Moving aggregate within a specified date range - python-3.x

Question

Using a sample credit card transactions data below:
df = pd.DataFrame({
'card_id' : [1, 1, 1, 2, 2],
'date' : [datetime(2020, 6, random.randint(1, 14)) for i in range(5)],
'amount' : [random.randint(1, 100) for i in range(5)]})
df
card_id date amount
0 1 2020-06-07 11
1 1 2020-06-11 45
2 1 2020-06-14 87
3 2 2020-06-04 48
4 2 2020-06-12 76
I'm trying to take the total amount spent in the past 7 days of a card at the point of the transaction. For example, if card_id 1 made a transaction on June 8, I want to get the total transactions from June 1 to June 7. This is what I was hoping to get:
card_id date amount sum_past_7d
0 1 2020-06-07 11 0
1 1 2020-06-11 45 11
2 1 2020-06-14 87 56
3 2 2020-06-04 48 0
4 2 2020-06-12 76 48
I'm currently using this function and pd.apply to generate my desired column but it's taking too long on the actual data (> 1 million rows).
df['past_week'] = df['date'].apply(lambda x: x - timedelta(days=7))
def myfunction(x):
return df.loc[(df['card_id'] == x.card_id) & \
(df['date'] >= x.past_week) & \
(df['date'] < x.date), :]['amount'].sum()
Is there a faster and more efficient way to do this?

Answer 1

Let's try rolling on date with groupby:
# make sure the data is sorted properly
# your sample is already sorted, so you can skip this
df = df.sort_values(['card_id', 'date'])
df['sum_past_7D'] = (df.set_index('date').groupby('card_id')
['amount'].rolling('7D').sum()
.groupby('card_id').shift(fill_value=0)
.values
)
Output:
card_id date amount sum_past_7D
0 1 2020-06-07 11 0.0
1 1 2020-06-11 45 11.0
2 1 2020-06-14 87 56.0
3 2 2020-06-04 48 0.0
4 2 2020-06-12 76 48.0