Expanded with more methods and examples
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README.md
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README.md
@ -29,7 +29,7 @@ Example:
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... ('2021-06-01', 20)
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...]
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>>> ts = fc.TimeSeries(time_series_data)
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>>> ts = pft.TimeSeries(time_series_data)
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```
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### Sample usage
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@ -46,12 +46,169 @@ With PyFacts, you never have to go into the hassle of creating datetime objects
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```
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>>> import pyfacts as pft
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>>> fc.PyfactsOptions.date_format = '%d-%m-%Y'
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>>> pft.PyfactsOptions.date_format = '%d-%m-%Y'
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```
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Now the library will automatically parse all dates as DD-MM-YYYY
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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.
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### Working with multiple time series
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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.
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Example:
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```
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>>> import pyfacts as pft
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>>> time_series_data = [
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... ('2021-01-01', 10),
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... ('2021-02-01', 12),
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... ('2021-03-01', 14),
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... ('2021-04-01', 16),
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... ('2021-05-01', 18),
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... ('2021-06-01', 20)
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...]
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>>> ts = pft.TimeSeries(time_series_data)
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>>> print(ts/100)
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TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 0.1),
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(datetime.datetime(2022, 1, 2, 0, 0), 0.12),
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(datetime.datetime(2022, 1, 3, 0, 0), 0.14),
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(datetime.datetime(2022, 1, 4, 0, 0), 0.16),
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(datetime.datetime(2022, 1, 6, 0, 0), 0.18),
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(datetime.datetime(2022, 1, 7, 0, 0), 0.2)], frequency='M')
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```
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Mathematical operations can also be done between time series as long as they have the same dates.
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Example:
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```
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>>> import pyfacts as pft
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>>> time_series_data = [
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... ('2021-01-01', 10),
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... ('2021-02-01', 12),
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... ('2021-03-01', 14),
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... ('2021-04-01', 16),
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... ('2021-05-01', 18),
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... ('2021-06-01', 20)
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...]
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>>> ts = pft.TimeSeries(time_series_data)
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>>> ts2 = pft.TimeSeries(time_series_data)
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>>> print(ts/ts2)
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TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 1.0),
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(datetime.datetime(2022, 1, 2, 0, 0), 1.0),
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(datetime.datetime(2022, 1, 3, 0, 0), 1.0),
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(datetime.datetime(2022, 1, 4, 0, 0), 1.0),
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(datetime.datetime(2022, 1, 6, 0, 0), 1.0),
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(datetime.datetime(2022, 1, 7, 0, 0), 1.0)], frequency='M')
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```
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However, if the dates are not in sync, PyFacts provides convenience methods for syncronising dates.
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Example:
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```
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>>> import pyfacts as pft
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>>> data1 = [
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... ('2021-01-01', 10),
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... ('2021-02-01', 12),
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... ('2021-03-01', 14),
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... ('2021-04-01', 16),
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... ('2021-05-01', 18),
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... ('2021-06-01', 20)
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...]
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>>> data2 = [
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... ("2022-15-01", 20),
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... ("2022-15-02", 22),
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... ("2022-15-03", 24),
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... ("2022-15-04", 26),
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... ("2022-15-06", 28),
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... ("2022-15-07", 30)
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...]
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>>> ts = pft.TimeSeries(data, frequency='M', date_format='%Y-%d-%m')
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>>> ts2 = pft.TimeSeries(data2, frequency='M', date_format='%Y-%d-%m')
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>>> ts.sync(ts2, fill_method='bfill') # Sync ts2 with ts1
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TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 20.0),
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(datetime.datetime(2022, 2, 1, 0, 0), 22.0),
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(datetime.datetime(2022, 3, 1, 0, 0), 24.0),
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(datetime.datetime(2022, 4, 1, 0, 0), 26.0),
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(datetime.datetime(2022, 6, 1, 0, 0), 28.0),
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(datetime.datetime(2022, 7, 1, 0, 0), 30.0)], frequency='M')
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```
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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.
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Example:
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```
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>>> data = [
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... ("2022-01-01", 10),
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... ("2022-02-01", 12),
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... ("2022-03-01", 14),
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... ("2022-04-01", 16),
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... ("2022-05-01", 18),
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... ("2022-06-01", 20)
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...]
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>>> ts = pft.TimeSeries(data, 'M')
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>>> ts.expand(to_frequency='W', method='ffill')
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TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10.0),
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(datetime.datetime(2022, 1, 8, 0, 0), 10.0),
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(datetime.datetime(2022, 1, 15, 0, 0), 10.0)
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...
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(datetime.datetime(2022, 5, 14, 0, 0), 18.0),
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(datetime.datetime(2022, 5, 21, 0, 0), 18.0),
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(datetime.datetime(2022, 5, 28, 0, 0), 18.0)], frequency='W')
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>>> ts.shrink(to_frequency='Q', method='ffill')
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TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 10.0),
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(datetime.datetime(2022, 4, 1, 0, 0), 16.0)], frequency='Q')
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```
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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.
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Example:
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```
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>>> data = [
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... ("2022-01-01", 10),
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... ("2022-02-01", 12),
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... ("2022-03-01", 14),
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... ("2022-04-01", 16),
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... ("2022-05-01", 18),
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... ("2022-06-01", 20),
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... ("2022-07-01", 22),
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... ("2022-08-01", 24),
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... ("2022-09-01", 26),
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... ("2022-10-01", 28),
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... ("2022-11-01", 30),
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... ("2022-12-01", 32)
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...]
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>>> ts = pft.TimeSeries(data, 'M')
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>>> ts.transform(to_frequency='Q', method='sum')
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TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 36.0),
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(datetime.datetime(2022, 4, 1, 0, 0), 54.0),
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(datetime.datetime(2022, 7, 1, 0, 0), 72.0),
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(datetime.datetime(2022, 10, 1, 0, 0), 90.0)], frequency='Q')
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>>> ts.transform(to_frequency='Q', method='mean')
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TimeSeries([(datetime.datetime(2022, 1, 1, 0, 0), 12.0),
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(datetime.datetime(2022, 4, 1, 0, 0), 18.0),
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(datetime.datetime(2022, 7, 1, 0, 0), 24.0),
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(datetime.datetime(2022, 10, 1, 0, 0), 30.0)], frequency='Q')
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```
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## To-do
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### Core features
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