diff --git a/numpy/concatenate /README.md b/numpy/concatenate /README.md
index e7c3f3d..1794ab3 100644
--- a/numpy/concatenate /README.md	
+++ b/numpy/concatenate /README.md	
@@ -114,6 +114,79 @@ print(b.shape)  # -> (10,)
 c = np.r_[a, b]
 print(c.shape)  # -> (20,)
 ```
+### More r_
+
+```python
+import numpy as np
+
+a = np.arange(0, 10)
+print(a.shape)  # -> (10,)
+b = np.arange(0, 10)
+print(b.shape)  # -> (10,)
+
+c = np.r_[a, b]
+print(c.shape)  # -> (20,)
+c = np.r_["-1", a, b]
+print(c.shape)  # -> (20,)
+c = np.r_["0, 2", a, b]
+print(c.shape)  # -> (2, 10)
+c = np.r_["1, 2", a, b]
+print(c.shape)  # -> (1, 20)
+
+c = np.r_["0, 1", a, b]
+print(c.shape)  # -> (20,)
+c = np.r_["0, 2", a, b]
+print(c.shape)  # -> (2, 10)
+c = np.r_["0, 3", a, b]
+print(c.shape)  # -> (2, 1, 10)
+
+c = np.r_["r", a, b]  # r == row
+print(c.shape)  # -> (1, 20)
+c = np.r_["c", a, b]  # c == column
+print(c.shape)  # -> (20, 1)
+```
+
+### Producing index arrays​ with r_
+
+```python
+import numpy as np
+
+idx = np.r_[2:10, 20:100, 10]
+print(idx)
+print(idx.shape)  # -> (89,)
+```
+
+Output: 
+
+```python
+[ 2  3  4  5  6  7  8  9 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
+ 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59
+ 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83
+ 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 10]
+```
+
+**If we use idx for indexing a matrix/ array then we use indexing and not slicing.** 
+
+### Producing index arrays​ with [numpy.s_](https://numpy.org/doc/stable/reference/generated/numpy.s_.html)
+
+```python
+numpy.s_ = <numpy.lib.index_tricks.IndexExpression object>
+```
+> A nicer way to build up index tuples for arrays.
+
+s_ is a simpler alternative to [slice](https://docs.python.org/dev/library/functions.html#slice): 
+
+```python
+class slice(stop)
+class slice(start, stop, step=None)
+```
+
+> Return a slice object representing the set of indices specified by range(start, stop, step). The start and step arguments default to None.
+> 
+> Slice objects have read-only data attributes start, stop, and step which merely return the argument values (or their default). They have no other explicit functionality; however, they are used by NumPy and other third-party packages.
+> 
+> Slice objects are also generated when extended indexing syntax is used. For example: a[start:stop:step] or a[start:stop, i]. 
+
 
 
 ## [numpy.c_](https://numpy.org/doc/stable/reference/generated/numpy.c_.html)
@@ -125,6 +198,20 @@ This is an optional topic!
 numpy.c_ = <numpy.lib.index_tricks.CClass object>
 ```
 
-
+> Translates slice objects to concatenation along the second axis.
+> 
+> This is short-hand for np.r_['-1,2,0', index expression], which is useful because of its common occurrence. In particular, arrays will be stacked along their last axis after being upgraded to at least 2-D with 1’s post-pended to the shape (column vectors made out of 1-D arrays).
+
+```python
+import numpy as np
+
+a = np.arange(0, 10)
+print(a.shape)  # -> (10,)
+b = np.arange(0, 10)
+print(b.shape)  # -> (10,)
+
+c = np.c_[a, b]
+print(c.shape)  # -> (10,2)
+```