CODEWITHSUNDEEP
pythonpandasdataframe
Madhu
Madhu

Reputation: 1

How to return max value from a row from pandas dataframe taking into account values from the last row?

Currently I'm returning column name of the max value in the each row.


    df['Active'] = df.idxmax(axis=1)

enter image description here

How do I take into account the Priority for each column? e.g. for Row 0, the Active column should have opC since it has a higher priority than opA. (Also Priority row shouldn't return anything in the Active column).

Update: Follow up scenario. Adding an additional row called 'minOccurrence'. Here's an example of it. Since opD doesn't have 3 straight "Actives" it isn't active at index 1 or 2 where previously it was Active based on 'Priority' column only.

df1 = pd.DataFrame({'opA': [1,1,1,1,0], 
               'opB': [1,1,1,0,1],
                'opC': [1,1,1,1,2], 
               'opD': [0,1,1,0,3],
               'Active': ['opC','opD', 'opD', 'opC', 0]})
df1 = df1.rename(index={df1.last_valid_index() : 'Priority'})
df1.loc['Priority','Active'] = ''
print(df1)

df1 = pd.DataFrame({'opA': [1,1,1,1,0,0], 
               'opB': [1,1,1,0,1,0],
                'opC': [1,1,1,1,2,0], 
               'opD': [0,1,1,0,3,3],
               'Active': ['opC','opC', 'opC', 'opC', 0,0]})
df1 = df1.rename(index={df1.last_valid_index() - 1 : 'Priority'})
df1 = df1.rename(index={df1.last_valid_index() : 'minOccurrence'})
df1.loc['Priority','Active'] = ''
df1.loc['minOccurrence','Active'] = ''
print(df1)

vs. if opD had a 1 at index 0.

df1 = pd.DataFrame({'opA': [1,1,1,1,0,0], 
               'opB': [1,1,1,0,1,0],
                'opC': [1,1,1,1,2,0], 
               'opD': [1,1,1,0,3,3],
               'Active': ['opD','opD', 'opD', 'opC', 0,0]})
df1 = df1.rename(index={df1.last_valid_index() - 1 : 'Priority'})
df1 = df1.rename(index={df1.last_valid_index() : 'minOccurrence'})
df1.loc['Priority','Active'] = ''
df1.loc['minOccurrence','Active'] = ''
print(df1)

Upvotes: 0

Views: 352

Answers (3)

mozway
mozway

Reputation: 260420

You can do everything in a single shot using indexing.

Using multiplication by the priority as suggested by @Meelad:

df['Active'] = (df
               .loc[df.index!='Priority']
               .mul(df.loc['Priority'])
               .idxmax(1)
               )

Or by sorting the columns as suggested by @Arnau:

df['Active'] = (df
                .loc[df.index!='Priority']
                .sort_index(axis=1, key=lambda x: -df.loc['Priority',x])
                .idxmax(1)
                )

Reproducible input:

np.random.seed(0)
df = pd.DataFrame(np.random.randint(0,2,(13,4)),
                  columns=['opA', 'opB', 'opC', 'opD'])
df.loc['Priority'] = range(4)

Output:

          opA  opB  opC  opD Active
0           0    1    1    0    opC
1           1    1    1    1    opD
2           1    1    1    0    opC
3           0    1    0    0    opB
4           0    0    0    1    opD
5           0    1    1    0    opC
6           0    1    1    1    opD
7           1    0    1    0    opC
8           1    0    1    1    opD
9           0    1    1    0    opC
10          0    1    0    1    opD
11          1    1    1    1    opD
Priority    0    1    2    3    NaN

Upvotes: 0

Meelad Ghazipour
Meelad Ghazipour

Reputation: 36

multiply column index by row column value , then pick up maximum result and sum all the row values , put it in new column , sort column.

Upvotes: 0

Arnau
Arnau

Reputation: 741

You need to resort the columns before using idxmax

temp_cols = df.columns
df = df.sort_index(axis=1,key=lambda x:df.loc['Priority',x],ascending=False)
df['Active'] = df.idxmax(axis=1)
df = df[list(temp_cols)+['Active']]
df.loc['Priority','Active'] = ''

Upvotes: 1

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