Python Pandas DataFrame：SettingWithCopyWarning：试图在 DataFrame 的切片副本上设置值
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Pandas DataFrame: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame
提问by Matthias
I know there are tons of posts about this warning, but I couldn't find a solution to my situation. Here's my code:
我知道有很多关于此警告的帖子，但我找不到解决我的情况的方法。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. Try using .loc[row_indexer,col_indexer] = value instead
SettingWithCopyWarning：试图在 DataFrame 中切片的副本上设置值。尝试使用 .loc[row_indexer,col_indexer] = value 代替
Even though I changed the code as suggested, I still get this warning? All I need to do is to convert the data type of one column.
即使我按照建议更改了代码，我仍然收到此警告？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). Therefore I change it to integer (4711) and then to string ('4711') - just to remove the decimal.
**备注：** 最初该列是具有一位小数的浮点类型（例如：4711.0）。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!
感谢你的帮助！
Update:The warning was a side effect on a filtering of the original data that was done just before. I was missing the DataFrame.copy(). Using the copy instead, solved the problem!
更新：警告是对之前完成的原始数据过滤的副作用。我错过了 DataFrame.copy()。改用副本，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!
回答by jezrael
I think need copyand omit locfor select columns:
我认为需要copy并省略loc选择列：
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 dflater you will find that the modifications do not propagate back to the original data (df), and that Pandas does warning.
如果df稍后修改值，您会发现修改不会传播回原始数据 ( df)，并且 Pandas 会发出警告。
回答by sudonym
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，它适用于您的代码 而无需创建副本：
# disable chained assignments
pd.options.mode.chained_assignment = None
回答by Guybrush
If you need to change the data type of a single column, it's easier to address that column directly:
如果您需要更改单个列的数据类型，直接寻址该列会更容易：
df['my_col'] = df['my_col'].astype(int)
Or using .assign:
或使用.assign：
df = df.assign(my_col=lambda d: d['my_col'].astype(int))
The .assignis useful if you only need the conversion once, and don't want to alter your dfoutside of that scope.
的.assign，如果你只需要一次转换，并且不想改变你的是有用df的是范围之外。
