As you might notice, the previous method simply labels to datetime timezone. The second approach is to convert existing `datetime` objects with no timezone to a certain `timezone`.

Let's consider the same `datetime` object as in the previous chapter, but this time with the second approach. The algorithm is the next:
1. Create `datetime` object with no timezone;
2. Create `timezone` object with a certain timezone name (*optional*);
3. Apply `.astimezone()` method to `datetime` object with `timezone` object as an argument.

# Load libraries and classes
import pytz
from pytz import timezone
from datetime import datetime

tz = timezone('Europe/Warsaw') # save timezone
dt = datetime(2021, 11, 1, 11, 25, 0) # create datetime object

dt_tz = dt.astimezone(tz) # convert dt to tz timezone

# Testing
print(f"Saved time: {dt}")
print(f"Converted time: {dt_tz}")

There you can see a clear difference: not only timezone was saved, but time was adjusted to timezone. Let's try to complete the task! Please note, that code is compiling on a dedicated server with GMT.

Dates and(or) times are one of the most common things in modern data: often we need to deal with observations containing dates and times. In this course, you will learn about datetime, pytz libraries, and how they help to deal with different formats of dates and times. Also, you will learn how to implement this knowledge to work with dirty or missing date/time values.

In this section, you will learn how to deal with dates in your data. Specifically, you will learn how to create date objects, how to perform arithmetic operations, like difference, how to convert dates into different local formats, and so on.

In this section, you will learn how to deal with data containing not only dates, but also times, or times only.

In this section, you will learn how to apply and adapt timezones to datetime objects and also how to deal with daylight (or summer) time.

In this section, you will learn how to implement acquired knowledge about manipulation with dates and times for different datasets using pandas library. In particular, you will handle with taxi rides dataset in Mexico City, analyzing it, correcting mistakes, and so on.

What Time is it in ... ?

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