peakina 0.13.0

Description:

peakina 0.13.0

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

Installation on macOS M1 chipset
install everything
brew install hdf5 snappy
HDF5_DIR="/opt/homebrew/Cellar/hdf5/1.12.1/" CPPFLAGS="-I/opt/homebrew/Cellar/snappy/1.1.9/include -L/opt/homebrew/Cellar/snappy/1.1.9/lib" poetry install

For more details, here is what is needed:
install pytables
brew install hdf5
HDF5_DIR="/opt/homebrew/Cellar/hdf5/1.12.1/" poetry run pip install tables

install python-snappy
brew install snappy
CPPFLAGS="-I/opt/homebrew/Cellar/snappy/1.1.9/include -L/opt/homebrew/Cellar/snappy/1.1.9/lib" poetry run pip install python-snappy