def create_grouped_kde(df, col, target=None, by_class=True, size=175,
ylabel=None, xlabel=None):
"""
Create an altair chart showing the kernel density estimate of a variable
with the option to plot a separate distribution by each class.
Parameters
----------
df : pandas DataFrame
col : string
The column of df to be plotted
target : string
The column of df that holds target labels
by_class : boolean, default True
Whether to plot data by class
size : integer
Size (width & height) of the returned plot
ylabel : string
xlabel : string
Returns
-------
Altair chart
"""
ylabel = ylabel if ylabel is not None else "density"
xlabel = xlabel if xlabel is not None else col
if by_class:
labels = df[target].unique()
colors = ["DarkMagenta", "MediumOrchid", "RebeccaPurple"]
chart = alt.Chart(df, title=col).transform_density(
density=col, counts = True, groupby=[target],
steps=len(df),extent=[df[col].min() * 0.8,
df[col].max() * 1.2], as_=[col, "density"]
).mark_area(
opacity=0.7,
line=alt.OverlayMarkDef(stroke="black", strokeWidth=3)
).encode(
x=alt.X(f"{col}:Q", title=xlabel),
y=alt.Y("density:Q", title=ylabel),
color=alt.Color(f"{target}:N", scale=alt.Scale(domain=labels,
range=colors[:len(labels)]))
)
else:
chart = alt.Chart(df, title=col).transform_density(
density=col, counts = True, steps=len(df),
extent=[df[col].min() * 0.8, df[col].max() * 1.2],
as_=[col, "density"]
).mark_area(
opacity=0.8,
line=alt.OverlayMarkDef(stroke="black",
strokeWidth=3, fill="DarkMagenta")
).encode(
x=alt.X(f"{col}:Q", title=xlabel),
y=alt.Y("density:Q", title=ylabel)
)
return chart.properties(width=size, height=size)
def make_chart(self, df):
sort_order = ['Confirmados', 'Probables', 'Muertes']
lines = alt.Chart(df).mark_line(
strokeWidth=3, point=alt.OverlayMarkDef(size=50)).encode(
x=alt.X('yearmonthdate(bulletin_date):O',
title="Fecha boletín",
axis=alt.Axis(format='%d/%m', titlePadding=10)),
y=alt.Y('value:Q', title=None),
color=alt.Color('variable', sort=sort_order, legend=None),
tooltip=[
'variable', 'bulletin_date',
alt.Tooltip(field='value',
type='quantitative',
format=".1f")
])
text = lines.mark_text(
align='center', baseline='line-top', size=15,
dy=10).encode(text=alt.Text('value:Q', format='.1f'))
return (lines + text).properties(width=550, height=37).facet(
row=alt.Row('variable', title=None, sort=sort_order))
def concat_ngdp_vis(year, geo):
nominal_gdp = alt.Chart(
eco, title="Total GDP").mark_bar().transform_aggregate(
groupby=['Geography', 'Year'], GDP='sum(Nominal GDP)').encode(
x=alt.X('sum(GDP):Q',
title='CA$ (MM)',
axis=alt.Axis(titleFontSize=20,
grid=False,
ticks=False,
labels=False)),
y=alt.Y('Geography:O',
sort='-x',
title=None,
axis=alt.Axis(labelFontSize=20,
ticks=False,
grid=False)),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography', equal=geo)).transform_filter(
alt.FieldEqualPredicate(
field='Year', equal=year)).properties(
height=200, width=400).mark_text(
dx=-175, color='darkblue',
size=40).encode(text=alt.Text(
'sum(GDP):Q', format=('$,')))
nominal_gdp_gr = alt.Chart(
eco_gr, title="Growth rate").mark_bar().transform_aggregate(
groupby=['Geography', 'Year'], GDP='sum(Nominal GDP)').encode(
x=alt.X('sum(GDP):Q',
title='Yearly %',
axis=alt.Axis(titleFontSize=20,
grid=False,
ticks=False,
labels=False)),
y=alt.Y('Geography:O',
sort='-x',
title=None,
axis=alt.Axis(grid=False, ticks=False, labels=False)),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography', equal=geo)).transform_filter(
alt.FieldEqualPredicate(
field='Year', equal=year)).properties(
height=200, width=400).mark_text(
dx=-175, color='darkblue',
size=40).encode(text=alt.Text(
'sum(GDP):Q', format=('.2%')))
nominal_gdp_evo = alt.Chart(eco, title="GDP evolution").mark_area(
point=alt.OverlayMarkDef(filled=False, fill='darkblue')).encode(
x=alt.X('Year',
title='Year',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('sum(Nominal GDP):Q',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
ticks=False,
format=('$,f')),
title=None),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('sum(Nominal GDP):Q', format=('$,'), title='GDP')
]).transform_filter(
alt.FieldEqualPredicate(field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2000, year]))
nominal_gdp_rank = alt.Chart(
eco, title="Contribution by province/territory").mark_bar().encode(
x=alt.X('sum(Nominal GDP):Q',
title='CA$ (MM)',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
format=('$,f'))),
y=alt.Y('Geography:O',
sort='-x',
axis=alt.Axis(labelFontSize=18),
title=None),
color=alt.condition((alt.datum.Geography == geo) |
(alt.datum.Geography == 'Canada'),
alt.value('darkblue'), alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('sum(Nominal GDP):Q', format=('$,'), title='GDP')
]).transform_filter(
alt.FieldEqualPredicate(field='Year', equal=year))
nominal_gdp_gr_evo = alt.Chart(eco_gr, title="GDP growth rates").mark_bar(
point=alt.OverlayMarkDef(filled=False, fill='darkblue'),
size=16).encode(x=alt.X('Year',
title='Year',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('sum(Nominal GDP):Q',
axis=alt.Axis(tickCount=3,
grid=False,
ticks=False,
format=('%')),
title=None),
color=alt.condition((alt.datum.Year == year),
alt.value('darkblue'),
alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography',
title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('sum(Nominal GDP):Q',
format=('.2%'),
title='Growth rate')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2000, year]))
nominal_gdp_gr_rank = alt.Chart(
eco_gr, title="By province/territory").mark_bar().encode(
x=alt.X('sum(Nominal GDP):Q',
title=None,
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
format=('%'))),
y=alt.Y('Geography:O',
sort='-x',
axis=alt.Axis(labelFontSize=18),
title=None),
color=alt.condition((alt.datum.Geography == geo) |
(alt.datum.Geography == 'Canada'),
alt.value('darkblue'), alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('sum(Nominal GDP):Q',
format=('.2%'),
title='Growth rate')
]).transform_filter(
alt.FieldEqualPredicate(field='Year', equal=year))
w = 300
h = 200
nominal_gdp = nominal_gdp.properties(width=w, height=h)
nominal_gdp_gr = nominal_gdp_gr.properties(width=w, height=h)
nominal_gdp_evo = nominal_gdp_evo.properties(width=w, height=h)
nominal_gdp_rank = nominal_gdp_rank.properties(width=w, height=h)
nominal_gdp_gr_evo = nominal_gdp_gr_evo.properties(width=w, height=h)
nominal_gdp_gr_rank = nominal_gdp_gr_rank.properties(width=w, height=h)
ngdp_summary = (nominal_gdp | nominal_gdp_gr)
ngdp_total = (nominal_gdp_evo | nominal_gdp_rank)
ngdp_gr = (nominal_gdp_gr_evo | nominal_gdp_gr_rank)
ngdp_vis = ((ngdp_summary & ngdp_total)
& ngdp_gr).configure_view(strokeOpacity=0).configure_axis(
domain=False).configure_title(fontSize=30)
return ngdp_vis.to_html()
def concat_employment_vis(year, geo):
er_evo = alt.Chart(eco, title="Unemployment rate evolution").mark_area(
point=alt.OverlayMarkDef(filled=False, fill='darkblue')).encode(
x=alt.X('Year',
title='Year',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(Unemployment rate):Q',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
ticks=False,
format=('%')),
title=None),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(Unemployment rate):Q',
format=('.2%'),
title='Unemployment rate')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography', equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2000, year])).properties(height=430)
er_rank = alt.Chart(
eco,
title="Unemployment rate by province/territory").mark_bar().encode(
x=alt.X('average(Unemployment rate):Q',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
ticks=False,
format=('%')),
title=None),
y=alt.Y('Geography:O',
sort='-x',
axis=alt.Axis(labelFontSize=18),
title=None),
color=alt.condition((alt.datum.Geography == geo) |
(alt.datum.Geography == 'Canada'),
alt.value('darkblue'), alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(Unemployment rate):Q',
format=('.2%'),
title='Unemployment rate')
]).transform_filter(
alt.FieldEqualPredicate(field='Year', equal=year))
er_ind_rank = alt.Chart(
labour, title="Unemployment rate by industry").mark_bar().encode(
x=alt.X('Unemployment rate:Q',
axis=alt.Axis(tickCount=3,
grid=False,
ticks=False,
format=('%')),
title=None),
y=alt.Y('Industry:O',
sort='-x',
axis=alt.Axis(labelFontSize=18),
title=None),
color=alt.value('lightblue'),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('Industry'),
alt.Tooltip('Unemployment rate:Q',
format=('.2%'),
title='Unemployment rate')
]).transform_filter(
alt.FieldEqualPredicate(
field='Year', equal=year)).transform_filter(
alt.FieldEqualPredicate(field='Geography', equal=geo))
w = 300
h = 200
# nominal_gdp = nominal_gdp.properties(
# width=w,
# height=h
# )
#
# nominal_gdp_gr = nominal_gdp_gr.properties(
# width=w,
# height=h
# )
#
# nominal_gdp_evo = nominal_gdp_evo.properties(
# width=w,
# height=h
# )
er_ind_rank = er_ind_rank.properties(width=w, height=h)
er_rank = er_rank.properties(width=w, height=h)
er_evo = er_evo.properties(width=w, height=h)
employment_plot = ((er_evo | er_rank) & er_ind_rank).configure_view(
strokeOpacity=0).configure_axis(domain=False).configure_title(
fontSize=30)
return employment_plot.to_html()
def concat_earnings_vis(year, geo):
all_ear_evo = alt.Chart(eco, title="All industries").mark_area(
point=alt.OverlayMarkDef(filled=False, fill='darkblue')).encode(
x=alt.X('Year',
title='CA$',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(All industries):Q',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
ticks=False,
format=('$,f')),
title=None),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(All industries):Q',
format=('$,'),
title='Earnings')
]).transform_filter(
alt.FieldEqualPredicate(field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2002, year]))
all_ear_gr_evo = alt.Chart(eco_gr, title="Growth rates").mark_bar(
point=alt.OverlayMarkDef(filled=False, fill='darkblue'),
size=16).encode(x=alt.X('Year',
title='Year',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(All industries):Q',
axis=alt.Axis(tickCount=3,
grid=False,
ticks=False,
format=('%')),
title=None),
color=alt.condition((alt.datum.Year == year),
alt.value('darkblue'),
alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography',
title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(All industries):Q',
format=('.2%'),
title='Growth rate')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2002, year]))
goods_ear_evo = alt.Chart(eco, title="Goods-producing sector").mark_area(
point=alt.OverlayMarkDef(filled=False, fill='darkblue')).encode(
x=alt.X('Year',
title='CA$',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(Goods-producing sector):Q',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
ticks=False,
format=('$,f')),
title=None),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(Goods-producing sector):Q',
format=('$,'),
title='Earnings')
]).transform_filter(
alt.FieldEqualPredicate(field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2002, year]))
goods_ear_gr_evo = alt.Chart(eco_gr, title="Growth rates").mark_bar(
point=alt.OverlayMarkDef(filled=False, fill='darkblue'),
size=16).encode(x=alt.X('Year',
title='Year',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(Goods-producing sector):Q',
axis=alt.Axis(tickCount=3,
grid=False,
ticks=False,
format=('%')),
title=None),
color=alt.condition((alt.datum.Year == year),
alt.value('darkblue'),
alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography',
title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(Goods-producing sector):Q',
format=('.2%'),
title='Growth rate')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2002, year]))
serv_ear_evo = alt.Chart(eco, title="Service-producing").mark_area(
point=alt.OverlayMarkDef(filled=False, fill='darkblue')).encode(
x=alt.X('Year',
title='CA$',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(Service-producing sector):Q',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
ticks=False,
format=('$,f')),
title=None),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(Service-producing sector):Q',
format=('$,'),
title='Earnings')
]).transform_filter(
alt.FieldEqualPredicate(field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2002, year]))
serv_ear_gr_evo = alt.Chart(eco_gr, title="Growth rates").mark_bar(
point=alt.OverlayMarkDef(filled=False, fill='darkblue'),
size=16).encode(x=alt.X('Year',
title='Year',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(Service-producing sector):Q',
axis=alt.Axis(tickCount=3,
grid=False,
ticks=False,
format=('%')),
title=None),
color=alt.condition((alt.datum.Year == year),
alt.value('darkblue'),
alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography',
title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(Service-producing sector):Q',
format=('.2%'),
title='Growth rate')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2002, year]))
w = 300
h = 200
serv_ear_gr_evo = serv_ear_gr_evo.properties(width=w, height=h)
serv_ear_evo = serv_ear_evo.properties(width=w, height=h)
goods_ear_gr_evo = goods_ear_gr_evo.properties(width=w, height=h)
goods_ear_evo = goods_ear_evo.properties(width=w, height=h)
all_ear_gr_evo = all_ear_gr_evo.properties(width=w, height=h)
all_ear_evo = all_ear_evo.properties(width=w, height=h)
all_ear = (all_ear_evo | all_ear_gr_evo)
goods_ear = (goods_ear_evo | goods_ear_gr_evo)
serv_ear = (serv_ear_evo | serv_ear_gr_evo)
earnings_plot = ((all_ear & serv_ear) & goods_ear).configure_view(
strokeOpacity=0).configure_axis(domain=False).configure_title(
fontSize=30)
return earnings_plot.to_html()
"""
Line Chart with Points
----------------------
This chart shows a simple line chart with points marking each value. Use
``point=True`` for points with default appearance or customize it with
``OverlayMarkDef()``.
"""
# category: line charts
import altair as alt
import numpy as np
import pandas as pd
x = np.arange(100)
source = pd.DataFrame({'x': x, 'f(x)': np.sin(x / 5)})
alt.Chart(source).mark_line(point=alt.OverlayMarkDef(color="red")).encode(
x='x', y='f(x)')
def concat_cpi_vis(year, geo):
all_cpi_evo = alt.Chart(eco, title="CPI all-items evolution").mark_area(
point=alt.OverlayMarkDef(filled=False, fill='darkblue')).encode(
x=alt.X('Year',
title='2002=100',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(All-items):Q',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
ticks=False,
format=(',.0f')),
title=None),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(All-items):Q',
format=('.0f'),
title='CPI')
]).transform_filter(
alt.FieldEqualPredicate(field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2000, year]))
all_cpi_gr = alt.Chart(eco_gr, title="Growth rates").mark_bar(
point=alt.OverlayMarkDef(filled=False, fill='darkblue'),
size=16).encode(x=alt.X('Year',
title='Year',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('sum(All-items):Q',
axis=alt.Axis(tickCount=3,
grid=False,
ticks=False,
format=('%')),
title=None),
color=alt.condition((alt.datum.Year == year),
alt.value('darkblue'),
alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography',
title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('sum(All-items):Q',
format=('.2%'),
title='Growth rate')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2000, year]))
gasoline_cpi_evo = alt.Chart(
eco, title="CPI gasoline evolution").mark_area(
point=alt.OverlayMarkDef(filled=False, fill='darkblue')).encode(
x=alt.X('Year',
title='2002=100',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('average(Gasoline):Q',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
ticks=False,
format=(',.0f')),
title=None),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(Gasoline):Q', title='CPI')
]).transform_filter(
alt.FieldEqualPredicate(field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2000, year]))
gasoline_cpi_gr = alt.Chart(eco_gr, title="Growth rates").mark_bar(
point=alt.OverlayMarkDef(filled=False, fill='darkblue'),
size=16).encode(x=alt.X('Year',
title='Year',
axis=alt.Axis(tickCount=5,
titleFontSize=20,
grid=False,
ticks=False,
format='Y')),
y=alt.Y('sum(Gasoline):Q',
axis=alt.Axis(tickCount=3,
grid=False,
ticks=False,
format=('%')),
title=None),
color=alt.condition((alt.datum.Year == year),
alt.value('darkblue'),
alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography',
title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('sum(Gasoline):Q',
format=('.2%'),
title='Growth rate')
]).transform_filter(
alt.FieldEqualPredicate(
field='Geography',
equal=geo)).transform_filter(
alt.FieldRangePredicate(
'Year', [2000, year]))
cpi_rank = alt.Chart(
eco, title="CPI all-items by province/territory").mark_bar().encode(
x=alt.X('average(All-items):Q',
title='2002=100',
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
format=(',.0f'))),
y=alt.Y('Geography:O',
sort='-x',
axis=alt.Axis(labelFontSize=18),
title=None),
color=alt.condition((alt.datum.Geography == geo) |
(alt.datum.Geography == 'Canada'),
alt.value('darkblue'), alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(All-items):Q', title='CPI')
]).transform_filter(
alt.FieldEqualPredicate(field='Year', equal=year))
cpi_gr_rank = alt.Chart(
eco_gr, title="Growth rate by province/territory").mark_bar().encode(
x=alt.X('average(All-items):Q',
title=None,
axis=alt.Axis(tickCount=3,
titleFontSize=20,
grid=False,
format=('%'))),
y=alt.Y('Geography:O',
sort='-x',
axis=alt.Axis(labelFontSize=18),
title=None),
color=alt.condition((alt.datum.Geography == geo) |
(alt.datum.Geography == 'Canada'),
alt.value('darkblue'), alt.value('lightblue')),
tooltip=[
alt.Tooltip('Geography', title='Province/territory'),
alt.Tooltip('Year'),
alt.Tooltip('average(All-items):Q',
format=('.2%'),
title='CPI')
]).transform_filter(
alt.FieldEqualPredicate(field='Year', equal=year))
w = 300
h = 200
all_cpi_evo = all_cpi_evo.properties(width=w, height=h)
all_cpi_gr = all_cpi_gr.properties(width=w, height=h)
gasoline_cpi_evo = gasoline_cpi_evo.properties(width=w, height=h)
gasoline_cpi_gr = gasoline_cpi_gr.properties(width=w, height=h)
cpi_rank = cpi_rank.properties(width=w, height=h)
cpi_gr_rank = cpi_gr_rank.properties(width=w, height=h)
all_cpi = (all_cpi_evo | all_cpi_gr)
gasoline_cpi = (gasoline_cpi_evo | gasoline_cpi_gr)
rank_cpi = (cpi_rank | cpi_gr_rank)
cpi_plot = ((all_cpi & gasoline_cpi)
& rank_cpi).configure_view(strokeOpacity=0).configure_axis(
domain=False).configure_title(fontSize=30)
return cpi_plot.to_html()