[英]Finding the maximum difference for a subset of columns with pandas
I have a dataframe: 我有一个数据帧:
A B C D E
0 a 34 55 43 aa
1 b 53 77 65 bb
2 c 23 100 34 cc
3 d 54 43 23 dd
4 e 23 67 54 ee
5 f 43 98 23 ff
I need to get the maximum difference between the column B,C and D and return the value in column A . 我需要获得B,C和D列之间的最大差异,并返回A列中的值。 in row 'a' maximum difference between columns is 55 - 34 = 21 . 在行'a'列之间的最大差异是55 - 34 = 21。 data is in a dataframe. 数据在数据框中。
The expected result is 预期的结果是
A B C D E
0 21 34 55 43 aa
1 24 53 77 65 bb
2 77 23 100 34 cc
3 31 54 43 23 dd
4 44 23 67 54 ee
5 75 43 98 23 ff
# df['A'] = np.ptp(df.loc[:, 'B':'D'], axis=1)
df['A'] = np.ptp(df[['B', 'C', 'D']], axis=1)
df
A B C D E
0 21 34 55 43 aa
1 24 53 77 65 bb
2 77 23 100 34 cc
3 31 54 43 23 dd
4 44 23 67 54 ee
5 75 43 98 23 ff
Or, find the max
and min
yourself: 或者,自己找到max
和min
:
df['A'] = df[['B', 'C', 'D']].max(1) - df[['B', 'C', 'D']].min(1)
df
A B C D E
0 21 34 55 43 aa
1 24 53 77 65 bb
2 77 23 100 34 cc
3 31 54 43 23 dd
4 44 23 67 54 ee
5 75 43 98 23 ff
If performance is important, you can do this in NumPy space: 如果性能很重要,您可以在NumPy空间中执行此操作:
v = df[['B', 'C', 'D']].values
df['A'] = v.max(1) - v.min(1)
df
A B C D E
0 21 34 55 43 aa
1 24 53 77 65 bb
2 77 23 100 34 cc
3 31 54 43 23 dd
4 44 23 67 54 ee
5 75 43 98 23 ff
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