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 module 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.
- fieldnames: 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 give you access to the following QuerySet methods:
to_datafameto_timeseriesto_pivot table
Returns a DataFrame from the 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,
- 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 data
- like object, decimal etc. to float if possible
Examples
Create a dataframe using all the fields in your model as follows
qs = MyModel.objects.all() df = qs.to_dataframe()
This will include you primary key create a DataFrame only from specified
field names:
df = qs.to_dataframe(['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')
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
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
- 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)