Python Pandas : summing value of two or more DataFrames with identical value in multiple columns

Question

我有两个数据帧，例如:

df1 = pd.DataFrame([["tom", 1, 2, 3], ["bob", 3, 4, 5], ["ali", 6, 7, 8]], columns=["name", "A", "B", "C"])
df1
Out[44]: 
  name  A  B  C
0  tom  1  2  3
1  bob  3  4  5
2  ali  6  7  8
df2 = pd.DataFrame([["rob", 1, 2, 3], ["ali", 6, 7, 8]], columns=["name", "A", "B", "D"])
df2
Out[46]: 
  name  A  B  D
0  rob  1  2  3
1  ali  6  7  8

如何对具有相同“名称”和相同列的值执行求和运算，并获得如下结果 DataFrame:

  name A   B   C   D
0  tom 1   2   3 NaN     # <- tom and bob don't shows up in df2, so the sum is identical
1  bob 3   4   5 NaN     #    to their values in df1
2  rob 1   2 NaN   3     # <- rob only shows up on df2, so the sum equal to its df2 values
3  ali 12 14   8   8     # <- ali's A and B are sum up, and C and D are identical to their
                         #    corresponding value in df1 and df2

请注意，我不知道两个 DataFrame 的“名称”列中会显示哪些名称。
而且，因为我有两个以上的这样的 DataFrame 需要总结，如果可能的话，我怎样才能在一次操作中对所有这些数据帧进行总结，而不是一个一个地总结呢？非常感谢。

Answer 1

希望这能解决您的问题。我已将 Nan 修改为 0。

import pandas as pd
df1 = pd.DataFrame([["tom", 1, 2, 3], ["bob", 3, 4, 5], ["ali", 6, 7, 8]], columns=["name", "A", "B", "C"])
df2 = pd.DataFrame([["rob", 1, 2, 3], ["ali", 6, 7, 8]], columns=["name", "A", "B", "D"])
df3=pd.concat([df1, df2], ignore_index=True, sort=False)
df4=df3.groupby(['name'])['A','B','C','D'].sum()
print(df4)