suntong/Plotly, Bar Chart From Pandas.ipynb

## Plotly, Bar Chart From Pandas.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "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",
    "# (*) Useful Python/Plotly tools\n",
    "import plotly.tools as tls\n",
    "\n",
    "import cufflinks as cf\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              A                  B         C         D         E\n",
      "0   foo foo foo        one one one    I1-one  0.432164 -0.543835\n",
      "1   bar bar bar        two two two    I2-two -1.405275 -0.258083\n",
      "2   foo foo foo  three three three  I3-three  0.372689 -0.342716\n",
      "3   bar bar bar        one one one   I4-four  2.035922 -0.667917\n",
      "4   foo foo foo        two two two    I1-one -0.757218  0.537525\n",
      "5   bar bar bar  three three three    I2-two -0.656761 -0.157566\n",
      "6   foo foo foo        one one one  I3-three -0.892129  0.969210\n",
      "7   bar bar bar        two two two   I4-four  0.248857  0.480221\n",
      "8   foo foo foo  three three three    I1-one  1.890949 -0.757290\n",
      "9   bar bar bar        one one one    I2-two  0.057599  0.116085\n",
      "10  foo foo foo        two two two  I3-three  0.251044 -0.234277\n",
      "11  bar bar bar  three three three   I4-four -0.474219 -0.927109\n",
      "C                                I1-one    I2-two  I3-three   I4-four\n",
      "A           B                                                        \n",
      "bar bar bar one one one             NaN  0.057599       NaN  2.035922\n",
      "            three three three       NaN -0.656761       NaN -0.474219\n",
      "            two two two             NaN -1.405275       NaN  0.248857\n",
      "foo foo foo one one one        0.432164       NaN -0.892129       NaN\n",
      "            three three three  1.890949       NaN  0.372689       NaN\n",
      "            two two two       -0.757218       NaN  0.251044       NaN\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo foo foo', 'bar bar bar']*6,\n",
    "                   'B' : ['one one one', 'two two two', 'three three three']*4,\n",
    "                  'C' : ['I1-one', 'I2-two', 'I3-three', 'I4-four']*3,\n",
    "                  'D' : np.random.randn(12),\n",
    "                  'E' : np.random.randn(12)})\n",
    "print(df)\n",
    "e = pd.pivot_table(df, index=['A','B'], columns='C',values='E')\n",
    "p = pd.pivot_table(df, index=['A','B'], columns='C',values='D')\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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+QqkalHS0pMdSv94uqW+D/X4WOBC4OW3bBxDwDUlPS5pXl6ukSyXdKGkmcKOkHpJ+lPY7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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107fec400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = p.plot(kind='barh')\n",
    "f = ax.invert_yaxis()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x107feccf8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Se99+1zpk9JAoM/vMzP5oZkdmq0LOOZdKUw6exUOGIKlJQ/GQIc23MY3gyYNr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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1080f4ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.close('all')  # close all open figures\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "# Plot With Error Bars\n",
    "p.plot(xerr=e, ax=ax, kind='barh')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Figure(\n",
      "    data=Data([\n",
      "        Scatter(\n",
      "            x=[nan, nan, nan, 0.97599951193918555, 2.6482391432369594, -1.2..],\n",
      "            y=[-0.1875, 0.8125, 1.8125, 2.8125, 3.8125, 4.8125],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            mode='markers',\n",
      "            name='_nolegend_',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Scatter(\n",
      "            x=[nan, nan, nan, -0.11167075183956393, 1.1336598283373407, -0...],\n",
      "            y=[-0.1875, 0.8125, 1.8125, 2.8125, 3.8125, 4.8125],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            mode='markers',\n",
      "            name='_nolegend_',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Scatter(\n",
      "            x=[-0.058485784438047361, -0.4991951463129855, -1.14719235991.., ],\n",
      "            y=[-0.0625, 0.9375, 1.9375, 2.9375, 3.9375, 4.9375],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            mode='markers',\n",
      "            name='_nolegend_',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Scatter(\n",
      "            x=[0.17368413589687096, -0.81432759074098926, -1.663358534013.., ],\n",
      "            y=[-0.0625, 0.9375, 1.9375, 2.9375, 3.9375, 4.9375],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            mode='markers',\n",
      "            name='_nolegend_',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Scatter(\n",
      "            x=[nan, nan, nan, -1.8613387562523367, 0.71540522914363935, 0.4..],\n",
      "            y=[0.0625, 1.0625, 2.0625, 3.0625, 4.0625, 5.0625],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            mode='markers',\n",
      "            name='_nolegend_',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Scatter(\n",
      "            x=[nan, nan, nan, 0.077081499605491488, 0.029973238326778784, 0..],\n",
      "            y=[0.0625, 1.0625, 2.0625, 3.0625, 4.0625, 5.0625],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            mode='markers',\n",
      "            name='_nolegend_',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Scatter(\n",
      "            x=[2.7038392374086837, 0.4528902025031718, -0.23136365140701978, ],\n",
      "            y=[0.1875, 1.1875, 2.1875, 3.1875, 4.1875, 5.1875],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            mode='markers',\n",
      "            name='_nolegend_',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Scatter(\n",
      "            x=[1.3680055029574745, -1.4013273410389135, 0.72907749532873667, ],\n",
      "            y=[0.1875, 1.1875, 2.1875, 3.1875, 4.1875, 5.1875],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            mode='markers',\n",
      "            name='_nolegend_',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Bar(\n",
      "            x=[0.0, 0.0, 0.0, 0.43216438004981078, 1.8909494857871501, 0.75..],\n",
      "            y=[-0.1875, 0.8125, 1.8125, 2.8125, 3.8125, 4.8125],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            orientation='h',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Bar(\n",
      "            x=[0.057599175729411797, 0.65676136852698741, 1.4052754469636.., ],\n",
      "            y=[-0.0625, 0.9375, 1.9375, 2.9375, 3.9375, 4.9375],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            orientation='h',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Bar(\n",
      "            x=[0.0, 0.0, 0.0, 0.89212862832342266, 0.37268923373520907, 0.2..],\n",
      "            y=[0.0625, 1.0625, 2.0625, 3.0625, 4.0625, 5.0625],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            orientation='h',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        ),\n",
      "        Bar(\n",
      "            x=[2.0359223701830791, 0.47421856926787087, 0.24885692196085843, ],\n",
      "            y=[0.1875, 1.1875, 2.1875, 3.1875, 4.1875, 5.1875],\n",
      "            marker=Marker(\n",
      "                line=Line()\n",
      "            ),\n",
      "            orientation='h',\n",
      "            xaxis='x1',\n",
      "            yaxis='y1'\n",
      "        )\n",
      "    ]),\n",
      "    layout=Layout(\n",
      "        annotations=Annotations([\n",
      "            Annotation(\n",
      "                x=0.83574148745519716,\n",
      "                y=0.646505376344086,\n",
      "                font=Font(),\n",
      "                text='C',\n",
      "                xanchor='left',\n",
      "                xref='paper',\n",
      "                yanchor='bottom',\n",
      "                yref='paper'\n",
      "            ),\n",
      "            Annotation(\n",
      "                x=0.82493279569892464,\n",
      "                y=0.57123655913978477,\n",
      "                font=Font(),\n",
      "                text='I1-one',\n",
      "                xanchor='left',\n",
      "                xref='paper',\n",
      "                yanchor='bottom',\n",
      "                yref='paper'\n",
      "            ),\n",
      "            Annotation(\n",
      "                x=0.82493279569892464,\n",
      "                y=0.49193548387096753,\n",
      "                font=Font(),\n",
      "                text='I2-two',\n",
      "                xanchor='left',\n",
      "                xref='paper',\n",
      "                yanchor='bottom',\n",
      "                yref='paper'\n",
      "            ),\n",
      "            Annotation(\n",
      "                x=0.82493279569892464,\n",
      "                y=0.41263440860215034,\n",
      "                font=Font(),\n",
      "                text='I3-three',\n",
      "                xanchor='left',\n",
      "                xref='paper',\n",
      "                yanchor='bottom',\n",
      "                yref='paper'\n",
      "            ),\n",
      "            Annotation(\n",
      "                x=0.82493279569892464,\n",
      "                y=0.33333333333333315,\n",
      "                font=Font(),\n",
      "                text='I4-four',\n",
      "                xanchor='left',\n",
      "                xref='paper',\n",
      "                yanchor='bottom',\n",
      "                yref='paper'\n",
      "            )\n",
      "        ]),\n",
      "        barmode='stack',\n",
      "        height=320,\n",
      "        hovermode='y',\n",
      "        margin=Margin(\n",
      "            r=47,\n",
      "            t=31,\n",
      "            b=40,\n",
      "            l=60,\n",
      "            pad=0\n",
      "        ),\n",
      "        showlegend=False,\n",
      "        width=480,\n",
      "        xaxis1=XAxis(\n",
      "            anchor='y1',\n",
      "            domain=[0.0, 1.0],\n",
      "            range=[-2.0, 3.0],\n",
      "            side='bottom',\n",
      "            tickfont=dict(),\n",
      "            type='linear'\n",
      "        ),\n",
      "        yaxis1=YAxis(\n",
      "            anchor='x1',\n",
      "            domain=[0.0, 1.0],\n",
      "            range=[-0.5, 5.5],\n",
      "            side='left',\n",
      "            tickfont=dict(),\n",
      "            tickmode=False,\n",
      "            title='A,B',\n",
      "            titlefont=dict(),\n",
      "            type='linear'\n",
      "        )\n",
      "    )\n",
      ")\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\plotly\\matplotlylib\\renderer.py:516: UserWarning:\n",
      "\n",
      "Looks like the annotation(s) you are trying \n",
      "to draw lies/lay outside the given figure size.\n",
      "\n",
      "Therefore, the resulting Plotly figure may not be \n",
      "large enough to view the full text. To adjust \n",
      "the size of the figure, use the 'width' and \n",
      "'height' keys in the Layout object. Alternatively,\n",
      "use the Margin object to adjust the figure's margins.\n",
      "\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\plotly\\matplotlylib\\renderer.py:445: UserWarning:\n",
      "\n",
      "Dang! That path collection is out of this world. I totally don't know what to do with it yet! Plotly can only import path collections linked to 'data' coordinates\n",
      "\n",
      "D:\\Programs\\Anaconda3\\lib\\site-packages\\plotly\\matplotlylib\\renderer.py:481: UserWarning:\n",
      "\n",
      "I found a path object that I don't think is part of a bar chart. Ignoring.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "py_fig1 = tls.mpl_to_plotly(fig, strip_style=True)\n",
    "print(py_fig1.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~xpt/90.embed\" height=\"320px\" width=\"480px\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert mpl figure object to Plotly plot \n",
    "py.iplot(py_fig1, filename='test/bars_pandas_witherr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "df = cf.datagen.lines()\n",
    "df.tail()\n",
    "\n",
    "# Plot!\n",
    "plot_url = py.plot([{\n",
    "    'x': df.index,\n",
    "    'y': df[col],\n",
    "    'name': col\n",
    "}  for col in df.columns], filename='cufflinks/simple-line')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-3-62db43ce601d>, line 18)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-3-62db43ce601d>\"\u001b[1;36m, line \u001b[1;32m18\u001b[0m\n\u001b[1;33m    }, for col in p.columns], kind='barh', layout=LayoutR, filename='test/barh-mg')\u001b[0m\n\u001b[1;37m         ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "LayoutR = go.Layout(\n",
    "    yaxis=dict(\n",
    "        autorange='reversed'\n",
    "    )\n",
    ")\n",
    "\n",
    "from plotly.graph_objs import *\n",
    "\n",
    "py.iplot([{\n",
    "    'data': [\n",
    "        Bar(**{\n",
    "            'x': p.index,\n",
    "            'y': p[col],\n",
    "            'name': col,\n",
    "            'type': 'barh'\n",
    "        })\n",
    "    ],\n",
    "}, for col in p.columns], kind='barh', layout=LayoutR, filename='test/barh-mg')\n",
    "\n",
    "#     'layout': LayoutR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">D</th>\n",
       "      <th colspan=\"4\" halign=\"left\">E</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>I1-one</th>\n",
       "      <th>I2-two</th>\n",
       "      <th>I3-three</th>\n",
       "      <th>I4-four</th>\n",
       "      <th>I1-one</th>\n",
       "      <th>I2-two</th>\n",
       "      <th>I3-three</th>\n",
       "      <th>I4-four</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar bar bar</th>\n",
       "      <th>one one one</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.840244</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.018895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.594734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.576189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three three three</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.722621</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.665297</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-2.419947</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.021346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two two two</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.335319</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.656017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.792627</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.789807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo foo foo</th>\n",
       "      <th>one one one</th>\n",
       "      <td>-0.693236</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.234695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.686581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.190331</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three three three</th>\n",
       "      <td>-0.638832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.624958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.748206</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.327225</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two two two</th>\n",
       "      <td>-0.391719</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.540170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.180333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.500611</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      D                                \\\n",
       "C                                I1-one    I2-two  I3-three   I4-four   \n",
       "A           B                                                           \n",
       "bar bar bar one one one             NaN -2.840244       NaN  0.018895   \n",
       "            three three three       NaN -0.722621       NaN  0.665297   \n",
       "            two two two             NaN  1.335319       NaN -0.656017   \n",
       "foo foo foo one one one       -0.693236       NaN  2.234695       NaN   \n",
       "            three three three -0.638832       NaN -0.624958       NaN   \n",
       "            two two two       -0.391719       NaN -0.540170       NaN   \n",
       "\n",
       "                                      E                                \n",
       "C                                I1-one    I2-two  I3-three   I4-four  \n",
       "A           B                                                          \n",
       "bar bar bar one one one             NaN -1.594734       NaN -0.576189  \n",
       "            three three three       NaN -2.419947       NaN  0.021346  \n",
       "            two two two             NaN -0.792627       NaN  0.789807  \n",
       "foo foo foo one one one        0.686581       NaN -1.190331       NaN  \n",
       "            three three three -0.748206       NaN -0.327225       NaN  \n",
       "            two two two       -0.180333       NaN -1.500611       NaN  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, index=['A','B'], columns='C',values=['D','E'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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  "language_info": {
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