/usr/lib/python3/dist-packages/seaborn/linearmodels.py is in python3-seaborn 0.7.1-4.
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1622 1623 1624 1625 1626 1627 1628 1629 1630 | """Plotting functions for linear models (broadly construed)."""
from __future__ import division
import copy
import itertools
from textwrap import dedent
import numpy as np
import pandas as pd
from scipy.spatial import distance
import matplotlib as mpl
import matplotlib.pyplot as plt
import warnings
try:
import statsmodels
assert statsmodels
_has_statsmodels = True
except ImportError:
_has_statsmodels = False
from .external.six import string_types
from .external.six.moves import range
from . import utils
from . import algorithms as algo
from .palettes import color_palette
from .axisgrid import FacetGrid, PairGrid, _facet_docs
from .distributions import kdeplot
__all__ = ["lmplot", "regplot", "residplot",
"coefplot", "interactplot",
"pairplot"]
class _LinearPlotter(object):
"""Base class for plotting relational data in tidy format.
To get anything useful done you'll have to inherit from this, but setup
code that can be abstracted out should be put here.
"""
def establish_variables(self, data, **kws):
"""Extract variables from data or use directly."""
self.data = data
# Validate the inputs
any_strings = any([isinstance(v, string_types) for v in kws.values()])
if any_strings and data is None:
raise ValueError("Must pass `data` if using named variables.")
# Set the variables
for var, val in kws.items():
if isinstance(val, string_types):
setattr(self, var, data[val])
else:
setattr(self, var, val)
def dropna(self, *vars):
"""Remove observations with missing data."""
vals = [getattr(self, var) for var in vars]
vals = [v for v in vals if v is not None]
not_na = np.all(np.column_stack([pd.notnull(v) for v in vals]), axis=1)
for var in vars:
val = getattr(self, var)
if val is not None:
setattr(self, var, val[not_na])
def plot(self, ax):
raise NotImplementedError
class _RegressionPlotter(_LinearPlotter):
"""Plotter for numeric independent variables with regression model.
This does the computations and drawing for the `regplot` function, and
is thus also used indirectly by `lmplot`.
"""
def __init__(self, x, y, data=None, x_estimator=None, x_bins=None,
x_ci="ci", scatter=True, fit_reg=True, ci=95, n_boot=1000,
units=None, order=1, logistic=False, lowess=False,
robust=False, logx=False, x_partial=None, y_partial=None,
truncate=False, dropna=True, x_jitter=None, y_jitter=None,
color=None, label=None):
# Set member attributes
self.x_estimator = x_estimator
self.ci = ci
self.x_ci = ci if x_ci == "ci" else x_ci
self.n_boot = n_boot
self.scatter = scatter
self.fit_reg = fit_reg
self.order = order
self.logistic = logistic
self.lowess = lowess
self.robust = robust
self.logx = logx
self.truncate = truncate
self.x_jitter = x_jitter
self.y_jitter = y_jitter
self.color = color
self.label = label
# Validate the regression options:
if sum((order > 1, logistic, robust, lowess, logx)) > 1:
raise ValueError("Mutually exclusive regression options.")
# Extract the data vals from the arguments or passed dataframe
self.establish_variables(data, x=x, y=y, units=units,
x_partial=x_partial, y_partial=y_partial)
# Drop null observations
if dropna:
self.dropna("x", "y", "units", "x_partial", "y_partial")
# Regress nuisance variables out of the data
if self.x_partial is not None:
self.x = self.regress_out(self.x, self.x_partial)
if self.y_partial is not None:
self.y = self.regress_out(self.y, self.y_partial)
# Possibly bin the predictor variable, which implies a point estimate
if x_bins is not None:
self.x_estimator = np.mean if x_estimator is None else x_estimator
x_discrete, x_bins = self.bin_predictor(x_bins)
self.x_discrete = x_discrete
else:
self.x_discrete = self.x
# Save the range of the x variable for the grid later
self.x_range = self.x.min(), self.x.max()
@property
def scatter_data(self):
"""Data where each observation is a point."""
x_j = self.x_jitter
if x_j is None:
x = self.x
else:
x = self.x + np.random.uniform(-x_j, x_j, len(self.x))
y_j = self.y_jitter
if y_j is None:
y = self.y
else:
y = self.y + np.random.uniform(-y_j, y_j, len(self.y))
return x, y
@property
def estimate_data(self):
"""Data with a point estimate and CI for each discrete x value."""
x, y = self.x_discrete, self.y
vals = sorted(np.unique(x))
points, cis = [], []
for val in vals:
# Get the point estimate of the y variable
_y = y[x == val]
est = self.x_estimator(_y)
points.append(est)
# Compute the confidence interval for this estimate
if self.x_ci is None:
cis.append(None)
else:
units = None
if self.units is not None:
units = self.units[x == val]
boots = algo.bootstrap(_y, func=self.x_estimator,
n_boot=self.n_boot, units=units)
_ci = utils.ci(boots, self.x_ci)
cis.append(_ci)
return vals, points, cis
def fit_regression(self, ax=None, x_range=None, grid=None):
"""Fit the regression model."""
# Create the grid for the regression
if grid is None:
if self.truncate:
x_min, x_max = self.x_range
else:
if ax is None:
x_min, x_max = x_range
else:
x_min, x_max = ax.get_xlim()
grid = np.linspace(x_min, x_max, 100)
ci = self.ci
# Fit the regression
if self.order > 1:
yhat, yhat_boots = self.fit_poly(grid, self.order)
elif self.logistic:
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.genmod.families import Binomial
yhat, yhat_boots = self.fit_statsmodels(grid, GLM,
family=Binomial())
elif self.lowess:
ci = None
grid, yhat = self.fit_lowess()
elif self.robust:
from statsmodels.robust.robust_linear_model import RLM
yhat, yhat_boots = self.fit_statsmodels(grid, RLM)
elif self.logx:
yhat, yhat_boots = self.fit_logx(grid)
else:
yhat, yhat_boots = self.fit_fast(grid)
# Compute the confidence interval at each grid point
if ci is None:
err_bands = None
else:
err_bands = utils.ci(yhat_boots, ci, axis=0)
return grid, yhat, err_bands
def fit_fast(self, grid):
"""Low-level regression and prediction using linear algebra."""
X, y = np.c_[np.ones(len(self.x)), self.x], self.y
grid = np.c_[np.ones(len(grid)), grid]
reg_func = lambda _x, _y: np.linalg.pinv(_x).dot(_y)
yhat = grid.dot(reg_func(X, y))
if self.ci is None:
return yhat, None
beta_boots = algo.bootstrap(X, y, func=reg_func,
n_boot=self.n_boot, units=self.units).T
yhat_boots = grid.dot(beta_boots).T
return yhat, yhat_boots
def fit_poly(self, grid, order):
"""Regression using numpy polyfit for higher-order trends."""
x, y = self.x, self.y
reg_func = lambda _x, _y: np.polyval(np.polyfit(_x, _y, order), grid)
yhat = reg_func(x, y)
if self.ci is None:
return yhat, None
yhat_boots = algo.bootstrap(x, y, func=reg_func,
n_boot=self.n_boot, units=self.units)
return yhat, yhat_boots
def fit_statsmodels(self, grid, model, **kwargs):
"""More general regression function using statsmodels objects."""
X, y = np.c_[np.ones(len(self.x)), self.x], self.y
grid = np.c_[np.ones(len(grid)), grid]
reg_func = lambda _x, _y: model(_y, _x, **kwargs).fit().predict(grid)
yhat = reg_func(X, y)
if self.ci is None:
return yhat, None
yhat_boots = algo.bootstrap(X, y, func=reg_func,
n_boot=self.n_boot, units=self.units)
return yhat, yhat_boots
def fit_lowess(self):
"""Fit a locally-weighted regression, which returns its own grid."""
from statsmodels.nonparametric.smoothers_lowess import lowess
grid, yhat = lowess(self.y, self.x).T
return grid, yhat
def fit_logx(self, grid):
"""Fit the model in log-space."""
X, y = np.c_[np.ones(len(self.x)), self.x], self.y
grid = np.c_[np.ones(len(grid)), np.log(grid)]
def reg_func(_x, _y):
_x = np.c_[_x[:, 0], np.log(_x[:, 1])]
return np.linalg.pinv(_x).dot(_y)
yhat = grid.dot(reg_func(X, y))
if self.ci is None:
return yhat, None
beta_boots = algo.bootstrap(X, y, func=reg_func,
n_boot=self.n_boot, units=self.units).T
yhat_boots = grid.dot(beta_boots).T
return yhat, yhat_boots
def bin_predictor(self, bins):
"""Discretize a predictor by assigning value to closest bin."""
x = self.x
if np.isscalar(bins):
percentiles = np.linspace(0, 100, bins + 2)[1:-1]
bins = np.c_[utils.percentiles(x, percentiles)]
else:
bins = np.c_[np.ravel(bins)]
dist = distance.cdist(np.c_[x], bins)
x_binned = bins[np.argmin(dist, axis=1)].ravel()
return x_binned, bins.ravel()
def regress_out(self, a, b):
"""Regress b from a keeping a's original mean."""
a_mean = a.mean()
a = a - a_mean
b = b - b.mean()
b = np.c_[b]
a_prime = a - b.dot(np.linalg.pinv(b).dot(a))
return (a_prime + a_mean).reshape(a.shape)
def plot(self, ax, scatter_kws, line_kws):
"""Draw the full plot."""
# Insert the plot label into the correct set of keyword arguments
if self.scatter:
scatter_kws["label"] = self.label
else:
line_kws["label"] = self.label
# Use the current color cycle state as a default
if self.color is None:
lines, = plt.plot(self.x.mean(), self.y.mean())
color = lines.get_color()
lines.remove()
else:
color = self.color
# Ensure that color is hex to avoid matplotlib weidness
color = mpl.colors.rgb2hex(mpl.colors.colorConverter.to_rgb(color))
# Let color in keyword arguments override overall plot color
scatter_kws.setdefault("color", color)
line_kws.setdefault("color", color)
# Draw the constituent plots
if self.scatter:
self.scatterplot(ax, scatter_kws)
if self.fit_reg:
self.lineplot(ax, line_kws)
# Label the axes
if hasattr(self.x, "name"):
ax.set_xlabel(self.x.name)
if hasattr(self.y, "name"):
ax.set_ylabel(self.y.name)
def scatterplot(self, ax, kws):
"""Draw the data."""
# Treat the line-based markers specially, explicitly setting larger
# linewidth than is provided by the seaborn style defaults.
# This would ideally be handled better in matplotlib (i.e., distinguish
# between edgewidth for solid glyphs and linewidth for line glyphs
# but this should do for now.
line_markers = ["1", "2", "3", "4", "+", "x", "|", "_"]
if self.x_estimator is None:
if "marker" in kws and kws["marker"] in line_markers:
lw = mpl.rcParams["lines.linewidth"]
else:
lw = mpl.rcParams["lines.markeredgewidth"]
kws.setdefault("linewidths", lw)
if not hasattr(kws['color'], 'shape') or kws['color'].shape[1] < 4:
kws.setdefault("alpha", .8)
x, y = self.scatter_data
ax.scatter(x, y, **kws)
else:
# TODO abstraction
ci_kws = {"color": kws["color"]}
ci_kws["linewidth"] = mpl.rcParams["lines.linewidth"] * 1.75
kws.setdefault("s", 50)
xs, ys, cis = self.estimate_data
if [ci for ci in cis if ci is not None]:
for x, ci in zip(xs, cis):
ax.plot([x, x], ci, **ci_kws)
ax.scatter(xs, ys, **kws)
def lineplot(self, ax, kws):
"""Draw the model."""
xlim = ax.get_xlim()
# Fit the regression model
grid, yhat, err_bands = self.fit_regression(ax)
# Get set default aesthetics
fill_color = kws["color"]
lw = kws.pop("lw", mpl.rcParams["lines.linewidth"] * 1.5)
kws.setdefault("linewidth", lw)
# Draw the regression line and confidence interval
ax.plot(grid, yhat, **kws)
if err_bands is not None:
ax.fill_between(grid, *err_bands, facecolor=fill_color, alpha=.15)
ax.set_xlim(*xlim)
_regression_docs = dict(
model_api=dedent("""\
There are a number of mutually exclusive options for estimating the
regression model: ``order``, ``logistic``, ``lowess``, ``robust``, and
``logx``. See the parameter docs for more information on these options.\
"""),
regplot_vs_lmplot=dedent("""\
Understanding the difference between :func:`regplot` and :func:`lmplot` can
be a bit tricky. In fact, they are closely related, as :func:`lmplot` uses
:func:`regplot` internally and takes most of its parameters. However,
:func:`regplot` is an axes-level function, so it draws directly onto an
axes (either the currently active axes or the one provided by the ``ax``
parameter), while :func:`lmplot` is a figure-level function and creates its
own figure, which is managed through a :class:`FacetGrid`. This has a few
consequences, namely that :func:`regplot` can happily coexist in a figure
with other kinds of plots and will follow the global matplotlib color
cycle. In contrast, :func:`lmplot` needs to occupy an entire figure, and
the size and color cycle are controlled through function parameters,
ignoring the global defaults.\
"""),
x_estimator=dedent("""\
x_estimator : callable that maps vector -> scalar, optional
Apply this function to each unique value of ``x`` and plot the
resulting estimate. This is useful when ``x`` is a discrete variable.
If ``x_ci`` is not ``None``, this estimate will be bootstrapped and a
confidence interval will be drawn.\
"""),
x_bins=dedent("""\
x_bins : int or vector, optional
Bin the ``x`` variable into discrete bins and then estimate the central
tendency and a confidence interval. This binning only influences how
the scatterplot is drawn; the regression is still fit to the original
data. This parameter is interpreted either as the number of
evenly-sized (not necessary spaced) bins or the positions of the bin
centers. When this parameter is used, it implies that the default of
``x_estimator`` is ``numpy.mean``.\
"""),
x_ci=dedent("""\
x_ci : "ci", int in [0, 100] or None, optional
Size of the confidence interval used when plotting a central tendency
for discrete values of ``x``. If "ci", defer to the value of the``ci``
parameter.\
"""),
scatter=dedent("""\
scatter : bool, optional
If ``True``, draw a scatterplot with the underlying observations (or
the ``x_estimator`` values).\
"""),
fit_reg=dedent("""\
fit_reg : bool, optional
If ``True``, estimate and plot a regression model relating the ``x``
and ``y`` variables.\
"""),
ci=dedent("""\
ci : int in [0, 100] or None, optional
Size of the confidence interval for the regression estimate. This will
be drawn using translucent bands around the regression line. The
confidence interval is estimated using a bootstrap; for large
datasets, it may be advisable to avoid that computation by setting
this parameter to None.\
"""),
n_boot=dedent("""\
n_boot : int, optional
Number of bootstrap resamples used to estimate the ``ci``. The default
value attempts to balance time and stability; you may want to increase
this value for "final" versions of plots.\
"""),
units=dedent("""\
units : variable name in ``data``, optional
If the ``x`` and ``y`` observations are nested within sampling units,
those can be specified here. This will be taken into account when
computing the confidence intervals by performing a multilevel bootstrap
that resamples both units and observations (within unit). This does not
otherwise influence how the regression is estimated or drawn.\
"""),
order=dedent("""\
order : int, optional
If ``order`` is greater than 1, use ``numpy.polyfit`` to estimate a
polynomial regression.\
"""),
logistic=dedent("""\
logistic : bool, optional
If ``True``, assume that ``y`` is a binary variable and use
``statsmodels`` to estimate a logistic regression model. Note that this
is substantially more computationally intensive than linear regression,
so you may wish to decrease the number of bootstrap resamples
(``n_boot``) or set ``ci`` to None.\
"""),
lowess=dedent("""\
lowess : bool, optional
If ``True``, use ``statsmodels`` to estimate a nonparametric lowess
model (locally weighted linear regression). Note that confidence
intervals cannot currently be drawn for this kind of model.\
"""),
robust=dedent("""\
robust : bool, optional
If ``True``, use ``statsmodels`` to estimate a robust regression. This
will de-weight outliers. Note that this is substantially more
computationally intensive than standard linear regression, so you may
wish to decrease the number of bootstrap resamples (``n_boot``) or set
``ci`` to None.\
"""),
logx=dedent("""\
logx : bool, optional
If ``True``, estimate a linear regression of the form y ~ log(x), but
plot the scatterplot and regression model in the input space. Note that
``x`` must be positive for this to work.\
"""),
xy_partial=dedent("""\
{x,y}_partial : strings in ``data`` or matrices
Confounding variables to regress out of the ``x`` or ``y`` variables
before plotting.\
"""),
truncate=dedent("""\
truncate : bool, optional
By default, the regression line is drawn to fill the x axis limits
after the scatterplot is drawn. If ``truncate`` is ``True``, it will
instead by bounded by the data limits.\
"""),
xy_jitter=dedent("""\
{x,y}_jitter : floats, optional
Add uniform random noise of this size to either the ``x`` or ``y``
variables. The noise is added to a copy of the data after fitting the
regression, and only influences the look of the scatterplot. This can
be helpful when plotting variables that take discrete values.\
"""),
scatter_line_kws=dedent("""\
{scatter,line}_kws : dictionaries
Additional keyword arguments to pass to ``plt.scatter`` and
``plt.plot``.\
"""),
)
_regression_docs.update(_facet_docs)
def lmplot(x, y, data, hue=None, col=None, row=None, palette=None,
col_wrap=None, size=5, aspect=1, markers="o", sharex=True,
sharey=True, hue_order=None, col_order=None, row_order=None,
legend=True, legend_out=True, x_estimator=None, x_bins=None,
x_ci="ci", scatter=True, fit_reg=True, ci=95, n_boot=1000,
units=None, order=1, logistic=False, lowess=False, robust=False,
logx=False, x_partial=None, y_partial=None, truncate=False,
x_jitter=None, y_jitter=None, scatter_kws=None, line_kws=None):
# Reduce the dataframe to only needed columns
need_cols = [x, y, hue, col, row, units, x_partial, y_partial]
cols = np.unique([a for a in need_cols if a is not None]).tolist()
data = data[cols]
# Initialize the grid
facets = FacetGrid(data, row, col, hue, palette=palette,
row_order=row_order, col_order=col_order,
hue_order=hue_order, size=size, aspect=aspect,
col_wrap=col_wrap, sharex=sharex, sharey=sharey,
legend_out=legend_out)
# Add the markers here as FacetGrid has figured out how many levels of the
# hue variable are needed and we don't want to duplicate that process
if facets.hue_names is None:
n_markers = 1
else:
n_markers = len(facets.hue_names)
if not isinstance(markers, list):
markers = [markers] * n_markers
if len(markers) != n_markers:
raise ValueError(("markers must be a singeton or a list of markers "
"for each level of the hue variable"))
facets.hue_kws = {"marker": markers}
# Hack to set the x limits properly, which needs to happen here
# because the extent of the regression estimate is determined
# by the limits of the plot
if sharex:
for ax in facets.axes.flat:
ax.scatter(data[x], np.ones(len(data)) * data[y].mean()).remove()
# Draw the regression plot on each facet
regplot_kws = dict(
x_estimator=x_estimator, x_bins=x_bins, x_ci=x_ci,
scatter=scatter, fit_reg=fit_reg, ci=ci, n_boot=n_boot, units=units,
order=order, logistic=logistic, lowess=lowess, robust=robust,
logx=logx, x_partial=x_partial, y_partial=y_partial, truncate=truncate,
x_jitter=x_jitter, y_jitter=y_jitter,
scatter_kws=scatter_kws, line_kws=line_kws,
)
facets.map_dataframe(regplot, x, y, **regplot_kws)
# Add a legend
if legend and (hue is not None) and (hue not in [col, row]):
facets.add_legend()
return facets
lmplot.__doc__ = dedent("""\
Plot data and regression model fits across a FacetGrid.
This function combines :func:`regplot` and :class:`FacetGrid`. It is
intended as a convenient interface to fit regression models across
conditional subsets of a dataset.
When thinking about how to assign variables to different facets, a general
rule is that it makes sense to use ``hue`` for the most important
comparison, followed by ``col`` and ``row``. However, always think about
your particular dataset and the goals of the visualization you are
creating.
{model_api}
The parameters to this function span most of the options in
:class:`FacetGrid`, although there may be occasional cases where you will
want to use that class and :func:`regplot` directly.
Parameters
----------
x, y : strings, optional
Input variables; these should be column names in ``data``.
{data}
hue, col, row : strings
Variables that define subsets of the data, which will be drawn on
separate facets in the grid. See the ``*_order`` parameters to control
the order of levels of this variable.
{palette}
{col_wrap}
{size}
{aspect}
markers : matplotlib marker code or list of marker codes, optional
Markers for the scatterplot. If a list, each marker in the list will be
used for each level of the ``hue`` variable.
{share_xy}
{{hue,col,row}}_order : lists, optional
Order for the levels of the faceting variables. By default, this will
be the order that the levels appear in ``data`` or, if the variables
are pandas categoricals, the category order.
legend : bool, optional
If ``True`` and there is a ``hue`` variable, add a legend.
{legend_out}
{x_estimator}
{x_bins}
{x_ci}
{scatter}
{fit_reg}
{ci}
{n_boot}
{units}
{order}
{logistic}
{lowess}
{robust}
{logx}
{xy_partial}
{truncate}
{xy_jitter}
{scatter_line_kws}
See Also
--------
regplot : Plot data and a conditional model fit.
FacetGrid : Subplot grid for plotting conditional relationships.
pairplot : Combine :func:`regplot` and :class:`PairGrid` (when used with
``kind="reg"``).
Notes
-----
{regplot_vs_lmplot}
Examples
--------
These examples focus on basic regression model plots to exhibit the
various faceting options; see the :func:`regplot` docs for demonstrations
of the other options for plotting the data and models. There are also
other examples for how to manipulate plot using the returned object on
the :class:`FacetGrid` docs.
Plot a simple linear relationship between two variables:
.. plot::
:context: close-figs
>>> import seaborn as sns; sns.set(color_codes=True)
>>> tips = sns.load_dataset("tips")
>>> g = sns.lmplot(x="total_bill", y="tip", data=tips)
Condition on a third variable and plot the levels in different colors:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips)
Use different markers as well as colors so the plot will reproduce to
black-and-white more easily:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips,
... markers=["o", "x"])
Use a different color palette:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips,
... palette="Set1")
Map ``hue`` levels to colors with a dictionary:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips,
... palette=dict(Yes="g", No="m"))
Plot the levels of the third variable across different columns:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="total_bill", y="tip", col="smoker", data=tips)
Change the size and aspect ratio of the facets:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="size", y="total_bill", hue="day", col="day",
... data=tips, aspect=.4, x_jitter=.1)
Wrap the levels of the column variable into multiple rows:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="total_bill", y="tip", col="day", hue="day",
... data=tips, col_wrap=2, size=3)
Condition on two variables to make a full grid:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="total_bill", y="tip", row="sex", col="time",
... data=tips, size=3)
Use methods on the returned :class:`FacetGrid` instance to further tweak
the plot:
.. plot::
:context: close-figs
>>> g = sns.lmplot(x="total_bill", y="tip", row="sex", col="time",
... data=tips, size=3)
>>> g = (g.set_axis_labels("Total bill (US Dollars)", "Tip")
... .set(xlim=(0, 60), ylim=(0, 12),
... xticks=[10, 30, 50], yticks=[2, 6, 10])
... .fig.subplots_adjust(wspace=.02))
""").format(**_regression_docs)
def regplot(x, y, data=None, x_estimator=None, x_bins=None, x_ci="ci",
scatter=True, fit_reg=True, ci=95, n_boot=1000, units=None,
order=1, logistic=False, lowess=False, robust=False,
logx=False, x_partial=None, y_partial=None,
truncate=False, dropna=True, x_jitter=None, y_jitter=None,
label=None, color=None, marker="o",
scatter_kws=None, line_kws=None, ax=None):
plotter = _RegressionPlotter(x, y, data, x_estimator, x_bins, x_ci,
scatter, fit_reg, ci, n_boot, units,
order, logistic, lowess, robust, logx,
x_partial, y_partial, truncate, dropna,
x_jitter, y_jitter, color, label)
if ax is None:
ax = plt.gca()
scatter_kws = {} if scatter_kws is None else copy.copy(scatter_kws)
scatter_kws["marker"] = marker
line_kws = {} if line_kws is None else copy.copy(line_kws)
plotter.plot(ax, scatter_kws, line_kws)
return ax
regplot.__doc__ = dedent("""\
Plot data and a linear regression model fit.
{model_api}
Parameters
----------
x, y: string, series, or vector array
Input variables. If strings, these should correspond with column names
in ``data``. When pandas objects are used, axes will be labeled with
the series name.
{data}
{x_estimator}
{x_bins}
{x_ci}
{scatter}
{fit_reg}
{ci}
{n_boot}
{units}
{order}
{logistic}
{lowess}
{robust}
{logx}
{xy_partial}
{truncate}
{xy_jitter}
label : string
Label to apply to ether the scatterplot or regression line (if
``scatter`` is ``False``) for use in a legend.
color : matplotlib color
Color to apply to all plot elements; will be superseded by colors
passed in ``scatter_kws`` or ``line_kws``.
marker : matplotlib marker code
Marker to use for the scatterplot glyphs.
{scatter_line_kws}
ax : matplotlib Axes, optional
Axes object to draw the plot onto, otherwise uses the current Axes.
Returns
-------
ax : matplotlib Axes
The Axes object containing the plot.
See Also
--------
lmplot : Combine :func:`regplot` and :class:`FacetGrid` to plot multiple
linear relationships in a dataset.
jointplot : Combine :func:`regplot` and :class:`JointGrid` (when used with
``kind="reg"``).
pairplot : Combine :func:`regplot` and :class:`PairGrid` (when used with
``kind="reg"``).
residplot : Plot the residuals of a linear regression model.
interactplot : Plot a two-way interaction between continuous variables
Notes
-----
{regplot_vs_lmplot}
It's also easy to combine combine :func:`regplot` and :class:`JointGrid` or
:class:`PairGrid` through the :func:`jointplot` and :func:`pairplot`
functions, although these do not directly accept all of :func:`regplot`'s
parameters.
Examples
--------
Plot the relationship between two variables in a DataFrame:
.. plot::
:context: close-figs
>>> import seaborn as sns; sns.set(color_codes=True)
>>> tips = sns.load_dataset("tips")
>>> ax = sns.regplot(x="total_bill", y="tip", data=tips)
Plot with two variables defined as numpy arrays; use a different color:
.. plot::
:context: close-figs
>>> import numpy as np; np.random.seed(8)
>>> mean, cov = [4, 6], [(1.5, .7), (.7, 1)]
>>> x, y = np.random.multivariate_normal(mean, cov, 80).T
>>> ax = sns.regplot(x=x, y=y, color="g")
Plot with two variables defined as pandas Series; use a different marker:
.. plot::
:context: close-figs
>>> import pandas as pd
>>> x, y = pd.Series(x, name="x_var"), pd.Series(y, name="y_var")
>>> ax = sns.regplot(x=x, y=y, marker="+")
Use a 68% confidence interval, which corresponds with the standard error
of the estimate:
.. plot::
:context: close-figs
>>> ax = sns.regplot(x=x, y=y, ci=68)
Plot with a discrete ``x`` variable and add some jitter:
.. plot::
:context: close-figs
>>> ax = sns.regplot(x="size", y="total_bill", data=tips, x_jitter=.1)
Plot with a discrete ``x`` variable showing means and confidence intervals
for unique values:
.. plot::
:context: close-figs
>>> ax = sns.regplot(x="size", y="total_bill", data=tips,
... x_estimator=np.mean)
Plot with a continuous variable divided into discrete bins:
.. plot::
:context: close-figs
>>> ax = sns.regplot(x=x, y=y, x_bins=4)
Fit a higher-order polynomial regression and truncate the model prediction:
.. plot::
:context: close-figs
>>> ans = sns.load_dataset("anscombe")
>>> ax = sns.regplot(x="x", y="y", data=ans.loc[ans.dataset == "II"],
... scatter_kws={{"s": 80}},
... order=2, ci=None, truncate=True)
Fit a robust regression and don't plot a confidence interval:
.. plot::
:context: close-figs
>>> ax = sns.regplot(x="x", y="y", data=ans.loc[ans.dataset == "III"],
... scatter_kws={{"s": 80}},
... robust=True, ci=None)
Fit a logistic regression; jitter the y variable and use fewer bootstrap
iterations:
.. plot::
:context: close-figs
>>> tips["big_tip"] = (tips.tip / tips.total_bill) > .175
>>> ax = sns.regplot(x="total_bill", y="big_tip", data=tips,
... logistic=True, n_boot=500, y_jitter=.03)
Fit the regression model using log(x) and truncate the model prediction:
.. plot::
:context: close-figs
>>> ax = sns.regplot(x="size", y="total_bill", data=tips,
... x_estimator=np.mean, logx=True, truncate=True)
""").format(**_regression_docs)
def residplot(x, y, data=None, lowess=False, x_partial=None, y_partial=None,
order=1, robust=False, dropna=True, label=None, color=None,
scatter_kws=None, line_kws=None, ax=None):
"""Plot the residuals of a linear regression.
This function will regress y on x (possibly as a robust or polynomial
regression) and then draw a scatterplot of the residuals. You can
optionally fit a lowess smoother to the residual plot, which can
help in determining if there is structure to the residuals.
Parameters
----------
x : vector or string
Data or column name in `data` for the predictor variable.
y : vector or string
Data or column name in `data` for the response variable.
data : DataFrame, optional
DataFrame to use if `x` and `y` are column names.
lowess : boolean, optional
Fit a lowess smoother to the residual scatterplot.
{x, y}_partial : matrix or string(s) , optional
Matrix with same first dimension as `x`, or column name(s) in `data`.
These variables are treated as confounding and are removed from
the `x` or `y` variables before plotting.
order : int, optional
Order of the polynomial to fit when calculating the residuals.
robust : boolean, optional
Fit a robust linear regression when calculating the residuals.
dropna : boolean, optional
If True, ignore observations with missing data when fitting and
plotting.
label : string, optional
Label that will be used in any plot legends.
color : matplotlib color, optional
Color to use for all elements of the plot.
{scatter, line}_kws : dictionaries, optional
Additional keyword arguments passed to scatter() and plot() for drawing
the components of the plot.
ax : matplotlib axis, optional
Plot into this axis, otherwise grab the current axis or make a new
one if not existing.
Returns
-------
ax: matplotlib axes
Axes with the regression plot.
See Also
--------
regplot : Plot a simple linear regression model.
jointplot (with kind="resid"): Draw a residplot with univariate
marginal distrbutions.
"""
plotter = _RegressionPlotter(x, y, data, ci=None,
order=order, robust=robust,
x_partial=x_partial, y_partial=y_partial,
dropna=dropna, color=color, label=label)
if ax is None:
ax = plt.gca()
# Calculate the residual from a linear regression
_, yhat, _ = plotter.fit_regression(grid=plotter.x)
plotter.y = plotter.y - yhat
# Set the regression option on the plotter
if lowess:
plotter.lowess = True
else:
plotter.fit_reg = False
# Plot a horizontal line at 0
ax.axhline(0, ls=":", c=".2")
# Draw the scatterplot
scatter_kws = {} if scatter_kws is None else scatter_kws
line_kws = {} if line_kws is None else line_kws
plotter.plot(ax, scatter_kws, line_kws)
return ax
def coefplot(formula, data, groupby=None, intercept=False, ci=95,
palette="husl"):
"""Plot the coefficients from a linear model.
Parameters
----------
formula : string
patsy formula for ols model
data : dataframe
data for the plot; formula terms must appear in columns
groupby : grouping object, optional
object to group data with to fit conditional models
intercept : bool, optional
if False, strips the intercept term before plotting
ci : float, optional
size of confidence intervals
palette : seaborn color palette, optional
palette for the horizonal plots
"""
if not _has_statsmodels:
raise ImportError("The `coefplot` function requires statsmodels")
import statsmodels.formula.api as sf
alpha = 1 - ci / 100
if groupby is None:
coefs = sf.ols(formula, data).fit().params
cis = sf.ols(formula, data).fit().conf_int(alpha)
else:
grouped = data.groupby(groupby)
coefs = grouped.apply(lambda d: sf.ols(formula, d).fit().params).T
cis = grouped.apply(lambda d: sf.ols(formula, d).fit().conf_int(alpha))
# Possibly ignore the intercept
if not intercept:
coefs = coefs.ix[1:]
n_terms = len(coefs)
# Plot seperately depending on groupby
w, h = mpl.rcParams["figure.figsize"]
hsize = lambda n: n * (h / 2)
wsize = lambda n: n * (w / (4 * (n / 5)))
if groupby is None:
colors = itertools.cycle(color_palette(palette, n_terms))
f, ax = plt.subplots(1, 1, figsize=(wsize(n_terms), hsize(1)))
for i, term in enumerate(coefs.index):
color = next(colors)
low, high = cis.ix[term]
ax.plot([i, i], [low, high], c=color,
solid_capstyle="round", lw=2.5)
ax.plot(i, coefs.ix[term], "o", c=color, ms=8)
ax.set_xlim(-.5, n_terms - .5)
ax.axhline(0, ls="--", c="dimgray")
ax.set_xticks(range(n_terms))
ax.set_xticklabels(coefs.index)
else:
n_groups = len(coefs.columns)
f, axes = plt.subplots(n_terms, 1, sharex=True,
figsize=(wsize(n_groups), hsize(n_terms)))
if n_terms == 1:
axes = [axes]
colors = itertools.cycle(color_palette(palette, n_groups))
for ax, term in zip(axes, coefs.index):
for i, group in enumerate(coefs.columns):
color = next(colors)
low, high = cis.ix[(group, term)]
ax.plot([i, i], [low, high], c=color,
solid_capstyle="round", lw=2.5)
ax.plot(i, coefs.loc[term, group], "o", c=color, ms=8)
ax.set_xlim(-.5, n_groups - .5)
ax.axhline(0, ls="--", c="dimgray")
ax.set_title(term)
ax.set_xlabel(groupby)
ax.set_xticks(range(n_groups))
ax.set_xticklabels(coefs.columns)
def interactplot(x1, x2, y, data=None, filled=False, cmap="RdBu_r",
colorbar=True, levels=30, logistic=False,
contour_kws=None, scatter_kws=None, ax=None, **kwargs):
"""Visualize a continuous two-way interaction with a contour plot.
Parameters
----------
x1, x2, y, strings or array-like
Either the two independent variables and the dependent variable,
or keys to extract them from `data`
data : DataFrame
Pandas DataFrame with the data in the columns.
filled : bool
Whether to plot with filled or unfilled contours
cmap : matplotlib colormap
Colormap to represent yhat in the countour plot.
colorbar : bool
Whether to draw the colorbar for interpreting the color values.
levels : int or sequence
Number or position of contour plot levels.
logistic : bool
Fit a logistic regression model instead of linear regression.
contour_kws : dictionary
Keyword arguments for contour[f]().
scatter_kws : dictionary
Keyword arguments for plot().
ax : matplotlib axis
Axis to draw plot in.
Returns
-------
ax : Matplotlib axis
Axis with the contour plot.
"""
if not _has_statsmodels:
raise ImportError("The `interactplot` function requires statsmodels")
from statsmodels.regression.linear_model import OLS
from statsmodels.genmod.generalized_linear_model import GLM
from statsmodels.genmod.families import Binomial
# Handle the form of the data
if data is not None:
x1 = data[x1]
x2 = data[x2]
y = data[y]
if hasattr(x1, "name"):
xlabel = x1.name
else:
xlabel = None
if hasattr(x2, "name"):
ylabel = x2.name
else:
ylabel = None
if hasattr(y, "name"):
clabel = y.name
else:
clabel = None
x1 = np.asarray(x1)
x2 = np.asarray(x2)
y = np.asarray(y)
# Initialize the scatter keyword dictionary
if scatter_kws is None:
scatter_kws = {}
if not ("color" in scatter_kws or "c" in scatter_kws):
scatter_kws["color"] = "#222222"
if "alpha" not in scatter_kws:
scatter_kws["alpha"] = 0.75
# Intialize the contour keyword dictionary
if contour_kws is None:
contour_kws = {}
# Initialize the axis
if ax is None:
ax = plt.gca()
# Plot once to let matplotlib sort out the axis limits
ax.plot(x1, x2, "o", **scatter_kws)
# Find the plot limits
x1min, x1max = ax.get_xlim()
x2min, x2max = ax.get_ylim()
# Make the grid for the contour plot
x1_points = np.linspace(x1min, x1max, 100)
x2_points = np.linspace(x2min, x2max, 100)
xx1, xx2 = np.meshgrid(x1_points, x2_points)
# Fit the model with an interaction
X = np.c_[np.ones(x1.size), x1, x2, x1 * x2]
if logistic:
lm = GLM(y, X, family=Binomial()).fit()
else:
lm = OLS(y, X).fit()
# Evaluate the model on the grid
eval = np.vectorize(lambda x1_, x2_: lm.predict([1, x1_, x2_, x1_ * x2_]))
yhat = eval(xx1, xx2)
# Default color limits put the midpoint at mean(y)
y_bar = y.mean()
c_min = min(np.percentile(y, 2), yhat.min())
c_max = max(np.percentile(y, 98), yhat.max())
delta = max(c_max - y_bar, y_bar - c_min)
c_min, cmax = y_bar - delta, y_bar + delta
contour_kws.setdefault("vmin", c_min)
contour_kws.setdefault("vmax", c_max)
# Draw the contour plot
func_name = "contourf" if filled else "contour"
contour = getattr(ax, func_name)
c = contour(xx1, xx2, yhat, levels, cmap=cmap, **contour_kws)
# Draw the scatter again so it's visible
ax.plot(x1, x2, "o", **scatter_kws)
# Draw a colorbar, maybe
if colorbar:
bar = plt.colorbar(c)
# Label the axes
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
if clabel is not None and colorbar:
clabel = "P(%s)" % clabel if logistic else clabel
bar.set_label(clabel, labelpad=15, rotation=270)
return ax
def corrplot(data, names=None, annot=True, sig_stars=True, sig_tail="both",
sig_corr=True, cmap=None, cmap_range=None, cbar=True,
diag_names=True, method=None, ax=None, **kwargs):
"""Plot a correlation matrix with colormap and r values.
NOTE: This function is deprecated in favor of :func:`heatmap` and will
be removed in a forthcoming release.
Parameters
----------
data : Dataframe or nobs x nvars array
Rectangular input data with variabes in the columns.
names : sequence of strings
Names to associate with variables if `data` is not a DataFrame.
annot : bool
Whether to annotate the upper triangle with correlation coefficients.
sig_stars : bool
If True, get significance with permutation test and denote with stars.
sig_tail : both | upper | lower
Direction for significance test. Also controls the default colorbar.
sig_corr : bool
If True, use FWE-corrected p values for the sig stars.
cmap : colormap
Colormap name as string or colormap object.
cmap_range : None, "full", (low, high)
Either truncate colormap at (-max(abs(r)), max(abs(r))), use the
full range (-1, 1), or specify (min, max) values for the colormap.
cbar : bool
If true, plot the colorbar legend.
method: None (pearson) | kendall | spearman
Correlation method to compute pairwise correlations. Methods other
than the default pearson correlation will not have a significance
computed.
ax : matplotlib axis
Axis to draw plot in.
kwargs : other keyword arguments
Passed to ax.matshow()
Returns
-------
ax : matplotlib axis
Axis object with plot.
"""
warnings.warn(("The `corrplot` function has been deprecated in favor "
"of `heatmap` and will be removed in a forthcoming "
"release. Please update your code."))
if not isinstance(data, pd.DataFrame):
if names is None:
names = ["var_%d" % i for i in range(data.shape[1])]
data = pd.DataFrame(data, columns=names, dtype=np.float)
# Calculate the correlation matrix of the dataframe
if method is None:
corrmat = data.corr()
else:
corrmat = data.corr(method=method)
# Pandas will drop non-numeric columns; let's keep track of that operation
names = corrmat.columns
data = data[names]
# Get p values with a permutation test
if annot and sig_stars and method is None:
p_mat = algo.randomize_corrmat(data.values.T, sig_tail, sig_corr)
else:
p_mat = None
# Sort out the color range
if cmap_range is None:
triu = np.triu_indices(len(corrmat), 1)
vmax = min(1, np.max(np.abs(corrmat.values[triu])) * 1.15)
vmin = -vmax
if sig_tail == "both":
cmap_range = vmin, vmax
elif sig_tail == "upper":
cmap_range = 0, vmax
elif sig_tail == "lower":
cmap_range = vmin, 0
elif cmap_range == "full":
cmap_range = (-1, 1)
# Find a colormapping, somewhat intelligently
if cmap is None:
if min(cmap_range) >= 0:
cmap = "OrRd"
elif max(cmap_range) <= 0:
cmap = "PuBu_r"
else:
cmap = "coolwarm"
if cmap == "jet":
# Paternalism
raise ValueError("Never use the 'jet' colormap!")
# Plot using the more general symmatplot function
ax = symmatplot(corrmat, p_mat, names, cmap, cmap_range,
cbar, annot, diag_names, ax, **kwargs)
return ax
def symmatplot(mat, p_mat=None, names=None, cmap="Greys", cmap_range=None,
cbar=True, annot=True, diag_names=True, ax=None, **kwargs):
"""Plot a symmetric matrix with colormap and statistic values.
NOTE: This function is deprecated in favor of :func:`heatmap` and will
be removed in a forthcoming release.
"""
warnings.warn(("The `symmatplot` function has been deprecated in favor "
"of `heatmap` and will be removed in a forthcoming "
"release. Please update your code."))
if ax is None:
ax = plt.gca()
nvars = len(mat)
if isinstance(mat, pd.DataFrame):
plotmat = mat.values.copy()
mat = mat.values
else:
plotmat = mat.copy()
plotmat[np.triu_indices(nvars)] = np.nan
if cmap_range is None:
vmax = np.nanmax(plotmat) * 1.15
vmin = np.nanmin(plotmat) * 1.15
elif len(cmap_range) == 2:
vmin, vmax = cmap_range
else:
raise ValueError("cmap_range argument not understood")
mat_img = ax.matshow(plotmat, cmap=cmap, vmin=vmin, vmax=vmax, **kwargs)
if cbar:
plt.colorbar(mat_img, shrink=.75)
if p_mat is None:
p_mat = np.ones((nvars, nvars))
if annot:
for i, j in zip(*np.triu_indices(nvars, 1)):
val = mat[i, j]
stars = utils.sig_stars(p_mat[i, j])
ax.text(j, i, "\n%.2g\n%s" % (val, stars),
fontdict=dict(ha="center", va="center"))
else:
fill = np.ones_like(plotmat)
fill[np.tril_indices_from(fill, -1)] = np.nan
ax.matshow(fill, cmap="Greys", vmin=0, vmax=0, zorder=2)
if names is None:
names = ["var%d" % i for i in range(nvars)]
if diag_names:
for i, name in enumerate(names):
ax.text(i, i, name, fontdict=dict(ha="center", va="center",
weight="bold", rotation=45))
ax.set_xticklabels(())
ax.set_yticklabels(())
else:
ax.xaxis.set_ticks_position("bottom")
xnames = names if annot else names[:-1]
ax.set_xticklabels(xnames, rotation=90)
ynames = names if annot else names[1:]
ax.set_yticklabels(ynames)
minor_ticks = np.linspace(-.5, nvars - 1.5, nvars)
ax.set_xticks(minor_ticks, True)
ax.set_yticks(minor_ticks, True)
major_ticks = np.linspace(0, nvars - 1, nvars)
xticks = major_ticks if annot else major_ticks[:-1]
ax.set_xticks(xticks)
yticks = major_ticks if annot else major_ticks[1:]
ax.set_yticks(yticks)
ax.grid(False, which="major")
ax.grid(True, which="minor", linestyle="-")
return ax
def pairplot(data, hue=None, hue_order=None, palette=None,
vars=None, x_vars=None, y_vars=None,
kind="scatter", diag_kind="hist", markers=None,
size=2.5, aspect=1, dropna=True,
plot_kws=None, diag_kws=None, grid_kws=None):
"""Plot pairwise relationships in a dataset.
By default, this function will create a grid of Axes such that each
variable in ``data`` will by shared in the y-axis across a single row and
in the x-axis across a single column. The diagonal Axes are treated
differently, drawing a plot to show the univariate distribution of the data
for the variable in that column.
It is also possible to show a subset of variables or plot different
variables on the rows and columns.
This is a high-level interface for :class:`PairGrid` that is intended to
make it easy to draw a few common styles. You should use :class`PairGrid`
directly if you need more flexibility.
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.
kind : {'scatter', 'reg'}, optional
Kind of plot for the non-identity relationships.
diag_kind : {'hist', 'kde'}, optional
Kind of plot for the diagonal subplots.
markers : single matplotlib marker code or list, optional
Either the marker to use for all datapoints or a list of markers with
a length the same as the number of levels in the hue variable so that
differently colored points will also have different scatterplot
markers.
size : scalar, optional
Height (in inches) of each facet.
aspect : scalar, optional
Aspect * size gives the width (in inches) of each facet.
dropna : boolean, optional
Drop missing values from the data before plotting.
{plot, diag, grid}_kws : dicts, optional
Dictionaries of keyword arguments.
Returns
-------
grid : PairGrid
Returns the underlying ``PairGrid`` instance for further tweaking.
See Also
--------
PairGrid : Subplot grid for more flexible plotting of pairwise
relationships.
Examples
--------
Draw scatterplots for joint relationships and histograms for univariate
distributions:
.. plot::
:context: close-figs
>>> import seaborn as sns; sns.set(style="ticks", color_codes=True)
>>> iris = sns.load_dataset("iris")
>>> g = sns.pairplot(iris)
Show different levels of a categorical variable by the color of plot
elements:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, hue="species")
Use a different color palette:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, hue="species", palette="husl")
Use different markers for each level of the hue variable:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, hue="species", markers=["o", "s", "D"])
Plot a subset of variables:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, vars=["sepal_width", "sepal_length"])
Draw larger plots:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, size=3,
... vars=["sepal_width", "sepal_length"])
Plot different variables in the rows and columns:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris,
... x_vars=["sepal_width", "sepal_length"],
... y_vars=["petal_width", "petal_length"])
Use kernel density estimates for univariate plots:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, diag_kind="kde")
Fit linear regression models to the scatter plots:
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, kind="reg")
Pass keyword arguments down to the underlying functions (it may be easier
to use :class:`PairGrid` directly):
.. plot::
:context: close-figs
>>> g = sns.pairplot(iris, diag_kind="kde", markers="+",
... plot_kws=dict(s=50, edgecolor="b", linewidth=1),
... diag_kws=dict(shade=True))
"""
if plot_kws is None:
plot_kws = {}
if diag_kws is None:
diag_kws = {}
if grid_kws is None:
grid_kws = {}
# Set up the PairGrid
diag_sharey = diag_kind == "hist"
grid = PairGrid(data, vars=vars, x_vars=x_vars, y_vars=y_vars, hue=hue,
hue_order=hue_order, palette=palette,
diag_sharey=diag_sharey,
size=size, aspect=aspect, dropna=dropna, **grid_kws)
# Add the markers here as PairGrid has figured out how many levels of the
# hue variable are needed and we don't want to duplicate that process
if markers is not None:
if grid.hue_names is None:
n_markers = 1
else:
n_markers = len(grid.hue_names)
if not isinstance(markers, list):
markers = [markers] * n_markers
if len(markers) != n_markers:
raise ValueError(("markers must be a singeton or a list of markers"
" for each level of the hue variable"))
grid.hue_kws = {"marker": markers}
# Maybe plot on the diagonal
if grid.square_grid:
if diag_kind == "hist":
grid.map_diag(plt.hist, **diag_kws)
elif diag_kind == "kde":
diag_kws["legend"] = False
grid.map_diag(kdeplot, **diag_kws)
# Maybe plot on the off-diagonals
if grid.square_grid and diag_kind is not None:
plotter = grid.map_offdiag
else:
plotter = grid.map
if kind == "scatter":
plot_kws.setdefault("edgecolor", "white")
plotter(plt.scatter, **plot_kws)
elif kind == "reg":
plotter(regplot, **plot_kws)
# Add a legend
if hue is not None:
grid.add_legend()
return grid
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