Often you may be interested in calculating the sum of one or more rows in a pandas DataFrame. Fortunately you can do this easily in pandas using the sum(axis=1) function.

This tutorial shows several examples of how to use this function on the following DataFrame:

import pandas as pd import numpy as np #create DataFrame df = pd.DataFrame({'rating': [90, 85, 82, 88, 94, 90, 76, 75, 87, 86], 'points': [25, 20, 14, 16, 27, 20, 12, 15, 14, 19], 'assists': [5, 7, 7, 8, 5, 7, 6, 9, 9, 5], 'rebounds': [8, np.nan, 10, 6, 6, 9, 6, 10, 10, 7]}) #view DataFrame df rating points assists rebounds 0 90 25 5 8.0 1 85 20 7 NaN 2 82 14 7 10.0 3 88 16 8 6.0 4 94 27 5 6.0 5 90 20 7 9.0 6 76 12 6 6.0 7 75 15 9 10.0 8 87 14 9 10.0 9 86 19 5 7.07

**Example 1: Find the Sum of Each Row**

We can find the sum of each row in the DataFrame by using the following syntax:

df.sum(axis=1) 0 128.0 1 112.0 2 113.0 3 118.0 4 132.0 5 126.0 6 100.0 7 109.0 8 120.0 9 117.0 dtype: float64

The output tells us:

- The sum of values in the first row is
**128**. - The sum of values in the second row is
**112**. - The sum of values in the third row is
**113**.

And so on.

**Example 2: Place the Row Sums in a New Column**

We can use the following code to add a column to our DataFrame to hold the row sums:

#define new DataFrame column 'row_sum' as the sum of each row df['row_sum'] = df.sum(axis=1) #view DataFrame df rating points assists rebounds row_sum 0 90 25 5 8.0 128.0 1 85 20 7 NaN 112.0 2 82 14 7 10.0 113.0 3 88 16 8 6.0 118.0 4 94 27 5 6.0 132.0 5 90 20 7 9.0 126.0 6 76 12 6 6.0 100.0 7 75 15 9 10.0 109.0 8 87 14 9 10.0 120.0 9 86 19 5 7.0 117.0

**Example 3: Find the Row Sums for a Short List of Specific Columns**

We can use the following code to find the row sum for a short list of specific columns:

#define new DataFrame column as sum of points and assists columns df['sum_pa'] = df['points'] + df['assists'] #view DataFrame df rating points assists rebounds sum_pa 0 90 25 5 8.0 30 1 85 20 7 NaN 27 2 82 14 7 10.0 21 3 88 16 8 6.0 24 4 94 27 5 6.0 32 5 90 20 7 9.0 27 6 76 12 6 6.0 18 7 75 15 9 10.0 24 8 87 14 9 10.0 23 9 86 19 5 7.0 24

**Example 4: Find the Row Sums for a Long List of Specific Columns**

We can use the following code to find the row sum for a longer list of specific columns:

#define col_list as a list of all DataFrame column names col_list= list(df) #remove the column 'rating' from the list col_list.remove('rating') #define new DataFrame column as sum of rows in col_list df['new_sum'] = df[col_list].sum(axis=1) #view DataFrame df rating points assists rebounds new_sum 0 90 25 5 8.0 38.0 1 85 20 7 NaN 27.0 2 82 14 7 10.0 31.0 3 88 16 8 6.0 30.0 4 94 27 5 6.0 38.0 5 90 20 7 9.0 36.0 6 76 12 6 6.0 24.0 7 75 15 9 10.0 34.0 8 87 14 9 10.0 33.0 9 86 19 5 7.0 31.0

*You can find the complete documentation for the pandas sum() function here.*