Why sorting and ranking matter in Pandas - Challenge Your Understanding

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of sorting a DataFrame by a column?

Given the DataFrame below, what will be the order of the 'Name' column after sorting by 'Score' in ascending order?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Score': [88, 92, 85, 90]
})
sorted_df = df.sort_values(by='Score')
print(sorted_df['Name'].tolist())

A ['Charlie', 'Alice', 'David', 'Bob']

B ['Bob', 'David', 'Alice', 'Charlie']

C ['Alice', 'Bob', 'Charlie', 'David']

D ['David', 'Charlie', 'Bob', 'Alice']

💡 Hint

Think about sorting numbers from smallest to largest and how that affects the order of names.

Sorting by 'Score' in ascending order arranges the rows from lowest to highest score. The scores are 85 (Charlie), 88 (Alice), 90 (David), and 92 (Bob), so the names follow that order.

❓ data_output

intermediate

What is the rank of each score in the DataFrame?

Using the same DataFrame, what is the rank of each 'Score' when ranked in ascending order with method='min'?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie', 'David'],
    'Score': [88, 92, 85, 90]
})
df['Rank'] = df['Score'].rank(method='min')
print(df[['Name', 'Rank']].sort_values('Rank').to_dict(orient='records'))

A [{'Name': 'Bob', 'Rank': 1.0}, {'Name': 'David', 'Rank': 2.0}, {'Name': 'Alice', 'Rank': 3.0}, {'Name': 'Charlie', 'Rank': 4.0}]

B [{'Name': 'Charlie', 'Rank': 1.0}, {'Name': 'Alice', 'Rank': 2.0}, {'Name': 'David', 'Rank': 3.0}, {'Name': 'Bob', 'Rank': 4.0}]

C [{'Name': 'Alice', 'Rank': 1.0}, {'Name': 'Bob', 'Rank': 2.0}, {'Name': 'Charlie', 'Rank': 3.0}, {'Name': 'David', 'Rank': 4.0}]

D [{'Name': 'David', 'Rank': 1.0}, {'Name': 'Charlie', 'Rank': 2.0}, {'Name': 'Bob', 'Rank': 3.0}, {'Name': 'Alice', 'Rank': 4.0}]

💡 Hint

Ranking assigns 1 to the smallest score, 2 to the next smallest, and so on.

The rank method='min' assigns the smallest rank to the lowest score. Scores 85, 88, 90, 92 get ranks 1, 2, 3, 4 respectively.

🔧 Debug

advanced

Why does this sorting code raise an error?

Consider this code snippet. Why does it raise an error?

Pandas

import pandas as pd

df = pd.DataFrame({'A': [3, 1, 2]})
sorted_df = df.sort_values('B')

A KeyError because column 'B' does not exist in the DataFrame

B TypeError because 'B' is not a valid argument type

C SyntaxError due to missing parentheses

D ValueError because sorting requires numeric data

💡 Hint

Check if the column you want to sort by exists in the DataFrame.

The DataFrame only has column 'A'. Trying to sort by 'B' causes a KeyError because 'B' is missing.

🚀 Application

advanced

How to find the top 2 scores and their names?

Given the DataFrame below, which code snippet correctly finds the top 2 scores and their corresponding names?

Pandas

import pandas as pd

df = pd.DataFrame({
    'Name': ['Anna', 'Brian', 'Cathy', 'Derek'],
    'Score': [75, 85, 95, 80]
})

A df.sort_values('Score').head(2)[['Name', 'Score']]

B df.nsmallest(2, 'Score')[['Name', 'Score']]

C df.sort_values('Score', ascending=False).tail(2)[['Name', 'Score']]

D df.nlargest(2, 'Score')[['Name', 'Score']]

💡 Hint

Use a method that directly selects the largest values.

nlargest(2, 'Score') returns the rows with the two highest scores. Other options either select lowest scores or wrong rows.

🧠 Conceptual

expert

Why is ranking important in data analysis?

Which of the following best explains why ranking data is useful in data science?

A Ranking removes duplicate data points to clean the dataset.

B Ranking sorts data alphabetically to improve readability.

C Ranking helps to identify the relative position of data points, which is useful for comparisons and decision making.

D Ranking converts numerical data into categorical data for visualization.

💡 Hint

Think about how knowing the order of values helps in understanding data.

Ranking assigns positions to data points based on their values, helping to compare and prioritize them effectively.