apply() on rows (axis=1) in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of apply() with axis=1 on a DataFrame

What is the output of this code snippet?

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

result = df.apply(lambda row: row['A'] + row['B'], axis=1)
print(result)

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

result = df.apply(lambda row: row['A'] + row['B'], axis=1)
print(result)

A

A    6
B   15
dtype: int64

B

0    4
1    5
2    6
dtype: int64

C

0    5
1    7
2    9
dtype: int64

D

0    1
1    2
2    3
dtype: int64

💡 Hint

Remember that axis=1 means the function is applied to each row, not each column.

The lambda function adds the values in columns 'A' and 'B' for each row. So for row 0: 1+4=5, row 1: 2+5=7, row 2: 3+6=9.

❓ data_output

intermediate

Resulting DataFrame after apply() with axis=1

Given this code, what is the resulting DataFrame?

import pandas as pd

df = pd.DataFrame({
    'X': [10, 20],
    'Y': [1, 2]
})

def add_columns(row):
    return pd.Series({'Sum': row['X'] + row['Y'], 'Diff': row['X'] - row['Y']})

result = df.apply(add_columns, axis=1)
print(result)

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [10, 20],
    'Y': [1, 2]
})

def add_columns(row):
    return pd.Series({'Sum': row['X'] + row['Y'], 'Diff': row['X'] - row['Y']})

result = df.apply(add_columns, axis=1)
print(result)

A

   Sum  Diff
0   11     9
1   22    18

B

   X   Y
0  11   9
1  22  18

C

   Sum  Diff
0   9    11
1  18    22

D

0    11
1    22
dtype: int64

💡 Hint

The function returns a Series with two keys, so the result will have two columns.

Each row returns a Series with 'Sum' and 'Diff'. For row 0: Sum=10+1=11, Diff=10-1=9; for row 1: Sum=20+2=22, Diff=20-2=18.

🔧 Debug

advanced

Identify the error in apply() usage with axis=1

What error does this code raise?

import pandas as pd

df = pd.DataFrame({'A': [1, 2]})

result = df.apply(lambda row: row['B'] * 2, axis=1)
print(result)

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2]})

result = df.apply(lambda row: row['B'] * 2, axis=1)
print(result)

A KeyError: 'B'

B TypeError: unsupported operand type(s) for *: 'int' and 'str'

C AttributeError: 'Series' object has no attribute 'B'

D No error, outputs a Series with doubled values

💡 Hint

Check if the column 'B' exists in the DataFrame.

The DataFrame has only column 'A'. Trying to access row['B'] causes a KeyError.

❓ visualization

advanced

Visualizing apply() results on rows

What does this code plot?

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Height': [150, 160, 170],
    'Weight': [50, 60, 70]
})

df['BMI'] = df.apply(lambda row: row['Weight'] / ((row['Height']/100) ** 2), axis=1)

plt.bar(df.index, df['BMI'])
plt.xlabel('Person Index')
plt.ylabel('BMI')
plt.title('BMI of Persons')
plt.show()

Pandas

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'Height': [150, 160, 170],
    'Weight': [50, 60, 70]
})

df['BMI'] = df.apply(lambda row: row['Weight'] / ((row['Height']/100) ** 2), axis=1)

plt.bar(df.index, df['BMI'])
plt.xlabel('Person Index')
plt.ylabel('BMI')
plt.title('BMI of Persons')
plt.show()

A A line chart showing Height vs Weight

B A bar chart showing BMI values for 3 persons indexed 0,1,2

C A scatter plot of Height vs BMI

D An empty plot with no bars

💡 Hint

Look at the plt.bar call and what data it uses.

The code calculates BMI for each person and plots a bar chart with person index on x-axis and BMI on y-axis.

🚀 Application

expert

Using apply() with axis=1 to classify data

Given this DataFrame, which option correctly adds a new column 'Category' that labels each row as 'High' if 'Score' > 75, else 'Low'?

import pandas as pd

df = pd.DataFrame({'Name': ['Anna', 'Bob', 'Cara'], 'Score': [80, 70, 90]})

# Add 'Category' column here

Pandas

import pandas as pd

df = pd.DataFrame({'Name': ['Anna', 'Bob', 'Cara'], 'Score': [80, 70, 90]})

# Add 'Category' column here

A df['Category'] = df.apply(lambda row: 'High' if row.Score > 75 else 'Low')

B df['Category'] = df['Score'].apply(lambda x: 'High' if x > 75 else 'Low')

C df['Category'] = df.apply(lambda col: 'High' if col['Score'] > 75 else 'Low', axis=0)

D df['Category'] = df.apply(lambda row: 'High' if row['Score'] > 75 else 'Low', axis=1)

💡 Hint

Remember axis=1 applies the function to each row, axis=0 applies to columns.

Option D correctly uses apply with axis=1 to check each row's 'Score' and assign 'High' or 'Low'. Option D works but uses apply on Series, not DataFrame with axis=1. Option D uses axis=0 which applies to columns, not rows. Option D misses axis=1 and will error.