Metadata-Version: 2.1
Name: python-datamuse
Version: 1.2.1
Summary: Python wrapper for the Datamuse API
Home-page: https://github.com/gmarmstrong/python-datamuse
Author: Guthrie McAfee Armstrong
Author-email: guthrie.armstrong@gmail.com
License: MIT
Keywords: datamuse,linguistics,language,wrapper
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: requests

[![Build Status](https://travis-ci.org/gmarmstrong/python-datamuse.svg?branch=master)](https://travis-ci.org/gmarmstrong/python-datamuse)

# python-datamuse

Python wrapper and scripts for the [Datamuse API](http://datamuse.com/api/).
Available on PyPI at <https://pypi.python.org/pypi/python-datamuse>. You can
install this library with `pip3 install python-datamuse`.

## Changelog

### Version 1.2.1

- Fix README formatting on PyPI

### Version 1.2.0

- Raise Python version to 3.6
- Mock the Datamuse API for tests
- Restructure project files
- Set README as PyPI long description

### Version 1.1.0

- Changed to Python 3
- Uploaded to PyPI, added instructions for PyPI installation
- Changed README example to reflect PyPI packaging
- Set up Travis CI
- Temporarily removed pandas
- Changed mode of scripts to executable

## Example

```python
>>> from datamuse import datamuse
>>> api = datamuse.Datamuse()
>>> orange_rhymes = api.words(rel_rhy='orange', max=5)
>>> orange_rhymes
[]
>>> orange_near_rhymes = api.words(rel_nry='orange', max=5)
>>> orange_near_rhymes
[{'score': 973, 'word': 'storage'}, {'score': 858, 'word': 'knowledge'}, {'score': 615, 'word': 'homage'}, {'score': 560, 'word': 'warrant'}]
>>>
>>>
>>> foo_complete = api.suggest(s='foo', max=10)
>>> foo_complete
[{u'score': 626, u'word': u'food'}, {u'score': 568, u'word': u'foot'}, {u'score': 520, u'word': u'fool'}, {u'score': 315, u'word': u'footage'}, {u'score': 297, u'word': u'foolish'}, {u'score': 279, u'word': u'football'}, {u'score': 272, u'word': u'footprint'}, {u'score': 232, u'word': u'footing'}, {u'score': 221, u'word': u'foof'}, {u'score': 185, u'word': u'foolproof'}]
>>> from datamuse import scripts
>>> foo_df = scripts.dm_to_df(foo_complete)
>>> foo_df
   score       word
0    626       food
1    568       foot
2    521       fool
3    315    footage
4    297    foolish
5    279   football
6    272  footprint
7    232    footing
8    221       foof
9    185  foolproof

[10 rows x 2 columns]
```

Note that the default number of results is set to 100. You can set the default
`max` to something else using the `set_max_default` method, e.g.
`api.set_max_default(300)`. Datamuse only returns 1000 results max.


