EdgeFrame unflatten_columns

---

unflatten_columns(self, columns, delimiter=None)

Compacts data from multiple rows based on cell data.

Parameters:

columns : list

Name of the column(s) to be used as keys for unflattening.

delimiter : unicode (default=None)

Separator for the data in the result columns. Default is comma (,).

Groups together cells in all columns (less the composite key) using ”,” as string delimiter. The original rows are deleted. The grouping takes place based on a composite key created from cell values. The column datatypes are changed to string.

Examples

Given a data file:

user1 1/1/2015 1 70
user1 1/1/2015 2 60
user2 1/1/2015 1 65

The commands to bring the data into a frame, where it can be worked on:

>>> my_csv = ta.CsvFile("original_data.csv", schema=[('a', str), ('b', str),('c', int32) ,('d', int32)])
>>> frame = ta.Frame(source=my_csv)

Looking at it:

>>> frame.inspect()
[#]  a      b         c  d
===========================
[0]  user1  1/1/2015  1  70
[1]  user1  1/1/2015  2  60
[2]  user2  1/1/2015  1  65

Unflatten the data using columns a & b:

>>> frame.unflatten_columns(['a','b'])
[===Job Progress===]

Check again:

>>> frame.inspect()
[#]  a      b         c    d
================================
[0]  user2  1/1/2015  1    65
[1]  user1  1/1/2015  1,2  70,60

Alternatively, unflatten_columns() also accepts a single column like:

>>> frame.unflatten_columns('a')
[===Job Progress===]

>>> frame.inspect()
[#]  a      b                  c    d
=========================================
[0]  user1  1/1/2015,1/1/2015  1,2  70,60
[1]  user2  1/1/2015           1    65