implementing Filtering a DataFrame While Retaining Original Index in Pandas
I tried several approaches but none seem to work... I've been banging my head against this for hours. I'm working with a question when trying to filter a pandas DataFrame based on certain conditions while wanting to keep the original index intact. I have a DataFrame with a multi-level index and I'm filtering it based on a condition applied to one of the columns. However, after filtering, the index gets reset and I lose the original multi-level index structure, which I need for further operations. Here’s a small example of what I'm trying to do: ```python import pandas as pd import numpy as np # Create a sample DataFrame with a multi-level index index = pd.MultiIndex.from_tuples([('A', 'foo'), ('A', 'bar'), ('B', 'foo'), ('B', 'bar')]) data = {'value': [10, np.nan, 30, 40]} df = pd.DataFrame(data, index=index) # Filter the DataFrame where 'value' is not NaN filtered_df = df[df['value'].notna()] print(filtered_df) ``` When I run this code, I expect to see the filtered DataFrame with the original multi-level index. However, what I get is: ``` value (A, bar) NaN (A, foo) 10.0 (B, foo) 30.0 (B, bar) 40.0 ``` The index is not reset, but the actual NaN value from the original DataFrame is still present. What I want is to keep the original index intact without any NaN values in the resulting DataFrame. Can anyone suggest how I can achieve this while keeping the index unchanged? I've tried using `reset_index()` before filtering, but that just complicates things further. I'm using pandas version 1.3.0. Any help would be greatly appreciated! For context: I'm using Python on macOS. I'm on Debian using the latest version of Python. I'd love to hear your thoughts on this.