describe() for statistics in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get basic statistics of the DataFrame.

Data Analysis Python

stats = df.[1]()

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

Atail

Binfo

Cdescribe

Dhead

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 basic statistics like mean, count, min, max, and quartiles for numerical columns.

2fill in blank

medium

Complete the code to include statistics for all columns, including non-numeric ones.

Data Analysis Python

stats_all = df.describe([1]='all')

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

Ainclude

Bexclude

Cnumeric_only

Dall

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'all=True' which is not a valid argument.

Using 'numeric_only=True' which limits to numeric columns only.

The include='all' argument tells describe() to include all columns, not just numeric ones.

3fill in blank

hard

Fix the error in the code to get statistics only for numeric columns.

Data Analysis Python

num_stats = df.describe([1]=True)

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

Anumeric_only

Bexclude

Cinclude

Dall

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'include=True' which includes all columns.

Using 'all=True' which is invalid.

To get statistics only for numeric columns, use numeric_only=True. This excludes non-numeric columns.

4fill in blank

hard

Fill both blanks to create a dictionary of statistics for words longer than 3 letters.

Data Analysis Python

lengths = {word: [1] for word in words if len(word) [2] 3 }

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

Alen(word)

B>

C<

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' which selects shorter words.

Using 'word' instead of 'len(word)' which stores the word itself.

This dictionary comprehension maps each word to its length only if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys and values greater than 0.

Data Analysis Python

result = { [1]: [2] for k, v in data.items() if v [3] 0 }

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'k' instead of 'k.upper()' which does not change the key case.

Using '<' instead of '>' which filters values less than zero.

This comprehension creates a dictionary with keys as uppercase strings and values only if they are greater than zero.

Practice

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

easy

A. A sorted version of the DataFrame

B. A plot of the data distribution

C. A list of all column names

D. A summary of main statistics like mean, median, and count

Solution

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

   The describe() function summarizes key statistics such as count, mean, std, min, max, and quartiles for numeric data.

2. Step 2: Compare with other options

   Options B, C, and D describe different functions or outputs unrelated to describe().

3. Final Answer:

   A summary of main statistics like mean, median, and count -> Option D

4. Quick Check:

   describe() = summary statistics [OK]

Hint: Remember: describe() = quick stats summary [OK]

Common Mistakes:

• Thinking describe() creates plots
• Confusing describe() with listing columns
• Assuming describe() sorts data

2. Which of the following is the correct way to call describe() on a pandas DataFrame named df?

easy

A. df.describe()

B. describe(df)

C. df.describe

D. df.describe[]

Solution

1. Step 1: Recall pandas method syntax

   Methods are called with parentheses after the method name, so df.describe() is correct.

2. Step 2: Check other options for syntax errors

   df.describe[] uses invalid square brackets; describe(df) incorrectly calls it as a standalone function; df.describe misses parentheses and prints the method object.

3. Final Answer:

   df.describe() -> Option A

4. Quick Check:

   Method call needs () = df.describe() [OK]

Hint: Call methods with parentheses: df.describe() [OK]

Common Mistakes:

• Forgetting parentheses when calling methods
• Using square brackets instead of parentheses
• Trying to call describe as a standalone function

3. Given the DataFrame df below:

import pandas as pd
df = pd.DataFrame({'A': [1, 2, 3, 4, 5], 'B': [10, 20, 30, 40, 50]})
print(df.describe())

What is the output for the mean of column A?

medium

A. 3.0

B. 2.5

C. 15.0

D. 10.0

Solution

1. Step 1: Calculate mean of column A

   Column A has values [1, 2, 3, 4, 5]. Mean = (1+2+3+4+5)/5 = 15/5 = 3.0.

2. Step 2: Verify other options

   Options B, C, and D do not match the calculated mean.

3. Final Answer:

   3.0 -> Option A

4. Quick Check:

   Mean of A = 3.0 [OK]

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

Common Mistakes:

• Mixing mean with median or mode
• Calculating sum instead of mean
• Using wrong column values

4. You run the code below but get unexpected output:

import pandas as pd
df = pd.DataFrame({'X': [5, 10, 15]})
print(df.describe)

What is the problem and how to fix it?

medium

A. DataFrame missing columns; add more columns

B. describe is not a pandas function; import missing

C. Missing parentheses after describe; fix with df.describe()

D. Syntax error in DataFrame creation

Solution

1. Step 1: Identify the problem

   The code references df.describe without parentheses, so it prints the method object, not the result.

2. Step 2: Fix the code

   Add parentheses to call the method: df.describe().

3. Final Answer:

   Missing parentheses after describe; fix with df.describe() -> Option C

4. Quick Check:

   Method call needs () = df.describe() [OK]

Hint: Always use () to call methods [OK]

Common Mistakes:

• Forgetting parentheses on method calls
• Assuming describe is not a method
• Thinking DataFrame needs more columns

5. You have a DataFrame df with numeric and categorical columns. You want describe() to include statistics for all columns, not just numeric ones. Which code will achieve this?

hard

A. df.describe(exclude='all')

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

C. df.describe(numeric_only=True)

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

Solution

1. Step 1: Understand describe() default behavior

   By default, describe() shows stats only for numeric columns.

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

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

3. Step 3: Check other options

   exclude='all' excludes all columns (invalid result); numeric_only=True limits to numeric only; include='numeric' matches default.

4. Final Answer:

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

5. Quick Check:

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

Hint: Use include='all' to describe all columns [OK]

Common Mistakes:

• Using numeric_only=True to get all columns
• Confusing include and exclude parameters
• Passing invalid values to include