👀 Views: 29 💬 Answers: 1 📅 Created: 2025-06-10

pandas csv dataframe Python

I'm dealing with Quick question that's been bugging me - I'm having trouble loading a CSV file into a Pandas DataFrame where some columns have mixed data types, and it's causing issues with further operations. The CSV contains a column for numeric IDs that sometimes includes 'N/A' as a string due to missing data, and I expect this column to be treated as integers. When I try to load it using `pd.read_csv`, it automatically converts the entire column to `object` type instead of `Int64`, which leads to problems when I later try to perform numerical operations. Here’s the code I used: ```python import pandas as pd # Attempting to read the CSV with specific dtypes file_path = 'data.csv' df = pd.read_csv(file_path, dtype={'id_column': 'Int64'}) ``` Despite specifying `Int64`, the column is still being treated as `object`. When I print `df.dtypes`, it shows: ``` id_column object dtype: object ``` I also tried using the `na_values` parameter to specify 'N/A', but it still doesn't work as intended: ```python df = pd.read_csv(file_path, dtype={'id_column': 'Int64'}, na_values='N/A') ``` I end up with a DataFrame that has `NaN` values, but I want it to properly represent missing values with the `Int64` dtype so I can perform calculations without errors. Is there a recommended way to handle this to ensure that my numeric operations can work without running into type issues? The Pandas version I'm using is 1.3.3. Is there a better approach? I'm using Python latest in this project.