5 Quick Ways to Add a Row‑wise Max Column in Pandas

Introduction

Hello, I’m a Python enthusiast. A follower in the Python community asked how to compare two columns in a DataFrame and create a new column with the row‑wise maximum value.

Problem Statement

Given a DataFrame with two columns (e.g., cell1 and cell2), we need to generate a new column that contains the larger of the two values for each row.

Solution Methods

Method 1 (by 月神)

Use DataFrame.max across axis 1.

df['max1'] = df[['cell1', 'cell2']].max(axis=1)
df

Method 2 (by 广深‑运营‑n)

Leverage loc with max on the selected columns.

df['max2'] = df.loc[:, ['cell1', 'cell2']].max(axis=1)
df

Method 3 (by 月神)

Apply the built‑in max function row‑wise.

df['max3'] = df[['cell1', 'cell2']].apply(max, axis=1)
df

Method 4 (by 常州‑销售‑MT)

Use assign together with max on the two columns.

df = df.assign(new=df[['cell1', 'cell2']].max(1))

Important detail to avoid errors:

Method 5 (by 上海‑数分‑长城)

Combine NumPy's where with Pandas columns.

df['max4'] = np.where(df['cell1'] > df['cell2'], df['cell1'], df['cell2'])
df

Conclusion

The five approaches above demonstrate straightforward ways to compute a row‑wise maximum between two columns in a Pandas DataFrame, offering both readability and performance options for Python learners.