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tracek/wealth.ipynb

Created March 29, 2018 13:04
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wealth.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a `bqplot` recreation of Mike Bostock's [Wealth of Nations](https://bost.ocks.org/mike/nations/). This was also done by [Gapminder](http://www.gapminder.org/world/#$majorMode=chart$is;shi=t;ly=2003;lb=f;il=t;fs=11;al=30;stl=t;st=t;nsl=t;se=t$wst;tts=C$ts;sp=5.59290322580644;ti=2013$zpv;v=0$inc_x;mmid=XCOORDS;iid=phAwcNAVuyj1jiMAkmq1iMg;by=ind$inc_y;mmid=YCOORDS;iid=phAwcNAVuyj2tPLxKvvnNPA;by=ind$inc_s;uniValue=8.21;iid=phAwcNAVuyj0XOoBL_n5tAQ;by=ind$inc_c;uniValue=255;gid=CATID0;by=grp$map_x;scale=log;dataMin=194;dataMax=96846$map_y;scale=lin;dataMin=23;dataMax=86$map_s;sma=49;smi=2.65$cd;bd=0$inds=;modified=60). It is originally based on a TED Talk by [Hans Rosling](http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import os\n",
"\n",
"from bqplot import (\n",
" LogScale, LinearScale, OrdinalColorScale, ColorAxis,\n",
" Axis, Scatter, Lines, CATEGORY10, Label, Figure, Tooltip\n",
")\n",
"\n",
"from ipywidgets import HBox, VBox, IntSlider, Play, jslink"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"initial_year = 1800"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Cleaning and Formatting JSON Data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"scrolled": false
},
"outputs": [],
"source": [
"data = pd.read_json(os.path.abspath('../data_files/nations.json'))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def clean_data(data):\n",
" for column in ['income', 'lifeExpectancy', 'population']:\n",
" data = data.drop(data[data[column].apply(len) <= 4].index)\n",
" return data\n",
"\n",
"def extrap_interp(data):\n",
" data = np.array(data)\n",
" x_range = np.arange(1800, 2009, 1.)\n",
" y_range = np.interp(x_range, data[:, 0], data[:, 1])\n",
" return y_range\n",
"\n",
"def extrap_data(data):\n",
" for column in ['income', 'lifeExpectancy', 'population']:\n",
" data[column] = data[column].apply(extrap_interp)\n",
" return data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = clean_data(data)\n",
"data = extrap_data(data)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"scrolled": true
},
"outputs": [],
"source": [
"income_min, income_max = np.min(data['income'].apply(np.min)), np.max(data['income'].apply(np.max))\n",
"life_exp_min, life_exp_max = np.min(data['lifeExpectancy'].apply(np.min)), np.max(data['lifeExpectancy'].apply(np.max))\n",
"pop_min, pop_max = np.min(data['population'].apply(np.min)), np.max(data['population'].apply(np.max))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def get_data(year):\n",
" year_index = year - 1800\n",
" income = data['income'].apply(lambda x: x[year_index])\n",
" life_exp = data['lifeExpectancy'].apply(lambda x: x[year_index])\n",
" pop = data['population'].apply(lambda x: x[year_index])\n",
" return income, life_exp, pop"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating the Tooltip to display the required fields\n",
"\n",
"`bqplot`'s native `Tooltip` allows us to simply display the data fields we require on a mouse-interaction."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"tt = Tooltip(fields=['name', 'x', 'y'], labels=['Country Name', 'Income per Capita', 'Life Expectancy'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating the Label to display the year\n",
"\n",
"Staying true to the `d3` recreation of the talk, we place a `Label` widget in the bottom-right of the `Figure` (it inherits the `Figure` co-ordinates when no scale is passed to it). With `enable_move` set to `True`, the `Label` can be dragged around. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"year_label = Label(x=[0.75], y=[0.10], default_size=46, font_weight='bolder', colors=['orange'],\n",
" text=[str(initial_year)], enable_move=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Defining Axes and Scales\n",
"\n",
"The inherent skewness of the income data favors the use of a `LogScale`. Also, since the color coding by regions does not follow an ordering, we use the `OrdinalColorScale`."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_sc = LogScale(min=income_min, max=income_max)\n",
"y_sc = LinearScale(min=life_exp_min, max=life_exp_max)\n",
"c_sc = OrdinalColorScale(domain=data['region'].unique().tolist(), colors=CATEGORY10[:6])\n",
"size_sc = LinearScale(min=pop_min, max=pop_max)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ax_y = Axis(label='Life Expectancy', scale=y_sc, orientation='vertical', side='left', grid_lines='solid')\n",
"ax_x = Axis(label='Income per Capita', scale=x_sc, grid_lines='solid')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating the Scatter Mark with the appropriate size and color parameters passed\n",
"\n",
"To generate the appropriate graph, we need to pass the population of the country to the `size` attribute and its region to the `color` attribute."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Start with the first year's data\n",
"cap_income, life_exp, pop = get_data(initial_year)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"wealth_scat = Scatter(x=cap_income, y=life_exp, color=data['region'], size=pop,\n",
" names=data['name'], display_names=False,\n",
" scales={'x': x_sc, 'y': y_sc, 'color': c_sc, 'size': size_sc},\n",
" default_size=4112, tooltip=tt, animate=True, stroke='Black',\n",
" unhovered_style={'opacity': 0.5})"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"nation_line = Lines(x=data['income'][0], y=data['lifeExpectancy'][0], colors=['Gray'],\n",
" scales={'x': x_sc, 'y': y_sc}, visible=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Creating the Figure"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"time_interval = 10"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fig = Figure(marks=[wealth_scat, year_label, nation_line], axes=[ax_x, ax_y],\n",
" title='Health and Wealth of Nations', animation_duration=time_interval)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Using a Slider to allow the user to change the year and a button for animation\n",
"\n",
"Here we see how we can seamlessly integrate `bqplot` into the jupyter widget infrastructure. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"year_slider = IntSlider(min=1800, max=2008, step=1, description='Year', value=initial_year)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When the `hovered_point` of the `Scatter` plot is changed (i.e. when the user hovers over a different element), the entire path of that country is displayed by making the `Lines` object visible and setting it's `x` and `y` attributes."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def hover_changed(change):\n",
" if change.new is not None:\n",
" nation_line.x = data[data['name'] == wealth_scat.names[change.new]]['income'].values[0]\n",
" nation_line.y = data[data['name'] == wealth_scat.names[change.new]]['lifeExpectancy'].values[0]\n",
" nation_line.visible = True\n",
" else:\n",
" nation_line.visible = False\n",
" \n",
"wealth_scat.observe(hover_changed, 'hovered_point')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On the slider value `callback` (a function that is triggered everytime the `value` of the slider is changed) we change the `x`, `y` and `size` co-ordinates of the `Scatter`. We also update the `text` of the `Label` to reflect the current year."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def year_changed(change):\n",
" wealth_scat.x, wealth_scat.y, wealth_scat.size = get_data(year_slider.value)\n",
" year_label.text = [str(year_slider.value)]\n",
"\n",
"year_slider.observe(year_changed, 'value')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Add an animation button"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"play_button = Play(min=1800, max=2008, interval=time_interval)\n",
"jslink((play_button, 'value'), (year_slider, 'value'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Displaying the GUI"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3b8da479329a46d6b83e1d7ea75a93a2",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>VBox</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"VBox(children=(HBox(children=(Play(value=1800, interval=10, max=2008, min=1800), IntSlider(value=1800, description='Year', max=2008, min=1800))), Figure(animation_duration=10, axes=[Axis(label='Income per Capita', scale=LogScale(max=119849.29, min=281.91), side='bottom'), Axis(label='Life Expectancy', orientation='vertical', scale=LinearScale(max=82.87, min=12.7), side='left')], fig_margin={'top': 60, 'bottom': 60, 'left': 60, 'right': 60}, layout=Layout(min_width='125px'), marks=[Scatter(color=array(['Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'Sub-Saharan Africa', 'Sub-Saharan Africa', 'Sub-Saharan Africa',\n",
" 'South Asia', 'South Asia', 'South Asia', 'South Asia',\n",
" 'South Asia', 'South Asia', 'South Asia', 'South Asia',\n",
" 'Middle East & North Africa', 'Middle East & North Africa',\n",
" 'Middle East & North Africa', 'Middle East & North Africa',\n",
" 'Middle East & North Africa', 'Middle East & North Africa',\n",
" 'Middle East & North Africa', 'Middle East & North Africa',\n",
" 'Middle East & North Africa', 'Middle East & North Africa',\n",
" 'Middle East & North Africa', 'Middle East & North Africa',\n",
" 'Middle East & North Africa', 'Middle East & North Africa',\n",
" 'Middle East & North Africa', 'Middle East & North Africa',\n",
" 'Middle East & North Africa', 'America', 'America', 'America',\n",
" 'America', 'America', 'America', 'America', 'America', 'America',\n",
" 'America', 'America', 'America', 'America', 'America', 'America',\n",
" 'America', 'America', 'America', 'America', 'America', 'America',\n",
" 'America', 'America', 'America', 'America', 'America', 'America',\n",
" 'America', 'America', 'America', 'America', 'America', 'America',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'Europe & Central Asia', 'Europe & Central Asia',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific', 'East Asia & Pacific',\n",
" 'East Asia & Pacific'], dtype=object), colors=['steelblue'], default_size=4112, display_names=False, interactions={'hover': 'tooltip'}, names=array(['Angola', 'Benin', 'Botswana', 'Burkina Faso', 'Burundi',\n",
" 'Cameroon', 'Cape Verde', 'Chad', 'Comoros', 'Congo, Dem. Rep.',\n",
" 'Congo, Rep.', \"Cote d'Ivoire\", 'Equatorial Guinea', 'Eritrea',\n",
" 'Ethiopia', 'Gabon', 'Ghana', 'Guinea', 'Guinea-Bissau', 'Kenya',\n",
" 'Lesotho', 'Liberia', 'Madagascar', 'Malawi', 'Mali', 'Mauritania',\n",
" 'Mauritius', 'Mozambique', 'Namibia', 'Niger', 'Nigeria',\n",
" 'Reunion', 'Rwanda', 'Sao Tome and Principe', 'Senegal',\n",
" 'Sierra Leone', 'Somalia', 'South Africa', 'Sudan', 'Swaziland',\n",
" 'Tanzania', 'Togo', 'Uganda', 'Zambia', 'Zimbabwe', 'Afghanistan',\n",
" 'Bangladesh', 'Bhutan', 'India', 'Maldives', 'Nepal', 'Pakistan',\n",
" 'Sri Lanka', 'Algeria', 'Bahrain', 'Djibouti', 'Iraq', 'Israel',\n",
" 'Jordan', 'Kuwait', 'Lebanon', 'Libya', 'Morocco', 'Oman', 'Qatar',\n",
" 'Saudi Arabia', 'Tunisia', 'United Arab Emirates',\n",
" 'West Bank and Gaza', 'Yemen, Rep.', 'Argentina', 'Aruba',\n",
" 'Barbados', 'Belize', 'Bolivia', 'Brazil', 'Canada', 'Chile',\n",
" 'Colombia', 'Costa Rica', 'Cuba', 'Ecuador', 'El Salvador',\n",
" 'French Guiana', 'Grenada', 'Guadeloupe', 'Guatemala', 'Guyana',\n",
" 'Haiti', 'Honduras', 'Jamaica', 'Martinique', 'Mexico',\n",
" 'Netherlands Antilles', 'Nicaragua', 'Panama', 'Paraguay', 'Peru',\n",
" 'Puerto Rico', 'Suriname', 'Trinidad and Tobago', 'United States',\n",
" 'Uruguay', 'Albania', 'Armenia', 'Austria', 'Azerbaijan',\n",
" 'Belarus', 'Belgium', 'Bosnia and Herzegovina', 'Bulgaria',\n",
" 'Croatia', 'Cyprus', 'Denmark', 'Estonia', 'Finland', 'France',\n",
" 'Georgia', 'Germany', 'Greece', 'Hungary', 'Iceland', 'Ireland',\n",
" 'Italy', 'Kazakhstan', 'Latvia', 'Lithuania', 'Luxembourg',\n",
" 'Macedonia, FYR', 'Malta', 'Moldova', 'Montenegro', 'Netherlands',\n",
" 'Norway', 'Poland', 'Portugal', 'Romania', 'Serbia',\n",
" 'Slovak Republic', 'Slovenia', 'Spain', 'Sweden', 'Switzerland',\n",
" 'Tajikistan', 'Turkey', 'Turkmenistan', 'Ukraine',\n",
" 'United Kingdom', 'Uzbekistan', 'Australia', 'Brunei', 'Cambodia',\n",
" 'China', 'Fiji', 'French Polynesia', 'Hong Kong, China',\n",
" 'Indonesia', 'Japan', 'Korea, Dem. Rep.', 'Korea, Rep.',\n",
" 'Macao, China', 'Malaysia', 'Micronesia, Fed. Sts.', 'Mongolia',\n",
" 'Myanmar', 'New Caledonia', 'New Zealand', 'Papua New Guinea',\n",
" 'Philippines', 'Samoa', 'Singapore', 'Solomon Islands', 'Taiwan',\n",
" 'Thailand', 'Timor-Leste', 'Tonga', 'Vietnam', 'Vanuatu'],\n",
" dtype=object), scales={'x': LogScale(max=119849.29, min=281.91), 'y': LinearScale(max=82.87, min=12.7), 'color': OrdinalColorScale(colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b'], domain=['Sub-Saharan Africa', 'South Asia', 'Middle East & North Africa', 'America', 'Europe & Central Asia', 'East Asia & Pacific']), 'size': LinearScale(max=1326856173.0, min=2128.0)}, scales_metadata={'x': {'orientation': 'horizontal', 'dimension': 'x'}, 'y': {'orientation': 'vertical', 'dimension': 'y'}, 'color': {'dimension': 'color'}, 'size': {'dimension': 'size'}, 'opacity': {'dimension': 'opacity'}, 'rotation': {'dimension': 'rotation'}, 'skew': {'dimension': 'skew'}}, size=array([1.5670280e+06, 6.3655900e+05, 1.2100000e+05, 1.6654210e+06,\n",
" 8.9909700e+05, 1.8600540e+06, 5.5716000e+04, 1.4320000e+06,\n",
" 5.6346000e+04, 5.1638190e+06, 3.1446500e+05, 1.0885300e+06,\n",
" 8.0377000e+04, 2.0501000e+05, 2.9489900e+06, 1.5822700e+05,\n",
" 2.0160340e+06, 9.8395900e+05, 2.1816700e+05, 2.5740000e+06,\n",
" 2.7636100e+05, 3.1354300e+05, 1.6830000e+06, 7.3700000e+05,\n",
" 1.4033980e+06, 3.8269600e+05, 1.8100000e+05, 2.0960000e+06,\n",
" 2.2900000e+05, 1.2448610e+06, 1.2100855e+07, 9.2744000e+04,\n",
" 9.2836800e+05, 2.2731000e+04, 1.0098860e+06, 7.9426400e+05,\n",
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" 1.0858140e+06, 3.2800000e+06, 2.0000000e+07, 3.9228700e+05,\n",
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" 3.3200000e+05, 5.3800000e+05, 2.6890000e+06, 3.1800000e+05,\n",
" 1.4092000e+04, 2.0910000e+06, 8.7500000e+05, 4.0153000e+04,\n",
" 1.6594400e+05, 2.5930000e+06, 5.3400000e+05, 1.9286000e+04,\n",
" 8.1729000e+04, 2.5526000e+04, 1.1000000e+06, 4.5070000e+06,\n",
" 8.1600000e+05, 7.7144700e+05, 1.2060000e+06, 5.3000000e+04,\n",
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