sort_values() by single column in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of sorting DataFrame by one column ascending

What is the output of this code snippet that sorts the DataFrame by column 'Age' in ascending order?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 20]
})
sorted_df = df.sort_values(by='Age')
print(sorted_df)

A

     Name  Age
2  Charlie   20
0    Alice   25
1      Bob   30

B

     Name  Age
0    Alice   25
1      Bob   30
2  Charlie   20

C

     Name  Age
1      Bob   30
0    Alice   25
2  Charlie   20

D

     Name  Age
2  Charlie   20
1      Bob   30
0    Alice   25

💡 Hint

Remember that sort_values(by='Age') sorts the rows by the 'Age' column in ascending order by default.

The DataFrame is sorted by the 'Age' column from smallest to largest. Charlie (20) comes first, then Alice (25), then Bob (30). The original index is preserved.

❓ data_output

intermediate

Number of rows after sorting by a single column

After sorting this DataFrame by the 'Score' column, how many rows does the resulting DataFrame have?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Student': ['Ann', 'Ben', 'Cara', 'Dan'],
    'Score': [88, 92, 85, 90]
})
sorted_df = df.sort_values(by='Score')
print(len(sorted_df))

A 0

B 3

C 4

D 1

💡 Hint

Sorting does not remove or add rows; it only changes their order.

Sorting a DataFrame by a column does not change the number of rows. The original 4 rows remain.

🔧 Debug

advanced

Identify the error in sorting by a single column

What error does this code produce when trying to sort the DataFrame by column 'Height'?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Eva', 'Frank'],
    'Weight': [55, 70]
})
sorted_df = df.sort_values(by='Height')

A ValueError: Cannot sort by multiple columns

B No error, returns sorted DataFrame

C TypeError: unsupported operand type(s) for <

D KeyError: 'Height'

💡 Hint

Check if the column name exists in the DataFrame before sorting.

The DataFrame does not have a 'Height' column, so pandas raises a KeyError when trying to sort by it.

🧠 Conceptual

advanced

Effect of inplace=True in sort_values()

What is the effect of using inplace=True in the sort_values() method on a DataFrame?

A The method raises an error because inplace is not a valid argument.

B The original DataFrame is sorted and updated without creating a new DataFrame.

C The DataFrame is sorted but the changes are lost after the method call.

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

💡 Hint

inplace=True means the operation modifies the original object directly.

Using inplace=True sorts the original DataFrame in place and returns None, so no new DataFrame is created.

🚀 Application

expert

Sorting and resetting index in a DataFrame

Given this DataFrame, which option correctly sorts by 'Salary' descending and resets the index to start from 0?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Employee': ['John', 'Jane', 'Doe'],
    'Salary': [50000, 60000, 55000]
})

A df.sort_values(by='Salary', ascending=False).reset_index(drop=True)

B df.sort_values(by='Salary', ascending=True).reset_index()

C df.reset_index().sort_values(by='Salary', ascending=False)

D df.sort_values('Salary', ascending=False, inplace=True).reset_index(drop=True)

💡 Hint

Remember to sort descending and reset index without keeping old index as a column.

Option A sorts by Salary descending and resets the index dropping the old one, producing a clean DataFrame with index 0,1,2.