sort_index() for index sorting in Pandas - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the sort_index() function do in pandas?

The sort_index() function sorts a DataFrame or Series by its index labels in ascending or descending order.

beginner

How do you sort a DataFrame by its index in descending order using sort_index()?

Use df.sort_index(ascending=False) to sort the DataFrame's index from highest to lowest.

intermediate

What parameter in sort_index() controls whether the sorting is done in place or returns a new object?

The inplace parameter. If inplace=True, the original DataFrame or Series is modified. Otherwise, a sorted copy is returned.

intermediate

Can sort_index() sort by multiple index levels in a MultiIndex DataFrame?

Yes. By default, it sorts by all index levels. You can also specify which level(s) to sort using the level parameter.

beginner

What is the default sorting order of sort_index()?

The default is ascending order (ascending=True).

What does df.sort_index() do by default?

A Sorts the DataFrame by its columns in ascending order

B Removes duplicate index labels

C Sorts the DataFrame by its values in descending order

D Sorts the DataFrame by its index in ascending order

By default, sort_index() sorts the DataFrame by its index labels in ascending order.

How do you sort a DataFrame's index in descending order?

A df.sort_index(ascending=False)

B df.sort_index(descending=True)

C df.sort_values(ascending=False)

D df.sort_index(ascending=True)

Use ascending=False to sort the index in descending order.

Which parameter lets you sort a MultiIndex DataFrame by a specific level?

A by

B level

C axis

D subset

The level parameter specifies which index level(s) to sort.

What happens if you set inplace=True in sort_index()?

A Sorts the DataFrame and modifies it directly

B Returns a new sorted DataFrame

C Raises an error

D Sorts columns instead of index

Setting inplace=True modifies the original DataFrame without returning a new one.

Which of these is NOT a valid use of sort_index()?

A Sorting a Series by its index

B Sorting a DataFrame by its index

C Sorting a DataFrame by column values

D Sorting a MultiIndex DataFrame by a specific level

sort_index() sorts by index, not by column values. Use sort_values() for column sorting.

Explain how to use sort_index() to sort a pandas DataFrame by its index in descending order and modify the original DataFrame.

Remember the parameters ascending and inplace.

Describe how sort_index() works with MultiIndex DataFrames and how to sort by a specific index level.

Think about the level parameter and default behavior.

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