Pandas question

[takaa]

Hi

I have a dataframe that looks like this:

Output:
      1     2     4  5  3
1  0.25     0  0.75  0  0
2     0  0.75     0  0  0
4  0.75     0  0.25  0  0
5     0     0     0  1  0
3     0     0     0  0  1

Now i want to know for each index which column contains the highest score and the corresponding score. 

    def matchResult():
        match = df.max(axis=1) # shows the highest score
        match1 = df.idxmax(axis=1) # shows the column containing the highest score
        print(match)
        print(match1)

Output:
1    0.75
2    0.75
4    0.75
5    1.00
3    1.00
dtype: float64
1    4
2    2
4    1
5    5
3    3
dtype: object

Does anybody know how I can combine them, so I get one output looking like:

index - column - score
1 4 0.75
2 2 0.75
3 3 1
4 1 0.75
5 5 1

thanks!

[takaa]

in case somebody is interested, the following is the answer to my question above.

        match = df.max(axis=1).to_frame() # shows the highest score
        match1 = df.idxmax(axis=1).to_frame() # shows the column of the highes score
        result = pd.concat([match1, match], axis=1) # combines both

New question, 

Does anybody know how to return the max value as stated above, with a minimum value condition? (e.g. exclude zeros or values below a certain amount?)

[snippsat]

it's much better if you could post code that could be run,
especially  when it comes to pandas and alike than many of use sporadically.
Here how it could be done.

import pandas as pd
from io import StringIO

data = StringIO('''\
1,2,4,5,3
0.25,0,0.75,0,0
0,0.75,0,0,0
0.75,0,0.25,0,0
0,0,0,1,0
0,0,0,0,1''')

df = pd.read_csv(data, sep=",")
print(df)
print('------------------')
# Minimum has to be over 0.1
print(df[df > .01].min(axis=1))

Output:
G:\Anaconda3
λ python pd_test.py
      1     2     4  5  3
0  0.25  0.00  0.75  0  0
1  0.00  0.75  0.00  0  0
2  0.75  0.00  0.25  0  0
3  0.00  0.00  0.00  1  0
4  0.00  0.00  0.00  0  1
------------------
0    0.25
1    0.75
2    0.25
3    1.00
4    1.00
dtype: float64

[takaa]

it's much better if you could post code that could be run,
especially  when it comes to pandas and alike than many of use sporadically.
Here how it could be done.
 

Good point, i'll take it into account. Thank you for the help, much appreciated!. I have only added .dropna() since I got nan values when the requirements were not met.