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Efficient way to recognize time series's granularity in dataframe?

I have multiple time-series as dataframes in Python3, imported from excel files looking like this:
But they come in varying levels of data granularity such as hourly, daily, monthly and yearly series. To be perfectly clear: within a single file the granularity is consistent, it only varies across the different files. However, there might be missing time stamps (predictable ones like daylight saving or unpredictable ones because of technical failure to record (e.g. in the context of weather data)).
I would like to efficiently recognize each series' granularity level with a function, assuming the first column is always datetime such that an hourly series would have datetime steps like 2022-11-11 01:00 and 2022-11-11 02:00 whereas a yearly series would have datetime steps like 2022-01-01 00:00 and 2023-01-01 00:00
As a first approach I thought about taking the difference between the datetime series against its lagged series and calculate the average over total horizon to infer on the granularity level, but that seems rather inefficient. I'm hoping there is some built-in function in the datetime package already or that someone can come up with a reliable and more efficient method.
Edit 1
The above screenshot shows an example of a df featuring daily time-series granularity. Here is another screenshot showing a time-series with hourly granularity:

Python - Convert UTC datetime.timetz() to local time

Having a hard time searching for this since it's mostly datetime or similar-but-different results and still seeming to struggle with getting suggestions for slightly different problems to work with this one.
I have some UTC datetime object: timestamp. What I'm doing is dealing with many timestamps and corresponding values and bucketing them hour by hour, so I'm bucketing them by timestamp.timetz().
Now I want to loop through these buckets, take this UTC time object, and convert it to Eastern time, e.g. I want 00:00 to become 20:00, keeping daylight savings in mind.
How do I accomplish this?

You can use the "pytz" package to get this done. Below is a example to get current time using pytz in eastern timezone.
import pytz
from datetime import datetime
est_tz = pytz.timezone('US/Eastern')
datetime.now(est_tz)
Hope it helps.
Happy Coding !! :)

Get the timestamp of the start of a day (local time) [duplicate]

This question already has an answer here:
How to get the first datetime of a day?
(1 answer)
Closed 7 years ago.
I wonder if there's any way to get the UNIX timestamp at the beginning of a certain day, i.e the midnight timestamp of each day given its timezone.

Assuming you do not care about daylight savings time and you know the UTC offset which was in effect at that time (as opposed to the UTC offset which is in effect now), you can just do this:
import datetime as dt
return dt.datetime(year, month, day, tzinfo=dt.timezone(utc_offset)).timestamp()
The hour, minute, and second default to zero, so you can skip them. The timezone class does not handle daylight savings time, historical changes in timezone definitions (e.g. British Double Summer Time), or any other temporal anomalies (e.g. there was no December 30, 2011 in Samoa); it is a "dumb" offset. It is equivalent (in this case) to adding or subtracting the offset directly onto the timestamp and then working in UTC. You must ensure this is correct for your use case. If you need better timekeeping, you should install and make use of pytz.

Convert GMT time to Local time in python

I have a date(datetime) which is stored in the database as GMT time.
I need to convert this to local time.How can i achieve this using python
Please have your suggestions
Thanks in Advance!!

Have you read the Python documentation, 8.1 datetime — Basic date and time types
and especially 8.3 tzinfo objects?
They describe the library function datetime.astimezone(tz=None), which may be exactly what you want.
Return a datetime object with new tzinfo attribute tz, adjusting the date and time data so the result is the same UTC time as self, but in tz‘s local time.
They have example code there for defining a time zone in terms of an offset from UTC, ending with a declaration like:
Pacific = USTimeZone(-8, "Pacific", "PST", "PDT")
Having read that, what is your question?
Do you have a date or a datetime? i.e. does the data have a time zone component? If no time component, then what is the meaning of changing the date to a different time zone?
Do you have a 'naive' or 'aware' datetime? From the docs,
An aware object has sufficient knowledge of applicable algorithmic and political time adjustments, such as time zone and daylight saving time information, to locate itself relative to other aware objects. An aware object is used to represent a specific moment in time that is not open to interpretation....
A naive object does not contain enough information to unambiguously locate itself relative to other date/time objects. Whether a naive object represents Coordinated Universal Time (UTC), local time, or time in some other timezone is purely up to the program, just like it is up to the program whether a particular number represents metres, miles, or mass. Naive objects are easy to understand and to work with, at the cost of ignoring some aspects of reality.
The Python documentation is really quite good. Perhaps it will answer your questions without you having to ask StackOverflow.

How to handle time zones in a CMS?

I'm storing MySQL DateTimes in UTC, and let the user select their time zone, storing that information.
However, I want to to some queries that uses group by a date. Is it better to store that datetime information in UTC (and do the calculation every time) or is it better to save it in the timezone given? Since time zones for users can change, I wonder.
Thanks

Generally always store in UTC and convert for display, it's the only sane way to do time differences etc. Or when somebody next year decides to change the summer time dates.

It's almost always better to save the time information in UTC, and convert it to local time when needed for presentation and display.
Otherwise, you will go stark raving mad trying to manipulate and compare dates and times in your system because you will have to convert each time to UTC time for comparison and manipulation.