Learn how to count the number of missing (NaN) values in a specific column after applying a conditional operation.
Identify the number of rows that have NaN in the Purchase Amount (USD) column after replacing values below $50.
Print the total count of such rows.
Python Code:
import pandas as pd
import numpy as np
nan_count = df['Purchase Amount (USD)'].isna().sum()
print("Number of rows with NaN in 'Purchase Amount (USD)':", nan_count)