In [1]:

# from https://github.com/thu-vu92/python-dashboard-panel/blob/main/Interactive_dashboard.ipynb

In [1]:

### this may work as a pyscript example later

In [2]:

import pandas as pd
import numpy as np
import panel as pn
pn.extension('tabulator')
# pn.extension(sizing_mode = 'stretch_width')
import hvplot.pandas

In [3]:

# cache data to improve dashboard performance
if 'data' not in pn.state.cache.keys():

    df = pd.read_csv('https://raw.githubusercontent.com/owid/co2-data/master/owid-co2-data.csv')
    #df = pd.read_csv("docs/owid-co2-data.csv")

    pn.state.cache['data'] = df.copy()

else: 

    df = pn.state.cache['data']

In [4]:

df

Out[4]:

	iso_code	country	year	co2	co2_per_capita	trade_co2	cement_co2	cement_co2_per_capita	coal_co2	coal_co2_per_capita	...	ghg_excluding_lucf_per_capita	methane	methane_per_capita	nitrous_oxide	nitrous_oxide_per_capita	population	gdp	primary_energy_consumption	energy_per_capita	energy_per_gdp
0	AFG	Afghanistan	1949	0.015	0.002	NaN	NaN	NaN	0.015	0.002	...	NaN	NaN	NaN	NaN	NaN	7624058.0	NaN	NaN	NaN	NaN
1	AFG	Afghanistan	1950	0.084	0.011	NaN	NaN	NaN	0.021	0.003	...	NaN	NaN	NaN	NaN	NaN	7752117.0	9.421400e+09	NaN	NaN	NaN
2	AFG	Afghanistan	1951	0.092	0.012	NaN	NaN	NaN	0.026	0.003	...	NaN	NaN	NaN	NaN	NaN	7840151.0	9.692280e+09	NaN	NaN	NaN
3	AFG	Afghanistan	1952	0.092	0.012	NaN	NaN	NaN	0.032	0.004	...	NaN	NaN	NaN	NaN	NaN	7935996.0	1.001733e+10	NaN	NaN	NaN
4	AFG	Afghanistan	1953	0.106	0.013	NaN	NaN	NaN	0.038	0.005	...	NaN	NaN	NaN	NaN	NaN	8039684.0	1.063052e+10	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
25984	ZWE	Zimbabwe	2016	10.738	0.765	1.415	0.639	0.046	6.959	0.496	...	2.076	11.50	0.820	6.21	0.443	14030338.0	2.096179e+10	46.666	3326.073	2.226
25985	ZWE	Zimbabwe	2017	9.582	0.673	1.666	0.678	0.048	5.665	0.398	...	2.023	11.62	0.816	6.35	0.446	14236599.0	2.194784e+10	45.936	3226.617	2.093
25986	ZWE	Zimbabwe	2018	11.854	0.821	1.308	0.697	0.048	7.101	0.492	...	2.173	11.96	0.828	6.59	0.456	14438812.0	2.271535e+10	47.502	3289.887	2.091
25987	ZWE	Zimbabwe	2019	10.949	0.748	1.473	0.697	0.048	6.020	0.411	...	NaN	NaN	NaN	NaN	NaN	14645473.0	NaN	49.427	3374.877	NaN
25988	ZWE	Zimbabwe	2020	10.531	0.709	NaN	0.697	0.047	6.257	0.421	...	NaN	NaN	NaN	NaN	NaN	14862927.0	NaN	NaN	NaN	NaN

25989 rows × 60 columns

In [5]:

df[df['country'] == 'North America']

Out[5]:

	iso_code	country	year	co2	co2_per_capita	trade_co2	cement_co2	cement_co2_per_capita	coal_co2	coal_co2_per_capita	...	ghg_excluding_lucf_per_capita	methane	methane_per_capita	nitrous_oxide	nitrous_oxide_per_capita	population	gdp	primary_energy_consumption	energy_per_capita	energy_per_gdp
16785	NaN	North America	1785	0.004	NaN	NaN	NaN	NaN	0.004	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
16786	NaN	North America	1786	0.004	NaN	NaN	NaN	NaN	0.004	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
16787	NaN	North America	1787	0.004	NaN	NaN	NaN	NaN	0.004	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
16788	NaN	North America	1788	0.004	NaN	NaN	NaN	NaN	0.004	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
16789	NaN	North America	1789	0.004	NaN	NaN	NaN	NaN	0.004	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
17016	NaN	North America	2016	6471.360	11.353	309.904	70.858	0.124	1509.631	2.648	...	NaN	NaN	NaN	NaN	NaN	573814461.0	NaN	NaN	NaN	NaN
17017	NaN	North America	2017	6412.982	11.155	335.009	72.914	0.127	1467.602	2.553	...	NaN	NaN	NaN	NaN	NaN	578582963.0	NaN	NaN	NaN	NaN
17018	NaN	North America	2018	6588.630	11.366	345.915	74.787	0.129	1398.979	2.413	...	NaN	NaN	NaN	NaN	NaN	582921781.0	NaN	NaN	NaN	NaN
17019	NaN	North America	2019	6460.726	11.056	354.353	76.925	0.132	1214.329	2.078	...	NaN	NaN	NaN	NaN	NaN	587512621.0	NaN	NaN	NaN	NaN
17020	NaN	North America	2020	5775.159	9.805	NaN	76.870	0.130	965.430	1.639	...	NaN	NaN	NaN	NaN	NaN	592062319.0	NaN	NaN	NaN	NaN

236 rows × 60 columns

In [6]:

# Fill NAs with 0s and create GDP per capita column
df = df.fillna(0)
df['gdp_per_capita'] = np.where(df['population']!= 0, df['gdp']/ df['population'], 0)

In [7]:

# Make DataFrame Pipeline Interactive
idf = df.interactive()

(1) CO2 emission over time by continent¶

In [8]:

# Define Panel widgets
year_slider = pn.widgets.IntSlider(name='Year slider', start=1750, end=2020, step=5, value=1850)
year_slider

Out[8]:

In [9]:

# Radio buttons for CO2 measures
yaxis_co2 = pn.widgets.RadioButtonGroup(
    name='Y axis', 
    options=['co2', 'co2_per_capita',],
    button_type='success'
)

In [10]:

continents = ['World', 'Asia', 'Oceania', 'Europe', 'Africa', 'North America', 'South America', 'Antarctica']

co2_pipeline = (
    idf[
        (idf.year <= year_slider) &
        (idf.country.isin(continents))
    ]
    .groupby(['country', 'year'])[yaxis_co2].mean()
    .to_frame()
    .reset_index()
    .sort_values(by='year')  
    .reset_index(drop=True)
)

In [11]:

co2_pipeline

Out[11]:

In [12]:

co2_plot = co2_pipeline.hvplot(x = 'year', by='country', y=yaxis_co2,line_width=2, title="CO2 emission by continent")
co2_plot

Out[12]:

(2) Table - CO2 emission over time by continent¶

In [13]:

co2_table = co2_pipeline.pipe(pn.widgets.Tabulator, pagination='remote', page_size = 10, sizing_mode='stretch_width') 
co2_table

WARNING:param.main: DataTabulator was not imported on instantiation and may not render in a notebook. Restart the notebook kernel and ensure you load it as part of the extension using:

pn.extension('tabulator')

Out[13]:

(3) CO2 vs GDP scatterplot¶

In [14]:

co2_vs_gdp_scatterplot_pipeline = (
    idf[
        (idf.year == year_slider) &
        (~ (idf.country.isin(continents)))
    ]
    .groupby(['country', 'year', 'gdp_per_capita'])['co2'].mean()
    .to_frame()
    .reset_index()
    .sort_values(by='year')  
    .reset_index(drop=True)
)

In [15]:

co2_vs_gdp_scatterplot_pipeline

Out[15]:

In [16]:

co2_vs_gdp_scatterplot = co2_vs_gdp_scatterplot_pipeline.hvplot(x='gdp_per_capita', 
                                                                y='co2', 
                                                                by='country', 
                                                                size=80, kind="scatter", 
                                                                alpha=0.7,
                                                                legend=False, 
                                                                height=500, 
                                                                width=500)
co2_vs_gdp_scatterplot

Out[16]:

(4) Bar chart with CO2 sources by continent¶

In [17]:

yaxis_co2_source = pn.widgets.RadioButtonGroup(
    name='Y axis', 
    options=['coal_co2', 'oil_co2', 'gas_co2'], 
    button_type='success'
)

continents_excl_world = ['Asia', 'Oceania', 'Europe', 'Africa', 'North America', 'South America', 'Antarctica']

co2_source_bar_pipeline = (
    idf[
        (idf.year == year_slider) &
        (idf.country.isin(continents_excl_world))
    ]
    .groupby(['year', 'country'])[yaxis_co2_source].sum()
    .to_frame()
    .reset_index()
    .sort_values(by='year')  
    .reset_index(drop=True)
)

In [18]:

co2_source_bar_plot = co2_source_bar_pipeline.hvplot(kind='bar', 
                                                     x='country', 
                                                     y=yaxis_co2_source, 
                                                     title='CO2 source by continent')
co2_source_bar_plot

Out[18]:

Creating Dashboard¶

In [ ]:

#Layout using Template
#before: template = pn.template.FastListTemplate(
template = pn.template.fast.list.FastListTemplate(
    title='World CO2 emission dashboard', 
    sidebar=[pn.pane.Markdown("# CO2 Emissions and Climate Change"), 
             pn.pane.Markdown("#### Carbon dioxide emissions are the primary driver of global climate change. It’s widely recognised that to avoid the worst impacts of climate change, the world needs to urgently reduce emissions. But, how this responsibility is shared between regions, countries, and individuals has been an endless point of contention in international discussions."), 
             pn.pane.PNG('https://github.com/thu-vu92/python-dashboard-panel/raw/main/climate_day.png', sizing_mode='scale_both'),
             pn.pane.Markdown("## Settings"),
             year_slider],
    main=[pn.Row(pn.Column(yaxis_co2, 
                           co2_plot.panel(width=600), margin=(0,25)), 
                 co2_table.panel(width=400)), 
          pn.Row(pn.Column(co2_vs_gdp_scatterplot.panel(width=600), margin=(0,25)), 
                 pn.Column(yaxis_co2_source, co2_source_bar_plot.panel(width=500)))],
    accent_base_color="#88d8b0",
    header_background="#88d8b0",
)
# template.show()
template.servable()