From 30028685861eeb77390196242a9f6bd70cc6c0e5 Mon Sep 17 00:00:00 2001
From: David Rotermund <54365609+davrot@users.noreply.github.com>
Date: Fri, 29 Dec 2023 19:10:19 +0100
Subject: [PATCH] Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
---
 numpy/unique/README.md | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)

diff --git a/numpy/unique/README.md b/numpy/unique/README.md
index 8064265..0f212bc 100644
--- a/numpy/unique/README.md
+++ b/numpy/unique/README.md
@@ -25,3 +25,37 @@ numpy.unique(ar, return_index=False, return_inverse=False, return_counts=False,
 > * the indices of the unique array that reconstruct the input array
 > * the number of times each unique value comes up in the input array
 
+**unique can be used on multi-dimensional arrays. However, the results are strange since empty places need to be filled for shaping the results into one common matrix.**
+
+```python
+import numpy as np
+
+
+a = np.arange(10, 21)
+print(a)  # -> [10 11 12 13 14 15 16 17 18 19 20]
+idx = np.r_[0:5, 3:8]
+print(idx)  # -> [0 1 2 3 4 3 4 5 6 7]
+print(a[idx])  # -> [10 11 12 13 14 13 14 15 16 17]
+
+print(np.unique(idx))  # -> [0 1 2 3 4 5 6 7]
+print(np.unique(a[idx]))  # -> [10 11 12 13 14 15 16 17]
+```
+
+## There are more return arguments available 
+
+```python
+import numpy as np
+
+
+a = np.r_[0:5, 3:8]
+print(a)  # -> [0 1 2 3 4 3 4 5 6 7]
+
+values, unique_index = np.unique(a, return_index=True)
+_, unique_inverse = np.unique(a, return_inverse=True)
+_, unique_counts = np.unique(a, return_counts=True)
+
+print(values)  # -> [0 1 2 3 4 5 6 7]
+print(unique_index)  # -> [0 1 2 3 4 7 8 9]
+print(unique_inverse)  # -> [0 1 2 3 4 3 4 5 6 7]
+print(unique_counts)  # -> [1 1 1 2 2 1 1 1]
+```