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Learn Challenge: Extreme Trips Durations | Working with Dates and Times in pandas
Dealing with Dates and Times in Python

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Challenge: Extreme Trips Durations

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.

Task

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  1. Create new column duration in df dataframe and save the result of subtracting dropoff_datetime and pickup_datetime columns.
  2. Sort the entire dataframe by newly created column in descending order. Save it in df_sort.
  3. Print the top-5 longest and shortest trips.

Solution

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SectionΒ 4. ChapterΒ 3
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Challenge: Extreme Trips Durations

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.

Task

Swipe to start coding

  1. Create new column duration in df dataframe and save the result of subtracting dropoff_datetime and pickup_datetime columns.
  2. Sort the entire dataframe by newly created column in descending order. Save it in df_sort.
  3. Print the top-5 longest and shortest trips.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

close

Awesome!

Completion rate improved to 3.23

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