👀 Views: 46 💬 Answers: 1 📅 Created: 2025-06-10

pandas dataframe filtering Python

I'm integrating two systems and I'm attempting to set up I'm testing a new approach and I'm trying to filter a Pandas DataFrame based on multiple conditions, but I'm running into issues when my DataFrame contains NaN values... Specifically, I want to filter rows where the 'age' column is greater than 25 and the 'salary' column is not NaN. However, I'm working with unexpected results. Here’s a simplified version of my DataFrame: ```python import pandas as pd import numpy as np # Sample DataFrame data = { 'name': ['Alice', 'Bob', 'Charlie', 'David', 'Eva'], 'age': [24, 30, 22, 35, np.nan], 'salary': [50000, np.nan, 40000, 60000, 55000] } df = pd.DataFrame(data) ``` When I attempt to filter like this: ```python filtered_df = df[(df['age'] > 25) & (df['salary'].notna())] ``` I expect to get only David's record, but the output is empty. I’ve confirmed that there is indeed a record where age is 35 and salary is 60000. I suspect the scenario may be related to how Pandas handles NaN values in logical operations. I've tried using `fillna()` before filtering and it didn't solve the question. Here’s what I’ve attempted so far: 1. Used `dropna()` on the DataFrame before applying the filter, but that removes rows I still want to consider. 2. Tried checking individual conditions to see their outputs separately, which confirmed that both conditions work when evaluated individually. What am I missing here? How can I filter the DataFrame accurately while considering NaN values in the 'salary' column? My team is using Python for this REST API. Cheers for any assistance! My team is using Python for this desktop app. I'd be grateful for any help. Has anyone dealt with something similar?