Training data preparation in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load data from a CSV file using pandas.

Prompt Engineering / GenAI

import pandas as pd
data = pd.read_csv([1])

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

A'data.csv'

Bdata.csv

Ccsv.data

Dread.csv

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the file name

Using incorrect function names

The read_csv function requires the file path as a string, so it must be in quotes.

2fill in blank

medium

Complete the code to split data into features and labels.

Prompt Engineering / GenAI

X = data.drop([1], axis=1)
y = data['label']

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

A'features'

B'label'

Clabel

Dfeatures

Attempts:

3 left

💡 Hint

Common Mistakes

Dropping the wrong column

Not using quotes for column name

We drop the column named 'label' to get features. The column name must be a string.

3fill in blank

hard

Fix the error in the code to split data into training and testing sets.

Prompt Engineering / GenAI

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], random_state=42)

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A50

B0.5

C0.2

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using integers instead of floats

Using values greater than 1

The test_size parameter expects a float between 0 and 1 representing the proportion of the test set.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

Prompt Engineering / GenAI

lengths = {word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B<

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong comparison operator

Mapping word instead of length

The dictionary maps each word to its length, but only includes words longer than 3 characters.

5fill in blank

hard

Fill all three blanks to create a filtered dictionary with uppercase keys, values as counts, and only include counts greater than 1.

Prompt Engineering / GenAI

result = { [1]: [2] for k, v in data.items() if v [3] 1 }

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

Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using original keys instead of uppercase

Using wrong comparison operator

The dictionary comprehension uses uppercase keys, values as counts, and filters counts greater than 1.