pandas: apply axvspan to all subplots

Question

I'm trying to shade the spring months in a pandas plot with subplots. But it's only shading the last subplot. How do I get it to shade all the plots?

I'm using axvspan to shade between April 1 and June 30 each year by looping through a grouped dataframe of those end-dates.

Here's the result.

import matplotlib.pyplot as plt

recent = daily[daily.Date.dt.year >= 2000]

# Get only April 1 and Jume 30 each year
spring_months = recent[((recent.Date.dt.month == 4) & (recent.Date.dt.day == 1)) | ( (recent.Date.dt.month == 6) & (recent.Date.dt.day == 30) )]['Date']

# Make pivot table with data, one measuring station per column.
recent = recent.pivot(index='Date', columns='Station', values = 'Niveau(m)')

recent.plot(figsize=[7,50], subplots=True)
plt.xlim(xmax='2017-07-10')

# Group the spring end-dates by year
years = spring_months.drop_duplicates().groupby(spring_months.dt.year)

# Loop through groups and add axvspan between April 1 and June 30 each year
for n, g in years:
    plt.axvspan(g.iloc[0], g.iloc[1], facecolor='g', alpha=0.5)
    if g.iloc[0].year == 2016:
        break

Answer 1

Using as much of your code as possible, I modified a bit to fake a dataset. The key is to capture the axes handles of the subplot with the ax = df.plot... statement.

Then you can use list comprehension to loop through all the axes and draw the axvspan.

Create a dataset:

ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index, columns=list('ABCD'))

df1 = df.rename_axis('date').reset_index()

import matplotlib.pyplot as plt

recent = df1[df1.date.dt.year >= 2000]

# Get only April 1 and Jume 30 each year
spring_months = df1[((df1.date.dt.month == 4) & (df1.date.dt.day == 1)) | ( (df1.date.dt.month == 6) & (df1.date.dt.day == 30) )]['date']

# Make pivot table with data, one measuring station per column.
#recent = recent.pivot(index='Date', columns='Station', values = 'Niveau(m)')

Get the handles for all the axes:

ax = df.plot(subplots=True, figsize=(6,6))


# Group the spring end-dates by year
years = spring_months.drop_duplicates().groupby(spring_months.dt.year)

Loop thru all the axes with list comprehension to draw axspans

# Loop through groups and add axvspan between April 1 and June 30 each year
for n, g in years:
    [i.axvspan(g.iloc[0], g.iloc[1], facecolor='g', alpha=0.5) for i in ax] 
    if g.iloc[0].year == 2016:
        break