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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: Fixing the Issues

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.


              
1

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

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

Tarea

  1. For all the trips with negative duration add 12 hours to dropoff_datetime column.
  2. Calculate column duration again.
  3. Print first 5 rows of updated df.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 4. Capítulo 7
toggle bottom row

bookChallenge: Fixing the Issues

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.


              
1

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

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

Tarea

  1. For all the trips with negative duration add 12 hours to dropoff_datetime column.
  2. Calculate column duration again.
  3. Print first 5 rows of updated df.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 4. Capítulo 7
toggle bottom row

bookChallenge: Fixing the Issues

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.


              
1

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

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

Tarea

  1. For all the trips with negative duration add 12 hours to dropoff_datetime column.
  2. Calculate column duration again.
  3. Print first 5 rows of updated df.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

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.


              
1

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

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

Tarea

  1. For all the trips with negative duration add 12 hours to dropoff_datetime column.
  2. Calculate column duration again.
  3. Print first 5 rows of updated df.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 4. Capítulo 7
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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