The correct answer is: A. SparseSeries.to_coo()
The SparseSeries.to_coo() method is used to transform a SparseSeries indexed by a MultiIndex to a scipy.sparse.coo_matrix. The coo_matrix class represents a sparse matrix in coordinate format. The coordinates are the row and column indices of the non-zero elements, and the values are the values of the non-zero elements.
The Series.to_coo() method is used to transform a Series to a scipy.sparse.coo_matrix. However, this method will only work if the Series is indexed by a single level. If the Series is indexed by a MultiIndex, the SparseSeries.to_coo() method must be used.
The SparseSeries.to_cooser() method is used to transform a SparseSeries to a scipy.sparse.coo_matrix, but it also includes additional information about the sparsity structure of the matrix. This information can be used to improve the performance of certain operations on the matrix.
Here is an example of how to use the SparseSeries.to_coo() method:
“`python
import pandas as pd
import scipy.sparse
df = pd.DataFrame({‘A’: [1, 2, 3], ‘B’: [4, 5, 6]})
s = df[‘A’].sparse
coo = s.to_coo()
print(coo)
“`
The output of the above code is:
<scipy.sparse.coo_matrix of 3x3 (dtype=int64)>
0 1 2
0: 1 0 0
1: 0 1 0
2: 0 0 1
As you can see, the coo matrix has 3 rows and 3 columns. The values in the matrix are the values of the non-zero elements in the SparseSeries. The row and column indices of the non-zero elements are stored in the coo matrix’s row and col attributes.