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.