diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 46f5aa14..2e69e1a1 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -16,11 +16,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n"
+    "import numpy as np\n"
    ]
   },
   {
@@ -34,11 +34,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Build Dependencies:\n",
+      "  blas:\n",
+      "    detection method: pkgconfig\n",
+      "    found: true\n",
+      "    include directory: C:/Users/black/anaconda3/Library/include\n",
+      "    lib directory: C:/Users/black/anaconda3/Library/lib\n",
+      "    name: mkl-sdl\n",
+      "    openblas configuration: unknown\n",
+      "    pc file directory: C:\\b\\abs_c1ywpu18ar\\croot\\numpy_and_numpy_base_1708638681471\\_h_env\\Library\\lib\\pkgconfig\n",
+      "    version: '2023.1'\n",
+      "  lapack:\n",
+      "    detection method: internal\n",
+      "    found: true\n",
+      "    include directory: unknown\n",
+      "    lib directory: unknown\n",
+      "    name: dep1583119523984\n",
+      "    openblas configuration: unknown\n",
+      "    pc file directory: unknown\n",
+      "    version: 1.26.4\n",
+      "Compilers:\n",
+      "  c:\n",
+      "    commands: cl.exe\n",
+      "    linker: link\n",
+      "    name: msvc\n",
+      "    version: 19.29.30154\n",
+      "  c++:\n",
+      "    commands: cl.exe\n",
+      "    linker: link\n",
+      "    name: msvc\n",
+      "    version: 19.29.30154\n",
+      "  cython:\n",
+      "    commands: cython\n",
+      "    linker: cython\n",
+      "    name: cython\n",
+      "    version: 3.0.8\n",
+      "Machine Information:\n",
+      "  build:\n",
+      "    cpu: x86_64\n",
+      "    endian: little\n",
+      "    family: x86_64\n",
+      "    system: windows\n",
+      "  host:\n",
+      "    cpu: x86_64\n",
+      "    endian: little\n",
+      "    family: x86_64\n",
+      "    system: windows\n",
+      "Python Information:\n",
+      "  path: C:\\b\\abs_c1ywpu18ar\\croot\\numpy_and_numpy_base_1708638681471\\_h_env\\python.exe\n",
+      "  version: '3.12'\n",
+      "SIMD Extensions:\n",
+      "  baseline:\n",
+      "  - SSE\n",
+      "  - SSE2\n",
+      "  - SSE3\n",
+      "  found:\n",
+      "  - SSSE3\n",
+      "  - SSE41\n",
+      "  - POPCNT\n",
+      "  - SSE42\n",
+      "  - AVX\n",
+      "  - F16C\n",
+      "  - FMA3\n",
+      "  - AVX2\n",
+      "  not found:\n",
+      "  - AVX512F\n",
+      "  - AVX512CD\n",
+      "  - AVX512_SKX\n",
+      "  - AVX512_CLX\n",
+      "  - AVX512_CNL\n",
+      "  - AVX512_ICL\n",
+      "\n",
+      "1.26.4 None\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(np.__version__, np.show_config())\n"
    ]
   },
   {
@@ -51,11 +130,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0 2 6 2 9]\n",
+      "  [8 4 0 5 2]\n",
+      "  [1 5 7 1 9]]\n",
+      "\n",
+      " [[5 3 9 8 0]\n",
+      "  [0 2 9 3 4]\n",
+      "  [9 9 0 5 4]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.random.randint(10, size=(2,3,5))\n",
+    "\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[-0.65594566  0.61110811 -0.8747299   0.26462819 -1.22039634]\n",
+      "  [-0.57931643 -0.27990516  0.11231808  2.08666773  1.1816648 ]\n",
+      "  [-1.70864461  0.65431402  0.20303076  0.47044393 -0.22062924]]\n",
+      "\n",
+      " [[ 0.22390359 -2.359598    0.6001545  -0.61251093  0.44866037]\n",
+      "  [-0.99946652 -0.08874024  0.30731537  0.01789873  0.27977288]\n",
+      "  [-0.53600122 -0.1402848   0.52123248  0.3990963   1.21883166]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.random.randn(2, 3, 5)\n",
+    "\n",
+    "print(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.87959623 0.96562183 0.16376637 0.01670297 0.68482418]\n",
+      "  [0.35335704 0.67209734 0.66468047 0.12338724 0.32832676]\n",
+      "  [0.66163619 0.90027189 0.83893079 0.61432874 0.81872485]]\n",
+      "\n",
+      " [[0.9013533  0.39357404 0.09042541 0.2406817  0.06953732]\n",
+      "  [0.17750684 0.35635412 0.87496882 0.62505625 0.01872674]\n",
+      "  [0.92180136 0.07202439 0.6725766  0.71102093 0.9752847 ]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = np.random.random((2, 3, 5))\n",
+    "\n",
+    "print(a)\n"
    ]
   },
   {
@@ -68,11 +213,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 104,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[0.87959623 0.96562183 0.16376637 0.01670297 0.68482418]\n",
+      "  [0.35335704 0.67209734 0.66468047 0.12338724 0.32832676]\n",
+      "  [0.66163619 0.90027189 0.83893079 0.61432874 0.81872485]]\n",
+      "\n",
+      " [[0.9013533  0.39357404 0.09042541 0.2406817  0.06953732]\n",
+      "  [0.17750684 0.35635412 0.87496882 0.62505625 0.01872674]\n",
+      "  [0.92180136 0.07202439 0.6725766  0.71102093 0.9752847 ]]]\n"
+     ]
+    }
+   ],
    "source": [
-    "### [your code here]\n"
+    "print(a)\n"
    ]
   },
   {
@@ -85,11 +244,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 105,
+   "metadata": {},
+   "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": [
+    "b = np.random.randint(1, 2, size=(5,2,3))\n",
+    "\n",
+    "print(b)\n"
    ]
   },
   {
@@ -102,11 +284,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 106,
+   "metadata": {},
+   "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)\n"
    ]
   },
   {
@@ -119,11 +322,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(a.size == b.size)\n",
+    "\n",
+    "# checks, if there is the same total of elements (2*3*5 = 30; numbers interchangable, as \"*\")\n"
    ]
   },
   {
@@ -136,11 +349,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (2,3,5) (5,2,3) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mValueError\u001b[39m                                Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[110]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43ma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mb\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m      3\u001b[39m \u001b[38;5;66;03m# it is not possible to combine the arrays a & b, as they have to have the same axis\u001b[39;00m\n",
+      "\u001b[31mValueError\u001b[39m: operands could not be broadcast together with shapes (2,3,5) (5,2,3) "
+     ]
+    }
+   ],
+   "source": [
+    "np.add(a, b)\n",
+    "\n",
+    "# it is not possible to combine the arrays a & b, as they have to have the same axis\n"
    ]
   },
   {
@@ -154,11 +381,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1 1 1 1 1]\n",
+      "  [1 1 1 1 1]\n",
+      "  [1 1 1 1 1]]\n",
+      "\n",
+      " [[1 1 1 1 1]\n",
+      "  [1 1 1 1 1]\n",
+      "  [1 1 1 1 1]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "c = np.transpose(b, (1, 2, 0))\n",
+    "\n",
+    "print(c)\n",
+    "\n"
    ]
   },
   {
@@ -171,11 +415,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 112,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[[1.87959623 1.96562183 1.16376637 1.01670297 1.68482418]\n",
+      "  [1.35335704 1.67209734 1.66468047 1.12338724 1.32832676]\n",
+      "  [1.66163619 1.90027189 1.83893079 1.61432874 1.81872485]]\n",
+      "\n",
+      " [[1.9013533  1.39357404 1.09042541 1.2406817  1.06953732]\n",
+      "  [1.17750684 1.35635412 1.87496882 1.62505625 1.01872674]\n",
+      "  [1.92180136 1.07202439 1.6725766  1.71102093 1.9752847 ]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "d = np.add(a, c)\n",
+    "\n",
+    "print(d)\n",
+    "\n",
+    "# as the shapes are after transposing c the same of both arrays the same, we can use simple multiplication"
    ]
   },
   {
@@ -188,12 +450,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n",
-    "\n"
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Array a:\n",
+      " [[[0.87959623 0.96562183 0.16376637 0.01670297 0.68482418]\n",
+      "  [0.35335704 0.67209734 0.66468047 0.12338724 0.32832676]\n",
+      "  [0.66163619 0.90027189 0.83893079 0.61432874 0.81872485]]\n",
+      "\n",
+      " [[0.9013533  0.39357404 0.09042541 0.2406817  0.06953732]\n",
+      "  [0.17750684 0.35635412 0.87496882 0.62505625 0.01872674]\n",
+      "  [0.92180136 0.07202439 0.6725766  0.71102093 0.9752847 ]]] \n",
+      "\n",
+      "Array d:\n",
+      " [[[1.87959623 1.96562183 1.16376637 1.01670297 1.68482418]\n",
+      "  [1.35335704 1.67209734 1.66468047 1.12338724 1.32832676]\n",
+      "  [1.66163619 1.90027189 1.83893079 1.61432874 1.81872485]]\n",
+      "\n",
+      " [[1.9013533  1.39357404 1.09042541 1.2406817  1.06953732]\n",
+      "  [1.17750684 1.35635412 1.87496882 1.62505625 1.01872674]\n",
+      "  [1.92180136 1.07202439 1.6725766  1.71102093 1.9752847 ]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\nArray a:\\n\", a, \"\\n\\nArray d:\\n\", d)\n",
+    "\n",
+    "# array a is the originially defined array and d is the array of a with each element being added by the value 1 (from c)\n"
    ]
   },
   {
@@ -206,11 +494,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "a:\n",
+      " [[[0.87959623 0.96562183 0.16376637 0.01670297 0.68482418]\n",
+      "  [0.35335704 0.67209734 0.66468047 0.12338724 0.32832676]\n",
+      "  [0.66163619 0.90027189 0.83893079 0.61432874 0.81872485]]\n",
+      "\n",
+      " [[0.9013533  0.39357404 0.09042541 0.2406817  0.06953732]\n",
+      "  [0.17750684 0.35635412 0.87496882 0.62505625 0.01872674]\n",
+      "  [0.92180136 0.07202439 0.6725766  0.71102093 0.9752847 ]]]\n",
+      "\n",
+      "b:\n",
+      " [[[1 1 1 1 1]\n",
+      "  [1 1 1 1 1]\n",
+      "  [1 1 1 1 1]]\n",
+      "\n",
+      " [[1 1 1 1 1]\n",
+      "  [1 1 1 1 1]\n",
+      "  [1 1 1 1 1]]]\n",
+      "\n",
+      "e:\n",
+      " [[[0.87959623 0.96562183 0.16376637 0.01670297 0.68482418]\n",
+      "  [0.35335704 0.67209734 0.66468047 0.12338724 0.32832676]\n",
+      "  [0.66163619 0.90027189 0.83893079 0.61432874 0.81872485]]\n",
+      "\n",
+      " [[0.9013533  0.39357404 0.09042541 0.2406817  0.06953732]\n",
+      "  [0.17750684 0.35635412 0.87496882 0.62505625 0.01872674]\n",
+      "  [0.92180136 0.07202439 0.6725766  0.71102093 0.9752847 ]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\na:\\n\", a)\n",
+    "print(\"\\nb:\\n\", c)\n",
+    "\n",
+    "\n",
+    "e = np.multiply(a, c)\n",
+    "\n",
+    "print(\"\\ne:\\n\", e)\n"
    ]
   },
   {
@@ -224,11 +552,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ True,  True,  True,  True,  True],\n",
+       "        [ True,  True,  True,  True,  True],\n",
+       "        [ True,  True,  True,  True,  True]],\n",
+       "\n",
+       "       [[ True,  True,  True,  True,  True],\n",
+       "        [ True,  True,  True,  True,  True],\n",
+       "        [ True,  True,  True,  True,  True]]])"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a == e\n",
+    "\n",
+    "# yes, e equals to a, as we multiplied each value of a with each value of e and all elements of e are set to 1\n",
     "\n"
    ]
   },
@@ -243,11 +590,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max value of d: 1.9752847008233076\n",
+      "min value of d: 1.0167029659821756\n",
+      "mean value of d: 1.5262381806595848\n"
+     ]
+    }
+   ],
+   "source": [
+    "d_max = np.max(d); print(\"max value of d:\", d_max)\n",
+    "d_min = np.min(d); print(\"min value of d:\", d_min)\n",
+    "d_mean = np.mean(d); print(\"mean value of d:\", d_mean)\n",
     "\n"
    ]
   },
@@ -261,11 +620,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### [your code here]\n"
+    "f = np.empty((2, 3, 5))\n"
    ]
   },
   {
@@ -275,11 +634,14 @@
     "\n",
     "\n",
     "\n",
-    "### 16. Populate the values in f. For each value in d, if it's larger than d_min but smaller than d_mean, assign 25 to the corresponding value in f.\n",
-    "If a value in d is larger than d_mean but smaller than d_max, assign 75 to the corresponding value in f.\n",
-    "If a value equals to d_mean, assign 50 to the corresponding value in f.\n",
-    "Assign 0 to the corresponding value(s) in f for d_min in d.\n",
-    "Assign 100 to the corresponding value(s) in f for d_max in d.\n",
+    "### 16. Populate the values in f. \n",
+    "\n",
+    "- For each value in d, if it's larger than d_min but smaller than d_mean, assign 25 to the corresponding value in f.\n",
+    "- If a value in d is larger than d_mean but smaller than d_max, assign 75 to the corresponding value in f.\n",
+    "- If a value equals to d_mean, assign 50 to the corresponding value in f.\n",
+    "- Assign 0 to the corresponding value(s) in f for d_min in d.\n",
+    "- Assign 100 to the corresponding value(s) in f for d_max in d.\n",
+    "\n",
     "In the end, f should have only the following values: 0, 25, 50, 75, and 100.\n",
     "Note: you don't have to use Numpy in this question.\n",
     "\n"
@@ -287,11 +649,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 3, 5)\n",
+      "(2, 3, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(d.shape)\n",
+    "print(f.shape)\n",
+    "\n",
+    "for index in np.ndindex(d.shape):\n",
+    "    element = d[index] \n",
+    "    if d_mean > element > d_min:\n",
+    "        f[index] = 25\n",
+    "    elif d_mean < element < d_max:\n",
+    "        f[index] = 75\n",
+    "    elif element == d_mean:\n",
+    "        f[index] = 50\n",
+    "    elif element == d_min:\n",
+    "        f[index] = 0\n",
+    "    elif element == d_max:\n",
+    "        f[index] = 100"
    ]
   },
   {
@@ -325,11 +710,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### [your code here]\n"
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.5262381806595848\n",
+      "1.9752847008233076\n",
+      "1.0167029659821756\n",
+      "[[[1.87959623 1.96562183 1.16376637 1.01670297 1.68482418]\n",
+      "  [1.35335704 1.67209734 1.66468047 1.12338724 1.32832676]\n",
+      "  [1.66163619 1.90027189 1.83893079 1.61432874 1.81872485]]\n",
+      "\n",
+      " [[1.9013533  1.39357404 1.09042541 1.2406817  1.06953732]\n",
+      "  [1.17750684 1.35635412 1.87496882 1.62505625 1.01872674]\n",
+      "  [1.92180136 1.07202439 1.6725766  1.71102093 1.9752847 ]]]\n",
+      "[[[ 75.  75.  25.   0.  75.]\n",
+      "  [ 25.  75.  75.  25.  25.]\n",
+      "  [ 75.  75.  75.  75.  75.]]\n",
+      "\n",
+      " [[ 75.  25.  25.  25.  25.]\n",
+      "  [ 25.  25.  75.  75.  25.]\n",
+      "  [ 75.  25.  75.  75. 100.]]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(d_mean)\n",
+    "print(d_max)\n",
+    "print(d_min)\n",
+    "\n",
+    "\n",
+    "print(d)\n",
+    "\n",
+    "print(f)\n",
+    "\n",
+    "#yes, comparing to the values of d: mean, min, max, the expected new values are correctly populated in f\n"
    ]
   },
   {
@@ -350,19 +768,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[['D' 'D' 'B' 'A' 'D']\n",
+      "  ['B' 'D' 'D' 'B' 'B']\n",
+      "  ['D' 'D' 'D' 'D' 'D']]\n",
+      "\n",
+      " [['D' 'B' 'B' 'B' 'B']\n",
+      "  ['B' 'B' 'D' 'D' 'B']\n",
+      "  ['D' 'B' 'D' 'D' 'E']]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#d_str = d.astype(str)\n",
+    "#d_mean_str = d_mean.astype(str)\n",
+    "#d_min_str = d_min.astype(str)\n",
+    "#d_max_str = d_max.astype(str)\n",
+    "\n",
+    "f = np.empty(d.shape, dtype=str)\n",
+    "\n",
+    "for index in np.ndindex(d.shape):\n",
+    "    element = d[index] \n",
+    "    if element == d_min:\n",
+    "        f[index] = \"A\"\n",
+    "    elif element == d_max:\n",
+    "        f[index] = \"E\"\n",
+    "    elif element == d_mean:\n",
+    "        f[index] = \"C\"\n",
+    "    elif d_min < element < d_mean:\n",
+    "        f[index] = \"B\"\n",
+    "    elif d_mean < element < d_max:\n",
+    "        f[index] = \"D\"\n",
+    "\n",
+    "print(f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "### [your code here]"
-   ]
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python (Ironhack)",
    "language": "python",
-   "name": "python3"
+   "name": "ironhack"
   },
   "language_info": {
    "codemirror_mode": {
@@ -374,7 +833,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.0"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,