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 0x128e791d0>

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
809 Jordan 1977 1937652.0 Asia 61.13400 2852.351568
373 Croatia 1957 3991242.0 Europe 64.77000 4338.231617
152 Bosnia and Herzegovina 1992 4256013.0 Europe 72.17800 2546.781445
1463 Swaziland 2007 1133066.0 Africa 39.61300 4513.480643
1399 Somalia 1987 6921858.0 Africa 44.50100 1093.244963
1115 Nicaragua 2007 5675356.0 Americas 72.89900 2749.320965
289 China 1957 637408000.0 Asia 50.54896 575.987001
1096 New Zealand 1972 2929100.0 Oceania 71.89000 16046.037280
187 Bulgaria 1987 8971958.0 Europe 71.34000 8239.854824
888 Liberia 1952 863308.0 Africa 38.48000 575.572996

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