Project 12: Incomplete Product Cleaner
Data Analyst
Builds on these lessons
Current Task
Objective
.dropna() removes any row containing a missing value — the simplest way to handle empty cells when you can afford to lose incomplete rows.
Task: create a products DataFrame with a missing name and price, drop incomplete rows, and print the result.
index.py
import pandas as pd
import numpy as np
products = pd.DataFrame({'name': ['Mug', 'Pen', None], 'price': [9.99, np.nan, 4.5]})
cleaned = products.dropna()
print(cleaned)
* Hint: Correct characters turn green, incorrect ones turn red.
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