How to implement guide with dataframe.apply() not applying function correctly when passing axis=1 with mixed data types
I can't seem to get I've searched everywhere and can't find a clear answer. I'm deploying to production and I'm working with an scenario when trying to use `DataFrame.apply()` on a DataFrame that contains mixed data types (integers, floats, and strings). I want to apply a custom function to each row that calculates a weighted score based on certain conditions. However, I'm getting unexpected results when passing `axis=1`. Here’s what I have: ```python import pandas as pd data = { 'A': [1, 2, 3], 'B': [4.5, 5.5, 6.5], 'C': ['low', 'medium', 'high'] } df = pd.DataFrame(data) # Custom function to calculate score def calculate_score(row): score = row['A'] * 2 # A simple calculation based on column 'A' if row['C'] == 'high': score += 10 elif row['C'] == 'medium': score += 5 return score result = df.apply(calculate_score, axis=1) print(result) ``` The output I expected was: ``` 0 12 1 10 2 16 dtype: int64 ``` but instead, I’m getting: ``` 0 12.0 1 10.0 2 NaN dtype: float64 ``` I’ve double-checked my function and it seems correct. I even tried explicitly converting the values in the function, but that didn’t help. I’m using Pandas version 1.3.0. Could this be related to how Pandas handles mixed data types, or is there something I'm missing in the function logic? Am I approaching this the right way? Am I missing something obvious? This issue appeared after updating to Python latest.