我想計算電視廣告 GRP 數據的結轉效應。我的輸入數據如下所示： Variable Date Causal Half_Life0 TV Model 2016-01-10 0 41 TV Model 2016-01-17 0 42 TV Model 2016-01-24 0 43 TV Model 2016-01-31 100 44 TV Model 2016-02-07 110 45 TV Model 2016-02-14 89 46 TV Model 2016-02-21 57 47 TV Model 2016-02-28 90 48 TV General 2016-01-10 0 49 TV General 2016-01-17 0 410 TV General 2016-01-24 0 411 TV General 2016-01-31 30 412 TV General 2016-02-07 32 413 TV General 2016-02-14 42 414 TV General 2016-02-21 39 415 TV General 2016-02-28 55 4我想根據以下條件計算一個新列 df['Adstock']：如果列 df.Variable 中組的第一行，則 df.Adstock = df.Causal 如果不是組中的第一行，則 df. Adstock = df.Causal + 0.5**(1/df.Half_life)*df.Adstock 來自上一行。我正在使用以下代碼：import pandas as pdimport numpy as npimport numpy.random as randomimport statsmodels.api as smimport statsmodels.tsa as tsaimport statsmodels.formula.api as smfimport datetimedf = pd.read_excel('RC Data.xlsx')df['Adstock'] = 0df['Adstock'] = np.where(df['Variable'] == df['Variable'].shift(1), df['Adstock'].shift(1)*(0.5**(1/df['Half_Life'])) + df['Causal'], df['Causal'])我得到的輸出如下： Variable Date Causal Half_Life Adstock0 TV Model 2016-01-10 0 4 0.01 TV Model 2016-01-17 0 4 0.02 TV Model 2016-01-24 0 4 0.03 TV Model 2016-01-31 100 4 100.04 TV Model 2016-02-07 110 4 110.05 TV Model 2016-02-14 89 4 89.06 TV Model 2016-02-21 57 4 57.07 TV Model 2016-02-28 90 4 90.08 TV General 2016-01-10 0 4 0.09 TV General 2016-01-17 0 4 0.010 TV General 2016-01-24 0 4 0.0
1 回答
墨色風雨
TA貢獻1853條經驗 獲得超6個贊
這是我的解決方案，我認為很難將其矢量化
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
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