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.