本文介绍了用逻辑(布尔)表达式切片Pandas Dataframe的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
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.
I am getting an exception as I try to slice with a logical expression my Pandas dataframe.
我的数据具有以下形式:
My data have the following form:
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]
推荐答案
我建议分别创建布尔型掩码,以提高可读性并简化错误处理.
I suggest create boolean masks separately for better readibility and also easier error handling.
在m1
和m2
代码中缺少()
,问题出在运算符优先级上:
Here are missing ()
in m1
and m2
code, problem is in operator precedence:
文档-6.16.参见&
的运算符优先级具有更高的优先级,例如>=
:
docs - 6.16. Operator precedence where see &
have higher priority as >=
:
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]
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