👀 Views: 807 💬 Answers: 1 📅 Created: 2025-06-10

pandas dataframe nan Python

I'm attempting to set up I'm working with an scenario when trying to filter a Pandas DataFrame using a boolean mask that involves NaN values. Specifically, I have a DataFrame with some numeric columns that contain NaN values, and I want to create a new DataFrame that only includes rows where a specific column meets a certain condition. However, when I apply my boolean mask, it doesn't seem to account for NaN values as I expected. I am using Pandas version 1.4.2. Here's a simplified version of my code: ```python import pandas as pd import numpy as np # Sample DataFrame data = { 'A': [1, 2, np.nan, 4], 'B': [5, np.nan, 7, 8], 'C': [np.nan, 2, 3, 4] } df = pd.DataFrame(data) # Attempting to filter where column 'A' is greater than 2 mask = df['A'] > 2 df_filtered = df.loc[mask] print(df_filtered) ``` When I run this code, I expect to see the rows where 'A' is greater than 2, but the result is an empty DataFrame: ``` Empty DataFrame Columns: [A, B, C] Index: [] ``` I've tried replacing NaN values in column 'A' with a placeholder using `df['A'].fillna(0)` but that doesn't seem to resolve the scenario. Is there a better way to handle NaN values in this context so that I can get the expected results? Any insights would be greatly appreciated! I recently upgraded to Python latest.