Pandas Timestamp Filtering optimization guide as Expected with Timezone-Aware Data
I'm a bit lost with I've encountered a strange issue with I've encountered a strange issue with I'm relatively new to this, so bear with me..... I'm trying to filter a Pandas DataFrame based on a timestamp column that contains timezone-aware datetimes. However, my filtering logic seems to be getting incorrect results. I have a DataFrame where one of the columns, 'event_time', is of type datetime with UTC timezone. Here's a snippet of how I'm creating the DataFrame: ```python import pandas as pd import pytz data = { 'event_time': [ pd.Timestamp('2023-10-01 10:00:00', tz='UTC'), pd.Timestamp('2023-10-01 12:00:00', tz='UTC'), pd.Timestamp('2023-10-01 15:00:00', tz='UTC') ], 'event': ['start', 'middle', 'end'] } df = pd.DataFrame(data) ``` Now, I want to filter this DataFrame to get events that occurred after '2023-10-01 11:00:00' UTC. I tried the following: ```python filter_time = pd.Timestamp('2023-10-01 11:00:00', tz='UTC') filtered_df = df[df['event_time'] > filter_time] ``` However, `filtered_df` is returning an empty DataFrame, which is unexpected, as I know there are events after that timestamp. When I print `filtered_df`, I get: ``` Empty DataFrame Columns: [event_time, event] Index: [] ``` I've checked the types of the columns in `df`, and they are correct: ```python print(df.dtypes) ``` This outputs: ``` event_time datetime64[ns, UTC] event object dtype: object ``` I suspect it might be an scenario with how I'm comparing the timestamps, but they both seem to be in the same timezone. I've also made sure to install the latest versions of Pandas (1.5.3) and pytz (2023.2). Can anyone guide to understand why this comparison isn't working as expected? Am I missing something in my filtering logic, or is there a nuance with timezone-aware datetimes that I need to consider? Has anyone dealt with something similar? Any pointers in the right direction?