WIP: Tab completion 2

altair/examples/attribute_syntax/airport_connections.py

@@ -0,0 +1,70 @@
+"""
+Connections Among U.S. Airports Interactive
+-------------------------------------------
+This example shows all the connections between major U.S. airports. Lookup transformations
+are used to find the coordinates of each airport and connecting airports. Connections
+are displayed on mouseover via a single selection.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+# Since these data are each more than 5,000 rows we'll import from the URLs
+airports = data.airports.url
+flights_airport = data.flights_airport.url
+
+states = alt.topo_feature(data.us_10m.url, feature="states")
+
+# Create mouseover selection
+select_city = alt.selection_point(
+    on="mouseover", nearest=True, fields=["origin"], empty=False
+)
+
+# Define which attributes to lookup from airports.csv
+lookup_data = alt.LookupData(
+    airports, key="iata", fields=["state", "latitude", "longitude"]
+)
+
+background = alt.Chart(states).mark_geoshape(
+    fill="lightgray",
+    stroke="white"
+).properties(
+    width=750,
+    height=500
+).project("albersUsa")
+
+connections = alt.Chart(flights_airport).mark_rule(opacity=0.35).encode(
+    latitude="latitude:Q",
+    longitude="longitude:Q",
+    latitude2="lat2:Q",
+    longitude2="lon2:Q"
+).transform_lookup(
+    lookup="origin",
+    from_=lookup_data
+).transform_lookup(
+    lookup="destination",
+    from_=lookup_data,
+    as_=["state", "lat2", "lon2"]
+).transform_filter(
+    select_city
+)
+
+points = alt.Chart(flights_airport).mark_circle().encode(
+    latitude="latitude:Q",
+    longitude="longitude:Q",
+    size=alt.Size("routes:Q").legend(None).scale(range=[0, 1000]),
+    order=alt.Order("routes:Q").sort("descending"),
+    tooltip=["origin:N", "routes:Q"]
+).transform_aggregate(
+    routes="count()",
+    groupby=["origin"]
+).transform_lookup(
+    lookup="origin",
+    from_=lookup_data
+).transform_filter(
+    (alt.datum.state != "PR") & (alt.datum.state != "VI")
+).add_params(
+    select_city
+)
+
+(background + connections + points).configure_view(stroke=None)

altair/examples/attribute_syntax/annual_weather_heatmap.py

@@ -0,0 +1,24 @@
+"""
+Annual Weather Heatmap
+----------------------
+"""
+# category: tables
+import altair as alt
+from vega_datasets import data
+
+source = data.seattle_weather()
+
+alt.Chart(source, title="Daily Max Temperatures (C) in Seattle, WA").mark_rect().encode(
+    alt.X("date(date):O").title("Day").axis(format="%e", labelAngle=0),
+    alt.Y("month(date):O").title("Month"),
+    alt.Color("max(temp_max)").title(None),
+    tooltip=[
+        alt.Tooltip("monthdate(date)", title="Date"),
+        alt.Tooltip("max(temp_max)", title="Max Temp"),
+    ],
+).configure_view(
+    step=13,
+    strokeWidth=0
+).configure_axis(
+    domain=False
+)

altair/examples/attribute_syntax/anscombe_plot.py

@@ -0,0 +1,20 @@
+"""
+Anscombe's Quartet
+------------------
+
+This example shows how to use the column channel to make a trellis plot. Anscombe's Quartet is a famous dataset constructed by Francis Anscombe. Common summary statistics are identical for each subset of the data, despite the subsets having vastly different characteristics.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+source = data.anscombe()
+
+alt.Chart(source).mark_circle().encode(
+    alt.X('X').scale(zero=False),
+    alt.Y('Y').scale(zero=False),
+    alt.Facet('Series', columns=2),
+).properties(
+    width=180,
+    height=180,
+)

altair/examples/attribute_syntax/bar_chart_trellis_compact.py

@@ -0,0 +1,48 @@
+"""
+Compact Trellis Grid of Bar Charts
+==================================
+This example shows a simple grid of bar charts to compare performance data..
+"""
+# category: bar charts
+import altair as alt
+import pandas as pd
+
+source = pd.DataFrame(
+    [
+        {"a": "a1", "b": "b1", "c": "x", "p": "0.14"},
+        {"a": "a1", "b": "b1", "c": "y", "p": "0.60"},
+        {"a": "a1", "b": "b1", "c": "z", "p": "0.03"},
+        {"a": "a1", "b": "b2", "c": "x", "p": "0.80"},
+        {"a": "a1", "b": "b2", "c": "y", "p": "0.38"},
+        {"a": "a1", "b": "b2", "c": "z", "p": "0.55"},
+        {"a": "a1", "b": "b3", "c": "x", "p": "0.11"},
+        {"a": "a1", "b": "b3", "c": "y", "p": "0.58"},
+        {"a": "a1", "b": "b3", "c": "z", "p": "0.79"},
+        {"a": "a2", "b": "b1", "c": "x", "p": "0.83"},
+        {"a": "a2", "b": "b1", "c": "y", "p": "0.87"},
+        {"a": "a2", "b": "b1", "c": "z", "p": "0.67"},
+        {"a": "a2", "b": "b2", "c": "x", "p": "0.97"},
+        {"a": "a2", "b": "b2", "c": "y", "p": "0.84"},
+        {"a": "a2", "b": "b2", "c": "z", "p": "0.90"},
+        {"a": "a2", "b": "b3", "c": "x", "p": "0.74"},
+        {"a": "a2", "b": "b3", "c": "y", "p": "0.64"},
+        {"a": "a2", "b": "b3", "c": "z", "p": "0.19"},
+        {"a": "a3", "b": "b1", "c": "x", "p": "0.57"},
+        {"a": "a3", "b": "b1", "c": "y", "p": "0.35"},
+        {"a": "a3", "b": "b1", "c": "z", "p": "0.49"},
+        {"a": "a3", "b": "b2", "c": "x", "p": "0.91"},
+        {"a": "a3", "b": "b2", "c": "y", "p": "0.38"},
+        {"a": "a3", "b": "b2", "c": "z", "p": "0.91"},
+        {"a": "a3", "b": "b3", "c": "x", "p": "0.99"},
+        {"a": "a3", "b": "b3", "c": "y", "p": "0.80"},
+        {"a": "a3", "b": "b3", "c": "z", "p": "0.37"},
+    ]
+)
+
+alt.Chart(source, width=60, height=alt.Step(8)).mark_bar().encode(
+    alt.Y("c:N").axis(None),
+    alt.X("p:Q").title(None).axis(format="%"),
+    alt.Color("c:N").title("settings").legend(orient="bottom", titleOrient="left"),
+    alt.Row("a:N").title("Factor A").header(labelAngle=0),
+    alt.Column("b:N").title("Factor B"),
+)

altair/examples/attribute_syntax/beckers_barley_trellis_plot.py

@@ -0,0 +1,28 @@
+"""
+Becker's Barley Trellis Plot
+----------------------------
+The example demonstrates the trellis charts created by Richard Becker, William Cleveland and others in the 1990s. Using the visualization technique below they identified an anomoly in a widely used agriculatural dataset, which they termed `"The Morris Mistake." <http://ml.stat.purdue.edu/stat695t/writings/Trellis.User.pdf>`_. It became their favored way of showcasing the power of this pioneering plot.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+alt.Chart(source, title="The Morris Mistake").mark_point().encode(
+    alt.X('yield:Q')
+        .title("Barley Yield (bushels/acre)")
+        .scale(zero=False)
+        .axis(grid=False),
+    alt.Y('variety:N')
+        .title("")
+        .sort('-x')
+        .axis(grid=True),
+    alt.Color('year:N')
+        .legend(title="Year"),
+    alt.Row('site:N')
+        .title("")
+        .sort(alt.EncodingSortField(field='yield', op='sum', order='descending'))
+).properties(
+    height=alt.Step(20)
+).configure_view(stroke="transparent")

altair/examples/attribute_syntax/beckers_barley_wrapped_facet.py

@@ -0,0 +1,22 @@
+"""
+Becker's Barley Trellis Plot (Wrapped Facet)
+--------------------------------------------
+The example demonstrates the trellis charts created by Richard Becker, William Cleveland and others in the 1990s. 
+This is the Altair replicate of `the VegaLite version <https://vega.github.io/vega-lite/docs/facet.html#facet-full>`_ 
+demonstrating the usage of `columns` argument to create wrapped facet.
+"""
+# category: advanced calculations
+import altair as alt
+from vega_datasets import data
+
+source = data.barley.url
+
+alt.Chart(source).mark_point().encode(
+    alt.X('median(yield):Q').scale(zero=False),
+    y='variety:O',
+    color='year:N',
+    facet=alt.Facet('site:O', columns=2),
+).properties(
+    width=200,
+    height=100,
+)

altair/examples/attribute_syntax/bump_chart.py

@@ -0,0 +1,29 @@
+"""
+Bump Chart
+----------
+This example shows a bump chart.  The data is first grouped into six-month
+intervals using pandas. The ranks are computed by Altair using a 
+window transform.
+"""
+# category: line charts
+
+import altair as alt
+from vega_datasets import data
+import pandas as pd
+
+stocks = data.stocks()
+source = stocks.groupby([pd.Grouper(key="date", freq="6M"),"symbol"]).mean().reset_index()
+
+alt.Chart(source).mark_line(point=True).encode(
+    x=alt.X("date:O").timeUnit("yearmonth").title("date"),
+    y="rank:O",
+    color=alt.Color("symbol:N")
+).transform_window(
+    rank="rank()",
+    sort=[alt.SortField("price", order="descending")],
+    groupby=["date"]
+).properties(
+    title="Bump Chart for Stock Prices",
+    width=600,
+    height=150,
+)

altair/examples/attribute_syntax/candlestick_chart.py

@@ -0,0 +1,40 @@
+"""
+Candlestick Chart
+=================
+A candlestick chart inspired from `Protovis <http://mbostock.github.io/protovis/ex/candlestick.html>`_. 
+This example shows the performance of the Chicago Board Options Exchange `Volatility Index <https://en.wikipedia.org/wiki/VIX>`_ (VIX) 
+in the summer of 2009. The thick bar represents the opening and closing prices, 
+while the thin bar shows intraday high and low prices; if the index closed higher on a given day, the bars are colored green rather than red.
+"""
+# category: advanced calculations
+import altair as alt
+from vega_datasets import data
+
+source = data.ohlc()
+
+open_close_color = alt.condition(
+    "datum.open <= datum.close",
+    alt.value("#06982d"),
+    alt.value("#ae1325")
+)
+
+base = alt.Chart(source).encode(
+    alt.X('date:T')
+        .axis(format='%m/%d', labelAngle=-45)
+        .title('Date in 2009'),
+    color=open_close_color
+)
+
+rule = base.mark_rule().encode(
+    alt.Y('low:Q')
+        .title('Price')
+        .scale(zero=False),
+    alt.Y2('high:Q')
+)
+
+bar = base.mark_bar().encode(
+    alt.Y('open:Q'),
+    alt.Y2('close:Q')
+)
+
+rule + bar

altair/examples/attribute_syntax/co2_concentration.py

@@ -0,0 +1,64 @@
+"""
+Atmospheric CO2 Concentration
+-----------------------------
+This example is a fully developed line chart that uses a window transformation.
+It was inspired by `Gregor Aisch's work at datawrapper
+<https://www.datawrapper.de/_/OHgEm/>`_.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+source = data.co2_concentration.url
+
+base = alt.Chart(
+    source,
+    title="Carbon Dioxide in the Atmosphere"
+).transform_calculate(
+    year="year(datum.Date)"
+).transform_calculate(
+    decade="floor(datum.year / 10)"
+).transform_calculate(
+    scaled_date="(datum.year % 10) + (month(datum.Date)/12)"
+).transform_window(
+    first_date='first_value(scaled_date)',
+    last_date='last_value(scaled_date)',
+    sort=[{"field": "scaled_date", "order": "ascending"}],
+    groupby=['decade'],
+    frame=[None, None]
+).transform_calculate(
+  end=(
+      "datum.first_date === datum.scaled_date ? 'first'"
+      ": datum.last_date === datum.scaled_date ? 'last'"
+      ": null"
+  )
+).encode(
+    alt.X("scaled_date:Q")
+        .title("Year into Decade")
+        .axis(tickCount=11),
+    alt.Y("CO2:Q")
+        .title("CO2 concentration in ppm")
+        .scale(zero=False)
+)
+
+line = base.mark_line().encode(
+    alt.Color("decade:O")
+        .scale(scheme="magma")
+        .legend(None)
+)
+
+text = base.encode(text="year:N")
+
+start_year = text.transform_filter(
+  alt.datum.end == 'first'
+).mark_text(baseline="top")
+
+end_year = text.transform_filter(
+  alt.datum.end == 'last'
+).mark_text(baseline="bottom")
+
+(line + start_year + end_year).configure_text(
+    align="left",
+    dx=1,
+    dy=3
+).properties(width=600, height=375)

altair/examples/attribute_syntax/comet_chart.py

@@ -0,0 +1,44 @@
+"""
+Comet Chart
+-----------
+Inspired by `Zan Armstrong's comet chart <https://www.zanarmstrong.com/infovisresearch>`_
+this plot uses ``mark_trail`` to visualize change of grouped data over time.
+A more elaborate example and explanation of creating comet charts in Altair
+is shown in `this blogpost <https://medium.com/de-dataverbinders/comet-charts-in-python-visualizing-statistical-mix-effects-and-simpsons-paradox-with-altair-6cd51fb58b7c>`_.
+"""
+# category: advanced calculations
+
+import altair as alt
+import vega_datasets
+
+alt.Chart(
+    vega_datasets.data.barley.url,
+    title='Barley Yield comparison between 1932 and 1931'
+).mark_trail().encode(
+    alt.X('year:O').title(None),
+    alt.Y('variety:N').title('Variety'),
+    alt.Size('yield:Q')
+        .scale(range=[0, 12])
+        .legend(values=[20, 60])
+        .title('Barley Yield (bushels/acre)'),
+    alt.Color('delta:Q')
+        .scale(domainMid=0)
+        .title('Yield Delta (%)'),
+    alt.Tooltip(['year:O', 'yield:Q']),
+    alt.Column('site:N').title('Site')
+).transform_pivot(
+    "year",
+    value="yield",
+    groupby=["variety", "site"]
+).transform_fold(
+    ["1931", "1932"],
+    as_=["year", "yield"]
+).transform_calculate(
+    calculate="datum['1932'] - datum['1931']",
+    as_="delta"
+).configure_legend(
+    orient='bottom',
+    direction='horizontal'
+).configure_view(
+    stroke=None
+)