Survey data analysis pattern in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load survey data from a CSV file into a DataFrame.

Data Analysis Python

import pandas as pd

data = pd.[1]('survey_data.csv')

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

Aread_json

Bread_csv

Cread_excel

Dread_table

Attempts:

3 left

💡 Hint

Common Mistakes

Using read_excel for a CSV file causes errors.

Trying to use read_json when the file is not JSON format.

The read_csv function loads CSV files into a DataFrame, which is perfect for survey data stored in CSV format.

2fill in blank

medium

Complete the code to calculate the average age from the survey data.

Data Analysis Python

average_age = data['age'].[1]()

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

Acount

Bsum

Cmean

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum() returns total, not average.

Using count() returns number of entries, not average.

The mean() function calculates the average value of the 'age' column.

3fill in blank

hard

Fix the error in the code to filter survey responses where satisfaction is greater than 3.

Data Analysis Python

high_satisfaction = data[data['satisfaction'] [1] 3]

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

A>

B==

C=

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' causes syntax error in filtering.

Using '==' filters only values equal to 3, not greater.

To filter rows where satisfaction is greater than 3, use the > operator.

4fill in blank

hard

Fill both blanks to create a dictionary of counts for each response in the 'feedback' column where length is more than 10 characters.

Data Analysis Python

feedback_counts = {response: data['feedback'].value_counts()[response] for response in data['feedback'] if len(response) [1] 10 and response [2] ''}

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

A>

B<

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' filters short feedback, not long.

Using '==' for empty string includes empty feedback.

We want feedback longer than 10 characters (>) and not empty (!= '') to count meaningful responses.

5fill in blank

hard

Fill all three blanks to create a dictionary with keys as uppercase feedback and values as counts for feedback longer than 5 characters.

Data Analysis Python

result = {response[1]: data['feedback'].value_counts()[response] for response in data['feedback'] if len(response) [2] 5 and response [3] ''}

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

A.upper()

B>

C!=

D.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using .lower() instead of .upper() changes case incorrectly.

Using '<' filters short feedback, not long.

Using '==' includes empty strings.

We convert feedback to uppercase keys (.upper()), filter length greater than 5 (>), and exclude empty strings (!=).

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