"""
Layered Bar Chart
-----------------
This example shows a bar chart showing the US population distribution of age groups and gender in 2000.
"""
# category: bar charts
import altair as alt
from altair.expr import datum, if_
from vega_datasets import data
source = data.population.url
alt.Chart(source).mark_bar(opacity=0.7).encode(
alt.X('age:O', scale=alt.Scale(rangeStep=17)),
alt.Y('sum(people):Q', axis=alt.Axis(title='population'), stack=None),
alt.Color('gender:N',
scale=alt.Scale(range=["#EA98D2", "#659CCA"]))).transform_filter(
datum.year == 2000).transform_calculate(
"gender", if_(datum.sex == 2, 'Female', 'Male'))
chart = alt.Chart(
currency_hist_20WBTC.reset_index(),
title="{} Price vs Time".format(df_choice)).mark_circle(
size=70, opacity=0.6).encode(
alt.X('index', axis=alt.Axis(title='Date')),
alt.Y('price_x',
axis=alt.Axis(title='Price'),
scale=alt.Scale(type='log')),
color=alt.Color(
'price above BTC 20W MA',
scale=alt.Scale(scheme='category20'),
legend=alt.Legend(
title="BTC Price"))).transform_calculate(
'price above BTC 20W MA',
if_(datum['price above BTC 20W MA'] == 0,
'BTC Below 20 Week MA',
'BTC Above 20 Week MA')).interactive()
#st.line_chart(currency_hist['price'])
st.altair_chart(chart, use_container_width=True)
else:
line_chart = alt.Chart(
currency_hist_20WBTC.reset_index(),
title="{} Price vs Time".format(df_choice)).mark_line().encode(
alt.X('index', axis=alt.Axis(title='Date')),
alt.Y('price_x',
axis=alt.Axis(title='Price'),
scale=alt.Scale(type='log'))).interactive()
st.altair_chart(line_chart, use_container_width=True)
"""
US Population Over Time
=======================
This chart visualizes the age distribution of the US population over time.
It uses a slider widget that is bound to the year to visualize the age
distribution over time.
"""
# category: interactive
import altair as alt
from altair.expr import datum, if_
from vega_datasets import data
pop = data.population.url
pink_blue = alt.Scale(domain=('Male', 'Female'), range=["steelblue", "salmon"])
slider = alt.binding_range(min=1900, max=2000, step=10)
year = alt.selection_single(name="year", fields=['year'], bind=slider)
chart = alt.Chart(pop).mark_bar().encode(
x=alt.X('sex:N', axis=alt.Axis(title=None)),
y=alt.Y('people:Q', scale=alt.Scale(domain=(0, 12000000))),
color=alt.Color('sex:N', scale=pink_blue),
column='age:O').properties(
width=20,
selection=year,
).transform_calculate("sex", if_(datum.sex == 1, "Male",
"Female")).transform_filter(year.ref())
It uses a slider widget that is bound to the year to visualize the age
distribution over time.
'''
# category: case studies
import altair as alt
from altair.expr import datum, if_
from vega_datasets import data
pop = data.population.url
slider = alt.binding_range(min=1850, max=2000, step=10)
select_year = alt.selection_single(name='year', fields=['year'], bind=slider)
base = alt.Chart(pop).add_selection(select_year).transform_filter(
select_year).transform_calculate(
gender=if_(datum.sex == 1, 'Male', 'Female'))
title = alt.Axis(title='population')
color_scale = alt.Scale(domain=['Male', 'Female'],
range=['#1f77b4', '#e377c2'])
left = base.transform_filter(datum.gender == 'Female').encode(
y=alt.X('age:O', axis=None),
x=alt.X('sum(people):Q', axis=title, sort=alt.SortOrder('descending')),
color=alt.Color('gender:N', scale=color_scale,
legend=None)).mark_bar().properties(title='Female')
middle = base.encode(
y=alt.X('age:O', axis=None),
text=alt.Text('age:Q'),
).mark_text().properties(width=20)
"""
US Population Over Time
=======================
This chart visualizes the age distribution of the US population over time.
It uses a slider widget that is bound to the year to visualize the age
distribution over time.
"""
# category: interactive charts
import altair as alt
from altair.expr import datum, if_
from vega_datasets import data
pop = data.population.url
pink_blue = alt.Scale(domain=('Male', 'Female'), range=["steelblue", "salmon"])
slider = alt.binding_range(min=1900, max=2000, step=10)
select_year = alt.selection_single(name="year", fields=['year'], bind=slider)
alt.Chart(pop).mark_bar().encode(
x=alt.X('sex:N', axis=alt.Axis(title=None)),
y=alt.Y('people:Q', scale=alt.Scale(domain=(0, 12000000))),
color=alt.Color('sex:N', scale=pink_blue),
column='age:O').properties(
width=20).add_selection(select_year).transform_calculate(
"sex", if_(datum.sex == 1, "Male",
"Female")).transform_filter(select_year)
"""
Grouped Bar Chart
-----------------------
This example shows a population broken out by gender and age for a specific year.
The grouping is achieved by building a trellis plot with narrow column
encoded on the age groups and x-axes encoded on gender.
"""
import altair as alt
from altair.expr import datum, if_
from vega_datasets import data
source = data.population.url
chart = alt.Chart(source).mark_bar(stroke = 'transparent').encode(
alt.X('gender:N', scale=alt.Scale(rangeStep = 12), axis=alt.Axis(title='')),
alt.Y('sum(people):Q', axis=alt.Axis(title='population', grid=False)),
color = alt.Color('gender:N', scale=alt.Scale(range=["#EA98D2", "#659CCA"])),
column = 'age:O'
).configure_view(
stroke='transparent'
).configure_axis(
domainWidth=0.8
).transform_filter(
datum.year == 2000
).transform_calculate(
'gender', if_(datum.sex == 2, 'Female', 'Male')
)
