Update README.md

Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>

numpy/extending/README.md

@@ -28,9 +28,9 @@ numpy.tile(A, reps)
 > 
 > Note : Although tile may be used for broadcasting, it is strongly recommended to use numpy’s broadcasting operations and functions.
 
-**Very very important!!!​ First use numpy.newaxis​ to create the required additional axis and then use tile.**
+**You might want to first use numpy.newaxis​ to create the required additional axis and then use tile.**
 
-Adding a new dimension:
+Adding a new dimension makes a copy:
 
 ```python
 import numpy as np
@@ -140,3 +140,102 @@ Output:
   [1 2 3 4]
   [1 2 3 4]]]
 ```
+
+### Don’t confuse tile() with repeat()!​
+
+```python
+numpy.repeat(a, repeats, axis=None)[source]
+```
+
+> Repeat each element of an array after themselves
+
+```python
+import numpy as np
+
+a = np.arange(1, 5)
+print(a)  # -> [1 2 3 4]
+print(a.shape)  # -> (4,)
+
+b = np.repeat(a, (4))
+print(b)  # -> [1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4]
+print(b.shape)  # -> (16,)
+```
+
+## [numpy.pad](https://numpy.org/doc/stable/reference/generated/numpy.pad.html)
+
+```python
+numpy.pad(array, pad_width, mode='constant', **kwargs)
+```
+
+> Pad an array.
+
+> **pad_width** : {sequence, array_like, int}
+> 
+> Number of values padded to the edges of each axis. ((before_1, after_1), ... (before_N, after_N)) unique pad widths for each axis. (before, after) or ((before, after),) yields same before and after pad for each axis. (pad,) or int is a shortcut for before = after = pad width for all axes.
+
+> **constant_values** : sequence or scalar, optional
+> 
+> Used in ‘constant’. The values to set the padded values for each axis.
+> 
+> ((before_1, after_1), ... (before_N, after_N)) unique pad constants for each axis.
+> 
+> (before, after) or ((before, after),) yields same before and after constants for each axis.
+> 
+> (constant,) or constant is a shortcut for before = after = constant for all axes.
+> 
+> Default is 0.
+
+```python
+import numpy as np
+
+a = np.arange(1, 5)
+print(a)  # -> [1 2 3 4]
+print(a.shape)  # -> (4,)
+
+
+print(np.pad(a, 2))  # -> [0 0 1 2 3 4 0 0]
+print(np.pad(a, [2, 3]))  # -> [0 0 1 2 3 4 0 0 0]
+print(np.pad(a, [2, 3], constant_values=-1))  # -> [-1 -1  1  2  3  4 -1 -1 -1]
+```
+
+```python
+import numpy as np
+
+a = np.arange(1, 5).reshape((1, 4))
+print(a)  # -> [[1 2 3 4]]
+print(a.shape)  # -> (1, 4)
+
+
+print(np.pad(a, [[2, 3], [1, 1]]))
+```
+
+Output:
+```python
+[[0 0 0 0 0 0]
+ [0 0 0 0 0 0]
+ [0 1 2 3 4 0]
+ [0 0 0 0 0 0]
+ [0 0 0 0 0 0]
+ [0 0 0 0 0 0]]
+```
+
+### Pad can do more complex padding patterns than just pad with constant values!
+
+> **mode** : str or function, optional
+> 
+> One of the following string values or a user supplied function.
+
+|||
+|---|---|
+|‘constant’ (default)|Pads with a constant value.|
+|‘edge’|Pads with the edge values of array.|
+|‘linear_ramp’|Pads with the linear ramp between end_value and the array edge value.|
+|‘maximum’|Pads with the maximum value of all or part of the vector along each axis.|
+|‘mean’|Pads with the mean value of all or part of the vector along each axis.|
+|‘median’|Pads with the median value of all or part of the vector along each axis.|
+|‘minimum’|Pads with the minimum value of all or part of the vector along each axis.|
+|‘reflect’|Pads with the reflection of the vector mirrored on the first and last values of the vector along each axis.|
+|‘symmetric’|Pads with the reflection of the vector mirrored along the edge of the array.|
+|‘wrap’|Pads with the wrap of the vector along the axis. The first values are used to pad the end and the end values are used to pad the beginning.|
+|‘empty’|Pads with undefined values.|
+|<function>|Padding function, see Notes.|