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Concatenate
{:.no_toc}
* TOC {:toc}The goal
Questions to David Rotermund
numpy.concatenate
numpy.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind")
Join a sequence of arrays along an existing axis.
import numpy as np
a = np.arange(0, 5)
print(a) # -> [0 1 2 3 4]
print(a.shape) # -> (5,)
b = np.arange(0, 8)
print(b) # -> [0 1 2 3 4 5 6 7]
print(b.shape) # -> (8,)
c = np.concatenate((a, b))
print(c) # -> [0 1 2 3 4 0 1 2 3 4 5 6 7]
print(c.shape) # -> (13,)
print(np.may_share_memory(a, c)) # -> False (Copy)
c = np.concatenate((a, b), axis=0)
print(c) # -> [0 1 2 3 4 0 1 2 3 4 5 6 7]
print(c.shape) # -> (13,)
print(np.may_share_memory(a, c)) # -> False (Copy)
c = np.concatenate(
(a, b), axis=1
) # AxisError: axis 1 is out of bounds for array of dimension 1
concatenate does not add necessary dimensions, you have to do that yourself:
import numpy as np
a = np.arange(0, 10)
print(a.shape) # -> (10,)
b = np.arange(0, 10)
print(b.shape) # -> (10,)
c = np.concatenate((a, b), axis=0)
print(c.shape) # -> (20,)
c = np.concatenate((a, b), axis=1) # AxisError: axis 1 is out of bounds for array of dimension 1
import numpy as np
a = np.arange(0, 10)[:, np.newaxis]
print(a.shape) # -> (10,1)
b = np.arange(0, 10)[:, np.newaxis]
print(b.shape) # -> (10,1)
c = np.concatenate((a, b), axis=0)
print(c.shape) # -> (20,1)
c = np.concatenate((a, b), axis=1)
print(c)
print(c.shape) # -> (10,2)
Output:
[[0 0]
[1 1]
[2 2]
[3 3]
[4 4]
[5 5]
[6 6]
[7 7]
[8 8]
[9 9]]
numpy.r_
{: .topic-optional} This is an optional topic!
numpy.r_ = <numpy.lib.index_tricks.RClass object>
Translates slice objects to concatenation along the first axis.
import numpy as np
a = np.arange(0, 10)
print(a.shape) # -> (10,)
b = np.arange(0, 10)
print(b.shape) # -> (10,)
c = np.r_[a, b]
print(c.shape) # -> (20,)
More r_
import numpy as np
a = np.arange(0, 10)
print(a.shape) # -> (10,)
b = np.arange(0, 10)
print(b.shape) # -> (10,)
c = np.r_[a, b]
print(c.shape) # -> (20,)
c = np.r_["-1", a, b]
print(c.shape) # -> (20,)
c = np.r_["0, 2", a, b]
print(c.shape) # -> (2, 10)
c = np.r_["1, 2", a, b]
print(c.shape) # -> (1, 20)
c = np.r_["0, 1", a, b]
print(c.shape) # -> (20,)
c = np.r_["0, 2", a, b]
print(c.shape) # -> (2, 10)
c = np.r_["0, 3", a, b]
print(c.shape) # -> (2, 1, 10)
c = np.r_["r", a, b] # r == row
print(c.shape) # -> (1, 20)
c = np.r_["c", a, b] # c == column
print(c.shape) # -> (20, 1)
Producing index arrays with r_
import numpy as np
idx = np.r_[2:10, 20:100, 10]
print(idx)
print(idx.shape) # -> (89,)
Output:
[ 2 3 4 5 6 7 8 9 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 10]
If we use idx for indexing a matrix/ array then we use indexing and not slicing.
Producing index arrays with numpy.s_
numpy.s_ = <numpy.lib.index_tricks.IndexExpression object>
A nicer way to build up index tuples for arrays.
s_ is a simpler alternative to slice:
class slice(stop)
class slice(start, stop, step=None)
Return a slice object representing the set of indices specified by range(start, stop, step). The start and step arguments default to None.
Slice objects have read-only data attributes start, stop, and step which merely return the argument values (or their default). They have no other explicit functionality; however, they are used by NumPy and other third-party packages.
Slice objects are also generated when extended indexing syntax is used. For example: a[start:stop:step] or a[start:stop, i].
import numpy as np
idx = np.s_[2:10, 20:100, 10]
print(idx) # -> (slice(2, 10, None), slice(20, 100, None), 10)
data = np.zeros((20, 101, 11))
x = data[idx]
print(x.shape) # -> (8, 80)
numpy.c_
{: .topic-optional} This is an optional topic!
numpy.c_ = <numpy.lib.index_tricks.CClass object>
Translates slice objects to concatenation along the second axis.
This is short-hand for np.r_['-1,2,0', index expression], which is useful because of its common occurrence. In particular, arrays will be stacked along their last axis after being upgraded to at least 2-D with 1’s post-pended to the shape (column vectors made out of 1-D arrays).
import numpy as np
a = np.arange(0, 10)
print(a.shape) # -> (10,)
b = np.arange(0, 10)
print(b.shape) # -> (10,)
c = np.c_[a, b]
print(c.shape) # -> (10,2)