Merge pull request #537 from eitanlees/non-local-url-fix

Nonlocal URL Fix

altair/vegalite/v2/examples/aggregate_bar_chart.py

@@ -7,7 +7,7 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.population()
+source = data.population.url
 
 chart = alt.Chart(source).mark_bar().encode(
     x = alt.X('sum(people):Q', axis = alt.Axis(title = 'population')),

altair/vegalite/v2/examples/binned_scatterplot.py

@@ -10,7 +10,7 @@
 source = data.movies()
 
 chart = alt.Chart(source).mark_circle().encode(
-    x = alt.X('IMDB_Rating',  bin = alt.BinParams(maxbins=10)),
-    y = alt.Y('Rotten_Tomatoes_Rating',  bin = alt.BinParams(maxbins=10)),
+    x = alt.X('IMDB_Rating:Q',  bin = alt.BinParams(maxbins=10)),
+    y = alt.Y('Rotten_Tomatoes_Rating:Q',  bin = alt.BinParams(maxbins=10)),
     size = 'count(*):Q'
 )  

altair/vegalite/v2/examples/boxplot_max_min.py

@@ -7,14 +7,15 @@
 https://vega.github.io/vega-lite/examples/box-plot_minmax_2D_vertical_normalized.html
 """
 import altair as alt
+from vega_datasets import data
 
-population = alt.load_dataset('population')
+population = data.population.url
 
 # Define aggregate fields
-lower_box = 'q1(people)'
-lower_whisker = 'min(people)'
-upper_box = 'q3(people)'
-upper_whisker = 'max(people)'
+lower_box = 'q1(people):Q'
+lower_whisker = 'min(people):Q'
+upper_box = 'q3(people):Q'
+upper_whisker = 'max(people):Q'
 
 # Compose each layer individually
 lower_plot = alt.Chart(population).mark_rule().encode(
@@ -37,11 +38,11 @@
 )
 
 middle_tick = alt.Chart(population).mark_tick(color='white', size=5.0).encode(
-    y='median(people)',
+    y='median(people):Q',
     x='age:O',
 )
 
 chart = alt.layer(lower_plot,
                   middle_plot,
                   upper_plot,
-                  middle_tick)
+                  middle_tick)

altair/vegalite/v2/examples/diverging_stacked_bar_chart.py

@@ -5,7 +5,6 @@
 """
 
 import altair as alt
-from vega_datasets import data
 
 data =     [
       {
@@ -363,4 +362,4 @@
     y = alt.Y('question:N', axis = y_axis),
     color = alt.Color('type:N',
         legend=alt.Legend( title='Response'),
-        scale = color_scale,))
+        scale = color_scale,))

altair/vegalite/v2/examples/gantt_chart.py

@@ -5,7 +5,6 @@
 """
 
 import altair as alt
-from vega_datasets import data
 
 data = [
       {"task": "A","start": 1, "end": 3},
@@ -15,7 +14,6 @@
 
 source = alt.pd.DataFrame(data)
 
-
 chart = alt.Chart(source).mark_bar().encode(
     x = 'start',
     x2 = 'end',

altair/vegalite/v2/examples/gapminder_bubble_plot.py

@@ -7,10 +7,10 @@
 import altair as alt
 from vega_datasets import data
 
-gapminder = data.gapminder_health_income()
+gapminder = data.gapminder_health_income.url
 
 chart = alt.Chart(gapminder).mark_circle().encode(
-    x = alt.X('income', scale = alt.Scale(type = 'log')), 
-    y = alt.Y('health', scale = alt.Scale(zero=False)), 
-    size = 'population'
+    x = alt.X('income:Q', scale = alt.Scale(type = 'log')), 
+    y = alt.Y('health:Q', scale = alt.Scale(zero=False)), 
+    size = 'population:Q'
 )

altair/vegalite/v2/examples/grouped_bar_chart.py

@@ -7,7 +7,7 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.population()
+source = data.population.url
 
 chart = alt.Chart(source).mark_bar(stroke = 'transparent').encode(
     x = alt.X('gender:N', scale = alt.Scale(rangeStep = 12), axis = alt.Axis(title = '')),

altair/vegalite/v2/examples/histogram.py

@@ -5,8 +5,9 @@
 https://vega.github.io/vega-lite/examples/histogram.html
 """
 import altair as alt
+from vega_datasets import data
 
-movies = alt.load_dataset('movies')
+movies = data.movies.url
 
 chart = alt.Chart(movies).mark_bar().encode(
     x=alt.X("IMDB_Rating",
@@ -15,4 +16,4 @@
                 maxbins=10,
             )),
     y='count(*):Q',
-)
+)

altair/vegalite/v2/examples/histogram_with_a_global_mean_overlay.py

@@ -7,7 +7,7 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.movies()
+source = data.movies.url
 
 bar = alt.Chart(source).mark_bar().encode(
     x = alt.X('IMDB_Rating:Q', bin = True, axis = None),

altair/vegalite/v2/examples/layered_bar_chart.py

@@ -7,7 +7,7 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.population()
+source = data.population.url
 
 chart = alt.Chart(source).mark_bar(opacity = 0.7).encode(
     x = alt.X('age:O', scale = alt.Scale(rangeStep = 17)), 

altair/vegalite/v2/examples/line_percent.py

@@ -7,18 +7,14 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.jobs()
-
-# The year here is stored by pandas as an integer. When treating columns as dates,
-# it is best to use either a string representation or a datetime representation.
-source.year = source.year.astype(str)
-
-welders = source[source.job == 'Welder']
+source = data.jobs.url
 
-chart = alt.Chart(welders).mark_line().encode(
-    x=alt.X('year', timeUnit='year'),
-    y=alt.Y('perc', axis=alt.Axis(format='%')),
-    color='sex'
+chart = alt.Chart(source).mark_line().encode(
+    x=alt.X('year:O'),
+    y=alt.Y('perc:Q', axis=alt.Axis(format='%')),
+    color='sex:N'
 ).properties(
     title='Percent of work-force working as Welders'
 )
+
+chart.transform = [{"filter": {"field": "job", "equal": "Welder"}}]

altair/vegalite/v2/examples/natural_disasters.py

@@ -7,14 +7,14 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.disasters()
+source = data.disasters.url
 
 chart = alt.Chart(source).mark_circle(opacity = 0.8, stroke = 'black', strokeWidth = 1).encode(
     x = alt.X('Year:O', axis = alt.Axis(labelAngle = 0)),
     y = alt.X('Entity:N'),
     size = alt.Size('Deaths:Q', scale = alt.Scale(range = [0, 5000]),
                    legend = alt.Legend(title = 'Annual Global Deaths')),
-    color = alt.Color('Entity', legend = None)
+    color = alt.Color('Entity:N', legend = None)
 ).properties(width=600, height=400)
 
 chart.transform = [{"filter": "datum.Entity !== 'All natural disasters'"}]

altair/vegalite/v2/examples/normalized_stacked_bar_chart.py

@@ -7,7 +7,7 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.population()
+source = data.population.url
 
 chart = alt.Chart(source).mark_bar().encode(
     x = alt.X('age:O', scale = alt.Scale(rangeStep = 17)), 

altair/vegalite/v2/examples/ranged_dot_plot.py

@@ -7,18 +7,18 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.countries()
+source = data.countries.url
 
 
 line = alt.Chart().mark_line(color='#db646f').encode(
-    x = 'life_expect',
-    y = 'country',
-    detail = 'country'
+    x = 'life_expect:Q',
+    y = 'country:N',
+    detail = 'country:N'
     ).interactive()
 
 point = alt.Chart().mark_point(size = 100, opacity = 1, filled = True).encode(
-    x = 'life_expect',
-    y = 'country',
+    x = 'life_expect:Q',
+    y = 'country:N',
     color=alt.Color('year:O',
         scale=alt.Scale(
             domain=['1955', '2000'],
@@ -29,4 +29,4 @@
 
 chart = alt.layer(line + point, data = source,
                     transform = [{'filter': {"field": 'country', "oneOf": ["China", "India", "United States", "Indonesia", "Brazil"]}},
-                                  {'filter': {"field": 'year', "oneOf": [1955, 2000]}}])
+                                  {'filter': {"field": 'year', "oneOf": [1955, 2000]}}])

altair/vegalite/v2/examples/streamgraph.py

@@ -7,12 +7,12 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.unemployment_across_industries()
+source = data.unemployment_across_industries.url
 
 
 chart = alt.Chart(source).mark_area().encode(
     x = alt.X('date:T', timeUnit = 'yearmonth',
               axis=alt.Axis(format='%Y', domain = False, tickSize = 0)),
-    y = alt.Y('sum(count)', stack = 'center', axis = None),
-    color = alt.Color('series', scale=alt.Scale(scheme='category20b'))
-    ).interactive()
+    y = alt.Y('sum(count):Q', stack = 'center', axis = None),
+    color = alt.Color('series:N', scale=alt.Scale(scheme='category20b'))
+    ).interactive()

altair/vegalite/v2/examples/table_binned_heatmap.py

@@ -7,11 +7,11 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.movies()
+source = data.movies.url
 
 chart = alt.Chart(source).mark_rect().encode(
-    x = alt.X('IMDB_Rating', bin = alt.BinParams(maxbins=60)), 
-    y = alt.Y('Rotten_Tomatoes_Rating', bin = alt.BinParams(maxbins=40)),
-    color = alt.Color('count(IMDB_Rating)',
+    x = alt.X('IMDB_Rating:Q', bin = alt.BinParams(maxbins=60)), 
+    y = alt.Y('Rotten_Tomatoes_Rating:Q', bin = alt.BinParams(maxbins=40)),
+    color = alt.Color('count(IMDB_Rating):Q',
                       scale=alt.Scale(scheme='greenblue'))
-)
+)

altair/vegalite/v2/examples/table_bubble_plot_github.py

@@ -7,9 +7,9 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.github()
+source = data.github.url
 
 chart = alt.Chart(source).mark_circle().encode(
     x = alt.X('time:O', timeUnit = 'hours'),
     y = alt.X('time:O', timeUnit = 'day'),
-    size = 'sum(count)')
+    size = 'sum(count):Q')

altair/vegalite/v2/examples/trellis_scatter_plot.py

@@ -7,10 +7,10 @@
 import altair as alt
 from vega_datasets import data
 
-source = data.movies()
+source = data.movies.url
 
 chart = alt.Chart(source).mark_point().encode(
-    x = 'Worldwide_Gross', 
-    y = 'US_DVD_Sales', 
-    column = 'MPAA_Rating'
-)
+    x = 'Worldwide_Gross:Q', 
+    y = 'US_DVD_Sales:Q', 
+    column = 'MPAA_Rating:N'
+)