在列组中查找结果为非零值
[英]Find Result as Non Zero Value in Group of Columns
问题描述
I have a Dataframe as below:
我有一个 Dataframe 如下：
SYS Date_Time Col_1 Col_2 Col_3 Col_4 Col_5 Col_6
0 SYSTEM1 2021-01-07 09:15:00 0 0 0 0 Y 0
1 SYSTEM1 2021-01-07 09:20:00 0 0 0 0 0 0
2 SYSTEM1 2021-01-07 09:25:00 R 0 0 0 0 0
3 SYSTEM1 2021-01-07 09:30:00 0 0 0 0 0 0
4 SYSTEM1 2021-01-07 09:35:00 0 0 0 0 0 0
5 SYSTEM1 2021-01-07 09:40:00 0 R 0 0 0 0
6 SYSTEM1 2021-01-07 09:45:00 0 0 0 0 0 0
7 SYSTEM1 2021-01-07 09:50:00 0 0 0 0 0 0
8 SYSTEM1 2021-01-07 09:55:00 0 0 0 0 0 0
9 SYSTEM1 2021-01-07 10:00:00 0 0 0 0 0 0
10 SYSTEM1 2021-01-07 10:05:00 0 0 0 0 0 0
11 SYSTEM1 2021-01-07 10:10:00 0 0 0 0 0 0
12 SYSTEM1 2021-01-07 10:15:00 0 0 0 0 0 G
13 SYSTEM1 2021-01-07 10:20:00 0 0 0 0 R 0
14 SYSTEM1 2021-01-07 10:25:00 0 0 0 0 0 0
I need to find the result color in column group of (Col_1, Col_2, Col_3, Col_4, Col_5, Col_6) where the color is not Zero.我需要在颜色不为零的 (Col_1, Col_2, Col_3, Col_4, Col_5, Col_6) 列组中找到结果颜色。
Two possible condition can exist in above dataframe:在上述 dataframe 中可能存在两种可能的情况：
- Only one out of 6 columns will be Non Zero. 6 列中只有一列将是非零。
- If all columns have Zero Value then result will be Zero.如果所有列的值为零，则结果将为零。
I want the Output as below:我想要 Output 如下：
SYS Date_Time Col_1 Col_2 Col_3 Col_4 Col_5 Col_6 Result
0 SYSTEM1 2021-01-07 09:15:00 0 0 0 0 Y 0 Y
1 SYSTEM1 2021-01-07 09:20:00 0 0 0 0 0 0 0
2 SYSTEM1 2021-01-07 09:25:00 R 0 0 0 0 0 R
3 SYSTEM1 2021-01-07 09:30:00 0 0 0 0 0 0 0
4 SYSTEM1 2021-01-07 09:35:00 0 0 0 0 0 0 0
5 SYSTEM1 2021-01-07 09:40:00 0 R 0 0 0 0 R
6 SYSTEM1 2021-01-07 09:45:00 0 0 0 0 0 0 0
7 SYSTEM1 2021-01-07 09:50:00 0 0 0 0 0 0 0
8 SYSTEM1 2021-01-07 09:55:00 0 0 0 0 0 0 0
9 SYSTEM1 2021-01-07 10:00:00 0 0 0 0 0 0 0
10 SYSTEM1 2021-01-07 10:05:00 0 0 0 0 0 0 0
11 SYSTEM1 2021-01-07 10:10:00 0 0 0 0 0 0 0
12 SYSTEM1 2021-01-07 10:15:00 0 0 0 0 0 G G
13 SYSTEM1 2021-01-07 10:20:00 0 0 0 0 R 0 R
14 SYSTEM1 2021-01-07 10:25:00 0 0 0 0 0 0 0
2 个解决方案
解决方案1
1 已采纳 2021-01-10 14:41:44
You can get it using the following code:您可以使用以下代码获取它：
-
joinall the columns that have 'Col_' in them, using alambdafunction inapply在apply中使用lambdafunctionjoin所有包含“Col_”的列 replaceall the numeric characters with '' which will keep only alphabets用 '' replace所有数字字符，这将只保留字母- Lastly,
replace'' with 0 to get exactly your output.最后，用 0 replace'' 以获得准确的 output。
df['result'] = df[[c for c in df.columns if 'Col_' in c]].apply(lambda row: ''.join(row.values.astype(str)), axis=1).str.replace('[^a-zA-Z]', '').replace('',0)
which prints:打印：
SYS Date_Time Col_1 Col_2 Col_3 Col_4 Col_5 Col_6 result
0 SYSTEM1 2021-01-07 0 0 0 0 Y 0 Y
1 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
2 SYSTEM1 2021-01-07 R 0 0 0 0 0 R
3 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
4 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
5 SYSTEM1 2021-01-07 0 R 0 0 0 0 R
6 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
7 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
8 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
9 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
10 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
11 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
12 SYSTEM1 2021-01-07 0 0 0 0 0 G G
13 SYSTEM1 2021-01-07 0 0 0 0 R 0 R
14 SYSTEM1 2021-01-07 0 0 0 0 0 0 0
There probably is a better more pythonic way to do this, but this is a one-liner and does the trick.可能有一种更好的更 Pythonic 的方式来做到这一点，但这是一个单行并且可以解决问题。
解决方案2
1 2021-01-10 14:48:01
You can filter the Col like columns, then change the dtype of these columns to str and take max along axis=1 .您可以filter Col like 列，然后将这些列的dtype更改为str并沿axis=1取max 。 The idea used here is that when you take max('0', some_alphabet) then the returned max value will always be some_alphabet :这里使用的想法是，当您使用max('0', some_alphabet)时，返回的最大值将始终为some_alphabet ：
m = df.filter(like='Col').astype(str).max(1)
df['Result'] = m.where(m.ne('0'), 0) # replace '0' with 0
0 Y
1 0
2 R
3 0
4 0
5 R
6 0
7 0
8 0
9 0
10 0
11 0
12 G
13 R
14 0
Name: Result, dtype: object
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