Count number of non zeros in numpy array

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Count number of non zeros in numpy array

Just some examples on usage of array_split, split, hsplit and vsplit: n [9]: a = np.random.randint(0,10,[4,4]) In [10]: a Out[10]: array([[2, 2, 7, 1], [5, 0, 3, 1], [2, 9, 8, 8], [5, 7, 7, 6]]) Some examples on using array_split: If you give an array or list as second argument you basically give the indices (before) which to 'cut' # split rows into 0|1 2|3 In [4]:

np.array_split(a, [1,3]) Out[4]: [array([[2, 2, 7, 1]]), array([[5, 0, 3, 1], [2, 9, 8, 8]]), array([[5, 7, 7, 6]])] # split columns into 0| 1 2 3 In [5]: np.array_split(a, [1], axis=1) Out[5]: [array([[2], [5], [2], [5]]), array([[2, 7, 1], [0, 3, 1], [9, 8, 8], [7, 7, 6]])] An integer as second arg. specifies the number of equal chunks: In [6]: np.array_split(a, 2, axis=1) Out[6]:

[array([[2, 2], [5, 0], [2, 9], [5, 7]]), array([[7, 1], [3, 1], [8, 8], [7, 6]])] split works the same but raises an exception if an equal split is not possible In addition to array_split you can use shortcuts vsplit and hsplit. vsplit and hsplit are pretty much self-explanatry: In [11]: np.vsplit(a, 2) Out[11]: [array([[2, 2, 7, 1], [5, 0, 3, 1]]), array([[2, 9, 8, 8], [5, 7, 7,

6]])] In [12]: np.hsplit(a, 2) Out[12]: [array([[2, 2], [5, 0], [2, 9], [5, 7]]), array([[7, 1], [3, 1], [8, 8], [7, 6]])] You can't perform that action at this time. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. Page 2 You can't perform that action at this time. You

signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_. All of them are based on the string methods in the Python standard library. Unlike the

standard numpy comparison operators, the ones in the char module strip trailing whitespace characters before performing the comparison. If most of the elements of the matrix have 0 value, then it is called a sparse matrix. The two major benefits of using sparse matrix instead of a simple matrix are:Storage: There are lesser non-zero elements than

zeros and thus lesser memory can be used to store only those puting time: Computing time can be saved by logically designing a data structure traversing only non-zero elements.Sparse matrices are generally utilized in applied machine learning such as in data containing data-encodings that map categories to count and also in entire

subfields of machine learning such as natural language processing (NLP).Example:0 0 3 0 4 0 0 5 7 0 0 0 0 0 0 0 2 6 0 0Representing a sparse matrix by a 2D array leads to wastage of lots of memory as zeroes in the matrix are of no use in most of the cases. So, instead of storing zeroes with non-zero elements, we only store non-zero elements. This

means storing non-zero elements with triples- (Row, Column, value).Create a Sparse Matrix in PythonPython's SciPy gives tools for creating sparse matrices using multiple data structures, as well as tools for converting a dense matrix to a sparse matrix. The function csr_matrix() is used to create a sparse matrix of compressed sparse row format

whereas csc_matrix() is used to create a sparse matrix of compressed sparse column format.# Using csr_matrix()Syntax:scipy.sparse.csr_matrix(shape=None, dtype=None)Parameters:shape: Get shape of a matrixdtype: Data type of the matrixExample 1:import numpy as npfrom scipy.sparse import csr_matrixsparseMatrix = csr_matrix((3,

4),

dtype = np.int8).toarray()print(sparseMatrix)Output:[[0 0 0 0] [0 0 0 0] [0 0 0 0]] Example 2:import numpy as npfrom scipy.sparse import csr_matrixrow = np.array([0, 0, 1, 1, 2, 1])col = np.array([0, 1, 2, 0, 2, 2])data = np.array([1, 4, 5, 8, 9, 6])sparseMatrix = csr_matrix((data, (row, col)),

shape = (3,

3)).toarray()print(sparseMatrix)Output:[[ 1 4 0] [ 8 0 11] [ 0 0 9]] # Using csc_matrix()Syntax:scipy.sparse.csc_matrix(shape=None, dtype=None)Parameters:shape: Get shape of a matrixdtype: Data type of the matrixExample 1:import numpy as npfrom scipy.sparse import csc_matrixsparseMatrix = csc_matrix((3, 4),

dtype =

np.int8).toarray()print(sparseMatrix)Output:[[0 0 0 0] [0 0 0 0] [0 0 0 0]] Example 2:import numpy as npfrom scipy.sparse import csc_matrixrow = np.array([0, 0, 1, 1, 2, 1])col = np.array([0, 1, 2, 0, 2, 2])data = np.array([1, 4, 5, 8, 9, 6])sparseMatrix = csc_matrix((data, (row, col)),

shape = (3, 3)).toarray()print(sparseMatrix)Output:[[ 1

4 0] [ 8 0 11] [ 0 0 9]] You can't perform that action at this time. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.

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