计算进位

Question

我想计算电视广告GRP数据的结转效应。 我的输入数据如下所示：

      Variable       Date  Causal  Half_Life
0     TV Model 2016-01-10       0          4
1     TV Model 2016-01-17       0          4
2     TV Model 2016-01-24       0          4
3     TV Model 2016-01-31     100          4
4     TV Model 2016-02-07     110          4
5     TV Model 2016-02-14      89          4
6     TV Model 2016-02-21      57          4
7     TV Model 2016-02-28      90          4
8   TV General 2016-01-10       0          4
9   TV General 2016-01-17       0          4
10  TV General 2016-01-24       0          4
11  TV General 2016-01-31      30          4
12  TV General 2016-02-07      32          4
13  TV General 2016-02-14      42          4
14  TV General 2016-02-21      39          4
15  TV General 2016-02-28      55          4

我想根据以下条件计算新列df['Adstock']：

If first row of the group from Column df.Variable, then df.Adstock = df.Causal If not the first row from the group then, df. Adstock = df.Causal + 0.5**(1/df.Half_life)*df.Adstock from the previous row.

我正在使用以下代码：

^{pr2}$

我得到的输出如下：

      Variable       Date  Causal  Half_Life  Adstock
0     TV Model 2016-01-10       0          4      0.0
1     TV Model 2016-01-17       0          4      0.0
2     TV Model 2016-01-24       0          4      0.0
3     TV Model 2016-01-31     100          4    100.0
4     TV Model 2016-02-07     110          4    110.0
5     TV Model 2016-02-14      89          4     89.0
6     TV Model 2016-02-21      57          4     57.0
7     TV Model 2016-02-28      90          4     90.0
8   TV General 2016-01-10       0          4      0.0
9   TV General 2016-01-17       0          4      0.0
10  TV General 2016-01-24       0          4      0.0
11  TV General 2016-01-31      30          4     30.0
12  TV General 2016-02-07      32          4     32.0
13  TV General 2016-02-14      42          4     42.0
14  TV General 2016-02-21      39          4     39.0
15  TV General 2016-02-28      55          4     55.0

但所需输出应如下所示：

      Variable       Date  Causal  Half_Life     Adstock
0     TV Model 2016-01-10       0          4    0.000000
1     TV Model 2016-01-17       0          4    0.000000
2     TV Model 2016-01-24       0          4    0.000000
3     TV Model 2016-01-31     100          4  100.000000
4     TV Model 2016-02-07     110          4  194.089642
5     TV Model 2016-02-14      89          4  252.209284
6     TV Model 2016-02-21      57          4  269.081883
7     TV Model 2016-02-28      90          4  316.269991
8   TV General 2016-01-10       0          4    0.000000
9   TV General 2016-01-17       0          4    0.000000
10  TV General 2016-01-24       0          4    0.000000
11  TV General 2016-01-31      30          4   30.000000
12  TV General 2016-02-07      32          4   57.226892
13  TV General 2016-02-14      42          4   90.121889
14  TV General 2016-02-21      39          4  114.783173
15  TV General 2016-02-28      55          4  151.520759

请帮忙。在

Answer 1

这是我的解决方案，我认为很难将其矢量化

l=[]
for x , y in df.groupby('Variable',sort=False):
    #print(y)
    l1=[]
    for s,t in y.iterrows():
        if len(l1)==0:
            l1.append(t['Causal'])
        else:
            l1.append(t['Causal'] + 0.5**(1/t['Half_Life'])*l1[-1])
    l.extend(l1)
df['New']=l
df
Out[982]: 
     Variable        Date  Causal  Half_Life         New
0     TVModel  2016-01-10       0          4    0.000000
1     TVModel  2016-01-17       0          4    0.000000
2     TVModel  2016-01-24       0          4    0.000000
3     TVModel  2016-01-31     100          4  100.000000
4     TVModel  2016-02-07     110          4  194.089642
5     TVModel  2016-02-14      89          4  252.209284
6     TVModel  2016-02-21      57          4  269.081883
7     TVModel  2016-02-28      90          4  316.269991
8   TVGeneral  2016-01-10       0          4    0.000000
9   TVGeneral  2016-01-17       0          4    0.000000
10  TVGeneral  2016-01-24       0          4    0.000000
11  TVGeneral  2016-01-31      30          4   30.000000
12  TVGeneral  2016-02-07      32          4   57.226892
13  TVGeneral  2016-02-14      42          4   90.121889
14  TVGeneral  2016-02-21      39          4  114.783173
15  TVGeneral  2016-02-28      55          4  151.520759