How to make List from Numpy Matrix in Python
Question:
I using the dot() function from numpy to multiply a matrix of 3×3 with a numpy.array of 1×3. The output is for example this:
[[ 0.16666667 0.66666667 0.16666667]]
which is of type:
<class 'numpy.matrixlib.defmatrix.matrix'>
how can I convert this to a list. Because I know the result will always be a matrix of 1×3 so it should be coverted to a list because I need to be able to loop through it later for calculation the pearson distance of two of those lists.
So to summarize: how can I make a list from this matrix?
Answers:
Use the tolist() method on the matrix object :
>>> import numpy
>>> m = numpy.matrix([1, 2, 3])
>>> type(m)
<class 'numpy.core.defmatrix.matrix'>
>>> m.tolist()
[[1, 2, 3]]
If a is your matrix, try
a.ravel().tolist()
but you don’t need to turn it into a list to iterate over it.
May not be the optimal way to do this but the following works:
a = numpy.matrix([[ 0.16666667, 0.66666667, 0.16666667]])
list(numpy.array(a).reshape(-1,))
or
numpy.array(a).reshape(-1,).tolist()
or
numpy.array(a)[0].tolist()
m = numpy.matrix([[ 0.16666667, 0.66666667, 0.16666667]])
a = numpy.array(m)[0]
for i in a:
print i
results in
0.16666667
0.66666667
0.16666667
Another way:
>>> import numpy as np
>>> m = np.matrix([1,2,3])
>>> np.array(m).flatten().tolist()
[1,2,3]
I came here looking for a way to convert numpy matrices to typical 2D lists.
For a numpy matrix m:
my_2d_list = map(list, list(m.A))
If you just want a one dimensional list from a 1 x n matrix m:
my_1d_list = list(list(m.A)[0])
Try this simplistic approach. It works with 1D arrays, do not know with higher dimensions.
import mumpy as np # to create a numpy array example
a = np.array([1,2.5,3]) # your 1D numpy array
b = [i for i in a] # your list out of the original numpy array
import numpy as np
a = np.matrix([[1,2,3,4]])
b = map(float, a.transpose())
This code snippet will apply the built-in function “float” – which converts something to a floating point number – to every element of a. Since the first element of a is an array itself, it has to be transposed, so that every number becomes an element of a itself. a.transpose() is equivalent to np.matrix([[1],[2],[3],[4]]) in this example.
why not simple:
list(a.flat)
for example:
>>> import numpy as np
>>> a = np.matrix([[ 0.16666667, 0.66666667, 0.16666667]])
>>> a
matrix([[ 0.16666667, 0.66666667, 0.16666667]])
>>> a.flat
<numpy.flatiter object at 0x0000000002DE8CC0>
>>> a.flat.tolist()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'numpy.flatiter' object has no attribute 'tolist'
>>> list(a.flat)
[0.16666666999999999, 0.66666667000000002, 0.16666666999999999]
I think getA1() can do the job.
From the documentation:
getA1()
Return self as a flattened ndarray.
Equivalent to np.asarray(x).ravel()
From https://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.getA1.html
I using the dot() function from numpy to multiply a matrix of 3×3 with a numpy.array of 1×3. The output is for example this:
[[ 0.16666667 0.66666667 0.16666667]]
which is of type:
<class 'numpy.matrixlib.defmatrix.matrix'>
how can I convert this to a list. Because I know the result will always be a matrix of 1×3 so it should be coverted to a list because I need to be able to loop through it later for calculation the pearson distance of two of those lists.
So to summarize: how can I make a list from this matrix?
Use the tolist() method on the matrix object :
>>> import numpy
>>> m = numpy.matrix([1, 2, 3])
>>> type(m)
<class 'numpy.core.defmatrix.matrix'>
>>> m.tolist()
[[1, 2, 3]]
If a is your matrix, try
a.ravel().tolist()
but you don’t need to turn it into a list to iterate over it.
May not be the optimal way to do this but the following works:
a = numpy.matrix([[ 0.16666667, 0.66666667, 0.16666667]])
list(numpy.array(a).reshape(-1,))
or
numpy.array(a).reshape(-1,).tolist()
or
numpy.array(a)[0].tolist()
m = numpy.matrix([[ 0.16666667, 0.66666667, 0.16666667]])
a = numpy.array(m)[0]
for i in a:
print i
results in
0.16666667
0.66666667
0.16666667
Another way:
>>> import numpy as np
>>> m = np.matrix([1,2,3])
>>> np.array(m).flatten().tolist()
[1,2,3]
I came here looking for a way to convert numpy matrices to typical 2D lists.
For a numpy matrix m:
my_2d_list = map(list, list(m.A))
If you just want a one dimensional list from a 1 x n matrix m:
my_1d_list = list(list(m.A)[0])
Try this simplistic approach. It works with 1D arrays, do not know with higher dimensions.
import mumpy as np # to create a numpy array example
a = np.array([1,2.5,3]) # your 1D numpy array
b = [i for i in a] # your list out of the original numpy array
import numpy as np
a = np.matrix([[1,2,3,4]])
b = map(float, a.transpose())
This code snippet will apply the built-in function “float” – which converts something to a floating point number – to every element of a. Since the first element of a is an array itself, it has to be transposed, so that every number becomes an element of a itself. a.transpose() is equivalent to np.matrix([[1],[2],[3],[4]]) in this example.
why not simple:
list(a.flat)
for example:
>>> import numpy as np
>>> a = np.matrix([[ 0.16666667, 0.66666667, 0.16666667]])
>>> a
matrix([[ 0.16666667, 0.66666667, 0.16666667]])
>>> a.flat
<numpy.flatiter object at 0x0000000002DE8CC0>
>>> a.flat.tolist()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'numpy.flatiter' object has no attribute 'tolist'
>>> list(a.flat)
[0.16666666999999999, 0.66666667000000002, 0.16666666999999999]
I think getA1() can do the job.
From the documentation:
getA1()
Return self as a flattened ndarray.
Equivalent to np.asarray(x).ravel()
From https://docs.scipy.org/doc/numpy/reference/generated/numpy.matrix.getA1.html