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Dealing with Dates and Times in Python
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Conteúdo do Curso

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?

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Challenge: Average Metrics Across Taxi Types

Great! As for now, we have our dataset cleared from abnormally long rides and rides with ending time preceded starting. As we investigated, it happened because of misusage of 12 and 24-hour formats.

Let's try to find out some interesting insights from this dataset.

Tarefa

Swipe to start coding

  1. Apply .total_seconds() function to duration column using map and lambda functions.
  2. Group observations by taxi type (vendor_id column). Then, choose columns dist_meters, duration, and calculate mean. Then apply function avg_m to dist_meters and avg_dur to duration. The functions are defined in the code.

Solução

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 4. Capítulo 8
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book
Challenge: Average Metrics Across Taxi Types

Great! As for now, we have our dataset cleared from abnormally long rides and rides with ending time preceded starting. As we investigated, it happened because of misusage of 12 and 24-hour formats.

Let's try to find out some interesting insights from this dataset.

Tarefa

Swipe to start coding

  1. Apply .total_seconds() function to duration column using map and lambda functions.
  2. Group observations by taxi type (vendor_id column). Then, choose columns dist_meters, duration, and calculate mean. Then apply function avg_m to dist_meters and avg_dur to duration. The functions are defined in the code.

Solução

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 4. Capítulo 8
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
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