Gapminders#

import param
import numpy as np 
import pandas as pd
import panel as pn

import altair as alt
import plotly.graph_objs as go
import plotly.io as pio
import matplotlib.pyplot as plt

pn.extension('vega', 'plotly', defer_load=True, template='fast')
import hvplot.pandas

Configuration#

Let us start by configuring some high-level variables and configure the template:

XLABEL = 'GDP per capita (2000 dollars)'
YLABEL = 'Life expectancy (years)'
YLIM = (20, 90)
ACCENT = "#00A170"

PERIOD = 1000 # milliseconds

pn.state.template.param.update(
    site_url="https://panel.holoviz.org",
    title="Hans Rosling's Gapminder",
    header_background=ACCENT,
    accent_base_color=ACCENT,
    favicon="static/extensions/panel/images/favicon.ico",
    theme_toggle=False
)
<param.parameterized._ParametersRestorer object at 0x112314250>

Extract the dataset#

First, we’ll get the data into a Pandas dataframe. We use the built in cache to speed up the app.

@pn.cache
def get_dataset():
    url = 'https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv'
    return pd.read_csv(url)

dataset = get_dataset()

YEARS = [int(year) for year in dataset.year.unique()]

dataset.sample(10)
country year pop continent lifeExp gdpPercap
638 Haiti 1962 3880130.0 Americas 43.59000 1796.589032
829 Korea, Dem. Rep. 1957 9411381.0 Asia 54.08100 1571.134655
883 Lesotho 1987 1599200.0 Africa 57.18000 773.993214
1630 Uruguay 2002 3363085.0 Americas 75.30700 7727.002004
290 China 1962 665770000.0 Asia 44.50136 487.674018
63 Australia 1967 11872264.0 Oceania 71.10000 14526.124650
875 Lebanon 2007 3921278.0 Asia 71.99300 10461.058680
1041 Mozambique 1997 16603334.0 Africa 46.34400 472.346077
881 Lesotho 1977 1251524.0 Africa 52.20800 745.369541
727 Iran 1987 51889696.0 Asia 63.04000 6642.881371

Set up widgets and description#

Next we will set up a periodic callback to allow cycling through the years, set up the widgets to control the application and write an introduction:

def play():
    if year.value == YEARS[-1]:
        year.value = YEARS[0]
        return

    index = YEARS.index(year.value)
    year.value = YEARS[index+1]    

year = pn.widgets.DiscreteSlider(
    value=YEARS[-1], options=YEARS, name="Year", width=280
)
show_legend = pn.widgets.Checkbox(value=True, name="Show Legend")

periodic_callback = pn.state.add_periodic_callback(play, start=False, period=PERIOD)
player = pn.widgets.Checkbox.from_param(periodic_callback.param.running, name="Autoplay")

widgets = pn.Column(year, player, show_legend, margin=(0,15))

desc = """## 🎓 Info

The [Panel](http://panel.holoviz.org) library from [HoloViz](http://holoviz.org)
lets you make widget-controlled apps and dashboards from a wide variety of 
plotting libraries and data types. Here you can try out four different plotting libraries
controlled by a couple of widgets, for Hans Rosling's 
[gapminder](https://demo.bokeh.org/gapminder) example.

Source: [pyviz-topics - gapminder](https://github.com/pyviz-topics/examples/blob/master/gapminders/gapminders.ipynb)
"""

settings = pn.Column(
    "## ⚙️ Settings", widgets, desc,
    sizing_mode='stretch_width'
).servable(area='sidebar')

settings

Define plotting functions#

Now let’s define helper functions and functions to plot this dataset with Matplotlib, Plotly, Altair, and hvPlot (using HoloViews and Bokeh).

@pn.cache
def get_data(year):
    df = dataset[(dataset.year==year) & (dataset.gdpPercap < 10000)].copy()
    df['size'] = np.sqrt(df['pop']*2.666051223553066e-05)
    df['size_hvplot'] = df['size']*6
    return df

def get_title(library, year):
    return f"{library}: Life expectancy vs. GDP, {year}"

def get_xlim(data):
    return (data['gdpPercap'].min()-100,data['gdpPercap'].max()+1000)

@pn.cache
def mpl_view(year=1952, show_legend=True):
    data = get_data(year)
    title = get_title("Matplotlib", year)
    xlim = get_xlim(data)

    plot = plt.figure(figsize=(10, 6), facecolor=(0, 0, 0, 0))
    ax = plot.add_subplot(111)
    ax.set_xscale("log")
    ax.set_title(title)
    ax.set_xlabel(XLABEL)
    ax.set_ylabel(YLABEL)
    ax.set_ylim(YLIM)
    ax.set_xlim(xlim)

    for continent, df in data.groupby('continent'):
        ax.scatter(df.gdpPercap, y=df.lifeExp, s=df['size']*5,
                   edgecolor='black', label=continent)

    if show_legend:
        ax.legend(loc=4)

    plt.close(plot)
    return plot

pio.templates.default = None

@pn.cache
def plotly_view(year=1952, show_legend=True):
    data = get_data(year)
    title = get_title("Plotly", year)
    xlim = get_xlim(data)

    traces = []
    for continent, df in data.groupby('continent'):
        marker=dict(symbol='circle', sizemode='area', sizeref=0.1, size=df['size'], line=dict(width=2))
        traces.append(go.Scatter(x=df.gdpPercap, y=df.lifeExp, mode='markers', marker=marker, name=continent, text=df.country))

    axis_opts = dict(gridcolor='rgb(255, 255, 255)', zerolinewidth=1, ticklen=5, gridwidth=2)
    layout = go.Layout(
        title=title, showlegend=show_legend,
        xaxis=dict(title=XLABEL, type='log', **axis_opts),
        yaxis=dict(title=YLABEL, **axis_opts),
        autosize=True, paper_bgcolor='rgba(0,0,0,0)',
    )
    
    return go.Figure(data=traces, layout=layout)

@pn.cache
def altair_view(year=1952, show_legend=True, height="container", width="container"):
    data = get_data(year)
    title = get_title("Altair/ Vega", year)
    xlim = get_xlim(data)
    legend= ({} if show_legend else {'legend': None})
    return (
        alt.Chart(data)
            .mark_circle().encode(
                alt.X('gdpPercap:Q', scale=alt.Scale(type='log'), axis=alt.Axis(title=XLABEL)),
                alt.Y('lifeExp:Q', scale=alt.Scale(zero=False, domain=YLIM), axis=alt.Axis(title=YLABEL)),
                size=alt.Size('pop:Q', scale=alt.Scale(type="log"), legend=None),
                color=alt.Color('continent', scale=alt.Scale(scheme="category10"), **legend),
                tooltip=['continent','country'])
            .configure_axis(grid=False)
            .properties(title=title, height=height, width=width, background='rgba(0,0,0,0)') 
            .configure_view(fill="white")
            .interactive()
    )

@pn.cache
def hvplot_view(year=1952, show_legend=True):
    data = get_data(year)
    title = get_title("hvPlot/ Bokeh", year)
    xlim = get_xlim(data)
    return data.hvplot.scatter(
        'gdpPercap', 'lifeExp', by='continent', s='size_hvplot', alpha=0.8,
        logx=True, title=title, responsive=True, legend='bottom_right',
        hover_cols=['country'], ylim=YLIM, xlim=xlim, ylabel=YLABEL, xlabel=XLABEL
    )

Bind the plot functions to the widgets#

mpl_view    = pn.bind(mpl_view,    year=year, show_legend=show_legend)
plotly_view = pn.bind(plotly_view, year=year, show_legend=show_legend)
altair_view = pn.bind(altair_view, year=year, show_legend=show_legend)
hvplot_view = pn.bind(hvplot_view, year=year, show_legend=show_legend)

plots = pn.GridBox(
    pn.pane.HoloViews(hvplot_view, sizing_mode='stretch_both', margin=10),
    pn.pane.Plotly(plotly_view, sizing_mode='stretch_both', margin=10),
    pn.pane.Matplotlib(mpl_view, format='png', sizing_mode='scale_both', tight=True, margin=10),
    pn.pane.Vega(altair_view, sizing_mode='stretch_both', margin=10),
    ncols=2,
    sizing_mode="stretch_both"
).servable()

plots