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减去相同行和列中但不同数据帧文件中的值

dataframe pandas

user7852656 · 技术社区 · 6 年前

我有两个数据集有许多列。我想为所有数据点减去同一行和列号中的值。以下是这两个数据的一小部分。

数据1:

4   6   8
4403    4403    4403
4640    4640    4640
0   0   0
12  0   12
0   0   0
0   0   0
0   0   0
0   0   0
0   12  0
0   0   0
0   12  0
0   0   0
0   0   0
0   0   12
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   12  0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
12  0   0
0   0   0
0   0   0
12  0   0
0   0   0
0   12  0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
12  0   0
0   0   0
0   0   0
127 60  60
357 275 317
1882    2144    1838
6726    6609    7915
9398    11180   12737
12784   18389   21361
15863   20111   24469
6739    10202   11897
1684    1921    2735
249 376 476
47  103 70
0   26  82
17  0   18
0   0   0
0   0   0
0   0   0
0   0   0
0   0   18
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   12
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   12  0
0   0   0
0   0   0
0   0   12
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
12  0   0
0   0   0
0   0   0
0   0   0
0   0   0
12  0   0
0   0   12
0   12  0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
12  0   0
0   0   0
12  0   0
0   0   0
0   0   12
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   18
0   0   0
0   0   0
0   0   0
12  0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   12  0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   12
12  0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   12  0
12  0   0
0   12  0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   12  0
0   0   0
0   0   0
12  0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
12  0   12
0   12  0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   12
0   0   0
12  0   0
12  0   0
0   0   0
0   0   0
0   12  0
0   0   0
0   0   0
0   0   0
0   0   0
13  0   0
0   0   12
0   0   0
0   0   13

数据2:

4   6   8
4400    4400    4400
4750    4750    4750
0   0   0
12  0   0
0   0   0
0   12  0
0   0   0
0   25  12
0   0   12
12  13  0
0   0   0
0   12  0
0   0   0
12  0   0
0   0   0
0   12  0
0   0   0
12  0   0
0   0   0
0   0   12
12  0   12
0   0   0
24  0   18
0   0   0
0   0   0
12  0   0
0   0   0
0   0   12
0   12  0
39  0   0
0   12  0
0   0   0
0   12  12
0   0   0
0   0   0
0   0   0
0   0   12
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   12  0
0   0   0
0   0   0
0   0   0
43  46  13
6711    11323   9375
91043   116679  123466
241572  307822  310620
250588  309749  314146
105123  139651  141462
16143   21264   23856
2521    3648    3243
1042    1022    1598
576 910 525
482 552 509
229 416 425
210 227 264
120 149 99
69  55  58
47  0   17
26  65  29
0   20  35
0   0   32
0   0   14
0   12  12
12  38  12
0   0   0
18  0   12
0   0   0
0   13  0
0   0   0
0   18  0
16  0   12
12  0   0
0   0   12
12  0   12
0   0   0
0   23  0
0   0   0
0   0   0
20  0   0
0   0   0
0   0   0
0   0   0
0   0   12
0   12  12
14  12  0
0   0   0
0   0   0
0   0   12
0   0   0
0   12  0
0   0   0
0   0   0
12  0   12
0   0   0
0   0   0
0   18  0
0   0   0
12  0   39
12  0   12
13  0   12
0   0   0
0   0   0
0   0   0
0   0   0
24  0   14
0   15  0
0   16  0
0   12  0
12  0   12
0   12  0
0   0   0
0   0   0
14  13  0
0   23  24
0   0   0
0   0   12
0   16  0
0   12  0
0   0   12
0   0   0
0   0   0
0   13  0
0   0   0
0   0   0
16  0   0
0   12  21
0   0   0
0   0   0
0   12  13
0   0   0
0   0   0
0   19  12
0   0   0
0   0   12
0   12  0
0   0   0
0   12  12
0   12  0
0   0   0
0   0   0
12  24  0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   12  0
12  0   0
0   0   0
0   0   29
0   0   0
0   0   12
0   0   0
0   12  0
12  12  0
0   0   0
12  0   0
0   0   0
0   0   0
0   0   12
0   12  0
0   0   0
12  0   0
0   0   0
0   0   0
12  0   0
0   0   0
12  0   0
0   0   12
13  16  12
24  17  0
0   0   0
12  0   41
0   0   0
0   0   0
12  0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
0   0   0
14  12  0
25  0   0
0   12  0
0   0   0
0   15  0
0   0   0
12  0   0
0   0   0
0   0   0
0   0   25
27  0   0
0   12  0
0   22  0
0   0   0
0   0   0
0   0   0

对于所有数据,列名为“4”、“6”、“8”。

因此,在数据1和2之间,列“4”中的行1的第一个减法将是=4403-4400。

单独做一个比较容易,但我正在寻找一种方法来高效地为所有列执行此操作。

有什么想法吗?

谢谢!

1 回复 | 直到 6 年前

David Gaertner 6 年前

编辑:这里有两个答案。上面指出的短:

result = Data1 - Data2

如果你想用更复杂的计算代替减法,你可能需要一个更冗长的答案。它首先创建结果数据帧。它假设data1和data2是相同的形状。正如在python中常见的那样,还有其他方法可以做到这一点。我这次测试了。:-)

result = pd.DataFrame(columns = ['4','6','8'], index = Data1.index)
for index, row in Data1.iterrows():
    for col in Data1.columns:
        result.iloc[index][col] = Data1.iloc[index][col] - Data2.iloc[index][col]
print (result)