vene/trollplot.ipynb

## trollplot.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The trolliest plot ever\n",
    "\n",
    "## Stacked log-scale bar plot\n",
    "\n",
    "(Author: @vene, License: Simplified BSD but please, for the love of all things holy, don't ever use this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A lot of inequality going on here..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 1100)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f63730d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"example\": {\"a\": 10, \"b\": 100, \"c\": 1000}}).T\n",
    "\n",
    "df.plot.bar()\n",
    "plt.ylim(0, 1100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait a second, things don't really look so bad!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UcaIN2iVJczOfVS77gBXTjpf3zkmSGtBPoAdv/Z1+O7AqIloRsQhYDzxeZ3GSpOqqLlvc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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f63644940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(log=True, stacked=True)\n",
    "plt.ylim(1, 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"# The trolliest plot ever\n",
	"\n",
	"## Stacked log-scale bar plot\n",
	"\n",
	"(Author: @vene, License: Simplified BSD but please, for the love of all things holy, don't ever use this)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"import pandas as pd\n",
	"\n",
	"import numpy as np\n",
	"import matplotlib.pyplot as plt\n",
	"\n",
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"A lot of inequality going on here..."
	]
	},
	{
	"cell_type": "code",
	"execution_count": 8,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"(0, 1100)"
	]
	},
	"execution_count": 8,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f4f63730d68>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"df = pd.DataFrame({\"example\": {\"a\": 10, \"b\": 100, \"c\": 1000}}).T\n",
	"\n",
	"df.plot.bar()\n",
	"plt.ylim(0, 1100)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"Wait a second, things don't really look so bad!"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 10,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"(1, 10000)"
	]
	},
	"execution_count": 10,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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UcaIN2iVJczOfVS77gBXTjpf3zkmSGtBPoAdv/Z1+O7AqIloRsQhYDzxeZ3GSpOqqLlvc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\nAAAAAElFTkSuQmCC\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f4f63644940>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"df.plot.bar(log=True, stacked=True)\n",
	"plt.ylim(1, 10000)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": true
	},
	"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": "3.4.5"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}