Matplotlib axvspan着色为pandas DataFrame子图基于其中一列

Question

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.

Answer 1

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: