describe() for statistical summary in Pandas - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get a statistical summary of the DataFrame.

Pandas

summary = df.[1]()

Drag options to blanks, or click blank then click option'

Ainfo

Btail

Chead

Ddescribe

Attempts:

3 left

💡 Hint

Common Mistakes

Using info() instead of describe() which shows data types, not statistics.

Using head() or tail() which show rows, not statistics.

The describe() method provides a quick statistical summary of numerical columns in a DataFrame.

2fill in blank

medium

Complete the code to include all columns (numeric and non-numeric) in the summary.

Pandas

summary = df.describe(include=[1])

Drag options to blanks, or click blank then click option'

A'numeric'

B'all'

C'object'

D'category'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'numeric' which is the default and excludes text columns.

Using 'object' or 'category' which only include specific types.

Using include='all' makes describe() show statistics for all columns, including text.

3fill in blank

hard

Fix the error in the code to get the summary of a DataFrame named data.

Pandas

summary = data.[1]()

Drag options to blanks, or click blank then click option'

Adescribe

Bsummary

Cdescribe_summary

Dsummarize

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent method names like summarize or summary.

Confusing method names with variable names.

The correct method name is describe(). Other options are not valid pandas methods.

4fill in blank

hard

Fill both blanks to create a summary that includes only categorical columns.

Pandas

summary = df.describe(include=[1], exclude=[2])

Drag options to blanks, or click blank then click option'

A'object'

B'number'

C'float64'

D'int64'

Attempts:

3 left

💡 Hint

Common Mistakes

Including numeric types which adds numbers to the summary.

Excluding object type which removes categorical columns.

Including 'object' selects text columns, and excluding 'number' removes numeric columns, so only categorical columns remain.

5fill in blank

hard

Fill all three blanks to create a summary showing only columns of type float64.

Pandas

summary = df.describe(include=[1], exclude=[2], percentiles=[[3]])

Drag options to blanks, or click blank then click option'

A'float64'

B'int64'

C0.5

D0.25

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong types in include or exclude.

Setting percentiles to values outside 0 to 1 range.

Including 'float64' selects float columns, excluding 'int64' removes integers, and setting percentiles to [0.5] shows the median only.

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