diff --git a/numpy/numba/README.md b/numpy/numba/README.md
index 2fed16d..507e15c 100644
--- a/numpy/numba/README.md
+++ b/numpy/numba/README.md
@@ -20,25 +20,54 @@ pip install numba
 
 ## [Numba basic types](https://numba.pydata.org/numba-doc/dev/reference/types.html)
 
+For the example that will show you the options of optimization we need to understand the numba naming schema. 
+
+### Numbers
+
+For the function signatures we need to be able to translate the usual np.dtype into numpy.types.
+
+For doing so we just replace **np.** by **numba.types.** .
 
 |Type name(s)|	Shorthand|	Comments|
 |---|---|---|
-|boolean	|b1|	represented as a byte|
-|uint8, byte	|u1|	8-bit unsigned byte|
-|uint16	|u2|	16-bit unsigned integer|
-|uint32	|u4|	32-bit unsigned integer|
-|uint64	|u8|	64-bit unsigned integer|
-|int8, char	|i1|	8-bit signed byte|
-|int16	|i2|	16-bit signed integer|
-|int32	|i4|	32-bit signed integer|
-|int64	|i8|	64-bit signed integer|
-|intc	|–|	C int-sized integer|
-|uintc	|–|	C int-sized unsigned integer|
-|intp	|–|	pointer-sized integer|
-|uintp	|–|	pointer-sized unsigned integer|
-|float32|	f4|	single-precision floating-point number|
-|float64, double|	f8|	double-precision floating-point number|
-|complex64|	c8|	single-precision complex number|
-|complex128|	c16|	double-precision complex number|
+|numba.types.boolean	|b1|	represented as a byte|
+|numba.types.uint8, byte	|u1|	8-bit unsigned byte|
+|numba.types.uint16	|u2|	16-bit unsigned integer|
+|numba.types.uint32	|u4|	32-bit unsigned integer|
+|numba.types.uint64	|u8|	64-bit unsigned integer|
+|numba.types.int8, char	|i1|	8-bit signed byte|
+|numba.types.int16	|i2|	16-bit signed integer|
+|numba.types.int32	|i4|	32-bit signed integer|
+|numba.types.int64	|i8|	64-bit signed integer|
+|numba.types.intc	|–|	C int-sized integer|
+|numba.types.uintc	|–|	C int-sized unsigned integer|
+|numba.types.intp	|–|	pointer-sized integer|
+|numba.types.uintp	|–|	pointer-sized unsigned integer|
+|numba.types.float32|	f4|	single-precision floating-point number|
+|numba.types.float64, double|	f8|	double-precision floating-point number|
+|numba.types.complex64|	c8|	single-precision complex number|
+|numba.types.complex128|	c16|	double-precision complex number|
 
+### Arrays
+
+If we have arrays in the function signature, which is a very likely senario, we might want to give as much information to numpy as possible about the numpy.ndarray. In some cases it is very benificial to make a np.ndarray an array with C memory layout and tell numba about it.
+
+We can use the numpy function **numpy.ascontiguousarray for** converting a numpy array into a C memory layout.
+
+We can also check a numpy array, let's call it X, if it is already in the C memory layout. This is done by looking at **X.flags['C_CONTIGUOUS']**.
+
+Some example for array signatures are: 
+
+|||
+|---|---|
+|numba.types.float32[:]|	1d array of float32 with no particular memory layout|
+|numba.types.float32[:,:]|	2d array of float32 with no particular memory layout|
+|numba.types.float32[:,:,:]|	3d array of float32 with no particular memory layout|
+|numba.types.float32[::1]|	1d array of float32 with C memory layout|
+|numba.types.float32[:,::1]|	2d array of float32 with C memory layout|
+|numba.types.float32[:,:,::1]|	3d array of float32 with C memory layout|
+|numba.types.float32[::1,:]|	2d array of float32 with Fortran memory layout|
+|numba.types.float32[::1,:,:]| 3d array of float32 with Fortran memory layout|
+
+## An example (up to 437x faster)