Pandas group by then apply 抛出警告

Question

我有代码行

df = df.groupby(by=['col_A','col_B'])['float_col_c']
df.loc[:,'amount_cumulative'] = df.apply(lambda x: x.cumsum())

引发警告：

/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:362: 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
  self.obj[key] = _infer_fill_value(value)
/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:543: 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
  self.obj[item] = s

通常，当我看到该错误时，我可以将某些内容更改为 .loc[] 来修复它，但在这种情况下，警告似乎指的是另一个问题。我知道我可以抑制警告，但我更愿意理解我用 Pandas 语法造成的问题。非常感谢任何有关如何更正此语法的建议。

Answer 1

~~我相信这是因为 .loc[:, 'amount_cumulative'] 索引，returns 是 df 的一部分，而不是对新列的引用~~

更新： df 正如@QuangHoang 正确指出的那样，它本身就是一个副本，在这种情况下，以下内容仍会引发错误。

你可以在没有警告的情况下得到预期的结果，就像这样简单：

df['amount_cumulative'] = df.groupby(['col_A','col_B'])['float_col_c'].cumsum()

Answer 2

很可能您的 df 已经是另一个数据框的副本。您的命名 df_rev_melt_trim 也表明了这一点。测试

old_df = pd.DataFrame({'A':np.random.randint(1,10,1000),
                   'B':np.random.randint(1,10,1000),
                   'C':np.random.uniform(0,1,1000)})

df = old_df[old_df['A'] > 5]

df['amount_cumulative'] = df.groupby(by=['A','B'])['C'].cumsum()

产生相同的警告。相反，您可以这样做：

old_df.loc[df.index,'amount_cumulative'] = df.groupby(by=['A','B'])['C'].cumsum()

并且没有显示警告。

Pandas group by then apply 抛出警告

Pandas group by then apply throwing a warning

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