semvec/pypi-packages huggingface dataset:
mlmorph_spellchecker 1.0.6
Malayalam morphology analyser based spellchecker
License: MIT
Keywords: Malayalam morphology FST analyser generator spellchecker

Spellchecker based on Malayalam morphology analyser
===================================================


.. image:: https://img.shields.io/pypi/v/mlmorph-spellchecker.svg
    :target: https://pypi.python.org/pypi/mlmorph-spellchecker
    :alt: PyPI Version




This is a Spellchcker for Malayalam language based on `Malayalam morphology analyser - mlmorph`_.

Installation
------------

Python 3 is required. Using with `venv`_ is recommended

  .. code-block:: console

    $ pip install mlmorph_spellchecker

Usage
-----

  .. code-block:: python

    from mlmorph import Analyser
    from mlmorph_spellchecker import spellcheck, getSuggestions
    analyser = Analyser()
    if spellcheck(word, analyser) is True:
        print('%s ✔️' % (word))
    else:
        print('%s ❌ Suggestions: %s' % (word, getSuggestions(word, analyser)))

Command line interface
----------------------

  .. code-block:: console

    $ python -m mlmorph_spellchecker --help
    usage: __main__.py [-h] word

    positional arguments:
    word        word to spellcheck

    optional arguments:
    -h, --help  show this help message and exit

.. _`Malayalam morphology analyser - mlmorph`: https://gitlab.com/smc/mlmorph
.. _`venv`: https://docs.python.org/3/library/venv.html
spackl 0.1.0
Utility for unified querying across data sources
License: MIT
Keywords: utility query database csv file

# Spackl
Abstracting data source idiosyncrasies so you can stop reading Q&A forums and start reading your data.

## Installation
```
pip install spackl
```

## Usage Overview
#### Query Data
```python
from spackl import db, file

conf = db.Config()

pg = db.Postgres(**conf.default)
bq = db.BigQuery(**conf.bq_datalake)
csv = file.CSV('/path/to/file.csv')

# Same method for all sources
pg_results = pg.query('SELECT id FROM schema.some_table')
bq_results = bq.query('SELECT id FROM dataset.some_table')
csv_results = csv.query()
```

#### Access Query Results
_by index_
```python
results[0]
# (1234,)
```

_by attribute_
```python
results.id
# (1234, 1235, 1236)
```

_by key_
```python
results['id']
# (1234, 1235, 1236)
```

_index by index_
```python
results[0][0]
# 1234
```

_attribute by index_
```python
results.id[0]
# 1234
```

_key by index_
```python
results['id'][0]
# 1234
```

_index by attribute_
```python
results[0].id
# 1234
```

_index by key_
```python
results[0]['id']
# 1234
```

#### Other Data Formats
```python
# Pandas Dataframe
results.df()

# JSON String
results.json()

# List of tuples
results.list()

# Vertical dictionary
results.dict()
```

# CHANGELOG

## 0.1.0 (2019-03-09)
-  initial release