Well, in the last chapter you might notice that for the first 10 rows all the trips started before 13 hours (1 p.m.), and ended close to 00 hours. What if it's just AM/PM missing? Let's try to check it.

How can it be checked? Well, in our dataframe we also have `trip_duration` column representing the trip duration in seconds. What if we just add this duration to `pickup_datetime` and compare it with `dropoff_datetime`?

> Please note, that you will need to use `timedelta` objects in this task since you want to 'shift' your `datetime` object for a certain time. To convert column to `timedelta` type use `pd.to_timedelta()` function with two arguments: column you want to convert, and `unit` - dimension used ('S' for seconds, 'm' for minutes, and so on).

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: Negative Trip Duration

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