Input.ipynb

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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9-final"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
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 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Nhập số lần thí nghiệm: \n"
     ]
    }
   ],
   "source": [
    "print(\"Nhập số lần thí nghiệm: \")\n",
    "n = int(input())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "Nhập x 0\n",
      "Nhập y 0\n",
      "\n",
      "Nhập x 1\n",
      "Nhập y 1\n",
      "\n",
      "Nhập x 2\n",
      "Nhập y 2\n",
      "\n",
      "Nhập x 3\n",
      "Nhập y 3\n",
      "\n",
      "Nhập x 4\n",
      "Nhập y 4\n",
      "\n",
      "Nhập x 5\n",
      "Nhập y 5\n",
      "\n",
      "Nhập x 6\n",
      "Nhập y 6\n",
      "\n",
      "Nhập x 7\n",
      "Nhập y 7\n",
      "\n",
      "Nhập x 8\n",
      "Nhập y 8\n",
      "\n",
      "Nhập x 9\n",
      "Nhập y 9\n"
     ]
    }
   ],
   "source": [
    "x = []\n",
    "y = []\n",
    "for i in range(n):\n",
    "    print(\"\\nNhập x\", i)\n",
    "    x.append(int(input()))\n",
    "    print(\"Nhập y\", i)\n",
    "    y.append(int(input()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Xuất các cặp dữ liệu\nCặp dữ liệu  0\n1 2\nCặp dữ liệu  1\n4 3\nCặp dữ liệu  2\n5 2\nCặp dữ liệu  3\n1 3\nCặp dữ liệu  4\n5 8\nCặp dữ liệu  5\n4 6\nCặp dữ liệu  6\n5 8\nCặp dữ liệu  7\n4 2\nCặp dữ liệu  8\n4 1\nCặp dữ liệu  9\n3 6\n"
     ]
    }
   ],
   "source": [
    "print(\"Xuất các cặp dữ liệu\")\n",
    "for i in range(n):\n",
    "    print(\"Cặp dữ liệu \",i)\n",
    "    print(x[i], y[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}