Survey data analysis pattern in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ data_output

intermediate

Calculate the average satisfaction score by gender

You have a survey dataset with columns Gender and Satisfaction (scale 1-5). What is the average satisfaction score for each gender?

Data Analysis Python

import pandas as pd

data = pd.DataFrame({
    'Gender': ['Male', 'Female', 'Female', 'Male', 'Female'],
    'Satisfaction': [4, 5, 3, 2, 4]
})

result = data.groupby('Gender')['Satisfaction'].mean()

A {'Female': 4.0, 'Male': 3.0}

B {'Female': 4.0, 'Male': 3.5}

C {'Female': 4.0, 'Male': 2.0}

D {'Female': 4.5, 'Male': 3.0}

💡 Hint

Use groupby on Gender and then calculate the mean of Satisfaction.

The average satisfaction for females is (5 + 3 + 4) / 3 = 4.0, and for males is (4 + 2) / 2 = 3.0.

🧠 Conceptual

intermediate

Identify the correct interpretation of a Likert scale distribution

A survey question uses a 5-point Likert scale (1=Strongly Disagree to 5=Strongly Agree). The distribution of responses is skewed towards 5. What does this indicate?

A Responses are evenly spread across all options.

B Most respondents strongly disagree with the statement.

C Most respondents strongly agree with the statement.

D Most respondents chose the neutral option.

💡 Hint

Think about what a skew towards 5 means on a 1 to 5 scale.

A skew towards 5 means many respondents selected the highest agreement level, indicating strong agreement.

🔧 Debug

advanced

Fix the error in filtering survey data by age group

The code below tries to filter respondents aged 30 or older but raises an error. Identify the cause.

Data Analysis Python

import pandas as pd

data = pd.DataFrame({'Age': [25, 30, 35, 40]})

filtered = data[data['Age'] >= 30]

A SyntaxError due to incorrect bracket placement in the filter condition.

B KeyError because 'Age' column does not exist.

C TypeError because comparison is done between string and int.

D No error; code runs correctly.

💡 Hint

Check how the condition inside the brackets is written.

The condition data['Age' >= 30] is invalid because the comparison is done inside the brackets incorrectly. It should be data['Age'] >= 30.

❓ visualization

advanced

Choose the correct plot to show satisfaction distribution by department

You want to visualize how satisfaction scores vary across different departments in a survey. Which plot type is best?

A Scatter plot of satisfaction vs. age for all respondents.

B Line plot connecting average satisfaction scores over time.

C Pie chart showing percentage of respondents per department.

D Box plot showing satisfaction score distribution per department.

💡 Hint

Think about showing distribution and comparison across categories.

A box plot effectively shows the spread and central tendency of satisfaction scores for each department, making it ideal for this purpose.

🚀 Application

expert

Calculate the response rate from survey data

You have a dataset with 1000 invited participants. The survey data contains 750 rows of responses. How do you calculate the response rate?

A Response rate = 750 - 1000 = -250

B Response rate = (750 / 1000) * 100 = 75%

C Response rate = (1000 / 750) * 100 = 133.33%

D Response rate = 1000 + 750 = 1750

💡 Hint

Response rate is the percentage of invited participants who responded.

Response rate is calculated by dividing the number of responses by the number invited, then multiplying by 100 to get a percentage.

Practice

1. What does the value_counts() function do when analyzing survey data in Python?

easy

A. Calculates the average of numerical answers

B. Groups data by categories for comparison

C. Counts how many times each unique answer appears

D. Removes missing values from the dataset

Solution

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

   This function counts the frequency of each unique value in a column, showing how many times each answer appears.

2. Step 2: Compare with other functions

   Calculating averages is done by mean(), grouping is done by groupby(), and removing missing values uses dropna().

3. Final Answer:

   Counts how many times each unique answer appears -> Option C

4. Quick Check:

   value_counts() = count unique answers [OK]

Hint: Remember: value_counts counts answers, not averages [OK]

Common Mistakes:

• Confusing value_counts with mean()
• Thinking value_counts groups data
• Assuming it removes missing data

2. Which of the following is the correct syntax to get the average rating from a survey column named rating in a pandas DataFrame df?

easy

A. df['rating'].mean()

B. df['rating'].count()

C. df.groupby('rating').sum()

D. df['rating'].value_counts()

Solution

1. Step 1: Identify the function for average calculation

   The mean() function calculates the average of numerical values in a column.

2. Step 2: Check other options

   count() counts non-missing values, groupby().sum() sums grouped data, and value_counts() counts unique values.

3. Final Answer:

   df['rating'].mean() -> Option A

4. Quick Check:

   Average = mean() [OK]

Hint: Use mean() to get average of numeric columns [OK]

Common Mistakes:

• Using count() instead of mean()
• Confusing value_counts() with average
• Trying to sum without grouping

3. Given the following code, what will be the output?

import pandas as pd
data = {'gender': ['M', 'F', 'F', 'M', 'F'], 'score': [5, 3, 4, 2, 5]}
df = pd.DataFrame(data)
result = df.groupby('gender')['score'].mean()
print(result)

medium

A. gender
F 3.5
M 4.0
Name: score, dtype: float64

B. gender
F 4.0
M 3.5
Name: score, dtype: float64

C. gender
M 4.0
F 3.5
Name: score, dtype: float64

D. gender
F 3.0
M 4.0
Name: score, dtype: float64

Solution

1. Step 1: Group scores by gender

   Group 'score' values by 'gender': For 'F' scores are [3,4,5], for 'M' scores are [5,2].

2. Step 2: Calculate mean for each group

   Mean for 'F' = (3+4+5)/3 = 4.0; Mean for 'M' = (5+2)/2 = 3.5.

3. Final Answer:

   gender F 4.0 M 3.5 Name: score, dtype: float64 -> Option B

4. Quick Check:

   Group mean = correct averages [OK]

Hint: Calculate group means by averaging grouped values [OK]

Common Mistakes:

• Mixing up group labels and values
• Calculating sums instead of means
• Ignoring group sizes

4. The following code is intended to count how many times each answer appears in the 'response' column, but it raises an error. What is the error and how to fix it?

import pandas as pd
df = pd.DataFrame({'response': ['Yes', 'No', 'Yes', 'Maybe']})
counts = df.response.value_count()
print(counts)

medium

A. KeyError because 'response' column does not exist

B. SyntaxError due to missing parentheses in print statement

C. TypeError because value_count() requires an argument

D. AttributeError because the method name should be value_counts()

Solution

1. Step 1: Identify the method name error

   The method value_count() is incorrect; the correct method is value_counts() with an 's' at the end.

2. Step 2: Correct the method name

   Changing value_count() to value_counts() fixes the AttributeError and allows counting unique values.

3. Final Answer:

   AttributeError because the method name should be value_counts() -> Option D

4. Quick Check:

   Correct method is value_counts() [OK]

Hint: Remember the method ends with 's': value_counts() [OK]

Common Mistakes:

• Misspelling method name without 's'
• Confusing print syntax errors
• Assuming column name is missing

5. You have a survey DataFrame df with columns age_group and satisfaction_score. How would you calculate the average satisfaction score for each age group and then find which age group has the highest average score?

hard

A. Use df.groupby('age_group')['satisfaction_score'].mean().idxmax()

B. Use df['satisfaction_score'].max() directly

C. Use df.value_counts('age_group') and pick max

D. Use df.groupby('satisfaction_score')['age_group'].mean()

Solution

1. Step 1: Group by age group and calculate average satisfaction

   Use groupby('age_group')['satisfaction_score'].mean() to get average scores per group.

2. Step 2: Find the age group with the highest average

   Use idxmax() on the resulting Series to get the group label with the highest average score.

3. Final Answer:

   Use df.groupby('age_group')['satisfaction_score'].mean().idxmax() -> Option A

4. Quick Check:

   Group mean + idxmax() finds highest average [OK]

Hint: Chain mean() and idxmax() to find top group quickly [OK]

Common Mistakes:

• Using max() on scores without grouping
• Confusing value_counts() with averages
• Grouping by wrong column