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')

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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]()

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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]

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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

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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).