rank() method and ranking methods in Pandas - Mini Project: Build & Apply

Using the rank() Method and Ranking Methods in pandas

📖 Scenario: You work in a sales team. You have a list of salespeople and their sales amounts. You want to find out their ranks based on sales to see who sold the most and who sold the least.

🎯 Goal: Build a small program that creates a sales data dictionary, sets a ranking method, applies the rank() method to find ranks of salespeople, and prints the ranked results.

📋 What You'll Learn

Create a dictionary called sales_data with these exact entries: 'Alice': 250, 'Bob': 300, 'Charlie': 150, 'Diana': 300, 'Evan': 200

Create a variable called ranking_method and set it to the string 'min'

Use pandas to create a Series from sales_data called sales_series

Use the rank() method on sales_series with the method=ranking_method and ascending=False to rank sales from highest to lowest

Store the result in a variable called sales_rank

Print the sales_rank Series

💡 Why This Matters

🌍 Real World

Ranking salespeople helps managers quickly identify top performers and those who may need support.

💼 Career

Data analysts and business intelligence professionals often rank data to create reports and dashboards that inform decisions.

1

Create the sales data dictionary

Create a dictionary called sales_data with these exact entries: 'Alice': 250, 'Bob': 300, 'Charlie': 150, 'Diana': 300, 'Evan': 200

Pandas

# Create the sales_data dictionary with exact entries
# Your code here

Hint

Use curly braces {} to create a dictionary with the exact names and sales numbers.

2

Set the ranking method

Create a variable called ranking_method and set it to the string 'min'

Pandas

sales_data = {'Alice': 250, 'Bob': 300, 'Charlie': 150, 'Diana': 300, 'Evan': 200}
# Set ranking_method to 'min'
# Your code here

Hint

Just assign the string 'min' to the variable ranking_method.

3

Create a pandas Series and apply rank()

Import pandas as pd. Create a Series called sales_series from the dictionary sales_data. Then create a variable called sales_rank by applying the rank() method on sales_series with method=ranking_method and ascending=False.

Pandas

sales_data = {'Alice': 250, 'Bob': 300, 'Charlie': 150, 'Diana': 300, 'Evan': 200}
ranking_method = 'min'
# Import pandas as pd
# Create sales_series from sales_data
# Create sales_rank by ranking sales_series
# Your code here

Hint

Use pd.Series() to convert the dictionary to a Series. Then use rank() with the given method and ascending=False to rank from highest to lowest.

4

Print the ranked sales results

Write a print statement to display the sales_rank Series.

Pandas

import pandas as pd

sales_data = {'Alice': 250, 'Bob': 300, 'Charlie': 150, 'Diana': 300, 'Evan': 200}
ranking_method = 'min'

sales_series = pd.Series(sales_data)
sales_rank = sales_series.rank(method=ranking_method, ascending=False)
# Print the sales_rank Series
# Your code here

Hint

Use print(sales_rank) to show the ranks.

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