implementing CSV containing mixed data types in Pandas - TypeError during data processing
I need some guidance on I'm working on a project and hit a roadblock... I'm trying to read a CSV file using Pandas (version 1.3.3) that contains a column with mixed data types. For example, one of the columns, 'Age', has integers for most entries, but there are a few strings like 'N/A' and 'unknown'. When I attempt to process this column, I'm getting a `TypeError: want to perform reduce with flexible type`. My current approach is to read the CSV and then convert the 'Age' column to numeric values using `pd.to_numeric()` with `errors='coerce'` to handle the non-numeric entries. However, I end up with NaN values that I need to deal with afterward. Here’s the code snippet I’m using: ```python import pandas as pd df = pd.read_csv('data.csv') df['Age'] = pd.to_numeric(df['Age'], errors='coerce') print(df['Age']) ``` After executing this, I expected all invalid entries to be converted to NaN, but when I try to fill these NaNs using `df['Age'].fillna(0)`, I receive a warning about trying to set values on a copy of a slice from a DataFrame. Additionally, some of my subsequent analyses that involve grouping by 'Age' are failing because they expect numeric types but receive NaNs. What is the best way to handle this mixed data type scenario and avoid the warnings while ensuring that my numeric operations run smoothly? For context: I'm using Python on Windows. Any help would be greatly appreciated! Thanks for your help in advance!