Question

我正在编写一个代码，该代码将为我提供每月的进度报告，以显示我的债务应在哪里以及还清债务需要多长时间。当支付了一笔债务时，我想从数组中删除该值，并从各自的数组中删除相应的利率和最低还款额，这样我就可以提取花在这些债务上的值（钱）来还清其余的更快。有没有一种方法可以对每个数组中的每个值编制索引，以便说到还清865美元的“债务5”时，每个数组中与该债务对应的每个值都被删除了？当前，每个债务的每个详细信息在各自的数组中都位于相同的位置（即，债务1的名称为[0]，债务的金额也为[0]，其利息也为[0]，依此类推）

import numpy as np
from numpy import round

# Max amount you can spend per month on debts
budget = 1000 

# name your debts, balances, interests, and minimum payments
name = np.array(["Debt 1:", "Debt 2:", "Debt 3:", "Debt 4:", "Debt 5:", "Debt 6:"])
debt = np.array([250,300,7215,5500,864,1066])
interest = np.array([.1715,.0376,.0376,.0445,.0445,0])
minPayment = np.array([15,60,60,60,60,70])

# Miscellaneous variables for data storage and counting
index = [0]
intAccrued = []
month = []
i = 0
j = 0

# Loop through to calculate debt progress
while any(debt >=0):    
    debt = debt - minPayment + debt*interest/12
    debt[debt <=0] = 0

## This deletes the zero values from the debt index, now I need a way to delete
## the corresponding minPayments and interest values from the other arrays
    debt = np.delete(debt, index)

    print (round(debt,2))
    if sum(debt) == 0:
        break
    month
    i += 1
print (i, "Months")

Answer 1

与使用numpy和使用pandas相比，我会走得更远。 pandas有一个DataFrame，基本上是一个表。您可以一次对整行或整列进行操作，例如通过在某种条件下过滤数据帧（此处为df[df.debt > 0]）：

import pandas as pd

df = pd.DataFrame({"name": name, "debt": debt, "interest": interest, "minimum_payment": minPayment}).set_index("name")
#          debt  interest  minimum_payment
# name                                    
# Debt 1:   250    0.1715               15
# Debt 2:   300    0.0376               60
# Debt 3:  7215    0.0376               60
# Debt 4:  5500    0.0445               60
# Debt 5:   864    0.0445               60
# Debt 6:  1066    0.0000               70

那么您的循环将是：

from itertools import count

for i in count(1):
    df.debt = df.debt - df.minimum_payment + df.debt * df.interest / 12
    df = df[df.debt > 0]
    if len(df) == 0:
        break
    print(f"Month {i}:")
    print(df)
    print()

哪个给出以下输出：

# Month 1:
#                 debt  interest  minimum_payment
# name                                           
# Debt 1:   215.226480    0.1715               15
# Debt 2:   122.264256    0.0376               60
# Debt 3:  7102.469139    0.0376               60
# Debt 4:  5380.746359    0.0445               60
# Debt 5:   692.979363    0.0445               60
# Debt 6:   856.000000    0.0000               70
#
# Month 2:
#                 debt  interest  minimum_payment
# name                                           
# Debt 1:   203.302425    0.1715               15
# Debt 2:    62.647351    0.0376               60
# Debt 3:  7064.723542    0.0376               60
# Debt 4:  5340.699960    0.0445               60
# Debt 5:   635.549162    0.0445               60
# Debt 6:   786.000000    0.0000               70
#
# ...
#
# Month 149:
#              debt  interest  minimum_payment
# name                                        
# Debt 3:  8.948222    0.0376               60

Answer 2

我建议在这里使用该类，并创建该类的引用数组。在这里，我有一个示例刚刚尝试过。

#creating a class with the required structure.
class Expence:
    name = ""
    dept = 0.0
    intrest = 0.0
    minPayment = 0

    def __init__(self,name,dept,intrest,minPayment):#here you can create methods to store the value to the class variables.
        self.name = name
        self.dept = dept
        self.intrest = intrest
        self.minPayment = minPayment

    def getData(self):
        return self.name, self.dept, self.intrest, self.minPayment

newExpence1 = Expence("Debt 1:",250,0.1715,15)
newExpence2 = Expence("Debt 2:",300,0.0376,60)
newExpence3 = Expence("Debt 3:",7215,0.0376,60)

expArray = [newExpence1,newExpence2,newExpence3]  #adding element to array

print expArray[0].getData()[0] # access array elements with index value.
expArray.remove(newExpence1) # remove array elements with index value.
print expArray[0].getData()[0]

希望它会对您有所帮助。

根据要从另一个数组中删除的项目从Python数组中删除项目

2 个答案: