与pandas有条件合并

df['commsdate'] = pd.to_datetime(df['commsdate'], format='%d/%m/%y')areaunique = df['area'].unique().tolist()from datetime import timedeltacalldate = pd.date_range(start=min(df['commsdate']), end=max(df['commsdate'])+timedelta(days=6), freq='d')data = {area: [] for area in areaunique}dfnew = pd.dataframe(data)dfnew['calldate'] = calldatedfnew = dfnew.melt(id_vars=['calldate'], var_name='area')dfnew = dfnew.pivot(index='calldate', columns='area', values='value')dfnew = dfnew.reset_index()dfnew = dfnew[['calldate'] + areaunique]

for i in range(1,len(areaunique)+1):    dfnew.columns(i) =

df['commsdate'] = pd.to_datetime(df['commsdate'], dayfirst=true)tmp = df.pivot(index='commsdate', columns='area')out = (tmp['day0 incremental']       .add(tmp['day1 incremental'].shift(freq='1d'), fill_value=0)       .add(tmp['day2 incremental'].shift(freq='2d'), fill_value=0)       .reset_index().rename_axis(columns=none)      )

from functools import reduceimport rereg = re.compile(r'day(d+)')df['commsdate'] = pd.to_datetime(df['commsdate'], dayfirst=true)tmp = df.pivot(index='commsdate', columns='area')out = reduce(lambda a,b: a.add(b, fill_value=0),             (tmp[d].shift(freq=f'{reg.search(d).group(1)}d') for d in              tmp.columns.get_level_values(0).unique())            ).reset_index().rename_axis(columns=none)

CommsDate  Sales  Service0 2024-01-01   43.0     85.01 2024-01-02   92.0    147.02 2024-01-03  119.0    195.03 2024-01-04   63.0    107.04 2024-01-05   24.0     46.0