Editing NumPy (section)

== Examples ==
<syntaxhighlight lang="numpy">
import numpy as np
from numpy.random import rand
from numpy.linalg import solve, inv
a = np.array([[1, 2, 3, 4], [3, 4, 6, 7], [5, 9, 0, 5]])
a.transpose()
</syntaxhighlight>

=== Basic operations ===
<syntaxhighlight lang="numpy">
>>> a = np.array([1, 2, 3, 6])
>>> b = np.linspace(0, 2, 4)  # create an array with four equally spaced points starting with 0 and ending with 2.
>>> c = a - b
>>> c
array([ 1.        ,  1.33333333,  1.66666667,  4.        ])
>>> a**2
array([ 1,  4,  9, 36])
</syntaxhighlight>

=== Universal functions ===
<syntaxhighlight lang="numpy">
>>> a = np.linspace(-np.pi, np.pi, 100) 
>>> b = np.sin(a)
>>> c = np.cos(a)
>>>
>>> # Functions can take both numbers and arrays as parameters.
>>> np.sin(1)
0.8414709848078965
>>> np.sin(np.array([1, 2, 3]))
array([0.84147098, 0.90929743, 0.14112001])
</syntaxhighlight>

=== Linear algebra ===
<syntaxhighlight lang="numpy">
>>> from numpy.random import rand
>>> from numpy.linalg import solve, inv
>>> a = np.array([[1, 2, 3], [3, 4, 6.7], [5, 9.0, 5]])
>>> a.transpose()
array([[ 1. ,  3. ,  5. ],
       [ 2. ,  4. ,  9. ],
       [ 3. ,  6.7,  5. ]])
>>> inv(a)
array([[-2.27683616,  0.96045198,  0.07909605],
       [ 1.04519774, -0.56497175,  0.1299435 ],
       [ 0.39548023,  0.05649718, -0.11299435]])
>>> b =  np.array([3, 2, 1])
>>> solve(a, b)  # solve the equation ax = b
array([-4.83050847,  2.13559322,  1.18644068])
>>> c = rand(3, 3) * 20  # create a 3x3 random matrix of values within [0,1] scaled by 20
>>> c
array([[  3.98732789,   2.47702609,   4.71167924],
       [  9.24410671,   5.5240412 ,  10.6468792 ],
       [ 10.38136661,   8.44968437,  15.17639591]])
>>> np.dot(a, c)  # matrix multiplication
array([[  53.61964114,   38.8741616 ,   71.53462537],
       [ 118.4935668 ,   86.14012835,  158.40440712],
       [ 155.04043289,  104.3499231 ,  195.26228855]])
>>> a @ c # Starting with Python 3.5 and NumPy 1.10
array([[  53.61964114,   38.8741616 ,   71.53462537],
       [ 118.4935668 ,   86.14012835,  158.40440712],
       [ 155.04043289,  104.3499231 ,  195.26228855]])
</syntaxhighlight>

===   Multidimensional arrays  ===
<syntaxhighlight lang="numpy">
>>> M = np.zeros(shape=(2, 3, 5, 7, 11))
>>> T = np.transpose(M, (4, 2, 1, 3, 0))
>>> T.shape
(11, 5, 3, 7, 2)
</syntaxhighlight>

=== Incorporation with OpenCV ===
<syntaxhighlight lang="numpy">
>>> import numpy as np
>>> import cv2
>>> r = np.reshape(np.arange(256*256)%256,(256,256))  # 256x256 pixel array with a horizontal gradient from 0 to 255 for the red color channel
>>> g = np.zeros_like(r)  # array of same size and type as r but filled with 0s for the green color channel
>>> b = r.T  # transposed r will give a vertical gradient for the blue color channel
>>> cv2.imwrite("gradients.png", np.dstack([b,g,r]))  # OpenCV images are interpreted as BGR, the depth-stacked array will be written to an 8bit RGB PNG-file called "gradients.png"
True
</syntaxhighlight>

=== Nearest-neighbor search ===
Iterative Python algorithm and vectorized NumPy version.

<syntaxhighlight lang="numpy">
>>> # # # Pure iterative Python # # #
>>> points = [[9,2,8],[4,7,2],[3,4,4],[5,6,9],[5,0,7],[8,2,7],[0,3,2],[7,3,0],[6,1,1],[2,9,6]]
>>> qPoint = [4,5,3]
>>> minIdx = -1
>>> minDist = -1
>>> for idx, point in enumerate(points):  # iterate over all points
...     dist = sum([(dp-dq)**2 for dp,dq in zip(point,qPoint)])**0.5  # compute the euclidean distance for each point to q
...     if dist < minDist or minDist < 0:  # if necessary, update minimum distance and index of the corresponding point
...         minDist = dist
...         minIdx = idx

>>> print(f"Nearest point to q: {points[minIdx]}")
Nearest point to q: [3, 4, 4]

>>> # # # Equivalent NumPy vectorization # # #
>>> import numpy as np
>>> points = np.array([[9,2,8],[4,7,2],[3,4,4],[5,6,9],[5,0,7],[8,2,7],[0,3,2],[7,3,0],[6,1,1],[2,9,6]])
>>> qPoint = np.array([4,5,3])
>>> minIdx = np.argmin(np.linalg.norm(points-qPoint, axis=1))  # compute all euclidean distances at once and return the index of the smallest one
>>> print(f"Nearest point to q: {points[minIdx]}")
Nearest point to q: [3 4 4]
</syntaxhighlight>

=== F2PY ===
Quickly wrap native code for faster scripts.<ref>{{cite web |title=F2PY docs from NumPy |url=https://numpy.org/doc/stable/f2py/usage.html?highlight=f2py |publisher=NumPy |access-date=18 April 2022}}</ref><ref>{{cite web |last1=Worthey |first1=Guy |title=A python vs. Fortran smackdown |url=https://guyworthey.net/2022/01/03/a-python-vs-fortran-smackdown/ |website=Guy Worthey |date=3 January 2022 |publisher=Guy Worthey |access-date=18 April 2022}}</ref><ref>{{cite web |last1=Shell |first1=Scott |title=Writing fast Fortran routines for Python |url=https://sites.engineering.ucsb.edu/~shell/che210d/f2py.pdf |website=UCSB Engineering Department |publisher=University of California, Santa Barbara |access-date=18 April 2022}}</ref>

<syntaxhighlight lang="fortran">
! Python Fortran native code call example
! f2py -c -m foo *.f90
! Compile Fortran into python named module using intent statements
! Fortran subroutines only not functions--easier than JNI with C wrapper
! requires gfortran and make
subroutine ftest(a, b, n, c, d)
  implicit none
  integer, intent(in)  :: a, b, n
  integer, intent(out) :: c, d
  integer :: i
  c = 0
  do i = 1, n
    c = a + b + c
  end do
  d = (c * n) * (-1)
end subroutine ftest
</syntaxhighlight>
<syntaxhighlight lang="pycon">
>>> import numpy as np
>>> import foo
>>> a = foo.ftest(1, 2, 3)  # or c,d = instead of a.c and a.d
>>> print(a)
(9,-27)
>>> help("foo.ftest")  # foo.ftest.__doc__
</syntaxhighlight>