django-pandas 0.1.0

to_dataframe

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')

to_timeseries

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)

to_pivot_table

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

• rowslist of column names or arrays to group on

  Keys to group on the x-axis of the pivot table

• colslist of column names or arrays to group on

  Keys to group on the y-axis of the pivot table

• aggfuncfunction, 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_valuescalar, default None

  Value to replace missing values with

• marginsboolean, 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)