python计算2019年新个税

四月莺飞草长，心情荡漾，清明小长假还有赶在路上的51黄金周，简直不能更愉快~然而，工资日到来的时候，有朋友傻眼了，前几月都挺正常的，为啥突然之间工资单少了快1000元？？？神马情况？？？(✿◡‿◡)。HR和会计小姐姐们好无奈，导入新个税系统算的，核实了好多遍都是这样，真不是她们算错了。。。。
进入正题，各位大大，你需要了解2019年新个税啦~~~

并不认真的新个税简介

详细请看
文章目录：2019新个税累计预扣法
Excel计算请参考这个
2019年新个税税率表及新个税政策下如何扣个税)

总之，2019年开始个税是按照年度计算的累计预扣缴税.
1、根据累计预扣预缴应纳税所得额算等级，也就是税率。
2、这个累计预扣预缴应纳税所得额=年度累计总收入-年度减除费用（5000每月）-年度累计专项扣除（也就是五险一金）-年度累计专项附加扣除（也就是租房补贴等）
所以工资掉下来的朋友，暴露了你们的高收入，你们已经升到累计预扣预缴应纳税所得额算二级啦，这个月开始个税预扣率由3%变成10%啦，基本上突变会发生在每次升级时候，貌似突变后的工资都会低于个人年均工资，也就是说相对而言前几个月工资比较多。

python计算个税并对比五险一金额度对总收入的影响

以月收入15000，五险一金500元为例，效果图如下。其中红线是年均税后月收入，绿线是应缴税累加值，蓝色柱形是每月应发工资。单位：元。
同时对比提高五险一金额度到1000元前后的收入情况，可以看到年度缴税总额减少了600元，即：月平均工资下降450元，五险一金账户月提高500元，少缴税部分进入了五险一金账户。

纯属练手，仅供参考，如实际工资有出入，敬请谅解。
PS：没考虑各位大大们的高额年终奖，这个扣税我还不会算
PS：从2019年1月开始累积预扣算税，不管1月发的是2018年12月的工资还是2019年1月的工资
PS：或许应该考虑分段求算税收，后期改进再干

import pandas as pd
import numpy as np
import matplotlib.pyplot as pltdef net_pay_calc(salary_list=[10000,10000,20000],social_insurance_fund=2010.65,social_insurance_addition_fund=1500):"""函数：计算几个月的税后工资输入：salary_list = [10000,10000,20000] #你的税前工资序列social_insurance_fund = 2010.65 #你的五险一金钱数或序列social_insurance_addition_fund = 1500 #你的专项附加扣除输出：打印这几个月的税后工资"""basic_fee = 5000 #减除费用：基本生活保障基数if isinstance(salary_list,list):passelse:salary_list = [salary_list]month = len(salary_list)if isinstance(basic_fee,float) or isinstance(basic_fee,int):basic_fee = [basic_fee] * monthif isinstance(social_insurance_fund,float) or isinstance(social_insurance_fund,int):social_insurance_fund = [social_insurance_fund] * monthif isinstance(social_insurance_addition_fund,float) or isinstance(social_insurance_addition_fund,int):social_insurance_addition_fund = [social_insurance_addition_fund] * monthi = 1tax_pay_list = [] #累计预扣预缴应纳税所得额net_pays = []while True:print('*'*50)salary_sum = sum(salary_list[:i])  #累计收入basic_fee_sum = sum(basic_fee[:i])  #累计减除费用social_insurance_fund_sum = sum(social_insurance_fund[:i])  #累计专项扣除social_insurance_addition_fund_sum = sum(social_insurance_addition_fund[:i])  #累计专项附加扣除#累计预扣预缴应纳税所得额 = 累计收入-累计减除费用-累计专项扣除-累计专项附加扣除taxable_income = salary_sum - (basic_fee_sum + social_insurance_fund_sum + social_insurance_addition_fund_sum)taxable_income = taxable_income if taxable_income>0 else 0#预扣率rate = np.where(taxable_income <= 36000,0.03,np.where(taxable_income <= 144000,0.1,np.where(taxable_income <= 300000,0.2,np.where(taxable_income <= 420000,0.25,np.where(taxable_income <= 660000,0.3,np.where(taxable_income <= 960000,0.35,0.4))))))#速算扣除数quick_deduction = np.where(taxable_income <= 36000,0,np.where(taxable_income <= 144000,2520,np.where(taxable_income <= 300000,16920,np.where(taxable_income <= 420000,31920,np.where(taxable_income <= 660000,52920,np.where(taxable_income <= 960000,85920,181920))))))print("%d月份税前收入：%.2f 元"%(i,salary_list[i-1]))print("本月预扣率%.2f,速算扣除数%d"%(rate,quick_deduction))#本期应预扣预缴税额=（累计预扣预缴应纳税所得额*预扣率-速算扣除数-累计已预扣预缴税额tax_paid = tax_pay_list[i-2] if i>1 else 0 #累计已预扣预缴税额tax_due = taxable_income * rate - quick_deduction #i月累计预扣应缴纳税额tax_pay_list.append(tax_due) tax_of_month = tax_due - tax_paid  #本期应预扣预缴税额net_pay = salary_list[i-1] - social_insurance_fund[i-1] - tax_of_monthnet_pays.append(net_pay)print("本月累计预扣预缴应纳税所得额：%.2f,本月预扣率%.2f,速算扣除数%d"%(taxable_income,rate,quick_deduction))print("本月累计应纳税额：%.2f,累计已缴税额：%.2f，本月应缴税额%.2f"%(tax_due,tax_paid,tax_of_month))print("本月的应发工资是：%.2f 元"%(net_pay))if i == month:breakelse:i += 1return net_pays,tax_pay_list#===============================================================
if __name__ == "__main__":salary_list = eval(input("请输入你的税前工资序列："))social_insurance_fund = eval(input("再请输入你的五险一金数或序列："))social_insurance_addition_fund = eval(input("最后请输入你的专项附加扣除额度："))cmp = eval(input("是否通过修改五险一金数来对比实际收入差别（是：1,否：0）："))net_pays,tax_pay_list = net_pay_calc(salary_list,social_insurance_fund,social_insurance_addition_fund)if cmp:       social_insurance_fund2 = eval(input("再请输入你的五险一金数："))net_pays2,tax_pay_list2 = net_pay_calc(salary_list,social_insurance_fund2,social_insurance_addition_fund)print('工资是：%.2f(RMB)'%salary_list[0])print('如果五险一金费用是%.2f，年度总缴税%.2f，月平均工资是%.2f'%(np.mean(social_insurance_fund),tax_pay_list[-1],np.mean(net_pays)))print('如果五险一金费用是%.2f，年度总缴税%.2f，月平均工资是%.2f'%(social_insurance_fund2,tax_pay_list2[-1],np.mean(net_pays2)))fig, (ax0, ax1) = plt.subplots(2, 1, sharex=True)ax0.bar(np.arange(len(salary_list))+1,net_pays, align='center',color='SkyBlue', ecolor='black')
#         ax0.set_xlabel('Month')ax0.set_ylabel('Net Pay(RMB)')ax0.set_ylim(min(net_pays)*0.95,max(net_pays)*1.05)ax0.set_title('Average social_insurance_fund:%.2f'%(np.mean(social_insurance_fund)))
#        ax0.set_title('social_insurance_fund:%d'%social_insurance_fund)ax0.axhline(y=np.mean(net_pays),linestyle='--',color='r')ax02 = ax0.twinx()  # instantiate a second axes that shares the same x-axiscolor = 'tab:green'ax02.set_ylabel('Tax Paid', color=color)  # we already handled the x-label with ax1ax02.plot(np.arange(len(salary_list))+1, tax_pay_list,'*-', color=color,ms=10)ax02.tick_params(axis='y', labelcolor=color)ax1.bar(np.arange(len(salary_list))+1,net_pays2, align='center',color='SkyBlue', ecolor='black')ax1.set_xlabel('Month')ax1.set_ylabel('Net Pay(RMB)')ax1.set_ylim(min(net_pays2)*0.95,max(net_pays2)*1.05)ax1.set_title('Average social_insurance_fund:%.2f'%social_insurance_fund2)ax1.axhline(y=np.mean(net_pays2),linestyle='--',color='r')ax12 = ax1.twinx()  # instantiate a second axes that shares the same x-axiscolor = 'tab:green'ax12.set_ylabel('Tax Paid', color=color)  # we already handled the x-label with ax1ax12.plot(np.arange(len(salary_list))+1, tax_pay_list2,'*-', color=color,ms=10)ax12.tick_params(axis='y', labelcolor=color)   else:print('工资序列是：%s(RMB),\n五险一金费用序列是%s,\n年度总缴税%.2f'%(salary_list,social_insurance_fund,tax_pay_list[-1]))fig, ax0 = plt.subplots()ax0.bar(np.arange(len(salary_list))+1,net_pays, align='center',color='SkyBlue', ecolor='black')ax0.set_xlabel('Month')ax0.set_ylabel('Net Pay(RMB)')ax0.set_ylim(min(net_pays)*0.95,max(net_pays)*1.05)ax0.set_title('Average social_insurance_fund:%.2f'%(np.mean(social_insurance_fund)))ax0.axhline(y=np.mean(net_pays),linestyle='--',color='r')ax02 = ax0.twinx()  # instantiate a second axes that shares the same x-axiscolor = 'tab:green'ax02.set_ylabel('Tax Paid', color=color)  # we already handled the x-label with ax1ax02.plot(np.arange(len(salary_list))+1, tax_pay_list,'*-', color=color,ms=10)ax02.tick_params(axis='y', labelcolor=color)