xs() for cross-section selection in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of xs() on MultiIndex DataFrame

What is the output of the following code snippet using xs() to select a cross-section from a MultiIndex DataFrame?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1), ('B', 2)], names=['letter', 'number'])
data = {'value': [10, 20, 30, 40]}
df = pd.DataFrame(data, index=index)

result = df.xs('A', level='letter')
print(result)

A

letter  value
A       10
A       20

B

   value
letter       
A        10
A        20

C

value
0     10
1     20

D

   value
number       
1        10
2        20

💡 Hint

Remember that xs() selects data at a particular level of a MultiIndex.

The xs() method selects rows at the specified level value. Here, selecting 'A' at level 'letter' returns rows where the first index level is 'A'. The resulting DataFrame has the remaining index level 'number' as its index.

❓ data_output

intermediate

Number of rows after xs() selection

Given the DataFrame below, how many rows does the result have after applying df.xs(2, level='number')?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('X', 1), ('X', 2), ('Y', 1), ('Y', 2), ('Z', 2)], names=['letter', 'number'])
data = {'score': [5, 10, 15, 20, 25]}
df = pd.DataFrame(data, index=index)

result = df.xs(2, level='number')

A 3

B 2

C 5

D 1

💡 Hint

Count how many rows have the index level 'number' equal to 2.

The xs() method selects all rows where the 'number' level equals 2. The tuples with 'number' 2 are ('X', 2), ('Y', 2), and ('Z', 2), so there are 3 rows.

🔧 Debug

advanced

Identify the error in xs() usage

What error does the following code raise when using xs() incorrectly?

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('a', 1), ('b', 2)], names=['letter', 'number'])
data = {'val': [100, 200]}
df = pd.DataFrame(data, index=index)

result = df.xs('c', level='letter')

A KeyError

B TypeError

C ValueError

D AttributeError

💡 Hint

Check what happens when the key is not found in the specified index level.

Using xs() with a key that does not exist in the specified level raises a KeyError because the key is missing from the MultiIndex.

🚀 Application

advanced

Using xs() to select columns in a MultiIndex DataFrame

Given a DataFrame with MultiIndex columns, which xs() call correctly selects the cross-section for the column level 'metric' with value 'height'?

Pandas

import pandas as pd

arrays = [['height', 'height', 'weight', 'weight'], ['min', 'max', 'min', 'max']]
columns = pd.MultiIndex.from_arrays(arrays, names=['metric', 'stat'])
 data = [[150, 180, 60, 80], [160, 190, 65, 85]]
df = pd.DataFrame(data, columns=columns)

# Which xs() call selects the 'height' metric columns?

A df.xs('height', level='stat', axis=0)

B df.xs('height', level='metric', axis=1)

C df.xs('height', level='metric', axis=0)

D df.xs('height', level='stat', axis=1)

💡 Hint

Remember that columns are selected with axis=1 and the level name must match the MultiIndex level.

The DataFrame has MultiIndex columns with levels 'metric' and 'stat'. To select columns where 'metric' is 'height', use xs('height', level='metric', axis=1).

🧠 Conceptual

expert

Effect of drop_level parameter in xs()

What is the effect of setting drop_level=False in the xs() method when selecting a cross-section from a MultiIndex DataFrame?

A The DataFrame is converted to a Series.

B The selected level is removed from the index after selection.

C The selected level remains in the index after selection.

D The selection returns all levels except the specified one.

💡 Hint

Think about whether the index level you select stays or is dropped.

By default, xs() drops the level used for selection from the resulting index. Setting drop_level=False keeps that level in the index.