Thanks for having clarified your question, try this. Here is sample data with two columns that have three and two levels respectively: set.seed(123) n <- 6 df <- data.frame(x = sample(c(“A”, “B”, “C”), n, TRUE), y = sample(c(“D”, “E”), n, TRUE)) # x y # 1 A E # 2 C E # 3 B … Read more
Pandas 0.20.0+: As of pandas version 0.20.0, released May 5, 2017, there is a one-liner for this: from scipy import sparse def sparse_df_to_csr(df): return sparse.csr_matrix(df.to_coo()) This uses the new to_coo() method. Earlier Versions: Building on Victor May’s answer, here’s a slightly faster implementation, but it only works if the entire SparseDataFrame is sparse with all … Read more
Here is a method that creates a sparse scipy matrix based on data and indices of person and thing. person_u and thing_u are lists representing the unique entries for your rows and columns of pivot you want to create. Note: this assumes that your count column already has the value you want in it. from … Read more
A direct conversion is not supported ATM. Contributions are welcome! Try this, should be ok on memory as the SpareSeries is much like a csc_matrix (for 1 column) and pretty space efficient In [37]: col = np.array([0,0,1,2,2,2]) In [38]: data = np.array([1,2,3,4,5,6],dtype=”float64″) In [39]: m = csc_matrix( (data,(row,col)), shape=(3,3) ) In [40]: m Out[40]: <3×3 … Read more
I think I’ve recreated the csr row indexing with: def extractor(indices, N): indptr=np.arange(len(indices)+1) data=np.ones(len(indices)) shape=(len(indices),N) return sparse.csr_matrix((data,indices,indptr), shape=shape) Testing on a csr I had hanging around: In [185]: M Out[185]: <30×40 sparse matrix of type ‘<class ‘numpy.float64′>’ with 76 stored elements in Compressed Sparse Row format> In [186]: indices=np.r_[0:20] In [187]: M[indices,:] Out[187]: <20×40 sparse … Read more
Sparsed arrays built with hashmaps are very inefficient for frequently read data. The most efficient implementations uses a Trie that allows access to a single vector where segments are distributed. A Trie can compute if an element is present in the table by performing only read-only TWO array indexing to get the effective position where … Read more
Yes, they are. They are actually hash tables internally, so you can use not only large integers but also strings, floats, or other objects. All keys get converted to strings via toString() before being added to the hash. You can confirm this with some test code: <script> var array = []; array[0] = “zero”; array[new … Read more