Question

在尝试用逻辑表达式对Pandas数据框进行切片时，我遇到了异常。

我的数据具有以下形式：

df
    GDP_norm    SP500_Index_deflated_norm
Year        
1980    2.121190    0.769400
1981    2.176224    0.843933
1982    2.134638    0.700833
1983    2.233525    0.829402
1984    2.395658    0.923654
1985    2.497204    0.922986
1986    2.584896    1.09770

df.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 38 entries, 1980 to 2017
Data columns (total 2 columns):
GDP_norm                     38 non-null float64
SP500_Index_deflated_norm    38 non-null float64
dtypes: float64(2)
memory usage: 912.0 bytes

命令如下：

df[((df['GDP_norm'] >=3.5 & df['GDP_norm'] <= 4.5) & (df['SP500_Index_deflated_norm'] > 3)) | (

   (df['GDP_norm'] >= 4.0 & df['GDP_norm'] <= 5.0) & (df['SP500_Index_deflated_norm'] < 3.5))]

错误消息如下：

TypeError: cannot compare a dtyped [float64] array with a scalar of type [bool]

您的建议将不胜感激。

Answer 1

我建议分别创建布尔掩码，以提高可读性并简化错误处理。

()和m1代码中缺少m2，问题出在运算符优先级上：

docs-6.16。参见&的操作符优先级具有>=更高的优先级：

Operator                                Description

lambda                                  Lambda expression
if – else                               Conditional expression
or                                      Boolean OR
and                                     Boolean AND
not x                                   Boolean NOT
in, not in, is, is not,                 Comparisons, including membership tests    
<, <=, >, >=, !=, ==                    and identity tests
|                                       Bitwise OR
^                                       Bitwise XOR
&                                       Bitwise AND

(expressions...), [expressions...],     Binding or tuple display, list display,       
{key: value...}, {expressions...}       dictionary display, set display

m1 = (df['GDP_norm'] >=3.5) & (df['GDP_norm'] <= 4.5)
m2 = (df['GDP_norm'] >= 4.0) & (df['GDP_norm'] <= 5.0)

m3 = m1 & (df['SP500_Index_deflated_norm'] > 3)
m4 = m2 & (df['SP500_Index_deflated_norm'] < 3.5)

df[m3 | m4]

Answer 2

您正遭受chained comparisons的影响。发生的情况是表达式df['GDP_norm'] >=3.5 & df['GDP_norm'] <= 4.5的计算方式类似于：

df['GDP_norm'] >= (3.5 & df['GDP_norm']) <= 4.5

当然，这失败了，因为无法将float与bool进行比较，如错误消息中所述。而是使用圆括号来分隔每个布尔掩码并分配给变量：

m1 = (df['GDP_norm'] >= 3.5) & (df['GDP_norm'] <= 4.5)
m2 = df['SP500_Index_deflated_norm'] > 3

m3 = (df['GDP_norm'] >= 4.0) & (df['GDP_norm'] <= 5.0)
m4 = df['SP500_Index_deflated_norm'] < 3.5

res = df[(m1 & m2) | (m3 & m4)]

用逻辑（布尔）表达式切片Pandas Dataframe

2 个答案: