CODEWITHSUNDEEP
pythonpandasdataset
Danila Ganchar
Danila Ganchar

Reputation: 11242

pandas group by chunks

I have a data set:

df = pd.DataFrame({
    'service': ['a', 'a', 'a', 'b', 'c', 'a', 'a'],
    'status': ['problem', 'problem', 'ok', 'problem', 'ok', 'problem', 'ok'],
    'created': [
        datetime(2019, 1, 1, 1, 1, 0),
        datetime(2019, 1, 1, 1, 1, 10),
        datetime(2019, 1, 1, 1, 2, 0),
        datetime(2019, 1, 1, 1, 3, 0),
        datetime(2019, 1, 1, 1, 5, 0),
        datetime(2019, 1, 1, 1, 10, 0),
        datetime(2019, 1, 1, 1, 20, 0),
    ],
})

print(df.head(10))

  service   status             created
0       a  problem 2019-01-01 01:01:00  # -\
1       a  problem 2019-01-01 01:01:10  #   --> one group
2       a       ok 2019-01-01 01:02:00  # -/
3       b  problem 2019-01-01 01:03:00
4       c       ok 2019-01-01 01:05:00
5       a  problem 2019-01-01 01:10:00  # -\
6       a       ok 2019-01-01 01:20:00  # - --> one group

As you can see a service changed status problem -> ok(0, 2 items; 5, 6 items). Also you can see that 3, 4 items has no changes(only 1 record - without group/chunk). I need to create the next data set:

  service  downtime_seconds
0       a        60  # `created` difference between 2 and 0
1       a       600  # `created` difference between 6 and 5

I can do it through iteration:

for i in range(len(df.index)):
    # if df.loc[i]['status'] blablabla...

Is it possible to do it using pandas without iteration? Maybe there is a more elegant method?

Thank you.

Upvotes: 2

Views: 2911

Answers (1)

BENY
BENY

Reputation: 323316

In your case we need create the groupby key by reverse the order and cumsum , then we just need to filter the df before we groupby , use nunique with transform 

s=df.status.eq('ok').iloc[::-1].cumsum()
con=df.service.groupby(s).transform('nunique')==1
df_g=df[con].groupby(s).agg({'service':'first','created':lambda x : (x.iloc[-1]-x.iloc[0]).seconds})
Out[124]: 
       service  created
status                 
1            a      600
3            a       60

Upvotes: 3

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