AttributeError：“”Series“”对象没有“”pipe“”属性“”
AttributeError：“”Series“”对象没有“”pipe“”属性“”
提问于 2018-08-03 12:50:10
当我在我的ec2实例上运行python3 keras代码时，我得到了以下错误。代码在Azure Jupyter Notebook上运行良好。
代码：
numpy.random.seed(7)
dataframe = pandas.read_csv("some_data.csv", header = None)
df = dataframe
char_cols = df.dtypes.pipe(lambda x: x[x == 'object']).index
for c in char_cols:
df[c] = pandas.factorize(df[c])[0]
dataframe = df错误：
Traceback (most recent call last):
File "pi_8_1st_year.py", line 12, in <module>
char_cols = df.dtypes.pipe(lambda x: x[x == 'object']).index
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 1815, in __getattr__
(type(self).__name__, name))
AttributeError: 'Series' object has no attribute 'pipe'我的配置：
ubuntu@ipxxxx:~$ python3
Python 3.4.3 (default, Nov 28 2017, 16:41:13)
[GCC 4.8.4] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import pandas
_frozen_importlib:321: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
/usr/lib/python3.4/importlib/_bootstrap.py:321: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
return f(*args, **kwds)
>>> pandas.__version__
'0.13.1'
>>> import numpy
>>> numpy.__version__
'1.15.0'
>>> import sklearn
/usr/lib/python3.4/importlib/_bootstrap.py:321: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
return f(*args, **kwds)
/usr/lib/python3.4/importlib/_bootstrap.py:321: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
return f(*args, **kwds)
>>> sklearn.__version__
'0.19.2'
>>> import keras
Using TensorFlow backend.
/usr/lib/python3.4/importlib/_bootstrap.py:321: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility
return f(*args, **kwds)
>>> keras.__version__
'2.2.2'
>>> 回答 1
Stack Overflow用户
回答已采纳
发布于 2018-08-03 13:04:34
如错误所示，您不能在序列对象上使用pipe。df.dtypes返回一个序列对象，因此出现错误。如果要查找对象类型的列，可以通过以下方法来实现：
s = (df.dtypes == 'object')
cols = s[s.values == True].index页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/51665516
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