View source for Making a new matrix

Making a new matrix…

Questions to [mailto:davrot@uni-bremen.de David Rotermund]

Using '''import numpy as np''' is the standard.

== Simple example – new [https://numpy.org/doc/stable/reference/generated/numpy.zeros.html np.zeros()] ==
Define the size of your new matrix with a tuple, e.g.<syntaxhighlight lang="python">M = numpy.zeros((DIM_0, DIM_1, DIM_2, …))</syntaxhighlight>

=== 1d ===
<syntaxhighlight lang="python">import numpy as np

M = np.zeros((2))
print(M)</syntaxhighlight>Output:<syntaxhighlight lang="python">[0. 0.]</syntaxhighlight>

=== 2d ===
<syntaxhighlight lang="python">import numpy as np

M = np.zeros((2, 3))
print(M)</syntaxhighlight>Output:<syntaxhighlight lang="python">[[0. 0. 0.]
 [0. 0. 0.]]</syntaxhighlight>

=== 3d ===
<syntaxhighlight lang="python">import numpy as np

M = np.zeros((2, 3, 4))
print(M)</syntaxhighlight>Output:<syntaxhighlight lang="python">[[[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]

 [[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]]</syntaxhighlight>

== Simple example – recycle [https://numpy.org/doc/stable/reference/generated/numpy.zeros_like.html np.zeros_like()] ==
If you have a matrix with the same size you want then you can use zeros_like. This will also copy other properties like the data type.

as a prototype use

N = numpy.zeros_like(M)<syntaxhighlight lang="python">import numpy as np

M = np.zeros((2, 3, 4))

N = np.zeros_like(M)
print(N)</syntaxhighlight>Output:<syntaxhighlight lang="python">[[[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]

 [[0. 0. 0. 0.]
  [0. 0. 0. 0.]
  [0. 0. 0. 0.]]]</syntaxhighlight>

== Remember unpacking ==
'''This is an optional topic!'''<syntaxhighlight lang="python">import numpy as np

d = (3, 4)
M = np.zeros((2, *d))

print(M)</syntaxhighlight>

== np.empty is not np.zeros ==
If you are sure that you don’t care about what is inside the matrix in the beginning use<syntaxhighlight lang="python">M = numpy.empty((DIM_0, DIM_1, DIM_2,...))</syntaxhighlight>Empty claims a region in the memory and uses it for a matrix. Zeros goes one step further. It fills the memory with zeros.

Thus random junk (i.e. data that was stored prior at that memory position) with be the content of a matrix if you use empty. However, np.empty() is faster than np.zeros().<syntaxhighlight lang="python">import numpy as np
M = np.empty((10, 4))
print(M)</syntaxhighlight><syntaxhighlight lang="python">[[1.66706425e-316 0.00000000e+000 6.89933729e-310 6.89933730e-310]
 [6.89933729e-310 6.89933730e-310 6.89933729e-310 6.89933730e-310]
 [6.89933730e-310 6.89933730e-310 6.89933729e-310 6.89933729e-310]
 [6.89933730e-310 6.89933729e-310 6.89933730e-310 6.89933729e-310]
 [6.89933730e-310 4.30513389e-317 4.30321296e-317 6.89933825e-310]
 [4.30389280e-317 6.89933822e-310 4.30366750e-317 6.89933822e-310]
 [4.30311810e-317 4.30480583e-317 4.30462401e-317 4.30336316e-317]
 [6.89933822e-310 4.30386513e-317 4.30358055e-317 4.30571886e-317]
 [4.30568724e-317 4.30659237e-317 6.89933822e-310 6.89933822e-310]
 [6.89933822e-310 6.89933822e-310 4.30289676e-317 6.89920336e-310]]</syntaxhighlight>

== [https://numpy.org/doc/stable/reference/routines.array-creation.html#from-shape-or-value From shape or value] ==
{| class="wikitable"
|[https://numpy.org/doc/stable/reference/generated/numpy.empty.html#numpy.empty empty](shape[, dtype, order, like])
|Return a new array of given shape and type, without initializing entries.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.empty_like.html#numpy.empty_like empty_like](prototype[, dtype, order, subok, …])
|Return a new array with the same shape and type as a given array.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.eye.html#numpy.eye eye](N[, M, k, dtype, order, like])
|Return a 2-D array with ones on the diagonal and zeros elsewhere.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.identity.html#numpy.identity identity](n[, dtype, like])
|Return the identity array.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.ones.html#numpy.ones ones](shape[, dtype, order, like])
|Return a new array of given shape and type, filled with ones.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.ones_like.html#numpy.ones_like ones_like](a[, dtype, order, subok, shape])
|Return an array of ones with the same shape and type as a given array.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.zeros.html#numpy.zeros zeros](shape[, dtype, order, like])
|Return a new array of given shape and type, filled with zeros.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.zeros_like.html#numpy.zeros_like zeros_like](a[, dtype, order, subok, shape])
|Return an array of zeros with the same shape and type as a given array.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.full.html#numpy.full full](shape, fill_value[, dtype, order, like])
|Return a new array of given shape and type, filled with fill_value.
|-
|[https://numpy.org/doc/stable/reference/generated/numpy.full_like.html#numpy.full_like full_like](a, fill_value[, dtype, order, …])
|Return a full array with the same shape and type as a given array.
|}