Pea Kina aka 'Giant Panda'

Wrapper around pandas library, which detects separator, encoding and type of the file. It allows to get a group of files with a matching pattern (python or glob regex). It can read both local and remote files (HTTP/HTTPS, FTP/FTPS/SFTP or S3/S3N/S3A).

The supported file types are csv, excel, json, parquet and xml.

ℹ️ If the desired type is not yet supported, feel free to open an issue or to directly open a PR with the code !

Please, read the documentation for more information

Installation

pip install peakina

Usage

Considering a file file.csv

a;b
0;0
0;1

Just type

>>> import peakina as pk
>>> pk.read_pandas('file.csv')
   a  b
0  0  0
1  0  1

Or files on a FTPS server:

• my_data_2015.csv
• my_data_2016.csv
• my_data_2017.csv
• my_data_2018.csv

You can just type

>>> pk.read_pandas('ftps://<path>/my_data_\\d{4}\\.csv$', match='regex', dtype={'a': 'str'})
    a   b     __filename__
0  '0'  0  'my_data_2015.csv'
1  '0'  1  'my_data_2015.csv'
2  '1'  0  'my_data_2016.csv'
3  '1'  1  'my_data_2016.csv'
4  '3'  0  'my_data_2017.csv'
5  '3'  1  'my_data_2017.csv'
6  '4'  0  'my_data_2018.csv'
7  '4'  1  'my_data_2018.csv'

Using cache

You may want to keep the last result in cache, to avoid downloading and extracting the file if it didn't change:

>>> from peakina.cache import Cache
>>> cache = Cache.get_cache('memory')  # in-memory cache
>>> df = pk.read_pandas('file.csv', expire=3600, cache=cache)

In this example, the resulting dataframe will be fetched from the cache, unless file.csv modification time has changed on disk, or unless the cache is older than 1 hour.

For persistent caching, use: cache = Cache.get_cache('hdf', cache_dir='/tmp')

Use only downloading feature

If you just want to download a file, without converting it to a pandas dataframe:

>>> uri = 'https://i.imgur.com/V9x88.jpg'
>>> f = pk.fetch(uri)
>>> f.get_str_mtime()
'2012-11-04T17:27:14Z'
>>> with f.open() as stream:
...     print('Image size:', len(stream.read()), 'bytes')
...
Image size: 60284 bytes