Pandas sum

import pandas as pd

data = {'Month': ['Jan ','Feb ','Mar ','Apr ','May ','Jun '],
        'Bill Commission': [1500,2200,3500,1800,3000,2800],
        'Maria Commission': [3200,4100,2500,3000,4700,3400], 
        'Jack Commission': [1700,3100,3300,2700,2400,3100]
        }

df = pd.DataFrame(data,columns=['Month','Bill Commission','Maria Commission','Jack Commission'])
sum_column = df.sum(axis=0)
print (sum_column)

Source: datatofish.com

Pandas sum

# select numeric columns and calculate the sums
sums = df.select_dtypes(pd.np.number).sum().rename('total')

# append sums to the data frame
df.append(sums)
#         X  MyColumn      Y      Z
#0        A      84.0   13.0   69.0
#1        B      76.0   77.0  127.0
#2        C      28.0   69.0   16.0
#3        D      28.0   28.0   31.0
#4        E      19.0   20.0   85.0
#5        F      84.0  193.0   70.0
#total  NaN     319.0  400.0  398.0

Source: stackoverflow.com

Pandas sum

df.at['Total', 'MyColumn'] = df['MyColumn'].sum()
print (df)
         X  MyColumn      Y      Z
0        A      84.0   13.0   69.0
1        B      76.0   77.0  127.0
2        C      28.0   69.0   16.0
3        D      28.0   28.0   31.0
4        E      19.0   20.0   85.0
5        F      84.0  193.0   70.0
Total  NaN     319.0    NaN    NaN

Source: stackoverflow.com

Pandas sum

import pandas as pd

data = {'Month': ['Jan ','Feb ','Mar ','Apr ','May ','Jun '],
        'Bill Commission': [1500,2200,3500,1800,3000,2800],
        'Maria Commission': [3200,4100,2500,3000,4700,3400], 
        'Jack Commission': [1700,3100,3300,2700,2400,3100]
        }

df = pd.DataFrame(data,columns=['Month','Bill Commission','Maria Commission','Jack Commission'])
print (df)

Source: datatofish.com

Pandas sum

>>> sum(ugds)16200904.0>>> ugds.sum()16200904.0

Source: medium.com

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