132 lines
2.6 KiB
Markdown
132 lines
2.6 KiB
Markdown
# Resize: Compensation for bad planning?
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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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## Top
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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**Try to avoid using this function!** Do better planning.
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Resize does its work on the linear memory segment.
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* If it is resized to a bigger array, the beginning of the original memory segment is copied into the new end segment.
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* If it is resized to a smaller array, the memory segment is shrunken by cutting of the end.
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## [numpy.resize](https://numpy.org/doc/stable/reference/generated/numpy.resize.html)
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```python
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numpy.resize(a, new_shape)
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```
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> Return a new array with the specified shape.
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>
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> If the new array is larger than the original array, then the new array is filled with repeated copies of a. Note that this behavior is different from a.resize(new_shape) which fills with zeros instead of repeated copies of a.
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```python
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import numpy as np
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a = np.arange(1, 10).reshape((3, 3))
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print(a)
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print()
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b = np.resize(a, (2, 3))
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print(b)
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print()
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b = np.resize(a, (3, 1))
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print(b)
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print()
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b = np.resize(a, (5, 5))
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print(b)
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```
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Output:
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```python
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[[1 2 3]
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[4 5 6]
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[7 8 9]]
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[[1 2 3]
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[4 5 6]]
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[[1]
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[2]
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[3]]
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[[1 2 3 4 5]
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[6 7 8 9 1]
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[2 3 4 5 6]
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[7 8 9 1 2]
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[3 4 5 6 7]]
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```
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## This is not [numpy.ndarray.resize](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.resize.html#numpy.ndarray.resize)
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```python
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ndarray.resize(new_shape, refcheck=True)
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```
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> Change shape and size of array in-place.
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I added a copy because it does not work on views (*ValueError: cannot resize this array: it does not own its data*) , which reshape creates.
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```python
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import numpy as np
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a = np.arange(1, 10).reshape((3, 3)).copy()
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print(a)
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print()
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a.resize((5, 5))
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print(a)
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```
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Output:
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```python
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[[1 2 3]
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[4 5 6]
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[7 8 9]]
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[[1 2 3 4 5]
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[6 7 8 9 0]
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[0 0 0 0 0]
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[0 0 0 0 0]
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[0 0 0 0 0]]
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```
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## More bad function
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**Try to avoid using these functions!**
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### [numpy.delete](https://numpy.org/doc/stable/reference/generated/numpy.delete.html)
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```python
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numpy.delete(arr, obj, axis=None)
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```
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> Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by arr[obj].
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### [numpy.insert](https://numpy.org/doc/stable/reference/generated/numpy.insert.html)
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```python
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numpy.insert(arr, obj, values, axis=None)
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```
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> Insert values along the given axis before the given indices.
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### [numpy.append](https://numpy.org/doc/stable/reference/generated/numpy.append.html)
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```python
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numpy.append(arr, values, axis=None)
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```
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> Append values to the end of an array.
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