Reputation: 117
I have the following dataframe.
data = {'bid':['23243', '23243', '23243', '12145', '12145', '12145', '12145'],
'lid':['54346786', '23435687', '23218987', '43454432', '21113567', '54789876', '22898721'],
'date':['2021-08-11','2021-08-12','2021-09-17','2021-05-02','2021-05-11','2021-05-20','2021-08-13'],
'val1':[44,34,54,76,89,33,27],
'val2':[11,55,43,89,76,44,88]}
df = pd.DataFrame(data)
What I am looking for is to randomly pick a lid
per month for the bid
column, and maintain a count of past instances until the point of the random sample, something similar to this:
I can think of separating the year and months into different columns and then apply pd.groupby on the bid, year and month with the pd.Series.sample function, but there must be a better way of doing it.
Upvotes: 1
Views: 499
Reputation: 862671
Use GroupBy.cumcount
per bid
and then per months and bid
use DataFrameGroupBy.sample
:
df['date'] = pd.to_datetime(df['date'])
#if necessary sorting
#df = df.sort_values(['bid','date'])
df['prev'] = df.groupby('bid').cumcount()
df1 = df.groupby(['bid', pd.Grouper(freq='M', key='date')], sort=False).sample(n=1)
print (df1)
bid lid date val1 val2 prev
1 23243 23435687 2021-08-12 34 55 1
2 23243 23218987 2021-09-17 54 43 2
5 12145 54789876 2021-05-20 33 44 2
6 12145 22898721 2021-08-13 27 88 3
Upvotes: 1
Reputation: 120409
IIUC, use groupby.sample
, assume date
column have datetime64
dtype:
out = df.groupby([df['date'].dt.month, 'bid']).sample(n=1).reset_index(drop=True)
print(out)
# Output
bid lid date val1 val2
0 12145 21113567 2021-05-11 89 76
1 12145 22898721 2021-08-13 27 88
2 23243 23435687 2021-08-12 34 55
3 23243 23218987 2021-09-17 54 43
Upvotes: 0