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Project 10: Missing Listings Finder

Data Analyst

Builds on these lessons

Current Task

Before analyzing data, you need to find what's missing — .isnull() returns a same-shaped DataFrame of True/False flags.

Task: create a listings DataFrame with a missing price, and print the result of .isnull().

index.py

import pandas as pd
import numpy as np

listings = pd.DataFrame({'price': [250000, np.nan, 180000]})
print(listings.isnull())

* Hint: Correct characters turn green, incorrect ones turn red.

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