax in enumerate(self.axes):
if not i % self._ncol:
axes.append(ax)
return np.array(axes, object).flat
@property
def _not_left_axes(self):
"""Return a flat array of axes that aren't on the left column."""
if self._col_wrap is None:
return self.axes[:, 1:].flat
else:
axes = []
for i, ax in enumerate(self.axes):
if i % self._ncol:
axes.append(ax)
return np.array(axes, object).flat
@property
def _bottom_axes(self):
"""Return a flat array of the bottom row of axes."""
if self._col_wrap is None:
return self.axes[-1, :].flat
else:
axes = []
n_empty = self._nrow * self._ncol - self._n_facets
for i, ax in enumerate(self.axes):
append = (i >= (self._ncol * (self._nrow - 1)) or
i >= (self._ncol * (self._nrow - 1) - n_empty))
if append:
axes.append(ax)
return np.array(axes, object).flat
@property
def _not_bottom_axes(self):
"""Return a flat array of axes that aren't on the bottom row."""
if self._col_wrap is None:
return self.axes[:-1, :].flat
else:
axes = []
n_empty = self._nrow * self._ncol - self._n_facets
for i, ax in enumerate(self.axes):
append = (i < (self._ncol * (self._nrow - 1)) and
i < (self._ncol * (self._nrow - 1) - n_empty))
if append:
axes.append(ax)
return np.array(axes, object).flat
class PairGrid(Grid):
"""Subplot grid for plotting pairwise relationships in a dataset."""
def __init__(self, data, hue=None, hue_order=None, palette=None,
vars=None, x_vars=None, y_vars=None,
diag_sharey=True, size=3, aspect=1,
despine=True, dropna=True):
"""Initialize the plot figure and PairGrid object.
Parameters
----------
data : DataFrame
Tidy (long-form) dataframe where each column is a variable and
each row is an observation.
hue : string (variable name), optional
Variable in ``data`` to map plot aspects to different colors.
hue_order : list of strings
Order for the levels of the hue variable in the palette
palette : dict or seaborn color palette
Set of colors for mapping the ``hue`` variable. If a dict, keys
should be values in the ``hue`` variable.
vars : list of variable names, optional
Variables within ``data`` to use, otherwise use every column with
a numeric datatype.
{x, y}_vars : lists of variable names, optional
Variables within ``data`` to use separately for the rows and
columns of the figure; i.e. to make a non-square plot.
size : scalar, optional
Height (in inches) of each facet.
aspect : scalar, optional
Aspect * size gives the width (in inches) of each facet.
despine : boolean, optional
Remove the top and right spines from the plots.
dropna : boolean, optional
Drop missing values from the data before plotting.
Returns
-------
self : PairGrid
Returns self for plotting onto the grid.
See Also
--------
FacetGrid : Subplot grid for plotting conditional relationships.
pairplot : Function for easily drawing common uses of PairGrid.
"""
# Sort out the variables that define the grid
if vars is not None:
x_vars = list(vars)
y_vars = list(vars)
elif (x_vars is not None) or (y_vars is not None):
if (x_vars is None) or (y_vars is None):
raise ValueError("Must specify `x_vars` and `y_vars`")
else:
numeric_cols = self._find_numeric_cols(data)
x_vars = numeric_cols
y_vars = numeric_cols
if np.isscalar(x_vars):
x_vars = [x_vars]
if np.isscalar(y_vars):
y_vars = [y_vars]
self.x_vars = list(x_vars)
self.y_vars = list(y_vars)
self.square_grid = self.x_vars == self.y_vars
# Create the figure and the array of subplots
figsize = len(x_vars) * size * aspect, len(y_vars) * size
fig, axes = plt.subplots(len(y_vars), len(x_vars),
figsize=figsize,
sharex="col", sharey="row",
squeeze=False)
self.fig = fig
self.axes = axes
self.data = data
# Save what we are going to do with the diagonal
self.diag_sharey = diag_sharey
self.diag_axes = None
# Label the axes
self._add_axis_labels()
# Sort out the hue variable
self._hue_var = hue
if hue is None:
self.hue_names = None
self.hue_vals = pd.Series(["_nolegend_"] * len(data))
else:
if hue_order is None:
hue_names = np.unique(np.sort(data[hue]))
else:
hue_names = hue_order
if dropna:
# Filter NA from the list of unique hue names
hue_names = list(filter(pd.notnull, hue_names))
self.hue_names = hue_names
self.hue_vals = data[hue]
self.palette = self._get_palette(data, hue, hue_order, palette, dropna)
self._legend_data = {}
# Make the plot look nice
if despine:
utils.despine(fig=fig)
fig.tight_layout()
def map(self, func, **kwargs):
"""Plot with the same function in every subplot.
Parameters
----------
func : callable plotting function
Must take x, y arrays as positional arguments and draw onto the
"currently active" matplotlib Axes.
"""
kw_color = kwargs.pop("color", None)
for i, y_var in enumerate(self.y_vars):
for j, x_var in enumerate(self.x_vars):
hue_grouped = self.data.groupby(self.hue_vals)
for k, (label_k, data_k) in enumerate(hue_grouped):
ax = self.axes[i, j]
plt.sca(ax)
color = self.palette[k] if kw_color is None else kw_color
func(data_k[x_var], data_k[y_var],
label=label_k, color=color, **kwargs)
self._clean_axis(ax)
self._update_legend_data(ax)
if kw_color is not None:
kwargs["color"] = kw_color
self._add_axis_labels()
def map_diag(self, func, **kwargs):
"""Plot with a univariate function on each diagonal subplot.
Parameters
----------
func : callable plotting function
Must take an x array as a positional arguments and draw onto the
"currently active" matplotlib Axes. There is a special case when
using a ``hue`` variable and ``plt.hist``; the histogram will be
plotted with stacked bars.
"""
# Add special diagonal axes for the univariate plot
if self.square_grid and self.diag_axes is None:
diag_axes = []
for i, (var, ax) in enumerate(zip(self.x_vars,
np.diag(self.axes))):
if i and self.diag_sharey:
diag_ax = ax._make_twin_axes(sharex=ax,
sharey=diag_axes[0],
frameon=False)
else:
diag_ax = ax._make_twin_axes(sharex=ax, frameon=False)
diag_ax.set_axis_off()
diag_axes.append(diag_ax)
self.diag_axes = np.array(diag_axes, np.object)
else:
self.diag_axes = None
# Plot on each of the diagonal axes
for i, var in enumerate(self.x_vars):
ax = self.diag_axes[i]
hue_grouped = self.data[var].groupby(self.hue_vals)
# Special-case plt.hist with stacked bars
if func is plt.hist:
plt.sca(ax)
vals = [v.values for g, v in hue_grouped]
func(vals, color=self.palette, histtype="barstacked",
**kwargs)
else:
for k, (label_k, data_k) in enumerate(hue_grouped):
plt.sca(ax)
func(data_k, label=label_k,
color=self.palette[k], **kwargs)
self._clean_axis(ax)
self._add_axis_labels()
def map_lower(self, func, **kwargs):
"""Plot with a bivariate function on the lower diagonal subplots.
Parameters
----------
func : callable plotting function
Must take x, y arrays as positional arguments and draw onto the
"currently active" matplotlib Axes.
"""
kw_color = kwargs.pop("color", None)
for i, j in zip(*np.tril_indices_from(self.axes, -1)):
hue_grouped = self.data.groupby(self.hue_vals)
for k, (label_k, data_k) in enumerate(hue_grouped):
ax = self.axes[i, j]
plt.sca(ax)
x_var = self.x_vars[j]
y_var = self.y_vars[i]
color = self.palette[k] if kw_color is None else kw_color
func(data_k[x_var], data_k[y_var], label=label_k,
color=color, **kwargs)
self._clean_axis(ax)
self._update_legend_data(ax)
if kw_color is not None:
kwargs["color"] = kw_color
self._add_axis_labels()
def map_upper(self, func, **kwargs):
"""Plot with a bivariate function on the upper diagonal subplots.
Parameters
----------
func : callable plotting function
Must take x, y arrays as positional arguments and draw onto the
"currently active" matplotlib Axes.
"""
kw_color = kwargs.pop("color", None)
for i, j in zip(*np.triu_indices_from(self.axes, 1)):
hue_grouped = self.data.groupby(self.hue_vals)
for k, (label_k, data_k) in enumerate(hue_grouped):
ax = self.axes[i, j]
plt.sca(ax)
x_var = self.x_vars[j]
y_var = self.y_vars[i]
color = self.palette[k] if kw_color is None else kw_color
func(data_k[x_var], data_k[y_var], label=label_k,
color=color, **kwargs)
self._clean_axis(ax)
self._update_legend_data(ax)
if kw_color is not None:
kwargs["color"] = kw_color
def map_offdiag(self, func, **kwargs):
"""Plot with a bivariate function on the off-diagonal subplots.
Parameters
----------
func : callable plotting function
Must take x, y arrays as positional arguments and draw onto the
"currently active" matplotlib Axes.
"""
self.map_lower(func, **kwargs)
self.map_upper(func, **kwargs)
def _add_axis_labels(self):
"""Add labels to the left and bottom Axes."""
for ax, label in zip(self.axes[-1, :], self.x_vars):
ax.set_xlabel(label)
for ax, label in zip(self.axes[:, 0], self.y_vars):
ax.set_ylabel(label)
def _find_numeric_cols(self, data):
"""Find which variables in a DataFrame are numeric."""
# This can't be the best way to do this, but I do not
# know what the best way might be, so this seems ok
numeric_cols = []
for col in data:
try:
data[col].astype(np.float)
numeric_cols.append(col)
except ValueError:
pass
return numeric_cols
class JointGrid(object):
"""Grid for drawing a bivariate plot with marginal univariate plots."""
def __init__(self, x, y, data=None, size=6, ratio=5, space=.2,
dropna=True, xlim=None, ylim=None):
"""Set up the grid of subplots.
Parameters
----------
x, y : strings or vectors
Data or names of variables in `data`.
data : DataFrame, optional
DataFrame when `x` and `y` are variable names.
size : numeric
Size of the figure (it will be square).
ratio : numeric
Ratio of joint axes size to marginal axes height.
space : numeric, optional
Space between the joint and marginal axes
dropna : bool, optional
If True, remove observations that are missing from `x` and `y`.
{x, y}lim : two-tuples, optional
Axis limits to set before plotting.
See Also
--------
jointplot : Inteface for drawing bivariate plots with several different
default plot kinds.
"""
# Set up the subplot grid
f = plt.figure(figsize=(size, size))
gs = plt.GridSpec(ratio + 1, ratio + 1)
ax_joint = f.add_subplot(gs[1:, :-1])
ax_marg_x = f.add_subplot(gs[0, :-1], sharex=ax_joint)
ax_marg_y = f.add_subplot(gs[1:, -1], sharey=ax_joint)
self.fig = f
self.ax_joint = ax_joint
self.ax_marg_x = ax_marg_x
self.ax_marg_y = ax_marg_y
# Turn off tick visibility for the measure axis on the marginal plots
plt.setp(ax_marg_x.get_xticklabels(), visible=False)
plt.setp(ax_marg_y.get_yticklabels(), visible=False)
# Turn off the ticks on the density axis for the marginal plots
plt.setp(ax_marg_x.yaxis.get_majorticklines(), visible=False)
plt.setp(ax_marg_x.yaxis.get_minorticklines(), visible=False)
plt.setp(ax_marg_y.xaxis.get_majorticklines(), visible=False)
plt.setp(ax_marg_y.xaxis.get_minorticklines(), visible=False)
plt.setp(ax_marg_x.get_yticklabels(), visible=False)
plt.setp(ax_marg_y.get_xticklabels(), visible=False)
ax_marg_x.yaxis.grid(False)
ax_marg_y.xaxis.grid(False)
# Possibly extract the variables from a DataFrame
if data is not None:
if x in data:
x = data[x]
if y in data:
y = data[y]
# Possibly drop NA
if dropna:
not_na = pd.notnull(x) & pd.notnull(y)
x = x[not_na]
y = y[not_na]
# Find the names of the variables
if hasattr(x, "name"):
xlabel = x.name
ax_joint.set_xlabel(xlabel)
if hasattr(y, "name"):
ylabel = y.name
ax_joint.set_ylabel(ylabel)
# Convert the x and y data to arrays for plotting
self.x = np.asarray(x)
self.y = np.asarray(y)
if xlim is not None:
ax_joint.set_xlim(xlim)
if ylim is not None:
ax_joint.set_ylim(ylim)
# Make the grid look nice
utils.despine(f)
utils.despine(ax=ax_marg_x, left=True)
utils.despine(ax=ax_marg_y, bottom=True)
f.tight_layout()
f.subplots_adjust(hspace=space, wspace=space)
def plot(self, joint_func, marginal_func, annot_func=None):
"""Shortcut to draw the full plot.
Use `plot_joint` and `plot_marginals` directly for more control.
Parameters
----------
joint_func, marginal_func: callables
Functions to draw the bivariate and univariate plots.
Returns
-------
self : JointGrid instance
Returns `self`.
"""
self.plot_marginals(marginal_func)
self.plot_joint(joint_func)
if annot_func is not None:
self.annotate(annot_func)
return self
def plot_joint(self, func, **kwargs):
"""Draw a bivariate plot of `x` and `y`.
Parameters
----------
func : plotting callable
This must take two 1d arrays of data as the first two
positional arguments, and it must plot on the "current" axes.
kwargs : key, value mappings
Keyword argument are passed to the plotting function.
Returns
-------
self : JointGrid instance
Returns `self`.
"""
plt.sca(self.ax_joint)
func(self.x, self.y, **kwargs)
return self
def plot_marginals(self, func, **kwargs):
"""Draw univariate plots for `x` and `y` separately.
Parameters
----------
func : plotting callable
This must take a 1d array of data as the first positional
argument, it must plot on the "current" axes, and it must
accept a "vertical" keyword argument to orient the measure
dimension of the plot vertically.
kwargs : key, value mappings
Keyword argument are passed to the plotting function.
Returns
-------
self : JointGrid instance
Returns `self`.
"""
plt.sca(self.ax_marg_x)
func(self.x, **kwargs)
kwargs["vertical"] = True
plt.sca(self.ax_marg_y)
func(self.y, **kwargs)
return self
def annotate(self, func, template=None, stat=None, loc="best", **kwargs):
"""Annotate the plot with a statistic about the relationship.
Parameters
----------
func : callable
Statistical function that maps the x, y vectors either to (val, p)
or to val.
template : string format template, optional
The template must have the format keys "stat" and "val";
if `func` returns a p value, it should also have the key "p".
stat : string, optional
Name to use for the statistic in the annotation, by default it
uses the name of `func`.
loc : string or int, optional
Matplotlib legend location code; used to place the annotation.
kwargs : key, value mappings
Other keyword arguments are passed to `ax.legend`, which formats
the annotation.
Returns
-------
self : JointGrid instance.
Returns `self`.
"""
default_template = "{stat} = {val:.2g}; p = {p:.2g}"
# Call the function and determine the form of the return value(s)
out = func(self.x, self.y)
try:
val, p = out
except TypeError:
val, p = out, None
default_template, _ = default_template.split(";")
# Set the default template
if template is None:
template = default_template
# Default to name of the function
if stat is None:
stat = func.__name__
# Format the annotation
if p is None:
annotation = template.format(stat=stat, val=val)
else:
annotation = template.format(stat=stat, val=val, p=p)
# Draw an invisible plot and use the legend to draw the annotation
# This is a bit of a hack, but `loc=best` works nicely and is not
# easily abstracted.
phantom, = self.ax_joint.plot(self.x, self.y, linestyle="", alpha=0)
self.ax_joint.legend([phantom], [annotation], loc=loc, **kwargs)
phantom.remove()
return self
def set_axis_labels(self, xlabel="", ylabel="", **kwargs):
"""Set the axis labels on the bivariate axes.
Parameters
----------
xlabel, ylabel : strings
Label names for the x and y variables.
kwargs : key, value mappings
Other keyword arguments are passed to the set_xlabel or
set_ylabel.
Returns
-------
self : JointGrid instance
returns `self`
"""
self.ax_joint.set_xlabel(xlabel, **kwargs)
self.ax_joint.set_ylabel(ylabel, **kwargs)
return self
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