diff --git a/Primos_clase_2junio.ipynb b/Primos_clase_2junio.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..03943a0982d2d2e179a4b69d947f0658ea9724bd
--- /dev/null
+++ b/Primos_clase_2junio.ipynb
@@ -0,0 +1,399 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "628f22b5-0cec-45ac-aaf9-facef0fbfe12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib. pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "39a4ff49-3acd-409b-ad76-4e387de2e1ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 es primo\n",
+      "4 no es primo\n",
+      "5 es primo\n",
+      "6 no es primo\n",
+      "7 es primo\n",
+      "8 no es primo\n",
+      "9 no es primo\n",
+      "10 no es primo\n",
+      "11 es primo\n",
+      "12 no es primo\n",
+      "13 es primo\n",
+      "14 no es primo\n",
+      "15 no es primo\n",
+      "16 no es primo\n",
+      "17 es primo\n",
+      "18 no es primo\n",
+      "19 es primo\n",
+      "20 no es primo\n",
+      "21 no es primo\n",
+      "22 no es primo\n",
+      "23 es primo\n",
+      "24 no es primo\n",
+      "25 no es primo\n",
+      "26 no es primo\n",
+      "27 no es primo\n",
+      "28 no es primo\n",
+      "29 es primo\n",
+      "30 no es primo\n",
+      "31 es primo\n",
+      "32 no es primo\n",
+      "33 no es primo\n",
+      "34 no es primo\n",
+      "35 no es primo\n",
+      "36 no es primo\n",
+      "37 es primo\n",
+      "38 no es primo\n",
+      "39 no es primo\n",
+      "40 no es primo\n",
+      "41 es primo\n",
+      "42 no es primo\n",
+      "43 es primo\n",
+      "44 no es primo\n",
+      "45 no es primo\n",
+      "46 no es primo\n",
+      "47 es primo\n",
+      "48 no es primo\n",
+      "49 no es primo\n",
+      "50 no es primo\n",
+      "51 no es primo\n",
+      "52 no es primo\n",
+      "53 es primo\n",
+      "54 no es primo\n",
+      "55 no es primo\n",
+      "56 no es primo\n",
+      "57 no es primo\n",
+      "58 no es primo\n",
+      "59 es primo\n",
+      "60 no es primo\n",
+      "61 es primo\n",
+      "62 no es primo\n",
+      "63 no es primo\n",
+      "64 no es primo\n",
+      "65 no es primo\n",
+      "66 no es primo\n",
+      "67 es primo\n",
+      "68 no es primo\n",
+      "69 no es primo\n",
+      "70 no es primo\n",
+      "71 es primo\n",
+      "72 no es primo\n",
+      "73 es primo\n",
+      "74 no es primo\n",
+      "75 no es primo\n",
+      "76 no es primo\n",
+      "77 no es primo\n",
+      "78 no es primo\n",
+      "79 es primo\n",
+      "80 no es primo\n",
+      "81 no es primo\n",
+      "82 no es primo\n",
+      "83 es primo\n",
+      "84 no es primo\n",
+      "85 no es primo\n",
+      "86 no es primo\n",
+      "87 no es primo\n",
+      "88 no es primo\n",
+      "89 es primo\n",
+      "90 no es primo\n",
+      "91 no es primo\n",
+      "92 no es primo\n",
+      "93 no es primo\n",
+      "94 no es primo\n",
+      "95 no es primo\n",
+      "96 no es primo\n",
+      "97 es primo\n",
+      "98 no es primo\n",
+      "99 no es primo\n",
+      "100 no es primo\n",
+      "101 es primo\n",
+      "102 no es primo\n",
+      "103 es primo\n",
+      "104 no es primo\n",
+      "105 no es primo\n",
+      "106 no es primo\n",
+      "107 es primo\n",
+      "108 no es primo\n",
+      "109 es primo\n",
+      "110 no es primo\n",
+      "111 no es primo\n",
+      "112 no es primo\n",
+      "113 es primo\n",
+      "114 no es primo\n",
+      "115 no es primo\n",
+      "116 no es primo\n",
+      "117 no es primo\n",
+      "118 no es primo\n",
+      "119 no es primo\n",
+      "120 no es primo\n",
+      "121 no es primo\n",
+      "122 no es primo\n",
+      "123 no es primo\n",
+      "124 no es primo\n",
+      "125 no es primo\n",
+      "126 no es primo\n",
+      "127 es primo\n",
+      "128 no es primo\n",
+      "129 no es primo\n",
+      "130 no es primo\n",
+      "131 es primo\n",
+      "132 no es primo\n",
+      "133 no es primo\n",
+      "134 no es primo\n",
+      "135 no es primo\n",
+      "136 no es primo\n",
+      "137 es primo\n",
+      "138 no es primo\n",
+      "139 es primo\n",
+      "140 no es primo\n",
+      "141 no es primo\n",
+      "142 no es primo\n",
+      "143 no es primo\n",
+      "144 no es primo\n",
+      "145 no es primo\n",
+      "146 no es primo\n",
+      "147 no es primo\n",
+      "148 no es primo\n",
+      "149 es primo\n",
+      "150 no es primo\n"
+     ]
+    }
+   ],
+   "source": [
+    "N= 150\n",
+    "num =np.arange(2, N+1)\n",
+    "num_prim = [num[0]]\n",
+    "\n",
+    "for i in np.arange(1,len(num)):\n",
+    "    for j in np.arange(0,i):\n",
+    "        cond = num[i]%num[j]\n",
+    "        if cond== 0:\n",
+    "            print(num[i], \"no es primo\")\n",
+    "            break\n",
+    "    if cond != 0:\n",
+    "        print(num[i], \"es primo\")\n",
+    "        num_prim.append(num[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "023fa8d7-dce7-40db-b3c8-5799ffd2b8c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(num_prim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "365d17da-08d7-498a-b76d-cdb27ce21741",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "35"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(num_prim)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "id": "ad9ff5e9-c7f2-4893-947f-5bbf57fdafd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def num_prim_func(N):\n",
+    "    \"\"\"retorna los numeros primos\"\"\"\n",
+    "    num =np.arange(2, N+1)\n",
+    "    num_prim = [num[0]]\n",
+    "\n",
+    "    for i in np.arange(1,len(num)):\n",
+    "        for j in np.arange(0,i):\n",
+    "            cond = num[i]%num[j]\n",
+    "            if cond== 0:\n",
+    "                #print(num[i], \"no es primo\")\n",
+    "                break\n",
+    "        if cond != 0:\n",
+    "            #print(num[i], \"es primo\")\n",
+    "            num_prim.append(num[i])\n",
+    "    return num_prim\n",
+    "\n",
+    "def counting_primes(N): \n",
+    "    num_prim = num_prim_func(N)\n",
+    "    return len(num_prim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "f7192b33-7448-4e2b-a98a-94ba6363e174",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1229"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "counting_primes(10000)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "7dfbcb81-064e-4fad-b544-d65d09c0c79f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nums=np.arange(2,1001)\n",
+    "y=[]\n",
+    "for i in np.arange(0,len(nums)): \n",
+    "    y.append(counting_primes(nums[i]))\n",
+    "y = np.array(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "05b80860-aa12-435d-b593-cae20c413a74",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1e8bcae22c0>]"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure()\n",
+    "plt.plot(nums,y,\".\")\n",
+    "plt.plot(nums,np.rint(nums/np.log(nums)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "c1ae88a4-4a65-4971-8646-cb0b3968eb39",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f=np.rint(nums/np.log(nums))\n",
+    "error_p= np.abs(y - f)/y*100\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "7503f174-54fe-40be-a0c0-36a2155a1190",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1e8bc2eba30>]"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure\n",
+    "plt.plot(nums[10:],error_p[10:])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01f20788-ff7e-4d32-a606-4ff5202cff71",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}