Find recurring transactions every month for at least "x" months every year - python-3.x

Question

df = pd.DataFrame.from_dict({"Card_No":[1234,1234,1234,4321,4321,4321],
"Merchant_Country":["USA", "USA", "USA", "USA","USA", "USA"],
"Merchant_Name":["BestBuy", "BestBuy", "BestBuy", "BestBuy","BestBuy", "BestBuy"],
"Date": ["2021-01-15", "2021-02-15", "2021-03-15", "2021-04-15", "2021-05-15", "2021-07-15"],
"TrxAmount": [99.99, 99.99, 99.99, 89.99, 89.99, 89.99]})
Find card numbers who had at least 3 recurring payments at the same merchant during all of 2020. So while 1234 qualifies as having had recurring payments, Card number 4321 does not fulfill the criteria.
A recurring transaction is at the same merchant, for the same amount, and in consecutive months.
I have been trying to break my head to find a solution but can't seem to solve this either in SQL or in Python.
One approach that might work is creating groups and then seeing how many transactions occurred in each group but I can't seem to crack the consecutive transaction piece of the problem.
Any help is appreciated. Also, the solution should scale to 3,4,5,..n recurring transactions.
groups = df.groupby(["Card_No", "Merchant_Name","Transaction Amount"])
for grp, data in groups:
lst_of_dates = data["date"].unique()

Answer 1

If you mean one payment per month for a merchant with the same amount on the same day of the month, then you can use lead() or lag(). So, to get the first of three "recurring" payments:
select t.*
from (select t.*,
lead(date, 1) over (partition by card, merchant, amount) as date_1,
lead(date, 2) over (partition by card, merchant, amount) as date_2
from t
) t
where date_2 = date + interval '2 month' and
date_1 = date + interval '1 month';

Answer 2

A pandas answer:
df = pd.DataFrame({'Card Number': {0: 1234, 1: 1234, 2: 1234, 3: 4321, 4: 4321, 5: 4321},
'Merchant Country': {0: 'USA', 1: 'USA', 2: 'USA', 3: 'USA', 4: 'USA', 5: 'USA'},
'Merchant Name': {0: 'Best Buy', 1: 'Best Buy', 2: 'Best Buy', 3: 'Best Buy', 4: 'Best Buy', 5: 'Best Buy'},
'Date': {0: '2020-02-15', 1: '2020-03-15', 2: '2020-04-15', 3: '2020-05-15', 4: '2020-06-15', 5: '2020-08-15'},
'Transaction Amount': {0: 99.99, 1: 99.99, 2: 99.99, 3: 99.99, 4: 99.99, 5: 99.99}})
df['Date'] = pd.to_datetime(df['Date'])
# Create monthly date for indexing
df['Month'] = df['Date']
# Set index as monthly & fill missing months
df = df.set_index('Month').resample('M').min()
# Fill card numbers for grouping
df['Card Number'] = df['Card Number'].ffill()
# Identify recurring transaction - same in month t and t+1, or month t and t-1
df['cond'] = df.groupby('Card Number')['Transaction Amount'].transform(
lambda x: (x == x.shift(1)) | (x == x.shift(-1)))
# Flag recurring transactions with total number of recurrences in sequence
df['Recurrences'] = df.groupby([df['Card Number'], df['cond'].eq(True)])['cond'].transform(
lambda x: x.cumsum().max())
Card Number Merchant Country Merchant Name Date Transaction Amount cond Recurrences
Month
2020-02-29 1234.0 USA Best Buy 2020-02-15 99.99 True 3
2020-03-31 1234.0 USA Best Buy 2020-03-15 99.99 True 3
2020-04-30 1234.0 USA Best Buy 2020-04-15 99.99 True 3
2020-05-31 4321.0 USA Best Buy 2020-05-15 99.99 True 2
2020-06-30 4321.0 USA Best Buy 2020-06-15 99.99 True 2
2020-07-31 4321.0 NaN NaN NaT NaN False 0
2020-08-31 4321.0 USA Best Buy 2020-08-15 99.99 False 0
From there, you can find recurrences of N months with df.loc[df['Recurrences'].eq(N).