embarrassment 0.1.0
Convenience functions to work with pandas triple dataframes 🐼🐼🐼
License: MIT
Requires Python: >=3.8,<4.0
Keywords: pandas,rdf,knowledge graph

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Convenience functions for pandas dataframes containing triples. Fun fact: a group of pandas (e.g. three) is commonly referred to as an [embarrassment](https://www.zmescience.com/feature-post/what-is-a-group-of-pandas-called-its-surprisingly-complicated/).

This library's main focus is to easily make commonly used functions available, when exploring [triples](https://en.wikipedia.org/wiki/Semantic_triple) stored in pandas dataframes. It is not meant to be an efficient graph analysis library.

Usage
=====
You can use a variety of convenience functions, let's create some simple example triples:
```python
>>> import pandas as pd
>>> rel = pd.DataFrame([("e1","rel1","e2"), ("e3", "rel2", "e1")], columns=["head","relation","tail"])
>>> attr = pd.DataFrame([("e1","attr1","lorem ipsum"), ("e2","attr2","dolor")], columns=["head","relation","tail"])
```
Search in attribute triples:
```python
>>> from embarrassment import search
>>> search(attr, "lorem ipsum")
  head relation         tail
0   e1    attr1  lorem ipsum
>>> search(attr, "lorem", method="substring")
  head relation         tail
0   e1    attr1  lorem ipsum
```
Select triples with a specific relation:
```python
>>> from embarrassment import select_rel
>>> select_rel(rel, "rel1")
  head relation tail
0   e1     rel1   e2
```
Perform operations on the immediate neighbor(s) of an entity, e.g. get the attribute triples:
```python
>>> from embarrassment import neighbor_attr_triples
>>> neighbor_attr_triples(rel, attr, "e1")
  head relation   tail
1   e2    attr2  dolor
```
Or just get the triples:
```python
>>> from embarrassment import neighbor_rel_triples
>>> neighbor_rel_triples(rel, "e1")
  head relation tail
1   e3     rel2   e1
0   e1     rel1   e2
```
By default you get in- and out-links, but you can specify a direction:
```python
>>> neighbor_rel_triples(rel, "e1", in_out_both="in")
  head relation tail
1   e3     rel2   e1
>>> neighbor_rel_triples(rel, "e1", in_out_both="out")
  head relation tail
0   e1     rel1   e2
```

Using pandas' [pipe](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pipe.html) operator you can chain operations.
Let's see a more elaborate example by loading a dataset from [sylloge](https://github.com/dobraczka/sylloge):

```python
>>> from sylloge import MovieGraphBenchmark
>>> from embarrassment import clean, neighbor_attr_triples, search, select_rel
>>> ds = MovieGraphBenchmark()
>>> # clean attribute triples
>>> cleaned_attr = clean(ds.attr_triples_left)
>>> # find uri of James Tolkan
>>> jt = search(cleaned_attr, query="James Tolkan")["head"].iloc[0]
>>> # get neighbor triples
>>> # and select triples with title and show values
>>> title_rel = "https://www.scads.de/movieBenchmark/ontology/title"
>>> neighbor_attr_triples(ds.rel_triples_left, cleaned_attr, jt).pipe(
            select_rel, rel=title_rel
        )["tail"]
    )
    12234    A Nero Wolfe Mystery
    12282           Door to Death
    12440          Die Like a Dog
    12461        The Next Witness
    Name: tail, dtype: object
```


Installation
============
You can install `embarrassment` via pip:
```
pip install embarrassment
```