Obviously we need to learn how to work with timestamps that differ from current. Let's begin with creating `datetime` objects.

As you remember from the previous chapter, `datetime` object has 9 arguments. Let's show the necessary ones in the example below.

# Load class from library
from datetime import datetime

# Create new datetime object
datetime(year, month, day, hour, minute, second, microsecond, ...)

The first 3 arguments are required. Like for `date` object you are free to use either keyword, either positional arguments. For example, let's create `datetime` object with the following value: "November 17, 2017, 12:34:56".

# Load class from library
from datetime import datetime

# Create datetime object using keyword arguments
d = datetime(month = 11, year = 2017, day = 17, hour = 12, minute = 34, second = 56)
# Create datetime object using positional arguments
t = datetime(2017, 11, 17, 12, 34, 56)

# Check their values
print("Are", d, "and", t, "equal:", d == t)

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.

Creating a datetime Object

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