Pandas Series: sparse.from_coo() function

Last update on September 15 2022 12:56:58 (UTC/GMT +8 hours)

Series-sparse.from_coo() function

The from_coo() function is used to create a SparseSeries from a scipy.sparse.coo_matrix.

Syntax:

classmethod sparse.from_coo(A, dense_index=False)

Pandas Series: sparse.from_coo() function

Parameters:

Name	Description	Type/Default Value	Required / Optional
dense_index	If False (default), the SparseSeries index consists of only the coords of the non-null entries of the original coo_matrix. If True, the SparseSeries index consists of the full sorted (row, col) coordinates of the coo_matrix.	bool, default False	Required

Returns: s : SparseSeries

Example:

Python-Pandas Code:

import numpy as np
import pandas as pd
from scipy import sparse
X = sparse.coo_matrix(([4.0, 2.0, 3.0], ([1, 0, 1], [0, 2, 3])),
                       shape=(3, 4))
X

Output:

<3x4 sparse matrix of type '<class 'numpy.float64'>'
	with 3 stored elements in COOrdinate format>

Python-Pandas Code:

import numpy as np
import pandas as pd
from scipy import sparse
X = sparse.coo_matrix(([4.0, 2.0, 3.0], ([1, 0, 1], [0, 2, 3])),
                       shape=(3, 4))
X.todense()

Output:

matrix([[0., 0., 2., 0.],
        [4., 0., 0., 3.],
        [0., 0., 0., 0.]])

Python-Pandas Code:

import numpy as np
import pandas as pd
from scipy import sparse
X = sparse.coo_matrix(([4.0, 2.0, 3.0], ([1, 0, 1], [0, 2, 3])),
                       shape=(3, 4))
ss = pd.SparseSeries.from_coo(X)
ss

Output:

0  2    2.0
1  0    4.0
   3    3.0
dtype: Sparse[float64, nan]
BlockIndex
Block locations: array([0])
Block lengths: array([3])

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