Divide arguments element-wise.
Parametersx1array_likeDividend array.
x2array_likeDivisor array. If x1.shape != x2.shape
, they must be broadcastable to a common shape [which becomes the shape of the output].
A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple [possible only as a keyword argument] must have length equal to the number of outputs.
wherearray_like, optionalThis condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an
uninitialized out array is created via the default out=None
, locations within it where the condition is False will remain uninitialized.
For other keyword-only arguments, see the ufunc docs.
Returnsyndarray or scalarThe quotient x1/x2
, element-wise.
This is a scalar if both x1 and x2 are scalars.
See also
seterr
Set whether to raise or warn on overflow, underflow and division by zero.
Notes
Equivalent to x1
/ x2
in terms of array-broadcasting.
The true_divide[x1, x2]
function is an alias for divide[x1, x2]
.
Examples
>>> np.divide[2.0, 4.0] 0.5 >>> x1 = np.arange[9.0].reshape[[3, 3]] >>> x2 = np.arange[3.0] >>> np.divide[x1, x2] array[[[nan, 1. , 1. ], [inf, 4. , 2.5], [inf, 7. , 4. ]]]
The /
operator can be used as a shorthand for np.divide
on ndarrays.
>>> x1 = np.arange[9.0].reshape[[3, 3]] >>> x2 = 2 * np.ones[3] >>> x1 / x2 array[[[0. , 0.5, 1. ], [1.5, 2. , 2.5], [3. , 3.5, 4. ]]]
Created: May-08, 2021 This tutorial will discuss the methods to divide a matrix by a vector in NumPy. A matrix is a 2D array, while a vector is just a 1D array. If we want to divide the elements of a matrix by the vector elements in each row, we have to add a new dimension to the vector. We can add a new dimension to the vector with the array slicing method in Python. The following code example shows us how to divide each row of a matrix by a vector with the array slicing method in Python. Output:numpy.reshape[]
FunctionDivide Matrix by Vector in NumPy With
the Array Slicing Method in Python
import numpy as np
matrix = np.array[[[2,2,2],[4,4,4],[6,6,6]]]
vector = np.array[[2,4,6]]
matrix = matrix / vector[:,None]
print[matrix]
[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
We first created the matrix and
the vector with the np.array[]
function. We then added a new axis to the vector with the slicing method. We then divided the matrix by the array and saved the result inside the matrix.
Divide Matrix by Vector in NumPy With the Transpose Method in NumPy
We can also transpose the matrix to divide each row of the matrix by each vector element. After that, we can transpose the result to return to the matrix’s previous orientation. See the following code example.
import numpy as np
matrix = np.array[[[2,2,2],[4,4,4],[6,6,6]]]
vector = np.array[[2,4,6]]
matrix = [matrix.T / vector].T
print[matrix]
Output:
[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
In the above code, we took a transpose of the matrix and divided it by the vector. After that, we took a transpose of the result and stored it inside the matrix
.
Divide Matrix by Vector in NumPy With the numpy.reshape[]
Function
The whole idea behind this approach is that we have
to convert the vector to a 2D array first. The numpy.reshape[]
function can be used to convert the vector into a 2D array where each row contains only one element. We can then easily divide each row of the matrix by each row of the vector.
import numpy as np
matrix = np.array[[[2,2,2],[4,4,4],[6,6,6]]]
vector = np.array[[2,4,6]]
matrix = matrix / vector.reshape[[3,1]]
print[matrix]
Output:
[[1. 1. 1.]
[1. 1. 1.]
[1. 1. 1.]]
In the above code, we converted the vector
to a 2D array with the np.reshape[]
function. After that, we divided the matrix
by the vector
and stored the result inside the
matrix
.