CODEWITHSUNDEEP
pythonpandasdataframetime-series
cona
cona

Reputation: 189

How to resample using custom frequency?

I know the Pandas .resample() method can down sample using a regular interval. What if I have irregular intervals? This code doesn't work. I don't want to reinvent the wheel if a function already exists.

d = {'ticker': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'], 
 'date':['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05', '2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04', '2021-01-05'],  
 'return': [.01, .011, .012, .013, .014, -.02, -.01, .01, .02, -.03]}
df = pd.DataFrame(d)
dates = ['2021-01-03', '2021-01-05']

enter image description here

df.resample(dates).sum()
# ValueError: Invalid frequency

What I want.

ticker date return
A 2021-01-03 0.033
A 2021-01-05 0.027
B 2021-01-03 -0.02
B 2021-01-05 -0.01

Upvotes: 0

Views: 217

Answers (1)

Sayandip Dutta
Sayandip Dutta

Reputation: 15872

You can use the following:

>>> mask = df.date.where(df['date'].isin(dates)).bfill()
>>> df.groupby([df.ticker, mask]).sum().reset_index()

  ticker        date  return
0      A  2021-01-03   0.033
1      A  2021-01-05   0.027
2      B  2021-01-03  -0.020
3      B  2021-01-05  -0.010

Upvotes: 1

Related Questions