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.

Practice

1. What does the sort_values() function do in pandas when used on a DataFrame?

easy

A. It filters rows based on a condition.

B. It changes the data type of a column.

C. It sorts the DataFrame rows based on the values in a specified column.

D. It adds a new column to the DataFrame.

Solution

1. Step 1: Understand the purpose of sort_values()

   This function is used to reorder rows in a DataFrame based on the values in one or more columns.

2. Step 2: Compare with other options

   Changing data types, filtering rows, or adding columns are done by different functions, not sort_values().

3. Final Answer:

   It sorts the DataFrame rows based on the values in a specified column. -> Option C

4. Quick Check:

   sort_values() = reorder rows by column [OK]

Hint: Remember: sort_values() orders rows by column values [OK]

Common Mistakes:

• Confusing sorting with filtering
• Thinking it changes data types
• Assuming it adds columns

2. Which of the following is the correct syntax to sort a DataFrame df by the column 'age' in ascending order?

easy

A. df.sort_values(by='age', ascending=True)

B. df.sort_values('age', ascending=False)

C. df.sort(by='age', ascending=True)

D. df.sort_values(by='age', ascending='yes')

Solution

1. Step 1: Check the correct function and parameters

   The function is sort_values() and it requires the parameter by='column_name'. The ascending parameter should be a boolean.

2. Step 2: Validate each option

   df.sort_values(by='age', ascending=True) uses correct function and parameters. df.sort_values('age', ascending=False) has ascending=False (descending). df.sort(by='age', ascending=True) uses wrong function sort(). df.sort_values(by='age', ascending='yes') uses a string instead of boolean for ascending.

3. Final Answer:

   df.sort_values(by='age', ascending=True) -> Option A

4. Quick Check:

   Correct syntax = df.sort_values(by='age', ascending=True) [OK]

Hint: Use by='column' and ascending=True/False in sort_values() [OK]

Common Mistakes:

• Using sort() instead of sort_values()
• Passing ascending as string instead of boolean
• Omitting 'by' keyword

3. Given the DataFrame:

import pandas as pd
df = pd.DataFrame({'name': ['Anna', 'Bob', 'Cara'], 'score': [88, 92, 85]})
sorted_df = df.sort_values(by='score')

What is the order of names in sorted_df?

medium

A. ['Cara', 'Anna', 'Bob']

B. ['Bob', 'Anna', 'Cara']

C. ['Anna', 'Bob', 'Cara']

D. ['Cara', 'Bob', 'Anna']

Solution

1. Step 1: Understand sorting by 'score'

   The scores are 88 (Anna), 92 (Bob), and 85 (Cara). Sorting ascending means smallest to largest.

2. Step 2: Order names by ascending scores

   Sorted scores: 85 (Cara), 88 (Anna), 92 (Bob). So names order is ['Cara', 'Anna', 'Bob'].

3. Final Answer:

   ['Cara', 'Anna', 'Bob'] -> Option A

4. Quick Check:

   Ascending sort by score = ['Cara', 'Anna', 'Bob'] [OK]

Hint: Sort ascending means smallest value first [OK]

Common Mistakes:

• Assuming descending order by default
• Mixing up names and scores
• Not using 'by' parameter

4. What is wrong with this code snippet?

df = pd.DataFrame({'A': [3, 1, 2]})
df.sort_values('A', ascending='False')

medium

A. The column name should be passed as a list.

B. The ascending parameter should be a boolean, not a string.

C. sort_values() does not accept the ascending parameter.

D. The DataFrame must be sorted inplace.

Solution

1. Step 1: Check the ascending parameter type

   The ascending parameter expects a boolean (True or False), but here it is passed as a string 'False'.

2. Step 2: Validate other options

   Passing column name as string is allowed. sort_values() accepts ascending. Sorting inplace is optional.

3. Final Answer:

   The ascending parameter should be a boolean, not a string. -> Option B

4. Quick Check:

   ascending must be True/False boolean [OK]

Hint: Use True/False, not 'True'/'False' for ascending [OK]

Common Mistakes:

• Passing ascending as string
• Thinking column name must be list
• Forcing inplace sorting unnecessarily

5. You have a DataFrame df with columns 'product' and 'price'. You want to sort the products by price descending but keep the original DataFrame unchanged. Which code achieves this?

hard

A. df.sort_values(by='price', ascending=False, inplace=True)

B. df.sort_values('price', inplace=False)

C. df.sort_values(by='price', ascending=True)

D. sorted_df = df.sort_values(by='price', ascending=False)

Solution

1. Step 1: Understand sorting without changing original

   To keep original DataFrame unchanged, do not use inplace=True. Instead, assign the sorted result to a new variable.

2. Step 2: Check options for descending order

   sorted_df = df.sort_values(by='price', ascending=False) sorts by 'price' descending and assigns to sorted_df. df.sort_values(by='price', ascending=False, inplace=True) changes original due to inplace=True. df.sort_values(by='price', ascending=True) sorts ascending, not descending. df.sort_values('price', inplace=False) sorts ascending by default and does not assign result.

3. Final Answer:

   sorted_df = df.sort_values(by='price', ascending=False) -> Option D

4. Quick Check:

   Assign sorted result without inplace for original unchanged [OK]

Hint: Assign sorted DataFrame to new variable, avoid inplace=True [OK]

Common Mistakes:

• Using inplace=True and losing original order
• Sorting ascending instead of descending
• Not assigning sorted DataFrame to a variable