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Expanded with more methods and examples

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Gourav Kumar 2022-06-12 21:36:26 +05:30
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@ -29,7 +29,7 @@ Example:
... ('2021-06-01', 20) ... ('2021-06-01', 20)
...] ...]
>>> ts = fc.TimeSeries(time_series_data) >>> ts = pft.TimeSeries(time_series_data)
``` ```
### Sample usage ### Sample usage
@ -46,12 +46,169 @@ With PyFacts, you never have to go into the hassle of creating datetime objects
``` ```
>>> import pyfacts as pft >>> import pyfacts as pft
>>> fc.PyfactsOptions.date_format = '%d-%m-%Y' >>> pft.PyfactsOptions.date_format = '%d-%m-%Y'
``` ```
Now the library will automatically parse all dates as DD-MM-YYYY Now the library will automatically parse all dates as DD-MM-YYYY
If you happen to have any one situation where you need to use a different format, all methods accept a date_format parameter to override the default. If you happen to have any one situation where you need to use a different format, all methods accept a date_format parameter to override the default.
### Working with multiple time series
While working with time series data, you will often need to perform calculations on the data. PyFacts supports all kinds of mathematical operations on time series.
Example:
```
>>> import pyfacts as pft
>>> time_series_data = [
... ('2021-01-01', 10),
... ('2021-02-01', 12),
... ('2021-03-01', 14),
... ('2021-04-01', 16),
... ('2021-05-01', 18),
... ('2021-06-01', 20)
...]
>>> ts = pft.TimeSeries(time_series_data)
>>> print(ts/100)
TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 0.1),
(datetime.datetime(2022, 1, 2, 0, 0), 0.12),
(datetime.datetime(2022, 1, 3, 0, 0), 0.14),
(datetime.datetime(2022, 1, 4, 0, 0), 0.16),
(datetime.datetime(2022, 1, 6, 0, 0), 0.18),
(datetime.datetime(2022, 1, 7, 0, 0), 0.2)], frequency='M')
```
Mathematical operations can also be done between time series as long as they have the same dates.
Example:
```
>>> import pyfacts as pft
>>> time_series_data = [
... ('2021-01-01', 10),
... ('2021-02-01', 12),
... ('2021-03-01', 14),
... ('2021-04-01', 16),
... ('2021-05-01', 18),
... ('2021-06-01', 20)
...]
>>> ts = pft.TimeSeries(time_series_data)
>>> ts2 = pft.TimeSeries(time_series_data)
>>> print(ts/ts2)
TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 1.0),
(datetime.datetime(2022, 1, 2, 0, 0), 1.0),
(datetime.datetime(2022, 1, 3, 0, 0), 1.0),
(datetime.datetime(2022, 1, 4, 0, 0), 1.0),
(datetime.datetime(2022, 1, 6, 0, 0), 1.0),
(datetime.datetime(2022, 1, 7, 0, 0), 1.0)], frequency='M')
```
However, if the dates are not in sync, PyFacts provides convenience methods for syncronising dates.
Example:
```
>>> import pyfacts as pft
>>> data1 = [
... ('2021-01-01', 10),
... ('2021-02-01', 12),
... ('2021-03-01', 14),
... ('2021-04-01', 16),
... ('2021-05-01', 18),
... ('2021-06-01', 20)
...]
>>> data2 = [
... ("2022-15-01", 20),
... ("2022-15-02", 22),
... ("2022-15-03", 24),
... ("2022-15-04", 26),
... ("2022-15-06", 28),
... ("2022-15-07", 30)
...]
>>> ts = pft.TimeSeries(data, frequency='M', date_format='%Y-%d-%m')
>>> ts2 = pft.TimeSeries(data2, frequency='M', date_format='%Y-%d-%m')
>>> ts.sync(ts2, fill_method='bfill') # Sync ts2 with ts1
TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 20.0),
(datetime.datetime(2022, 2, 1, 0, 0), 22.0),
(datetime.datetime(2022, 3, 1, 0, 0), 24.0),
(datetime.datetime(2022, 4, 1, 0, 0), 26.0),
(datetime.datetime(2022, 6, 1, 0, 0), 28.0),
(datetime.datetime(2022, 7, 1, 0, 0), 30.0)], frequency='M')
```
Even if you need to perform calculations on data with different frequencies, PyFacts will let you easily handle this with the expand and shrink methods.
Example:
```
>>> data = [
... ("2022-01-01", 10),
... ("2022-02-01", 12),
... ("2022-03-01", 14),
... ("2022-04-01", 16),
... ("2022-05-01", 18),
... ("2022-06-01", 20)
...]
>>> ts = pft.TimeSeries(data, 'M')
>>> ts.expand(to_frequency='W', method='ffill')
TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10.0),
(datetime.datetime(2022, 1, 8, 0, 0), 10.0),
(datetime.datetime(2022, 1, 15, 0, 0), 10.0)
...
(datetime.datetime(2022, 5, 14, 0, 0), 18.0),
(datetime.datetime(2022, 5, 21, 0, 0), 18.0),
(datetime.datetime(2022, 5, 28, 0, 0), 18.0)], frequency='W')
>>> ts.shrink(to_frequency='Q', method='ffill')
TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10.0),
(datetime.datetime(2022, 4, 1, 0, 0), 16.0)], frequency='Q')
```
If you want to shorten the timeframe of the data with an aggregation function, the transform method will help you out. Currently it supports sum and mean.
Example:
```
>>> data = [
... ("2022-01-01", 10),
... ("2022-02-01", 12),
... ("2022-03-01", 14),
... ("2022-04-01", 16),
... ("2022-05-01", 18),
... ("2022-06-01", 20),
... ("2022-07-01", 22),
... ("2022-08-01", 24),
... ("2022-09-01", 26),
... ("2022-10-01", 28),
... ("2022-11-01", 30),
... ("2022-12-01", 32)
...]
>>> ts = pft.TimeSeries(data, 'M')
>>> ts.transform(to_frequency='Q', method='sum')
TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 36.0),
(datetime.datetime(2022, 4, 1, 0, 0), 54.0),
(datetime.datetime(2022, 7, 1, 0, 0), 72.0),
(datetime.datetime(2022, 10, 1, 0, 0), 90.0)], frequency='Q')
>>> ts.transform(to_frequency='Q', method='mean')
TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 12.0),
(datetime.datetime(2022, 4, 1, 0, 0), 18.0),
(datetime.datetime(2022, 7, 1, 0, 0), 24.0),
(datetime.datetime(2022, 10, 1, 0, 0), 30.0)], frequency='Q')
```
## To-do ## To-do
### Core features ### Core features