👀 Views: 93 💬 Answers: 1 📅 Created: 2025-06-25

python csv parsing Python

I'm wondering if anyone has experience with I'm deploying to production and I'm working with scenarios while trying to parse a CSV file in Python that contains mixed data types across rows. The CSV has strings, integers, and floats, but I'm working with unexpected behavior when reading it using the built-in `csv` module. For instance, some integer columns are being interpreted as strings, especially when the CSV has leading spaces or inconsistent formatting. Here's a snippet of the CSV I'm working with: ``` name,age,income Alice,30,55000.50 Bob, 28,46000 Charlie,35, 60000.00 ``` I'm using the following code to read the CSV: ```python import csv with open('data.csv', newline='') as csvfile: reader = csv.DictReader(csvfile) for row in reader: print(row) ``` The output I get is as follows: ``` {'name': 'Alice', 'age': '30', 'income': '55000.50'} {'name': 'Bob', 'age': ' 28', 'income': '46000'} {'name': 'Charlie', 'age': '35', 'income': ' 60000.00'} ``` As you can see, the age for Bob retains a leading space, and all values are strings, which complicates any further data manipulation. I tried using `int()` and `float()` to convert the values after reading but ran into `ValueError` for rows with spaces or if the income field is blank. I also looked into using the `pandas` library, but I really want to solve this with the standard library. Is there a more reliable way to ensure consistent data types when parsing CSV files with mixed data? Any suggestions on handling leading spaces or converting data after reading would be appreciated! Has anyone else encountered this?