本文介绍了使用 copy() 后的 SettingWithCopyWarning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
我的代码如下.
import pandas as pd
import numpy as np
data = [['Alex',10,5,0],['Bob',12,4,1],['Clarke',13,6,0],['brke',15,1,0]]
df = pd.DataFrame(data,columns=['Name','Age','weight','class'],dtype=float)
df_numeric=df.select_dtypes(include='number')#, exclude=None)[source]
df_non_numeric=df.select_dtypes(exclude='number')
df_non_numeric['class']=df_numeric['class'].copy()
它给了我下面的信息
__main__:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
我想让 df_non_numeric 独立于 df_numeric
我根据其他帖子中的建议使用了 df_numeric['class'].copy().
i used df_numeric['class'].copy() based upon suggestions given in other posts.
我怎样才能避免这条消息?
How could i avoid the message?
推荐答案
我认为你需要 copy 因为 DataFrame.select_dtypes 是切片操作，按列类型过滤，勾选问题3:
I think you need copy because DataFrame.select_dtypes is slicing operation, filtering by types of column, check Question 3:
df_numeric=df.select_dtypes(include='number').copy()
df_non_numeric=df.select_dtypes(exclude='number').copy()
如果您稍后修改 df_non_numeric 中的值，您会发现修改不会传播回原始数据 (df)，并且 Pandas 会发出警告.
If you modify values in df_non_numeric later you will find that the modifications do not propagate back to the original data (df), and that Pandas does warning.
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