python - Pandas aggregation on timedelta and its behaviour -


i struggling aggregation on timedelta including plotting. raw data available here data has submit (datetime) , resolved (datetime) , pausetime (timedelta) , resolved-submit-pause ( actual time resolve )

click here data

test_df = pd.read_csv('test_df.csv')  #convert date time stamps test_df[['submit','resolved']] = test_df[['submit','resolved']].apply(pd.to_datetime)  #convert pausetime , resolved-submit-pausetime timedelta  test_df['pausetime']=pd.to_timedelta(test_df['pausetime']) test_df['resolved-submit-pausetime'] = pd.to_timedelta(test_df['resolved-submit-pausetime'])

i trying aggregate mean each day of 'resolved'

test_df.groupby([pd.grouper(key='resolved', freq='d')])['resolved-submit-pausetime'].mean()

which gives me error - 'dataerror: no numeric types aggregate'

1) how can aggregate on mean .

2) guidance plotting trend of mean time resolve (x axis have dates , y axis agg mean timedelta of 'resolved-submit-pausetime')

use step convert time delta column seconds:

test_df['resolved-submit-pausetime'] = test_df['resolved-submit-pausetime'].astype('timedelta64[s]')  0      1234.0 1     27380.0 2     33017.0 3      5454.0 4       433.0 5      2302.0 6     21753.0 7      3405.0 8      4779.0 9      3974.0 10     3389.0 11      114.0 name: resolved-submit-pausetime, dtype: float64

then run groupby statement compute mean:

test_df.groupby([pd.grouper(key='resolved', freq='d')])['resolved-submit-pausetime'].mean()  resolved 2017-04-01    20543.666667 2017-04-02     7485.500000 2017-04-03     3132.200000 name: resolved-submit-pausetime, dtype: float64

you can use pandas built in plotting tools quick , dirty plot of mean time respect groupby day:

test_df.groupby([pd.grouper(key='resolved', freq='d')])['resolved-submit-pausetime'].mean().plot()

enter image description here


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