使用 copy() 后的 SettingWithCopyWarning

SettingWithCopyWarning after using copy()(使用 copy() 后的 SettingWithCopyWarning)

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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