Convert sparse matrix to pandas dataframe

Question

import numpy as np
from scipy.sparse import csr_matrix

csr = csr_matrix(np.array(
    [[0, 0, 4],
     [1, 0, 0],
     [2, 0, 0],]))

# Return a Coordinate (coo) representation of the csr matrix.
coo = csr.tocoo(copy=False)

# Access `row`, `col` and `data` properties of coo matrix.
df = pd.DataFrame({'index': coo.row, 'col': coo.col, 'data': coo.data})[['index', 'col', 'data']]

>>> df.head()
   index  col  data
0    0     2     4
1    1     0     1
2    2     0     2

I tried to convert a scipy csr_matrix matrix to a dataframe, where the columns represent the index, column, and data of the matrix.

The only issue is that what I tried above does not produce rows for the columns where the values are 0. Here is what I'd like the output to look like:

>>> df.head()
   index  col  data
0    0     0     0
1    0     1     0
2    0     2     4
3    1     0     1
4    1     1     0
5    1     2     0
6    2     0     2
7    2     1     0
8    2     2     0

You'll see that the code snippet above is taken from this answer in this thread .

My request/question : Is there a way to convert the matrix to a df and also include the elements of the matrix where the value is 0?

Answer 1

One approach is to create a filling DataFrame and combine it (using combine_first ) with the one you already have:

df = pd.DataFrame({'index': coo.row, 'col': coo.col, 'data': coo.data}).set_index(["index", "col"])

n_rows, n_cols = coo.shape
rows, cols = map(np.ndarray.flatten, np.mgrid[:n_rows, :n_cols])
filling = pd.DataFrame({"index": rows, "col": cols, "data": np.repeat(0, n_rows * n_cols)}) \
    .set_index(["index", "col"])

res = df.combine_first(filling).reset_index()

print(res)

Output

   index  col  data
0      0    0   0.0
1      0    1   0.0
2      0    2   4.0
3      1    0   1.0
4      1    1   0.0
5      1    2   0.0
6      2    0   2.0
7      2    1   0.0
8      2    2   0.0

Answer 2

• Convert sparse matrix to dense matrix to fill in 0s
• Convert dense matrix to pandas dataframe
• melt dataframe from 'wide' to 'long' format

df = your_sparse_matrix_data.todense()
(pd.DataFrame(df)
    .melt()
    .reset_index()
    .rename(columns = {'index':'row','variable':'column'}))