CODEWITHSUNDEEP
pythonpandasloopspivot-tablemulti-index
Caiotru
Caiotru

Reputation: 325

Python Pivot Table multi Sub-totals in column

I would like to be able to show the sub-total column from a multi-index pivot table in different ways for example, I would like to show the sum for a selected row and the max for another, is this possible?

I managed to get half code correct but I am stuck in replicating the code without offsetting the previous one and I am not able to loop this code over.

In my example I want to get the max value from Toyota and the sum value from Honda shown in the newly created Total column.

cars = {'Brand': ['Honda','Toyota', 'Honda','Toyota'],
        'Target': ['A','B', 'A','B'],
        'Speed': [20, 80, 30 , 10],
        'Date' : ['13/02/2019', '18/02/2019', '18/02/2019', '13/02/2019']
       }

df = pd.DataFrame(cars)


table = pd.pivot_table(df, values=['Speed'],
                       index=['Target', 'Brand'],
                       columns=['Date'],
                       fill_value=0, aggfunc=np.sum, dropna=True)
table

enter image description here

the code craeted: (which works only for the last line as it overwrites the first one)

table['Total'] = table.loc(axis=0)[:, ['Toyota']].max(axis=1)
table['Total'] = table.loc(axis=0)[:, ['Honda']].sum(axis=1)

Current output:

enter image description here

Disired Output:

I would like to be able to see also the max value for Toyota which would be 80.

Upvotes: 1

Views: 118

Answers (1)

jezrael
jezrael

Reputation: 863226

Use slicers for set new values in both sides, here : means all values for levels:

idx = pd.IndexSlice
table.loc[idx[:, 'Toyota'], 'Total'] = table.max(axis=1)
table.loc[idx[:, 'Honda'], 'Total'] = table.sum(axis=1)
print (table)
                   Speed            Total
Date          13/02/2019 18/02/2019      
Target Brand                             
A      Honda          20         30  50.0
B      Toyota         10         80  80.0

You can set and select in both sides:

idx = pd.IndexSlice
table.loc[idx[:, 'Toyota'], 'Total'] = table.loc[idx[:, 'Toyota'], :].max(axis=1)
table.loc[idx[:, 'Honda'], 'Total'] = table.loc[idx[:, 'Honda'], :].sum(axis=1)

Upvotes: 2

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