In the previous chapter, you saw one timezone: *America/Edmonton*. But the total number of even common timezones is quite big: more than 400! But you can see, that timezone by default contain continent name and city. So, we can explore the variety of timezones across the specific continent.

Since `.all_timezones` and `common_timezones` methods return lists, we can easily iterate over them. For example, using list comprehension, we can easily count the number of timezones containing 'America'.

# Load library
import pytz

# Count number of american timezones using list comprehension
american_timezones = len([x for x in pytz.common_timezones if 'America' in x])
print(f"There are {american_timezones} american timezones available in pytz.common_timesones.")

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.

Timezone Examples

Solution

Timezone Examples

Solution