def geojson_feature(data, feature='features', **kwargs):
"""A convenience function for extracting features from a geojson object or url
Parameters
----------
data : anyOf(string, geojson.GeoJSON)
string is interpreted as URL from which to load the data set.
geojson.GeoJSON is interpreted as data set itself.
feature : string
The JSON property containing the GeoJSON object set to convert to
a GeoJSON feature collection. For example ``features[0].geometry``.
\**kwargs :
additional keywords passed to JsonDataFormat
"""
if isinstance(data, six.string_types):
return alt.UrlData(url=data,
format=alt.JsonDataFormat(type='json',
property=feature,
**kwargs))
elif hasattr(data, '__geo_interface__'):
if isinstance(data, gpd.GeoDataFrame):
data = alt.utils.sanitize_dataframe(data)
return alt.InlineData(values=data.__geo_interface__,
format=alt.JsonDataFormat(type='json',
property=feature,
**kwargs))
else:
warnings.warn("data of type {0} not recognized".format(type(data)))
return data
def make_heatmap(year_slider=[1950, 2000], genre="Comedy"):
'''
Takes in a year range and genre and filters with the criteria
to create our Altair figure
A heatmap of box office profit ratio against IMDb ratings
Inputs :
year_info : List with 2 values, takes in the year information from the callback above
[1970,1985]
genre_input : (To be programmed) : takes in the genre information
'Drama', 'Any'
Returns
A heatmap Altair object
'''
df = alt.UrlData(data.movies.url)
heatmap = alt.Chart(df).mark_rect().transform_filter(
alt.datum.Production_Budget > 0).transform_calculate(
Release_Year="year(datum.Release_Date)",
Profit="datum.Worldwide_Gross - datum.Production_Budget",
Profit_Ratio="datum.Profit / datum.Production_Budget"
).transform_filter(
alt.datum.Release_Year <= year_slider[1]).transform_filter(
alt.datum.Release_Year >= year_slider[0]
).transform_filter(alt.datum.Profit_Ratio < 10).transform_filter(
alt.datum.Major_Genre == genre).encode(
alt.X('IMDB_Rating:Q',
bin=alt.Bin(maxbins=20),
axis=alt.Axis(title='IMDB Rating')),
alt.Y('Profit_Ratio:Q',
bin=alt.Bin(maxbins=20),
axis=alt.Axis(
title='Profit Ratio ((Gross - Budget)/Budget)')),
alt.Color('mean(Profit_Ratio):Q',
scale=alt.Scale(scheme='yelloworangebrown'),
legend=alt.Legend(orient="bottom")),
tooltip=['Title:N']).properties(
title="Profit Ratio against IMDb Ratings",
height=300,
width=300)
return heatmap
def make_areachart(year_slider=[1950, 2000], genre="Comedy"):
'''
Takes in a year range and genre and filters with the criteria
to create our Altair figure
An area chart of box office of movies of selected genre in selected year range
with domestic and international data
Inputs :
year_info : List with 2 values, takes in the year information from the callback above
[1970,1985]
genre_input : (To be programmed) : takes in the genre information
'Drama', 'Any'
Returns
A area chart Altair object
'''
df = alt.UrlData(data.movies.url)
areachart = alt.Chart(df).mark_area().transform_calculate(
Release_Year="year(datum.Release_Date)",
International_Gross="datum.Worldwide_Gross - datum.US_Gross"
).transform_filter(
alt.datum.Release_Year <= year_slider[1]).transform_filter(
alt.datum.Release_Year >= year_slider[0]).transform_filter(
alt.datum.Major_Genre == genre).transform_fold(
['International_Gross', 'US_Gross'], ).encode(
alt.Y('value:Q',
aggregate="mean",
axis=alt.Axis(title='Dollars')),
alt.X('Release_Year:O', axis=alt.Axis(title='Year')),
color=alt.Color(
"key:N",
legend=alt.Legend(orient="bottom"),
scale=alt.Scale(
scheme='yelloworangebrown'))).properties(
title="Average Gross",
height=300,
width=300)
return areachart
def make_highlight_hist(year_slider=[1950, 2000], genre="Comedy"):
'''
Takes in a year range and genre and filters with the criteria
to create our Altair figure
A highlighted histogram of counts of movies of different genres
Inputs :
year_info : List with 2 values, takes in the year information from the callback above
[1970,1985]
genre_input : (To be programmed) : takes in the genre information
'Drama', 'Any'
Returns
A histogram Altair object
'''
df = alt.UrlData(data.movies.url)
genre_hist = alt.Chart(df).mark_bar().transform_calculate(
Release_Year="year(datum.Release_Date)",
International_Gross="datum.Worldwide_Gross - datum.US_Gross"
).transform_filter(
alt.datum.Release_Year <= year_slider[1]).transform_filter(
alt.datum.Major_Genre != None).transform_filter(
alt.datum.Release_Year >= year_slider[0]).encode(
alt.Y('Major_Genre:N', axis=alt.Axis(title='Genre')),
alt.X('count()'),
color=alt.condition(alt.datum.Major_Genre == genre,
alt.value("orange"),
alt.value("gray"))).properties(
title="Histogram of Genres",
width=300,
height=300)
return genre_hist
"""
Repeated Choropleth Map
=======================
Three choropleths representing disjoint data from the same table.
"""
# category: geographic
import altair as alt
from vega_datasets import data
pop_eng_hur = alt.UrlData(data.population_engineers_hurricanes.url)
states = alt.UrlData(data.us_10m.url,
format=alt.TopoDataFormat(type='topojson',
feature='states'))
variable_list = ['population', 'engineers', 'hurricanes']
chart = alt.Chart(states).mark_geoshape().properties(
projection={
'type': 'albersUsa'
}, width=500, height=300).transform_lookup(
lookup='id',
from_=alt.LookupData(pop_eng_hur, 'id', variable_list)).encode(
color=alt.Color(alt.repeat('row'), type='quantitative')).repeat(
row=variable_list).resolve_scale(color='independent')
"""
Repeated Choropleth Map
=======================
Three choropleths representing disjoint data from the same table.
"""
# category: geographic
import altair as alt
from vega_datasets import data
states = alt.topo_feature(data.us_10m.url, 'states')
pop_eng_hur = alt.UrlData(data.population_engineers_hurricanes.url)
variable_list = ['population', 'engineers', 'hurricanes']
alt.Chart(states).mark_geoshape().properties(
projection={
'type': 'albersUsa'
}, width=500, height=300).encode(
alt.Color(alt.repeat('row'), type='quantitative')).transform_lookup(
lookup='id',
from_=alt.LookupData(pop_eng_hur, 'id', variable_list)).repeat(
row=variable_list).resolve_scale(color='independent')
"""
Interactive Crossfilter
=======================
This example shows a multi-panel view of the same data, where you can interactively
select a portion of the data in any of the panels to highlight that portion in any
of the other panels.
"""
# category: interactive charts
import altair as alt
from vega_datasets import data
flights = alt.UrlData(data.flights_2k.url, format={'parse': {'date': 'date'}})
brush = alt.selection(type='interval', encodings=['x'])
# Define the base chart, with the common parts of the
# background and highlights
base = alt.Chart().mark_bar().encode(x=alt.X(alt.repeat('column'),
type='quantitative',
bin=alt.Bin(maxbins=20)),
y='count()').properties(width=180,
height=130)
# blue background with selection
background = base.properties(selection=brush)
# yellow highlights on the transformed data
highlight = base.encode(color=alt.value('goldenrod')).transform_filter(brush)
# layer the two charts & repeat
alt.layer(background, highlight, data=flights).transform_calculate(
"""
U.S. state capitals overlayed on a map of the U.S
-------------------------------------------------
This is a layered geographic visualization that shows US capitals
overlayed on a map.
"""
# category: geographic
import altair as alt
from vega_datasets import data
states = alt.UrlData(data.us_10m.url,
format=alt.TopoDataFormat(type='topojson',
feature='states'))
capitals = data.us_state_capitals.url
# US states background
background = alt.Chart(states).mark_geoshape(
fill='lightgray',
stroke='white'
).properties(
title='US State Capitols',
projection={'type': 'albersUsa'},
width=700,
height=400
)
# Points and text
hover = alt.selection(type='single', on='mouseover', nearest=True,
fields=['lat', 'lon'])
import altair as alt
from vega_datasets import data
alt.renderers.enable('altair_viewer')
movies = alt.UrlData(data.movies.url,
format=alt.DataFormat(parse={"Release_Date": "date"}))
ratings = ['G', 'NC-17', 'PG', 'PG-13', 'R']
genres = [
'Action', 'Adventure', 'Black Comedy', 'Comedy', 'Concert/Performance',
'Documentary', 'Drama', 'Horror', 'Musical', 'Romantic Comedy',
'Thriller/Suspense', 'Western'
]
base = alt.Chart(movies, width=200,
height=200).mark_point(filled=True).transform_calculate(
Rounded_IMDB_Rating="floor(datum.IMDB_Rating)",
Hundred_Million_Production=
"datum.Production_Budget > 100000000.0 ? 100 : 10",
Release_Year="year(datum.Release_Date)").transform_filter(
alt.datum.IMDB_Rating > 0).transform_filter(
alt.FieldOneOfPredicate(
field='MPAA_Rating',
oneOf=ratings)).encode(x=alt.X(
'Worldwide_Gross:Q',
scale=alt.Scale(domain=(100000, 10**9),
clamp=True)),
y='IMDB_Rating:Q',
tooltip="Title:N")
# A slider filter
year_slider = alt.binding_range(min=1969, max=2018, step=1)
slider_selection = alt.selection_single(bind=year_slider,
"""
London Tube Lines
=================
This example shows the London tube lines against the background of the
borough boundaries. It is based on the vega-lite example at
https://vega.github.io/vega-lite/examples/geo_layer_line_london.html.
"""
# category: geographic
import altair as alt
from vega_datasets import data
boroughs = alt.UrlData(url=data.londonBoroughs.url,
format=alt.TopoDataFormat(type='topojson',
feature='boroughs'))
centroids = data.londonCentroids.url
tubelines = alt.UrlData(url=data.londonTubeLines.url,
format=alt.TopoDataFormat(type='topojson',
feature='line'))
background = alt.Chart(boroughs).mark_geoshape(
stroke='white',
strokeWidth=2).encode(color=alt.value('#eee'), ).properties(width=700,
height=500)
labels = alt.Chart(centroids).mark_text().encode(
longitude='cx:Q',
latitude='cy:Q',
text='bLabel:N',
size=alt.value(8),
opacity=alt.value(0.6)
"""
Choropleth Map
==============
A choropleth map of unemployment rate per county in the US
"""
# category: geographic
import altair as alt
from vega_datasets import data
counties = alt.topo_feature(data.us_10m.url,'counties')
unemp_data = alt.UrlData(data.unemployment.url)
alt.Chart(counties).mark_geoshape().properties(
projection={'type': 'albersUsa'},
width=500,
height=300
).encode(
color='rate:Q'
).transform_lookup(
lookup='id',
from_=alt.LookupData(unemp_data, 'id', ['rate'])
)
import numpy as np
import pandas as pd
import altair as alt
#import altair.vega.v5 as alt
from vega_datasets import data
import streamlit as alt
source = pd.DataFrame({
'a': ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I'],
'b': [28, 55, 43, 91, 81, 53, 19, 87, 52]
})
chart = alt.Chart(
data=alt.UrlData(
url='https://vega.github.io/vega-datasets/data/cars.json'),
mark='point',
encoding=alt.FacetedEncoding(
x=alt.PositionFieldDef(field='Horsepower', type='quantitative'),
y=alt.PositionFieldDef(field='Miles_per_Gallon', type='quantitative'),
color=alt.StringFieldDefWithCondition(field='Origin', type='nominal')),
config=alt.Config(
view=alt.ViewConfig(continuousHeight=300, continuousWidth=400)))
st.altair_chart(chart)
"""
Choropleth Map
==============
A choropleth map of unemployment rate per county in the US
"""
# category: geographic
import altair as alt
from vega_datasets import data
unemp_data = alt.UrlData(data.unemployment.url)
counties = alt.UrlData(data.us_10m.url,
format=alt.TopoDataFormat(type='topojson',
feature='counties'))
chart = alt.Chart(counties).mark_geoshape().properties(
projection={
'type': 'albersUsa'
}, width=500, height=300).encode(color='rate:Q').transform_lookup(
lookup='id', from_=alt.LookupData(unemp_data, 'id', ['rate']))
"""
Cumulative Wikipedia Donations
==============================
This chart shows cumulative donations to Wikipedia over the past 10 years. This chart was inspired by https://www.reddit.com/r/dataisbeautiful/comments/7guwd0/cumulative_wikimedia_donations_over_the_past_10/ but using lines instead of areas.
Data comes from https://frdata.wikimedia.org/.
"""
import altair as alt
data = alt.UrlData("https://frdata.wikimedia.org/donationdata-vs-day.csv")
chart = alt.Chart(data).mark_line().encode(
x=alt.X('date:T',
timeUnit='monthdate',
axis=alt.Axis(format='%B', title='Month')),
y=alt.Y('max(ytdsum):Q',
stack=None,
axis=alt.Axis(title='Cumulative Donations')),
color=alt.Color('date:O', timeUnit='year',
legend=alt.Legend(title='Year')),
order=alt.Order('data:O', timeUnit='year'))
