In two previous sections, we worked with dates and times, without taking into account the timezone. While planning the meeting with foreigners, planning to fly to another country, or planning to watch some sporting event, you have to take this into account.

We might mention about `tzinfo` argument of the `datetime` object. But to handle timezones, we will use `pytz` library, which brings the **tz database** (also known as *Olson database*) in Python. At this time we will not use `from library import something` since there is no need in loading specific methods for `pytz`.

Let's start with the exploration of available timezones. There are two methods available in `pytz`: `.all_timezones` will return a list with all the available timezones in `pytz`, and `.common_timezones` will return only the common ones.

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.

The Variery of Timezones

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