Remember the `timedelta` object we learned in the first section? At that time we missed the time part in `timedelta` objects since we compared two dates only. Now there is even more sense in using `timedelta` since we can perform different operations with time also.

For example, imagine that we have both departure and arrival times on the ticket. Using `timedelta` we can easily calculate the expected duration for this trip.
- **Departure:** 25 May 2021, 18:20
- **Arrive:** 26 May 2021, 07:40

# Load classes from library
from datetime import datetime
from datetime import timedelta

# Create two datetimes objects
dep = datetime(2021, 5, 25, 18, 20)
arr = datetime(2021, 5, 26, 7, 40)

# Calculate estimated trip duration
print(f"Estimated trip duration: {arr - dep}")

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.

Date and Time Difference

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