Python 计算不存储在DataFrame中，但可打印

我有以下计算：

np.最大值（0，np.产品（[perf_-asset，calc_-arr['val']]）-支付金额-np.产品（[exposure，calc_-arr['delta_-1']]）

如果写出来，这将是：

MAX(0, 0.8 × 105.015038 - 80 - TRUE × 5.3135)
MAX(0, 84.0120307692 - 80 - 5.3135)
= 0

如果我打印此文件，输出实际上可以工作，但如果我想将其存储在数据帧中，则不会：

calc_arr['added_amt']=np.max（0，np.prod（[perf_-asset，calc_arr['val']）-金额支出-np.prod（[exposure，calc_arr['delta_1']）

计算突然停止了。在此之前，我甚至不必使用

np.prod

和

np.sum

。老实说，我完全糊涂了

完整循环：

j = 1
for i in [0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,-0.053,-0.0698,-0.1011,-0.1767,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271]:
    risky_return = i
    risk_free_return = (-0.0125/260)

    stock_val = stock_calc(j, stock_val['amt_payout'], stock_val['alloc_risky'], stock_val['alloc_risk_free'], stock_val['delta_1'], risky_return, risk_free_return)
    stock_vals = stock_vals.append(stock_val)
    j = j + 1

然后检索

calc_arr['val']

：

calc_arr['val'] = np.sum([np.prod([(1 + perf_risky), alloc_risky]), np.prod([(1 + perf_risk_free), alloc_risk_free])])

---

我认为您需要

将
值附加到
列表
-
股票价值
中，然后将其分配到列：

stock_vals = []
for i in [0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,-0.053,-0.0698,-0.1011,-0.1767,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271]:
    risky_return = i
    risk_free_return = (-0.0125/260)

    stock_val = stock_calc(j, stock_val['amt_payout'], stock_val['alloc_risky'], stock_val['alloc_risk_free'], stock_val['delta_1'], risky_return, risk_free_return)
    stock_vals.append(stock_val)


calc_arr['val'] = stock_vals

我尝试将其重写以列出理解：

L = [0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,-0.053,-0.0698,-0.1011,-0.1767,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271]

calc_arr['val'] = [stock_vals.append(stock_calc(j, stock_val['amt_payout'], stock_val['alloc_risky'], stock_val['alloc_risk_free'], stock_val['delta_1'], risky_return, (-0.0125/260))) for risky_return in L]

我认为您需要
将
值附加到
列表
-
股票价值
中，然后将其分配到列：

stock_vals = []
for i in [0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,-0.053,-0.0698,-0.1011,-0.1767,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271]:
    risky_return = i
    risk_free_return = (-0.0125/260)

    stock_val = stock_calc(j, stock_val['amt_payout'], stock_val['alloc_risky'], stock_val['alloc_risk_free'], stock_val['delta_1'], risky_return, risk_free_return)
    stock_vals.append(stock_val)


calc_arr['val'] = stock_vals

我尝试将其重写以列出理解：

L = [0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,0.0627,-0.053,-0.0698,-0.1011,-0.1767,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271,-0.6271]

calc_arr['val'] = [stock_vals.append(stock_calc(j, stock_val['amt_payout'], stock_val['alloc_risky'], stock_val['alloc_risk_free'], stock_val['delta_1'], risky_return, (-0.0125/260))) for risky_return in L]

这很有趣。如果使用
a=np.maximum（0，np.prod（[perf_-asset，calc_-arr['val']）金额支付-np.prod（[exposure，calc_-arr['delta_-1']]）
和
cppi['added_-amt']=a
它也不起作用？或者可能需要
cppi.ix[0'，added_-amt']=a
，因为
cppi['added_-amt']]，在数组中也返回
NaN
，但打印正确。因此
a
的值是
NaN
？不，a的值应该是0。这很有趣。如果使用
a=np.maximum（0，np.prod（[perf_-asset，calc_-arr['val']）金额支付-np.prod（[exposure，calc_-arr['delta_-1']]）
和
cppi['added_-amt']=a
它也不起作用？或者可能需要
cppi.ix[0'，added_-amt']=a
，因为
cppi['added_-amt']]，在数组中也返回
NaN
，但打印正确。因此
a
的值是
NaN
？否，a的值是0，应该是这样的。