Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Lære Challenge: Extreme Trips Durations | Working with Dates and Times in pandas
Dealing with Dates and Times in Python
course content

Kursinnhold

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?

book
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.

Oppgave

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.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 4. Kapittel 3
toggle bottom row

book
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.

Oppgave

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.

Løsning

Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Alt var klart?

Hvordan kan vi forbedre det?

Takk for tilbakemeldingene dine!

Seksjon 4. Kapittel 3
Switch to desktopBytt til skrivebordet for virkelighetspraksisFortsett der du er med et av alternativene nedenfor
Vi beklager at noe gikk galt. Hva skjedde?
some-alt