Time-series boxplot in pandas

If its an option for you, i would recommend using Seaborn, which is a wrapper for Matplotlib. You could do it yourself by looping over the groups from your timeseries, but that's much more work.

import pandas as pd
import numpy as np
import seaborn
import matplotlib.pyplot as plt

n = 480
ts = pd.Series(np.random.randn(n), index=pd.date_range(start="2014-02-01", periods=n, freq="H"))


fig, ax = plt.subplots(figsize=(12,5))
seaborn.boxplot(ts.index.dayofyear, ts, ax=ax)

Which gives:

Note that i'm passing the day of year as the grouper to seaborn, if your data spans multiple years this wouldn't work. You could then consider something like:

ts.index.to_series().apply(lambda x: x.strftime('%Y%m%d'))

Edit, for 3-hourly you could use this as a grouper, but it only works if there are no minutes or lower defined. :

[(dt - datetime.timedelta(hours=int(dt.hour % 3))).strftime('%Y%m%d%H') for dt in ts.index]

(Not enough rep to comment on accepted solution, so adding an answer instead.)

The accepted code has two small errors: (1) need to add numpy import and (2) nned to swap the x and y parameters in the boxplot statement. The following produces the plot shown.

import numpy as np
import pandas as pd
import seaborn
import matplotlib.pyplot as plt

n = 480
ts = pd.Series(np.random.randn(n), index=pd.date_range(start="2014-02-01", periods=n, freq="H"))

fig, ax = plt.subplots(figsize=(12,5))
seaborn.boxplot(ts.index.dayofyear, ts, ax=ax)