Question

我有一个数据框，其中包含名称，现金，日期等列。在数据框b中，我要动态填充xnpv值

def xnpv(rate, values, dates):
    if rate <= -1.0:
        return float('inf')
    d0 = dates.min()   # or min(dates)
    return sum([ vi / (1.0 + rate)**((di - d0).days / 365.0) for vi, di in zip(values, dates)])

for cl in range(2,ctr_max+1,1):    
    grouped = b.groupby('Name')         
    b["XNPV"+str(cl)]=grouped.apply(lambda x: xnpv(0.1, 
    x[str(cl)+"cash"], x['Value Date']))

使用上面的代码，我想用值1cash，2cash，3cash动态填充xnpv1，xnpv2，xnpv3之类的值。结果是上面的代码为NaN，但是它确实生成了xnpv1，xnpv2，xnpv3列，但是带有NaN值。我该如何解决？

Answer 1

我相信您需要自定义功能：

b = pd.DataFrame({"Name":['a','a','a','a','b','b','c','c'],
              "2cash":[1,1,3,4,1,2,4,5],
              "3cash":[4,5,3,2,4,5,7,9],
              "4cash":[1,1,2,4,5,1,3,4],
              "Value Date":['2017-01-01','2017-02-01','2017-03-01','2017-04-01',
                     '2017-01-01','2017-02-01','2017-03-01','2017-04-01']
             })
b["Value Date"] = pd.to_datetime(b["Value Date"])

def xnpv(rate, values, dates):
    if rate <= -1.0:
        return float('inf')
    d0 = dates.min()   # or min(dates)
    return sum([ vi / (1.0 + rate)**((di - d0).days/ 365.0) for vi, di in zip(values, dates)])

ctr_max = 4
def f(x):
    for cl in range(2,ctr_max+1,1): 
        x["XNPV{}".format(cl)] = xnpv(0.1, x["{}cash".format(cl)], x['Value Date'])
    return x

df = b.groupby('Name').apply(f)
print (df)
  Name  2cash  3cash  4cash Value Date     XNPV2      XNPV3     XNPV4
0    a      1      4      1 2017-01-01  8.853165  13.867370  7.868453
1    a      1      5      1 2017-02-01  8.853165  13.867370  7.868453
2    a      3      3      2 2017-03-01  8.853165  13.867370  7.868453
3    a      4      2      4 2017-04-01  8.853165  13.867370  7.868453
4    b      1      4      5 2017-01-01  2.983876   8.959689  5.991938
5    b      2      5      1 2017-02-01  2.983876   8.959689  5.991938
6    c      4      7      3 2017-03-01  8.959689  15.927441  6.967751
7    c      5      9      4 2017-04-01  8.959689  15.927441  6.967751

动态分配值

1 个答案: