[FIXED] Efficiently create sparse pivot tables in pandas?

Issue

I'm working turning a list of records with two columns (A and B) into a matrix representation. I have been using the pivot function within pandas, but the result ends up being fairly large. Does pandas support pivoting into a sparse format? I know I can pivot it and then turn it into some kind of sparse representation, but isn't as elegant as I would like. My end goal is to use it as the input for a predictive model.

Alternatively, is there some kind of sparse pivot capability outside of pandas?

edit: here is an example of a non-sparse pivot

import pandas as pd
frame=pd.DataFrame()
frame['person']=['me','you','him','you','him','me']
frame['thing']=['a','a','b','c','d','d']
frame['count']=[1,1,1,1,1,1]

frame

  person thing  count
0     me     a      1
1    you     a      1
2    him     b      1
3    you     c      1
4    him     d      1
5     me     d      1

frame.pivot('person','thing')

        count            
thing       a   b   c   d
person                   
him       NaN   1 NaN   1
me          1 NaN NaN   1
you         1 NaN   1 NaN

This creates a matrix that could contain all possible combinations of persons and things, but it is not sparse.

http://docs.scipy.org/doc/scipy/reference/sparse.html

Sparse matrices take up less space because they can imply things like NaN or 0. If I have a very large data set, this pivoting function can generate a matrix that should be sparse due to the large number of NaNs or 0s. I was hoping that I could save a lot of space/memory by generating something that was sparse right off the bat rather than creating a dense matrix and then converting it to sparse.

Solution

The answer posted previously by @khammel was useful, but unfortunately no longer works due to changes in pandas and Python. The following should produce the same output:

from scipy.sparse import csr_matrix
from pandas.api.types import CategoricalDtype

person_c = CategoricalDtype(sorted(frame.person.unique()), ordered=True)
thing_c = CategoricalDtype(sorted(frame.thing.unique()), ordered=True)

row = frame.person.astype(person_c).cat.codes
col = frame.thing.astype(thing_c).cat.codes
sparse_matrix = csr_matrix((frame["count"], (row, col)), \
                           shape=(person_c.categories.size, thing_c.categories.size))

>>> sparse_matrix
<3x4 sparse matrix of type '<class 'numpy.int64'>'
     with 6 stored elements in Compressed Sparse Row format>

>>> sparse_matrix.todense()
matrix([[0, 1, 0, 1],
        [1, 0, 0, 1],
        [1, 0, 1, 0]], dtype=int64)


dfs = pd.SparseDataFrame(sparse_matrix, \
                         index=person_c.categories, \
                         columns=thing_c.categories, \
                         default_fill_value=0)
>>> dfs
        a   b   c   d
 him    0   1   0   1
  me    1   0   0   1
 you    1   0   1   0

The main changes were:

• .astype() no longer accepts "categorical". You have to create a CategoricalDtype object.
• sort() doesn't work anymore

Other changes were more superficial:

• using the category sizes instead of a length of the uniqued Series objects, just because I didn't want to make another object unnecessarily
• the data input for the csr_matrix (frame["count"]) doesn't need to be a list object
• pandas SparseDataFrame accepts a scipy.sparse object directly now

Answered By - Alnilam