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  1. {
  2.  "cells": [
  3.   {
  4.    "cell_type": "code",
  5.    "execution_count": 1,
  6.    "metadata": {},
  7.    "outputs": [],
  8.    "source": [
  9.     "%matplotlib inline"
  10.    ]
  11.   },
  12.   {
  13.    "cell_type": "code",
  14.    "execution_count": 4,
  15.    "metadata": {},
  16.    "outputs": [],
  17.    "source": [
  18.     "from pylab import *\n",
  19.     "from numpy import *"
  20.    ]
  21.   },
  22.   {
  23.    "cell_type": "code",
  24.    "execution_count": 2,
  25.    "metadata": {},
  26.    "outputs": [],
  27.    "source": [
  28.     "from matplotlib.pyplot import *"
  29.    ]
  30.   },
  31.   {
  32.    "cell_type": "code",
  33.    "execution_count": 5,
  34.    "metadata": {},
  35.    "outputs": [],
  36.    "source": [
  37.     "x = linspace(0, 5, 10)\n",
  38.     "y = x ** 2"
  39.    ]
  40.   },
  41.   {
  42.    "cell_type": "code",
  43.    "execution_count": 6,
  44.    "metadata": {},
  45.    "outputs": [
  46.     {
  47.      "data": {
  48.       "image/png": 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\n",
  49.       "text/plain": [
  50.        "<Figure size 432x288 with 1 Axes>"
  51.       ]
  52.      },
  53.      "metadata": {},
  54.      "output_type": "display_data"
  55.     }
  56.    ],
  57.    "source": [
  58.     "figure()\n",
  59.     "plot(x,y,'r')\n",
  60.     "xlabel('x')\n",
  61.     "ylabel('y')\n",
  62.     "title('Title')\n",
  63.     "show()"
  64.    ]
  65.   },
  66.   {
  67.    "cell_type": "code",
  68.    "execution_count": 7,
  69.    "metadata": {},
  70.    "outputs": [
  71.     {
  72.      "data": {
  73.       "text/plain": [
  74.        "Button(description='Click me!', style=ButtonStyle())"
  75.       ]
  76.      },
  77.      "execution_count": 0,
  78.      "metadata": {},
  79.      "output_type": "execute_result"
  80.     }
  81.    ],
  82.    "source": [
  83.     "from ipywidgets import widgets\n",
  84.     "from IPython.display import display\n",
  85.     "button=widgets.Button(description=\"Click me!\")\n",
  86.     "display(button)"
  87.    ]
  88.   },
  89.   {
  90.    "cell_type": "code",
  91.    "execution_count": 5,
  92.    "metadata": {},
  93.    "outputs": [],
  94.    "source": []
  95.   },
  96.   {
  97.    "cell_type": "code",
  98.    "execution_count": null,
  99.    "metadata": {},
  100.    "outputs": [],
  101.    "source": []
  102.   }
  103.  ],
  104.  "metadata": {
  105.   "kernelspec": {
  106.    "display_name": "Python 3",
  107.    "language": "python",
  108.    "name": "python3"
  109.   },
  110.   "language_info": {
  111.    "codemirror_mode": {
  112.     "name": "ipython",
  113.     "version": 3
  114.    },
  115.    "file_extension": ".py",
  116.    "mimetype": "text/x-python",
  117.    "name": "python",
  118.    "nbconvert_exporter": "python",
  119.    "pygments_lexer": "ipython3",
  120.    "version": "3.6.3"
  121.   }
  122.  },
  123.  "nbformat": 4,
  124.  "nbformat_minor": 1
  125. }
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