Pandas DataFrame from CSV with mixed data types - malformed data handling
I tried several approaches but none seem to work. I'm converting an old project and I've looked through the documentation and I'm still confused about I'm trying to read a CSV file that contains mixed data types within the same column, which is causing issues when I attempt to convert the DataFrame to numeric types later on. My CSV looks something like this: ```csv id,name,value 1,Alice,10 2,Bob,twelve 3,Charlie,15 4,David,NaN 5,Eve,20.5 ``` When I use `pandas.read_csv()`, it loads the data, but the 'value' column ends up as an object type due to the presence of the string 'twelve' and 'NaN'. Here’s the code I’m using: ```python import pandas as pd df = pd.read_csv('data.csv') print(df.dtypes) ``` This prints: ``` id int64 name object value object dtype: object ``` I’ve tried using `pd.to_numeric()` on the 'value' column: ```python df['value'] = pd.to_numeric(df['value'], errors='coerce') ``` This successfully converts 'twelve' to NaN, but now my DataFrame ends up with missing values. I want to either skip rows with malformed data or handle them differently without losing the entire dataset's integrity. Is there a way to implement this without compromising my DataFrame's structure? Also, how can I validate the data types in such cases before attempting to convert? Any suggestions on best practices for handling this type of situation would be greatly appreciated. I'm working on a application that needs to handle this. Any suggestions would be helpful.