pandas sum
df.loc['Total'] = pd.Series(df['MyColumn'].sum(), index = ['MyColumn'])
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 NaNpandas 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)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 319.0 400.0 398.0pandas 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 NaNpandas 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)Copyright © 2021 Codeinu
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