Python3.x 有没有办法使用重采样矢量化添加缺失的月份

不确定是否更快，但更简单的代码是:

df = df.sort_index().groupby('ID').apply(lambda x: x.asfreq('MS'))

df1 = df.groupby('ID').apply(lambda x: x.asfreq('MS'))
df2 = df.set_index(df.index).groupby('ID').apply(add_missing_months)

print (df1.equals(df2))
True

编辑:为了提高性能，创建Series.dt.to_period个月周期，将最小值和最大值与重复索引的差值相加Index.repeat，最后添加计数器GroupBy.cumcount个foa append months ranges，通过Series.dt.to_timestamp转换为时间戳，最后使用左连接:

df1 = (df.assign(year_month = df['year_month'].dt.to_period('m'))
         .groupby(['ID'])['year_month']
         .agg(['min', 'max']))

diff = df1['max'].astype('int').sub(df1['min'].astype('int')) + 1
df1 = df1.loc[df1.index.repeat(diff)]
df1 = (df1['min'].add(df1.groupby(level=0).cumcount()))
                 .dt.to_timestamp()
                 .reset_index(name='year_month'))

df = df1.merge(df.rename_axis(None), how='left')

Performance:

In [276]: %timeit jez(df)
126 ms ± 7.26 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

In [277]: %timeit vogel(df)
312 ms ± 32.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

df = pd.DataFrame({'ID': [1, 1, 1, 2, 2, 3], 'year_month': ['2020-01-01','2020-08-01','2020-10-01','2020-01-01','2020-07-01','2021-05-01'], 'product':['A','B','C','A','D','C']})

# Enlarge dataset to 60 000 rows
for i in range(9999):
    df2 = df.iloc[-6:].copy()
    df2['ID'] = df2['ID'] + 3
    df = pd.concat([df,df2], axis=0, ignore_index=True)

df['year_month'] = pd.to_datetime(df['year_month'])
df.index = pd.to_datetime(df['year_month'], format = '%Y%m%d')

def jez(df):

    df1 = df.assign(year_month = df['year_month'].dt.to_period('m')).groupby(['ID'])['year_month'].agg(['min', 'max'])
    df1 = df1.loc[df1.index.repeat( df1['max'].astype('int').sub(df1['min'].astype('int')) + 1)]
    df1 = (df1['min'] +  df1.groupby(level=0).cumcount()).dt.to_timestamp().reset_index(name='year_month')
    
    return df1.merge(df.rename_axis(None), how='left')

def vogel(df):
    min_d = df['year_month'].min()
    max_d = df['year_month'].max()
    
    # generate all possible combinations of date and ID
    df_agg = df.groupby(['ID'])['year_month'].agg(['min', 'max'])
    df = pd.DataFrame(
        index=pd.MultiIndex.from_product(
            [pd.date_range(min_d, max_d, freq='MS'), df_agg.index]
        )
    )
    
    # reduce to only relevant dates
    df = df.merge(df_agg, left_on='ID', right_index=True)
    df = df.reset_index().rename(columns={'level_0': 'year_month'})
    df = df[df['year_month'].between(df['min'], df['max'])]
    df = df.drop(columns=['min', 'max'])
    
    # add product information
    df = df.merge(df, how='left')
        
    return  df