Is there a way to make a Python Altair plot showing two standards of deviation? Using mark_errorbar(extent='stdev') shows just one standard deviation.
# Only shows one standard deviation.
alt.Chart(data).mark_errorbar(extent='stdev').encode(
x=alt.X('quantity:O', title='Quantity'),
y=alt.Y('value:Q', title='Value')
)
There isn't a parameter values that allows you to do this with mark_errorbar, but you could pre-calculate it in pandas and use mark_rule with y + y2:
import altair as alt
from vega_datasets import data
source = data.cars()
hp_agg = (
source
.groupby('Origin')
['Horsepower']
.agg(['mean', 'std'])
.assign(error_lower = lambda df: df['mean'] - 2 * df['std'],
error_upper = lambda df: df['mean'] + 2 * df['std'])
.reset_index())
error_bars = alt.Chart(hp_agg).mark_rule().encode(
x='Origin',
y='error_lower',
y2='error_upper')
means = alt.Chart(hp_agg).mark_circle(color='black').encode(
x='Origin',
y='mean')
error_bars + means
You can also achieve the same result using the transforms in Altair instead of using pandas.
import altair as alt
from vega_datasets import data
source = data.cars()
error_bars = (
alt.Chart(source).mark_rule().encode(
x='Origin',
y='error_lower:Q',
y2='error_upper:Q')
.transform_aggregate(
mean = 'mean(Horsepower)',
stdev = 'stdev(Horsepower)',
groupby=['Origin'])
.transform_calculate(
error_lower = 'datum.mean - 2 * datum.stdev',
error_upper = 'datum.mean + 2 * datum.stdev'))
means = alt.Chart(source).mark_circle(color='black').encode(
x='Origin',
y='mean(Horsepower)')
error_bars + means
