Numpy TypeError：输入类型不支持ufunc'bitwise_and'，

Question

我有以下数据框和列表值

import pandas as pd
import numpy as np
df_merge = pd.DataFrame({'column1': ['a', 'c', 'e'],
               'column2': ['b', 'd', 'f'],
               'column3': [0.5, 0.6, .04],
               'column4': [0.7, 0.8, 0.9]
               })

bb = ['b','h']
dd = ['d', 'I']
ff = ['f', 'l']

我正在尝试使用 np.where 和 np.select 来代替 IF FUNCTION:

condition = [((df_merge['column1'] == 'a') & (df_merge['column2'] == df_merge['column2'].isin(bb))),((df_merge['column1'] == 'c') & (df_merge['column2'] == df_merge['column2'].isin(dd))), ((df_merge['column1'] == 'e') & (df_merge['column2'] == df_merge['column2'].
isin(ff)))]

choices1 = [((np.where(df_merge['column3'] >= 1, 'should not have, ','correct')) & (np.where(df_merge['column4'] >= 0.45, 'should not have, ','correct')))]

df_merge['Reason'] = np.select(condition, choices1, default='correct')

但是，当我尝试运行choices1 的代码行时，我收到以下错误：

TypeError: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

我不确定我们是否可以在上面提到的选择中使用 np.where。

np.where 应该应用于两列。预期输出如下：

df_merge = pd.DataFrame({'column1': ['a', 'c', 'e'],
               'column2': ['b', 'd', 'f'],
               'column3': [0.5, 0.6, .04],
               'column4': [0.7, 0.8, 0.9],
               'Reason': ['correct, should not have', 'correct, should not have', 'correct, should not have'],
               })

非常感谢任何帮助/指导/替代方案。

Answer 1

condition 列表的第一个长度必须与 choices1 相同，因此长度为 2 的最后一个条件被注释（删除）。

那么如果通过isin比较输出是条件（掩码），那么与列比较就没有意义了。

最后一个问题是需要长度为 2 的列表，因此将 & 替换为 , 并删除了 choices1 列表中的括号以避免元组：

condition = [(df_merge['column1'] == 'a') & df_merge['column2'].isin(bb),
             (df_merge['column1'] == 'c') & df_merge['column2'].isin(dd)
#             (df_merge['column1'] == 'e') & df_merge['column2'].isin(ff),
             ]

choices1 = [np.where(df_merge['column3'] >= 1, 'should not have','correct'),
            np.where(df_merge['column4'] >= 0.45, 'should not have','correct')]

df_merge['Reason'] = np.select(condition, choices1, default='correct')
print (df_merge)
  column1 column2  column3  column4           Reason
0       a       b     0.50      0.7          correct
1       c       d     0.60      0.8  should not have
2       e       f     0.04      0.9          correct