django-pandas 0.4.2

Tools for working with pydata.pandas in your Django projects

==============
Django Pandas
==============
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Tools for working with `pandas <http://pandas.pydata.org>`_ in your Django
projects

Contributors
============
* `Christopher Clarke <https://github.com/chrisdev>`_
* `Bertrand Bordage <https://github.com/BertrandBordage>`_
* `Guillaume Thomas <https://github.com/gtnx>`_
* `Parbhat Puri <https://parbhatpuri.com/>`_
* `Fredrik Burman (coachHIPPO) <https://www.coachhippo.com>`_
* `Safe Hammad <http://safehammad.com>`_
* `Jeff Sternber <https://www.linkedin.com/in/jeffsternberg>`_
* `@MiddleFork <https://github.com/MiddleFork>`_
- `Daniel Andrlik <https://github.com/andrlik>`_

What's New
===========

- Compatibility with `pandas 0.20.1`
- Support for Python 2.7 and 3.5 with Django versions 1.8+
- Suport for Python 3.6 and Django 1.11
- We still support legacy versions (Python 2.7 and Django 1.4)

Dependencies
=============
``django-pandas`` supports `Django`_ (>=1.4.5) or later
and requires `django-model-utils`_ (>= 1.4.0) and `Pandas`_ (>= 0.12.0).
**Note** because of problems with the ``requires`` directive of setuptools
you probably need to install ``numpy`` in your virtualenv before you install
this package or if you want to run the test suite ::

pip install numpy
python setup.py test

Some ``pandas`` functionality requires parts of the Scipy stack.
You may wish to consult http://www.scipy.org/install.html
for more information on installing the ``Scipy`` stack.

.. _Django: http://djangoproject.com/
.. _django-model-utils: http://pypi.python.org/pypi/django-model-utils
.. _Pandas: http://pandas.pydata.org

Contributing
============

Please file bugs and send pull requests to the `GitHub repository`_ and `issue
tracker`_.

.. _GitHub repository: https://github.com/chrisdev/django-pandas/
.. _issue tracker: https://github.com/chrisdev/django-pandas/issues


Installation
=============
Start by creating a new ``virtualenv`` for your project ::

mkvirtualenv myproject

Next install ``numpy`` and ``pandas`` and optionally ``scipy`` ::

pip install numpy
pip install pandas

You may want to consult the `scipy documentation`_ for more information
on installing the ``Scipy`` stack.

.. _scipy documentation: http://www.scipy.org/install.html

Finally, install ``django-pandas`` using ``pip``::

pip install django-pandas

or install the development version from ``github`` ::

pip install https://github.com/chrisdev/django-pandas/tarball/master

Usage
======


IO Module
----------
The ``django-pandas.io`` module provides some convenience methods to
facilitate the creation of DataFrames from Django QuerySets.

read_frame
^^^^^^^^^^^

**Parameters**

- qs: A Django QuerySet.

- fieldnames: A list of model field names to use in creating the ``DataFrame``.
You can span a relationship in the usual Django way
by using double underscores to specify a related field
in another model

- index_col: Use specify the field name to use for the ``DataFrame`` index.
If the index
field is not in the field list it will be appended

- coerce_float : Boolean, defaults to True
Attempt to convert values to non-string,
non-numeric objects (like decimal.Decimal)
to floating point.

- verbose: If this is ``True`` then populate the DataFrame with the
human readable versions of any foreign key or choice fields
else use the actual values set in the model.


Examples
^^^^^^^^^
Assume that this is your model::

class MyModel(models.Model):

full_name = models.CharField(max_length=25)
age = models.IntegerField()
department = models.CharField(max_length=3)
wage = models.FloatField()

First create a query set::

from django_pandas.io import read_frame
qs = MyModel.objects.all()

To create a dataframe using all the fields in the underlying model ::

df = read_frame(qs)

The `df` will contain human readable column values for foreign key and choice
fields. The `DataFrame` will include all the fields in the underlying
model including the primary key.
To create a DataFrame using specified field names::

df = read_frame(qs, fieldnames=['age', 'wage', 'full_name'])

To set ``full_name`` as the ``DataFrame`` index ::

qs.to_dataframe(['age', 'wage', index='full_name'])

You can use filters and excludes ::

qs.filter(age__gt=20, department='IT').to_dataframe(index='full_name')


DataFrameManager
-----------------
``django-pandas`` provides a custom manager to use with models that
you want to render as Pandas Dataframes. The ``DataFrameManager``
manager provides the ``to_dataframe`` method that returns
your models queryset as a Pandas DataFrame. To use the DataFrameManager, first
override the default manager (`objects`) in your model's definition
as shown in the example below ::

#models.py

from django_pandas.managers import DataFrameManager

class MyModel(models.Model):

full_name = models.CharField(max_length=25)
age = models.IntegerField()
department = models.CharField(max_length=3)
wage = models.FloatField()

objects = DataFrameManager()


This will give you access to the following QuerySet methods:

- ``to_dataframe``
- ``to_timeseries``
- ``to_pivot_table``

to_dataframe
^^^^^^^^^^^^^

Returns a DataFrame from the QuerySet

**Parameters**

- fieldnames: The model field names to utilise in creating the frame.
to span a relationship, use the field name of related
fields across models, separated by double underscores,


- index: specify the field to use for the index. If the index
field is not in the field list it will be appended

- coerce_float: Attempt to convert the numeric non-string data
like object, decimal etc. to float if possible

- verbose: If this is ``True`` then populate the DataFrame with the
human readable versions of any foreign key or choice fields
else use the actual value set in the model.

Examples
^^^^^^^^^

Create a dataframe using all the fields in your model as follows ::

qs = MyModel.objects.all()

df = qs.to_dataframe()

This will include your primary key. To create a DataFrame using specified
field names::

df = qs.to_dataframe(fieldnames=['age', 'department', 'wage'])

To set ``full_name`` as the index ::

qs.to_dataframe(['age', 'department', 'wage'], index='full_name'])

You can use filters and excludes ::

qs.filter(age__gt=20, department='IT').to_dataframe(index='full_name')

to_timeseries
--------------

A convenience method for creating a time series i.e the
DataFrame index is instance of a DateTime or PeriodIndex

**Parameters**

- fieldnames: The model field names to utilise in creating the frame.
to span a relationship, just use the field name of related
fields across models, separated by double underscores,

- index: specify the field to use for the index. If the index
field is not in the field list it will be appended. This
is mandatory.

- storage: Specify if the queryset uses the `wide` or `long` format
for data.

- pivot_column: Required once the you specify `long` format
storage. This could either be a list or string identifying
the field name or combination of field. If the pivot_column
is a single column then the unique values in this column become
a new columns in the DataFrame
If the pivot column is a list the values in these columns are
concatenated (using the '-' as a separator)
and these values are used for the new timeseries columns

- values: Also required if you utilize the `long` storage the
values column name is use for populating new frame values

- freq: the offset string or object representing a target conversion

- rs_kwargs: Arguments based on pandas.DataFrame.resample

- verbose: If this is ``True`` then populate the DataFrame with the
human readable versions of any foreign key or choice fields
else use the actual value set in the model.

Examples
^^^^^^^^^

Using a *long* storage format ::

#models.py

class LongTimeSeries(models.Model):
date_ix = models.DateTimeField()
series_name = models.CharField(max_length=100)
value = models.FloatField()

objects = DataFrameManager()

Some sample data:::

======== ===== =====
date_ix series_name value
======== ===== ======
2010-01-01 gdp 204699

2010-01-01 inflation 2.0

2010-01-01 wages 100.7

2010-02-01 gdp 204704

2010-02-01 inflation 2.4

2010-03-01 wages 100.4

2010-02-01 gdp 205966

2010-02-01 inflation 2.5

2010-03-01 wages 100.5
========== ========== ======


Create a QuerySet ::

qs = LongTimeSeries.objects.filter(date_ix__year__gte=2010)

Create a timeseries dataframe ::

df = qs.to_timeseries(index='date_ix',
pivot_columns='series_name',
values='value',
storage='long')
df.head()

date_ix gdp inflation wages

2010-01-01 204966 2.0 100.7

2010-02-01 204704 2.4 100.4

2010-03-01 205966 2.5 100.5


Using a *wide* storage format ::

class WideTimeSeries(models.Model):
date_ix = models.DateTimeField()
col1 = models.FloatField()
col2 = models.FloatField()
col3 = models.FloatField()
col4 = models.FloatField()

objects = DataFrameManager()

qs = WideTimeSeries.objects.all()

rs_kwargs = {'how': 'sum', 'kind': 'period'}
df = qs.to_timeseries(index='date_ix', pivot_columns='series_name',
values='value', storage='long',
freq='M', rs_kwargs=rs_kwargs)

to_pivot_table
--------------
A convenience method for creating a pivot table from a QuerySet

**Parameters**

- fieldnames: The model field names to utilise in creating the frame.
to span a relationship, just use the field name of related
fields across models, separated by double underscores,
- values : column to aggregate, optional
- rows : list of column names or arrays to group on
Keys to group on the x-axis of the pivot table
- cols : list of column names or arrays to group on
Keys to group on the y-axis of the pivot table
- aggfunc : function, default numpy.mean, or list of functions
If list of functions passed, the resulting pivot table will have
hierarchical columns whose top level are the function names
(inferred from the function objects themselves)
- fill_value : scalar, default None
Value to replace missing values with
- margins : boolean, default False
Add all row / columns (e.g. for subtotal / grand totals)
- dropna : boolean, default True

**Example**
::

# models.py
class PivotData(models.Model):
row_col_a = models.CharField(max_length=15)
row_col_b = models.CharField(max_length=15)
row_col_c = models.CharField(max_length=15)
value_col_d = models.FloatField()
value_col_e = models.FloatField()
value_col_f = models.FloatField()

objects = DataFrameManager()

Usage ::

rows = ['row_col_a', 'row_col_b']
cols = ['row_col_c']

pt = qs.to_pivot_table(values='value_col_d', rows=rows, cols=cols)


.. end-here


CHANGES
========
0.4.1 (2016-02-05)
-------------------
- Address the incompatibility with Django 1.9 due to the removal of
specialized query sets like the
`ValuesQuerySet <https://code.djangoproject.com/ticket/24211>`_
- Address the removal of the ``PassThrougManager`` from ``django-model-utils``
version ``2.4``. We've removed the dependency on django-model-utils and
included the PassThroughManger (which was always a standalone tool
distributed a part of django-model-utils) for compatibility with
earlier versions of Django (<= 1.8). For more recent versions of
Django we're using Django's built in ``QuerySet.as_manager()``.
- Now supports Pandas 0.14.1 and above
- The fall in Coverage in this release largely reflects the integration of
the PassThroughManager into the code base. We'll add the required test
coverage for the PassThroughManager in subsequent releases.

0.3.1 (2015-10-25)
-------------------
- Extends the ability to span a ForeignKey relationship with double underscores
to OneToOneField too thanks to Safe Hammad
- Provide better support for ManyToMany and OneToMany relations thanks to
Jeff Sternberg and @MiddleFork

0.3.0 (2015-06-16)
---------------------
- This version supports Django 1.8
- Support for Pandas 0.16

0.2.2 (2015-03-02)
---------------------
- Added Support for Django 1.7

0.2.1 (2015-01-28)
---------------------
- Added Support for Values QuerySets
- Support for Python 2.6
- Note we still have limited support for Django 1.7 but this will be coming in
the next release

0.2.0 (2014-06-15)
--------------------

- Added the ``io`` module so that DataFrames can be created from any
queryset so you don't need to to add a ``DataFrame manager`` to your
models. This is good for working with legacy projects.
- added a Boolean ``verbose`` argument to all methods (which defaults to ``True``)
This populate the DataFrame columns with the human readable versions of
foreign key or choice fields.
- Improved the performance DataFrame creation by removing dependency on
``np.core.records.fromrecords``
- Loads of bug fixes, more tests and improved coverage and better
documentation`