sort_values() by multiple columns in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to sort the DataFrame by the 'age' column in ascending order.

Pandas

df_sorted = df.sort_values(by=[1])

Drag options to blanks, or click blank then click option'

A'age'

B'salary'

C'name'

D'height'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that does not exist in the DataFrame.

Not putting the column name inside quotes.

The sort_values() function sorts the DataFrame by the column name given in the by parameter. Here, we want to sort by 'age'.

2fill in blank

medium

Complete the code to sort the DataFrame by 'age' ascending and then by 'salary' descending.

Pandas

df_sorted = df.sort_values(by=['age', [1]], ascending=[True, False])

Drag options to blanks, or click blank then click option'

A'salary'

B'height'

C'name'

D'department'

Attempts:

3 left

💡 Hint

Common Mistakes

Not passing a list to the by parameter.

Mixing up the order of columns in the list.

To sort by multiple columns, pass a list of column names to by. Here, we sort first by 'age' ascending, then by 'salary' descending.

3fill in blank

hard

Fix the error in the code to correctly sort by 'department' ascending and 'age' descending.

Pandas

df_sorted = df.sort_values(by=['department', [1]], ascending=[True, False])

Drag options to blanks, or click blank then click option'

A'salary'

B'age'

C'name'

D'height'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong column name that does not exist.

Not matching the order of columns with the ascending list.

The second column to sort by is 'age' as specified. The code needs the correct column name in the list.

4fill in blank

hard

Fill both blanks to create a dictionary that sorts by 'age' ascending and 'salary' descending.

Pandas

df_sorted = df.sort_values(by=[1], ascending=[2])

Drag options to blanks, or click blank then click option'

A['age', 'salary']

B[True, False]

C[False, True]

D['salary', 'age']

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up the order of columns and booleans.

Using a single boolean instead of a list for multiple columns.

The by parameter takes a list of columns to sort by. The ascending parameter takes a list of booleans matching the order of columns.

5fill in blank

hard

Fill all three blanks to create a sorted DataFrame by 'department' ascending, 'age' descending, and 'salary' ascending.

Pandas

df_sorted = df.sort_values(by=[[1], [2], [3]], ascending=[True, False, True])

Drag options to blanks, or click blank then click option'

A'department'

B'age'

C'salary'

D'name'

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the columns in the wrong order.

Not matching the length of by and ascending lists.

The by list contains the columns in the order to sort. The ascending list matches the order with True or False for ascending or descending.

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