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Please enable JavaScript in your browser settings or update your browser.Challenge: Negative Trip Duration | Working with Dates and Times in pandas
Dealing with Dates and Times in Python
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Dealing with Dates and Times in Python

Dealing with Dates and Times in Python

1. Working with Dates
What Day is it Today?Creating a Date ObjectDate AttributesDate MethodsDate FormatsOne More FormattingDate DifferenceArithmetic Operations with timedelta Objects
2. Working with Times
What Time is it Now?Creating a datetime ObjectDatetime AttributesDatetime MethodsDatetime Formats12-Hour and 24-Hour FormatsDate and Time DifferenceReading Date and Time from String
3. Timezones and Daylight Savings Time (DST)
The Variery of TimezonesTimezone ExamplesApplying Timezone to Datetime ObjectWhat Time is it in ... ?Daylight Savings Time
4. Working with Dates and Times in pandas
Challenge: What Will We Work With?Challenge: Converting Columns into datetime TypeChallenge: Extreme Trips DurationsChallenge: InvestigationChallenge: Negative Trip DurationChallenge: Is this Common Issue?Challenge: Fixing the IssuesChallenge: Average Metrics Across Taxi TypesChallenge: Corrected Metrics Across Taxi TypesChallenge: Group by Period?

bookChallenge: Negative Trip Duration

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).

Завдання

  1. Convert trip_duration column into timedelta type. Do not forget that trip_duration measures in seconds.
  2. Create new column dropoff_calculated as the result of adding pickup_datetime and trip_duration.
  3. Print first 10 rows for updated dataframe.

Please, do not change column names in square brackets.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 4. Розділ 5
toggle bottom row

bookChallenge: Negative Trip Duration

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).

Завдання

  1. Convert trip_duration column into timedelta type. Do not forget that trip_duration measures in seconds.
  2. Create new column dropoff_calculated as the result of adding pickup_datetime and trip_duration.
  3. Print first 10 rows for updated dataframe.

Please, do not change column names in square brackets.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

Секція 4. Розділ 5
toggle bottom row

bookChallenge: Negative Trip Duration

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).

Завдання

  1. Convert trip_duration column into timedelta type. Do not forget that trip_duration measures in seconds.
  2. Create new column dropoff_calculated as the result of adding pickup_datetime and trip_duration.
  3. Print first 10 rows for updated dataframe.

Please, do not change column names in square brackets.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

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).

Завдання

  1. Convert trip_duration column into timedelta type. Do not forget that trip_duration measures in seconds.
  2. Create new column dropoff_calculated as the result of adding pickup_datetime and trip_duration.
  3. Print first 10 rows for updated dataframe.

Please, do not change column names in square brackets.

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 4. Розділ 5
Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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