sort_values() by multiple columns in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Sorting a DataFrame by two columns

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Anna', 'Bob', 'Charlie', 'Bob', 'Anna'],
    'Age': [25, 30, 35, 22, 25],
    'Score': [88, 92, 85, 95, 90]
})

sorted_df = df.sort_values(by=['Name', 'Age'])
print(sorted_df.reset_index(drop=True))

A

    Name  Age  Score
0   Anna   25     88
1   Anna   25     90
2    Bob   30     92
3    Bob   22     95
4 Charlie   35     85

B

    Name  Age  Score
0   Anna   25     90
1   Anna   25     88
2    Bob   22     95
3    Bob   30     92
4 Charlie   35     85

C

    Name  Age  Score
0   Anna   25     90
1   Anna   25     88
2    Bob   30     92
3    Bob   22     95
4 Charlie   35     85

D

    Name  Age  Score
0   Anna   25     88
1   Anna   25     90
2    Bob   22     95
3    Bob   30     92
4 Charlie   35     85

💡 Hint

Remember that sort_values sorts first by the first column, then by the second within groups.

The DataFrame is sorted first by 'Name' alphabetically, then by 'Age' ascending within each 'Name'. Both 'Anna' rows have Age 25, so they appear first, followed by 'Bob' rows sorted by Age 22 then 30, then 'Charlie'.

❓ data_output

intermediate

Number of rows after sorting with duplicates

After sorting the DataFrame below by 'Category' and 'Value', how many rows does the sorted DataFrame contain?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Category': ['A', 'B', 'A', 'C', 'B', 'A'],
    'Value': [10, 20, 10, 30, 20, 15]
})

sorted_df = df.sort_values(by=['Category', 'Value'])
print(len(sorted_df))

A 6

B 5

C 4

D 7

💡 Hint

Sorting does not remove rows, it only changes their order.

Sorting the DataFrame does not change the number of rows. The original DataFrame has 6 rows, so the sorted DataFrame also has 6 rows.

🔧 Debug

advanced

Identify the error in sorting by multiple columns

What error will this code raise?

Pandas

import pandas as pd

df = pd.DataFrame({
    'X': [1, 2, 3],
    'Y': [4, 5, 6]
})

sorted_df = df.sort_values(by='X', 'Y')

A SyntaxError: positional argument follows keyword argument

B No error, sorts by 'X' then 'Y'

C ValueError: 'by' must be a string or list of strings

D TypeError: sort_values() got multiple values for argument 'by'

💡 Hint

Check the function call syntax and argument order.

The code passes 'by' as a keyword argument and then another positional argument 'Y'. Python does not allow positional arguments after keyword arguments, causing a SyntaxError.

🚀 Application

advanced

Sorting with different ascending orders

You want to sort a DataFrame by 'City' ascending and 'Temperature' descending. Which code achieves this?

Pandas

import pandas as pd

df = pd.DataFrame({
    'City': ['Paris', 'Paris', 'London', 'London'],
    'Temperature': [20, 25, 15, 10]
})

A df.sort_values(by=['City', 'Temperature'], ascending=[False, True])

B df.sort_values(by=['City', 'Temperature'], ascending=[True, False])

C df.sort_values(by=['City', 'Temperature'], ascending=True)

D df.sort_values(by=['City', 'Temperature'], ascending=False)

💡 Hint

The ascending parameter can be a list matching the columns.

To sort 'City' ascending and 'Temperature' descending, pass ascending=[True, False]. Other options either reverse the order or apply the same order to both columns.

🧠 Conceptual

expert

Effect of inplace parameter in sort_values()

What is the effect of using inplace=true in the sort_values() method when sorting by multiple columns?

A The method raises an error because inplace=true is not supported with multiple columns.

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

C The original DataFrame is sorted and changed directly; the method returns null.

D The method sorts only the first column inplace and ignores others.

💡 Hint

Recall what inplace=true means for pandas methods.

Using inplace=true modifies the original DataFrame directly and returns null. This works regardless of how many columns are used for sorting.

Practice

1.

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

easy

A. It sorts the DataFrame rows based on the values in one or more columns.

B. It filters rows that match a condition.

C. It changes the data types of columns.

D. It adds new columns to the DataFrame.

Solution

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

   This function organizes the rows of a DataFrame by sorting them according to the values in specified columns.

2. Step 2: Compare with other DataFrame operations

   Filtering selects rows, changing data types modifies columns, and adding columns is unrelated to sorting.

3. Final Answer:

   It sorts the DataFrame rows based on the values in one or more columns. -> Option A

4. Quick Check:

   Sorting = Organizing rows by column values [OK]

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

Common Mistakes:

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

2.

Which of the following is the correct syntax to sort a DataFrame df by columns 'A' ascending and 'B' descending?

df.sort_values(??? )

easy

A. columns=['A', 'B'], ascending=[False, True]

B. by=['A', 'B'], ascending=[True, False]

C. by=['A', 'B'], ascending=True

D. sort=['A', 'B'], order=[True, False]

Solution

1. Step 1: Identify correct parameter names

   The method uses by to specify columns and ascending to specify order per column.

2. Step 2: Match order for each column

   To sort 'A' ascending and 'B' descending, use ascending=[True, False].

3. Final Answer:

   by=['A', 'B'], ascending=[True, False] -> Option B

4. Quick Check:

   Correct parameters = by=['A', 'B'], ascending=[True, False] [OK]

Hint: Use by=columns and ascending=list of booleans [OK]

Common Mistakes:

• Using wrong parameter names like columns or sort
• Passing a single boolean for multiple columns
• Mixing ascending order values

3.

Given the DataFrame:

import pandas as pd
df = pd.DataFrame({
  'Name': ['Anna', 'Bob', 'Anna', 'Bob'],
  'Score': [90, 92, 85, 80]
})

What is the result of df.sort_values(by=['Name', 'Score'], ascending=[True, False])?

medium

A. Rows sorted by Name ascending, then Score descending: Anna(90), Anna(85), Bob(92), Bob(80)

B. Rows sorted by Name descending, then Score ascending: Bob(80), Bob(92), Anna(85), Anna(90)

C. Rows sorted by Score ascending only: Bob(80), Anna(85), Anna(90), Bob(92)

D. Rows sorted by Score descending only: Bob(92), Anna(90), Anna(85), Bob(80)

Solution

1. Step 1: Sort by 'Name' ascending

   Names order: Anna, Anna, Bob, Bob.

2. Step 2: Within each Name, sort 'Score' descending

   For Anna: 90 then 85; for Bob: 92 then 80.

3. Final Answer:

   Rows sorted by Name ascending, then Score descending: Anna(90), Anna(85), Bob(92), Bob(80) -> Option A

4. Quick Check:

   Sort by Name ↑, Score ↓ = Rows sorted by Name ascending, then Score descending: Anna(90), Anna(85), Bob(92), Bob(80) [OK]

Hint: Sort first column ascending, second descending as list [OK]

Common Mistakes:

• Mixing ascending and descending order
• Sorting only by one column
• Confusing order of sorting columns

4.

What is wrong with this code snippet?

df.sort_values(by=['A', 'B'], ascending=True)

Assuming df has columns 'A' and 'B'.

medium

A. The method sort_values does not accept the ascending parameter.

B. The by parameter should be a string, not a list.

C. The ascending parameter should be a list when sorting by multiple columns.

D. The code is correct and will run without errors.

Solution

1. Step 1: Recall sort_values() parameters

   by accepts list of columns, ascending accepts bool (applies to all) or list of bools.

2. Step 2: Validate the code

   The code uses ascending=True, which correctly sorts both columns ascending. No errors.

3. Final Answer:

   The code is correct and will run without errors. -> Option D

4. Quick Check:

   ascending=True valid for multi-column sort [OK]

Hint: ascending=True sorts all columns ascending; use list for mixed orders [OK]

Common Mistakes:

• Believing ascending must be a list for multiple columns
• Using string instead of list for 'by' parameter
• Assuming sort_values lacks ascending parameter

5.

You have a DataFrame df with columns 'City', 'Temperature', and 'Humidity'. You want to sort the data so that cities are in alphabetical order, but within each city, rows with higher temperature come first, and if temperatures are equal, rows with lower humidity come first.

Which sort_values() call achieves this?

hard

A. df.sort_values(by=['City', 'Temperature', 'Humidity'], ascending=[False, True, False])

B. df.sort_values(by=['City', 'Humidity', 'Temperature'], ascending=[True, True, False])

C. df.sort_values(by=['City', 'Temperature', 'Humidity'], ascending=[True, False, True])

D. df.sort_values(by=['Temperature', 'City', 'Humidity'], ascending=[False, True, True])

Solution

1. Step 1: Sort 'City' alphabetically ascending

   Set ascending=True for 'City' to get alphabetical order.

2. Step 2: Sort 'Temperature' descending within each city

   Use ascending=False for 'Temperature' to have higher temperatures first.

3. Step 3: Sort 'Humidity' ascending when temperatures tie

   Use ascending=True for 'Humidity' to get lower humidity first.

4. Final Answer:

   df.sort_values(by=['City', 'Temperature', 'Humidity'], ascending=[True, False, True]) -> Option C

5. Quick Check:

   City ↑, Temp ↓, Humidity ↑ = df.sort_values(by=['City', 'Temperature', 'Humidity'], ascending=[True, False, True]) [OK]

Hint: Match ascending list to desired order per column [OK]

Common Mistakes:

• Mixing order of columns in 'by' list
• Using wrong ascending booleans
• Sorting humidity before temperature