👀 Views: 0 💬 Answers: 1 📅 Created: 2025-07-05

pandas dataframe boolean-indexing python

I'm working through a tutorial and I'm writing unit tests and I'm experiencing unexpected behavior when trying to filter a DataFrame using boolean indexing with NaN values. I have a DataFrame with a column that contains both numerical values and NaN entries, and I want to select rows based on certain conditions. Here's a simplified version of my DataFrame: ```python import pandas as pd import numpy as np df = pd.DataFrame({ 'A': [1, 2, np.nan, 4], 'B': [5, np.nan, 7, 8] }) ``` Now, I want to select rows where column 'A' is greater than 2. I thought I could do this with the following line: ```python result = df.loc[df['A'] > 2] ``` However, when I print `result`, I am surprised to find that it returns only the rows with valid entries, and the row with NaN in column 'A' is completely omitted: ```python print(result) ``` This outputs: ``` A B 3 4.0 8.0 ``` I was expecting to see some indication that a row was filtered out due to NaN values, but it looks like it's just completely removed from the result. I would like to know if there’s a way to include these NaN values in my result set while still applying my condition. I’ve tried using the `fillna` method, but it doesn’t seem to give me what I want either. Here’s what I tried: ```python df.fillna(0, inplace=True) result = df.loc[df['A'] > 2] ``` This still excludes the NaN entries which seem to be replaced with 0, but it changes the original data. Is there a way to keep the NaN rows in the result without losing information, or is this behavior expected? Any help would be appreciated! I'm working on a application that needs to handle this. What am I doing wrong? My development environment is Windows. I'm developing on Ubuntu 22.04 with Python. Has anyone dealt with something similar? Has anyone dealt with something similar?