From 4fab448f601c2581eecaae40139ba8c71ece7afe Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Thu, 14 Dec 2023 14:55:37 +0100
Subject: [PATCH] Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
---
 numpy/reshape/README.md | 51 ++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 50 insertions(+), 1 deletion(-)

diff --git a/numpy/reshape/README.md b/numpy/reshape/README.md
index 8713275..829d4b1 100644
--- a/numpy/reshape/README.md
+++ b/numpy/reshape/README.md
@@ -1,4 +1,4 @@
-# Reshape, flatten 
+# Reshape and flatten 
 {:.no_toc}
 
 <nav markdown="1" class="toc-class">
@@ -8,6 +8,7 @@
 
 ## The goal
 
+Sometimes you have to change the shape of a matrix. 
 
 Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
 
@@ -133,3 +134,51 @@ Output:
   [28. 29.]]]
 ```
 
+## [.flatten()](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flatten.html)
+
+**.flatten()** is the "inverse" operation of reshape
+
+```python
+ndarray.flatten(order='C')
+```
+
+> Return a copy of the array collapsed into one dimension.
+
+> **order** : {‘C’, ‘F’, ‘A’, ‘K’}, **optional**
+>   ‘C’ means to flatten in row-major (C-style) order. ‘F’ means to flatten in column-major (Fortran- style) order. ‘A’ means to flatten in column-major order if a is Fortran contiguous in memory, row-major order otherwise. ‘K’ means to flatten a in the order the elements occur in memory. The default is ‘C’.
+
+Example:
+
+```python
+import numpy as np
+
+a = np.arange(0, 30)
+b_3d = np.reshape(a, (5, 3, 2))
+
+c = b_3d.flatten()
+print(f"View: {np.may_share_memory(c, b_3d)}")  # -> View: False
+print(c.shape)  # -> (30,)
+print(c) # -> [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29]
+```
+
+**Note: This is not a view!**
+
+If you want a view, you can use reshape too. 
+
+```python
+import numpy as np
+
+a = np.arange(0, 30)
+b_3d = np.reshape(a, (5, 3, 2))
+
+c = b_3d.reshape((np.prod(b_3d.shape),))
+print(f"View: {np.may_share_memory(c, b_3d)}")  # -> View: True
+print(c.shape)  # -> (30,)
+print(c) # -> [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29]
+```
+
+
+
+
+
+