Manipulating panda dataframes more python-like

[snippsat]

Unfortunately, I don't think I'm doing it in a python-efficient way as the script has been executing for over three hours. Here's some example code. Is there a better way to compare time stamps and not rely as heavily on for loops?

Pandas often need a tolal diffent approch than how stuff are done in Python,the loops are usually out 🚀
Look at this blog post.
So try to work on whole dataframs(build in tools) using vectorization(this mean avoid Python-level loops)
To give exampe how to compare time between two or more dataframs.

import pandas as pd

# Create Dataframe 1
date1 = ['2022-01-01 00:00:00', '2022-01-02 12:00:00', '2022-01-03 18:00:00']
wind_speed1 = [5, 6, 7]
df1 = pd.DataFrame({'date_time': pd.to_datetime(date1), 'wind_speed': wind_speed1})

# Create Dataframe 2
date2 = ['2022-01-02 08:00:00', '2022-01-03 15:00:00', '2022-01-04 20:00:00']
wind_speed2 = [8, 9, 10]
df2 = pd.DataFrame({'date_time': pd.to_datetime(date2), 'wind_speed': wind_speed2})

# Convert the specific time to a 'pandas.Timestamp' object
specific_time = df1.iloc[1]['date_time'].time()
specific_time = pd.to_datetime(specific_time, format='%H:%M:%S').time()

# Compare time less than a specific time and show the result
print(df2[df2['date_time'].dt.time < specific_time])

Output:
     date_time        wind_speed
0   2022-01-02 08:00:00   8