I have a dataframe like as shown below (run the full code below)
df1 = pd.DataFrame({'person_id': [11,21,31,41,51],
'date_birth': ['05/29/1967', '01/21/1957', '7/27/1959','01/01/1961','12/31/1961']})
df1 = df1.melt('person_id', value_name='date_birth')
df1['birth_dates'] = pd.to_datetime(df1['date_birth'])
df_ranges = df1.assign(until_prev_year_days=(df1['birth_dates'].dt.dayofyear - 1),
until_next_year_days=((df1['birth_dates'] + pd.offsets.YearEnd(0)) - df1['birth_dates']).dt.days)
f = {'until_prev_year_days': 'min', 'until_next_year_days': 'min'}
min_days = df_ranges.groupby('person_id',as_index=False).agg(f)
min_days.columns = ['person_id','no_days_to_prev_year','no_days_to_next_year']
df_offset = pd.merge(df_ranges[['person_id','birth_dates']], min_days, on='person_id',how='inner')
See below on what I tried to get the range
df_offset['range_to_shift'] = "[" + (-1 * df_offset['no_days_to_prev_year']).map(str) + "," + df_offset['no_days_to_next_year'].map(str) + "]"
Though my approach works, I would like to is there any better and elegant way to do the same
Please note that for values from no_days_to_prev_year
, we have to prefix minus
sign
I expect my output to be like as shown below
Use DataFrame.mul
along with DataFrame.to_numpy
:
cols = ['no_days_to_prev_year', 'no_days_to_next_year']
df_offset['range_to_shift'] = df_offset[cols].mul([-1, 1]).to_numpy().tolist()
Result:
# print(df_offset)
person_id birth_dates no_days_to_prev_year no_days_to_next_year range_to_shift
0 11 1967-05-29 148 216 [-148, 216]
1 21 1957-01-21 20 344 [-20, 344]
2 31 1959-07-27 207 157 [-207, 157]
3 41 1961-01-01 0 364 [0, 364]
4 51 1961-12-31 364 0 [-364, 0]
timeit
performance results:
df_offset.shape
(50000, 5)
%%timeit -n100
cols = ['no_days_to_prev_year', 'no_days_to_next_year']
df_offset['range_to_shift'] = df_offset[cols].mul([-1, 1]).to_numpy().tolist()
15.5 ms ± 464 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)