如何将数据框日期与保持工作日和假期分开的行和列的值进行比较

How to compare dataframe dates with the values of row and column keeping weekdays and holidays apart

我有一个代表这个的数据框:

我需要创建另一个列 'Mark',这就是它复杂的原因。 对于值 'C',执行日是 Sunday 8/11/2018。第二天将是 Monday 9/11/2018。 所以我需要计算 previous weekweekdays 值。对于这种情况,我需要计算 1/11/20182/11/20183/11/20184/11/20185/11/2018.

但是,如果 execution daynext dayFridaySaturday,我需要取前一周的值 'Friday''Saturday'。例如,BThursday 12/11/2018' 上执行。后天是'Friday'。所以我需要计算前一周的FridaySaturday的平均值,分别是6/11/20187/11/2018

最初我没有 Day 列,我使用

添加了后记
df['Execution']=pd.to_datetime(df['Execution'])
df['Day']=df['Execution'].dt.weekday_name

如果 execution datecolumn dates 之一匹配,我可以打印一些东西。这是代码-

for j,row in df.iterrows():
x=str(row['Execution'])
x=x[slicing]

for i, val in enumerate (df.columns.values):
    print(df.columns[i])

    if i<l1:
        val=str(val)
        val=val[slicing]
        if x==val: #Execution date matches column date
            print('yay')

我正在尝试自学 python,我已经开始学习 pandas dataframe
但是,现在我迷路了,无法弄清楚继续进行的逻辑。谁能给我指路?

这是对我有用的代码,并附有解释:

for i,row in df.iterrows():
  for j, val in enumerate (range(0,l1-1)):   #l1 is the number of columns 
               #subtracted 1 to not take last column in account as I only need the dates

    if df.columns[j+1]==row['Execution']: #to match the date of column execution,with the column dates

        a=pd.to_datetime(df.columns[j+1+1])
        a=a.day_name() #to convert the date in to weekday name
#As for friday I would need previous week's friday and saturday values.
#Therefore, I subtracted 7 and 8 to get the required value. For all the other days I calculated carefully this way so that I get the days right.
        if (a=='Friday'): 
            mark=(df.iloc[i,(j+1+1-7)]+df.iloc[i,(j+1+1-6)])/2
#df.iloc(row,column) was used to get the values right
            markList.append(mark)
        elif (a=='Saturday'):
            mark=(df.iloc[i,(j+1+1-7)]+df.iloc[i,(j+1+1-8)])/2
            markList.append(mark)
        elif (a=='Sunday'):
            mark=(df.iloc[i,(j+1+1-7)]+df.iloc[i,(j+1+1-6)]+df.iloc[i,(j+1+1-5)]+MPDr.iloc[i,(j+1+1-4)]+df.iloc[i,(j+1+1-3)])/5
            markList.append(mark)
        elif (a=='Monday'):
            mark=(df.iloc[i,(j+1+1-7)]+df.iloc[i,(j+1+1-6)]+df.iloc[i,(j+1+1-5)]+df.iloc[i,(j+1+1-4)]+df.iloc[i,(j+1+1-8)])/5
            markList.append(mark)
        elif (a=='Tuesday'):
            mark=(df.iloc[i,(j+1+1-7)]+df.iloc[i,(j+1+1-6)]+df.iloc[i,(j+1+1-5)]+df.iloc[i,(j+1+1-8)]+df.iloc[i,(j+1+1-9)])/5
            markList.append(mark)
        elif (a=='Wednesday'):
            mark=(df.iloc[i,(j+1+1-7)]+df.iloc[i,(j+1+1-6)]+df.iloc[i,(j+1+1-8)]+df.iloc[i,(j+1+1-9)]+df.iloc[i,(j+1+1-10)])/5
            markList.append(mark)  
        elif (a=='Thursday'):
            mark=(df.iloc[i,(j+1+1-7)]+df.iloc[i,(j+1+1-8)]+df.iloc[i,(j+1+1-9)]+df.iloc[i,(j+1+1-10)]+df.iloc[i,(j+1+1-11)])/5
            markList.append(mark)

df['mark']=markList #To add at the end of the dataframe