将具有相似行值的值相加
问题内容：
我有一个像这样的熊猫数据集
city difference
NY 6
SF 8
LA 8
NY 9
SF 10
我想difference基于该city列总结该列的值，以便最终数据集看起来像
city difference total difference
NY 6 15
NY 9
LA 8 8
SF 10 10
我试过了
df['total difference'] = df.groupby('city')['difference'].sum()
但这没用。我什至尝试过如何对熊猫中特定行的值求和？但获得NaN了新列的值。请帮忙！
问题答案：
我认为您需要transform：
df['total difference'] = df.groupby('city')['difference'].transform(sum)
print (df)
city difference total difference
0 NY 6 15
1 SF 8 18
2 LA 8 8
3 NY 9 15
4 SF 10 18
并且如果还需要排序列：
df['total difference'] = df.groupby('city')['difference'].transform('sum')
df = df.sort_values('city')
print (df)
city difference total difference
2 LA 8 8
0 NY 6 15
3 NY 9 15
1 SF 8 18
4 SF 10 18
我对功能上的差异和时序非常相似感兴趣：
#[10000000 rows x 2 columns]
np.random.seed(100)
df = pd.DataFrame(np.random.randint(1000, size=(10000000,2)), columns=['city','difference'])
#print (df)
In [293]: %timeit (df.groupby('city')['difference'].transform('sum'))
1 loop, best of 3: 570 ms per loop
In [294]: %timeit (df.groupby('city')['difference'].transform(sum))
1 loop, best of 3: 567 ms per loop
In [295]: %timeit (df.groupby('city')['difference'].transform(np.sum))
1 loop, best of 3: 561 ms per loop
