AseiSugiyama/Seaborn Test Drive.ipynb

## Seaborn Test Drive.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Seaborn Test Drive.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMTiBqS/hcEu69AgzJ0evWo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/AseiSugiyama/a8d0a52d1b6b1acc3505006718701a68/untitled2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6I43TYDyqbmG",
        "colab_type": "text"
      },
      "source": [
        "`import seaborn as sns` するだけで matplotlib のグラフのスタイルが変わったように記憶していたんだけれど、最近の振る舞いは違うかもしれないという指摘を受けたので検証する。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8AUqAQspx6w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9KKhIB2bp10g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "d1aab6aa-66e3-4bbc-abb2-1b791ae50f1e"
      },
      "source": [
        "x = np.linspace(0,2*np.pi,1024)\n",
        "plt.plot(x, np.sin(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1fb8ac2860>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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xN1kFpcxPybU6jlJOQ1xxf2lSUpJJSUmxOsb/3PfBJpL35PPdwxcTEexvdRx1GsYYrn19DfuLylj58CgC/XysjqRUqxGRjcaYpFOX65XFdtqSU8zi7Qf49QXdtQRcgIjw2MRe5JdU8M4Pe62Oo5RT0CKwgzGG575OJSzIj19fqHcXdRVJcR0Y2zuS11dmcqS00uo4SllOi8AO36UVsDariPvHJNDWX3cxuJJHJvSktLKamTrZvVJaBGerptbw3Ne7iekQyA3DY6yOo5opMTKYa4ZG896afeQeKbM6jlKW0iI4S4u25LH7YAm/v6SnzjXgoh4YmwgCLy5LszqKUpbS32Bnobyqhn8uTaNfVAiX9+985g2UU+rSrg23j4xj4eY8dh/UW08oz6VFcBbeX7uPvOITPDahN156KwmXds+oHgT7+/D817utjqKUZbQImqm0opp/r8zkvPgwzk/QW0m4unaBftwzKp7kPQWk7HW5axmVcggtgmZ698e9FJZW8tD4nlZHUQ4ydWQs4W39+edSPVagPJMWQTMcPVHFG99lMqZXR4bEuPT8OaqeQD8f7h3VgzVZhfyYcdjqOEq1Oi2CZpizOptj5dX8blyi1VGUg904IoZOIQH8c1ma3qZaeRwtgiYqKq3k7dXZTOzXiX5RoVbHUQ4W4OvNjDHxbNx3hJVpzjnxkVItRYugid5YlUlppY4G3Nm1Q7vStUMb/rl0j44KlEfRImiC/JJy5v64l8kDu5AYGWx1HNVC/Hy8+O2YRHbkHWPJzkNWx1Gq1WgRNMG/V2ZSVWP47VgdDbi7Kwd1oXtEEC8tS6O2VkcFyjM4pAhEZIKI7BGRDBF5rIH3HxSRXbbJ61eISGy992pEZIvt8cWp21rtp+ITfLB2P9cMidZJZzyAj7cXvxubyJ5DJfx3+wGr4yjVKuwuAhHxBmYCE4E+wA0i0ueU1TYDSbbJ6xcAf6/33gljzCDbYxJO5rXkDAyGGWPirY6iWsll/TvTq1MwLy9L0yktlUdwxIhgOJBhjMkyxlQC84DJ9VcwxiQbY07e4nEtdZPUO739hWXM35DDDcNjiG4faHUc1Uq8vITfjUsk63ApCzfnWR1HqRbniCKIAnLqvc61LWvMncDX9V4HiEiKiKwVkSsb20hEptnWSykoaJ3T+15ZkY63l3DfxToa8DTj+0TSPyqUV1akU6WjAuXmWvVgsYjcDCQBL9RbHGubQ/NG4GUR6dHQtsaY2caYJGNMUkRERItn3Xu4lM+35HHzObFEhgS0+Pcp5yIiPDgukdwjJ/hsk050r9ybI4ogD+ha73W0bdnPiMhY4I/AJGNMxcnlxpg8279ZwEpgsAMy2e215Ax8vITfXKRTUHqqUT0jGBAdymvJGToqUG7NEUWwAUgQkW4i4gdMAX529o+IDAbeoK4E8ustby8i/rbn4cB5wC4HZLLL/sIyFm7O48YRMXQM1tGApxIRHhibQE7RCRZu0mMFyn3ZXQTGmGpgOrAESAXmG2N2isjTInLyLKAXgLbAJ6ecJtobSBGRrUAy8JwxxvIimJmcgbeXcPdFDe6lUh7k4p4ddVSg3J5DZlw3xnwFfHXKsifqPR/byHY/Av0dkcFRcorK+HRTLjeNiNFjAwoR4f7RCdz1Xgqfb87j2qSuZ95IKRejVxafYtbKTLxEuHuUjgZUnTG9O9IvKoTXkjP0ugLllrQI6skrPsGCjTlcP6wrnUPbWB1HOQkR4bdjEtlXWMbnW36yOo5SDqdFUM+s5Aygbh5bpeob27sjfbuE8K9v03VUoNyOFoHNT8UnmJ+Sw7VJXenSTkcD6ufqRgUJ7CssY5GOCpSb0SKwef27TADu1dGAasS4PpH06azHCpT70SIADh4tZ976HK4ZGq33FFKNEhF+OzaB7MOlfLFVRwXKfWgRUDcaqDWGe0fpPYXU6Y3vE0nvziG89q2OCpT78PgiOHSsnA/X7+dXQ6Lo2kFHA+r0Th4ryDpcypfbdFSg3IPHF8Eb32VRU2uYfnGC1VGUixjfJ5JenYL514oManQWM+UGPLoI8kvK+WDdPq4aHEVMmI4GVNN4edUbFeixAuUGPLoIZn+XRVVNrc43oJrtkr6d6NUpmFe/TddRgXJ5HlsEh49X8P66fVw5KErnIlbN5uUl3D8mgayCUhbr3MbKxXlsEby5KovK6lqmj9bRgDo7E/p2IjGyLf9akU6tjgqUC/PIIig8XsF7a/YxaWAXuke0tTqOclFeXsKM0Qmk5x/nqx06KlCuyyOL4M3vsymvrtHRgLLbpf07E9+xLa/qqEC5MI8rgqLSSt5bs5fLB3QhvmOw1XGUi/P2EmaMjift0HGW7DxodRylzopDikBEJojIHhHJEJHHGnjfX0Q+tr2/TkTi6r33uG35HhG5xBF5TmfO6ixOVNUwQ0cDykEuH9CF7hFBvKKjAuWi7C4CEfEGZgITgT7ADSLS55TV7gSOGGPigZeA523b9qFujuO+wARglu3zWkRxWSVzf9zHpf06kxipowHlGCdHBbsPlrB01yGr4yjVbI4YEQwHMowxWcaYSmAeMPmUdSYDc23PFwBjRERsy+cZYyqMMdlAhu3zWsSc1dkcr6hmxhgdDSjHumJAF7qFB/HqinSM0VGBcryM/OPc/s569heWOfyzHVEEUUBOvde5tmUNrmOb7P4oENbEbQEQkWkikiIiKQUFBWcVtKi0kssGdKZXp5Cz2l6pxvh4ezH94nh2HTjG8tR8q+MoNzQzOYO1WUUE+Tt+p4nLHCw2xsw2xiQZY5IiIiLO6jOeuao/r04Z7OBkStWZPKgLsWGBvLIiTUcFyqGyD5eyaEset5wbS1hbf4d/viOKIA/oWu91tG1Zg+uIiA8QChQ2cVuH8vaSlvx45cF8vL247+J4duQd49vdOipQjjMzOQNfby/uuqBbi3y+I4pgA5AgIt1ExI+6g79fnLLOF8BU2/NrgG9N3Z9MXwBTbGcVdQMSgPUOyKSUJa4aHEXXDm14RY8VKAfZV1jKws153DQilo7BAS3yHXYXgW2f/3RgCZAKzDfG7BSRp0Vkkm21OUCYiGQADwKP2bbdCcwHdgHfAPcZY2rszaSUVXxtxwq25R5lZdrZHctSqr5ZyZl4ewm/uah7i32HuOJfLUlJSSYlJcXqGEo1qLK6lov/sZKIYH8W3juSuhPklGq+nKIyLv7HSm4+J5anJvW1+/NEZKMxJunU5S5zsFgpV+HnU3esYEtOMavSD1sdR7mwWSsz8ZKWHQ2AFoFSLeKaodFEtWvDK8v1DCJ1dvKKT7BgYw7XD+tK59A2LfpdWgRKtQA/Hy/uGdWDTfuL+SGj0Oo4ygX9e2UGAHeP6tHi36VFoFQLuTYpms6hAXpdgWq2A0dPMH9DLtcmdSWqXcuOBkCLQKkW4+/jzT2jerBh7xHWZOqoQDXd6yszqTWGey5q+dEAaBEo1aKuS+pKZIg/L69ItzqKchGHjpXz0YYcrhkaTdcOga3ynVoESrWgAF9v7rmoB+uzi1ibpaMCdWavf5dJTa3h3lGtd3NMLQKlWtiU4TFEBPvzynIdFajTyy8p58N1+/nV4ChiwlpnNABaBEq1uABfb+6+qAdrsgpZn11kdRzlxGZ/l0V1reG+i1v3VvlaBEq1gptGxBDe1p9XVqRZHUU5qcPHK3h/3T4mD+pCXHhQq363FoFSraBuVNCdHzIKSdmrowL1S2+uyqKyurbVRwOgRaBUq7lxRAxhQX68omcQqVMUHq/gvTX7uGJgF3pEtG3179ciUKqVBPr5MO3C7nyffphN+49YHUc5kTmrsymvrmHGaGum0dUiUKoV3XJuLB2C/PQMIvU/R0ormfvjXi7r35n4jsGWZNAiUKoVBfr58OsLuvNdWgFbcoqtjqOcwJzV2ZRW1nD/mATLMmgRKNXKbjk3lnaBvryqxwo8XlFpJe/8kM3lAzqTGGnNaADsLAIR6SAiy0Qk3fZv+wbWGSQia0Rkp4hsE5Hr6733rohki8gW22OQPXmUcgVt/etGBd/uzmdbro4KPNkbqzI5UVXDA2OtGw2A/SOCx4AVxpgEYIXt9anKgFuNMX2BCcDLItKu3vsPG2MG2R5b7MyjlEu49dxYQtvoqMCTFZRU8N6P+5g8KMqyYwMn2VsEk4G5tudzgStPXcEYk2aMSbc9/wnIByLs/F6lXFpwgC93nd+N5an57Mg7anUcZYHXv8uksqbW0mMDJ9lbBJHGmAO25weByNOtLCLDAT8gs97iZ2y7jF4SEf/TbDtNRFJEJKWgQCcFV65v6nlxhAT46KjAAx06Vs77a/dx1eAourXyVcQNOWMRiMhyEdnRwGNy/fVM3cwbjc6+ISKdgf8Atxtjam2LHwd6AcOADsCjjW1vjJltjEkyxiRFROiAQrm+kABf7ji/G0t3HWLXT8esjqNa0azkDGpqDfePtn40AE0oAmPMWGNMvwYei4BDtl/wJ3/R5zf0GSISAiwG/miMWVvvsw+YOhXAO8BwR/xQSrmK28/rRnCADy8t13sQeYqfik/w0focrk2KbtU7jJ6OvbuGvgCm2p5PBRaduoKI+AELgfeMMQtOee9kiQh1xxd22JlHKZcS2saXaRd0Z9muQ3pdgYd4LTkDg2G6k4wGwP4ieA4YJyLpwFjba0QkSUTesq1zHXAhcFsDp4l+ICLbge1AOPBXO/Mo5XJuP78bHYL8+OfSPVZHUS0sp6iM+RtymDIsplXmIm4qH3s2NsYUAmMaWJ4C3GV7/j7wfiPbj7bn+5VyB239fbh3VA/+ujiVNZmFnNsjzOpIqoW89m0GXl5iyR1GT0evLFbKCdx8TiydQgL4x9I91J13odzN3sOlLNiUy00jYugUGmB1nJ/RIlDKCQT4ejNjTDwb9x1h5R49PdodvboiHV9v4Z5RPayO8gtaBEo5ieuSuhLTIZB/LN1Dba2OCtzJnoMlLNySx9Rz4+gY7FyjAdAiUMpp+Hp78cDYBHb+dIyvdxy0Oo5yoBeW7KGtv49TjgZAi0AppzJ5UBQJHdvy4rI91OiowC1s3FfE8tRD3H1RD9oF+lkdp0FaBEo5EW8v4aHxiWQWlLJwc57VcZSdjDE8//Uewtv6c/t5cVbHaZQWgVJO5pK+negfFcrLy9OorK498wbKaa1MK2D93iJ+OyaeQD+7ztZvUVoESjkZkbpRQe6RE8zbsN/qOOos1dYa/v7NHmI6BHL9sBir45yWFoFSTuiixAhGdOvAK8vTOV5RbXUcdRa+3PYTqQeO8dD4RPx8nPtXrXOnU8pDiQiPX9qbwtJKZq/KsjqOaqbK6lr+uTSNXp2CuWJAF6vjnJEWgVJOalDXdlw+oDNvrsoi/1i51XFUM3ycksP+ojIemdATLy+xOs4ZaREo5cQevqQn1bW1vLRcJ69xFWWV1by6Ip1hce25uGdHq+M0iRaBUk4sNiyIm0bE8vGG/WTkl1gdRzXB7FVZFJRU8NjEXtTdYd/5aREo5eRmjI4nyM+H57/R21Q7u0PHynnjuywu7d+JobEdrI7TZFoESjm5sLb+3D2qB8t2HWJ9dpHVcdRp/HPpHqpra3l0Qi+rozSLXUUgIh1EZJmIpNv+bd/IejX1JqX5ot7ybiKyTkQyRORj22xmSqlT3HFeNyJD/Hn2q1S9TbWT2vXTMT7ZmMvUc+OIDbN+QvrmsHdE8BiwwhiTAKywvW7ICWPMINtjUr3lzwMvGWPigSPAnXbmUcottfHz5qFxPdmSU8xX2/WGdM7GGMOzX6US2saXGU40BWVT2VsEk4G5tudzqZt3uEls8xSPBk7OY9ys7ZXyNFcPjaZnZDB/+zqV8qoaq+OoelamFbA64zD3j04gNNDX6jjNZm8RRBpjDtieHwQiG1kvQERSRGStiJz8ZR8GFBtjTl42mQtENfZFIjLN9hkpBQU6cYfyPN5ewhNX9CH3yAnmrM62Oo6yqa6p5dnFqcSFBXLzObFWxzkrZ7wLkogsBzo18NYf678wxhgRaWznZawxJk9EugPf2iasP9qcoMaY2cBsgKSkJN1JqjzSefHhjO8TyczkDK4ZGk1kiPNNcuJpPk7JIT3/OK/fPMTpbyXRmDOmNsaMNcb0a+CxCDgkIvRoVLUAAA+tSURBVJ0BbP/mN/IZebZ/s4CVwGCgEGgnIifLKBrQ++4qdQZ/vKw31TV1NzRT1jp6oooXl6YxLK49l/Rt6O9l12BvfX0BTLU9nwosOnUFEWkvIv625+HAecAuU3fqQzJwzem2V0r9XGxYEHec341PN+WyJafY6jge7eXlaRSVVfLkFX1d5uKxhthbBM8B40QkHRhre42IJInIW7Z1egMpIrKVul/8zxljdtneexR4UEQyqDtmMMfOPEp5hOmj44kI9ufPX+7U00ktsudgCe+t2ceNw2PoFxVqdRy72DVTgjGmEBjTwPIU4C7b8x+B/o1snwUMtyeDUp6orb8PD1/Sk0cWbGPRlp+4cnCj51moFmCM4akvdhIc4MPvx/e0Oo7dXPPIhlKKa4ZE0z8qlOe+3q1zFrSyr7YfZE1WIQ+N70n7INe/DlaLQCkX5eUl/HlyXw4eK+eV5WlWx/EYZZXVPLN4F306h3DjcOeeeayptAiUcmFDYtpzw/CuvP3DXnYfPGZ1HI/w75WZ/HS0nD9P7ou3C8w10BRaBEq5uEcu6UVoG1/+b+EOamv1wHFLyio4zhursrhyUBeGxbnO3UXPRItAKRfXPsiPxyb2ImXfERZszLU6jtsyxvDHhTvw9/HiD5f1tjqOQ2kRKOUGrhkSzbC49vzt61SOlFZaHcctfbYpjzVZhTw2sRcdg93rim4tAqXcgJeX8Jcr+3GsvJrnv9ltdRy3U1RayV8X72JobHtuGOYeB4jr0yJQyk306hTCned3Y96GHJ3AxsGe/SqVkvJqnr2qv0tMRt9cWgRKuZEHxibQtUMbHv10m96q2kHWZBayYGMu0y7sTs9OwVbHaRFaBEq5kUA/H5771QCyD5fy0jK9tsBeJypr+MPC7cR0COT+Ma434UxTaREo5WbOiw/nhuFdefP7LLbqTens8o+le8g+XMpzv+pPgK+31XFajBaBUm7o8Ut70zE4gIcXbKWiWncRnY0Ne4t4+4dsbj4nhpHx4VbHaVFaBEq5oZAAX579VT/SDh1nZnKm1XFczonKGh7+ZCtR7drw+ET3umagIVoESrmp0b0iuWpwFLOSM9iWq7uImuPvS3azt7CMF64ZSJC/XTdpdglaBEq5saeu6EtEsD8PzNtCWaXeobQp1mYV8s4Pe5l6bizn9gizOk6r0CJQyo2FBvryz2sHkl1YyjOLU62O4/SOllXx4MdbiAsL5NGJvayO02rsKgIR6SAiy0Qk3fZv+wbWuVhEttR7lIvIlbb33hWR7HrvDbInj1Lql0bGh/PrC7rzwbr9rEg9ZHUcp2WM4bHPtpFfUsErUwYT6Of+u4ROsndE8BiwwhiTAKywvf4ZY0yyMWaQMWYQMBooA5bWW+Xhk+8bY7bYmUcp1YCHxifSq1MwjyzYRkFJhdVxnNLHG3L4esdBfn9JTwZ2bWd1nFZlbxFMBubans8FrjzD+tcAXxtjyuz8XqVUM/j7ePPqDYMpqajmwflbqNHbVf9MRv5x/vzlLkb2CGPaBd2tjtPq7C2CSGPMAdvzg0DkGdafAnx0yrJnRGSbiLwkIv6NbSgi00QkRURSCgoK7IislGdKjAzmySv68H36YV77NsPqOE6jvKqG+z/aTICvFy9dP8gt7yV0JmcsAhFZLiI7GnhMrr+eMcYAjf6ZISKdqZvEfkm9xY8DvYBhQAfg0ca2N8bMNsYkGWOSIiIizhRbKdWAG4fHcNXgKF5ekcbq9MNWx7GcMYY/fb6DXQeO8Y9rBxIZ4l63l26qMxaBMWasMaZfA49FwCHbL/iTv+jzT/NR1wELjTFV9T77gKlTAbwDDLfvx1FKnY6I8MxV/Ujo2Jb7523mwNETVkey1LwNOXyyMZcZo+MZ0/tMOzTcl727hr4AptqeTwUWnWbdGzhlt1C9EhHqji/ssDOPUuoMAv18mHXTUMqrapj+4WYqq2utjmSJLTnFPLloJxckhPPA2ESr41jK3iJ4DhgnIunAWNtrRCRJRN46uZKIxAFdge9O2f4DEdkObAfCgb/amUcp1QTxHdvy/NUD2LjvCE9+sYO6Pbue4/DxCu59fyMRwf68OmWw20xCf7bsOlHWGFMIjGlgeQpwV73Xe4GoBtYbbc/3K6XO3hUDu7D74DFmJmfSI6Itd3nI2TLlVTVMey+FwtJKFtw9kvZBflZHspznXDGhlPqFh8b1JDO/lGe/SqV7RBCje7n3fnJjDA8v2Mam/cXMumkI/aNDrY7kFPQWE0p5MC8v4cXrB9KnSwj3f7SF1APHrI7Uol5ans6XW3/ikQk9ubR/Z6vjOA0tAqU8XKCfD2/dOowgf2+mvr2enCL3vN5z3vr9vLoinWuHRnPPRT2sjuNUtAiUUnQKDeA/d46gorqWm+esc7vbUPx32088vnA7FyVG8MxV/ak7UVGdpEWglALqrjx+5/Zh5B+r4Na313P0RNWZN3IBK/fk87uPt5AU257Xbx6Kn4/+2juV/hdRSv3PkJj2vHHLUDLyS7hlzjqKyyqtjmSXHzIOc/f7G0mMDGbObcNo4+e+8w7bQ4tAKfUzFyZG8PrNQ9l9oIQb31xHUalrlsG3uw9x+7sbiO0QxNw7hhMS4Gt1JKelRaCU+oUxvSN5c2oSmQXHuWH2WvJLyq2O1Cxfbz/Ab/6zkZ6Rwcybdg7hbRu9n6VCi0Ap1YiLEiN457Zh7C8q41ezfiQjv8TqSE3yn7X7uO/DTQyIbscHvx6hF4w1gRaBUqpRI+PD+fg351BeVcuvZv3ImsxCqyM1qqbW8Jf/7uJPn+9gVM+OvKe7g5pMi0ApdVoDotux8N6RdAwJ4JY563jnh2ynuzdRcVkl095LYc7qbG4bGcebtyYR5K83TmgqLQKl1Bl17RDIp3eP5KLECP785S6mf7SZ4xXVVscCYPP+I1z26mpWpRfwl8l9eWpSX4+/iVxzaREopZokNNCXN29N4tEJvfh6+wEufeV71mZZt6uoqqaWmckZXPv6GkTgk7tHcsu5cZblcWVaBEqpJvPyEu4Z1YN5085FBKbMXssTi3ZQUt66F59tzz3KpNd+4IUlexjfN5LFMy5gkIdNOO9I4mz7+poiKSnJpKSkWB1DKY9WVlnNP5ak8c6P2XQI9OOBcYlMGdYVX++W+/vy4NFyXl6exvyUHMLb+vP05H5M6Nepxb7P3YjIRmNM0i+WaxEopeyxLbeYZxansi67iO7hQUy7sDtXDo4iwNdxV/HmFJXx7o97+WDdPmpqDTefE8sDYxMJbaNnBTVHixSBiFwLPAX0BobbJqRpaL0JwCuAN/CWMebkTGbdgHlAGLARuMUYc8bLGLUIlHIuxhiWp+bz0rI0dh04RnhbP64eEs2kQV3o0znkrG7yVl5Vw8o9BXy+OY+luw4iIkwa2IUHxyXStUNgC/wU7q+liqA3UAu8Afy+oSIQEW8gDRgH5AIbgBuMMbtEZD7wmTFmnoi8Dmw1xvz7TN+rRaCUczLGsCazkLd/2MvKPflU1xpiwwIZ2SOMEd3C6NkpmG7hQb8YLRhjKCytJPtwKVtzilmbVcS6rEJKKqoJC/LjmqHRTB0ZR5d2bSz6ydxDY0Vg71SVqbYPP91qw4EMY0yWbd15wGQRSQVGAzfa1ptL3ejijEWglHJOIsLI+HBGxodzpLSSxdsPkLw7n/9uPcBH63Ns60BIgC9t/X3w8/GirLKakvJqyipr/vc5cWGBXD6wMxP7dWZkjzB8WvC4g2qdqSqjgJx6r3OBEdTtDio2xlTXW/6LeY1PEpFpwDSAmJiYlkmqlHKY9kF+3HxOLDefE0tNrSHtUAkZ+cfJKiilqLSC4xU1VNbUEuTnTaCfD107tCEuPIjenULoFBpgdXyPcsYiEJHlQEOH5f9ojFnk+EgNM8bMBmZD3a6h1vpepZT9vL2E3p1D6N05xOooqgFnLAJjzFg7vyMP6FrvdbRtWSHQTkR8bKOCk8uVUkq1otbY8bYBSBCRbiLiB0wBvjB1R6mTgWts600FWm2EoZRSqo5dRSAiV4lILnAusFhEltiWdxGRrwBsf+1PB5YAqcB8Y8xO20c8CjwoIhnUHTOYY08epZRSzacXlCmllIdo7PRRPSdLKaU8nBaBUkp5OC0CpZTycFoESinl4VzyYLGIFAD7znLzcOCwA+O0NlfPD67/M7h6fnD9n8HV84M1P0OsMSbi1IUuWQT2EJGUho6auwpXzw+u/zO4en5w/Z/B1fODc/0MumtIKaU8nBaBUkp5OE8sgtlWB7CTq+cH1/8ZXD0/uP7P4Or5wYl+Bo87RqCUUurnPHFEoJRSqh4tAqWU8nAeVQQiMkFE9ohIhog8ZnWe5hCRt0UkX0R2WJ3lbIhIVxFJFpFdIrJTRH5rdabmEpEAEVkvIlttP8Ofrc50NkTEW0Q2i8h/rc5yNkRkr4hsF5EtIuJyd58UkXYiskBEdotIqoica3kmTzlGICLeQBowjrppMTcANxhjdlkarIlE5ELgOPCeMaaf1XmaS0Q6A52NMZtEJBjYCFzpKv/9AaRucu4gY8xxEfEFVgO/NcastThas4jIg0ASEGKMudzqPM0lInuBJGOMS15QJiJzge+NMW/Z5mgJNMYUW5nJk0YEw4EMY0yWMaYSmAdMtjhTkxljVgFFVuc4W8aYA8aYTbbnJdTNTdHoHNXOyNQ5bnvpa3u41F9SIhINXAa8ZXUWTyQiocCF2OZeMcZUWl0C4FlFEAXk1Hudi4v9InIXIhIHDAbWWZuk+Wy7VbYA+cAyY4yr/QwvA48AtVYHsYMBlorIRhGZZnWYZuoGFADv2HbPvSUiQVaH8qQiUE5ARNoCnwIPGGOOWZ2nuYwxNcaYQdTNsT1cRFxmN52IXA7kG2M2Wp3FTucbY4YAE4H7bLtNXYUPMAT4tzFmMFAKWH680pOKIA/oWu91tG2ZaiW2/eqfAh8YYz6zOo89bMP5ZGCC1Vma4Txgkm0f+zxgtIi8b22k5jPG5Nn+zQcWUrfb11XkArn1RpILqCsGS3lSEWwAEkSkm+0AzRTgC4szeQzbgdY5QKox5kWr85wNEYkQkXa2522oO/Fgt7Wpms4Y87gxJtoYE0fd///fGmNutjhWs4hIkO1kA2y7VMYDLnMmnTHmIJAjIj1ti8YAlp8w4WN1gNZijKkWkenAEsAbeNsYs9PiWE0mIh8Bo4BwEckFnjTGzLE2VbOcB9wCbLftYwf4gzHmKwszNVdnYK7tDDQvYL4xxiVPwXRhkcDCur8r8AE+NMZ8Y22kZpsBfGD7gzQLuN3iPJ5z+qhSSqmGedKuIaWUUg3QIlBKKQ+nRaCUUh5Oi0AppTycFoFSSnk4LQKllPJwWgRKKeXh/h+5tCJg3fu0ogAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpvXUZ_fp4f_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "055eab76-30aa-48e4-8bd3-ca9dcf92c058"
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oj9cpuySp9qV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "9962783a-ba5d-4f76-ba1a-f6ea3d67c947"
      },
      "source": [
        "x = np.linspace(0,2*np.pi,1024)\n",
        "plt.plot(x, np.sin(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f1fadbbfc18>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkAJtSATqp8p",
        "colab_type": "text"
      },
      "source": [
        "どうやら今は変わらない様子。"
      ]
    }
  ]
}