Python 3.x 更改函数参数以生成多个输出时出错

当我在函数更改一个参数以获得第二个结果集后取消注释此行时：

weights2 = weights(flags, 0, .4, .3, .3, 0, .4) ## This line is the problem

我收到这个错误：

TypeError:“tuple”对象不可调用

输入：

功能：

这条线用从函数返回的元组权重来阴影函数权重

当您在第二次调用元组时尝试调用函数时，因此错误元组是不可调用的

例如，必须为函数或返回值使用不同的名称

calculated_weights1 = weights(flags, 0, .4, .025, .3, 0, .4)
calculated_weights2 = weights(flags, 0, .4, .3, .3, 0, .4)

---

将结果名称更改为out1、out2等后没有错误

import pandas as pd
import numpy as np

flags = pd.DataFrame({'Date': ['2017-11-01','2017-12-01','2018-01-01'],
                    'flag_11': [2, 2, 2],
                   'flag_12': [2, 2, 2]})

flags = flags.set_index('Date')
print(flags)

def weights(dfin, wt1, wt2, wt3, wmin06, wmin1012, wmax1012):
    dfin = pd.DataFrame(dfin)
    dfout = pd.DataFrame()
    dfcum = pd.DataFrame()
    mapping = {1:wt1,2:wt2,3:wt3}
    dfout['flag_12']=dfin['flag_12'].replace(mapping)
    dfcum['flag_12']=dfin['flag_12'].replace(mapping)
    i = 11
    conditions = [
    (dfin["flag_{}".format(i)]==1) & (wt1 > (wmax1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==1) & (wt1 < (wmin1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==1),
    (dfin["flag_{}".format(i)]==2) & (wt2 > (wmax1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==2) & (wt2 < (wmin1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==2),
    (dfin["flag_{}".format(i)]==3) & (wt3 > (wmax1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==3) & (wt3 < (wmin1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==3)]   
    choices = [
    (wmax1012 - dfout["flag_{}".format(i+1)]),
    (wmin1012 - dfout["flag_{}".format(i+1)]),
    (wt1),
    (wmax1012 - dfout["flag_{}".format(i+1)]),
    (wmin1012 - dfout["flag_{}".format(i+1)]),
    (wt2),
    (wmax1012 - dfout["flag_{}".format(i+1)]),
    (wmin1012 - dfout["flag_{}".format(i+1)]),
    (wt3)]
    dfout["flag_{}".format(i)] = np.select(conditions, choices)
    dfcum["flag_{}".format(i)] = np.select(conditions, choices)+dfcum["flag_{}".format(i+1)]
    dfout=dfout.iloc[:,::-1]
    dfcum=dfcum.iloc[:,::-1]
    return(dfout,dfcum)
out1 = weights(flags, 0, .4, .025, .3, 0, .4)
out2 = weights(flags, 0, .4, .3, .3, 0, .4) ## This line is the problem
print(out1[0])
print(out1[1])
print(out2[0])
print(out2[1])

---

清楚了，谢谢。将在8分钟内时间限制到期后接受答复。

weights = weights(flags, 0, .4, .025, .3, 0, .4)

calculated_weights1 = weights(flags, 0, .4, .025, .3, 0, .4)
calculated_weights2 = weights(flags, 0, .4, .3, .3, 0, .4)

import pandas as pd
import numpy as np

flags = pd.DataFrame({'Date': ['2017-11-01','2017-12-01','2018-01-01'],
                    'flag_11': [2, 2, 2],
                   'flag_12': [2, 2, 2]})

flags = flags.set_index('Date')
print(flags)

def weights(dfin, wt1, wt2, wt3, wmin06, wmin1012, wmax1012):
    dfin = pd.DataFrame(dfin)
    dfout = pd.DataFrame()
    dfcum = pd.DataFrame()
    mapping = {1:wt1,2:wt2,3:wt3}
    dfout['flag_12']=dfin['flag_12'].replace(mapping)
    dfcum['flag_12']=dfin['flag_12'].replace(mapping)
    i = 11
    conditions = [
    (dfin["flag_{}".format(i)]==1) & (wt1 > (wmax1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==1) & (wt1 < (wmin1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==1),
    (dfin["flag_{}".format(i)]==2) & (wt2 > (wmax1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==2) & (wt2 < (wmin1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==2),
    (dfin["flag_{}".format(i)]==3) & (wt3 > (wmax1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==3) & (wt3 < (wmin1012 - dfout["flag_{}".format(i+1)])),
    (dfin["flag_{}".format(i)]==3)]   
    choices = [
    (wmax1012 - dfout["flag_{}".format(i+1)]),
    (wmin1012 - dfout["flag_{}".format(i+1)]),
    (wt1),
    (wmax1012 - dfout["flag_{}".format(i+1)]),
    (wmin1012 - dfout["flag_{}".format(i+1)]),
    (wt2),
    (wmax1012 - dfout["flag_{}".format(i+1)]),
    (wmin1012 - dfout["flag_{}".format(i+1)]),
    (wt3)]
    dfout["flag_{}".format(i)] = np.select(conditions, choices)
    dfcum["flag_{}".format(i)] = np.select(conditions, choices)+dfcum["flag_{}".format(i+1)]
    dfout=dfout.iloc[:,::-1]
    dfcum=dfcum.iloc[:,::-1]
    return(dfout,dfcum)
out1 = weights(flags, 0, .4, .025, .3, 0, .4)
out2 = weights(flags, 0, .4, .3, .3, 0, .4) ## This line is the problem
print(out1[0])
print(out1[1])
print(out2[0])
print(out2[1])