work bqplot in nbveiwer.

bqplot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import bqplot.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = 100\n",
    "scale = 100.\n",
    "np.random.seed(0)\n",
    "x_data = np.arange(size)\n",
    "y_data = np.cumsum(np.random.randn(size)  * scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f90cd08cf1045adbde3e5ad28ac4dbc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top':…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(title='First Example')\n",
    "plt.plot(y_data)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This image can be saved by calling the `save_png` function of the `Figure` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.save_png()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Chart with dates as x data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = np.arange('2005-02', '2005-03', dtype='datetime64[D]')\n",
    "size = len(dates)\n",
    "prices = scale + 5 * np.cumsum(np.random.randn(size))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6398587bfa36440ca9efebbb50795125",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(label='Date', scale=DateScale(), tick_format='%m/%d'), Axis(label='Price', orientation='vert…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(title='Changing Styles', background_style={'fill': 'lightgreen'},\n",
    "                 title_style={'font-size': '20px','fill': 'DarkOrange'})\n",
    "axes_options = {'x': {'label': 'Date', 'tick_format': '%m/%d'},\n",
    "                'y': {'label': 'Price', 'tick_format': '0.0f'}}\n",
    "plt.plot(dates, prices, 'b', axes_options=axes_options) # third argument is the marker string\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f299a1fdc9494ef3a71edb148372632c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(scale=LinearScale()), Axis(orientation='vertical', scale=LinearScale())], fig_margin={'top':…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes_options = {'x': {'label': 'Date', 'tick_format': '%m/%d'},\n",
    "                'y': {'label': 'Price', 'tick_format': '0.0f'}}\n",
    "\n",
    "plt.scatter(x_data, y_data, colors=['red'], stroke='black')\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c55302fb2ad74a80a5b879b3b57a1978",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(orientation='vertical', scale=LinearScale()), Axis(scale=LinearScale())], fig_margin={'top':…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "plt.hist(y_data)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2394e2e4861b471e8d69d341df16219e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Figure(axes=[Axis(label='X', scale=OrdinalScale()), Axis(label='Y', orientation='vertical', scale=LinearScale(…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import string\n",
    "\n",
    "fig = plt.figure(padding_x=0)\n",
    "axes_options = {'x': {'label': 'X'}, 'y': {'label': 'Y'}}\n",
    "plt.bar(x=list(string.ascii_uppercase), y=np.abs(y_data[:20]), axes_options=axes_options)\n",
    "fig"
   ]
  }
 ],
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