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+# Resize: Compensation for bad planning?
+{:.no_toc}
+
+<nav markdown="1" class="toc-class">
+* TOC
+{:toc}
+</nav>
+
+## Top
+
+Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
+
+**Try to avoid using this function!** Do better planning. 
+
+
+Resize does its work on the linear memory segment.
+* If it is resized to a bigger array, the beginning of the original memory segment is copied into the new end segment.
+* If it is resized to a smaller array, the memory segment is shrunken by cutting of the end.
+
+## [numpy.resize](https://numpy.org/doc/stable/reference/generated/numpy.resize.html)
+
+```python
+numpy.resize(a, new_shape)
+```
+
+> Return a new array with the specified shape.
+> 
+> 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.
+
+```python
+import numpy as np
+
+a = np.arange(1, 10).reshape((3, 3))
+print(a)
+print()
+b = np.resize(a, (2, 3))
+print(b)
+print()
+b = np.resize(a, (3, 1))
+print(b)
+print()
+b = np.resize(a, (5, 5))
+print(b)
+```
+
+Output:
+
+```python
+[[1 2 3]
+ [4 5 6]
+ [7 8 9]]
+
+[[1 2 3]
+ [4 5 6]]
+
+[[1]
+ [2]
+ [3]]
+
+[[1 2 3 4 5]
+ [6 7 8 9 1]
+ [2 3 4 5 6]
+ [7 8 9 1 2]
+ [3 4 5 6 7]]
+```
+