rank() method and ranking methods in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of rank() with default method

What is the output of the following code snippet using pandas rank() method with default parameters?

Pandas

import pandas as pd
s = pd.Series([3, 1, 4, 1, 5])
ranks = s.rank()
print(ranks.tolist())

A [3.0, 1.5, 4.0, 1.5, 5.0]

B [3, 1, 4, 1, 5]

C [3.0, 2.0, 4.0, 2.0, 5.0]

D [3, 2, 4, 2, 5]

💡 Hint

The default method is 'average', which assigns the average rank to tied values.

The rank() method assigns ranks starting at 1. The tied values (1 and 1) get the average rank (1 and 2 average to 1.5). Other values get their natural ranks.

❓ data_output

intermediate

Ranking with method='min'

Given the pandas Series below, what is the output of s.rank(method='min')?

Pandas

import pandas as pd
s = pd.Series([7, 3, 7, 1, 3])
ranks = s.rank(method='min')
print(ranks.tolist())

A [3.0, 2.0, 3.0, 1.0, 2.0]

B [4.0, 2.0, 4.0, 1.0, 2.0]

C [4, 2, 4, 1, 2]

D [4.0, 3.0, 4.0, 1.0, 3.0]

💡 Hint

The 'min' method assigns the lowest rank to all tied values.

For tied values, 'min' assigns the smallest rank among them. The two 7's get rank 4, the two 3's get rank 2, and 1 gets rank 1.

❓ visualization

advanced

Visualizing different ranking methods

Which ranking method produces the following ranks for the Series [10, 20, 20, 30]? Ranks: [1.0, 3.0, 3.0, 4.0]

Pandas

import pandas as pd
import matplotlib.pyplot as plt
s = pd.Series([10, 20, 20, 30])
methods = ['average', 'min', 'max', 'dense']
ranks = {m: s.rank(method=m).tolist() for m in methods}
print(ranks)

A dense

B min

C average

D max

💡 Hint

The 'max' method assigns the highest rank to tied values.

The 'max' method assigns the maximum rank to tied values. For the two 20's, ranks are 3 and 3, so output is [1.0, 3.0, 3.0, 4.0]. The given ranks match the 'max' method.

🧠 Conceptual

advanced

Understanding 'dense' ranking method

Which statement correctly describes the 'dense' ranking method in pandas rank()?

A 'dense' ranking assigns ranks randomly to tied values.

B 'dense' ranking assigns average ranks to tied values, possibly creating gaps.

C 'dense' ranking assigns ranks with no gaps, incrementing by 1 for each distinct value.

D 'dense' ranking assigns the minimum rank to tied values, skipping ranks after ties.

💡 Hint

Think about how 'dense' differs from 'min' and 'max' in rank numbering.

'Dense' ranking assigns ranks starting at 1 and increments by 1 for each distinct value without gaps, even if there are ties.

🔧 Debug

expert

Identify the error in rank() usage

What error will the following code raise when executed? import pandas as pd s = pd.Series([1, 2, 3]) ranks = s.rank(method='median') print(ranks.tolist())

Pandas

import pandas as pd
s = pd.Series([1, 2, 3])
ranks = s.rank(method='median')
print(ranks.tolist())

A ValueError: method must be one of ['average', 'min', 'max', 'first', 'dense']

B TypeError: unsupported method argument

C AttributeError: 'Series' object has no attribute 'rank'

D No error, output: [1.0, 2.0, 3.0]

💡 Hint

Check the allowed values for the 'method' parameter in pandas rank().

The 'median' method is not a valid option for pandas Series.rank(). It raises a ValueError listing valid methods.

Practice

1. What does the rank() method in pandas do?

easy

A. Assigns ranks to values in a Series or DataFrame column based on their order

B. Sorts the data in ascending order permanently

C. Removes duplicate values from the data

D. Calculates the sum of all values in the data

Solution

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

   The rank() method assigns a rank number to each value based on its position in sorted order.

2. Step 2: Differentiate from sorting and aggregation

   Unlike sorting or summing, rank() does not change order or calculate totals but gives position numbers.

3. Final Answer:

   Assigns ranks to values in a Series or DataFrame column based on their order -> Option A

4. Quick Check:

   rank() = Assign ranks [OK]

Hint: Remember: rank() gives position, not sorting or sums [OK]

Common Mistakes:

• Confusing rank() with sort()
• Thinking rank() removes duplicates
• Assuming rank() calculates sums

2. Which of the following is the correct syntax to rank a pandas Series s with method 'min'?

easy

A. s.rank(min='method')

B. s.rank(method='min')

C. rank(s, method='min')

D. s.rank(method=min)

Solution

1. Step 1: Recall the correct method call syntax

   In pandas, the rank method is called on the Series object with parameters inside parentheses and method as a string.

2. Step 2: Check each option for syntax errors

   s.rank(method='min') uses correct syntax: s.rank(method='min'). s.rank(min='method') wrongly uses parameter name. rank(s, method='min') uses wrong function call. s.rank(method=min) misses quotes around 'min'.

3. Final Answer:

   s.rank(method='min') -> Option B

4. Quick Check:

   Method name as string in rank() call [OK]

Hint: Use object.method(parameter='value') syntax [OK]

Common Mistakes:

• Omitting quotes around method name
• Using wrong parameter names
• Calling rank as a standalone function

3. Given the pandas Series s = pd.Series([3, 1, 4, 4, 2]), what is the output of s.rank(method='average')?

medium

A. [2.0, 1.0, 4.5, 4.5, 3.0]

B. [3.0, 1.0, 4.0, 5.0, 2.0]

C. [2.0, 1.0, 5.0, 5.0, 3.0]

D. [3.0, 1.0, 4.5, 4.5, 2.0]

Solution

1. Step 1: Sort values and assign ranks with 'average' method

   Sorted values: 1 (rank 1), 2 (rank 2), 3 (rank 3), 4 and 4 (ranks 4 and 5). For ties (4 and 4), average ranks = (4+5)/2 = 4.5.

2. Step 2: Map ranks back to original positions

   Original values: 3 -> rank 3, 1 -> rank 1, 4 -> rank 4.5, 4 -> rank 4.5, 2 -> rank 2. So ranks are [3,1,4.5,4.5,2].

3. Step 3: Check options carefully

   [2.0, 1.0, 4.5, 4.5, 3.0] matches ranks but with 3 replaced by 2.0. Re-examining, 3 is the third smallest value, so rank 3.0 is correct. [2.0, 1.0, 4.5, 4.5, 3.0] shows 2.0 for first value which is 3, so [2.0, 1.0, 4.5, 4.5, 3.0] is incorrect. [3.0, 1.0, 4.5, 4.5, 2.0] shows [3.0,1.0,4.5,4.5,2.0] which matches our calculation.

4. Final Answer:

   [3.0, 1.0, 4.5, 4.5, 2.0] -> Option D

5. Quick Check:

   Average method averages tie ranks correctly [OK]

Hint: Average method assigns mean rank to ties [OK]

Common Mistakes:

• Not averaging ranks for ties
• Assigning ranks in descending order by mistake
• Mixing up rank positions

4. You run df['rank'] = df['score'].rank(method='first') but get unexpected ranks. What is a likely cause?

medium

A. The 'method' parameter does not accept 'first'

B. You forgot to import pandas

C. The 'score' column contains duplicate values and 'first' ranks by order of appearance

D. You need to sort the DataFrame before ranking

Solution

1. Step 1: Understand 'first' method behavior

   The 'first' method assigns ranks in order of appearance for ties, so duplicates get ranks based on their position in the data.

2. Step 2: Identify why ranks seem unexpected

   If duplicates exist, ranks may not be sorted by value but by order, causing confusion if expecting sorted ranks.

3. Final Answer:

   The 'score' column contains duplicate values and 'first' ranks by order of appearance -> Option C

4. Quick Check:

   'first' ranks ties by appearance order [OK]

Hint: Check if duplicates affect 'first' method ranking [OK]

Common Mistakes:

• Assuming 'first' sorts before ranking
• Thinking 'first' is invalid method
• Forgetting duplicates affect rank order

5. You have a DataFrame df with a 'score' column. You want to rank scores in descending order, assign average ranks for ties, and get the rank as a percentage of total rows. Which code achieves this?

hard

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

B. df['rank'] = df['score'].rank(ascending=True, method='average', pct=True)

C. df['rank'] = df['score'].rank(ascending=False, method='min', pct=False)

D. df['rank'] = df['score'].rank(method='average', pct=True)

Solution

1. Step 1: Set descending order and average method

   To rank highest scores first, set ascending=False. To assign average ranks for ties, use method='average'.

2. Step 2: Get rank as percentage

   Set pct=True to get ranks as percentage of total rows.

3. Step 3: Verify options

   df['rank'] = df['score'].rank(ascending=False, method='average', pct=True) matches all requirements. df['rank'] = df['score'].rank(ascending=True, method='average', pct=True) ranks ascending, wrong order. df['rank'] = df['score'].rank(ascending=False, method='min', pct=False) uses 'min' method and no percentage. df['rank'] = df['score'].rank(method='average', pct=True) misses descending order.

4. Final Answer:

   df['rank'] = df['score'].rank(ascending=False, method='average', pct=True) -> Option A

5. Quick Check:

   Descending + average + pct=True = df['rank'] = df['score'].rank(ascending=False, method='average', pct=True) [OK]

Hint: Use ascending=False and pct=True for descending percent ranks [OK]

Common Mistakes:

• Forgetting ascending=False for descending rank
• Using wrong method for ties
• Missing pct=True for percentage rank