Sorting by index in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ query_result

intermediate

Sorting a DataFrame by index ascending

Given the DataFrame below, what is the output after sorting by index in ascending order?

import pandas as pd

df = pd.DataFrame({
  'A': [3, 1, 2],
  'B': [9, 8, 7]
}, index=['c', 'a', 'b'])

sorted_df = df.sort_index()

Pandas

import pandas as pd

df = pd.DataFrame({
  'A': [3, 1, 2],
  'B': [9, 8, 7]
}, index=['c', 'a', 'b'])

sorted_df = df.sort_index()
print(sorted_df)

A

   A  B
a  1  8
b  2  7
c  3  9

B

   A  B
c  3  9
b  2  7
a  1  8

C

   A  B
b  2  7
a  1  8
c  3  9

D

   A  B
a  3  9
b  1  8
c  2  7

💡 Hint

Sorting by index with default parameters sorts in ascending order alphabetically.

The DataFrame is sorted by its index labels alphabetically: 'a', 'b', then 'c'.

❓ query_result

intermediate

Sorting a DataFrame by index descending

What is the output of sorting the following DataFrame by index in descending order?

import pandas as pd

df = pd.DataFrame({
  'X': [10, 20, 30],
  'Y': [100, 200, 300]
}, index=[2, 1, 3])

sorted_df = df.sort_index(ascending=False)
print(sorted_df)

Pandas

import pandas as pd

df = pd.DataFrame({
  'X': [10, 20, 30],
  'Y': [100, 200, 300]
}, index=[2, 1, 3])

sorted_df = df.sort_index(ascending=False)
print(sorted_df)

A

    X    Y
1  20  200
2  10  100
3  30  300

B

    X    Y
3  30  300
2  10  100
1  20  200

C

    X    Y
3  30  300
1  20  200
2  10  100

D

    X    Y
2  10  100
3  30  300
1  20  200

💡 Hint

Descending order means largest index first.

Sorting by index descending orders rows by index 3, 2, then 1.

📝 Syntax

advanced

Identify the syntax error in sorting by index

Which option contains a syntax error when trying to sort a DataFrame by index?

Pandas

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

A df.sort_index(ascending=True)

B df.sort_index()

C df.sort_index(ascending=False)

D df.sort_index(ascending==False)

💡 Hint

Check the use of assignment vs comparison operators in function arguments.

Using 'ascending==False' is a comparison expression, not a keyword argument assignment, causing a syntax error.

❓ query_result

advanced

Sorting a MultiIndex DataFrame by level

Given the MultiIndex DataFrame below, what is the output after sorting by the second level of the index?

import pandas as pd

index = pd.MultiIndex.from_tuples([('a', 2), ('a', 1), ('b', 1), ('b', 2)])
df = pd.DataFrame({'val': [10, 20, 30, 40]}, index=index)
sorted_df = df.sort_index(level=1)
print(sorted_df)

Pandas

import pandas as pd

index = pd.MultiIndex.from_tuples([('a', 2), ('a', 1), ('b', 1), ('b', 2)])
df = pd.DataFrame({'val': [10, 20, 30, 40]}, index=index)
sorted_df = df.sort_index(level=1)
print(sorted_df)

A

       val
(a, 1) 20
(b, 1) 30
(a, 2) 10
(b, 2) 40

B

       val
(a, 2) 10
(a, 1) 20
(b, 1) 30
(b, 2) 40

C

       val
(b, 1) 30
(a, 1) 20
(a, 2) 10
(b, 2) 40

D

       val
(a, 1) 20
(a, 2) 10
(b, 1) 30
(b, 2) 40

💡 Hint

Sorting by level=1 sorts by the second index value across all first-level groups.

Rows are ordered by the second index level: 1, 1, 2, 2, preserving first level order within ties.

🧠 Conceptual

expert

Effect of inplace parameter in sort_index

What is the effect of using inplace=True when calling df.sort_index() on a DataFrame?

A The DataFrame is sorted by index but the changes are only temporary and lost after the next operation.

B A new sorted DataFrame is returned and the original DataFrame remains unchanged.

C The DataFrame is sorted by index and the original DataFrame is modified directly without returning a new DataFrame.

D The DataFrame is sorted by index and a copy is returned, but the original DataFrame is also modified.

💡 Hint

Consider what inplace means in pandas methods.

Using inplace=True modifies the original DataFrame directly and returns None.