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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?

Challenge: Group by Period?

Previously, across other courses and chapters, you used to group observations by some columns. But can we do it with some time-series data? For example, can we summarize data by each week presented in dataset? Sounds like a complicated task.

Actually, pandas can handle even with that. There is .resample function available to group by different periods. Let's consider the structure of this function.


              
1

            
df.resample(rule, axis = 0, closed = None, label = None, convention = 'start', kind = None, loffset = None, base = None, on = None, level = None, origin = 'start_day', offset = None)
copy

The most important and the only one required argument is rule - the offset string or object representing target conversion. Easier, it's the period we want to divide our data by. There is a list of offset aliases used for resampling. You can find them in the table below the task.

Завдання

  1. Set pickup_datetime column of df dataframe as an index of df.
  2. Calculate the number of trips each month available in dataset.

Завдання

  1. Set pickup_datetime column of df dataframe as an index of df.
  2. Calculate the number of trips each month available in dataset.
AliasMeaning
BBusiness day frequency
CCustom business day frequency
DCalendar day frequency
WWeekly frequency
MMonth end frequency
QQuarter end frequency

There are many more aliases available. You can read about it in documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases (Offset aliases)

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 4. Розділ 10
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Challenge: Group by Period?

Previously, across other courses and chapters, you used to group observations by some columns. But can we do it with some time-series data? For example, can we summarize data by each week presented in dataset? Sounds like a complicated task.

Actually, pandas can handle even with that. There is .resample function available to group by different periods. Let's consider the structure of this function.


              
1

            
df.resample(rule, axis = 0, closed = None, label = None, convention = 'start', kind = None, loffset = None, base = None, on = None, level = None, origin = 'start_day', offset = None)
copy

The most important and the only one required argument is rule - the offset string or object representing target conversion. Easier, it's the period we want to divide our data by. There is a list of offset aliases used for resampling. You can find them in the table below the task.

Завдання

  1. Set pickup_datetime column of df dataframe as an index of df.
  2. Calculate the number of trips each month available in dataset.

Завдання

  1. Set pickup_datetime column of df dataframe as an index of df.
  2. Calculate the number of trips each month available in dataset.
AliasMeaning
BBusiness day frequency
CCustom business day frequency
DCalendar day frequency
WWeekly frequency
MMonth end frequency
QQuarter end frequency

There are many more aliases available. You can read about it in documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases (Offset aliases)

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Секція 4. Розділ 10
toggle bottom row

Challenge: Group by Period?

Previously, across other courses and chapters, you used to group observations by some columns. But can we do it with some time-series data? For example, can we summarize data by each week presented in dataset? Sounds like a complicated task.

Actually, pandas can handle even with that. There is .resample function available to group by different periods. Let's consider the structure of this function.


              
1

            
df.resample(rule, axis = 0, closed = None, label = None, convention = 'start', kind = None, loffset = None, base = None, on = None, level = None, origin = 'start_day', offset = None)
copy

The most important and the only one required argument is rule - the offset string or object representing target conversion. Easier, it's the period we want to divide our data by. There is a list of offset aliases used for resampling. You can find them in the table below the task.

Завдання

  1. Set pickup_datetime column of df dataframe as an index of df.
  2. Calculate the number of trips each month available in dataset.

Завдання

  1. Set pickup_datetime column of df dataframe as an index of df.
  2. Calculate the number of trips each month available in dataset.
AliasMeaning
BBusiness day frequency
CCustom business day frequency
DCalendar day frequency
WWeekly frequency
MMonth end frequency
QQuarter end frequency

There are many more aliases available. You can read about it in documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases (Offset aliases)

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Previously, across other courses and chapters, you used to group observations by some columns. But can we do it with some time-series data? For example, can we summarize data by each week presented in dataset? Sounds like a complicated task.

Actually, pandas can handle even with that. There is .resample function available to group by different periods. Let's consider the structure of this function.


              
1

            
df.resample(rule, axis = 0, closed = None, label = None, convention = 'start', kind = None, loffset = None, base = None, on = None, level = None, origin = 'start_day', offset = None)
copy

The most important and the only one required argument is rule - the offset string or object representing target conversion. Easier, it's the period we want to divide our data by. There is a list of offset aliases used for resampling. You can find them in the table below the task.

Завдання

  1. Set pickup_datetime column of df dataframe as an index of df.
  2. Calculate the number of trips each month available in dataset.
AliasMeaning
BBusiness day frequency
CCustom business day frequency
DCalendar day frequency
WWeekly frequency
MMonth end frequency
QQuarter end frequency

There are many more aliases available. You can read about it in documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases (Offset aliases)

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Секція 4. Розділ 10
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
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