Cursusinhoud
Dealing with Dates and Times in Python
Dealing with Dates and Times in Python
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.
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.
Swipe to start coding
- Set
pickup_datetimecolumn ofdfdataframe as an index ofdf. - Calculate the number of trips each month available in dataset.
Oplossing
| 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)
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande optiesBedankt voor je feedback!
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.
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.
Swipe to start coding
- Set
pickup_datetimecolumn ofdfdataframe as an index ofdf. - Calculate the number of trips each month available in dataset.
Oplossing
| 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)
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande optiesBedankt voor je feedback!
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties