Pandas scenarios to parse CSV with irregularly quoted fields and trailing spaces
I'm collaborating on a project where I'm having trouble reading a CSV file with `pandas` (version 1.3.0) that contains fields that are irregularly quoted and sometimes have trailing spaces. For example, my CSV looks like this: ```csv "Name", "Age", "Location" "John Doe", "30", " New York " "Jane Smith", 25, "Los Angeles" "Mark Johnson", "35", " " ``` When I try to read this CSV using the following code: ```python import pandas as pd # Attempting to read the CSV try: df = pd.read_csv('data.csv') print(df) except Exception as e: print(f'behavior: {e}') ``` I'm getting the following warning: `DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.` The resulting DataFrame has 'object' types for all columns instead of the expected types. Additionally, the trailing spaces in the 'Location' column are preserved, which I want to trim. I've tried using the `converters` parameter to clean up the data, but it hasn't resolved the mixed types scenario. Here’s what I attempted: ```python # Using converters to trim spaces converters = {0: lambda x: x.strip(), 1: lambda x: int(x.strip()), 2: lambda x: x.strip()} df = pd.read_csv('data.csv', converters=converters) ``` This approach still leads to the same dtype warning, and I end up with a DataFrame where 'Age' is still an object type for some entries. What can I do to successfully read this CSV while ensuring that the types are consistent and trailing spaces are removed? I recently upgraded to Python stable. Any help would be greatly appreciated!