Get Max value comparing multiple columns and return specific values

Without using numpy wizardry:

• First, there are some really great solutions to this problem, by others.
• Data will be that provided in the question, as df

# find the max value in the Duration columns
max_value = max(df.filter(like='Dur', axis=1).max().tolist())

# get a Boolean match of the dataframe for max_value
df_max = df[df == mv]

# get the row index
max_index = df_max.dropna(how='all').index[0]

# get the column name
max_col = df_max.dropna(axis=1, how='all').columns[0]

# get column index
max_col_index = df.columns.get_loc(max_col)

# final
df.iloc[max_index, [0, max_col_index, max_col_index + 1]]

Output:

Sequence     1008
Duration3     981
Value3         82
Name: 7, dtype: int64

Update

• Last night, actually 4 a.m., I dismissed a better solution, because I was overly tired.
  • I used max_value = max(df.filter(like='Dur', axis=1).max().tolist()), to return the maximum value within the Duration columns
  • Instead of max_col_name = df.filter(like='Dur', axis=1).max().idxmax(), to return the column name where the maximum value occurs
  • I did that because my addled brain told me I was returning the max value of the column names, instead of the maximum value in the column. For example:

test = ['Duration5', 'Duration2', 'Duration3']
print(max(test))
>>> 'Duration5'

• This is why being overtired, is a poor problem solving condition
• With sleep, and coffee, a more efficient solution
  • Similar to others, in the use of idmax

New & Improved Solution:

# column name with max duration value
max_col_name = df.filter(like='Dur', axis=1).max().idxmax()

# index of max_col_name
max_col_idx =df.columns.get_loc(max_col_name)

# row index of max value in max_col_name
max_row_idx = df[max_col_name].idxmax()

# output with .loc
df.iloc[max_row_idx, [0, max_col_idx, max_col_idx + 1 ]]

Output:

Sequence     1008
Duration3     981
Value3         82
Name: 7, dtype: int64

Methods used:

• pandas.DataFrame.max
• pandas.DataFrame.filter
• pandas.DataFrame.idxmax
• pandas.Index.get_loc
• pandas.DataFrame.iloc

With wide data it can be easier to first reshape with wide_to_long. This creates 2 columns ['Duration', 'Value'], and the MultiIndex tells us which number it was. There is no reliance on any specific column ordering.

import pandas as pd

df = pd.wide_to_long(df, i='Sequence', j='num', stubnames=['Duration', 'Value'])
df.loc[[df.Duration.idxmax()]]

              Duration  Value
Sequence num                 
1008     3         981     82

Try the following, quite short code, based mainly on Numpy:

vv = df.iloc[:, 1::2].values
iRow, iCol = np.unravel_index(vv.argmax(), vv.shape)
iCol = iCol * 2 + 1
result = df.iloc[iRow, [0, iCol, iCol + 1]]

The result is a Series:

Sequence     1008
Duration3     981
Value3         82
Name: 7, dtype: int64

If you want to "rehape" it (first index values, then actual values), you can get something like this executing:

pd.DataFrame([result.values], columns=result.index)