What is the most elegant way to shade a pandas subplots based on one of the columns in a DataFrame?
A simple example:
In [8]:
from random import *
import pandas as pd
randBinList = lambda n: [randint(0,1) for b in range(1,n+1)]
rng = pd.date_range('1/1/2011', periods=72, freq='H')
ts = pd.DataFrame({'Value1': randn(len(rng)),'Value2': randn(len(rng)),'OnOff': randBinList(len(rng))}, index=rng)
ts.plot(subplots=True)
Results in the following plot:
Ideally, I would like a subplot of just Value1
and Value2
with both plots being shaded using axvspan
where On
(values with 1.0
in the OnOff
) are shaded and Off
is not shaded.
You're set up to do this very well. I think you'll need to interact with matplotlib directly, however.
If you set up your DataFrame like this (what you have already):
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
randBinList = lambda n: [np.random.randint(0,2) for b in range(1,n+1)]
rng = pd.date_range('1/1/2011', periods=72, freq='H')
ts = pd.DataFrame({
'Value1': np.random.randn(len(rng)),
'Value2': np.random.randn(len(rng)),
'OnOff': randBinList(len(rng))
}, index=rng)
Then you you can use the fill_between
command with the where
kwarg:
fig, (ax1, ax2) = plt.subplots(nrows=2)
ax1.plot(ts.index, ts['Value1'], 'k-')
ax1.fill_between(ts.index, ts['Value1'], y2=-6, where=ts['OnOff'])
ax2.plot(ts.index, ts['Value2'], 'k-')
ax2.fill_between(ts.index, ts['Value2'], y2=-6, where=ts['OnOff'])
fig.tight_layout()
Which gives me:
The technical post webpages of this site follow the CC BY-SA 4.0 protocol. If you need to reprint, please indicate the site URL or the original address.Any question please contact:yoyou2525@163.com.