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https://github.com/staircase-dev/piso
Pandas Interval Set Operations: providing methods for set operations, analytics, lookups and joins on pandas' Interval, IntervalArray and IntervalIndex
https://github.com/staircase-dev/piso
data-analysis data-science data-structures interval interval-arithmetic interval-set pandas set set-operations set-theory
Last synced: 6 days ago
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Pandas Interval Set Operations: providing methods for set operations, analytics, lookups and joins on pandas' Interval, IntervalArray and IntervalIndex
- Host: GitHub
- URL: https://github.com/staircase-dev/piso
- Owner: staircase-dev
- License: mit
- Created: 2021-10-05T04:21:29.000Z (about 3 years ago)
- Default Branch: master
- Last Pushed: 2024-06-25T09:18:27.000Z (5 months ago)
- Last Synced: 2024-09-27T18:36:40.363Z (about 2 months ago)
- Topics: data-analysis, data-science, data-structures, interval, interval-arithmetic, interval-set, pandas, set, set-operations, set-theory
- Language: Python
- Homepage: https://piso.readthedocs.io
- Size: 343 KB
- Stars: 25
- Watchers: 3
- Forks: 2
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# piso - pandas interval set operations
**piso** exists to bring set operations (union, intersection, difference + more), analytical methods, and lookup and join functionality to [pandas'](https://pandas.pydata.org/) interval classes, specifically
- pandas.Interval
- pandas.arrays.IntervalArray
- pandas.IntervalIndex
Currently, there is a lack of such functionality in pandas, although it has been earmarked for development. Until this eventuates, piso aims to fill the void. Many of the methods can be used via accessors, which can be registered to `pandas.arrays.IntervalArray` and `pandas.IntervalIndex` classes, for example:
```python
>>> import pandas as pd
>>> import piso
>>> piso.register_accessors()
>>> arr = pd.arrays.IntervalArray.from_tuples(
... [(1,5), (3,6), (2,4)]
... )
>>> arr.piso.intersection()
[(3, 4]]
Length: 1, closed: right, dtype: interval[int64]
>>> arr.piso.contains([2, 3, 5])
2 3 5
(1, 5] True True True
(3, 6] False False True
(2, 4] False True False
>>> df = pd.DataFrame(
... {"A":[4,3], "B":["x","y"]},
... index=pd.IntervalIndex.from_tuples([(1,3), (5,7)]),
... )
>>> s = pd.Series(
... [True, False],
... index=pd.IntervalIndex.from_tuples([(2,4), (5,6)]),
... name="C",
... )
>>> piso.join(df, s)
A B C
(1, 2] 4 x NaN
(2, 3] 4 x True
(5, 6] 3 y False
(6, 7] 3 y NaN
>>> piso.join(df, s, how="inner")
A B C
(2, 3] 4 x True
(5, 6] 3 y False
```
The domain of the intervals can be either numerical, `pandas.Timestamp` or `pandas.Timedelta`.
Several [case studies](https://piso.readthedocs.io/en/latest/user_guide/case_studies/index.html) using piso can be found in the [user guide](https://piso.readthedocs.io/en/latest/user_guide/index.html). Further examples, and a detailed explanation of functionality, are provided in the [API reference](https://piso.readthedocs.io/en/latest/reference/index.html).
Visit [https://piso.readthedocs.io](https://piso.readthedocs.io/) for the documentation.
## Installation
`piso` can be installed from PyPI or Anaconda.
To install the latest version from PyPI::
```sh
python -m pip install piso
```
To install the latest version through conda-forge::
```sh
conda install -c conda-forge piso
```
## Versioning
[SemVer](http://semver.org/) is used by piso for versioning releases. For versions available, see the [tags on this repository](https://github.com/staircase-dev/piso/tags).
## License
This project is licensed under the [MIT License](https://github.com/staircase-dev/piso/blob/master/LICENSE)
## Acknowledgments
Currently, piso is a pure-python implentation which relies heavily on [staircase](https://www.staircase.dev) and [pandas](https://pandas.pydata.org/). It is designed to operate as part of the *pandas ecosystem*. The colours for the piso logo have been assimilated from pandas as a homage, and is not to intended to imply and affiliation with, or endorsement by, pandas.
Additionally, two classes have been borrowed, almost verbatim, from the pandas source code:
- `pandas.util._decorators.Appender`
- `pandas.core.accessor.CachedAccessor`