peakina 0.19.4

pandas readers on steroids (remote files, glob patterns, cache, etc.)

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.

:information_source: 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