describe() for statistical summary in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of describe() on numeric DataFrame

What is the output of the following code snippet?

Pandas

import pandas as pd
import numpy as np
data = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [5, 4, 3, 2, 1]})
summary = data.describe()
print(summary)

A {'A': {'count': 5.0, 'mean': 3.0, 'std': 1.58, 'min': 1.0, '25%': 2.0, '50%': 3.0, '75%': 4.0, 'max': 5.0}, 'B': {'count': 5.0, 'mean': 3.0, 'std': 1.58, 'min': 1.0, '25%': 2.0, '50%': 3.0, '75%': 4.0, 'max': 5.0}}

B A DataFrame with count=5, mean=3, std=1.58, min=1, 25%=2, 50%=3, 75%=4, max=5 for column A and count=5, mean=3, std=1.58, min=5, 25%=4, 50%=3, 75%=2, max=1 for column B

C A DataFrame with count=5, mean=3, std=1.58, min=1, 25%=2, 50%=3, 75%=4, max=5 for both columns

D A DataFrame with count=5, mean=3, std=1.58, min=1, 25%=2, 50%=3, 75%=4, max=5 for column A and reversed for B

💡 Hint

The describe() method returns a summary with count, mean, std, min, quartiles, and max for numeric columns.

The describe() method returns a DataFrame summarizing count, mean, standard deviation, min, 25th percentile, median (50%), 75th percentile, and max for each numeric column. Both columns A and B have the same statistics because B is just A reversed.

❓ data_output

intermediate

Count of non-numeric columns in describe() output

Given the DataFrame below, how many columns will appear in the output of describe() by default?

Pandas

import pandas as pd
data = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35], 'City': ['NY', 'LA', 'SF']})
summary = data.describe()
print(summary)

A 2

B 1

C 3

D 0

💡 Hint

By default, describe() summarizes only numeric columns.

By default, describe() includes only numeric columns. Here, 'Age' is numeric, 'Name' and 'City' are strings, so only 1 column appears.

🧠 Conceptual

advanced

Understanding percentiles in describe()

Which of the following statements about the percentiles shown in describe() output is TRUE?

A The 50% value is the median of the data.

B The 25% and 75% values represent the mean minus and plus one standard deviation.

C The 25%, 50%, and 75% values are the minimum, median, and maximum respectively.

D Percentiles in describe() are calculated only for categorical columns.

💡 Hint

Think about what the 50% percentile means in statistics.

The 50% percentile is the median, the middle value of the sorted data. The 25% and 75% are the first and third quartiles, not related to mean and std. Percentiles are for numeric data.

🔧 Debug

advanced

Error when calling describe() on empty DataFrame

What error will this code produce?

Pandas

import pandas as pd
data = pd.DataFrame()
summary = data.describe()
print(summary)

A Empty DataFrame with no columns and no rows

B TypeError: describe() missing required argument

C ValueError: No numeric types to describe

D KeyError: 'describe'

💡 Hint

What does describe() return if the DataFrame has no data?

Calling describe() on an empty DataFrame returns an empty DataFrame with no columns and no rows, no error is raised.

🚀 Application

expert

Using describe() to compare two groups

You have a DataFrame with a numeric column 'Score' and a categorical column 'Group' with values 'A' and 'B'. Which code snippet correctly produces descriptive statistics of 'Score' for each group separately?

A data.describe().groupby('Group')['Score']

B data.describe('Score').groupby('Group')

C data['Score'].describe().groupby(data['Group'])

D data.groupby('Group')['Score'].describe()

💡 Hint

Think about how to group data before summarizing.

Using groupby on 'Group' and then calling describe() on 'Score' gives descriptive stats for each group separately.

Practice

1. What does the describe() function in pandas primarily provide for a DataFrame?

easy

A. A statistical summary including count, mean, std, min, and max

B. A list of all column names in the DataFrame

C. A plot of the data distribution

D. A sorted version of the DataFrame

Solution

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

   The describe() function gives a quick statistical summary of numerical columns by default.

2. Step 2: Identify what the summary includes

   This summary includes count, mean, standard deviation, minimum, maximum, and quartiles.

3. Final Answer:

   A statistical summary including count, mean, std, min, and max -> Option A

4. Quick Check:

   describe() = statistical summary [OK]

Hint: Remember: describe() shows stats, not column names or plots [OK]

Common Mistakes:

• Thinking describe() lists column names
• Confusing describe() with plotting functions
• Assuming describe() sorts data

2. Which of the following is the correct way to get a statistical summary of a pandas DataFrame named df?

easy

A. df.summary()

B. df.describe()

C. describe(df)

D. df.stats()

Solution

1. Step 1: Recall pandas method syntax

   In pandas, methods are called on DataFrame objects using dot notation.

2. Step 2: Identify the correct method name

   The method to get a statistical summary is describe(), so df.describe() is correct.

3. Final Answer:

   df.describe() -> Option B

4. Quick Check:

   Method call on DataFrame = df.describe() [OK]

Hint: Use dot notation: df.describe() for summary [OK]

Common Mistakes:

• Using non-existent methods like summary() or stats()
• Calling describe() as a standalone function
• Missing parentheses after describe

3. Given the DataFrame:

import pandas as pd
data = {'A': [1, 2, 3, 4, 5], 'B': [5, 4, 3, 2, 1]}
df = pd.DataFrame(data)
print(df.describe())

What is the output for the mean of column A?

medium

A. 1

B. 2.5

C. 15

D. 3.0

Solution

1. Step 1: Calculate the mean of column A

   Column A values are [1, 2, 3, 4, 5]. The mean is (1+2+3+4+5)/5 = 15/5 = 3.0.

2. Step 2: Confirm output from describe()

   The describe() method prints the mean as 3.0 for column A.

3. Final Answer:

   3.0 -> Option D

4. Quick Check:

   Mean of [1,2,3,4,5] = 3.0 [OK]

Hint: Add values and divide by count for mean [OK]

Common Mistakes:

• Confusing sum with mean
• Using median instead of mean
• Misreading the output values

4. What is wrong with this code snippet to get a statistical summary of a DataFrame df?

summary = df.describe

medium

A. Missing parentheses to call the method

B. DataFrame does not have describe method

C. describe() requires arguments

D. Variable name 'summary' is invalid

Solution

1. Step 1: Check method call syntax

   The code assigns df.describe without parentheses, so it assigns the method itself, not the result.

2. Step 2: Identify the fix

   Adding parentheses df.describe() calls the method and returns the summary.

3. Final Answer:

   Missing parentheses to call the method -> Option A

4. Quick Check:

   Method call needs () to execute [OK]

Hint: Always add () to call methods like describe() [OK]

Common Mistakes:

• Forgetting parentheses after method names
• Thinking describe needs arguments
• Confusing method with variable name errors

5. You have a DataFrame df with both numeric and categorical columns. How can you use describe() to get a summary that includes statistics for all columns?

hard

A. df.describe(include='numeric')

B. df.describe(include='categorical')

C. df.describe(include='all')

D. df.describe(include=None)

Solution

1. Step 1: Understand default behavior of describe()

   By default, describe() summarizes only numeric columns.

2. Step 2: Use include='all' to summarize all columns

   Passing include='all' makes describe() include numeric and categorical columns.

3. Final Answer:

   df.describe(include='all') -> Option C

4. Quick Check:

   include='all' shows all columns summary [OK]

Hint: Use include='all' to summarize all column types [OK]

Common Mistakes:

• Using include='numeric' which is default
• Using include='categorical' which is invalid
• Passing include=None which defaults to numeric only