Bqplot in HTML

requirements.txt

#
# This file is autogenerated by pip-compile
# To update, run:
#
#    pip-compile
#
numpy>=1, <2
scipy>=1, <2
IPython>=6, <8
nbconvert>=5.3, <6
traitlets>=4.3, <5
toolz>=0.8, <1
docopt>=0.6.2, <1
nbformat>=4.4.0, <5
Jinja2>=2.10, <3
bqplot==0.11.0
ipywidgets>=7.5.0, <8
xlrd>= 1.0.0, <2

Terminal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import ipywidgets as widgets\n",
    "from bqplot import pyplot as plt\n",
    "from bqplot import topo_load\n",
    "from bqplot.interacts import panzoom\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initializing data to be plotted\n",
    "np.random.seed(0)\n",
    "size = 100\n",
    "y_data = np.cumsum(np.random.randn(size) * 100.0)\n",
    "y_data_2 = np.cumsum(np.random.randn(size))\n",
    "y_data_3 = np.cumsum(np.random.randn(size) * 100.)\n",
    "\n",
    "x = np.linspace(0.0, 10.0, size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_data = pd.DataFrame(np.cumsum(np.random.randn(150, 2).dot([[0.5, 0.8], [0.8, 1.0]]), axis=0) + 100,\n",
    "                          columns=['Security 1', 'Security 2'],\n",
    "                          index=pd.date_range(start='01-01-2007', periods=150))\n",
    "\n",
    "symbol = 'Security 1'\n",
    "dates_all = price_data.index.values\n",
    "final_prices = price_data[symbol].values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "price_data.index.names = ['date']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y_data)\n",
    "plt.xlabel('Time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = plt.ylabel('Stock Price')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the title for the current figure\n",
    "plt.title('Brownian Increments')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot('Security 1', data=price_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(title='Scatter Plot with colors')\n",
    "plt.scatter(y_data_2, y_data_3, color=y_data, stroke='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Horizontal and Vertical Lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adding a horizontal line at y=0\n",
    "plt.hline(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adding a vertical line at x=4 with stroke_width and colors being passed.\n",
    "plt.vline(4., stroke_width=2, colors=['orangered'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.scatter('Security 1', 'Security 2', color='date', data=price_data.reset_index(), stroke='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist(y_data, colors=['OrangeRed'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.hist('Security 1', data=price_data, colors=['MediumSeaGreen'])\n",
    "plt.xlabel('Hello')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bar Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "bar_x=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'U']\n",
    "plt.bar(bar_x, y_data_3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.bar('date', 'Security 2', data=price_data.reset_index()[:10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "d = abs(y_data_2[:5])\n",
    "plt.pie(d)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.pie('Security 2', color='Security 1', data=price_data[:4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OHLC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = np.arange(dt.datetime(2014, 1, 2), dt.datetime(2014, 1, 30), dt.timedelta(days=1))\n",
    "\n",
    "prices = np.array([[ 187.21  ,  187.4   ,  185.2   ,  185.53  ],\n",
    "       [ 185.83  ,  187.35  ,  185.3   ,  186.64  ],\n",
    "       [ 187.15  ,  187.355 ,  185.3   ,  186.    ],\n",
    "       [ 186.39  ,  190.35  ,  186.38  ,  189.71  ],\n",
    "       [ 189.33  ,  189.4175,  187.26  ,  187.97  ],\n",
    "       [ 189.02  ,  189.5   ,  186.55  ,  187.38  ],\n",
    "       [ 188.31  ,  188.57  ,  186.28  ,  187.26  ],\n",
    "       [ 186.26  ,  186.95  ,  183.86  ,  184.16  ],\n",
    "       [ 185.06  ,  186.428 ,  183.8818,  185.92  ],\n",
    "       [ 185.82  ,  188.65  ,  185.49  ,  187.74  ],\n",
    "       [ 187.53  ,  188.99  ,  186.8   ,  188.76  ],\n",
    "       [ 188.04  ,  190.81  ,  187.86  ,  190.09  ],\n",
    "       [ 190.23  ,  190.39  ,  186.79  ,  188.43  ],\n",
    "       [ 181.28  ,  183.5   ,  179.67  ,  182.25  ],\n",
    "       [ 181.43  ,  183.72  ,  180.71  ,  182.73  ],\n",
    "       [ 181.25  ,  182.8141,  179.64  ,  179.64  ],\n",
    "       [ 179.605 ,  179.65  ,  177.66  ,  177.9   ],\n",
    "       [ 178.05  ,  178.45  ,  176.16  ,  176.85  ],\n",
    "       [ 175.98  ,  178.53  ,  175.89  ,  176.4   ],\n",
    "       [ 177.17  ,  177.86  ,  176.36  ,  177.36  ]])\n",
    "\n",
    "plt.figure()\n",
    "plt.ohlc(dates, prices)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.boxplot(np.arange(10), np.random.randn(10, 100))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.geo(map_data='WorldMap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(padding_y=0)\n",
    "plt.heatmap(x * x[:, np.newaxis])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GridHeatMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(padding_y=0)\n",
    "plt.gridheatmap(x[:10] * x[:10, np.newaxis])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(dates_all, final_prices)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Editing existing axes properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adding grid lines and changing the side of the axis in the figure above\n",
    "_ = plt.axes(options={'x': {'grid_lines': 'solid'}, 'y': {'side': 'right', 'grid_lines': 'dashed'}})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Advanced Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Marks on the same Figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y_data_3, colors=['orangered'])\n",
    "plt.scatter(x, y_data, stroke='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using marker strings in Line Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mark_x = np.arange(10)\n",
    "plt.figure(title='Using Marker Strings')\n",
    "plt.plot(mark_x, 3 * mark_x + 5, 'g-.s') # color=green, line_style=dash_dotted, marker=square\n",
    "plt.plot(mark_x ** 2, 'm:d') # color=magenta, line_style=None, marker=diamond\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Partially changing the scales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "plt.plot(x, y_data)\n",
    "\n",
    "## preserving the x scale and changing the y scale\n",
    "plt.scales(scales={'x': plt.Keep})\n",
    "plt.plot(x, y_data_2, colors=['orange'], axes_options={'y': {'side': 'right', 'color': 'orange',\n",
    "                                                             'grid_lines': 'none'}})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a label to the chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "line = plt.plot(dates_all, final_prices)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adds the label to the figure created above\n",
    "_ = plt.label(['Pie Day'], x=[np.datetime64('2007-03-14')], y=[final_prices.mean()], scales=line.scales,\n",
    "              colors=['orangered'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changing context figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1)\n",
    "plt.plot(x,y_data_3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(2)\n",
    "plt.plot(x[:20],y_data_3[:20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Re-editing first figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## adds the new line to the first figure\n",
    "fig = plt.figure(1, title='New title')\n",
    "plt.plot(x,y_data, colors=['orangered'])\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing the properties of the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "marks = plt.current_figure().marks\n",
    "marks[0].get_state()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing a second view of the first figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clearing the figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Clearing the figure above\n",
    "plt.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deleting a figure and all its views. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.show(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.close(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactions in Pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brush Selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "plt.scatter(y_data_2, y_data_3, colors=['orange'], stroke='black')\n",
    "\n",
    "label = widgets.Label()\n",
    "\n",
    "def callback(name, value):\n",
    "    label.value = str(value)\n",
    "## click and drag on the figure to see the selector\n",
    "plt.brush_selector(callback)\n",
    "\n",
    "widgets.VBox([fig, label])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fast Interval Selector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = plt.figure()\n",
    "n = 100\n",
    "plt.plot(np.arange(n), y_data_3)\n",
    "label = widgets.Label()\n",
    "\n",
    "def callback(name, value):\n",
    "    label.value = str(value)\n",
    "\n",
    "## click on the figure to activate the selector\n",
    "plt.int_selector(callback)\n",
    "\n",
    "widgets.VBox([fig, label])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Brush Interval Selector with call back on brushing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# click and drag on chart to make a selection\n",
    "def callback(name, value):\n",
    "    label.value = 'Brushing: ' + str(value)\n",
    "_ = plt.brush_int_selector(callback, 'brushing')"
   ]
  }
 ],
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