CSV行与Python的比较

Question

我整理了我的CSV文件做了一些计算。Python 2.7版

import pandas as pd
df = pd.read_csv('Cliente_x_Pais_Sitio.csv', sep=',')
df1 = df.sort_values(by=['Cliente','Auth_domain','Sitio',"Country"])
df1.to_csv('test.csv')

CSV数据（test.csv）：

Cliente,Fecha,Auth_domain,Sitio,Country,ECPM_medio
FF,15/12/2017,@ff,ff_Color,Afganistán,0.53
FF,15/01/2018,@ff,ff_Color,Afganistán,0.5
FF,15/01/2017,@ff,ff_Color,Alemania,0.34
FF,15/12/2017,@ff,ff_Color,Alemania,0.38
FF,15/01/2018,@ff,ff_Color,Alemania,0.37

我需要的是：

if (15/12/2017 ECPM) ≤ (15/01/2018 ECPM):
    if ((15/12/2017 ECPM)*0.8) ≥ (15/01/2017 ECPM):
        r = (15/01/2017 ECPM)
    else:
        r = ((15/12/2017 ECPM)*0.8)
else:
    if (15/01/2018 ECPM) ≥ (15/01/2017 ECPM):
        r = (15/01/2017 ECPM)
    else:
        r = (15/01/2018 ECPM)

在填写真实数据时，前两行是：

if 0.53 ≤ 0.5:
    if 0.5 ≥ 0: #if we don't have the cell value I would like to add a 0 True
        r = 0.5

记住，我有10000多行，我需要一个多表格

新的CSV应该显示：

Cliente,Auth_domain,Sitio,Country,Recomendation_ECPM
FF,@ff,ff_Color,Afganistán,0.5
FF,@ff,ff_Color,Alemania,0.34

Answer 1

我不确定我有没有正确的答案

1. 日期选择在setval或
2. compare_val中的返回值逻辑

但不管这些管道使用排序、分组和转换。因为我们要将边与nan（首先是shift(-1)，最后是shift(1)）进行比较，所以我们必须在最后删除它们。你知道吗

# build data
from StringIO import StringIO
import pandas as pd
df = pd.read_csv(StringIO("""Cliente,Fecha,Auth_domain,Sitio,Country,ECPM_medio
FF,15/12/2017,@ff,ff_Color,Afganistán,0.53
FF,15/01/2018,@ff,ff_Color,Afganistán,0.5
FF,15/01/2017,@ff,ff_Color,Alemania,0.34
FF,15/12/2017,@ff,ff_Color,Alemania,0.38
FF,15/01/2018,@ff,ff_Color,Alemania,0.37
""")).sort_values(by='Fecha')

# functions to parse
def compare_val(cur,past,future):
   if cur <= past:
       cur_adj = cur * .8
       if cur_adj >= past:
            return(past)
       else:
            return(cur_adj)
   else:
        if future >= past:
           return(past)
        else:
           return(future)

def setval(v):
      cur, past, future = v, v.shift(-1), v.shift(1)
      v = [ compare_val(*x) for x in zip(cur,past,future)]
      return(v)

# do the work
df['Recomendation_ECPM'] = df.\
      groupby(['Cliente','Auth_domain','Sitio',"Country"])['ECPM_medio'].\
      transform(setval)

df[ pd.notna(df['Recomendation_ECPM']) ]