loc for label-based selection in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of loc with single label selection

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [10, 20, 30],
    'B': [40, 50, 60]
}, index=['x', 'y', 'z'])

result = df.loc['y']
print(result)

A

   A   B
x  10  40
y  20  50
z  30  60

B

A    20
B    50
Name: y, dtype: int64

C

A    10
B    40
Name: x, dtype: int64

D KeyError: 'y'

💡 Hint

Remember loc selects rows by label. Here 'y' is a row label.

Using df.loc['y'] selects the row with label 'y'. It returns a Series with values from columns A and B for that row.

❓ data_output

intermediate

Selecting multiple rows and columns with loc

What is the output of this code?

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [1, 2, 3, 4],
    'Y': [5, 6, 7, 8],
    'Z': [9, 10, 11, 12]
}, index=['a', 'b', 'c', 'd'])

result = df.loc[['b', 'd'], ['Y', 'Z']]
print(result)

A KeyError: "['Y', 'Z'] not in index"

B

   X   Y
b  2   6
d  4   8

C

   Y   Z
b  5   9
d  7  11

D

   Y   Z
b  6  10
d  8  12

💡 Hint

loc uses labels for both rows and columns. Check the selected labels carefully.

df.loc[['b', 'd'], ['Y', 'Z']] selects rows 'b' and 'd' and columns 'Y' and 'Z'. The values correspond to those positions.

🔧 Debug

advanced

Error when using loc with missing label

What error does this code raise?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 2, 3]}, index=['p', 'q', 'r'])

result = df.loc['s']
print(result)

A KeyError: 's'

B IndexError: single positional indexer is out-of-bounds

C ValueError: cannot index with vector containing NA / NaN values

D TypeError: unhashable type: 'list'

💡 Hint

Check if the label 's' exists in the DataFrame index.

Using loc with a label not in the index raises a KeyError.

🚀 Application

advanced

Using loc to update values in a DataFrame

Given the DataFrame below, which option correctly updates the value in row 'b' and column 'Y' to 100 using loc?

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [10, 20, 30],
    'Y': [40, 50, 60]
}, index=['a', 'b', 'c'])

A df.loc[['b'], 'Y'] = 100

B df.loc['b', ['Y']] = 100

C df.loc['b', 'Y'] = 100

D df.loc['b']['Y'] = 100

💡 Hint

Use loc with row and column labels directly for assignment.

Option C correctly assigns 100 to the cell at row 'b' and column 'Y'. Option C returns a copy and does not update the original DataFrame. Options A and C assign to slices and may cause unexpected behavior.

🧠 Conceptual

expert

Understanding loc with slice labels

Consider this DataFrame:

import pandas as pd

df = pd.DataFrame({'Val': [10, 20, 30, 40]}, index=['a', 'b', 'c', 'd'])

result = df.loc['b':'d']
print(result)

What is the output?

A

   Val
b   20
c   30
d   40

B

   Val
b   20
c   30

C

   Val
c   30
d   40

D KeyError: 'b':'d'

💡 Hint

loc slice includes both start and end labels.

When slicing with loc, both the start and end labels are included in the result.