sort_index() for index sorting in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

What is the output of sorting a DataFrame by index with ascending=False?

Given the DataFrame df with index [3, 1, 2] and values ["a", "b", "c"], what is the result of df.sort_index(ascending=False)?

Pandas

import pandas as pd
df = pd.DataFrame({'value': ['a', 'b', 'c']}, index=[3, 1, 2])
result = df.sort_index(ascending=False)

A {3: 'a', 2: 'c', 1: 'b'}

B {1: 'b', 2: 'c', 3: 'a'}

C {2: 'c', 3: 'a', 1: 'b'}

D {1: 'b', 3: 'a', 2: 'c'}

💡 Hint

Remember that ascending=False sorts from highest index to lowest.

The sort_index method sorts the DataFrame by its index. With ascending=False, the order is from the largest index to the smallest, so indices 3, 2, then 1.

📝 Syntax

intermediate

Which option correctly sorts a DataFrame by its index in place?

You want to sort the DataFrame df by its index and modify it directly without creating a new object. Which code does this correctly?

Pandas

import pandas as pd
df = pd.DataFrame({'val': [10, 20, 30]}, index=[2, 0, 1])

A df.sort_index(inplace=False)

B df.sort_index(inplace=True)

C df.sort_index(inplace=1)

D df.sort_index(in_place=True)

💡 Hint

Check the exact parameter name and its boolean value.

The parameter to sort_index that modifies the DataFrame in place is 'inplace=True'. Other options either use wrong parameter names or values.

❓ optimization

advanced

How to efficiently sort a MultiIndex DataFrame by the second level index?

Given a DataFrame df with a MultiIndex of two levels, which code sorts the DataFrame by the second level of the index only?

Pandas

import pandas as pd
index = pd.MultiIndex.from_tuples([(1, 'b'), (2, 'a'), (1, 'a')], names=['num', 'char'])
df = pd.DataFrame({'val': [10, 20, 30]}, index=index)

A df.sort_index(level=1)

B df.sort_index(level=0)

C df.sort_index(axis=1, level=1)

D df.sort_index(by=1)

💡 Hint

The 'level' parameter specifies which index level to sort by.

Using level=1 sorts by the second level of the MultiIndex. Option A sorts by the first level. Option A uses a non-existent parameter 'by'. Option A sorts columns, not index.

🔧 Debug

advanced

Why does this sort_index call raise an error?

Consider the code: df.sort_index(axis=2). Why does this raise an error?

Pandas

import pandas as pd
df = pd.DataFrame({'a': [1, 2], 'b': [3, 4]})
df.sort_index(axis=2)

A The DataFrame columns are not sorted alphabetically by default.

B The DataFrame has no index to sort.

C sort_index requires a 'level' parameter for sorting.

D Axis 2 does not exist for a DataFrame; valid axes are 0 (index) and 1 (columns).

💡 Hint

Check the valid axis values for DataFrame methods.

DataFrames have only two axes: 0 for rows (index) and 1 for columns. Axis=2 is invalid and causes an error.

🧠 Conceptual

expert

What happens when you sort a DataFrame with a non-unique index using sort_index()?

If a DataFrame has duplicate index values, what is the behavior of df.sort_index()?

A It removes duplicate index rows before sorting.

B It raises an error because index values must be unique to sort.

C It sorts all rows by index values, keeping duplicates in order as they appear.

D It sorts only the unique index values and drops duplicates.

💡 Hint

Think about whether sorting requires unique index values.

sort_index sorts rows by their index values regardless of duplicates. It does not raise errors or remove duplicates.

Practice

1. What does the sort_index() function do in pandas?

easy

A. It filters rows based on a condition.

B. It sorts the DataFrame by its values.

C. It sorts the DataFrame by its index labels.

D. It resets the index to default integers.

Solution

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

   This function organizes the DataFrame by sorting its row or column labels (index), not the data values.

2. Step 2: Compare options

   Only It sorts the DataFrame by its index labels. correctly describes sorting by index labels. Options B, C, and D describe other functions.

3. Final Answer:

   It sorts the DataFrame by its index labels. -> Option C

4. Quick Check:

   sort_index() = sort by index [OK]

Hint: Remember: sort_index() sorts labels, not data values [OK]

Common Mistakes:

• Confusing sort_index() with sort_values()
• Thinking it filters rows
• Assuming it resets the index

2. Which of the following is the correct syntax to sort a DataFrame df by its index in descending order?

easy

A. df.sort_index(order='desc')

B. df.sort_index(ascending=False)

C. df.sort_index(descending=True)

D. df.sort_index(reverse=True)

Solution

1. Step 1: Recall the parameter for sorting order

   The correct parameter is ascending, which accepts True or False.

2. Step 2: Check each option

   Only df.sort_index(ascending=False) uses ascending=False correctly. Options A, B, and D use invalid parameters.

3. Final Answer:

   df.sort_index(ascending=False) -> Option B

4. Quick Check:

   Use ascending=False for descending sort [OK]

Hint: Use ascending=False to sort index descending [OK]

Common Mistakes:

• Using invalid parameter names like descending or reverse
• Confusing ascending with descending
• Missing parentheses after sort_index

3. Given the DataFrame df with index labels [3, 1, 2] and values ["a", "b", "c"], what will df.sort_index() return as the index?

medium

A. [1, 2, 3]

B. [3, 2, 1]

C. ["a", "b", "c"]

D. [2, 3, 1]

Solution

1. Step 1: Understand the initial index

   The DataFrame index is [3, 1, 2], unordered.

2. Step 2: Apply sort_index()

   This sorts the index labels in ascending order: [1, 2, 3].

3. Final Answer:

   [1, 2, 3] -> Option A

4. Quick Check:

   sort_index() sorts index ascending by default [OK]

Hint: sort_index() sorts index ascending by default [OK]

Common Mistakes:

• Confusing index labels with values
• Expecting descending order without specifying
• Thinking values get sorted instead of index

4. What is wrong with this code snippet to sort the columns of DataFrame df by their labels?

df.sort_index(axis=1, inplace=False)

medium

A. It does not modify df because inplace=False is used.

B. The axis parameter should be 0 to sort columns.

C. sort_index() cannot sort columns, only rows.

D. inplace must be True to return a sorted DataFrame.

Solution

1. Step 1: Understand inplace parameter

   With inplace=False, the original DataFrame df is not changed; a sorted copy is returned.

2. Step 2: Check axis parameter

   axis=1 correctly specifies sorting columns, so no error there.

3. Step 3: Identify the issue

   Since the returned sorted DataFrame is not assigned, df remains unsorted.

4. Final Answer:

   It does not modify df because inplace=False is used. -> Option A

5. Quick Check:

   inplace=False returns new DataFrame, original unchanged [OK]

Hint: Use inplace=True or assign result to keep changes [OK]

Common Mistakes:

• Assuming inplace=False modifies original DataFrame
• Confusing axis=0 and axis=1 for columns
• Thinking sort_index() can't sort columns

5. You have a DataFrame df with a MultiIndex on rows. How can you sort the DataFrame by the second level of the index using sort_index()?

hard

A. df.sort_index(index=1)

B. df.sort_index(axis=1, level=1)

C. df.sort_index(by=1)

D. df.sort_index(level=1)

Solution

1. Step 1: Understand MultiIndex sorting

   To sort by a specific level in a MultiIndex, use the level parameter with the level number.

2. Step 2: Check each option

   df.sort_index(level=1) correctly uses level=1 to sort by the second index level. Options A, B, and C use invalid parameters.

3. Final Answer:

   df.sort_index(level=1) -> Option D

4. Quick Check:

   Use level parameter to sort MultiIndex levels [OK]

Hint: Use level=1 to sort by second index level [OK]

Common Mistakes:

• Using axis=1 to sort rows
• Using invalid parameters like by or index
• Not specifying level for MultiIndex sorting