practicas

05-NumPy&Pandas.ipynb

@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -173,12 +173,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[20 30 40 50]\n",
+      "[0 1 2 3]\n"
+     ]
+    }
+   ],
    "source": [
     "a = np.array([20,30,40,50] )\n",
-    "b = np.arange( 4 )"
+    "b = np.arange( 4 )\n",
+    "print(a)\n",
+    "print(b)"
    ]
   },
   {
@@ -761,12 +772,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "166.39999999999998\n"
+     ]
+    }
+   ],
    "source": [
+    "def getProdPunto(a,b):\n",
+    "    return sum([i*b[idx] for idx,i in enumerate(a)])\n",
+    "\n",
     "a = [2, 5.6, 9, 8, 10]\n",
-    "b = [1, 3, 2.4, 2, 11]"
+    "b = [1, 3, 2.4, 2, 11]    \n",
+    "prductopunto = getProdPunto(a,b)\n",
+    "print(prductopunto)\n"
    ]
   },
   {
@@ -778,12 +802,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1   7   7\n",
+      "2   9   18\n",
+      "3   11   33\n",
+      "------ 58\n",
+      "1   8   8\n",
+      "2   10   20\n",
+      "3   12   36\n",
+      "------ 64\n",
+      "4   7   28\n",
+      "5   9   45\n",
+      "6   11   66\n",
+      "------ 139\n",
+      "4   8   32\n",
+      "5   10   50\n",
+      "6   12   72\n",
+      "------ 154\n",
+      "[[58, 64], [139, 154]]\n"
+     ]
+    }
+   ],
    "source": [
+    "def getProdMat(A,B):\n",
+    "    prodmat = []\n",
+    "    if(len(A) == len(B[0])):\n",
+    "        for indiceA, elemA in enumerate(A):\n",
+    "            elemProdMat = []\n",
+    "            for cont in range(len(A)):\n",
+    "                auxSuma = 0\n",
+    "                for subIndiceA, subElemA in enumerate(elemA):\n",
+    "                    auxSuma += subElemA*B[subIndiceA][cont]\n",
+    "                    print(subElemA, \" \", B[subIndiceA][cont], \" \", subElemA*B[subIndiceA][cont])\n",
+    "                elemProdMat.append(auxSuma)\n",
+    "                print(\"------\", auxSuma)\n",
+    "            prodmat.append(elemProdMat)\n",
+    "        return prodmat\n",
+    "    else:\n",
+    "        return \"La matriz debe ser A=mxn y B=nxp\"\n",
+    "        \n",
     "A = [[1,2,3],[4,5,6]]\n",
-    "B = [[7,8],[9,10],[11,12]]"
+    "B = [[7,8],[9,10],[11,12]]\n",
+    "print(getProdMat(A,B))"
    ]
   },
   {
@@ -795,10 +861,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "166.39999999999998\n",
+      "[[ 58  64]\n",
+      " [139 154]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "a = np.array([2, 5.6, 9, 8, 10])\n",
+    "b = np.array([1, 3, 2.4, 2, 11])\n",
+    "print(a.dot(b))\n",
+    "a = np.array([[1,2,3],[4,5,6]])\n",
+    "b = np.array([[7,8],[9,10],[11,12]])\n",
+    "print(np.matmul(a,b))"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -1232,7 +1316,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,