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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.


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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)

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

Swipe to start coding

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

Рішення

Alias	Meaning
`B`	Business day frequency
`C`	Custom business day frequency
`D`	Calendar day frequency
`W`	Weekly frequency
`M`	Month end frequency
`Q`	Quarter 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)

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