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.

Practice

1. What does the xs() method do in pandas?

easy

A. Removes duplicate rows from the DataFrame.

B. Sorts the DataFrame by index values.

C. Selects data by label from rows or columns, especially in multi-index DataFrames.

D. Converts DataFrame to a NumPy array.

Solution

1. Step 1: Understand the purpose of xs()

   xs() is used to select data by label from rows or columns, especially useful for multi-index DataFrames.

2. Step 2: Compare with other options

   Sorting, removing duplicates, or converting to arrays are different methods and not related to xs().

3. Final Answer:

   Selects data by label from rows or columns, especially in multi-index DataFrames. -> Option C

4. Quick Check:

   xs() = select by label [OK]

Hint: Remember: xs() picks data by label from multi-index [OK]

Common Mistakes:

• Confusing xs() with sorting methods
• Thinking xs() removes duplicates
• Assuming xs() converts data formats

2. Which of the following is the correct syntax to select the cross-section labeled 'B' from the first level of a multi-index DataFrame df using xs()?

easy

A. df.xs('B', level=0)

B. df.xs(level=0, 'B')

C. df.xs('B', axis=1)

D. df.xs('B', level=1)

Solution

1. Step 1: Recall xs() syntax

   The correct syntax is df.xs(key, level=level_number) where key is the label and level is the index level.

2. Step 2: Match the label and level

   To select label 'B' from level 0, use df.xs('B', level=0). Other options have wrong argument order or wrong level.

3. Final Answer:

   df.xs('B', level=0) -> Option A

4. Quick Check:

   xs(key, level=0) = correct syntax [OK]

Hint: Use xs('label', level=level_number) for multi-index [OK]

Common Mistakes:

• Swapping arguments order in xs()
• Using wrong level number
• Confusing axis parameter with level

3. Given the following DataFrame df with a multi-index:

import pandas as pd
index = pd.MultiIndex.from_tuples([('A', 1), ('A', 2), ('B', 1), ('B', 2)])
data = {'Value': [10, 20, 30, 40]}
df = pd.DataFrame(data, index=index)
print(df.xs('B', level=0))

What is the output?

medium

A. Value
1 30
2 40

B. Value
0 30
1 40

C. Value 70
Name: B, dtype: int64

D. KeyError: 'B'

Solution

1. Step 1: Understand the DataFrame structure

   The DataFrame has a multi-index with levels: first level 'A' or 'B', second level 1 or 2, and values 10, 20, 30, 40.

2. Step 2: Apply xs('B', level=0)

   This selects all rows where the first index level is 'B', returning rows with second level 1 and 2 and their values.

3. Final Answer:

   Value 1 30 2 40 -> Option A

4. Quick Check:

   xs('B', level=0) returns rows with first index 'B' [OK]

Hint: xs() filters rows by label at specified level [OK]

Common Mistakes:

• Expecting sum instead of filtered rows
• Confusing index levels
• Mistaking output format

4. Consider this code snippet:

import pandas as pd
index = pd.MultiIndex.from_tuples([('X', 'a'), ('X', 'b'), ('Y', 'a')])
data = {'Score': [5, 10, 15]}
df = pd.DataFrame(data, index=index)
result = df.xs('a')
print(result)

What is the issue with this code?

medium

A. It works fine and prints the rows with second level 'a'.

B. It returns all rows because xs() without level selects all.

C. It returns only the first row incorrectly.

D. It raises a KeyError because level is not specified in xs().

Solution

1. Step 1: Check the multi-index levels

   The DataFrame has two index levels: first level 'X' or 'Y', second level 'a' or 'b'.

2. Step 2: Understand xs() without level

   Calling xs('a') without specifying level causes pandas to look for 'a' in the outermost level, which does not exist, raising a KeyError.

3. Final Answer:

   It raises a KeyError because level is not specified in xs(). -> Option D

4. Quick Check:

   xs() needs level for multi-index [OK]

Hint: Always specify level in xs() for multi-index to avoid errors [OK]

Common Mistakes:

• Assuming xs() guesses the level automatically
• Expecting xs() to return partial matches
• Ignoring the need for level parameter

5. You have a DataFrame df with a three-level index: ['Country', 'Year', 'City']. You want to select all data for the year 2020 regardless of country or city. Which xs() call will achieve this?

hard

A. df.xs('Year', level=2020)

B. df.xs(2020, level='Year')

C. df.xs(2020, level='City')

D. df.xs('2020', level='Year')

Solution

1. Step 1: Identify the index level for selection

   The index levels are 'Country', 'Year', and 'City'. We want to select by the 'Year' level with value 2020.

2. Step 2: Use xs() with correct key and level

   Use df.xs(2020, level='Year') to select all rows where 'Year' is 2020. Other options have wrong argument order, wrong level, or wrong data type.

3. Final Answer:

   df.xs(2020, level='Year') -> Option B

4. Quick Check:

   xs(value, level='Year') selects year 2020 [OK]

Hint: Use xs(value, level='LevelName') to select multi-index slices [OK]

Common Mistakes:

• Swapping level and key arguments
• Using string '2020' instead of integer 2020
• Selecting wrong index level