Data analysis agent pipeline in Agentic AI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a data analysis agent pipeline that loads data.

Agentic AI

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

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

Aload_data

Bopen

Cread_file

Dpd.read_csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using open() returns a file object, not a DataFrame.

Using load_data is not a standard pandas function.

The pd.read_csv function loads CSV data into a DataFrame, which is essential for analysis.

2fill in blank

medium

Complete the code to filter rows where the 'age' column is greater than 30.

Agentic AI

filtered_data = data[data['age'] [1] 30]

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

A<=

B>

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' selects ages less than or equal to 30.

Using '==' selects only ages exactly 30.

The operator > filters rows where 'age' is greater than 30.

3fill in blank

hard

Fix the error in the code to calculate the mean of the 'salary' column.

Agentic AI

average_salary = data['salary'].[1]()

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

Amean

Bcount

Cmedian

Dsum

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 'salary' column.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length if the length is greater than 3.

Agentic AI

{word: [1] for word in words if [2]

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

Alen(word)

Bword > 3

Clen(word) > 3

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'word > 3' compares string to number, causing error.

Not using len() returns the word itself, not its length.

The dictionary comprehension maps each word to its length using len(word). The condition len(word) > 3 filters words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their values if the value is greater than 0.

Agentic AI

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.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() does not convert keys to uppercase.

Using '<' instead of '>' filters wrong values.

The comprehension maps keys converted to uppercase (k.upper()) to their values (v) only if the value is greater than zero (> 0).

Practice

1. What is the main purpose of a data analysis agent pipeline?

easy

A. To store data in a database

B. To organize multiple data steps into one automated flow

C. To create visual charts manually

D. To write code without running it

Solution

1. Step 1: Understand the pipeline concept

   A data analysis agent pipeline links several data tasks into a sequence.

2. Step 2: Identify the main goal

   The goal is to automate and organize these steps for easy reuse and flow.

3. Final Answer:

   To organize multiple data steps into one automated flow -> Option B

4. Quick Check:

   Pipeline = organized automated flow [OK]

Hint: Pipelines automate step-by-step data tasks [OK]

Common Mistakes:

• Confusing pipelines with data storage
• Thinking pipelines create visuals manually
• Assuming pipelines only write code

2. Which of the following is the correct way to define a step in a data analysis agent pipeline?

easy

A. step = agent.add_step('clean_data', function=clean)

B. step = agent.run('clean_data')

C. step = agent.delete_step('clean_data')

D. step = agent.save('clean_data')

Solution

1. Step 1: Identify how to add a step

   Adding a step uses add_step with a name and function.

2. Step 2: Check options for correct syntax

   Only step = agent.add_step('clean_data', function=clean) uses add_step correctly with parameters.

3. Final Answer:

   step = agent.add_step('clean_data', function=clean) -> Option A

4. Quick Check:

   Add step uses add_step() method [OK]

Hint: Add steps with add_step(name, function) [OK]

Common Mistakes:

• Using run() instead of add_step() to define steps
• Trying to delete or save steps when defining
• Missing function parameter in add_step

3. Given this pipeline code snippet:

agent = Agent()
agent.add_step('load', function=load_data)
agent.add_step('clean', function=clean_data)
agent.add_step('analyze', function=analyze_data)
result = agent.run()

What will result contain?

medium

A. An error because run needs parameters

B. The output of load_data function

C. A list of all step names

D. The output of analyze_data function

Solution

1. Step 1: Understand the pipeline run process

   Running the pipeline executes steps in order, passing data along.

2. Step 2: Identify final output

   The last step's output (analyze_data) is returned as result.

3. Final Answer:

   The output of analyze_data function -> Option D

4. Quick Check:

   Pipeline run returns last step output [OK]

Hint: Pipeline run returns last step's result [OK]

Common Mistakes:

• Thinking run returns first step output
• Expecting a list of step names as output
• Assuming run requires extra parameters

4. You wrote this pipeline code:

agent = Agent()
agent.add_step('load', function=load_data)
agent.add_step('clean', function=clean_data)
result = agent.run()

But you get an error: KeyError: 'analyze'. What is the likely cause?

medium

A. The 'load_data' function returns None

B. The 'clean_data' function has a syntax error

C. Missing the 'analyze' step before running

D. The agent.run() method is called with wrong parameters

Solution

1. Step 1: Analyze the error message

   The error KeyError: 'analyze' means the pipeline expects an 'analyze' step.

2. Step 2: Check pipeline steps

   The code only adds 'load' and 'clean' steps, missing 'analyze'.

3. Final Answer:

   Missing the 'analyze' step before running -> Option C

4. Quick Check:

   KeyError means missing step [OK]

Hint: Check all required steps added before run [OK]

Common Mistakes:

• Blaming function syntax errors without checking steps
• Assuming run parameters cause KeyError
• Ignoring missing step names in pipeline

5. You want to create a pipeline that loads data, filters out rows with missing values, and then calculates the average of a column. Which pipeline step order is correct?

hard

A. load_data -> filter_missing -> calculate_average

B. calculate_average -> load_data -> filter_missing

C. filter_missing -> calculate_average -> load_data

D. calculate_average -> filter_missing -> load_data

Solution

1. Step 1: Understand logical data flow

   First, data must be loaded before any processing.

2. Step 2: Order filtering before calculation

   Filtering missing values must happen before calculating averages to avoid errors.

3. Step 3: Confirm step order

   Correct order is load_data, then filter_missing, then calculate_average.

4. Final Answer:

   load_data -> filter_missing -> calculate_average -> Option A

5. Quick Check:

   Load before filter before calculate [OK]

Hint: Load data first, then clean, then analyze [OK]

Common Mistakes:

• Calculating before loading data
• Filtering after calculating averages
• Mixing step order randomly