Python - 获取“SettingWithCopyWarning"尽管使用 df.loc [英] Python - Getting "SettingWithCopyWarning" despite using df.loc

问题描述

尽管使用了推荐的方法，我还是收到了 SettingWithCopyWarning.我错过了什么?我该如何更正或取消此特定警告?

I am getting the SettingWithCopyWarning despite using the recommended method. What am I missing? How shall I correct it or suppress this particular warning?

import numpy as np
import pandas as pd

df = pd.DataFrame(np.random.randint(1,100, size=(6,4)), columns=list('abcd'))
print(df)
#>     a   b   c   d
#> 0  25  76  90  82
#> 1   2  19  97  44
#> 2  52  36  96  26
#> 3  37  48  55  49
#> 4  54  98  71  99
#> 5  42  26  86  76

var = ('a b').split()
print(var)
#> ['a', 'b']
df2 = df[var]
print(df2)
#>     a   b
#> 0  25  76
#> 1   2  19
#> 2  52  36
#> 3  37  48
#> 4  54  98
#> 5  42  26

df2.loc[:,'e'] = pd.Series(df.a/df.d*100)
#> C:\Users\Rahul\anaconda3\lib\site-packages\pandas\core\indexing.py:1596: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
#>   self.obj[key] = _infer_fill_value(value)
#> C:\Users\Rahul\anaconda3\lib\site-packages\pandas\core\indexing.py:1745: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
#>   isetter(ilocs[0], value)
print(df2)
#>     a   b           e
#> 0  25  76   30.487805
#> 1   2  19    4.545455
#> 2  52  36  200.000000
#> 3  37  48   75.510204
#> 4  54  98   54.545455
#> 5  42  26   55.263158

^{由 reprexpy 包} 于 2021 年 1 月 10 日创建

^{Created on 2021-01-10 by the reprexpy package}

推荐答案

使用以下代码创建第二个数据框:

Create the 2nd dataframe with the following code :

# create subset
df2 = df[var].copy()

# create new column
df2['e'] = df.a/df.d*100
df2
#     a   b           e
# 0  19  20   25.333333
# 1  52  36  400.000000
# 2   7  62   53.846154
# 3   5  93    7.575758
# 4  69  32  215.625000
# 5  55  36   59.782609

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