sort_index() for index sorting in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to sort the DataFrame by its index in ascending order.

Pandas

df_sorted = df.[1]()

Drag options to blanks, or click blank then click option'

Asort_index

Bsort_values

Csort

Dorder

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort_values() instead of sort_index()

Using sort() which is deprecated

Trying to use order() which is not a pandas method

The sort_index() method sorts the DataFrame by its index labels.

2fill in blank

medium

Complete the code to sort the DataFrame index in descending order.

Pandas

df_sorted = df.sort_index([1]=False)

Drag options to blanks, or click blank then click option'

Aascending

Bdescending

Creverse

Dorder

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'descending' parameter which does not exist

Using 'reverse' parameter which is invalid

Confusing parameter names

The ascending parameter controls the sort order. Setting it to True sorts ascending, False sorts descending.

3fill in blank

hard

Fix the error in the code to sort the DataFrame index in descending order.

Pandas

df_sorted = df.sort_index(ascending=[1])

Drag options to blanks, or click blank then click option'

A'False'

BTrue

CFalse

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Passing 'False' as a string instead of boolean

Passing 0 which is interpreted differently

Using True which sorts ascending

To sort descending, set ascending=False as a boolean, not as a string or number.

4fill in blank

hard

Fill both blanks to sort the DataFrame index in descending order and reset the index.

Pandas

df_sorted = df.sort_index([1]=False).[2](drop=True)

Drag options to blanks, or click blank then click option'

Aascending

Breset_index

Csort_values

Dsort_index

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort_values instead of reset_index

Not setting drop=True when resetting index

Using ascending=True which sorts ascending

First, sort the index descending by setting ascending=False. Then reset the index with reset_index(drop=True) to get a clean integer index.

5fill in blank

hard

Fill both blanks to sort the DataFrame index in ascending order, keep the index, and select only rows where index is greater than 2.

Pandas

df_filtered = df.sort_index([1]=True).loc[[2] > 2, :]

Drag options to blanks, or click blank then click option'

Aascending

Bdf.index

C:

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'index' instead of 'df.index' for filtering

Using False for ascending which sorts descending

Not selecting columns properly

Sort ascending by setting ascending=True. Use df.index to filter rows where index > 2. Use : to select all columns.

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