👀 Views: 59 💬 Answers: 1 📅 Created: 2025-06-17

pandas csv dataframe Python

This might be a silly question, but I've looked through the documentation and I'm still confused about I'm working with an scenario while reading a CSV file using Pandas version 1.3.5 where the data types are not being interpreted as expected... I have a CSV with several columns, but I specifically want to ensure that a column named 'amount' is interpreted as 'float64'. Here’s what my code looks like: ```python import pandas as pd dtypes = {'amount': 'float64'} data = pd.read_csv('data.csv', dtype=dtypes) ``` However, when I inspect the DataFrame using `data.dtypes`, it shows that the 'amount' column is being interpreted as 'object' instead of 'float64'. I’ve double-checked the contents of 'data.csv', and it appears that there are some rows with non-numeric characters, such as commas and spaces, in the 'amount' column. I tried using `error_bad_lines=False` to skip problematic rows, but it still doesn't seem to apply the dtype correctly: ```python import pandas as pd data = pd.read_csv('data.csv', dtype=dtypes, error_bad_lines=False) ``` I also considered using `converters` to preprocess the 'amount' column, but I’m unsure how to handle non-numeric data effectively. Here's an example of what one of the rows looks like: ``` item,amount "item1", "1,234" "item2", "45.67" "item3", "N/A" ``` What would be the best approach to ensure that the 'amount' column is read correctly as 'float64', while also handling or ignoring the non-numeric values? Any help would be appreciated. For context: I'm using Python on macOS. The project is a application built with Python. Could someone point me to the right documentation? I recently upgraded to Python 3.9. Any ideas how to fix this? I'm working with Python in a Docker container on Linux. Cheers for any assistance! My team is using Python for this web app. Could someone point me to the right documentation?