在Pandas中合并2个数据帧：加入一些列，总结其他列

Question

I want to merge two dataframes on specific columns (key1, key2) and sum up the values for another column (value).

>>> df1 = pd.DataFrame({'key1': range(4), 'key2': range(4), 'value': range(4)})
   key1  key2  value
0     0     0      0
1     1     1      1
2     2     2      2
3     3     3      3

>>> df2 = pd.DataFrame({'key1': range(2, 6), 'key2': range(2, 6), 'noise': range(2, 6), 'value': range(10, 14)})
   key1  key2  noise  value
0     2     2      2     10
1     3     3      3     11
2     4     4      4     12
3     5     5      5     13

I want this result:

   key1  key2  value
0     0     0      0
1     1     1      1
2     2     2     12
3     3     3     14
4     4     4     12
5     5     5     13

In SQL terms, I want:

SELECT df1.key1, df1.key2, df1.value + df2.value AS value
FROM df1 OUTER JOIN df2 ON key1, key2

I tried two approaches:

approach 1

concatenated = pd.concat([df1, df2])
grouped = concatenated.groupby(['key1', 'key2'], as_index=False)
summed = grouped.agg(np.sum)
result = summed[['key1', 'key2', 'value']]

approach 2

joined = pd.merge(df1, df2, how='outer', on=['key1', 'key2'], suffixes=['_1', '_2'])
joined = joined.fillna(0.0)
joined['value'] = joined['value_1'] + joined['value_2']
result = joined[['key1', 'key2', 'value']]

Both approaches give the result I want, but I wonder if there is a simpler way.

Answer 1

I don't know about simpler, but you can get a little more concise:

>>> pd.concat([df1, df2]).groupby(["key1", "key2"], as_index=False)["value"].sum()
   key1  key2  value
0     0     0      0
1     1     1      1
2     2     2     12
3     3     3     14
4     4     4     12
5     5     5     13

Depending on your tolerance for chaining ops, you might want to break this onto multiple lines anyway, though (four tends to be close to my upper limit, in this case concat-groupby-select-sum).