get the sign change count from Dataframe - pandas

Question

I have df like this
Date amount
0 2021-06-18 14
1 2021-06-19 -8
2 2021-06-20 -8
3 2021-06-21 17
4 2021-07-02 -8
5 2021-07-05 77
6 2021-07-06 -10
7 2021-08-02 -78
8 2021-08-06 77
9 2021-07-08 10
i went the count of sign change in amount month wise of count each month like in
count = [{"June-2021": 2},{"July-2021" : 3},{"Aug-2021" : 1}]
Note: Last Date of each month and first date of next month is different then count as in different count
i want a function for this

Answer 1

You can use (x.mul(x.shift()) < 0).sum() (current entry multiply by last entry being negative indicates a sign change) to get the count of sign changes within a group of month-year, as follows:
count = (df.groupby(df['Date'].dt.strftime('%b-%Y'), sort=False)['amount']
.agg(lambda x: (x.mul(x.shift()) < 0).sum())
.to_dict()
)
Result:
print(count)
{'Jun-2021': 2, 'Jul-2021': 3, 'Aug-2021': 1}
Edit
If you want list of dict, you can use:
count = (df.groupby(df['Date'].dt.strftime('%b-%Y'), sort=False)['amount']
.agg(lambda x: (x.mul(x.shift()) < 0).sum())
.reset_index()
.apply(lambda x: {x['Date']: x['amount']}, axis=1)
.to_list()
)
Result:
print(count)
[{'Jun-2021': 2}, {'Jul-2021': 3}, {'Aug-2021': 1}]