Python中向已有数据帧追加重影行的优化算法

Question

我有一个数据帧，我想将虚行（现有行的副本）附加到数据帧

       id   month  as_of_date1 turn  age 
119 5712    201401  2014-01-01  9   0
120 5712    201402  2014-02-01  9   1
121 5712    201403  2014-03-01  9   2
122 5712    201404  2014-04-01  9   3
123 5712    201405  2014-05-01  9   4
124 5712    201406  2014-06-01  9   5
125 9130    201401  2014-01-01  9   0
126 9130    201402  2014-02-01  9   1
127 9130    201403  2014-03-01  9   2
128 9130    201404  2014-04-01  9   3
129 9130    201405  2014-05-01  9   4

重影行由以下条件选择： 如果age小于turn，我们需要将最新的行追加到age== turn of或as_of_date1 == now()

现在我正在使用下面的代码，但是由于数据很大，大约有20万行，有100个字段，所以它永远需要

tdf1=tdf.loc[(tdf['age']<tdf['turn'])]
tdf2=tdf1.drop_duplicates(subset=['id'],keep='last')
leads=tdf2.index.tolist()
for lead in leads:
    ttdf=tdf.loc[[lead]]
    diff1 = relativedelta.relativedelta(datetime.datetime(2018,6,1),tdf.loc[lead,'as_of_date1']).months
    diff2=tdf.loc[lead,'turn']-tdf.loc[lead,'age']
    diff=min(diff1,diff2)
    for i in range(0,diff):
        tdf = tdf.append(ttdf, ignore_index=True)

预期结果：

    id   month  as_of_date1 turn  age 
119 5712    201401  2014-01-01  9   0
120 5712    201402  2014-02-01  9   1
121 5712    201403  2014-03-01  9   2
122 5712    201404  2014-04-01  9   3
123 5712    201405  2014-05-01  9   4
124 5712    201406  2014-06-01  9   5
125 9130    201401  2014-01-01  9   0
126 9130    201402  2014-02-01  9   1
127 9130    201403  2014-03-01  9   2
128 9130    201404  2014-04-01  9   3
129 9130    201405  2014-05-01  9   4
130 5712    201406  2014-06-01  9   5
131 5712    201406  2014-06-01  9   5
132 5712    201406  2014-06-01  9   5
133 5712    201406  2014-06-01  9   5
134 9130    201405  2014-05-01  9   4
135 9130    201405  2014-05-01  9   4
136 9130    201405  2014-05-01  9   4
137 9130    201405  2014-05-01  9   4
138 9130    201405  2014-05-01  9   4

如果有人知道更快的算法我会很感激的

Answer 1

正如@Parfit在注释中提到的那样，附加到数据帧实际上是占用内存的，在循环中执行它一点也不可取。所以我用了下面的方法，难以置信地提高了速度

a=[]
for lead in leads:
    ttdf=tdf.loc[[lead]]
    diff1 = relativedelta.relativedelta(datetime.datetime(2018,6,1),tdf.loc[lead,'as_of_date1']).months
    diff2=tdf.loc[lead,'turn']-tdf.loc[lead,'age']
    diff=min(diff1,diff2)
    for i in range(0,diff):
        a.append(ttdf)

tdf = tdf.append(a, ignore_index=True)