Pandas：将所有列从字符串转换为数字，除了两个？
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Pandas: convert all column from string to number except two?
提问by Learner132
Suppose we have
假设我们有
>>> df.dtype
Name object
Height object
Weight object
Age object
Job object
>>> df.dtype
Name object
Height object
Weight object
Age object
Job object
Is there any simple way to covert all columns except Name and Job columns with .to_numeric() method?
是否有任何简单的方法可以使用 .to_numeric() 方法隐藏除 Name 和 Job 列之外的所有列？
I have tried but it doesn't work
我试过了，但没有用
df.iloc[df.columns != Name & df.columns != Job] = pd.to_numeric(df.iloc[df.columns != Name & df.columns != Job], errors='coerce')
df.iloc[df.columns != Name & df.columns != Job] = pd.to_numeric(df.iloc[df.columns != Name & df.columns != Job], errors='coerce')
回答by baloo
The simplest way that comes to my mind would be to make a list of all the columns except Name and Job and then iterate pandas.to_numericover them:
我想到的最简单的方法是列出除 Name 和 Job 之外的所有列，然后遍历pandas.to_numeric它们：
cols=[i for i in df.columns if i not in ["Name","Job"]]
for col in cols:
df[col]=pd.to_numeric(df[col])
Edit:
编辑：
If you absolutely want to use numbers instead of columns names and already know at which indice they are:
如果您绝对想使用数字而不是列名并且已经知道它们位于哪个索引：
for i in [i for i in list(range(len(df.columns))) if i not in [0,4]]:
df.iloc[:,i]=pandas.to_numeric(df.iloc[:,i])
That's more complicated than necessary though.
但这比必要的要复杂。
回答by Allen
Suppose you have DF:
假设你有 DF：
df
Out[125]:
Name Height Weight Age Job
0 0 2 3 4 5
df.dtypes
Out[126]:
Name object
Height object
Weight object
Age object
Job object
dtype: object
If you have to use pd.to_numeric to convert those columns, you can do it this way:
如果您必须使用 pd.to_numeric 来转换这些列，您可以这样做：
df2 = pd.concat([pd.DataFrame([pd.to_numeric(df[e],errors='coerce') \
for e in df.columns if e not in ['Name','Job']]).T,\
df[['Name','Job']]],axis=1)
df2
Out[138]:
Height Weight Age Name Job
0 2 3 4 0 5
df2.dtypes
Out[139]:
Height int64
Weight int64
Age int64
Name object
Job object
dtype: object
