boxplot (original) (raw)

A Box Plot of autompg data. This example demonstrates combining multiple basic glyphs to create a more complicated chart.

Details

Sampledata:

bokeh.sampledata.autompg2

Bokeh APIs:

figure.vbar

More info:

Boxplot

Keywords:

bars, boxplot, categorical, pandas

import pandas as pd

from bokeh.models import ColumnDataSource, Whisker from bokeh.plotting import figure, show from bokeh.sampledata.autompg2 import autompg2 from bokeh.transform import factor_cmap

df = autompg2[["class", "hwy"]].rename(columns={"class": "kind"})

kinds = df.kind.unique()

compute quantiles

grouper = df.groupby("kind") qs = grouper.hwy.quantile([0.25, 0.5, 0.75]).unstack().reset_index() qs.columns = ["kind", "q1", "q2", "q3"]

compute IQR outlier bounds

iqr = qs.q3 - qs.q1 qs["upper"] = qs.q3 + 1.5iqr qs["lower"] = qs.q1 - 1.5iqr

update the whiskers to actual data points

for kind, group in grouper: qs_idx = qs.query(f"kind=={kind!r}").index[0] data = group["hwy"]

# the upper whisker is the maximum between p3 and upper
q3 = qs.loc[qs_idx, "q3"]
upper = qs.loc[qs_idx, "upper"]
wiskhi = group[(q3 <= data) & (data <= upper)]["hwy"]
qs.loc[qs_idx, "upper"] = q3 if len(wiskhi) == 0 else wiskhi.max()

# the lower whisker is the minimum between q1 and lower
q1 = qs.loc[qs_idx, "q1"]
lower = qs.loc[qs_idx, "lower"]
wisklo = group[(lower <= data) & (data<= q1)]["hwy"]
qs.loc[qs_idx, "lower"] = q1 if len(wisklo) == 0 else wisklo.min()

df = pd.merge(df, qs, on="kind", how="left")

source = ColumnDataSource(qs)

p = figure(x_range=kinds, tools="", toolbar_location=None, title="Highway MPG distribution by vehicle class", background_fill_color="#eaefef", y_axis_label="MPG")

outlier range

whisker = Whisker(base="kind", upper="upper", lower="lower", source=source) whisker.upper_head.size = whisker.lower_head.size = 20 p.add_layout(whisker)

quantile boxes

cmap = factor_cmap("kind", "TolRainbow7", kinds) p.vbar("kind", 0.7, "q2", "q3", source=source, color=cmap, line_color="black") p.vbar("kind", 0.7, "q1", "q2", source=source, color=cmap, line_color="black")

outliers

outliers = df[~df.hwy.between(df.lower, df.upper)] p.scatter("kind", "hwy", source=outliers, size=6, color="black", alpha=0.3)

p.xgrid.grid_line_color = None p.axis.major_label_text_font_size="14px" p.axis.axis_label_text_font_size="12px"

show(p)