Sorting by index 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](ascending=True)

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

Asort_values

Bsort_index

Corder_by

Darrange

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort_values() instead of sort_index()

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

The sort_index() method sorts the DataFrame by its index. Other options either sort by values or do not exist in pandas.

2fill in blank

medium

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

Pandas

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

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

Adescending

Breverse

Cascending

Dorder

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'descending' as a parameter name which does not exist

Using 'reverse' which is not a valid parameter here

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

3fill in blank

hard

Fix the error in the code to sort the DataFrame by index in place.

Pandas

df.sort_index(inplace=[1])

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

ATrue

B"True"

C1

Dyes

Attempts:

3 left

💡 Hint

Common Mistakes

Passing 'True' as a string instead of boolean True

Using 1 or 'yes' which are not valid boolean values here

The inplace parameter expects a boolean value True or False, not a string or other types.

4fill in blank

hard

Fill both blanks to sort the DataFrame by index in descending order and keep the result in a new variable.

Pandas

df_sorted = df.[1](ascending=[2])

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

Asort_index

Bsort_values

CFalse

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort_values instead of sort_index

Setting ascending to True which sorts ascending

Use sort_index to sort by index, and set ascending=False for descending order.

5fill in blank

hard

Fill all three blanks to sort the DataFrame by index in ascending order, modify the original DataFrame, and return None.

Pandas

result = df.[1](ascending=[2], inplace=[3])

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

Asort_index

BTrue

Dsort_values

Attempts:

3 left

💡 Hint

Common Mistakes

Using sort_values instead of sort_index

Setting inplace to False which returns a new DataFrame

Passing strings instead of booleans for parameters

sort_index sorts by index, ascending=True sorts ascending, and inplace=True modifies the original DataFrame returning None.

Practice

1. What does sorting by index in pandas do?

easy

A. It changes the data values to alphabetical order.

B. It filters rows based on a condition.

C. It removes duplicate rows from the DataFrame.

D. It arranges data rows or columns based on their labels.

Solution

1. Step 1: Understand the meaning of sorting by index

   Sorting by index means arranging the data based on the row or column labels, not the data values.

2. Step 2: Compare options with the definition

   Only It arranges data rows or columns based on their labels. correctly describes sorting by index as arranging rows or columns by their labels.

3. Final Answer:

   It arranges data rows or columns based on their labels. -> Option D

4. Quick Check:

   Sorting by index = arrange by labels [OK]

Hint: Sorting by index means ordering by labels, not values [OK]

Common Mistakes:

• Confusing sorting by index with sorting by values
• Thinking it filters or removes data
• Assuming it changes data values

2. Which of the following is the correct syntax to sort a DataFrame df by its row index in descending order without changing the original DataFrame?

easy

A. df.sort_index(ascending=False, inplace=False)

B. df.sort_index(axis=1, ascending=False)

C. df.sort_index(ascending=True, inplace=False)

D. df.sort_index(ascending=False, inplace=True)

Solution

1. Step 1: Identify parameters for sorting by index

   To sort by row index descending, use ascending=False. To avoid changing original, use inplace=False.

2. Step 2: Check each option

   df.sort_index(ascending=False, inplace=False) uses ascending=False and inplace=False, which matches the requirement. df.sort_index(ascending=False, inplace=True) changes original (inplace=True). df.sort_index(ascending=True, inplace=False) sorts ascending. df.sort_index(axis=1, ascending=False) sorts columns (axis=1).

3. Final Answer:

   df.sort_index(ascending=False, inplace=False) -> Option A

4. Quick Check:

   Descending + no inplace = ascending=False, inplace=False [OK]

Hint: Use inplace=False to keep original unchanged [OK]

Common Mistakes:

• Using inplace=True when original should not change
• Confusing ascending=True with descending order
• Sorting columns instead of rows by mistake

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 order?

medium

A. [a, b, c]

B. [1, 2, 3]

C. [3, 2, 1]

D. [c, b, a]

Solution

1. Step 1: Understand default sort_index behavior

   By default, sort_index sorts row labels in ascending order.

2. Step 2: Apply sorting to index [3, 1, 2]

   Sorting ascending gives [1, 2, 3]. Values remain associated with their labels.

3. Final Answer:

   [1, 2, 3] -> Option B

4. Quick Check:

   Default sort_index sorts index ascending [OK]

Hint: Default sort_index sorts index ascending [OK]

Common Mistakes:

• Confusing index labels with values
• Assuming sort_index sorts values instead of index
• Thinking sort_index reverses order by default

4. You run df.sort_index(axis=2) on a DataFrame but get an error. What is the likely cause?

medium

A. axis=2 is invalid; axis must be 0 or 1.

B. sort_index does not accept axis parameter.

C. DataFrame index labels are missing.

D. inplace parameter is required.

Solution

1. Step 1: Check valid axis values for sort_index

   sort_index accepts axis=0 (rows) or axis=1 (columns) only.

2. Step 2: Identify error cause with axis=2

   axis=2 is invalid and causes an error.

3. Final Answer:

   axis=2 is invalid; axis must be 0 or 1. -> Option A

4. Quick Check:

   axis must be 0 or 1 in sort_index [OK]

Hint: axis can only be 0 (rows) or 1 (columns) [OK]

Common Mistakes:

• Using axis values other than 0 or 1
• Assuming sort_index ignores invalid axis
• Forgetting axis parameter is optional but must be valid

5. You have a DataFrame df with a multi-level index (two levels). How do you sort df by the second level of the index in ascending order without changing the original DataFrame?

hard

A. df.sort_values(level=1, ascending=True)

B. df.sort_index(level=0, ascending=True, inplace=True)

C. df.sort_index(level=1, ascending=True, inplace=False)

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

Solution

1. Step 1: Understand sorting multi-level index

   Use sort_index with level parameter to specify which index level to sort.

2. Step 2: Choose correct parameters

   To sort by second level (level=1), ascending order, and keep original unchanged, use inplace=False.

3. Final Answer:

   df.sort_index(level=1, ascending=True, inplace=False) -> Option C

4. Quick Check:

   Sort multi-level index by level=1, inplace=False [OK]

Hint: Use level parameter to sort multi-level index [OK]

Common Mistakes:

• Using sort_values instead of sort_index for index sorting
• Sorting wrong index level (level=0 instead of 1)
• Using inplace=True when original should not change