README-template.rst

outputty

{{INTRO}}

Installation

As simple as executing:

pip install outputty

Example

Code time!:

>>> from outputty import Table
>>> my_table = Table(headers=['name', 'age']) # headers are the columns
>>> my_table.append(('Álvaro Justen', 24)) # a row as tuple
>>> my_table.append({'name': 'Other User', 'age': 99}) # a row as dict
>>> print my_table # a text representation of Table
+---------------+-----+
|      name     | age |
+---------------+-----+
| Álvaro Justen |  24 |
|    Other User |  99 |
+---------------+-----+

>>> print 'First row:', my_table[0] # Table is indexable
First row: [u'\xc1lvaro Justen', 24]

>>> print 'Sum of ages:', sum(my_table['age']) # you can get columns too
Sum of ages: 123

>>> my_table.write('csv', 'my-table.csv') # CSV plugin will save its contents in a file
>>> # let's see what's in the file...
>>> print open('my-table.csv').read()
"name","age"
"Álvaro Justen","24"
"Other User","99"

>>> # let's use HTML plugin!
>>> print my_table.write('html') # without filename ``write`` will return a string
<table>
  <thead>
    <tr class="header">
      <th>name</th>
      <th>age</th>
    </tr>
  </thead>
  <tbody>
    <tr class="odd">
      <td>Álvaro Justen</td>
      <td>24</td>
    </tr>
    <tr class="even">
      <td>Other User</td>
      <td>99</td>
    </tr>
  </tbody>
</table>

Table have a lot of other features. To learn more (by examples), read outputty tutorial and see examples folder. Enjoy!

New Features

Yes, there are a lot of features to add (it's just the begining). If you want to contribute, please see our outputty wishlist.

You can also use the outputty Issue Tracking System on GitHub to report bugs.

Contributing

If you want to contribute to this project, please:

• Install dependencies for development running pip install -r requirements/development.txt.
• Execute make test to run all tests -- please run all tests before pushing.
  • To run just one test file execute: nosetests --with-coverage --cover-package outputty tests/test_your-test-file.py
  • Try to have a test-coverage of 100%.
  • Use Test-driven development.
• Use nvie's gitflow - to learn, read A Successful Git branching model.
• Create/update documentation (README/docstrings/man page)
  • Do NOT edit README.rst and tutorial.rst, edit README-template.rst or tutorial-template.rst instead and run make create-docs to create the new README.rst and tutorial.rst (before committing). The tutorial will be created based on files in examples folder.

New Plugins

If you want to create a new plugin to import/export from/to some new resource, please see files outputty/plugin_*.py as examples. They are so simple, please follow these steps:

• Create a file named outputty/plugin_name.py, where name is the name of your plugin.
• Create read and/or write functions in this file. These functions receive the Table object and optional parameters.
  • read: should read data from the resource specified in parameters and put this data in Table (using Table.append or Table.extend).
  • write: should read data from Table (iterating over it, using slicing etc.) and write this data to the resource specified in parameters.
• Call your plugin executing my_table.write('name', optional_parameters...) or my_table.read('name', optional_parameters...) (where name is your plugin's name) - when you execute it outputty will call outputty.plugin_name.read/outputty.plugin_name.write.

Encoding and Decoding

Your plugin's read function must put all data inside in unicode and your plugin's write function will receive a Table object with all data in unicode (it should not change this). But if you need to decode/encode before/after doing some actions in your plugin, you can use Table.decode() and Table.encode().

Contributors

{{AUTHORS}}

Related Software

• outputty-like:

  • tablib: format-agnostic tabular dataset library.
  • PyTables: package for managing hierarchical datasets and designed to efficiently and easily cope with extremely large amounts of data.
  • csvstudio: Python tool to analyze csv files.
  • csvsimple: a simple tool to handle CSV data.
  • toolshed: less boiler-plate.
  • buzhug: a fast, pure-Python database engine.

• Data analysis:

  • pyf: framework and platform dedicated to large data processing, mining, transforming, reporting and more.
  • pygrametl: Python framework which offers commonly used functionality for development of Extract-Transform-Load (ETL) processes.
  • etlpy seems to be a dead project.
  • orange: data visualization and analysis for novice and experts.
  • Ruffus: lightweight python module to run computational pipelines.
  • webstore: web-api enabled datastore backed onto sql databases

• Command-line tools:

  • fabulous: library designed to make the output of terminal applications look fabulous.
  • termcolor: ANSII Color formatting for output in terminal.
  • clint: Python Command-line Application Tools.

• Other:

  • pyspread: non-traditional spreadsheet application.