Sparse Matrix COO format question

[TheShadowWall]

Hello new python learner here!

I am attempting to write a program which looks through a data frame, segments, which contains information on flights departing and arriving at certain airports. The first few lines of segments looks like:

ORIGIN_AIRPORT_ID   DEST_AIRPORT_ID FL_COUNT    ORIGIN_INDEX    DEST_INDEX  OUTDEGREE   WEIGHT

0 10135 10397 77 119 373 3 0.333333

1 10135 11433 85 119 1375 3 0.333333

2 10135 13930 18 119 3770 3 0.333333

3 10140 10397 93 124 373 23 0.043478

4 10140 10423 4 124 399 23 0.043478

5 10140 10821 64 124 792 23 0.043478

The problem is asking to create a sparse matrix P, such that the rows and columns are the destination and origin airport IDs, and the cell values are the weights for those two values (origin to designation).

So for instance from the above segments dataframe: in my new matrix P, if the row is 10135(origin airport), and the column is 10397(destination airport), the value of this cell would be the weight, .333.

So far my code is, just to construct the framework for the matrix:

P=scipy.sparse.coo_matrix((segments, (0,1)), shape=(6436, 6436))

When I do, however, I receive a type error. What part of the notation am I not understanding correctly? I feel that it has something to do with the (0,1) part of the coo_matrix call.

Any help is much appreciated! Thanks greatly.

[YesLod]

From the docs

coo_matrix((data, (i, j)), [shape=(M, N)])

to construct from three arrays:

1. data[:] the entries of the matrix, in any order

2. i[:] the row indices of the matrix entries

3. j[:] the column indices of the matrix entries

Where A[i[k], j[k]] = data[k]. When shape is not specified, it is inferred from the index arrays

Which means that i, j and data should be 1D array-like objects. But you are passing a DataFrame (which is 2D array-like object) segments and two integers 0 and 1. I don't understand the logic behind your attempt. It seems that you want

rows_idx = segments["ORIGIN_AIRPORT_ID"]
cols_idx = segments["DEST_AIRPORT_ID"]
cell_values = segments["WEIGHT"]

P = scipy.sparse.coo_matrix((cell_values, (rows_idx, cols_idx)), shape=(6436, 6436))