import numpy as np
import pandas as pd
df = pd.DataFrame({'a': [10,20],'b':[100,200],'c': ['a','b']})
df.loc['Column_Total']= df.sum(numeric_only=True, axis=0)
df.loc[:,'Row_Total'] = df.sum(numeric_only=True, axis=1)
print(df)
a b c Row_Total
0 10.0 100.0 a 110.0
1 20.0 200.0 b 220.0
Column_Total 30.0 300.0 NaN 330.0df.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
# 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
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)
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)
# sum over the column axis.
df.sum(axis = 1, skipna = True)