Convenient time series aggregation with TimeGrouper in pandas

As a reminder, I didn't have any information about TimeGrouper like the extension of GroupBy in pandas. Please let me know if there is a more de facto standard method!

Example: Useful when you want to aggregate within a specific period

Example

• Aggregate every 6 months from data with monthly values
• Monthly aggregation from data with daily values
• Aggregate daily from data that has transaction records with time stamps

Related stackoverflow

• How to groupby time series by 10 minutes using pandas? [1]

• timegrouper-pandas

Example 1: Aggregate every 6 months from data with monthly values (from [1])

ts = pd.date_range('7/1/2008', periods=30, freq='MS') 
df = pd.DataFrame(pd.Series(range(len(ts)), index=ts))
df[0] += 1
df # 2008/7/Create a monthly data frame starting from 1

df2 = pd.DataFrame([0], index = [df.index.shift(-1, freq='MS')[0]])
df2

df2 can be aggregated every 6 months

df2.append(df).groupby(pd.TimeGrouper(freq='6M')).aggregate(np.sum)[1:] 

Example 2: Aggregate daily from data that has a transaction record with a time stamp

rng = pd.date_range(start = '2014-01-01',periods = 100, freq='H')
df_original = pd.DataFrame({'Volume' : np.random.randint(100,2000,len(rng))}, index=rng)
df_original

Out[148]: 
	Volume
2014-01-01 00:00:00	1484
2014-01-01 01:00:00	1635
2014-01-01 02:00:00	984
2014-01-01 03:00:00	1239
2014-01-01 04:00:00	785
2014-01-01 05:00:00	871
2014-01-01 06:00:00	614
2014-01-01 07:00:00	119
2014-01-01 08:00:00	933
2014-01-01 09:00:00	624
...	...
2014-01-04 19:00:00	1832
2014-01-04 20:00:00	1996
2014-01-04 21:00:00	1040
2014-01-04 22:00:00	1867
2014-01-04 23:00:00	1098
2014-01-05 00:00:00	1397
2014-01-05 01:00:00	1996
2014-01-05 02:00:00	610
2014-01-05 03:00:00	1242
100 rows × 1 columns

df_tmp = pd.DataFrame({'Volume':[0]}, index = [df_original.index.shift(-1, freq='D')[0]])
df_daily=df_tmp.append(df_original).groupby(pd.TimeGrouper(freq='D')).aggregate(np.sum)[1:] 
df_daily

P.S I want to embed jupyter in Qiita