apply() on columns in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of apply() with a lambda on DataFrame columns

What is the output of this code when applying a lambda function to each column of the DataFrame?

Pandas

import pandas as pd

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

result = df.apply(lambda x: x.max() - x.min())
print(result)

A

A    2
B    2
dtype: int64

B

A    3
B    6
dtype: int64

C

A    1
B    4
dtype: int64

D

A    6
B    9
dtype: int64

💡 Hint

Think about what max() and min() do for each column.

The lambda function calculates the difference between the maximum and minimum values in each column. For column A, max is 3 and min is 1, difference is 2. For column B, max is 6 and min is 4, difference is 2.

❓ data_output

intermediate

Result of apply() with a custom function on columns

Given this DataFrame, what is the result of applying the custom function to each column?

Pandas

import pandas as pd

def custom_func(col):
    return col.sum() / len(col)

df = pd.DataFrame({
    'X': [10, 20, 30],
    'Y': [5, 15, 25]
})

result = df.apply(custom_func)
print(result)

A

X    30
Y    25
dtype: int64

B

X    60
Y    45
dtype: int64

C

X    10
Y    5
dtype: int64

D

X    20.0
Y    15.0
dtype: float64

💡 Hint

The function calculates the average of each column.

The function sums the values in each column and divides by the number of rows, which is the mean. For X: (10+20+30)/3 = 20. For Y: (5+15+25)/3 = 15.

🔧 Debug

advanced

Identify the error in apply() usage on columns

What error does this code raise when applying the function to each column?

Pandas

import pandas as pd

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

result = df.apply(lambda x: x["nonexistent"])
print(result)

A KeyError: 'nonexistent'

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

C TypeError: 'int' object is not subscriptable

D ValueError: cannot index with vector

💡 Hint

Check if the column has the key you are trying to access.

The lambda tries to access a key 'nonexistent' in each column (which is a Series). Since this key does not exist, a KeyError is raised.

❓ visualization

advanced

Visualize the effect of apply() on columns with a plotting function

Which option correctly applies a function to plot the sum of each column as a bar chart?

Pandas

import pandas as pd
import matplotlib.pyplot as plt

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

# Which code snippet below correctly plots the sum of each column as a bar chart?

A

df.sum().apply(lambda x: x).plot(kind='bar')
plt.show()

B

df.plot(kind='bar').apply(sum)
plt.show()

C

df.apply(sum).plot(kind='bar')
plt.show()

D

df.apply(lambda x: x.plot(kind='bar'))
plt.show()

💡 Hint

Sum the columns first, then plot the result.

Option C sums each column using apply(sum), resulting in a Series with sums, then plots it as a bar chart. Other options either apply plot incorrectly or apply sum in wrong order.

🚀 Application

expert

Use apply() on columns to normalize data

You want to normalize each column in a DataFrame by subtracting the mean and dividing by the standard deviation. Which code snippet correctly does this using apply() on columns?

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [10, 20, 30],
    'Y': [5, 15, 25]
})

# Choose the correct code to normalize columns

A df.apply(lambda x: (x - df.mean()) / df.std())

B df.apply(lambda x: (x - x.mean()) / x.std())

C df.apply(lambda x: (x - x.median()) / x.std())

D df.apply(lambda x: (x - x.min()) / (x.max() - x.min()))

💡 Hint

Normalization uses mean and standard deviation of each column separately.

Option B correctly normalizes each column by subtracting its own mean and dividing by its own standard deviation. Option B uses overall DataFrame mean/std which is incorrect. Option B uses median instead of mean. Option B does min-max scaling, not normalization.