Question

我有一个股票数据框。和要运行许多if语句来分配值。

c1 = np.where((blackCondition & whitePCondition) & (close_gt_oP & open_ge_cP),'UP',None)
c2 = np.where((blackCondition & whitePCondition) & (open_lt_cP & close_le_oP),'DOWN',None)

c3 = np.where((blackCondition & whitePCondition) & (close_gt_oP & open_lt_cP),'OUTSIDE',None)
c4 = np.where((blackCondition & whitePCondition) & (close_le_oP & open_ge_cP),'INSIDE',None)
#IF2
c5 = np.where((blackCondition & blackPCondition) & (open_gt_oP & close_ge_cP),'UP',None)
c6 = np.where((blackCondition & blackPCondition) & (close_lt_cP & open_le_oP),'DOWN',None)

c7 = np.where((blackCondition & blackPCondition) & (open_gt_oP & close_lt_cP),'OUTSIDE',None)
c8 = np.where((blackCondition & blackPCondition) & (open_le_oP & close_ge_cP),'INSIDE',None)
#IF3

c9 = np.where((whiteCondition & whitePCondition) & (close_gt_cP & open_ge_oP),'UP',None)
c10 = np.where((whiteCondition & whitePCondition) & (open_lt_oP & close_le_cP),'DOWN',None)

c11 = np.where((whiteCondition & whitePCondition) & (close_gt_cP & open_lt_oP),'OUTSIDE',None)
c12 = np.where((whiteCondition & whitePCondition) & (close_le_cP & open_ge_oP),'INSIDE',None)
#IF4
c13 = np.where((whiteCondition & blackPCondition) & (open_gt_cP & close_ge_oP),'UP',None)
c14 = np.where((whiteCondition & blackPCondition) & (close_lt_oP & open_le_cP),'DOWN',None)

c15 = np.where((whiteCondition & blackPCondition) & (open_gt_cP & close_lt_oP),'OUTSIDE',None)
c16 = np.where((whiteCondition & blackPCondition) & (open_le_cP & close_ge_oP),'INSIDE',None)

如何将语句的TRUE VALUE赋值给列？

df [＆＃39; pos＆＃39;] =如果本声明的任何内容是正确的

例如：

if(c1) => df['pos'] = TRUE_CONDITION_OF_C1

Answer 1

对于大熊猫，我不知道这样做的一种非常优雅的方式。但有几个选择。

您可以重复使用.map()一个丑陋的面具，或者您可以使用.apply()或df['pos'] = ( (blackCondition & whitePCondition) & (close_gt_oP & open_ge_cP) ).map({True: "UP", False: "DOWN"})方法。

以下示例假设掩码无效，且与df本身具有相同的形状。

// Create a file named index.mjs (the extension is the key)

import axios from 'axios';

console.log(axios);

那只会做第一行。

Answer 2

我认为条件设置值需要numpy.select，我也尝试将相同条件重新分配给变量m1-m3以获得更好的效果：

m1 = (blackCondition & whitePCondition)
m2 = (blackCondition & blackPCondition)
m3 = (whiteCondition & whitePCondition)
#same like m1
#m4 = (whitePCondition & blackCondition)

c1 = m1 & (close_gt_oP & open_ge_cP)
c2 = m1 & (open_lt_cP & close_le_oP)
c5 = m2 & (open_gt_oP & close_ge_cP)
c6 = m2 & (close_lt_cP & open_le_oP)
c9 = m3 & (close_gt_cP & open_ge_oP)
c10 = m3 & (open_lt_oP & close_le_cP)
c13 = m1 & (open_gt_cP & close_ge_oP)
c14 = m1 & (close_lt_oP & open_le_cP)

c3 = m1 & (close_gt_oP & open_lt_cP)
c4 = m1 & (close_le_oP & open_ge_cP)
c7 = m2 & (open_gt_oP & close_lt_cP)
c8 = m2 & (open_le_oP & close_ge_cP)
c11 = m3 & (close_gt_cP & open_lt_oP)
c12 = m3 & (close_le_cP & open_ge_oP)
c15 = m1 & (open_gt_cP & close_lt_oP)
c16 = m1 & (open_le_cP & close_ge_oP)

| OR的链条件：

cup = c1 | c5 | c9 | c13
cdown = c2 | c6 | c10 | c14

coutside = c3 | c7 | c11 | c15
cinside = c4 | c8 | c12 | c16

df['pos'] = np.select([cup, cdown, coutside, cinside], 
                      ['UP','DOWN','OUTSIDE','INSIDE'], default=None)

Pandas

2 个答案: