Tools for documentation-aware data reading, writing, and analysis
Project description
=======
MetaCSV
=======
.. image:: https://travis-ci.org/delgadom/metacsv.svg?branch=master
:target: https://travis-ci.org/delgadom/metacsv
.. image:: https://badge.fury.io/py/metacsv.svg
:target: https://badge.fury.io/py/metacsv
.. image:: https://coveralls.io/repos/github/delgadom/metacsv/badge.svg?branch=master
:target: https://coveralls.io/github/delgadom/metacsv?branch=master
``metacsv`` - Tools for documentation-aware data reading, writing, and analysis
See the full documentation at ReadTheDocs_
.. _ReadTheDocs: http://metacsv.rtfd.org
Overview
=========
Read in CSV data with a yaml-compliant header directly into
a ``pandas`` ``Series``, ``DataFrame``, or ``Panel`` or an ``xarray``
``DataArray`` or ``Dataset``.
Data specification
----------------------------
Data can be specified using a yaml-formatted header, with the doc-separation string
above and below the yaml block. Only one yaml block is allowed. If the doc-separation
string is not the first (non-whitespace) line in the file, all of the file's contents
will be interpreted by the csv reader. The yaml data can have arbitrary complexity.
.. code-block:: python
>>> import metacsv, io
>>> doc = io.StringIO('''
---
author: A Person
date: 2000-01-01
variables:
pop:
name: Population
unit: millions
gdp:
name: Product
unit: 2005 $Bn
---
region,year,pop,gdp
USA,2010,309.3,13599.3
USA,2011,311.7,13817.0
CAN,2010,34.0,1240.0
CAN,2011,34.3,1276.7
''')
Special attributes
~~~~~~~~~~~~~~~~~~~~~~~
The ``coords`` and ``variables`` attributes are keywords and are not simply passed
to the MetaCSV object's ``attrs`` attribute.
``variables`` describes columns in the resulting ``DataFrame`` or ``Data variables``
in the resulting ``xarray.Dataset``. Variables is not used when the CSV has only one
column and the argumetn ``squeeze=True`` is passed to ``read_csv``.
``coords`` describes indices in the resulting ``DataFrame``/``Series``, or
``Coordinates`` in the resulting ``xarray.Dataset/xarray.DataArray``. Coordinates
are categorical or independent variables which index the object's ``values``.
Using MetaCSV-formatted files in python
--------------------------------------------
Read MetaCSV-formatted data into python using pandas-like syntax:
.. code-block:: python
>>> metacsv.read_csv(doc, index_col=[0,1])
>>> df
<metacsv.core.containers.DataFrame (4, 2)>
pop gdp
region year
USA 2010 309.3 13599.3
2011 311.7 13817.0
CAN 2010 34.0 1240.0
2011 34.3 1276.7
Coordinates
* region (region) object CAN, USA
* year (year) int64 2010, 2011
Variables
pop
gdp
Attributes
date: 2000-01-01
author: A Person
Exporting MetaCSV data to other formats
-----------------------------------------------
CSV
~~~~~~~~~
A MetaCSV ``Series`` or ``DataFrame`` can be written as a yaml-prefixed CSV using
the same ``to_csv`` syntax as it's ``pandas`` counterpart:
.. code-block:: python
>>> df.attrs['new attribute'] = 'changed in python!'
>>> # includes changes to data, attributes, variables, and coordinates
... df.to_csv('my_new_data.csv')
pandas
~~~~~~~~~~~~~~~
The coordinates and MetaCSV attributes can be easily stripped from a MetaCSV Container:
.. code-block:: python
>>> df.to_pandas()
pop gdp
region year
USA 2010 309.3 13599.3
2011 311.7 13817.0
CAN 2010 34.0 1240.0
2011 34.3 1276.7
xarray/netCDF
~~~~~~~~~~~~~~~
``xarray`` provides a pandas-like interface to operating on indexed ``ndarray`` data. It
is modeled on the ``netCDF`` data storage format used frequently in climate science, but
is useful for many applications with higher-order data.
.. code-block:: python
>>> ds = df.to_xarray()
>>> ds
<xarray.Dataset>
Dimensions: (region: 2, year: 2)
Coordinates:
* region (region) object 'USA' 'CAN'
* year (year) int64 2010 2011
Data variables:
pop (region, year) float64 309.3 311.7 34.0 34.3
gdp (region, year) float64 1.36e+04 1.382e+04 1.24e+03 1.277e+03
Attributes:
date: 2000-01-01
author: A Person
>>> ds.to_netcdf('my_netcdf_data.nc')
Others
~~~~~~~~~
Currently, MetaCSV only supports conversion back to CSV and to
netCDF through the ``xarray`` module. However, feel free to suggest
additional features and to contribute your own!
TODO
============
* Make ``coords`` and ``attrs`` persistent across slicing operations
(try ``df['pop'].to_xarray()`` from above example and watch it
fail...)
* Improve hooks between ``pandas`` and ``metacsv``:
- update ``coord`` names on ``df.index.names`` assignment
- update ``coords`` on stack/unstack
- update ``coords`` on
* Handle attributes indexed by coord/variable names --> assign to
coord/variable-specific ``attrs``
* Let's start an issue tracker and get rid of this section!
* Should we rethink "special attributes," e.g. coords? Maybe these should
have some special prefix like ``_coords`` when included in yaml headers to
avoid confusion with other generic attributes...
* Allow special attributes (``coords``, ``variables``) in ``read_csv`` call
* Allow external file headers
* Write tests
* Write documentation
* Maybe steal xarray's coordinate handling and save ourselves a whole lotta work?
Feature Requests
==================
* Create syntax for ``multi-csv`` --> ``Panel`` or combining using filename regex
* Eventually? allow for on-disk manipulation of many/large files with dask/xarray
* Eventually? add xml, SQL, other structured syntax language conversions
============== ==========================================================
Python support Python 2.7, >= 3.3
Source https://github.com/delgadom/metacsv
Docs http://metacsv.rtfd.org
Changelog http://metacsv.readthedocs.org/en/latest/history.html
API http://metacsv.readthedocs.org/en/latest/api.html
Issues https://github.com/delgadom/metacsv/issues
Travis http://travis-ci.org/delgadom/metacsv
Test coverage https://coveralls.io/r/delgadom/metacsv
pypi https://pypi.python.org/pypi/metacsv
Ohloh https://www.ohloh.net/p/metacsv
License `BSD`_.
git repo .. code-block:: bash
$ git clone https://github.com/delgadom/metacsv.git
install dev .. code-block:: bash
$ git clone https://github.com/delgadom/metacsv.git metacsv
$ cd ./metacsv
$ virtualenv .env
$ source .env/bin/activate
$ pip install -e .
tests .. code-block:: bash
$ python setup.py test
============== ==========================================================
.. _BSD: http://opensource.org/licenses/BSD-3-Clause
.. _Documentation: http://metacsv.readthedocs.org/en/latest/
.. _API: http://metacsv.readthedocs.org/en/latest/api.html
=========
Changelog
=========
Here you can find the recent changes to MetaCSV..
.. changelog::
:version: dev
:released: Ongoing
.. change::
:tags: docs
Updated CHANGES.
.. changelog::
:version: 0.0.1
:released: 2016-05-04
.. change::
:tags: project
First release on PyPi.
.. todo:: vim: set filetype=rst:
MetaCSV
=======
.. image:: https://travis-ci.org/delgadom/metacsv.svg?branch=master
:target: https://travis-ci.org/delgadom/metacsv
.. image:: https://badge.fury.io/py/metacsv.svg
:target: https://badge.fury.io/py/metacsv
.. image:: https://coveralls.io/repos/github/delgadom/metacsv/badge.svg?branch=master
:target: https://coveralls.io/github/delgadom/metacsv?branch=master
``metacsv`` - Tools for documentation-aware data reading, writing, and analysis
See the full documentation at ReadTheDocs_
.. _ReadTheDocs: http://metacsv.rtfd.org
Overview
=========
Read in CSV data with a yaml-compliant header directly into
a ``pandas`` ``Series``, ``DataFrame``, or ``Panel`` or an ``xarray``
``DataArray`` or ``Dataset``.
Data specification
----------------------------
Data can be specified using a yaml-formatted header, with the doc-separation string
above and below the yaml block. Only one yaml block is allowed. If the doc-separation
string is not the first (non-whitespace) line in the file, all of the file's contents
will be interpreted by the csv reader. The yaml data can have arbitrary complexity.
.. code-block:: python
>>> import metacsv, io
>>> doc = io.StringIO('''
---
author: A Person
date: 2000-01-01
variables:
pop:
name: Population
unit: millions
gdp:
name: Product
unit: 2005 $Bn
---
region,year,pop,gdp
USA,2010,309.3,13599.3
USA,2011,311.7,13817.0
CAN,2010,34.0,1240.0
CAN,2011,34.3,1276.7
''')
Special attributes
~~~~~~~~~~~~~~~~~~~~~~~
The ``coords`` and ``variables`` attributes are keywords and are not simply passed
to the MetaCSV object's ``attrs`` attribute.
``variables`` describes columns in the resulting ``DataFrame`` or ``Data variables``
in the resulting ``xarray.Dataset``. Variables is not used when the CSV has only one
column and the argumetn ``squeeze=True`` is passed to ``read_csv``.
``coords`` describes indices in the resulting ``DataFrame``/``Series``, or
``Coordinates`` in the resulting ``xarray.Dataset/xarray.DataArray``. Coordinates
are categorical or independent variables which index the object's ``values``.
Using MetaCSV-formatted files in python
--------------------------------------------
Read MetaCSV-formatted data into python using pandas-like syntax:
.. code-block:: python
>>> metacsv.read_csv(doc, index_col=[0,1])
>>> df
<metacsv.core.containers.DataFrame (4, 2)>
pop gdp
region year
USA 2010 309.3 13599.3
2011 311.7 13817.0
CAN 2010 34.0 1240.0
2011 34.3 1276.7
Coordinates
* region (region) object CAN, USA
* year (year) int64 2010, 2011
Variables
pop
gdp
Attributes
date: 2000-01-01
author: A Person
Exporting MetaCSV data to other formats
-----------------------------------------------
CSV
~~~~~~~~~
A MetaCSV ``Series`` or ``DataFrame`` can be written as a yaml-prefixed CSV using
the same ``to_csv`` syntax as it's ``pandas`` counterpart:
.. code-block:: python
>>> df.attrs['new attribute'] = 'changed in python!'
>>> # includes changes to data, attributes, variables, and coordinates
... df.to_csv('my_new_data.csv')
pandas
~~~~~~~~~~~~~~~
The coordinates and MetaCSV attributes can be easily stripped from a MetaCSV Container:
.. code-block:: python
>>> df.to_pandas()
pop gdp
region year
USA 2010 309.3 13599.3
2011 311.7 13817.0
CAN 2010 34.0 1240.0
2011 34.3 1276.7
xarray/netCDF
~~~~~~~~~~~~~~~
``xarray`` provides a pandas-like interface to operating on indexed ``ndarray`` data. It
is modeled on the ``netCDF`` data storage format used frequently in climate science, but
is useful for many applications with higher-order data.
.. code-block:: python
>>> ds = df.to_xarray()
>>> ds
<xarray.Dataset>
Dimensions: (region: 2, year: 2)
Coordinates:
* region (region) object 'USA' 'CAN'
* year (year) int64 2010 2011
Data variables:
pop (region, year) float64 309.3 311.7 34.0 34.3
gdp (region, year) float64 1.36e+04 1.382e+04 1.24e+03 1.277e+03
Attributes:
date: 2000-01-01
author: A Person
>>> ds.to_netcdf('my_netcdf_data.nc')
Others
~~~~~~~~~
Currently, MetaCSV only supports conversion back to CSV and to
netCDF through the ``xarray`` module. However, feel free to suggest
additional features and to contribute your own!
TODO
============
* Make ``coords`` and ``attrs`` persistent across slicing operations
(try ``df['pop'].to_xarray()`` from above example and watch it
fail...)
* Improve hooks between ``pandas`` and ``metacsv``:
- update ``coord`` names on ``df.index.names`` assignment
- update ``coords`` on stack/unstack
- update ``coords`` on
* Handle attributes indexed by coord/variable names --> assign to
coord/variable-specific ``attrs``
* Let's start an issue tracker and get rid of this section!
* Should we rethink "special attributes," e.g. coords? Maybe these should
have some special prefix like ``_coords`` when included in yaml headers to
avoid confusion with other generic attributes...
* Allow special attributes (``coords``, ``variables``) in ``read_csv`` call
* Allow external file headers
* Write tests
* Write documentation
* Maybe steal xarray's coordinate handling and save ourselves a whole lotta work?
Feature Requests
==================
* Create syntax for ``multi-csv`` --> ``Panel`` or combining using filename regex
* Eventually? allow for on-disk manipulation of many/large files with dask/xarray
* Eventually? add xml, SQL, other structured syntax language conversions
============== ==========================================================
Python support Python 2.7, >= 3.3
Source https://github.com/delgadom/metacsv
Docs http://metacsv.rtfd.org
Changelog http://metacsv.readthedocs.org/en/latest/history.html
API http://metacsv.readthedocs.org/en/latest/api.html
Issues https://github.com/delgadom/metacsv/issues
Travis http://travis-ci.org/delgadom/metacsv
Test coverage https://coveralls.io/r/delgadom/metacsv
pypi https://pypi.python.org/pypi/metacsv
Ohloh https://www.ohloh.net/p/metacsv
License `BSD`_.
git repo .. code-block:: bash
$ git clone https://github.com/delgadom/metacsv.git
install dev .. code-block:: bash
$ git clone https://github.com/delgadom/metacsv.git metacsv
$ cd ./metacsv
$ virtualenv .env
$ source .env/bin/activate
$ pip install -e .
tests .. code-block:: bash
$ python setup.py test
============== ==========================================================
.. _BSD: http://opensource.org/licenses/BSD-3-Clause
.. _Documentation: http://metacsv.readthedocs.org/en/latest/
.. _API: http://metacsv.readthedocs.org/en/latest/api.html
=========
Changelog
=========
Here you can find the recent changes to MetaCSV..
.. changelog::
:version: dev
:released: Ongoing
.. change::
:tags: docs
Updated CHANGES.
.. changelog::
:version: 0.0.1
:released: 2016-05-04
.. change::
:tags: project
First release on PyPi.
.. todo:: vim: set filetype=rst:
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
File details
Details for the file MetaCSV-0.0.3-py2.py3-none-any.whl
.
File metadata
- Download URL: MetaCSV-0.0.3-py2.py3-none-any.whl
- Upload date:
- Size: 20.4 kB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 78775b5ebabfa93d6fc971ffb990ca8cb724fd8d30111a2696e914a63a33daa2 |
|
MD5 | e8abb19967feb076bd70929e0f5810c7 |
|
BLAKE2b-256 | a14c0a74253cba955d57e707c23814f5c7eb38a5e8d2d4f17e92b2e5238dc0be |