CSV行與Python的比較

Question

我對CSV文件進行了排序以進行一些計算。 Python 2.7

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數據（ 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：

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)

填寫實際數據，前兩行為：

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

請記住，我有10,000多行，我需要多種形式

新的CSV應該顯示以下內容：

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

Answer 1

我不確定我是否正確

1. setval日期選擇或
2. compare_val的返回值邏輯

但是，無論使用哪種管道，都使用sort，group_by和transform。 因為我們將邊緣與nan （首先是shift(-1) ，最后是shift(1) ）進行比較，所以我們必須在最后刪除它們。

# 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']) ]