changhons :
I am new and maybe my question is silly so I apologise in advance.
Basically I have data like this,
ID | Scope 1 | Scope 2 | Scope 3 | Scope 4 | ... | Scope 30|
1 | True | True | True | False | ... | True |
2 | True | True | True | False | ... | False |
3 | True | True | True | False | ... | True |
4 | True | False | False | False | ... | False |
I would like to make a new column called Top Scope and have the highest scope number that has True as output.
I am trying for loop but I am not failing. :( Can you please help me? I would really appreciate it.
jezrael :
Use DataFrame.filter for all columns, check order of columns by DataFrame.iloc and slicing and last use DataFrame.idxmax:
df['Top Scope'] = df.filter(like='Scope').idxmax(axis=1)
#seelcting all columns without first with reversed order
#df['Top Scope'] = df.iloc[:, :0:-1].idxmax(axis=1)
print (df)
ID Scope 1 Scope 2 Scope 3 Scope 4 Scope 30 Top Scope
0 1 True True True False True Scope 30
1 2 True True True False False Scope 3
2 3 True True True False True Scope 30
3 4 True False False False False Scope 1
More general solution is necessary for avoid wrong output if all False values with numpy.where and DataFrame.any for test at least one True per rows:
df1 = df.filter(like='Scope').iloc[:, ::-1]
df['Top Scope'] = np.where(df1.any(axis=1), df1.idxmax(axis=1), 'no match')
print (df)
ID Scope 1 Scope 2 Scope 3 Scope 4 Scope 30 Top Scope
0 1 True True True False True Scope 30
1 2 True True True False False Scope 3
2 3 True True True False True Scope 30
3 4 True False False False False Scope 1
4 5 False False False False False no match
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