pandas sum rows

1# Basic syntax:
2df.sum(axis=1)
3
4# Create new column consisting of row sums across specific columns:
5df['sums'] = df.iloc[:, 6:23].sum(axis=1)
6
7# Where:
8#	- iloc allows you to specify the rows and columns with slicing. Here
9#		I select all rows and sum over columns 6-22
10#	- df['sums'] is how you assign a new column named 'sums' to the df
11
12# Example usage:
13import pandas as pd
14import numpy as np
15
16# Create dataframe:
17df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), 
18                  columns=['a', 'b', 'c'])
19
20print(df)
21   a  b  c
220  1  2  3
231  4  5  6
242  7  8  9
25
26# Sum columns 1-2:
27df['sums'] = df.iloc[:, 1:3].sum(axis=1)
28
29print(df)
30   a  b  c  sums
310  1  2  3     5
321  4  5  6    11
332  7  8  9    17

1import numpy as np
2import pandas as pd
3
4
5df = pd.DataFrame({'a': [10,20],'b':[100,200],'c': ['a','b']})
6
7df.loc['Column_Total']= df.sum(numeric_only=True, axis=0)
8df.loc[:,'Row_Total'] = df.sum(numeric_only=True, axis=1)
9
10print(df)
11
12                 a      b    c  Row_Total
130             10.0  100.0    a      110.0
141             20.0  200.0    b      220.0
15Column_Total  30.0  300.0  NaN      330.0

1df.groupby(['Fruit','Name'])['Number'].sum()

1df.sum(axis=1)

1# select numeric columns and calculate the sums
2sums = df.select_dtypes(pd.np.number).sum().rename('total')
3
4# append sums to the data frame
5df.append(sums)
6#         X  MyColumn      Y      Z
7#0        A      84.0   13.0   69.0
8#1        B      76.0   77.0  127.0
9#2        C      28.0   69.0   16.0
10#3        D      28.0   28.0   31.0
11#4        E      19.0   20.0   85.0
12#5        F      84.0  193.0   70.0
13#total  NaN     319.0  400.0  398.0
14