Tools for working with pandas in your Django projects
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.
Please file bugs and send pull requests to the GitHub repository and issue tracker.
Start by creating a new virtualenv
for your project :
mkvirtualenv myproject
Next install numpy
and optionally pandas
:
pip install numpy
pip install pandas
You may want to consult the scipy documentation for more information on installing the Scipy
stack.
Finally, install the development version of django-pandas
from the github repository using pip
:
pip install https://github.com/chrisdev/django-pandas/tarball/master
To use django-pandas
in your Django project, modify the INSTALLED_APPS
in your settings module to include django_pandas
.
The django-pandas.io
provides some convenience methods to facilitate the creation of DataFrames from Django QuerySets and saving data to the underlying models.
Parameters
- qs: The Django QuerySet.
- fields: The model field names to use in creating the frame.
You can span a relationship in the usual Django way by using double underscores to specify a related field in another model You can span a relationship in the usual Django way by using double underscores to specify a related field in another model
- index_col: specify the field to use for the index. If the index
field is not in the field list it will be appended
- coerce_float : boolean, default True
Attempt to convert values to non-string, non-numeric objects (like decimal.Decimal) to floating point, useful for SQL result sets
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 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 qive you access to the following QuerySet methods:
to_datafame
to_timeseries
to_pivot table
Returns a DataFrame from the QuerySet
Parameters
- fields: The model fields 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
- fill_na: fill in missing observations using one of the following
this is a string specifying a pandas fill method {'backfill, 'bill', 'pad', 'ffill'} or a scalar value
- coerce_float: Attempt to convert the numeric non-string fields
like object, decimal etc. to float if possible
Examples
Create a dataframe using all the fields in your model as follows :
df = MyModel.to_dataframe()
This will include you primary key create a DataFrame only from secified fields:
df = MyData.to_dataframe('age', 'department', 'wage')
To set full_name
as the index :
MyData.to_dataframe('age', 'department', 'wage', index='full_name')
You can use filters and excludes :
MyData.filter(age__gt=20, department='IT').to_dataframe(index='full_name')
A convenience method for creating a time series i.e the DataFrame index is instance of a DateTime or PeriodIndex
Parameters
- fields: The model fields 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
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 mame 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 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)
A convenience method for creating a pivot table from a QuerySet
Parameters
- fields: The model fields 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)