Skip to main content

Excel table reader library.

Project description

About xlref

xlref is an useful library to capture by a simple reference (e.g., A1(RD):..:RD) a table with non-empty cells from Excel-sheets when its exact position is not known beforehand.

This code was inspired by the xleash module of the pandalone library. The reason of developing a similar tool was to have a smaller library to install and improve the performances of reading .xlsx files.

Installation

To install it use (with root privileges):

$ pip install xlref

Or download the last git version and use (with root privileges):

$ python setup.py install

Tutorial

A typical example is capturing a table with a “header” row and convert into a dictionary. The code below shows how to do it:

>>> import xlref as xl
>>> _ref = 'excel.xlsx#ref!A1(RD):RD[%s]'
>>> ref = xl.Ref(_ref % '"dict"')
>>> ref.range  # Captured range.
B2:C28
>>> values = ref.values; values  # Captured values.
{...}
>>> values['st-cell-move']
'#D5(RU):H1(DL)'

You can notice from the code above that all the values of the dictionary are references. To parse it recursively, there are two options:

  1. add the “recursive” filter before the “dict”:

    >>> values = xl.Ref(_ref % '"recursive", "dict"').values
    >>> values['st-cell-move'].tolist()
    [[1.0, 2.0, 3.0],
     [4.0, 5.0, 6.0],
     [7.0, 8.0, 9.0]]
    
  2. apply a filter onto dictionary’ values using the extra functionality of the “dict” filter:

    >>> values = xl.Ref(_ref % '{"fun": "dict", "value":"ref"}').values
    >>> values['st-cell-move'].tolist()
    [[1.0, 2.0, 3.0],
     [4.0, 5.0, 6.0],
     [7.0, 8.0, 9.0]]
    

You have also the possibility to define and use your custom filters as follows:

>>> import numpy as np
>>> xl.FILTERS['my-filter'] = lambda parent, x: np.sum(x)
>>> xl.Ref('#D5(RU):H1(DL)["my-filter"]', ref).values
45.0

An alternative way is to use directly the methods of the filtered results as follows:

>>> xl.Ref('#D5(RU):H1(DL)["sum"]', ref).values
45.0

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

xlref-1.2.0.tar.gz (57.6 kB view details)

Uploaded Source

Built Distribution

xlref-1.2.0-py2.py3-none-any.whl (17.2 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file xlref-1.2.0.tar.gz.

File metadata

  • Download URL: xlref-1.2.0.tar.gz
  • Upload date:
  • Size: 57.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for xlref-1.2.0.tar.gz
Algorithm Hash digest
SHA256 8ca953d2b474e02d74811f88a2ff285dc14abffe5334e3c9a6b80b909906249d
MD5 4aa6388eb077dbe188363b51bf3b17d3
BLAKE2b-256 05c8ca9c0c7587ce8f16e2c0b364ef7bc3266553374a41d13617bf548c4cfa28

See more details on using hashes here.

File details

Details for the file xlref-1.2.0-py2.py3-none-any.whl.

File metadata

  • Download URL: xlref-1.2.0-py2.py3-none-any.whl
  • Upload date:
  • Size: 17.2 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.0.1 pkginfo/1.7.0 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.54.1 CPython/3.9.1

File hashes

Hashes for xlref-1.2.0-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 33efd9b15127f0ee7142b2b44e070752e311eee35a96ef3fd38c08b572e018ad
MD5 ff7d56c33ecc8ddcd593924d946ad5be
BLAKE2b-256 b3a68ab84fa968ff115e0c50fabe01a57d454887b6192143889d6b3f03eb3a93

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page