How do you calculate the standard deviation for each row in a pandas DataFrame?

---

You can use the following basic syntax to calculate the standard deviation of values for each row in a pandas DataFrame:

df.std(axis=1, numeric_only=True)

The argument axis=1 tells pandas to perform the calculation for each row (instead of each column) and numeric_only=True tells pandas to only consider numeric columns when performing the calculation.

The following example shows how to use this syntax in practice.

Example: Calculate Standard Deviation for Each Row in Pandas

Suppose we have the following pandas DataFrame that contains information about the points scored by various basketball players during four different games:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'],
                   'game1': [18, 22, 19, 14, 14, 11, 20, 28],
                   'game2': [5, 7, 7, 9, 12, 9, 9, 4],
                   'game3': [11, 8, 10, 6, 6, 5, 9, 12],
                   'game4': [9, 8, 8, 9, 14, 15, 10, 11]})
                   
#view DataFrame
print(df)

  player  game1  game2  game3  game4
0      A     18      5     11      9
1      B     22      7      8      8
2      C     19      7     10      8
3      D     14      9      6      9
4      E     14     12      6     14
5      F     11      9      5     15
6      G     20      9      9     10
7      H     28      4     12     11

We can use the following syntax to calculate the standard deviation of points scored by each player:

#calculate standard deviation for each row
df.std(axis=1, numeric_only=True)

0     5.439056
1     7.182154
2     5.477226
3     3.316625
4     3.785939
5     4.163332
6     5.354126
7    10.144785
dtype: float64

Here’s how to interpret the output:

• The standard deviation of points scored by player A is 5.439.
• The standard deviation of points scored by player B is 7.182.
• The standard deviation of points scored by player C is 5.477.

And so on.

Note that the std() function calculates the sample standard deviation by default.

If you would instead like to calculate the population standard deviation, you must use the argument ddof=0:

#calculate population standard deviation for each row
df.std(axis=1, ddof=0, numeric_only=True)

0    4.747351
1    5.881366
2    4.807037
3    3.384910
4    3.983518
5    3.915150
6    4.892772
7    8.091179
dtype: float64

Related:

To assign the standard deviation values to a new column, you can use the following syntax:

#add new column to display standard deviation for each row
df['points_std'] = df.std(axis=1, numeric_only=True)

#view updated DataFrame
print(df)

  player  game1  game2  game3  game4  points_std
0      A     18      5     11      9    5.439056
1      B     22      7      8      8    7.182154
2      C     19      7     10      8    5.477226
3      D     14      9      6      9    3.316625
4      E     14     12      6     14    3.785939
5      F     11      9      5     15    4.163332
6      G     20      9      9     10    5.354126
7      H     28      4     12     11   10.144785

The standard deviation of values for each row in the game1, game2, game3 and game4 columns is now shown in the points_std column.