Successfully resolved TypeError: distplot() got an unexpected keyword argument'y'
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Solve the problem
TypeError: distplot() got an unexpected keyword argument 'y'
Solutions
Type error: distplot() got an unexpected keyword argument'y'
Solution
fg=sns.JointGrid(x=cols[0],y=cols[1],data=data_frame,)
fg.plot_marginals(sns.distplot)In the distplot() function, only one input data is accepted, that is, there is x but no y
def distplot Found at: seaborn.distributions
def distplot(a=None, bins=None, hist=True, kde=True, rug=False, fit=None,
hist_kws=None, kde_kws=None, rug_kws=None, fit_kws=None,
color=None, vertical=False, norm_hist=False, axlabel=None,
label=None, ax=None, x=None):
"""DEPRECATED: Flexibly plot a univariate distribution of observations.
.. warning::
This function is deprecated and will be removed in a future version.
Please adapt your code to use one of two new functions:
- :func:`displot`, a figure-level function with a similar flexibility
over the kind of plot to draw
- :func:`histplot`, an axes-level function for plotting histograms,
including with kernel density smoothing
This function combines the matplotlib ``hist`` function (with automatic
calculation of a good default bin size) with the seaborn :func:`kdeplot`
and :func:`rugplot` functions. It can also fit ``scipy.stats``
distributions and plot the estimated PDF over the data.
Parameters
----------
a : Series, 1d-array, or list.
Observed data. If this is a Series object with a ``name`` attribute,
the name will be used to label the data axis.
bins : argument for matplotlib hist(), or None, optional
Specification of hist bins. If unspecified, as reference rule is used
that tries to find a useful default.
hist : bool, optional
Whether to plot a (normed) histogram.
kde : bool, optional
Whether to plot a gaussian kernel density estimate.
rug : bool, optional
Whether to draw a rugplot on the support axis.
fit : random variable object, optional
An object with `fit` method, returning a tuple that can be passed to a
`pdf` method a positional arguments following a grid of values to
evaluate the pdf on.
hist_kws : dict, optional
Keyword arguments for :meth:`matplotlib.axes.Axes.hist`.
kde_kws : dict, optional
Keyword arguments for :func:`kdeplot`.
rug_kws : dict, optional
Keyword arguments for :func:`rugplot`.
color : matplotlib color, optional
Color to plot everything but the fitted curve in.
vertical : bool, optional
If True, observed values are on y-axis.
norm_hist : bool, optional
If True, the histogram height shows a density rather than a count.
This is implied if a KDE or fitted density is plotted.
axlabel : string, False, or None, optional
Name for the support axis label. If None, will try to get it
from a.name if False, do not set a label.
label : string, optional
Legend label for the relevant component of the plot.
ax : matplotlib axis, optional
If provided, plot on this axis.
Returns
-------
ax : matplotlib Axes
Returns the Axes object with the plot for further tweaking.
See Also
--------
kdeplot : Show a univariate or bivariate distribution with a kernel
density estimate.
rugplot : Draw small vertical lines to show each observation in a
distribution.
Examples
--------
Show a default plot with a kernel density estimate and histogram with bin
size determined automatically with a reference rule:
.. plot::
:context: close-figs
>>> import seaborn as sns, numpy as np
>>> sns.set_theme(); np.random.seed(0)
>>> x = np.random.randn(100)
>>> ax = sns.distplot(x)
Use Pandas objects to get an informative axis label:
.. plot::
:context: close-figs
>>> import pandas as pd
>>> x = pd.Series(x, name="x variable")
>>> ax = sns.distplot(x)
Plot the distribution with a kernel density estimate and rug plot:
.. plot::
:context: close-figs
>>> ax = sns.distplot(x, rug=True, hist=False)
Plot the distribution with a histogram and maximum likelihood gaussian
distribution fit:
.. plot::
:context: close-figs
>>> from scipy.stats import norm
>>> ax = sns.distplot(x, fit=norm, kde=False)
Plot the distribution on the vertical axis:
.. plot::
:context: close-figs
>>> ax = sns.distplot(x, vertical=True)
Change the color of all the plot elements:
.. plot::
:context: close-figs
>>> sns.set_color_codes()
>>> ax = sns.distplot(x, color="y")
Pass specific parameters to the underlying plot functions:
.. plot::
:context: close-figs
>>> ax = sns.distplot(x, rug=True, rug_kws={"color": "g"},
... kde_kws={"color": "k", "lw": 3, "label": "KDE"},
... hist_kws={"histtype": "step", "linewidth": 3,
... "alpha": 1, "color": "g"})
"""
if kde and not hist:
axes_level_suggestion = "`kdeplot` (an axes-level function for kernel
density plots)."
else:
axes_level_suggestion = "`histplot` (an axes-level function for
histograms)."
msg = "`distplot` is a deprecated function and will be removed in a future
version. "\
"Please adapt your code to use either `displot` (a figure-level function
with "\
"similar flexibility) or " + axes_level_suggestion
warnings.warn(msg, FutureWarning)
if ax is None:
ax = plt.gca()
# Intelligently label the support axis
label_ax = bool(axlabel)
if axlabel is None and hasattr(a, "name"):
axlabel = a.name
if axlabel is not None:
label_ax = True
# Support new-style API
if x is not None:
a = x
# Make a a 1-d float array
a = np.asarray(a, float)
if a.ndim > 1:
a = a.squeeze()
# Drop null values from array
a = remove_na(a)
# Decide if the hist is normed
norm_hist = norm_hist or kde or fit is not None
# Handle dictionary defaults
hist_kws = {} if hist_kws is None else hist_kws.copy()
kde_kws = {} if kde_kws is None else kde_kws.copy()
rug_kws = {} if rug_kws is None else rug_kws.copy()
fit_kws = {} if fit_kws is None else fit_kws.copy()
# Get the color from the current color cycle
if color is None:
if vertical:
line, = ax.plot(0, a.mean())
else:
line, = ax.plot(a.mean(), 0)
color = line.get_color()
line.remove()
# Plug the label into the right kwarg dictionary
if label is not None:
if hist:
hist_kws["label"] = label
elif kde:
kde_kws["label"] = label
elif rug:
rug_kws["label"] = label
elif fit:
fit_kws["label"] = label
if hist:
if bins is None:
bins = min(_freedman_diaconis_bins(a), 50)
hist_kws.setdefault("alpha", 0.4)
hist_kws.setdefault("density", norm_hist)
orientation = "horizontal" if vertical else "vertical"
hist_color = hist_kws.pop("color", color)
ax.hist(a, bins, orientation=orientation, color=hist_color, **hist_kws)
if hist_color != color:
hist_kws["color"] = hist_color
if kde:
kde_color = kde_kws.pop("color", color)
kdeplot(a, vertical=vertical, ax=ax, color=kde_color, **kde_kws)
if kde_color != color:
kde_kws["color"] = kde_color
if rug:
rug_color = rug_kws.pop("color", color)
axis = "y" if vertical else "x"
rugplot(a, axis=axis, ax=ax, color=rug_color, **rug_kws)
if rug_color != color:
rug_kws["color"] = rug_color
if fit is not None:
def pdf(x):
return fit.pdf(x, *params)
fit_color = fit_kws.pop("color", "#282828")
gridsize = fit_kws.pop("gridsize", 200)
cut = fit_kws.pop("cut", 3)
clip = fit_kws.pop("clip", (-np.inf, np.inf))
bw = stats.gaussian_kde(a).scotts_factor() * a.std(ddof=1)
x = _kde_support(a, bw, gridsize, cut, clip)
params = fit.fit(a)
y = pdf(x)
if vertical:
x, y = y, x
ax.plot(x, y, color=fit_color, **fit_kws)
if fit_color != "#282828":
fit_kws["color"] = fit_color
if label_ax:
if vertical:
ax.set_ylabel(axlabel)
else:
ax.set_xlabel(axlabel)
return ax