Why sorting and ranking matter in Pandas - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

Pandas

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

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

A'score'

B'name'

C'age'

D'date'

Attempts:

3 left

💡 Hint

Common Mistakes

Using a column name that doesn't exist in the DataFrame.

We sort the DataFrame by the 'score' column to organize data based on scores.

2fill in blank

medium

Complete the code to rank the 'score' column in descending order (highest score gets rank 1).

Pandas

df['rank'] = df['score'].rank(ascending=[1])

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

ATrue

BFalse

CNone

D0

Attempts:

3 left

💡 Hint

Common Mistakes

Setting ascending=True which ranks lowest scores as 1.

Setting ascending=False ranks highest scores as 1, which is typical for ranking.

3fill in blank

hard

Fix the error in the code to sort the DataFrame by 'age' in descending order.

Pandas

sorted_df = df.sort_values(by='age', [1]=False)

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

Adescend

Bascend

Cascending

Dorder

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names like 'ascend' or 'descend'.

The correct parameter name is 'ascending' to specify sort order.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Pandas

{word: [1] for word in words if [2]

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

Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as value instead of its length.

Wrong condition filtering.

The dictionary maps each word to its length (len(word)) only if the length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their scores only if score is above 50.

Pandas

{ [1]: [2] for word, score in data.items() if [3] }

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

Aword.upper()

Bscore

Cscore > 50

Dword.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase words as keys.

Not filtering scores correctly.

The dictionary keys are uppercase words, values are scores, filtered by scores above 50.

Practice

1.

Why is sorting data important in pandas?

easy

A. It arranges data to make patterns easier to see.

B. It deletes duplicate rows automatically.

C. It changes data types to numeric.

D. It creates new columns with summary statistics.

Solution

1. Step 1: Understand sorting purpose

   Sorting arranges data in order, making it easier to spot trends or patterns.

2. Step 2: Compare options

   Only It arranges data to make patterns easier to see. correctly describes sorting's role; others describe different operations.

3. Final Answer:

   It arranges data to make patterns easier to see. -> Option A

4. Quick Check:

   Sorting = Arrange data for clarity [OK]

Hint: Sorting helps see data order and patterns fast [OK]

Common Mistakes:

• Confusing sorting with removing duplicates
• Thinking sorting changes data types
• Assuming sorting creates new columns

2.

Which of the following is the correct pandas code to sort a DataFrame df by column 'score' in descending order?

easy

A. df.order('score', ascending=False)

B. df.sort(by='score', ascending=True)

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

D. df.sort_index(ascending=False)

Solution

1. Step 1: Recall pandas sorting syntax

   The correct method is sort_values with by parameter for column name.

2. Step 2: Check ascending parameter

   To sort descending, ascending=False is needed.

3. Final Answer:

   df.sort_values(by='score', ascending=False) -> Option C

4. Quick Check:

   sort_values + ascending=False = correct syntax [OK]

Hint: Use sort_values(by=col, ascending=False) to sort descending [OK]

Common Mistakes:

• Using sort() instead of sort_values()
• Confusing sort_index() with sorting by column
• Wrong ascending parameter value

3.

Given the DataFrame df:

   name  score
0  Amy     90
1  Bob     75
2  Cara    90
3  Dan     60

What is the output of df['rank'] = df['score'].rank(method='min', ascending=False)?

medium

A. [1.0, 3.0, 1.0, 4.0]

B. [2.0, 3.0, 2.0, 4.0]

C. [1.0, 4.0, 1.0, 3.0]

D. [4.0, 3.0, 4.0, 1.0]

Solution

1. Step 1: Understand ranking with method='min' and descending order

   Ranking assigns 1 to highest score (90), ties get lowest rank among them.

2. Step 2: Assign ranks to scores

   Scores 90 get rank 1, 75 gets rank 3, 60 gets rank 4.

3. Final Answer:

   [1.0, 3.0, 1.0, 4.0] -> Option A

4. Quick Check:

   Rank highest score as 1 with method='min' [OK]

Hint: Rank highest value as 1 using ascending=False and method='min' [OK]

Common Mistakes:

• Ranking ascending instead of descending
• Using method='average' instead of 'min'
• Confusing rank numbers for ties

4.

Consider this code snippet:

df = pd.DataFrame({'value': [10, 20, 15]})
df['rank'] = df['value'].rank(ascending=True, method='max')
df_sorted = df.sort_values(by='rank', ascending=False)

What is the main issue with this code if the goal is to sort df by value in ascending order and assign ranks accordingly?

medium

A. Rank function cannot be applied to numeric columns.

B. Using method='max' causes syntax error.

C. DataFrame creation syntax is incorrect.

D. Sorting by rank descending reverses the intended order.

Solution

1. Step 1: Analyze ranking and sorting steps

   Ranking is ascending with method='max', so lowest value gets rank 1.

2. Step 2: Check sorting by rank descending

   Sorting by rank descending reverses order, so highest value appears first, opposite of goal.

3. Final Answer:

   Sorting by rank descending reverses the intended order. -> Option D

4. Quick Check:

   Sorting rank descending ≠ ascending value order [OK]

Hint: Sort rank ascending to match ascending value order [OK]

Common Mistakes:

• Assuming method='max' causes error
• Thinking rank can't apply to numbers
• Ignoring sorting order after ranking

5.

You have a DataFrame df with columns 'player' and 'score'. You want to create a leaderboard that shows players ranked by score descending, but if two players have the same score, the player who achieved it first (earlier row) should have the higher rank. Which pandas approach correctly achieves this?

hard

A. Use df['rank'] = df['score'].rank(method='average', ascending=False)

B. Use df['rank'] = df['score'].rank(method='first', ascending=False)

C. Use df['rank'] = df['score'].rank(method='dense', ascending=True)

D. Use df.sort_values('score', ascending=True).rank()

Solution

1. Step 1: Understand ranking with ties and order

   Method 'first' ranks ties by order of appearance (row order).

2. Step 2: Apply descending order for higher scores first

   Set ascending=False to rank highest scores as 1.

3. Step 3: Confirm other methods don't respect row order

   Methods 'dense' and 'average' assign ranks ignoring row order; sorting ascending reverses desired order.

4. Final Answer:

   Use df['rank'] = df['score'].rank(method='first', ascending=False) -> Option B

5. Quick Check:

   Rank with method='first' + descending = correct leaderboard [OK]

Hint: Use rank(method='first', ascending=False) for stable descending ranks [OK]

Common Mistakes:

• Using ascending=True reverses leaderboard
• Using method='average' ignores row order
• Sorting ascending before ranking reverses logic