Python 3.x 属性错误:';数据帧';对象没有属性';推断对象';
每次我尝试使用expert_objects()方法时,即使在遵循官方文档()时,我也会遇到以下错误:Python 3.x 属性错误:';数据帧';对象没有属性';推断对象';,python-3.x,pandas,jupyter-notebook,Python 3.x,Pandas,Jupyter Notebook,每次我尝试使用expert_objects()方法时,即使在遵循官方文档()时,我也会遇到以下错误: AttributeError: 'DataFrame' object has no attribute 'infer_objects' 代码示例: import pandas as pd df = pd.DataFrame({"A": ["a", 1, 2, 3]}) df = df.iloc[1:] df = df.infer_objects() 为什么会出现这个错误?我可以支持Jon C
AttributeError: 'DataFrame' object has no attribute 'infer_objects'
代码示例:
import pandas as pd
df = pd.DataFrame({"A": ["a", 1, 2, 3]})
df = df.iloc[1:]
df = df.infer_objects()
为什么会出现这个错误?我可以支持Jon Clements的答案和F.Varlets的问题:更新熊猫作品 为了避免和: 手动设置数据类型:
In [21]: df=pd.DataFrame([['a','1'],['b','2']], columns=['x','y'])
In [22]: df.dtypes
Out[22]:
x object
y object
dtype: object
In [23]: for k in {'x':'object','y':'int'}:
...: df[k]=pd.to_numeric(df[k], errors='ignore')
...:
In [24]: df.dtypes
Out[24]:
x object
y int64
dtype: object
In [10]: df=pd.DataFrame([['a','1'],['b','2']], columns=['x','y'])
In [11]: df.dtypes
Out[11]:
x object
y object
dtype: object
In [12]: for k in list(df):
...: ...: df[k]=pd.to_numeric(df[k], errors='ignore')
...:
In [13]: df.dtypes
Out[13]:
x object
y int64
dtype: object
自动数据类型转换:
In [21]: df=pd.DataFrame([['a','1'],['b','2']], columns=['x','y'])
In [22]: df.dtypes
Out[22]:
x object
y object
dtype: object
In [23]: for k in {'x':'object','y':'int'}:
...: df[k]=pd.to_numeric(df[k], errors='ignore')
...:
In [24]: df.dtypes
Out[24]:
x object
y int64
dtype: object
In [10]: df=pd.DataFrame([['a','1'],['b','2']], columns=['x','y'])
In [11]: df.dtypes
Out[11]:
x object
y object
dtype: object
In [12]: for k in list(df):
...: ...: df[k]=pd.to_numeric(df[k], errors='ignore')
...:
In [13]: df.dtypes
Out[13]:
x object
y int64
dtype: object
注意,上面写着:0.21.0版中的新版本。-pd.\uuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuuu。我的版本是0.20.3。更新后,它现在可以工作了。谢谢你的帮助;)这很奇怪,我上周安装了一个新的Anaconda和Pandas。没错,我们也可以这样做:)最后,我更喜欢手动设置我的列的类型,只要我不需要在太多的列上定义数据类型