Pandas基于多列值新增列时np.select触发类型错误如何解决？

1. np.select要求传入的条件列表每个元素是和原数据集长度一致的布尔数组，用于逐行匹配条件。原代码中使用df_1.loc[判断条件]返回的是符合条件的子DataFrame，不满足参数要求，直接触发TypeError: invalid entry 0 in condlist: should be boolean ndarray报错。
2. 数据中B、C列混入了空字符串，列类型为object而非数值类型，直接做数值比较也可能引发类型异常，部分低版本numpy、pandas混用场景下会触发参数不匹配的na关键字相关报错。

先将B、C列转为数值类型，空值统一处理为NaN，再调整条件列表的写法即可：

import pandas as pd
import numpy as np

data_1 = {'A': ['Emo/3', 'Emo/4', 'Emo/1','Emo/3', '','Emo/3', 'Emo/4', 'Emo/1','Emo/3', '', 'Neu/5', 'Neu/2','Neu/5', 'Neu/2'],
        'Pos': ["repeat3", "repeat3", "repeat3", "repeat3", '',"repeat1", "repeat1", "repeat1", "repeat1", '', "repeat2", "repeat2","repeat2", "repeat2"],
        'B': [0, 0, 0, 0, '', 1, 2, 3, 4, '', 4, 2, 3, 1],'C': [0, 2, 1, 3, '', 4, 2, 3, 1, '', 4, 2, 3, 1]}
df_1 = pd.DataFrame(data_1)

# 处理B、C列为数值类型，空值转为NaN
df_1['B'] = pd.to_numeric(df_1['B'], errors='coerce')
df_1['C'] = pd.to_numeric(df_1['C'], errors='coerce')

# 条件直接写布尔判断表达式，无需套loc
conditions = [
    (df_1['B'] == 1) & (df_1['C'] == 1),
    (df_1['B'] == 2) & (df_1['C'] == 1),
    (df_1['B'] == 3) & (df_1['C'] == 1)
]
choices = [1,1,0]
df_1['D'] = np.select(conditions, choices, default='')

运行后新列D会按照规则赋值，不满足条件的位置留空，符合需求。

内容的提问来源于stack exchange，提问作者Catherine