227 lines
5 KiB
Markdown
227 lines
5 KiB
Markdown
# Concatenate
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{:.no_toc}
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<nav markdown="1" class="toc-class">
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* TOC
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{:toc}
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</nav>
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## The goal
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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## [numpy.concatenate]()
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```python
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numpy.concatenate((a1, a2, ...), axis=0, out=None, dtype=None, casting="same_kind")
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```
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> Join a sequence of arrays along an existing axis.
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```python
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import numpy as np
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a = np.arange(0, 5)
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print(a) # -> [0 1 2 3 4]
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print(a.shape) # -> (5,)
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b = np.arange(0, 8)
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print(b) # -> [0 1 2 3 4 5 6 7]
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print(b.shape) # -> (8,)
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c = np.concatenate((a, b))
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print(c) # -> [0 1 2 3 4 0 1 2 3 4 5 6 7]
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print(c.shape) # -> (13,)
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print(np.may_share_memory(a, c)) # -> False (Copy)
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c = np.concatenate((a, b), axis=0)
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print(c) # -> [0 1 2 3 4 0 1 2 3 4 5 6 7]
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print(c.shape) # -> (13,)
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print(np.may_share_memory(a, c)) # -> False (Copy)
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c = np.concatenate(
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(a, b), axis=1
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) # AxisError: axis 1 is out of bounds for array of dimension 1
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```
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concatenate does not add necessary dimensions, you have to do that yourself:
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```python
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import numpy as np
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a = np.arange(0, 10)
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print(a.shape) # -> (10,)
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b = np.arange(0, 10)
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print(b.shape) # -> (10,)
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c = np.concatenate((a, b), axis=0)
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print(c.shape) # -> (20,)
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c = np.concatenate((a, b), axis=1) # AxisError: axis 1 is out of bounds for array of dimension 1
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```
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```python
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import numpy as np
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a = np.arange(0, 10)[:, np.newaxis]
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print(a.shape) # -> (10,1)
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b = np.arange(0, 10)[:, np.newaxis]
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print(b.shape) # -> (10,1)
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c = np.concatenate((a, b), axis=0)
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print(c.shape) # -> (20,1)
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c = np.concatenate((a, b), axis=1)
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print(c)
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print(c.shape) # -> (10,2)
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```
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Output:
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```python
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[[0 0]
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[1 1]
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[2 2]
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[3 3]
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[4 4]
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[5 5]
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[6 6]
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[7 7]
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[8 8]
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[9 9]]
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```
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## [numpy.r_](https://numpy.org/doc/stable/reference/generated/numpy.r_.html)
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{: .topic-optional}
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This is an optional topic!
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```python
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numpy.r_ = <numpy.lib.index_tricks.RClass object>
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```
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> Translates slice objects to concatenation along the first axis.
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```python
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import numpy as np
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a = np.arange(0, 10)
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print(a.shape) # -> (10,)
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b = np.arange(0, 10)
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print(b.shape) # -> (10,)
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c = np.r_[a, b]
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print(c.shape) # -> (20,)
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```
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### More r_
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```python
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import numpy as np
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a = np.arange(0, 10)
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print(a.shape) # -> (10,)
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b = np.arange(0, 10)
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print(b.shape) # -> (10,)
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c = np.r_[a, b]
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print(c.shape) # -> (20,)
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c = np.r_["-1", a, b]
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print(c.shape) # -> (20,)
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c = np.r_["0, 2", a, b]
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print(c.shape) # -> (2, 10)
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c = np.r_["1, 2", a, b]
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print(c.shape) # -> (1, 20)
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c = np.r_["0, 1", a, b]
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print(c.shape) # -> (20,)
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c = np.r_["0, 2", a, b]
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print(c.shape) # -> (2, 10)
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c = np.r_["0, 3", a, b]
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print(c.shape) # -> (2, 1, 10)
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c = np.r_["r", a, b] # r == row
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print(c.shape) # -> (1, 20)
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c = np.r_["c", a, b] # c == column
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print(c.shape) # -> (20, 1)
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```
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### Producing index arrays with r_
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```python
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import numpy as np
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idx = np.r_[2:10, 20:100, 10]
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print(idx)
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print(idx.shape) # -> (89,)
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```
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Output:
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```python
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[ 2 3 4 5 6 7 8 9 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
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36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
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60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
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84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 10]
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```
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**If we use idx for indexing a matrix/ array then we use indexing and not slicing.**
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### Producing index arrays with [numpy.s_](https://numpy.org/doc/stable/reference/generated/numpy.s_.html)
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```python
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numpy.s_ = <numpy.lib.index_tricks.IndexExpression object>
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```
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> A nicer way to build up index tuples for arrays.
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s_ is a simpler alternative to [slice](https://docs.python.org/dev/library/functions.html#slice):
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```python
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class slice(stop)
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class slice(start, stop, step=None)
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```
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> Return a slice object representing the set of indices specified by range(start, stop, step). The start and step arguments default to None.
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>
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> 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.
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>
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> Slice objects are also generated when extended indexing syntax is used. For example: a[start:stop:step] or a[start:stop, i].
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```python
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import numpy as np
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idx = np.s_[2:10, 20:100, 10]
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print(idx) # -> (slice(2, 10, None), slice(20, 100, None), 10)
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data = np.zeros((20, 101, 11))
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x = data[idx]
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print(x.shape) # -> (8, 80)
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```
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## [numpy.c_](https://numpy.org/doc/stable/reference/generated/numpy.c_.html)
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{: .topic-optional}
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This is an optional topic!
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```python
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numpy.c_ = <numpy.lib.index_tricks.CClass object>
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```
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> Translates slice objects to concatenation along the second axis.
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>
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> 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).
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```python
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import numpy as np
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a = np.arange(0, 10)
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print(a.shape) # -> (10,)
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b = np.arange(0, 10)
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print(b.shape) # -> (10,)
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c = np.c_[a, b]
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print(c.shape) # -> (10,2)
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```
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