Python 按日期范围从变量计算累积占用(求和)

使用:

#convert column to datetimes
df['entry_date'] = pd.to_datetime(df['entry_date'])

#repeat rows by days column
df = df.loc[df.index.repeat(df['days'])]

#create days timedeltas
td = pd.to_timedelta(df.groupby(level=0).cumcount(), unit='d')

#add timedeltas by datetiems and count to 2 columns DataFrame
df1 = (df['entry_date'].add(td)
                       .value_counts()
                       .sort_index()
                       .rename_axis('date')
                       .reset_index(name='ocupation'))
print (df1)

         date  ocupation
0  2022-02-01         1
1  2022-02-02         2
2  2022-02-03         2
3  2022-02-04         1
4  2022-02-05         1
5  2022-04-10         1
6  2022-04-11         2
7  2022-04-12         3
8  2022-04-13         2
9  2022-11-10         1
10 2022-11-11         1
11 2022-11-12         1
12 2022-11-13         1
13 2022-11-14         1
14 2022-11-15         1

Performance:样本数据重复Performance0次:

df = pd.concat([df] * 1000, ignore_index=True)

def jez(df):
    #convert column to datetimes
    df['entry_date'] = pd.to_datetime(df['entry_date'], dayfirst=True)
    
    #repeat rows by days column
    df = df.loc[df.index.repeat(df['days'])]
    
    #create days timedeltas
    td = pd.to_timedelta(df.groupby(level=0).cumcount(), unit='d')
    
    #add timedeltas by datetiems and count to 2 columns DataFrame
    return (df['entry_date'].add(td)
                           .value_counts()
                           .sort_index()
                           .rename_axis('date')
                           .reset_index(name='ocupation'))
    


def moz(df):
    return (pd.Series([d for start, end in zip(df['entry_date'], df['exit_date'])
            for d in pd.date_range(start, end, freq='D')], name='date')
   .value_counts(sort=False)
   .reset_index(name='ocupation')
 )

In [122]: %timeit jez(df)
15.3 ms ± 470 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [123]: %timeit moz(df)
2.31 s ± 140 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)