👀 Views: 0 💬 Answers: 1 📅 Created: 2025-06-19

pandas dataframe apply data-transformation Python

I'm a bit lost with I'm experimenting with Quick question that's been bugging me - I'm working with an scenario when trying to apply a custom transformation function to a column in my DataFrame using Pandas version 1.4.2. The function should convert strings to title case, but it seems to be skipping some entries and returning `NaN` instead. Here's the code I'm using: ```python import pandas as pd data = {'name': ['alice', 'BOB', None, 'charlie', 'dAVE']} df = pd.DataFrame(data) # Custom function to convert to title case def to_title_case(s): if isinstance(s, str): return s.title() return None # Applying the function with apply() df['name_transformed'] = df['name'].apply(to_title_case) print(df) ``` When I run this code, I expect the `name_transformed` column to contain `['Alice', 'Bob', None, 'Charlie', 'Dave']`, but I get: ``` name_transformed 0 Alice 1 NaN 2 None 3 Charlie 4 NaN ``` It seems the `apply()` method is not handling `None` values correctly, and I'm unsure why `BOB` is being transformed to `NaN`. I tried to add a debug print statement inside the function, and it shows that the function is indeed being called for all entries, including `BOB`. Any insights on how I can fix this scenario? Am I missing something in how Pandas handles `None` values? Also, is there a more efficient way to apply transformations to large datasets, as I suspect this might impact performance if my DataFrame grows significantly? I'm working on a mobile app that needs to handle this. For context: I'm using Python on Windows 10.