Well, in the last chapter you saw, that there were only two rides with negative durations where minutes in both columns were different. But if paid your attention to seconds, you might notice, that that were the minute ending and starting (59 seconds, and 00 respectively). It means that all the inconsistencies can be interpreted as misuages of 12 and 24-hour formats.

Since we have investigated the real reason for the issue, we can now fix it! Let me remind you of one of the ways to replace values in dataframe based on some condition - `.where` function.

df['col_name'].where(~(condition), inplace = True, other = values_to_replace)

Using the following approach all the values in `col_name` will be replaced with `values_to_replace` if `(condition)` is True.

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: Fixing the Issues

Lösung

Challenge: Fixing the Issues

Lösung