Seaborn fontsize issue

seaborn_fontsize_issue

{
 "metadata": {
  "name": "",
  "signature": "sha256:b831c65c39ae1487614e2d20cfbb244a6eea2f552ae5221987fe357497b31aa5"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#Seaborn fontsize issue"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!pip install seaborn --upgrade"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rc={'axes.labelsize': 32, 'font.size': 32, 'legend.fontsize': 32.0, 'axes.titlesize': 32}\n",
      "plt.rcParams.update(**rc)\n",
      "\n",
      "plt.plot([2,1,3], label='one')\n",
      "plt.title('test')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "<matplotlib.legend.Legend at 0xa038b70>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G5s2bERISYleF/Pz85O1r165ZtU9dXZ28bZg9EZFnYb+Se7D5Vl56ejree+89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\nDpQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa0188d0>"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import seaborn as sns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot([2,1,3], label='one')\n",
      "plt.title('test')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.legend.Legend at 0x145edac8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LrTgGIhDu2hmGYfzmN78xSktL7TgammDp0qVGTk6OcccddzR4Oc87d2js+hkG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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x144ef470>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.set(rc=rc)\n",
      "\n",
      "plt.plot([2,1,3], label='one')\n",
      "plt.title('test')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.legend.Legend at 0x145eddd8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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o/wTgadRzojH8Ipwl6j8BeBb1nGgMvwnnStR/AnA36jnRWH4XzhL1nwDch3pO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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x14502550>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.mpl.rcParams.update(rc)\n",
      "\n",
      "plt.plot([2,1,3], label='one')\n",
      "plt.title('test')\n",
      "plt.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.legend.Legend at 0x145fad30>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x159f3080>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    }
   ],
   "metadata": {}
  }
 ]
}