[英]Pandas DataFrame: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame
I know there are tons of posts about this warning, but I couldn't find a solution to my situation. 我知道有很多关于这个警告的帖子,但我无法找到解决方案。 Here's my code: 这是我的代码:
df.loc[:, 'my_col'] = df.loc[:, 'my_col'].astype(int)
#df.loc[:, 'my_col'] = df.loc[:, 'my_col'].astype(int).copy()
#df.loc[:, 'my_col'] = df['my_col'].astype(int)
It produces the warning: 它产生警告:
SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. SettingWithCopyWarning:尝试在DataFrame的切片副本上设置值。 Try using .loc[row_indexer,col_indexer] = value instead 尝试使用.loc [row_indexer,col_indexer] = value
Even though I changed the code as suggested, I still get this warning? 即使我按照建议更改了代码,我仍然会收到此警告? All I need to do is to convert the data type of one column. 我需要做的就是转换一列的数据类型。
**Remark: ** Originally the column is of type float having one decimal (example: 4711.0). **备注:**最初该列的类型为float,具有一位小数(例如:4711.0)。 Therefore I change it to integer (4711) and then to string ('4711') - just to remove the decimal. 因此我将其更改为整数(4711)然后更改为字符串('4711') - 只是为了删除小数。
Appreciate your help! 感谢您的帮助!
Update: The warning was a side effect on a filtering of the original data that was done just before. 更新:该警告对过滤之前完成的原始数据的过滤产生了副作用。 I was missing the DataFrame.copy(). 我错过了DataFrame.copy()。 Using the copy instead, solved the problem! 使用副本代替,解决了问题!
df = df[df['my_col'].notnull()].copy()
df.loc[:, 'my_col'] = df['my_col'].astype(int).astype(str)
#df['my_col'] = df['my_col'].astype(int).astype(str) # works too!
I think need copy
and omit loc
for select columns: 我认为需要copy
并省略选择列的loc
:
df = df[df['my_col'].notnull()].copy()
df['my_col'] = df['my_col'].astype(int).astype(str)
Explanation : 说明 :
If you modify values in df
later you will find that the modifications do not propagate back to the original data ( df
), and that Pandas does warning. 如果稍后修改df
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。
another way is to disable chained assignments, which works on your code without the need to create a copy : 另一种方法是禁用链式赋值,它可以在您的代码上运行, 而无需创建副本 :
# disable chained assignments
pd.options.mode.chained_assignment = None
If you need to change the data type of a single column, it's easier to address that column directly: 如果需要更改单个列的数据类型,则可以更直接地处理该列:
df['my_col'] = df['my_col'].astype(int)
Or using .assign
: 或者使用.assign
:
df = df.assign(my_col=lambda d: d['my_col'].astype(int))
The .assign
is useful if you only need the conversion once, and don't want to alter your df
outside of that scope. 如果您只需要转换一次,并且不希望在该范围之外更改您的df
,则.assign
非常有用。
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