根据边际税率表计算税收负债 [英] Calculate tax liabilities based on a marginal tax rate schedule

问题描述

所得税计算python 询问在给定边际税率表的情况下如何计算税收，以及

income tax calculation python asks how to calculate taxes given a marginal tax rate schedule, and its answer provides a function that works (below).

但是，它仅适用于单个收入值.我将如何调整它以使其适用于收入列表/numpy数组/熊猫系列?也就是说，如何矢量化此代码?

However, it works only for a single value of income. How would I adapt it to work for a list/numpy array/pandas Series of income values? That is, how do I vectorize this code?

from bisect import bisect

rates = [0, 10, 20, 30]   # 10%  20%  30%

brackets = [10000,        # first 10,000
            30000,        # next  20,000
            70000]        # next  40,000

base_tax = [0,            # 10,000 * 0%
            2000,         # 20,000 * 10%
            10000]        # 40,000 * 20% + 2,000

def tax(income):
    i = bisect(brackets, income)
    if not i:
        return 0
    rate = rates[i]
    bracket = brackets[i-1]
    income_in_bracket = income - bracket
    tax_in_bracket = income_in_bracket * rate / 100
    total_tax = base_tax[i-1] + tax_in_bracket
    return total_tax

推荐答案

创建了两个数据框，一个用于税收参数，一个用于收入. 对于每个收入，我们使用"searchsorted"方法从税表中获取相应的行索引. 使用该索引，我们创建一个新表(df_tax.loc [rows])并将其与收入表连接起来， 然后计算税收，并删除不必要的列.

Two data frames are created, one for the tax parameters and one for the incomes. For each income, we get the corresponding row indexes from the tax table, using the "searchsorted" method. With that index we create a new table (df_tax.loc[rows]) and concatenate it with the income table, then calculate the taxes, and drop the unnecessary columns.

import numpy as np, pandas as pd

    # Test data:
    df=pd.DataFrame({"name":["Bob","Julie","Mary","John","Bill","George","Andie"], \
                    "income":[0, 9_000, 10_000, 11_000, 30_000, 69_999, 200_000]})   
    OUT:
         name  income
    0     Bob       0
    1   Julie    9000
    2    Mary   10000
    3    John   11000
    4    Bill   30000
    5  George   69999
    6   Andie  200000

df_tax=pd.DataFrame({"brackets": [0, 10_000, 30_000, 70_000 ],   # lower limits
                     "rates":    [0,  .10,    .20,    .30   ],
                     "base_tax": [0,   0,    2_000,  10_000 ]} )


rows= df_tax["brackets"].searchsorted(df["income"], side="right") - 1  # aka bisect()
OUT:
[0 0 1 1 2 2 3]

df= pd.concat([df,df_tax.loc[rows].reset_index(drop=True)], axis=1) 

df["total_tax"]= df["income"].sub(df["brackets"]).mul(df["rates"]).add(df["base_tax"])

OUT:
     name  income  brackets  rates  base_tax  total_tax
0     Bob       0         0    0.0         0        0.0
1   Julie    9000         0    0.0         0        0.0
2    Mary   10000     10000    0.1         0        0.0
3    John   11000     10000    0.1         0      100.0
4    Bill   30000     30000    0.2      2000     2000.0
5  George   69999     30000    0.2      2000     9999.8
6   Andie  200000     70000    0.3     10000    49000.0

df=df.reindex(columns=["name","income","total_tax"])
OUT:
     name  income  total_tax
0     Bob       0        0.0
1   Julie    9000        0.0
2    Mary   10000        0.0
3    John   11000      100.0
4    Bill   30000     2000.0
5  George   69999     9999.8
6   Andie  200000    49000.0

在开始时，您也可以计算base_tax:

At the beginning, you can calculate the base_tax, too:

df_tax["base_tax"]= df_tax.brackets   #edit2
                .sub(df_tax.brackets.shift(fill_value=0))
                .mul(df_tax.rates.shift(fill_value=0))
                .cumsum()

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