describe() for statistics in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of describe() on numeric data

What is the output of the describe() method on this DataFrame's numeric column?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'scores': [10, 20, 20, 30, 40]})
result = df['scores'].describe()
print(result)

A

count    5.0
mean    24.0
std     12.25
min     10.0
25%     15.0
50%     20.0
75%     30.0
max     40.0
Name: scores, dtype: float64

B

count    5
mean    24
std     12.25
min     10
25%     20
50%     20
75%     30
max     40
Name: scores, dtype: int64

C

count    5.0
mean    24.0
std     11.40
min     10.0
25%     20.0
50%     20.0
75%     30.0
max     40.0
Name: scores, dtype: float64

D

count    5.0
mean    24.0
std     10.0
min     10.0
25%     20.0
50%     20.0
75%     30.0
max     40.0
Name: scores, dtype: float64

💡 Hint

Remember that describe() returns count, mean, std, min, quartiles, and max for numeric data.

The describe() method calculates statistics including count, mean, standard deviation, min, 25th percentile, median (50%), 75th percentile, and max. The values match option C exactly.

❓ data_output

intermediate

describe() on categorical data output

What does describe() return when applied to a categorical column in a DataFrame?

Data Analysis Python

import pandas as pd

df = pd.DataFrame({'colors': ['red', 'blue', 'red', 'green', 'blue', 'blue']})
result = df['colors'].describe()
print(result)

A

count       6
unique      3
top       blue
freq        3
Name: colors, dtype: object

B

count       6
mean      NaN
std       NaN
min       blue
max       red
Name: colors, dtype: object

C

count       6
unique      4
top       red
freq        2
Name: colors, dtype: object

D

count       6
unique      3
top       green
freq        1
Name: colors, dtype: object

💡 Hint

For categorical data, describe() shows count, unique values, most frequent value (top), and its frequency (freq).

The describe() method for categorical data returns count, number of unique values, the most common value (top), and how often it appears (freq). Here, 'blue' appears 3 times, which matches option A.

🧠 Conceptual

advanced

Understanding describe() output for mixed data

If a DataFrame has both numeric and categorical columns, what does describe() return by default?

A It returns descriptive statistics only for numeric columns.

B It returns descriptive statistics for all columns, numeric and categorical combined in one table.

C It returns descriptive statistics only for categorical columns.

D It raises an error because of mixed data types.

💡 Hint

By default, describe() focuses on numeric data unless specified otherwise.

By default, pandas describe() returns statistics only for numeric columns. To include categorical columns, you must specify include='all'.

🔧 Debug

advanced

Error when calling describe() on empty DataFrame

What error occurs when calling describe() on an empty DataFrame with no columns?

Data Analysis Python

import pandas as pd

df = pd.DataFrame()
result = df.describe()
print(result)

A AttributeError: 'DataFrame' object has no attribute 'describe'

B

Empty DataFrame
Columns: []
Index: []

C TypeError: describe() missing 1 required positional argument

D ValueError: No objects to describe

💡 Hint

Check what pandas returns when describe() is called on an empty DataFrame.

Calling describe() on an empty DataFrame returns an empty DataFrame with no columns and no rows, not an error.

🚀 Application

expert

Using describe() to compare two datasets

You have two DataFrames df1 and df2 with a numeric column 'age'. You want to quickly compare their age distributions. Which code snippet correctly prints the summary statistics side by side?

Data Analysis Python

import pandas as pd

df1 = pd.DataFrame({'age': [23, 45, 31, 35, 40]})
df2 = pd.DataFrame({'age': [30, 22, 40, 50, 60]})

A print(df1['age'].describe().merge(df2['age'].describe()))

B print(df1['age'].describe() + df2['age'].describe())

C print(df1['age'].describe().append(df2['age'].describe()))

D print(pd.concat([df1['age'].describe(), df2['age'].describe()], axis=1))

💡 Hint

Think about how to combine two Series objects side by side in pandas.

pd.concat with axis=1 stacks the two describe() Series side by side as columns, allowing easy comparison. Other options either add values incorrectly, append rows, or use merge which is not valid for Series.