Temporary .rolling () error is not executed using group

Question

Here's a snippet of code from Pandas Problem No. 13966

dates = pd.date_range(start='2016-01-01 09:30:00', periods=20, freq='s')
df = pd.DataFrame({'A': [1] * 20 + [2] * 12 + [3] * 8,
                   'B': np.concatenate((dates, dates)),
                   'C': np.arange(40)})

Fails:

df.groupby('A').rolling('4s', on='B').C.mean()
ValueError: B must be monotonic

Due to the problem related above, this seems to be a mistake. Does anyone have a good workaround?

Answer 1

Set Bas an index first to use the method Groupby.resampleon it.

df.set_index('B', inplace=True)

A . , ffill ( fillna NaN 0). rolling, 4 (- freq=4s) C, :

for _, grp in df.groupby('A'):
    print (grp.resample('s').ffill(limit=0).rolling(4)['C'].mean().head(10)) #Remove head() 

:

B
2016-01-01 09:30:00    NaN
2016-01-01 09:30:01    NaN
2016-01-01 09:30:02    NaN
2016-01-01 09:30:03    1.5
2016-01-01 09:30:04    2.5
2016-01-01 09:30:05    3.5
2016-01-01 09:30:06    4.5
2016-01-01 09:30:07    5.5
2016-01-01 09:30:08    6.5
2016-01-01 09:30:09    7.5
Freq: S, Name: C, dtype: float64
B
2016-01-01 09:30:00     NaN
2016-01-01 09:30:01     NaN
2016-01-01 09:30:02     NaN
2016-01-01 09:30:03    21.5
2016-01-01 09:30:04    22.5
2016-01-01 09:30:05    23.5
2016-01-01 09:30:06    24.5
2016-01-01 09:30:07    25.5
2016-01-01 09:30:08    26.5
2016-01-01 09:30:09    27.5
Freq: S, Name: C, dtype: float64
B
2016-01-01 09:30:12     NaN
2016-01-01 09:30:13     NaN
2016-01-01 09:30:14     NaN
2016-01-01 09:30:15    33.5
2016-01-01 09:30:16    34.5
2016-01-01 09:30:17    35.5
2016-01-01 09:30:18    36.5
2016-01-01 09:30:19    37.5
Freq: S, Name: C, dtype: float64

---

TL; DR

groupby.apply :

# tested in version - 0.19.1
df.groupby('A').apply(lambda grp: grp.resample('s').ffill(limit=0).rolling(4)['C'].mean())

()

# Tested in OP version - 0.19.0
df.groupby('A').apply(lambda grp: grp.resample('s').ffill().rolling(4)['C'].mean())

.

Answer 2

>>> df.sort_values('B').set_index('B').groupby('A').rolling('4s').C.mean()