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]()

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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]

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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] ''}

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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] ''}

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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 (!=).