ValueError：太多的值无法解压 Pandas

问题描述

我有以下代码。但是我在执行时出错。我希望该函数计算计划日期并输出相同的新数据帧。似乎错误在 for 循环中。我是新手，所以只是想弄清楚

import QuantLib as ql
import pandas as pd
import numpy as np
import datetime
from dateutil.parser import parse

data = pd.read_excel (r'C:\Users\Avishen\Desktop\Python\BONDDATA.xlsx')

for ISIN,issuedate,maturitydate in data:
    issueDate = ql.Date(issuedate,'%d-%m-%Y')
    maturityDate = ql.Date(maturitydate,'%d-%m-%Y')
    tenor = ql.Period(ql.Semiannual)
    day_count = ql.Thirty360
    calendar = ql.UnitedStates()
    businessConvention = ql.Unadjusted
    dateGeneration = ql.DateGeneration.Backward
    monthEnd = False

    # Dates in Bond Period
    df['Test']= df['t'].apply(ISIN)
    df['Test']= df['t1'].apply(ql.Schedule (issueDate,maturityDate,tenor,calendar,businessConvention,dateGeneration,monthEnd))

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-14-b6dc0a0bd926> in <module>
      8 
      9 
---> 10 for ISIN,maturitydate in data:
     11     issueDate = ql.Date(issuedate,'%d-%m-%Y')
     12     maturityDate = ql.Date(maturitydate,'%d-%m-%Y')

ValueError: too many values to unpack (expected 3)

期望输出

t       t1
Test1   Date(22,10,2019)
Test1   Date(8,2,2020)
Test1   Date(8,8,2021)
Test1   Date(8,2022)
Test1   Date(8,2023)
Test1   Date(8,2023)

Test2   Date(31,7,2020)
Test2   Date(21,9,3,2021)
Test2   Date(21,2022)
Test2   Date(21,2023)
Test2   Date(21,2024)
Test2   Date(21,2025)

解决方法

我假设您想要做的是逐行修改数据框（如果还有更多可能有更好的解决方案）：

df['new_issueDate'] = df['issuedate'].apply(lambda row: ql.Date(row,'%d-%m-%Y')
df['new_maturityDate'] = df['maturityDate'].apply(lambda row: ql.Date(row,'%d-%m-%Y')

编辑：您只想遍历行：

t_list = []
t1_list = []
for _,row in data.iterrows():
    issueDate = ql.Date(row['issuedate'],'%d-%m-%Y')
    maturityDate = ql.Date(row['maturitydate'],'%d-%m-%Y')
    tenor = ql.Period(ql.Semiannual)
    day_count = ql.Thirty360
    calendar = ql.UnitedStates()
    businessConvention = ql.Unadjusted
    dateGeneration = ql.DateGeneration.Backward
    monthEnd = False
    t_list.append(row['ISIN'])
    t1_list.append(ql.Schedule(issueDate,maturityDate,tenor,calendar,businessConvention,dateGeneration,monthEnd))

new_df = pd.DataFrame({'t':t_list,'t1':t1_list})

还有其他方法可以逐行附加到数据帧，但这种方式也很好。

pandas python quantlib