Ascending and descending order in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Sorting a DataFrame by one column ascending

What is the output of this code snippet sorting the DataFrame by column 'A' in ascending order?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [3, 1, 2], 'B': [9, 8, 7]})
sorted_df = df.sort_values(by='A', ascending=True)
print(sorted_df)

A

   A  B
2  2  7
1  1  8
0  3  9

B

   A  B
1  1  8
2  2  7
0  3  9

C

   A  B
0  3  9
2  2  7
1  1  8

D

   A  B
0  3  9
1  1  8
2  2  7

💡 Hint

Remember ascending=True means smallest to largest values.

The DataFrame is sorted by column 'A' from smallest to largest. The index order changes accordingly.

❓ Predict Output

intermediate

Sorting a DataFrame by multiple columns with mixed order

What is the output of this code sorting by column 'A' ascending and 'B' descending?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [1, 1, 2, 2], 'B': [3, 4, 1, 2]})
sorted_df = df.sort_values(by=['A', 'B'], ascending=[True, False])
print(sorted_df)

A

   A  B
2  2  1
3  2  2
0  1  3
1  1  4

B

   A  B
0  1  3
1  1  4
2  2  1
3  2  2

C

   A  B
3  2  2
2  2  1
1  1  4
0  1  3

D

   A  B
1  1  4
0  1  3
3  2  2
2  2  1

💡 Hint

First sort by 'A' ascending, then by 'B' descending within each 'A' group.

Rows with 'A' = 1 come first, sorted by 'B' descending (4 then 3). Then rows with 'A' = 2 sorted by 'B' descending (2 then 1).

❓ data_output

advanced

Number of rows after sorting by descending order

After sorting this DataFrame by column 'score' descending, how many rows remain in the result?

Pandas

import pandas as pd

df = pd.DataFrame({'name': ['Ann', 'Bob', 'Cara'], 'score': [88, 92, 85]})
sorted_df = df.sort_values(by='score', ascending=False)
print(len(sorted_df))

A 3

B 2

C 0

D 1

💡 Hint

Sorting does not remove rows, it only changes order.

Sorting keeps all rows, so the length remains the same as original DataFrame.

🔧 Debug

advanced

Identify the error in sorting code

What error does this code raise when trying to sort a DataFrame?

Pandas

import pandas as pd

df = pd.DataFrame({'X': [1, 2], 'Y': [3, 4]})
sorted_df = df.sort_values(by='Z')

A KeyError: 'Z'

B TypeError: unsupported operand type(s)

C ValueError: axis must be 0 or 1

D AttributeError: 'DataFrame' object has no attribute 'sort_values'

💡 Hint

Check if the column name exists in the DataFrame.

The column 'Z' does not exist in the DataFrame, so pandas raises a KeyError.

🚀 Application

expert

Sorting and resetting index in a DataFrame

After sorting the DataFrame by column 'age' descending, which option correctly resets the index to default integers?

Pandas

import pandas as pd

df = pd.DataFrame({'name': ['Tom', 'Jerry', 'Spike'], 'age': [5, 3, 7]})
sorted_df = df.sort_values(by='age', ascending=False)

A

sorted_df.reset_index(drop=True)
print(sorted_df)

B

sorted_df.reset_index(drop=False, inplace=True)
print(sorted_df)

C

sorted_df.reset_index(drop=True, inplace=True)
print(sorted_df)

D

sorted_df.reset_index(inplace=False)
print(sorted_df)

💡 Hint

To remove old index and update in place, use drop=True and inplace=True.

Option C resets the index in place and drops the old index, so the DataFrame has a clean integer index.