Manage time series data with explicit frequency and unit.
Project description
TimeSeriesX
The eXtended time series library.
Manage time series data with explicit time zone, frequency and unit.
Free software: MIT license
Documentation: https://timeseriesx.readthedocs.io.
About
TimeSeriesX is motivated by handling time series data in a convenient way. Almost all the features are actually already provided by pandas. TimeSeriesX extends the pandas time series functionality by the unit functionalities of pint and pint-pandas. Further, TimeSeriesX offers an easy and convenient interface to work with time series without the need to dig deep into these libraries, which nevertheless is still recommended, since they go way beyond time series data.
The main challenges that arise when handling time series data are time zones and frequencies. Since time series data is often obtained by measurements, the values are associated with units. Then these units can be confused easily, since the units are often not modeled in code.
TimeSeriesX forces the user to handle time zones, frequencies and units explicitly, while taking care of validation and convenient formats. It also supports deriving these attributes from raw time series data. It offers a limited set of actions on time series that are translated to pandas or pint functionality under the hood. It was designed to guarantee that every transformation of time series data results in a new valid time series, which would require quite some pandas code if done “manually”.
Features
model time series data with explicit frequency, time zone and unit
convert time zone or unit
resample data to new frequency
fill and get gaps
join time series
perform calculations on time series with python standard operators
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
History
0.1.13 (2022-07-19)
fix a few bugs by avoiding is_compatible_with in convert_unit
raise only ValueError instead of DimensionalityError on unit dimensionality mismatch
remove pandas installation dependency because of transitive dependency via pint-pandas
loosen requirements for pytz and dateutil, no special version requirements known
extend __getitem__ functionality by supporting iterables of timestamps or positional indices
explicitly support indexing by time zone naive timestamps, which is deprecated by pandas
make coerce_unit behave like coerce_freq and coerce_time_zone by passing through None
0.1.12 (2022-03-16)
fix equals method
update documentation
0.1.11 (2022-03-07)
fix resampling method to support nan-values
update dependencies
0.1.10 (2022-01-21)
fix equals method
update dependencies
0.1.9 (2021-11-19)
allow aggregation functions to return magnitudes or quantities
update dependencies
0.1.8 (2021-09-28)
fix time zone bug in gap handling
update dependencies
add more tests
0.1.7 (2021-09-28)
improve gap handling
update dependencies
improve documentation
fix calculations with quantity scalar
0.1.6 (2021-09-13)
fix time zone issue with UTC in basic calculations for TimestampSeries as 2nd operand
update pint-pandas version dependency
use pint’s default unit registry
add support of callables as arguments for frequency resampling
add more tests
0.1.5 (2021-09-10)
fix time zone issue with UTC in basic calculations
add round-method for TimestampSeries
fix map-function for series with unit
add more tests
0.1.4 (2021-09-09)
improve test coverage
improve TimeSeries equality check
support NaN-removal in as_pd_series-method
0.1.3 (2021-09-08)
remove manual timezone checks because it is handled by pandas
fix skipped tests
fix repr() method of TimestampSeries
fix basic calculation with units involved
0.1.2 (2021-09-07)
fix timezone handling
First release on PyPI Index.
0.1.1 (2021-02-16)
First release on PyPI Test Index.
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