asnramos/dino_viz.ipynb

## dino_viz.ipynb
{
  "cells": [
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# Jupyter Slides Demo"
    },
    {
      "metadata": {
        "trusted": true,
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "code",
      "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nplt.style.use('seaborn-white')\n%matplotlib inline",
      "execution_count": 23,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "code",
      "source": "# dataset resource : http://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html\n\ndatasauras  = pd.read_csv('DatasaurusDozen.tsv',sep='\\t')",
      "execution_count": 28,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "slideshow": {
          "slide_type": "subslide"
        }
      },
      "cell_type": "code",
      "source": "dino = datasauras[datasauras['dataset']=='dino']",
      "execution_count": 29,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "slideshow": {
          "slide_type": "fragment"
        }
      },
      "cell_type": "code",
      "source": "datasauras.head()",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>dataset</th>\n      <th>x</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>dino</td>\n      <td>55.3846</td>\n      <td>97.1795</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>dino</td>\n      <td>51.5385</td>\n      <td>96.0256</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>dino</td>\n      <td>46.1538</td>\n      <td>94.4872</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>dino</td>\n      <td>42.8205</td>\n      <td>91.4103</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>dino</td>\n      <td>40.7692</td>\n      <td>88.3333</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "  dataset        x        y\n0    dino  55.3846  97.1795\n1    dino  51.5385  96.0256\n2    dino  46.1538  94.4872\n3    dino  42.8205  91.4103\n4    dino  40.7692  88.3333"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "markdown",
      "source": "# Visualization"
    },
    {
      "metadata": {
        "trusted": true,
        "slideshow": {
          "slide_type": "slide"
        }
      },
      "cell_type": "code",
      "source": "plt.scatter(dino['x'],dino['y'])\nplt.title(\"Dino Plot\",fontsize=18)",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "text/plain": "<matplotlib.text.Text at 0x21860d17438>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UY4/vP6QDWGI1cRASSiYhraYGyDbLesQ8C/zY3acAN7MXiNJnfQuBQu9/HsKJqgNYuiSkTEIaTQ2QbZZ11dndwJvMbMzMlgInAdt7tRuAdcBdBbQvKDqAZZC2rsxqat2xqTWxNsvUQ7r7t8zsfOA7RMFqI7ADuMbMFgAPAlsLayVhrCJp6ghPytPklVlJhJBJSCuUVLsckXko7u7vG/DwqhxtGSqUk1kHsMymwnOYmhgg26wROZ+QTmYdwDKT6nYi8RoRaHQyS5XSpGlVtxOJ14izQSdzN2Wty+Wp5w1K0/7DE8/xW4tPYO/U4TnbU92ueUKo93ZNI3pqnczdk7Uul7eeNyhN+/j+Qzy+f//A7alu1yyh1Hu7phE31WzqMkvJLuvtT/LeNmVYmnbU9vp1u9svXc7X1i3TcRmwpt5Wp+kaMaMBFeGbKmuaImtdLm89b1iaNuv2mqQLKSXVe+vRmEAjzZMnTZG1Lpe3njcoTZtne03RlZSS6r31aETqbJi2XpEduqT7PU+aIuvV3XmvCp+dpr34zJPm3LG7jfXBrqSUdNeAejQ2jHdlBBaaNPs9T5oia5G9iOL87DRtP6XU5mJ/V1JKWrxRj8YGmpAu4uySNPs9b5oia12u6HpeF+qDXUopdeHfMzSNPYraPAIrqyhbxHbT7HctS28O/Vsd0YVFEVVrbKDJMgLLewDlvRAwyXvLSgkWtd00+11piubQv1VEKflyNDbQpB2B5T2A8rw/zXvLSgkWtd20+11piubQv5VS8mVp7KqztBdx5l1Vk+f9ad5bVkqwqO224eJZrVaUYdqckq9TY2c0kG4ElvcAyvP+NO8tqyhb5HabPPJVakRG6dKiiCo1dkaTVt4DKM/707w36zr/uFG6rh+IdOV6EclG50k5OhOm866qyfP+NO/NUpRNMkpXsTei1IiMovOkHJ0JNHkPoDzvT/vetKmppAXMJqe8itK11IiW6qan86R47Ty7hsh7AOV5f5kHr0bpyXXpehHVo4qloJ1dpwJNW3VtlJ5Hl1IjWqpbHAXtfNQTtUCXRulFaGJqJMtoWjPd4iho56NA0wJdGqWP0qTURpq2Zh1Na6ZbnLig3aRjrw464hpk1MHcxFF6kZqU2kjb1qyjac10izMqaDfp2KtLZ66jabr+wbzF9zIxeYAtvpe1Nz6qq9p7mnR9TNq2Zk2BteEuDqEYdX1Nk469umhG0xDKEY9WZz0ibdokbVvzpMC6PtMtyqj0tGph8RRoGkIHc2RYp15XPSJL2iRtW5UCC8OwoK1aWDylzhpCB/Po9GFdtw7JkjZJ21alwMKm29bE604v1XAa1canD+tYeZdlpplllaBSYOHSqs94CjQNoYM5vlOvozPOOtNU4GiXUf+eWvqsQNMoXemc6q7DpOkYNNOUUbT0OaJAI0EZdWJW0amn7Rg005RRkqwW7cKMR4GmBl04sLKquw6TZRl5V2aakl6SOwp0YcajQFOxrhxYWVVVhxkW7LWMXIoUl+7tyvVxuQKNmS0Gvg+sBQ4C1wHTwAPARnc/nLeBbdOVAyurKuowo4L9+Pyxge9ZNORxkVHi0r1dGdhkvo7GzOYDnwee7z10NXCVu58HjAEX529e+3TlwMqqimsSRgX76bHBAWXY4yKjxF0DlXRgFfdV7aHLM0z8GPA54P29388G7uz9vA24ELgpx/ZbSRdejlZFcX1UsJ8e8p59U5qcSzaj0r1JFri0Id2eqXczs7cDP3P3W8ysH2jG3L1/nu4DxgtoX+toOWy8sovrWYK9BgJShiQDqzak27OePZcB02b2RuA3ga8AM3vKhcDunG1rJS2HPaKu1XdxwT7tQECrCCWPuIFVG9LtmQKNu5/f/9nMJoB3Ah81s9XuPgGsA+4oooFtpOWw9aYD4oJ9moFAG9IaErY2pNuLbOl7gWvMbAHwILC1wG1Ly1SRDsj6RXFpBgJtSGtI2NqQbs8daNx99YxfV+XdXhMoVZJf2emAqmYabUhrSNiSpttD7peaM/cKhFIlg6U9yMtOB1Q102hDWkPCFzfLDr1f0vfRpHTFxFP62tZZsnzNdNnXy1Q109B3kUgIQv86aQWaFHbsmeKWxw4MfK7LqZIsB3nZX+ZV1UxDX0omIQg9hav5fQqb7nmaFw4NvqSvy6mSrAd5ntV3cam6KguoWkUodQs9hRtGKxpiWId6/Dw6nSqp+iBPko/W9UrSJaGvTFOgSWFYh3rhGSd3ugOr+iBPWugPYaYR8kogaY+sA6uqjk8FmhSGdaifXLWkxlbVr+rZQ+j56L7QVwJJu6QdWFV5fCrQpKB0zHBVzh5Cz0f36WJOCVmVx2dYZ2YDhJCOqUqoaZ/Q89F9TZl5STdVeXwq0KSUpfMNtcMeJeS0T1Nmlk2ZeUk3VXl86ohPIUvnW3WHXVRQqzPtk+RvaMLMsikzL+mmKo9PBZoUsnS+VXbYRQa1utI+Ic+k0mrKzEu6qcrjU4EmhSydb9r35JmRFBnU6kr7tK2A3oSZl3RXVcenAk0KWTrfNO/JO5ovchZSV9pHBXSR9tG9zlLIcgPFNO/Je2O8Imchdd3DSwV0kfbR2ZtCv/O9YuIp7tv1AtPAb7zsuETvSZIHzTuaL3oWUkfap6sF9CauTBRJSoEmgwd+PsVPnz8EwM2P7OdHzz46crSftMPOO5oPufictCMN+W8oS5sWQIgMokCTUpnF6iJG8yEWn9N2pCH+DWVq2wIIkdlUo0mpzGJ1W7/bJPQvZaqbFkBI22lGk1LZxeo2jubVkY6mBRDSdprRpJR0FdmOPVNs2DbJBVt3smHb5MivNW47daSj6eugpe10pqeUpFit4u7RurqSLKkuLoCQblGgySAuvVX1bWdGreYqY9ls2m2qI43XxpSpSJ8CTQmKrEmM6tTjZk5Jns9yJ+osszV1pOnp2hppCwWaEhRVk4jr1ONmTqOe37xycaaAoaW41VD6VdpEiwFKUFRxN25ZcNzMadTzWZccawVZNYb9+1wx8VRNLRLJTjOaEhRVk4jr1ONmTqOezxowtIKsGsP+fW597AA79kxpViONohlNSfo1idsvXc7X1i3L1DHEdepxM6dRz2cNGFqKW41h/z7PH5qu7ELXrEv0tbRfZtMwNGBxy4LjZk6jns+65FgryKqxeeVivvGTvbxwaO5zVaQps9aIVFuSQRRoApakU49bzTXs+TwBQyvIynfm+AIuPONkbn5k/5zn0qQps65cy7roQ4tFZBAFmsCV2akrYITtk6uW8KNnH818oWue2UXWGp4Wi8ggqtGIBCrvTVbz3Mw0aw1Pi0VkEP3riwQsz6wzz+wiaw1PtxuSQTIFGjObD1wLLAeOAz4M/BNwHTANPABsdPfDhbRSROaIq7/kmV1kreFpsYgMknVGswF41t3/yMxeBtwP/AC4yt0nzOxzwMXATQW1U0RmSFJ/yTu7yDqbUu1PZstao/k6sGnG7weBs4E7e79vA96Yo10irRJ3bUnaa0+S1F/a+kV60jyZZjTuvh/AzBYCW4GrgI+5+3TvJfuA8UJaKNJweW9+OkjS+otmFxKCzKvOzOw04A7gq+5+AzCzHrMQ2J2zbSKJZLkSPe/V62neHzf7yLI6TKu7pEmyLgZ4JXAr8B/cfXvv4fvNbLW7TwDriIKQSCZJLzTMMhvIe/V62vfnufnpMFrdJU2SdUZzJfArwCYzmzCzCaL02QfN7F5gAVFKTSS1fke+xfcyMXmALb6XtTc+OnDWkGU2kOf6kizvz3Pz02FUf5EmyVqjeTfw7gFPrcrXHJF0tzHJMhvIe/V62vfHzT7y3HdO9RdpAiV0JThpOvIss4G89Y20789z81ORNlCgkUolqb2k6cizzAby1jeyvD/rzU9F2kCBRiqTtIiepiPPMhvIO4PQDEQkHQUaqcyoryc+ecG8o2Y5ozryQbOiuNlAlveMohmISHIKNFKZUV9P/Pyh6V/+3p/lDOrI61jOLCL56GsCJJc0Fy6O+nrimUYtFS5zObO+glikHJrRSGZpZwqDai/HzyPV1xWXtZxZsx6R8mhGI5mlnV0MusjwwjNOHvjaYUuFy1rOnPciThEZTjMaySzL7GJ2EX3HnqlUX1c8aFZ08vwxHu6lvQat/kqyik1fQSxSHgUayayIGzumXSo88/UP736RB34+xf6Xprlv1wu9/+amu5J8hm5SKVIenUWSWVE3dky7VLj/+g3bJrnvpy8e9dywW9XEfYZuUilSHgUayayoCxeT3ql5tiLTXboIU6Q8CjSSS94LF/Os9kqS7koTxIb9LWm2kTVoFrGtIj87z/aKaEfRf4vUS4FGKjGs40hzp+bZ4tJdRSxZTrONIpdIp91W0cuzs26v6n0uzaDlzVK6Ud8vk/Qal0EXUsZ9J0sRS5bTbKPIJdJpt1X08uys26t6n0szaEYjpRvVccSlv+JGt6NSd0XUcNJso8iaUdptFb08O+v2qt7n0gya0UjpRnUcm1cuZsX4/KMen5n+yjO6LWLJcpptFLlEOu22hr1+0fyxTLfVyfq3VL3PpRkUaKR0ozqOuPRXntFtXBBLIs02ivi8rNsa9PrTTp7H/c+8mOgrsfN+ft73Fb0NCcvY9PR0/KsKYGbLgR3bt29n2TLdXr1LBqW/VozPT1Tc3bBtki2+d87j621RotVu/UUIRSy/TrKNIj4v67Zmv37fS4e5+ZH9c15X9r6rep9L/SYnJ1mzZg3Ame6+c/bzCjRSiTydVtYg1XUXbN3JxOSBuY8vO5HbL11efYOkteICjZKeUoms19voQsrsVOuQUOiIk1IUecGdvs0yG91WR0KhQCOF0wV3YdBsUEKhVWdSOF1wF47+bPBLa5cCcNltT+rbQ6VymtFI4XTBXVg0w5S6aUYjhVMROiyaYUrdFGikcLrgLiyaYUrdNMSUwqkIHZbx+WMDH9cMU6qiI01KoSXJYdixZ4r7n3lxzuOnnTxPM0ypjFJnIi226Z6neWzf3BTZby0+QTNMqYwCjUiLDavP7Js6XHFLpMsUaERaTCsAJQQKNCItphWAEoJChzVmdgzwWeC1wIvAv3P3nxT5GSKSnFYASgiKnj+/FTje3f+Vmf0O8HHg4oI/Q0RS0ApAqVvRqbM3AP8LwN3vA3674O2LiEjDFB1oFgF7Zvx+yMxUdRQR6bCiA81eYOHM7bu77nMhItJhRQeabwO/C9Cr0fyw4O2LiEjDFJ3WuglYa2b3AGPAn8x4bh7Arl27Cv5IERGp04x+fd6g58emp6craYiZvQG4q5IPExGROpzn7nfPfrDKQv13gfOAp4BDFX6uiIiUax5wKlE/P0dlMxoREekm3YJGRERKVcs1LmY2H7gWWA4cB3wY+CfgOmAaeADY6O7B3GLWzOYB1wBG1MZ3Ai8QcJv7zGwx8H1gLXCQwNtsZvdz5HqsHcDngb8iavut7v7Buto2jJm9H3gLsIDoNkx3EvB+NrO3A2/v/Xo88JvAagLez71+43qifuMQ8A4CP57N7Djgy8CriC7/2Ai8jED3s5mdC/ylu682s1czYN+a2QeA3yNq/3vc/Ttx261rRrMBeNbdzwPWAZ8Brgau6j02Rni3rvnXAO7+euAq4COE3+b+yfl54PneQ0G32cyOB3D31b3//gT4HPBviO48ca6Zva7ONs5mZquBlcDrgVXAaQS+n939uv4+JhqEvIvA9zPRpRPHuvtK4EM04xx8B7Df3X8H+DOivi7I/Wxm7wO+SDTwgAH7ttfWVcC5wB8Cf51k23UFmq8Dm2b8fhA4m2gUCLANeGPVjRrF3b8JXN779QxgN4G3uedjRAf2k73fQ2/za4ETzexWM7vdzM4HjnP3h919GrgFWFNvE+e4iOiasZuAvwO+Rfj7GQAz+23gN4D/Qfj7+SHg2N7NexcBLxH+fv4XRO3C3R04h3D388PAJTN+H7Rv30A0C5t298eI/j1eEbfhWgKNu+93931mthDYSjRDGOvteIB9wHgdbRvF3Q+a2fXAp4EtBN7mXnrkZ+5+y4yHg24zcIDXx0XOAAACS0lEQVQoOF5ElJ78cu+xvhDb/HKi+/r9AVGbtxDdFSPk/dx3JfBBoo5774zHQ2zzfqK02Y+J0tifIvzj+QfAm81srHcR+zjR39EXTJvd/RtEwbtv0L6dfZuxRO2vbTGAmZ0G3AF81d1vAGbmVRcSzRiC4+5vA15DdKCfMOOpENt8GdEFtBNEOfivADO/iCTENj8EfK03YnqI6KD+1RnPh9jmZ4Fb3H2qN2p9gaNPvhDbjJmdAvy6u9/B3NtHhdjmK4j282uIZr7XE9XE+kJs87VE+/Yu4PeBfwROmvF8iG3uG9QnZzpOagk0ZvZK4Fbgv7j7tb2H7+/luiGq2wR1caeZ/VGv4AvRCPsw8L2Q2+zu57v7ql4e/gfAHwPbQm4zUXD8OICZLQVOBJ4zsxVmNkY00wmtzXcDb+qNWpcSdSTbA9/PAOcD/xvA3fcCU4Hv519wZDT9c2A+gfcbRKmy7e7+BqKSwUOEv5/7Bu3bbwMXmdkxZnY60cz9mbgN1XVn5SuBXwE2mVm/VvNu4FNmtgB4kCilFpIbgS+b2T8QHeDvIWrnNQG3eZD3EnabvwRcZ2Z3E612uYwoqG8huijsVnf/PzW2bw53/1avlvQdosHbRqLVciHvZ4hWUD4y4/d+2i/I/Qx8ArjWzO4imslcCXyPsPfzPwObzey/Eo38/y1wOmHv5745fYW7H+rt/3s5cqzH0gWbIiJSKl2wKSIipVKgERGRUinQiIhIqRRoRESkVAo0IiJSKgUaEREplQKNiIiUSoFGRERK9f8B28UwfLDUngMAAAAASUVORK5CYII=\n",
            "text/plain": "<matplotlib.figure.Figure at 0x21860c9de80>"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "language_info": {
      "file_extension": ".py",
      "codemirror_mode": {
        "version": 3,
        "name": "ipython"
      },
      "pygments_lexer": "ipython3",
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
      "version": "3.5.4",
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "celltoolbar": "Slideshow",
    "gist": {
      "id": "",
      "data": {
        "description": "Jupyter slides/dinosaur-viz.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 2
}
	{
	"cells": [
	{
	"metadata": {
	"slideshow": {
	"slide_type": "slide"
	}
	},
	"cell_type": "markdown",
	"source": "# Jupyter Slides Demo"
	},
	{
	"metadata": {
	"trusted": true,
	"slideshow": {
	"slide_type": "slide"
	}
	},
	"cell_type": "code",
	"source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nplt.style.use('seaborn-white')\n%matplotlib inline",
	"execution_count": 23,
	"outputs": []
	},
	{
	"metadata": {
	"trusted": true,
	"slideshow": {
	"slide_type": "slide"
	}
	},
	"cell_type": "code",
	"source": "# dataset resource : http://blog.revolutionanalytics.com/2017/05/the-datasaurus-dozen.html\n\ndatasauras = pd.read_csv('DatasaurusDozen.tsv',sep='\\t')",
	"execution_count": 28,
	"outputs": []
	},
	{
	"metadata": {
	"trusted": true,
	"slideshow": {
	"slide_type": "subslide"
	}
	},
	"cell_type": "code",
	"source": "dino = datasauras[datasauras['dataset']=='dino']",
	"execution_count": 29,
	"outputs": []
	},
	{
	"metadata": {
	"trusted": true,
	"slideshow": {
	"slide_type": "fragment"
	}
	},
	"cell_type": "code",
	"source": "datasauras.head()",
	"execution_count": 30,
	"outputs": [
	{
	"output_type": "execute_result",
	"execution_count": 30,
	"data": {
	"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>dataset</th>\n <th>x</th>\n <th>y</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>dino</td>\n <td>55.3846</td>\n <td>97.1795</td>\n </tr>\n <tr>\n <th>1</th>\n <td>dino</td>\n <td>51.5385</td>\n <td>96.0256</td>\n </tr>\n <tr>\n <th>2</th>\n <td>dino</td>\n <td>46.1538</td>\n <td>94.4872</td>\n </tr>\n <tr>\n <th>3</th>\n <td>dino</td>\n <td>42.8205</td>\n <td>91.4103</td>\n </tr>\n <tr>\n <th>4</th>\n <td>dino</td>\n <td>40.7692</td>\n <td>88.3333</td>\n </tr>\n </tbody>\n</table>\n</div>",
	"text/plain": " dataset x y\n0 dino 55.3846 97.1795\n1 dino 51.5385 96.0256\n2 dino 46.1538 94.4872\n3 dino 42.8205 91.4103\n4 dino 40.7692 88.3333"
	},
	"metadata": {}
	}
	]
	},
	{
	"metadata": {
	"slideshow": {
	"slide_type": "slide"
	}
	},
	"cell_type": "markdown",
	"source": "# Visualization"
	},
	{
	"metadata": {
	"trusted": true,
	"slideshow": {
	"slide_type": "slide"
	}
	},
	"cell_type": "code",
	"source": "plt.scatter(dino['x'],dino['y'])\nplt.title(\"Dino Plot\",fontsize=18)",
	"execution_count": 31,
	"outputs": [
	{
	"output_type": "execute_result",
	"execution_count": 31,
	"data": {
	"text/plain": "<matplotlib.text.Text at 0x21860d17438>"
	},
	"metadata": {}
	},
	{
	"output_type": "display_data",
	"data": {
	"image/png": 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\n",
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