👀 Views: 1162 💬 Answers: 1 📅 Created: 2025-06-25

pandas csv data-cleaning Python

I'm trying to implement I'm converting an old project and I'm working on a personal project and I'm working with a frustrating scenario while importing a CSV file with mixed data types using Pandas in Python 3.9. My CSV file has a header row followed by data rows, but I'm seeing misaligned columns and unexpected data types upon import. Here's a snapshot of the CSV content: ```csv id,name,age,salary 1,John Doe,30,55000 2,Jane Smith,twenty-five,65000 3,Bob Johnson,,75000 4,Alice Brown,29,not_a_number ``` When I try to read this file using the following code: ```python import pandas as pd df = pd.read_csv('data.csv') print(df) ``` I get the following output: ``` id name age salary 0 1 John Doe 30 55000 1 2 Jane Smith twenty-five 65000 2 3 Bob Johnson NaN 75000 3 4 Alice Brown 29 not_a_number ``` The first scenario is that the `age` column has a string instead of an integer, and the `salary` column sometimes has non-numeric entries. I've tried using the `dtype` parameter in `read_csv` to enforce data types, but it causes errors when the data doesn't match the expected types: ```python df = pd.read_csv('data.csv', dtype={'age': 'int', 'salary': 'float'}) ``` This results in: ``` ValueError: Unable to parse string "twenty-five" at position 1 ``` I also attempted to deal with missing values using the `na_values` parameter, but it doesn’t solve the type issues for mixed data. Is there a recommended approach to handle this situation where I can read the data without working with type errors while still being able to identify and clean up the problematic entries later? Any best practices on managing such mixed type columns would be greatly appreciated! I'm working on a API that needs to handle this. How would you solve this? Any ideas what could be causing this? I recently upgraded to Python latest. What's the best practice here?