implementing CSV Date Parsing in Pandas - Incorrect Formats Leading to NaT
I'm testing a new approach and I'm having trouble reading a CSV file where the date formats are inconsistent, leading to `NaT` values in my DataFrame when using Pandas version 1.3.0... The CSV contains dates formatted in both `MM/DD/YYYY` and `YYYY-MM-DD`, and when I try to parse them using `pd.to_datetime`, it's returning `NaT` for several entries. Here's what my code looks like: ```python import pandas as pd df = pd.read_csv('data.csv') df['date'] = pd.to_datetime(df['date'], errors='coerce') ``` I've tried using `errors='coerce'` to handle parsing errors, but I'm still left with a lot of `NaT` values. Upon inspecting the data, here's a sample of what I found: ``` 03/25/2020 2020-03-26 03-27-2020 2020/03/28 ``` It seems that the mix of formats is causing the scenario. I attempted to preprocess the date column by replacing the slashes with dashes, but that didn't help either: ```python df['date'] = df['date'].str.replace('/', '-') df['date'] = pd.to_datetime(df['date'], errors='coerce') ``` Now I just get more `NaT` values, and I'm not sure how to effectively handle this mixed format to ensure all dates are parsed correctly. What strategies or functions can I use to manage this situation more effectively? Are there best practices for handling multiple date formats in a single CSV column? What am I doing wrong?