Survey data analysis pattern in Data Analysis Python - Mini Project: Build & Apply

Survey Data Analysis Pattern

📖 Scenario: You work for a company that collects customer satisfaction surveys. Each survey has a customer's name and their satisfaction score from 1 to 10. You want to analyze this data to find which customers are satisfied based on a score threshold.

🎯 Goal: Build a simple program that stores survey data, sets a satisfaction threshold, filters customers who meet or exceed this threshold, and prints their names and scores.

📋 What You'll Learn

Create a dictionary with exact customer names and scores

Create a variable for the satisfaction threshold

Use a dictionary comprehension to filter satisfied customers

Print the filtered dictionary

💡 Why This Matters

🌍 Real World

Companies often collect survey data to understand customer satisfaction and improve their services.

💼 Career

Data analysts use similar patterns to filter and analyze survey or feedback data to generate insights.

1

Create the survey data dictionary

Create a dictionary called survey_data with these exact entries: 'Alice': 7, 'Bob': 4, 'Charlie': 9, 'Diana': 6, 'Ethan': 3

Data Analysis Python

# Create the survey_data dictionary with customer names and scores
# Your code here

Hint

Use curly braces {} to create a dictionary with keys as names and values as scores.

2

Set the satisfaction threshold

Create a variable called threshold and set it to 6 to represent the minimum score for satisfaction

Data Analysis Python

survey_data = {'Alice': 7, 'Bob': 4, 'Charlie': 9, 'Diana': 6, 'Ethan': 3}
# Create the threshold variable with value 6
# Your code here

Hint

Just assign the number 6 to a variable named threshold.

3

Filter satisfied customers using dictionary comprehension

Use a dictionary comprehension to create a new dictionary called satisfied_customers that includes only customers from survey_data with scores greater than or equal to threshold

Data Analysis Python

survey_data = {'Alice': 7, 'Bob': 4, 'Charlie': 9, 'Diana': 6, 'Ethan': 3}
threshold = 6
# Create satisfied_customers dictionary using comprehension
# Your code here

Hint

Use {name: score for name, score in survey_data.items() if score >= threshold} to filter.

4

Print the satisfied customers

Write a print statement to display the satisfied_customers dictionary

Data Analysis Python

survey_data = {'Alice': 7, 'Bob': 4, 'Charlie': 9, 'Diana': 6, 'Ethan': 3}
threshold = 6
satisfied_customers = {name: score for name, score in survey_data.items() if score >= threshold}
# Print the satisfied_customers dictionary
# Your code here

Hint

Use print(satisfied_customers) to show the filtered dictionary.

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