Improving speed of iterrows() query that is utilizing a mask

Question

I have a large dataset that looks similar to this in terms of content:

test = pd.DataFrame({'date':['2018-08-01','2018-08-01','2018-08-02','2018-08-03','2019-09-01','2019-09-02','2019-09-03','2020-01-02','2020-01-03','2020-01-04','2020-10-04','2020-10-05'],
                 'account':['a','a','a','a','b','b','b','c','c','c','d','e']})

For each account, I am attempting to create a column that specifies "Yes" to rows that have the earliest date (even if that earliest date repeats), and "No" otherwise. I am using the following code which works nicely on a smaller subset of this data, but not on my entire (larger) dataset.

first_date = test.groupby('account').agg({'date':np.min})

test['first_date'] = 'No'
for row in first_date.iterrows():
    account = row[0]
    date = row[1].date
    mask = (test.account == account) & (test.date == date)
    test.loc[mask, 'first_date'] = 'Yes'

Any ideas for improvement? I'm fairly new to python and already having runtime issues for larger datasets that use pandas DataFrame. Thanks in advance.

Answer 1

Generally when we use pandas or numpy we want to avoid iterating over our data and use the provided vectorized methods.

Use groupby.transform to get a min date on each row, then use np.where to create your conditional column:

m = test['date'] == test.groupby('account')['date'].transform('min')
test['first_date'] = np.where(m, 'Yes', 'No')


          date account first_date
0   2018-08-01       a        Yes
1   2018-08-01       a        Yes
2   2018-08-02       a         No
3   2018-08-03       a         No
4   2019-09-01       b        Yes
5   2019-09-02       b         No
6   2019-09-03       b         No
7   2020-01-02       c        Yes
8   2020-01-03       c         No
9   2020-01-04       c         No
10  2020-10-04       d        Yes
11  2020-10-05       e        Yes