{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a883dc0a-988d-4524-b5c6-3d51bc0738d3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67f3cfed-a6c9-454a-b5a0-f2ffd9bce1aa",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241m.\u001b[39marange(\u001b[38;5;241m100\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "x = np.arange(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56eeae4f-4644-4507-aabc-e640f1252c37",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mplot(x,x\u001b[38;5;241m*\u001b[39mx,x,x\u001b[38;5;241m*\u001b[39mx\u001b[38;5;241m*\u001b[39mx)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(x,x*x,x,x*x*x);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "63792d95-a178-4ea1-87f8-77a23be2e5e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_663/3399443285.py:1: RuntimeWarning: divide by zero encountered in divide\n",
      "  plt.plot(x,1/x)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fa714f46a10>]"
      ]
     },
     "execution_count": 6,
     "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.plot(x,1/x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70ab6ef2-62f4-4fb9-96c5-4020e0220acd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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
}