seaborn.distplot

seaborn.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:

  • displot(), a figure-level function with a similar flexibility over the kind of plot to draw

  • 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 kdeplot() and rugplot() functions. It can also fit scipy.stats distributions and plot the estimated PDF over the data.

Parameters
aSeries, 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.

binsargument for matplotlib hist(), or None, optional

Specification of hist bins. If unspecified, as reference rule is used that tries to find a useful default.

histbool, optional

Whether to plot a (normed) histogram.

kdebool, optional

Whether to plot a gaussian kernel density estimate.

rugbool, optional

Whether to draw a rugplot on the support axis.

fitrandom 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_kwsdict, optional

Keyword arguments for matplotlib.axes.Axes.hist().

kde_kwsdict, optional

Keyword arguments for kdeplot().

rug_kwsdict, optional

Keyword arguments for rugplot().

colormatplotlib color, optional

Color to plot everything but the fitted curve in.

verticalbool, optional

If True, observed values are on y-axis.

norm_histbool, optional

If True, the histogram height shows a density rather than a count. This is implied if a KDE or fitted density is plotted.

axlabelstring, 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.

labelstring, optional

Legend label for the relevant component of the plot.

axmatplotlib axis, optional

If provided, plot on this axis.

Returns
axmatplotlib 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:

>>> import seaborn as sns, numpy as np
>>> sns.set_theme(); np.random.seed(0)
>>> x = np.random.randn(100)
>>> ax = sns.distplot(x)
../_images/seaborn-distplot-1.png

Use Pandas objects to get an informative axis label:

>>> import pandas as pd
>>> x = pd.Series(x, name="x variable")
>>> ax = sns.distplot(x)
../_images/seaborn-distplot-2.png

Plot the distribution with a kernel density estimate and rug plot:

>>> ax = sns.distplot(x, rug=True, hist=False)
../_images/seaborn-distplot-3.png

Plot the distribution with a histogram and maximum likelihood gaussian distribution fit:

>>> from scipy.stats import norm
>>> ax = sns.distplot(x, fit=norm, kde=False)
../_images/seaborn-distplot-4.png

Plot the distribution on the vertical axis:

>>> ax = sns.distplot(x, vertical=True)
../_images/seaborn-distplot-5.png

Change the color of all the plot elements:

>>> sns.set_color_codes()
>>> ax = sns.distplot(x, color="y")
../_images/seaborn-distplot-6.png

Pass specific parameters to the underlying plot functions:

>>> 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"})
../_images/seaborn-distplot-7.png