CODEWITHSUNDEEP
python-3.xpandaspandas-groupbyrolling-computation
LeCoconutWhisperer
LeCoconutWhisperer

Reputation: 319

Number of rows in a rolling window of 30 days

I have a sample dataframe

Account     Date         Amount 
10          2020-06-01   100
10          2020-06-11   500
10          2020-06-21   600
10          2020-06-25   900
10          2020-07-11   1000
10          2020-07-15   600
11          2020-06-01   100
11          2020-06-11   200
11          2020-06-21   500
11          2020-06-25   1500
11          2020-07-11   2500
11          2020-07-15   6700

I want to get the number of rows in each 30 day interval for each account ie

Account     Date         Amount 
10          2020-06-01   1
10          2020-06-11   2
10          2020-06-21   3
10          2020-06-25   4
10          2020-07-11   4
10          2020-07-15   4
11          2020-06-01   1
11          2020-06-11   2
11          2020-06-21   3
11          2020-06-25   4
11          2020-07-11   4
11          2020-07-15   4

I have tried Grouper and resampling but those give me the counts per each 30 days and not the rolling counts.
Thanks in advance!

Upvotes: 6

Views: 1153

Answers (3)

Ethan Kershner
Ethan Kershner

Reputation: 131

df = df.resample('30D').agg({'date':'count','Amount':'sum'})

This will aggregate the 'Date' column by count, getting the data you want.

However, since you will need to first set date as your index for resampling, you could create a "dummy" column containing zeros:

df['dummy'] = pd.Series(np.zeros(len(df))

Upvotes: 2

ALollz
ALollz

Reputation: 59579

You can use broadcasting within group to check how many rows fall within X days.

import pandas as pd

def within_days(s, days):
    arr = ((s.to_numpy() >= s.to_numpy()[:, None]) 
           & (s.to_numpy() <= (s + pd.offsets.DateOffset(days=days)).to_numpy()[:, None])).sum(axis=0)
    return pd.Series(arr, index=s.index)

df['Amount'] = df.groupby('Account')['Date'].apply(within_days, days=30)

    Account       Date  Amount
0        10 2020-06-01       1
1        10 2020-06-11       2
2        10 2020-06-21       3
3        10 2020-06-25       4
4        10 2020-07-11       4
5        10 2020-07-15       4
6        11 2020-06-01       1
7        11 2020-06-11       2
8        11 2020-06-21       3
9        11 2020-06-25       4
10       11 2020-07-11       4
11       11 2020-07-15       4

Upvotes: 2

Andrej Kesely
Andrej Kesely

Reputation: 195573

def get_rolling_amount(grp, freq):
    return grp.rolling(freq, on="Date", closed="both").count()


df["Date"] = pd.to_datetime(df["Date"])
df["Amount"] = df.groupby("Account").apply(get_rolling_amount, "30D").values
print(df)

Prints:

    Account       Date Amount
0        10 2020-06-01      1
1        10 2020-06-11      2
2        10 2020-06-21      3
3        10 2020-06-25      4
4        10 2020-07-11      4
5        10 2020-07-15      4
6        11 2020-06-01      1
7        11 2020-06-11      2
8        11 2020-06-21      3
9        11 2020-06-25      4
10       11 2020-07-11      4
11       11 2020-07-15      4

Upvotes: 5

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