Pandas：在 DataFrame 中创建聚合列
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Pandas: Creating aggregated column in DataFrame
提问by foglerit
With the DataFrame below as an example,
以下面的DataFrame为例，
In [83]:
df = pd.DataFrame({'A':[1,1,2,2],'B':[1,2,1,2],'values':np.arange(10,30,5)})
df
Out[83]:
A B values
0 1 1 10
1 1 2 15
2 2 1 20
3 2 2 25
What would be a simple way to generate a new column containing some aggregation of the data over one of the columns?
生成包含其中一列数据聚合的新列的简单方法是什么？
For example, if I sum valuesover items in A
例如，如果我values对中的项目求和A
In [84]:
df.groupby('A').sum()['values']
Out[84]:
A
1 25
2 45
Name: values
How can I get
我怎样才能得到
A B values sum_values_A
0 1 1 10 25
1 1 2 15 25
2 2 1 20 45
3 2 2 25 45
回答by Wouter Overmeire
In [20]: df = pd.DataFrame({'A':[1,1,2,2],'B':[1,2,1,2],'values':np.arange(10,30,5)})
In [21]: df
Out[21]:
A B values
0 1 1 10
1 1 2 15
2 2 1 20
3 2 2 25
In [22]: df['sum_values_A'] = df.groupby('A')['values'].transform(np.sum)
In [23]: df
Out[23]:
A B values sum_values_A
0 1 1 10 25
1 1 2 15 25
2 2 1 20 45
3 2 2 25 45
回答by foglerit
I found a way using join:
我找到了一种使用方法join：
In [101]:
aggregated = df.groupby('A').sum()['values']
aggregated.name = 'sum_values_A'
df.join(aggregated,on='A')
Out[101]:
A B values sum_values_A
0 1 1 10 25
1 1 2 15 25
2 2 1 20 45
3 2 2 25 45
Anyone has a simpler way to do it?
任何人都有更简单的方法来做到这一点？
回答by joaquin
This is not so direct but I found it very intuitive (the use of map to create new columns from another column) and can be applied to many other cases:
这不是那么直接，但我发现它非常直观（使用 map 从另一列创建新列）并且可以应用于许多其他情况：
gb = df.groupby('A').sum()['values']
def getvalue(x):
return gb[x]
df['sum'] = df['A'].map(getvalue)
df
回答by Garrett
In [15]: def sum_col(df, col, new_col):
....: df[new_col] = df[col].sum()
....: return df
In [16]: df.groupby("A").apply(sum_col, 'values', 'sum_values_A')
Out[16]:
A B values sum_values_A
0 1 1 10 25
1 1 2 15 25
2 2 1 20 45
3 2 2 25 45
