Now we have the respective columns converted into the correct type. It means now we can manipulate them using the learned methods.

We already have column `trip_duration` in our dataset, so why do we need to work with `datetime` objects? Yes, we have, but this number is in seconds, which is not readable at all (since there are 60 seconds in one minute, not 100). Let's see if there are outliers in our dataset.

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.

Challenge: Extreme Trips Durations

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