Pandas CSV Read guide: Unexpected NaN Values When Importing Timestamps
I'm upgrading from an older version and After trying multiple solutions online, I still can't figure this out. I'm having a hard time understanding I'm updating my dependencies and I've been struggling with this for a few days now and could really use some help... I'm sure I'm missing something obvious here, but I'm working with an scenario while trying to read a CSV file containing timestamp data using Pandas in Python 3.9. The CSV file has a column labeled 'timestamp' formatted as 'YYYY-MM-DD HH:MM:SS'. However, when I read the CSV, some of the timestamps are being interpreted as NaN values. Here's the code I'm using: ```python import pandas as pd # Attempting to read the CSV file_path = 'data.csv' df = pd.read_csv(file_path) print(df) ``` The output shows that some rows in the 'timestamp' column are converted to NaN: ``` id timestamp 0 1 2023-08-01 12:00:00 1 2 NaN 2 3 2023-08-01 12:05:00 3 4 NaN ``` I verified that the CSV file has no irregularities (like extra whitespace or non-standard characters) that may cause this scenario. I also tried using `pd.read_csv(file_path, parse_dates=['timestamp'])` to force the conversion, but I still see NaN values in the result. Also, when I open the CSV in a text editor, the timestamps look perfectly fine. I've considered adding a specific date parser, but that seems unnecessary. Here’s a snippet of the CSV data for clarity: ``` id,timestamp 1,2023-08-01 12:00:00 2,2023-08-01 12:03:00 3,2023-08-01 12:05:00 4,2023-08-01 12:12:00 ``` Is there a specific reason Pandas would interpret some of these timestamps as NaN? How can I ensure that all timestamps are correctly read without any NaN values? This is part of a larger application I'm building. Any ideas what could be causing this? For context: I'm using Python on Windows. Thanks in advance! This is for a service running on Debian. The stack includes Python and several other technologies. Am I approaching this the right way? Thanks for taking the time to read this! Has anyone dealt with something similar?