suntong/Plotly, Grouped Horizontal Bar.ipynb

## Plotly, Grouped Horizontal Bar.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import cufflinks as cf\n",
    "\n",
    "cf.set_config_file(offline=False, world_readable=True, theme='ggplot')\n",
    "\n",
    "# Reversed Y-Axes\n",
    "invert_y = go.Layout(\n",
    "    margin=dict(\n",
    "        l=500\n",
    "    ), \n",
    "    xaxis=dict(\n",
    "        fixedrange=True\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        autorange='reversed',\n",
    "        fixedrange=True\n",
    "    )\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>person</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA...</td>\n",
       "      <td>1.489575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB...</td>\n",
       "      <td>1.004093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...</td>\n",
       "      <td>0.275537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD...</td>\n",
       "      <td>-1.644944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE...</td>\n",
       "      <td>-2.594555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF...</td>\n",
       "      <td>0.559019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              person     score\n",
       "0  AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA...  1.489575\n",
       "1  BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB...  1.004093\n",
       "2  CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...  0.275537\n",
       "3  DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD... -1.644944\n",
       "4  EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE... -2.594555\n",
       "5  FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF...  0.559019"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'score':np.random.randn(6),\n",
    "                   'person':[x*50 for x in list('ABCDEF')]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x5d1d6a278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df.set_index(['person']).plot(kind='barh')\n",
    "ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~xpt/33.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(['person']).iplot(kind='barh', layout=invert_y, filename='test/grouped')"
   ]
  },
  {
   "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": "3.5.0"
  }
 },
 "nbformat": 4,
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	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 12,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"%matplotlib inline\n",
	"\n",
	"import numpy as np\n",
	"import pandas as pd\n",
	"\n",
	"import matplotlib.pyplot as plt\n",
	"\n",
	"import plotly.plotly as py\n",
	"import plotly.graph_objs as go\n",
	"\n",
	"import cufflinks as cf\n",
	"\n",
	"cf.set_config_file(offline=False, world_readable=True, theme='ggplot')\n",
	"\n",
	"# Reversed Y-Axes\n",
	"invert_y = go.Layout(\n",
	" margin=dict(\n",
	" l=500\n",
	" ), \n",
	" xaxis=dict(\n",
	" fixedrange=True\n",
	" ),\n",
	" yaxis=dict(\n",
	" autorange='reversed',\n",
	" fixedrange=True\n",
	" )\n",
	")\n"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 13,
	"metadata": {
	"collapsed": false,
	"scrolled": true
	},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<div>\n",
	"<table border=\"1\" class=\"dataframe\">\n",
	" <thead>\n",
	" <tr style=\"text-align: right;\">\n",
	" <th></th>\n",
	" <th>person</th>\n",
	" <th>score</th>\n",
	" </tr>\n",
	" </thead>\n",
	" <tbody>\n",
	" <tr>\n",
	" <th>0</th>\n",
	" <td>AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA...</td>\n",
	" <td>1.489575</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>1</th>\n",
	" <td>BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB...</td>\n",
	" <td>1.004093</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>2</th>\n",
	" <td>CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC...</td>\n",
	" <td>0.275537</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>3</th>\n",
	" <td>DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD...</td>\n",
	" <td>-1.644944</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>4</th>\n",
	" <td>EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE...</td>\n",
	" <td>-2.594555</td>\n",
	" </tr>\n",
	" <tr>\n",
	" <th>5</th>\n",
	" <td>FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF...</td>\n",
	" <td>0.559019</td>\n",
	" </tr>\n",
	" </tbody>\n",
	"</table>\n",
	"</div>"
	],
	"text/plain": [
	" person score\n",
	"0 AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA... 1.489575\n",
	"1 BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB... 1.004093\n",
	"2 CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC... 0.275537\n",
	"3 DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD... -1.644944\n",
	"4 EEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEE... -2.594555\n",
	"5 FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF... 0.559019"
	]
	},
	"execution_count": 13,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"df = pd.DataFrame({'score':np.random.randn(6),\n",
	" 'person':[x*50 for x in list('ABCDEF')]})\n",
	"df"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 14,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"image/png": 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5nNYZUw+OE8D/Bq7OzOcA19Cjv6lWco+Iw4HdMvPZwLuAf57oONqNqfCTzNyn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\nSRX1/wEzMG2U7pyCYgAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x5d1d6a278>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"ax = df.set_index(['person']).plot(kind='barh')\n",
	"ax.invert_yaxis()"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": []
	},
	{
	"cell_type": "code",
	"execution_count": 15,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/html": [
	"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~xpt/33.embed\" height=\"525px\" width=\"100%\"></iframe>"
	],
	"text/plain": [
	"<plotly.tools.PlotlyDisplay object>"
	]
	},
	"execution_count": 15,
	"metadata": {},
	"output_type": "execute_result"
	}
	],
	"source": [
	"df.set_index(['person']).iplot(kind='barh', layout=invert_y, filename='test/grouped')"
	]
	},
	{
	"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": "3.5.0"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}