Hist

import hvplot.pandas  # noqa

# hvplot.extension("matplotlib")

hist is often a good way to start looking at continuous data to get a sense of the distribution. Similar methods include kde (also available as density).

from bokeh.sampledata.autompg import autompg_clean

autompg_clean.sample(n=5)

	mpg	cyl	displ	hp	weight	accel	yr	origin	name	mfr
263	17.5	8	318.0	140	4080	13.7	78	North America	dodge magnum xe	dodge
366	29.0	4	135.0	84	2525	16.0	82	North America	dodge aries se	dodge
40	14.0	8	318.0	150	4096	13.0	71	North America	plymouth fury iii	plymouth
309	32.1	4	98.0	70	2120	15.5	80	North America	chevrolet chevette	chevrolet
100	23.0	6	198.0	95	2904	16.0	73	North America	plymouth duster	plymouth

autompg_clean.hvplot.hist("weight")

When using by the plots are overlaid by default. To create subplots instead, use subplots=True.

autompg_clean.hvplot.hist("weight", by="origin", subplots=True, width=250)

You can also plot histograms of datetime data

import pandas as pd
from bokeh.sampledata.commits import data as commits

commits = commits.reset_index().sort_values("datetime")
commits.head(3)

	datetime	day	time
4915	2012-12-29 11:57:50-06:00	Sat	11:57:50
4914	2013-01-02 17:46:43-06:00	Wed	17:46:43
4913	2013-01-03 16:28:49-06:00	Thu	16:28:49

commits.hvplot.hist(
    "datetime",
    bin_range=(pd.Timestamp('2012-11-30'), pd.Timestamp('2017-05-01')),
    bins=54,   
)

If you want to plot the distribution of a categorical column you can calculate the distribution using Pandas’ method value_counts and plot it using .hvplot.bar.

autompg_clean["mfr"].value_counts().hvplot.bar(invert=True, flip_yaxis=True, height=500)

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