Average trip duration across different taxi types looks a bit strange. Every taxi type has an average trip duration greater than 1 hour (most of them even greater than 2 hours), while the average distance is less than 10 km. That's extremely slow!

Let's make some corrections and assume that not all noisy data were removed.

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: Corrected Metrics Across Taxi Types

Ratkaisu

Challenge: Corrected Metrics Across Taxi Types

Ratkaisu