Pandas Trick-2: Extracting First and Second Maximum Values from Each Row in a Pandas DataFrame

import pandas as pd

# Sample DataFrame

df = pd.DataFrame({

'A': [5, 1, 3],

'B': [2, 8, 7],

'C': [9, 4, 6]

})

# Get first and second max values for each row

top_2 = df.apply(lambda row: row.nlargest(2).values, axis=1)

# Convert to DataFrame

top_2_df = pd.DataFrame(top_2.tolist(), columns=['First_Max', 'Second_Max'])

# Concatenate with original DataFrame (optional)

result = pd.concat([df, top_2_df], axis=1)

print(result)

Notes:

• row.nlargest(2).values gives you a NumPy array of top 2 values in descending order.

• This works row-wise (axis=1).