在Pandas中分配列时处理SettingWithCopyWarning [英] Dealing with SettingWithCopyWarning when assigning columns in Pandas

问题描述

我有一个DataFrame，我想用包含上一行数据的列进行扩展.

I have a DataFrame which I want to extend with columns that contain data from the previous row.

此脚本可以完成这项工作:

This script does the job:

#!/usr/bin/env python3

import numpy as np
import pandas as pd

n = 2

df = pd.DataFrame({'A': [1,2,3,4,5], 'B': [0,1,1,0,0]}, columns=['A', 'B'])

df2 = df[df['B'] == 0]
print(df2)

for i in range(1, n+1):
    df2['A_%d' % i] = df2['A'].shift(i)

print(df2)

它输出:

   A  B
0  1  0
3  4  0
4  5  0

   A  B  A_1  A_2
0  1  0  NaN  NaN
3  4  0  1.0  NaN
4  5  0  4.0  1.0

这正是我想要的. DataFrame现在具有另外两个列A_1和A_2，它们之前包含列A 1 和 2 的值.

which is exactly what I want. The DataFrame now has two additional columns A_1 and A_2 that contain the value of column A 1 and 2 rows before.

但是，我也收到警告:

./my_script.py:14: 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
  df2['A_%d' % i] = df2['A'].shift(i)

问题肯定来自我创建df2之前的过滤.如果我直接在df上工作，则不会发生此问题. 在我的应用程序中，我需要分别处理原始DataFrame的多个部分，因此绝对需要进行过滤.所有不同的部分(如此处的df2)稍后会串联在一起.

The problem definitely comes from the filtering before where I create df2. If I work on df directly, the problem does not occur. In my application I need to work on multiple parts of my original DataFrame separately ant therefore the filtering and is definitely required. All the different parts (like df2 here) get concatenated later.

我在>如何处理熊猫中的SettingWithCopyWarning问题中找到了类似的问题?和 Pandas SettingWithCopyWarning ，但是那里的解决方案不能解决问题.

I found similar issues in How to deal with SettingWithCopyWarning in Pandas? and Pandas SettingWithCopyWarning but the solutions from there do not fix the problem.

例如写作

df2[:, 'A_%d' % i] = df2['A'].shift(i)

同样的警告仍然发生.

我正在使用Python 3.5.2和Pandas 0.19.2

I am working with Python 3.5.2 and Pandas 0.19.2

推荐答案

我认为您需要

I think you need copy:

df2 = df[df['B'] == 0].copy()

如果稍后在df2中修改值，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 df2 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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