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.