Python pandas 删除 SettingWithCopyWarning
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Python pandas removing SettingWithCopyWarning
提问by user3084006
So I made an empty dataframe using
所以我使用了一个空的数据框
df=data[['ID','Matrix','Name','Country', 'Units']]
df['Value']=''
and I am filling it in with code like this, which finds strings containing values of 'Good', 'Bad' in df.Matrixand filling them with values in sch[i]:
我用这样的代码填充它，它找到包含“好”、“坏”值的字符串，df.Matrix并用值填充它们sch[i]：
df.loc[df.Matrix.str.contains('Good'),'Value'] = sch[2]
df.loc[df.Matrix.str.contains('Bad'),'Value'] = sch[6]
df.loc[df.Matrix.str.contains('Excellent'),'Value'] = sch[8]
I have been getting a bunch of errors like both of these two different ones:
我收到了一堆错误，例如这两个不同的错误：
C:\Python33\lib\site-packages\pandas\core\strings.py:184: UserWarning: This pattern has match groups. To actually get the groups, use str.extract.
" groups, use str.extract.", UserWarning)
C:\Userspd.options.mode.chained_assignment = None
\Desktop\python\Sorter.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame
df.loc[df.Matrix.str.contains('Bad'),'Value'] = sch[6]
So far I am suppressing the code using
到目前为止，我正在使用
In [13]: df = DataFrame(index=range(5))
In [14]: df['Value'] = ''
In [15]: df.loc[[1,4],'Value'] = 'bad'
In [16]: df.loc[[0,3],'Value'] = 'good'
In [17]: df
Out[17]:
Value
0 good
1 bad
2
3 good
4 bad
[5 rows x 1 columns]
If I do not suppress the error messages I will get about 20 of them. Is there another format I can change the data so that I do not get the error message?
如果我不抑制错误消息，我将收到大约 20 条错误消息。是否有另一种格式可以更改数据以便我不会收到错误消息？
I am using python 3 and pandas 0.131 if it helps
如果有帮助，我正在使用 python 3 和 pandas 0.131
回答by Jeff
Here is a good explanation of why this warning was turned on:
这里很好地解释了为什么打开这个警告：
Are you sure that is all of your code? Pls show all of what you are doing.
你确定这是你的全部代码吗？请展示您正在做的所有事情。
In [1]: df = DataFrame(index=range(5))
In [2]: df['Value'] = ''
In [3]: df2 = DataFrame(dict(A=['foo','foo','bar','bar','bah']))
In [4]: df
Out[4]:
Value
0
1
2
3
4
[5 rows x 1 columns]
In [5]: df2
Out[5]:
A
0 foo
1 foo
2 bar
3 bar
4 bah
[5 rows x 1 columns]
In [6]: df.loc[df2.A.str.contains('foo'),'Value'] = 'good'
In [7]: df.loc[df2.A.str.contains('bar'),'Value'] = 'bad'
In [8]: df
Out[8]:
Value
0 good
1 good
2 bad
3 bad
4
[5 rows x 1 columns]
2nd example
第二个例子
##代码##