diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 46f5aa14..50c6e11e 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -16,11 +16,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n"
+    "import numpy as np\n"
    ]
   },
   {
@@ -34,11 +34,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.2.5\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "print(np.version.version)"
    ]
   },
   {
@@ -51,11 +59,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.00981632 0.10487195 0.75172023 0.24717833 0.1757527 ]\n",
+      "  [0.28466374 0.11573415 0.02898033 0.19788668 0.55923292]\n",
+      "  [0.3909844  0.2197333  0.93232603 0.58334253 0.51506052]]\n",
+      "\n",
+      " [[0.77208879 0.1081357  0.42335421 0.41416298 0.02054184]\n",
+      "  [0.97612701 0.68891587 0.46166182 0.87802931 0.1344402 ]\n",
+      "  [0.12471473 0.971848   0.16558616 0.59028948 0.17461716]]]\n",
+      "Way 1 result.\n",
+      "[[[0.10815675 0.20357791 0.04645035 0.74097967 0.50536846]\n",
+      "  [0.87056147 0.05694009 0.56844548 0.48487816 0.8391152 ]\n",
+      "  [0.11785172 0.79252638 0.84433558 0.37761322 0.35078954]]\n",
+      "\n",
+      " [[0.58563343 0.45460649 0.60805108 0.86105639 0.35747511]\n",
+      "  [0.08480654 0.66506703 0.47416759 0.48257034 0.41581101]\n",
+      "  [0.3278739  0.01095876 0.42482839 0.51821374 0.17643662]]]\n",
+      "Way 2 result.\n",
+      "[[[9.57002042 6.27715117 5.84649964 5.75971897 5.29035103]\n",
+      "  [8.15113081 6.43689239 9.15706219 9.94508772 5.74917366]\n",
+      "  [6.0035414  5.28826462 9.54471226 8.92331808 5.92352057]]\n",
+      "\n",
+      " [[7.69727074 7.1177014  6.6091268  9.87589836 9.11025159]\n",
+      "  [9.39751299 8.31129871 6.46875273 7.34732826 8.91197435]\n",
+      "  [7.93392557 8.24437613 6.2450613  9.06761935 7.42428969]]]\n",
+      "Way 3 result.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Way 1 --> Using a tuple (2, 3, 5) as an argument\n",
+    "a = np.random.random((2, 3, 5))\n",
+    "\n",
+    "print(a)\n",
+    "\n",
+    "print(\"Way 1 result.\")\n",
+    "\n",
+    "# Way 2 --> Using dimensions directly instead of a tuple to define the array. \n",
+    "a = np.random.rand(2, 3, 5)\n",
+    "\n",
+    "print(a)\n",
+    "\n",
+    "print(\"Way 2 result.\")\n",
+    "\n",
+    "# Way 3 --> This is similar to way 1, but the first two places of the argument are to set the min and maax value of the range from which we should generate the random values. \n",
+    "a = np.random.uniform(5, 10, (2, 3, 5))\n",
+    "\n",
+    "print(a)\n",
+    "\n",
+    "print(\"Way 3 result.\")\n",
+    "\n",
+    "\n",
+    "#For the next exercise, let's use the first iteration of a:\n",
+    "\n",
+    "a = np.random.random((2, 3, 5))"
    ]
   },
   {
@@ -68,11 +131,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.04351799 0.71964368 0.98239922 0.80435276 0.6917323 ]\n",
+      "  [0.8062494  0.10107773 0.10673246 0.04102788 0.47390678]\n",
+      "  [0.00869559 0.17332658 0.81141575 0.76646175 0.82315202]]\n",
+      "\n",
+      " [[0.25904348 0.80230673 0.37430907 0.86166923 0.33292779]\n",
+      "  [0.80761588 0.04442697 0.72960752 0.22502103 0.01414849]\n",
+      "  [0.36082474 0.82508008 0.19013151 0.81064563 0.48328153]]]\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "print(a)"
    ]
   },
   {
@@ -85,11 +162,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n"
+    "b = np.ones((5, 2, 3))"
    ]
   },
   {
@@ -102,11 +179,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1. 1. 1.]\n",
+      "  [1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1.]\n",
+      "  [1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1.]\n",
+      "  [1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1.]\n",
+      "  [1. 1. 1.]]\n",
+      "\n",
+      " [[1. 1. 1.]\n",
+      "  [1. 1. 1.]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(b)"
    ]
   },
   {
@@ -119,11 +217,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "a.size == b.size\n"
    ]
   },
   {
@@ -136,11 +245,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We cannot add them, as they don't have the same shape. \n"
+     ]
+    }
+   ],
+   "source": [
+    "def add_arrays(a, b):\n",
+    "    try:\n",
+    "        result = np.add(a, b)\n",
+    "\n",
+    "        return result\n",
+    "    \n",
+    "    except ValueError:\n",
+    "        print(\"We cannot add them, as they don't have the same shape. \")\n",
+    "\n",
+    "add_arrays(a, b)\n"
    ]
   },
   {
@@ -154,11 +280,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3, 5)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = b.ravel()\n",
+    "\n",
+    "b = b.reshape(5, 3, 2)\n",
+    "\n",
+    "c = np.transpose(b)\n",
+    "\n",
+    "c.shape\n",
+    "\n"
    ]
   },
   {
@@ -171,11 +315,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n"
+    "d = np.add(a, c)\n",
+    "\n",
+    "#Now it works, because after reshaping and transposing b, the shape is 2x3x5, which matches with the one from a. \n"
    ]
   },
   {
@@ -188,12 +334,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.04351799 0.71964368 0.98239922 0.80435276 0.6917323 ]\n",
+      "  [0.8062494  0.10107773 0.10673246 0.04102788 0.47390678]\n",
+      "  [0.00869559 0.17332658 0.81141575 0.76646175 0.82315202]]\n",
+      "\n",
+      " [[0.25904348 0.80230673 0.37430907 0.86166923 0.33292779]\n",
+      "  [0.80761588 0.04442697 0.72960752 0.22502103 0.01414849]\n",
+      "  [0.36082474 0.82508008 0.19013151 0.81064563 0.48328153]]]\n",
+      "End of a\n",
+      "[[[1.04351799 1.71964368 1.98239922 1.80435276 1.6917323 ]\n",
+      "  [1.8062494  1.10107773 1.10673246 1.04102788 1.47390678]\n",
+      "  [1.00869559 1.17332658 1.81141575 1.76646175 1.82315202]]\n",
+      "\n",
+      " [[1.25904348 1.80230673 1.37430907 1.86166923 1.33292779]\n",
+      "  [1.80761588 1.04442697 1.72960752 1.22502103 1.01414849]\n",
+      "  [1.36082474 1.82508008 1.19013151 1.81064563 1.48328153]]]\n",
+      "End of d\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n",
-    "\n"
+    "\n",
+    "print(a)\n",
+    "\n",
+    "print(\"End of a\")\n",
+    "\n",
+    "print(d)\n",
+    "\n",
+    "print(\"End of d\")\n",
+    "\n",
+    "#The relation between the values of a and d is that a values are exactly the same as d values, but substracting 1. This makes sense, as c was a \"ones\" array, so the sum will add 1 to all the values from array a. "
    ]
   },
   {
@@ -206,11 +383,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n"
+    "e = np.multiply(a, c)"
    ]
   },
   {
@@ -224,12 +401,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n",
-    "\n"
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array_equal(e, a)\n",
+    "\n",
+    "# e equals a, as when mulyipying a by c, as all the values in c are 1, the result of the multiplication will return the exact same values that were originally in array a. That is why, all the values from e (resulting from a * c multiplication) will be the same as the values from a.\n"
    ]
   },
   {
@@ -243,12 +432,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n",
-    "\n"
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.9823992239262962\n",
+      "1.0086955911402782\n",
+      "1.4824910524208432\n"
+     ]
+    }
+   ],
+   "source": [
+    "d_max = d.max()\n",
+    "\n",
+    "print(d_max)\n",
+    "\n",
+    "d_min = d.min()\n",
+    "\n",
+    "print(d_min)\n",
+    "\n",
+    "d_mean = d.mean()\n",
+    "\n",
+    "print(d_mean)\n"
    ]
   },
   {
@@ -261,11 +469,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[1.04351799, 1.71964368, 1.98239922, 1.80435276, 1.6917323 ],\n",
+       "        [1.8062494 , 1.10107773, 1.10673246, 1.04102788, 1.47390678],\n",
+       "        [1.00869559, 1.17332658, 1.81141575, 1.76646175, 1.82315202]],\n",
+       "\n",
+       "       [[1.25904348, 1.80230673, 1.37430907, 1.86166923, 1.33292779],\n",
+       "        [1.80761588, 1.04442697, 1.72960752, 1.22502103, 1.01414849],\n",
+       "        [1.36082474, 1.82508008, 1.19013151, 1.81064563, 1.48328153]]])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f = np.empty((2, 3, 5))\n",
+    "\n",
+    "f"
    ]
   },
   {
@@ -287,11 +514,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 25,  75, 100,  75,  75],\n",
+       "        [ 75,  25,  25,  25,  25],\n",
+       "        [  0,  25,  75,  75,  75]],\n",
+       "\n",
+       "       [[ 25,  75,  25,  75,  25],\n",
+       "        [ 75,  25,  75,  25,  25],\n",
+       "        [ 25,  75,  25,  75,  75]]])"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = d.ravel()\n",
+    "\n",
+    "f = f.ravel()\n",
+    "\n",
+    "i = 0\n",
+    "\n",
+    "for element in d:\n",
+    "    if d[i] == d_max:\n",
+    "        f[i] = 100\n",
+    "        i += 1\n",
+    "    elif d_mean < d[i] < d_max:\n",
+    "        f[i] = 75\n",
+    "        i += 1\n",
+    "    elif d[i] == d_mean:\n",
+    "        f[i] = 50\n",
+    "        i += 1\n",
+    "    elif d_mean > d[i] > d_min:\n",
+    "        f[i] = 25\n",
+    "        i += 1\n",
+    "    elif d[i] == d_min:\n",
+    "        f[i] = 0\n",
+    "        i += 1\n",
+    "\n",
+    "d = d.reshape(2, 3, 5)\n",
+    "\n",
+    "f = f.reshape(2, 3, 5)\n",
+    "\n",
+    "f = f.astype(int)\n",
+    "\n",
+    "f"
    ]
   },
   {
@@ -325,11 +598,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1.04351799 1.71964368 1.98239922 1.80435276 1.6917323 ]\n",
+      "  [1.8062494  1.10107773 1.10673246 1.04102788 1.47390678]\n",
+      "  [1.00869559 1.17332658 1.81141575 1.76646175 1.82315202]]\n",
+      "\n",
+      " [[1.25904348 1.80230673 1.37430907 1.86166923 1.33292779]\n",
+      "  [1.80761588 1.04442697 1.72960752 1.22502103 1.01414849]\n",
+      "  [1.36082474 1.82508008 1.19013151 1.81064563 1.48328153]]]\n",
+      "End of array d.\n",
+      "[[[ 25  75 100  75  75]\n",
+      "  [ 75  25  25  25  25]\n",
+      "  [  0  25  75  75  75]]\n",
+      "\n",
+      " [[ 25  75  25  75  25]\n",
+      "  [ 75  25  75  25  25]\n",
+      "  [ 25  75  25  75  75]]]\n",
+      "End of array f.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(d)\n",
+    "\n",
+    "print(\"End of array d.\")\n",
+    "\n",
+    "print(f)\n",
+    "\n",
+    "print(\"End of array f.\")\n"
    ]
   },
   {
@@ -350,11 +652,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]"
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[['B', 'D', 'E', 'D', 'D'],\n",
+       "        ['D', 'B', 'B', 'B', 'B'],\n",
+       "        ['A', 'B', 'D', 'D', 'D']],\n",
+       "\n",
+       "       [['B', 'D', 'B', 'D', 'B'],\n",
+       "        ['D', 'B', 'D', 'B', 'B'],\n",
+       "        ['B', 'D', 'B', 'D', 'D']]], dtype='<U21')"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d = d.ravel()\n",
+    "\n",
+    "f = f.ravel()\n",
+    "\n",
+    "i = 0\n",
+    "\n",
+    "f = f.astype(str)\n",
+    "\n",
+    "for element in d:\n",
+    "    if d[i] == d_max:\n",
+    "        f[i] = \"E\"\n",
+    "        i += 1\n",
+    "    elif d_mean < d[i] < d_max:\n",
+    "        f[i] = \"D\"\n",
+    "        i += 1\n",
+    "    elif d[i] == d_mean:\n",
+    "        f[i] = \"C\"\n",
+    "        i += 1\n",
+    "    elif d_mean > d[i] > d_min:\n",
+    "        f[i] = \"B\"\n",
+    "        i += 1\n",
+    "    elif d[i] == d_min:\n",
+    "        f[i] = \"A\"\n",
+    "        i += 1\n",
+    "\n",
+    "d = d.reshape(2, 3, 5)\n",
+    "\n",
+    "f = f.reshape(2, 3, 5)\n",
+    "\n",
+    "f"
    ]
   }
  ],
@@ -374,7 +722,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,