熊猫中的Groupby和interpolate

我的数据有一个星期号、帐户id和几个使用列。我想a)按帐户ID分组,b)将每周数据重新采样为每日数据,c)平均插值每日数据(将每周数据除以7),然后将所有数据重新组合在一起。我把大部分都记下来了,但是 groupby 我有点困惑。它也很慢,这让我觉得这可能不是最佳的解决方案。

    Account Id  year week         views stats foo_col 
31133   213     2017-03-05          4.0     2.0     11.0
10085   456     2017-03-12          1.0     6.0     3.0
49551   789     2017-03-26          1.0     6.0     27.0

def interpolator(mini_df):
    mini_df = mini_df[cols_to_interpolate].set_index('year week')
    return mini_df.resample('D').ffill().interpolate() / 7

example = list(grp)[0][1]
interpolator(example) # This works perfectly

df.groupby('Account Id').agg(interpolator)                # doesn't work
df.groupby('Account Id').transform(interpolator)          # doesn't work

for name,group in grp:
    group = group[cols_to_interpolate].set_index('year week')
    group = group.resample('D').ffill().interpolate() / 7 # doesn't work

for acc_id in df['Account Id'].unique():
    mask = df.loc[df['Account Id'] == acc_id]
    print(df[mask])                                     # doesn't work