How to Compute Row-wise Means in Pandas with One-Liner Code

Introduction

Hello, I'm PiPi. Recently a question about processing data with Pandas was asked in a Python community. The original data is shown below.

df = pd.DataFrame({
    'student_id': ['S001','S002','S003'],
    'marks': [[88,89,90],[78,81,60],[84,83,91]]
})
df

The expected output is a DataFrame with an additional column containing the mean of each marks list.

Implementation

Method 1

The following code uses a lambda function with np.mean to compute the row‑wise mean.

df['dmean'] = df['marks'].map(lambda x: np.mean(x))

Method 2

An optimized version applies NumPy's mean directly via map or apply.

df['dmean'] = df['marks'].map(np.mean)
# or
df['dmean'] = df['marks'].apply(np.mean)

Conclusion

This article presented two concise solutions for adding a column of average marks to a Pandas DataFrame, helping readers solve the original problem efficiently.