semvec/pypi-packages huggingface dataset:
katottglib 0.3.1
A Python lib to operate KATOTTG addresses (classified administrative division of Ukraine).
License: MIT

===========
KATOTTG lib
===========

Quick start
-----------

- Install with `pip install katottglib`
- Import `import katottglib`
- Search for entities with `find_by_name` `katottglib.find_by_name(name="Lviv")`

Release notes
-------------

v0.3.1 (2024-06-17)
...................

Unpin pandas and openpyxl versions - previous version was uninstallable if pandas 2.1 required.

v0.3.0 (2024-06-16)
...................

Update kodifikator.xlsx to 2024-01-19

v0.2.6 (2023-07-10)
...................

Try to fix continuation import errors on some installations.

v0.2.5 (2023-07-10)
...................

Fix import statements to be consistent.


v0.2.4 (2023-07-10)
...................

Fix installation by adding setup.py install_requires


v0.2.3 (2023-07-10)
...................

Fix installation by refining the structure

v0.2.2 (2023-07-10)
...................

Updated setup.py config


v0.2.1 (2023-07-09)
...................

Fixes and improvements to performance.

- data/kodifikator.xlsx is included into the dist.

v0.2.0 (2023-07-01)
...................

Fixes and improvements to performance.

- Dataframe is now persisted with Pickle.

v0.1.0 (2023-06-10)
...................

Initial release.

- Search by name with filter by level (``find_by_name``).
- Search by identifier (``find_by_id``)
ipython-sparql-pandas 1.6
Jupyter/IPython Extension for Sparql Pandas dataframe queries
License: MIT
Requires Python: >=3.6
Keywords: sparql, ipython, jupyter extension, magics, ipython, pandas

# IPython Sparql Pandas Extension

Jupyter/IPython Extension for Sparql Pandas dataframe queries.

## Install
Via pip:

```bash
pip install ipython-sparql-pandas
```

## Usage

Load the extension:

```
%load_ext ipython_sparql_pandas
```

Query:

```sparql
%%sparql http://dbpedia.org/sparql/ -qs foo
PREFIX dct: <http://purl.org/dc/terms/>
PREFIX dcb: <http://dbpedia.org/resource/Category:>
PREFIX dbp: <http://dbpedia.org/property/>

SELECT DISTINCT ?name ?capital ?populationTotal WHERE {
    ?capital dct:subject dcb:Capitals_in_Europe ;
             dbp:populationTotal ?populationTotal ; 
             foaf:name ?name. 
}
ORDER BY DESC(?populationTotal)
LIMIT 5
```

The variable `foo` is now a Pandas dataframe of SPARQL results:

```python
foo.plot.barh('name', 'populationTotal').invert_yaxis()
```

![plot](https://raw.githubusercontent.com/bennokr/ipython_sparql_pandas/main/plot.png)

## Acknowledgements
This package is inspired by [ipython_sparql](https://github.com/baito/ipython_sparql).