first commit

.venv/Lib/site-packages/altair/__init__.py

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+# flake8: noqa
+__version__ = "4.2.0"
+
+from .vegalite import *
+from . import examples
+
+
+def load_ipython_extension(ipython):
+    from ._magics import vega, vegalite
+
+    ipython.register_magic_function(vega, "cell")
+    ipython.register_magic_function(vegalite, "cell")

.venv/Lib/site-packages/altair/_magics.py

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+"""
+Magic functions for rendering vega/vega-lite specifications
+"""
+__all__ = ["vega", "vegalite"]
+
+import json
+import warnings
+
+import IPython
+from IPython.core import magic_arguments
+import pandas as pd
+from toolz import curried
+
+from altair.vegalite import v3 as vegalite_v3
+from altair.vegalite import v4 as vegalite_v4
+from altair.vega import v5 as vega_v5
+
+try:
+    import yaml
+
+    YAML_AVAILABLE = True
+except ImportError:
+    YAML_AVAILABLE = False
+
+
+RENDERERS = {
+    "vega": {"5": vega_v5.Vega},
+    "vega-lite": {"3": vegalite_v3.VegaLite, "4": vegalite_v4.VegaLite},
+}
+
+
+TRANSFORMERS = {
+    "vega": {
+        # Vega doesn't yet have specific data transformers; use vegalite
+        "5": vegalite_v4.data_transformers,
+    },
+    "vega-lite": {
+        "3": vegalite_v3.data_transformers,
+        "4": vegalite_v4.data_transformers,
+    },
+}
+
+
+def _prepare_data(data, data_transformers):
+    """Convert input data to data for use within schema"""
+    if data is None or isinstance(data, dict):
+        return data
+    elif isinstance(data, pd.DataFrame):
+        return curried.pipe(data, data_transformers.get())
+    elif isinstance(data, str):
+        return {"url": data}
+    else:
+        warnings.warn("data of type {} not recognized".format(type(data)))
+        return data
+
+
+def _get_variable(name):
+    """Get a variable from the notebook namespace."""
+    ip = IPython.get_ipython()
+    if ip is None:
+        raise ValueError(
+            "Magic command must be run within an IPython "
+            "environemnt, in which get_ipython() is defined."
+        )
+    if name not in ip.user_ns:
+        raise NameError(
+            "argument '{}' does not match the "
+            "name of any defined variable".format(name)
+        )
+    return ip.user_ns[name]
+
+
+@magic_arguments.magic_arguments()
+@magic_arguments.argument(
+    "data",
+    nargs="*",
+    help="local variable name of a pandas DataFrame to be used as the dataset",
+)
+@magic_arguments.argument("-v", "--version", dest="version", default="5")
+@magic_arguments.argument("-j", "--json", dest="json", action="store_true")
+def vega(line, cell):
+    """Cell magic for displaying Vega visualizations in CoLab.
+
+    %%vega [name1:variable1 name2:variable2 ...] [--json] [--version='5']
+
+    Visualize the contents of the cell using Vega, optionally specifying
+    one or more pandas DataFrame objects to be used as the datasets.
+
+    If --json is passed, then input is parsed as json rather than yaml.
+    """
+    args = magic_arguments.parse_argstring(vega, line)
+
+    version = args.version
+    assert version in RENDERERS["vega"]
+    Vega = RENDERERS["vega"][version]
+    data_transformers = TRANSFORMERS["vega"][version]
+
+    def namevar(s):
+        s = s.split(":")
+        if len(s) == 1:
+            return s[0], s[0]
+        elif len(s) == 2:
+            return s[0], s[1]
+        else:
+            raise ValueError("invalid identifier: '{}'".format(s))
+
+    try:
+        data = list(map(namevar, args.data))
+    except ValueError:
+        raise ValueError("Could not parse arguments: '{}'".format(line))
+
+    if args.json:
+        spec = json.loads(cell)
+    elif not YAML_AVAILABLE:
+        try:
+            spec = json.loads(cell)
+        except json.JSONDecodeError:
+            raise ValueError(
+                "%%vega: spec is not valid JSON. "
+                "Install pyyaml to parse spec as yaml"
+            )
+    else:
+        spec = yaml.load(cell, Loader=yaml.FullLoader)
+
+    if data:
+        spec["data"] = []
+        for name, val in data:
+            val = _get_variable(val)
+            prepped = _prepare_data(val, data_transformers)
+            prepped["name"] = name
+            spec["data"].append(prepped)
+
+    return Vega(spec)
+
+
+@magic_arguments.magic_arguments()
+@magic_arguments.argument(
+    "data",
+    nargs="?",
+    help="local variablename of a pandas DataFrame to be used as the dataset",
+)
+@magic_arguments.argument("-v", "--version", dest="version", default="4")
+@magic_arguments.argument("-j", "--json", dest="json", action="store_true")
+def vegalite(line, cell):
+    """Cell magic for displaying vega-lite visualizations in CoLab.
+
+    %%vegalite [dataframe] [--json] [--version=3]
+
+    Visualize the contents of the cell using Vega-Lite, optionally
+    specifying a pandas DataFrame object to be used as the dataset.
+
+    if --json is passed, then input is parsed as json rather than yaml.
+    """
+    args = magic_arguments.parse_argstring(vegalite, line)
+    version = args.version
+    assert version in RENDERERS["vega-lite"]
+    VegaLite = RENDERERS["vega-lite"][version]
+    data_transformers = TRANSFORMERS["vega-lite"][version]
+
+    if args.json:
+        spec = json.loads(cell)
+    elif not YAML_AVAILABLE:
+        try:
+            spec = json.loads(cell)
+        except json.JSONDecodeError:
+            raise ValueError(
+                "%%vegalite: spec is not valid JSON. "
+                "Install pyyaml to parse spec as yaml"
+            )
+    else:
+        spec = yaml.load(cell, Loader=yaml.FullLoader)
+
+    if args.data is not None:
+        data = _get_variable(args.data)
+        spec["data"] = _prepare_data(data, data_transformers)
+
+    return VegaLite(spec)

.venv/Lib/site-packages/altair/datasets.py

@@ -0,0 +1,22 @@
+"""Datasets for use with Altair (deprecated)
+
+The functions here are merely thin wrappers for data access routines in the
+vega_datasets package.
+"""
+from altair.utils.deprecation import deprecated
+
+
+@deprecated("load_dataset is deprecated. " "Use the vega_datasets package instead.")
+def load_dataset(name):
+    """Load a dataset by name as a pandas.DataFrame."""
+    import vega_datasets
+
+    return vega_datasets.data(name)
+
+
+@deprecated("load_dataset is deprecated. " "Use the vega_datasets package instead.")
+def list_datasets():
+    """List the available datasets."""
+    import vega_datasets
+
+    return vega_datasets.data.list_datasets()

.venv/Lib/site-packages/altair/examples/__init__.py

@@ -0,0 +1,17 @@
+import os
+
+
+def iter_examples():
+    """Iterate over the examples in this directory.
+
+    Each item is a dict with the following keys:
+    - "name" : the unique name of the example
+    - "filename" : the full file path to the example
+    """
+    example_dir = os.path.abspath(os.path.dirname(__file__))
+    for filename in os.listdir(example_dir):
+        name, ext = os.path.splitext(filename)
+        if name.startswith('_') or ext != '.py':
+            continue
+        yield {'name': name,
+               'filename': os.path.join(example_dir, filename)}

.venv/Lib/site-packages/altair/examples/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_single(
+    on="mouseover", nearest=True, fields=["origin"], empty="none"
+)
+
+# 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", scale=alt.Scale(range=[0, 1000]), legend=None),
+    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_selection(
+    select_city
+)
+
+(background + connections + points).configure_view(stroke=None)

.venv/Lib/site-packages/altair/examples/airports.py

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+"""
+Locations of US Airports
+========================
+This is a layered geographic visualization that shows the positions of US
+airports on a background of US states.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+airports = data.airports()
+states = alt.topo_feature(data.us_10m.url, feature='states')
+
+# US states background
+background = alt.Chart(states).mark_geoshape(
+    fill='lightgray',
+    stroke='white'
+).properties(
+    width=500,
+    height=300
+).project('albersUsa')
+
+# airport positions on background
+points = alt.Chart(airports).mark_circle(
+    size=10,
+    color='steelblue'
+).encode(
+    longitude='longitude:Q',
+    latitude='latitude:Q',
+    tooltip=['name', 'city', 'state']
+)
+
+background + points

.venv/Lib/site-packages/altair/examples/airports_count.py

@@ -0,0 +1,39 @@
+"""
+Locations of US Airports
+========================
+This is a layered geographic visualization that shows the positions of US
+airports on a background of US states.
+"""
+# category: maps
+import altair as alt
+from vega_datasets import data
+
+airports = data.airports.url
+states = alt.topo_feature(data.us_10m.url, feature='states')
+
+# US states background
+background = alt.Chart(states).mark_geoshape(
+    fill='lightgray',
+    stroke='white'
+).properties(
+    width=500,
+    height=300
+).project('albersUsa')
+
+# airport positions on background
+points = alt.Chart(airports).transform_aggregate(
+    latitude='mean(latitude)',
+    longitude='mean(longitude)',
+    count='count()',
+    groupby=['state']
+).mark_circle().encode(
+    longitude='longitude:Q',
+    latitude='latitude:Q',
+    size=alt.Size('count:Q', title='Number of Airports'),
+    color=alt.value('steelblue'),
+    tooltip=['state:N','count:Q']
+).properties(
+    title='Number of airports in US'
+)
+
+background + points

.venv/Lib/site-packages/altair/examples/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=alt.Scale(zero=False)),
+    alt.Y('Y', scale=alt.Scale(zero=False)),
+    alt.Facet('Series', columns=2),
+).properties(
+    width=180,
+    height=180,
+)

.venv/Lib/site-packages/altair/examples/area_chart_gradient.py

@@ -0,0 +1,30 @@
+"""
+Area Chart with Gradient
+------------------------
+This example shows how to make an area chart with a gradient fill. 
+For more information about gradient options see the Vega-Lite `Gradient documentation <https://vega.github.io/vega-lite/docs/types.html#gradient>`_.
+"""
+# category: area charts
+
+import altair as alt
+from vega_datasets import data
+
+source = data.stocks()
+
+alt.Chart(source).transform_filter(
+    'datum.symbol==="GOOG"'
+).mark_area(
+    line={'color':'darkgreen'},
+    color=alt.Gradient(
+        gradient='linear', 
+        stops=[alt.GradientStop(color='white', offset=0), 
+               alt.GradientStop(color='darkgreen', offset=1)], 
+        x1=1, 
+        x2=1, 
+        y1=1, 
+        y2=0
+    )
+).encode(
+    alt.X('date:T'), 
+    alt.Y('price:Q')
+)

.venv/Lib/site-packages/altair/examples/bar_and_line_with_dual_axis.py

@@ -0,0 +1,22 @@
+"""
+Bar Chart with Line on Dual Axis
+--------------------------------
+This example shows how to combine two plots and keep their axes.
+
+For a more polished version of this chart, see :ref:`gallery_wheat_wages`.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.wheat()
+
+base = alt.Chart(source).encode(x='year:O')
+
+bar = base.mark_bar().encode(y='wheat:Q')
+
+line =  base.mark_line(color='red').encode(
+    y='wages:Q'
+)
+
+(bar + line).properties(width=600)

.venv/Lib/site-packages/altair/examples/bar_chart_horizontal.py

@@ -0,0 +1,15 @@
+"""
+Horizontal Bar Chart
+--------------------
+This example is a bar chart drawn horizontally by putting the quantitative value on the x axis.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.wheat()
+
+alt.Chart(source).mark_bar().encode(
+    x='wheat:Q',
+    y="year:O"
+).properties(height=700)

.venv/Lib/site-packages/altair/examples/bar_chart_sorted.py

@@ -0,0 +1,15 @@
+"""
+Sorted Bar Chart
+================
+This example shows a bar chart sorted by a calculated value.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+alt.Chart(source).mark_bar().encode(
+    x='sum(yield):Q',
+    y=alt.Y('site:N', sort='-x')
+)

.venv/Lib/site-packages/altair/examples/bar_chart_trellis_compact.py

@@ -0,0 +1,50 @@
+"""
+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(
+    y=alt.Y("c:N", axis=None),
+    x=alt.X("p:Q", title=None, axis=alt.Axis(format="%")),
+    color=alt.Color(
+        "c:N", title="settings", legend=alt.Legend(orient="bottom", titleOrient="left")
+    ),
+    row=alt.Row("a:N", title="Factor A", header=alt.Header(labelAngle=0)),
+    column=alt.Column("b:N", title="Factor B"),
+)

.venv/Lib/site-packages/altair/examples/bar_chart_with_highlighted_bar.py

@@ -0,0 +1,21 @@
+"""
+Bar Chart with Highlighted Bar
+------------------------------
+This example shows a basic bar chart with a single bar highlighted.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.wheat()
+
+alt.Chart(source).mark_bar().encode(
+    x='year:O',
+    y="wheat:Q",
+    # The highlight will be set on the result of a conditional statement
+    color=alt.condition(
+        alt.datum.year == 1810,  # If the year is 1810 this test returns True,
+        alt.value('orange'),     # which sets the bar orange.
+        alt.value('steelblue')   # And if it's not true it sets the bar steelblue.
+    )
+).properties(width=600)

.venv/Lib/site-packages/altair/examples/bar_chart_with_highlighted_segment.py

@@ -0,0 +1,30 @@
+"""
+Bar Chart with Highlighted Segment
+----------------------------------
+This example shows a bar chart that highlights values beyond a threshold.
+"""
+import altair as alt
+import pandas as pd
+from vega_datasets import data
+
+source = data.wheat()
+threshold = pd.DataFrame([{"threshold": 90}])
+
+bars = alt.Chart(source).mark_bar().encode(
+    x="year:O",
+    y="wheat:Q",
+)
+
+highlight = alt.Chart(source).mark_bar(color="#e45755").encode(
+    x='year:O',
+    y='baseline:Q',
+    y2='wheat:Q'
+).transform_filter(
+    alt.datum.wheat > 90
+).transform_calculate("baseline", "90")
+
+rule = alt.Chart(threshold).mark_rule().encode(
+    y='threshold:Q'
+)
+
+(bars + highlight + rule).properties(width=600)

.venv/Lib/site-packages/altair/examples/bar_chart_with_labels.py

@@ -0,0 +1,25 @@
+"""
+Bar Chart with Labels
+=====================
+This example shows a basic horizontal bar chart with labels created with Altair.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.wheat()
+
+bars = alt.Chart(source).mark_bar().encode(
+    x='wheat:Q',
+    y="year:O"
+)
+
+text = bars.mark_text(
+    align='left',
+    baseline='middle',
+    dx=3  # Nudges text to right so it doesn't appear on top of the bar
+).encode(
+    text='wheat:Q'
+)
+
+(bars + text).properties(height=900)

.venv/Lib/site-packages/altair/examples/bar_chart_with_mean_line.py

@@ -0,0 +1,21 @@
+"""
+Bar Chart with Line at Mean
+---------------------------
+This example shows the mean value overlayed on a bar chart.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.wheat()
+
+bar = alt.Chart(source).mark_bar().encode(
+    x='year:O',
+    y='wheat:Q'
+)
+
+rule = alt.Chart(source).mark_rule(color='red').encode(
+    y='mean(wheat):Q'
+)
+
+(bar + rule).properties(width=600)

.venv/Lib/site-packages/altair/examples/bar_chart_with_negatives.py

@@ -0,0 +1,20 @@
+"""
+Bar Chart with Negative Values
+==============================
+This example shows a bar chart with both positive and negative values.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.us_employment()
+
+alt.Chart(source).mark_bar().encode(
+    x="month:T",
+    y="nonfarm_change:Q",
+    color=alt.condition(
+        alt.datum.nonfarm_change > 0,
+        alt.value("steelblue"),  # The positive color
+        alt.value("orange")  # The negative color
+    )
+).properties(width=600)

.venv/Lib/site-packages/altair/examples/bar_rounded.py

@@ -0,0 +1,19 @@
+"""
+Bar Chart with rounded edges
+----------------------------
+This example shows how to create a bar chart with rounded edges.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.seattle_weather()
+
+alt.Chart(source).mark_bar(
+    cornerRadiusTopLeft=3,
+    cornerRadiusTopRight=3
+).encode(
+    x='month(date):O',
+    y='count():Q',
+    color='weather:N'
+)

.venv/Lib/site-packages/altair/examples/bar_with_rolling_mean.py

@@ -0,0 +1,27 @@
+"""
+Bar Chart with Rolling Mean
+---------------------------
+A bar chart overlayed with a rolling mean. In this example the average of values over the previous decade is displayed as a line.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.wheat()
+
+bar = alt.Chart(source).mark_bar().encode(
+    x='year:O',
+    y='wheat:Q'
+)
+
+line = alt.Chart(source).mark_line(color='red').transform_window(
+    # The field to average
+    rolling_mean='mean(wheat)',
+    # The number of values before and after the current value to include.
+    frame=[-9, 0]
+).encode(
+    x='year:O',
+    y='rolling_mean:Q'
+)
+
+(bar + line).properties(width=600)

.venv/Lib/site-packages/altair/examples/beckers_barley_trellis_plot.py

@@ -0,0 +1,33 @@
+"""
+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=alt.Scale(zero=False),
+        axis=alt.Axis(grid=False)
+    ),
+    alt.Y(
+        'variety:N',
+        title="",
+        sort='-x',
+        axis=alt.Axis(grid=True)
+    ),
+    color=alt.Color('year:N', legend=alt.Legend(title="Year")),
+    row=alt.Row(
+        'site:N',
+        title="",
+        sort=alt.EncodingSortField(field='yield', op='sum', order='descending'),
+    )
+).properties(
+    height=alt.Step(20)
+).configure_view(stroke="transparent")

.venv/Lib/site-packages/altair/examples/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: other charts
+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=alt.Scale(zero=False)),
+    y='variety:O',
+    color='year:N',
+    facet=alt.Facet('site:O', columns=2),
+).properties(
+    width=200,
+    height=100,
+)

.venv/Lib/site-packages/altair/examples/binned_heatmap.py

@@ -0,0 +1,16 @@
+"""
+Binned Heatmap
+--------------
+This example shows how to make a heatmap from binned quantitative data.
+"""
+# category: other charts
+import altair as alt
+from vega_datasets import data
+
+source = data.movies.url
+
+alt.Chart(source).mark_rect().encode(
+    alt.X('IMDB_Rating:Q', bin=alt.Bin(maxbins=60)),
+    alt.Y('Rotten_Tomatoes_Rating:Q', bin=alt.Bin(maxbins=40)),
+    alt.Color('count():Q', scale=alt.Scale(scheme='greenblue'))
+)

.venv/Lib/site-packages/altair/examples/binned_scatterplot.py

@@ -0,0 +1,16 @@
+"""
+Binned Scatterplot
+------------------
+This example shows how to make a binned scatterplot.
+"""
+# category: scatter plots
+import altair as alt
+from vega_datasets import data
+
+source = data.movies.url
+
+alt.Chart(source).mark_circle().encode(
+    alt.X('IMDB_Rating:Q', bin=True),
+    alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
+    size='count()'
+)

.venv/Lib/site-packages/altair/examples/boxplot.py

@@ -0,0 +1,18 @@
+"""
+Boxplot with Min/Max Whiskers
+------------------------------
+This example shows how to make a boxplot using US Population data from 2000.  
+Note that the default value of the `extent` property is 1.5,
+which represents the convention of extending the whiskers
+to the furthest points within 1.5 * IQR from the first and third quartile.
+"""
+# category: other charts
+import altair as alt
+from vega_datasets import data
+
+source = data.population.url
+
+alt.Chart(source).mark_boxplot(extent='min-max').encode(
+    x='age:O',
+    y='people:Q'
+)

.venv/Lib/site-packages/altair/examples/bubble_plot.py

@@ -0,0 +1,16 @@
+"""
+Bubble Plot
+-----------------
+This example shows how to make a bubble plot.
+"""
+# category: scatter plots
+import altair as alt
+from vega_datasets import data
+
+source = data.cars()
+
+alt.Chart(source).mark_point().encode(
+    x='Horsepower',
+    y='Miles_per_Gallon',
+    size='Acceleration'
+)

.venv/Lib/site-packages/altair/examples/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,
+)

.venv/Lib/site-packages/altair/examples/candlestick_chart.py

@@ -0,0 +1,44 @@
+"""
+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: other charts
+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=alt.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=alt.Scale(zero=False),
+    ),
+    alt.Y2('high:Q')
+)
+
+bar = base.mark_bar().encode(
+    alt.Y('open:Q'),
+    alt.Y2('close:Q')
+)
+
+rule + bar

.venv/Lib/site-packages/altair/examples/choropleth.py

@@ -0,0 +1,23 @@
+"""
+Choropleth Map
+==============
+A choropleth map of unemployment rate per county in the US
+"""
+# category: maps
+import altair as alt
+from vega_datasets import data
+
+counties = alt.topo_feature(data.us_10m.url, 'counties')
+source = data.unemployment.url
+
+alt.Chart(counties).mark_geoshape().encode(
+    color='rate:Q'
+).transform_lookup(
+    lookup='id',
+    from_=alt.LookupData(source, 'id', ['rate'])
+).project(
+    type='albersUsa'
+).properties(
+    width=500,
+    height=300
+)

.venv/Lib/site-packages/altair/examples/choropleth_repeat.py

@@ -0,0 +1,28 @@
+"""
+Repeated Choropleth Map
+=======================
+Three choropleths representing disjoint data from the same table.
+"""
+# category: maps
+import altair as alt
+from vega_datasets import data
+
+states = alt.topo_feature(data.us_10m.url, 'states')
+source = data.population_engineers_hurricanes.url
+variable_list = ['population', 'engineers', 'hurricanes']
+
+alt.Chart(states).mark_geoshape().encode(
+    alt.Color(alt.repeat('row'), type='quantitative')
+).transform_lookup(
+    lookup='id',
+    from_=alt.LookupData(source, 'id', variable_list)
+).properties(
+    width=500,
+    height=300
+).project(
+    type='albersUsa'
+).repeat(
+    row=variable_list
+).resolve_scale(
+    color='independent'
+)

.venv/Lib/site-packages/altair/examples/co2_concentration.py

@@ -0,0 +1,65 @@
+"""
+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(
+    x=alt.X(
+        "scaled_date:Q",
+        axis=alt.Axis(title="Year into Decade", tickCount=11)
+    ),
+    y=alt.Y(
+        "CO2:Q",
+        title="CO2 concentration in ppm",
+        scale=alt.Scale(zero=False)
+    )
+)
+
+line = base.mark_line().encode(
+    color=alt.Color(
+        "decade:O",
+        scale=alt.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)

.venv/Lib/site-packages/altair/examples/comet_chart.py

@@ -0,0 +1,32 @@
+"""
+Comet Chart Example
+----------------------------
+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: other charts
+
+import altair as alt
+import vega_datasets
+
+(
+    alt.Chart(vega_datasets.data.barley.url)
+    .transform_pivot("year", value="yield", groupby=["variety", "site"])
+    .transform_fold(["1931", "1932"], as_=["year", "yield"])
+    .transform_calculate(calculate="datum['1932'] - datum['1931']", as_="delta")
+    .mark_trail()
+    .encode(
+        x=alt.X('year:O', title=None), 
+        y=alt.Y('variety:N', title='Variety'),
+        size=alt.Size('yield:Q', scale=alt.Scale(range=[0, 12]), legend=alt.Legend(values=[20, 60], title='Barley Yield (bushels/acre)')),
+        color=alt.Color('delta:Q', scale=alt.Scale(domainMid=0), legend=alt.Legend(title='Yield Delta (%)')),
+        tooltip=alt.Tooltip(['year:O', 'yield:Q']),
+        column=alt.Column('site:N', title='Site')
+
+    )
+    .configure_view(stroke=None)
+    .configure_legend(orient='bottom', direction='horizontal')
+    .properties(title='Barley Yield comparison between 1932 and 1931')
+)

.venv/Lib/site-packages/altair/examples/connected_scatterplot.py

@@ -0,0 +1,18 @@
+"""
+Connected Scatterplot (Lines with Custom Paths)
+-----------------------------------------------
+
+This example show how the order encoding can be used to draw a custom path. The dataset tracks miles driven per capita along with gas prices annually from 1956 to 2010.
+It is based on Hannah Fairfield's article 'Driving Shifts Into Reverse'. See https://archive.nytimes.com/www.nytimes.com/imagepages/2010/05/02/business/02metrics.html for the original.
+"""
+# category: scatter plots
+import altair as alt
+from vega_datasets import data
+
+source = data.driving()
+
+alt.Chart(source).mark_line(point=True).encode(
+    alt.X('miles', scale=alt.Scale(zero=False)),
+    alt.Y('gas', scale=alt.Scale(zero=False)),
+    order='year'
+)

.venv/Lib/site-packages/altair/examples/cumulative_count_chart.py

@@ -0,0 +1,21 @@
+"""
+Cumulative Count Chart
+----------------------
+This example shows an area chart with cumulative count.
+Adapted from https://vega.github.io/vega-lite/examples/area_cumulative_freq.html
+
+"""
+# category: area charts
+
+import altair as alt
+from vega_datasets import data
+
+source = data.movies.url
+
+alt.Chart(source).transform_window(
+    cumulative_count="count()",
+    sort=[{"field": "IMDB_Rating"}],
+).mark_area().encode(
+    x="IMDB_Rating:Q",
+    y="cumulative_count:Q"
+)

.venv/Lib/site-packages/altair/examples/cumulative_wiki_donations.py

@@ -0,0 +1,17 @@
+"""
+Cumulative Wikipedia Donations
+==============================
+
+This chart shows cumulative donations to Wikipedia over the past 10 years. Inspired by this `Reddit post <https://www.reddit.com/r/dataisbeautiful/comments/7guwd0/cumulative_wikimedia_donations_over_the_past_10/>`_ but using lines instead of areas.
+"""
+# category: case studies
+import altair as alt
+
+source = "https://frdata.wikimedia.org/donationdata-vs-day.csv"
+
+alt.Chart(source).mark_line().encode(
+    alt.X('monthdate(date):T', title='Month', axis=alt.Axis(format='%B')),
+    alt.Y('max(ytdsum):Q', title='Cumulative Donations', stack=None),
+    alt.Color('year(date):O', legend=alt.Legend(title='Year')),
+    alt.Order('year(data):O')
+)

.venv/Lib/site-packages/altair/examples/density_facet.py

@@ -0,0 +1,28 @@
+"""
+Faceted Density Estimates
+-------------------------
+Density estimates of measurements for each iris flower feature
+"""
+# category: area charts
+
+import altair as alt
+from vega_datasets import data
+
+source = data.iris()
+
+alt.Chart(source).transform_fold(
+    ['petalWidth', 
+     'petalLength', 
+     'sepalWidth', 
+     'sepalLength'], 
+    as_ = ['Measurement_type', 'value']
+).transform_density(
+    density='value', 
+    bandwidth=0.3, 
+    groupby=['Measurement_type'], 
+    extent= [0, 8]
+).mark_area().encode(
+    alt.X('value:Q'), 
+    alt.Y('density:Q'),
+    alt.Row('Measurement_type:N')
+).properties(width=300, height=50)

.venv/Lib/site-packages/altair/examples/density_stack.py

@@ -0,0 +1,35 @@
+"""
+Stacked Density Estimates
+-------------------------
+To plot a stacked graph of estimates, use a shared ``extent`` and a fixed
+number of subdivision ``steps`` to ensure that the points for each area align
+well.  Density estimates of measurements for each iris flower feature are plot
+in a stacked method.  In addition, setting ``counts`` to true multiplies the
+densities by the number of data points in each group, preserving proportional
+differences.
+"""
+# category: area charts
+
+import altair as alt
+from vega_datasets import data
+
+source = data.iris()
+
+alt.Chart(source).transform_fold(
+    ['petalWidth', 
+     'petalLength', 
+     'sepalWidth', 
+     'sepalLength'], 
+    as_ = ['Measurement_type', 'value']
+).transform_density(
+    density='value', 
+    bandwidth=0.3, 
+    groupby=['Measurement_type'], 
+    extent= [0, 8], 
+    counts = True, 
+    steps=200
+).mark_area().encode(
+    alt.X('value:Q'), 
+    alt.Y('density:Q', stack='zero'),
+    alt.Color('Measurement_type:N')
+).properties(width=400, height=100)

.venv/Lib/site-packages/altair/examples/diverging_stacked_bar_chart.py

@@ -0,0 +1,367 @@
+"""
+Diverging Stacked Bar Chart
+---------------------------
+This example shows a diverging stacked bar chart for sentiments towards a set of eight questions, displayed as percentages with neutral responses straddling the 0% mark.
+"""
+# category: bar charts
+import altair as alt
+
+source = alt.pd.DataFrame([
+      {
+        "question": "Question 1",
+        "type": "Strongly disagree",
+        "value": 24,
+        "percentage": 0.7,
+        "percentage_start": -19.1,
+        "percentage_end": -18.4
+      },
+      {
+        "question": "Question 1",
+        "type": "Disagree",
+        "value": 294,
+        "percentage": 9.1,
+        "percentage_start": -18.4,
+        "percentage_end": -9.2
+      },
+      {
+        "question": "Question 1",
+        "type": "Neither agree nor disagree",
+        "value": 594,
+        "percentage": 18.5,
+        "percentage_start": -9.2,
+        "percentage_end": 9.2
+      },
+      {
+        "question": "Question 1",
+        "type": "Agree",
+        "value": 1927,
+        "percentage": 59.9,
+        "percentage_start": 9.2,
+        "percentage_end": 69.2
+      },
+      {
+        "question": "Question 1",
+        "type": "Strongly agree",
+        "value": 376,
+        "percentage": 11.7,
+        "percentage_start": 69.2,
+        "percentage_end": 80.9
+      },
+
+      {
+        "question": "Question 2",
+        "type": "Strongly disagree",
+        "value": 2,
+        "percentage": 18.2,
+        "percentage_start": -36.4,
+        "percentage_end": -18.2
+      },
+      {
+        "question": "Question 2",
+        "type": "Disagree",
+        "value": 2,
+        "percentage": 18.2,
+        "percentage_start": -18.2,
+        "percentage_end": 0
+      },
+      {
+        "question": "Question 2",
+        "type": "Neither agree nor disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": 0,
+        "percentage_end": 0
+      },
+      {
+        "question": "Question 2",
+        "type": "Agree",
+        "value": 7,
+        "percentage": 63.6,
+        "percentage_start": 0,
+        "percentage_end": 63.6
+      },
+      {
+        "question": "Question 2",
+        "type": "Strongly agree",
+        "value": 11,
+        "percentage": 0,
+        "percentage_start": 63.6,
+        "percentage_end": 63.6
+      },
+
+      {
+        "question": "Question 3",
+        "type": "Strongly disagree",
+        "value": 2,
+        "percentage": 20,
+        "percentage_start": -30,
+        "percentage_end": -10
+      },
+      {
+        "question": "Question 3",
+        "type": "Disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": -10,
+        "percentage_end": -10
+      },
+      {
+        "question": "Question 3",
+        "type": "Neither agree nor disagree",
+        "value": 2,
+        "percentage": 20,
+        "percentage_start": -10,
+        "percentage_end": 10
+      },
+      {
+        "question": "Question 3",
+        "type": "Agree",
+        "value": 4,
+        "percentage": 40,
+        "percentage_start": 10,
+        "percentage_end": 50
+      },
+      {
+        "question": "Question 3",
+        "type": "Strongly agree",
+        "value": 2,
+        "percentage": 20,
+        "percentage_start": 50,
+        "percentage_end": 70
+      },
+
+      {
+        "question": "Question 4",
+        "type": "Strongly disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": -15.6,
+        "percentage_end": -15.6
+      },
+      {
+        "question": "Question 4",
+        "type": "Disagree",
+        "value": 2,
+        "percentage": 12.5,
+        "percentage_start": -15.6,
+        "percentage_end": -3.1
+      },
+      {
+        "question": "Question 4",
+        "type": "Neither agree nor disagree",
+        "value": 1,
+        "percentage": 6.3,
+        "percentage_start": -3.1,
+        "percentage_end": 3.1
+      },
+      {
+        "question": "Question 4",
+        "type": "Agree",
+        "value": 7,
+        "percentage": 43.8,
+        "percentage_start": 3.1,
+        "percentage_end": 46.9
+      },
+      {
+        "question": "Question 4",
+        "type": "Strongly agree",
+        "value": 6,
+        "percentage": 37.5,
+        "percentage_start": 46.9,
+        "percentage_end": 84.4
+      },
+
+      {
+        "question": "Question 5",
+        "type": "Strongly disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": -10.4,
+        "percentage_end": -10.4
+      },
+      {
+        "question": "Question 5",
+        "type": "Disagree",
+        "value": 1,
+        "percentage": 4.2,
+        "percentage_start": -10.4,
+        "percentage_end": -6.3
+      },
+      {
+        "question": "Question 5",
+        "type": "Neither agree nor disagree",
+        "value": 3,
+        "percentage": 12.5,
+        "percentage_start": -6.3,
+        "percentage_end": 6.3
+      },
+      {
+        "question": "Question 5",
+        "type": "Agree",
+        "value": 16,
+        "percentage": 66.7,
+        "percentage_start": 6.3,
+        "percentage_end": 72.9
+      },
+      {
+        "question": "Question 5",
+        "type": "Strongly agree",
+        "value": 4,
+        "percentage": 16.7,
+        "percentage_start": 72.9,
+        "percentage_end": 89.6
+      },
+
+      {
+        "question": "Question 6",
+        "type": "Strongly disagree",
+        "value": 1,
+        "percentage": 6.3,
+        "percentage_start": -18.8,
+        "percentage_end": -12.5
+      },
+      {
+        "question": "Question 6",
+        "type": "Disagree",
+        "value": 1,
+        "percentage": 6.3,
+        "percentage_start": -12.5,
+        "percentage_end": -6.3
+      },
+      {
+        "question": "Question 6",
+        "type": "Neither agree nor disagree",
+        "value": 2,
+        "percentage": 12.5,
+        "percentage_start": -6.3,
+        "percentage_end": 6.3
+      },
+      {
+        "question": "Question 6",
+        "type": "Agree",
+        "value": 9,
+        "percentage": 56.3,
+        "percentage_start": 6.3,
+        "percentage_end": 62.5
+      },
+      {
+        "question": "Question 6",
+        "type": "Strongly agree",
+        "value": 3,
+        "percentage": 18.8,
+        "percentage_start": 62.5,
+        "percentage_end": 81.3
+      },
+
+      {
+        "question": "Question 7",
+        "type": "Strongly disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": -10,
+        "percentage_end": -10
+      },
+      {
+        "question": "Question 7",
+        "type": "Disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": -10,
+        "percentage_end": -10
+      },
+      {
+        "question": "Question 7",
+        "type": "Neither agree nor disagree",
+        "value": 1,
+        "percentage": 20,
+        "percentage_start": -10,
+        "percentage_end": 10
+      },
+      {
+        "question": "Question 7",
+        "type": "Agree",
+        "value": 4,
+        "percentage": 80,
+        "percentage_start": 10,
+        "percentage_end": 90
+      },
+      {
+        "question": "Question 7",
+        "type": "Strongly agree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": 90,
+        "percentage_end": 90
+      },
+
+      {
+        "question": "Question 8",
+        "type": "Strongly disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": 0,
+        "percentage_end": 0
+      },
+      {
+        "question": "Question 8",
+        "type": "Disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": 0,
+        "percentage_end": 0
+      },
+      {
+        "question": "Question 8",
+        "type": "Neither agree nor disagree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": 0,
+        "percentage_end": 0
+      },
+      {
+        "question": "Question 8",
+        "type": "Agree",
+        "value": 0,
+        "percentage": 0,
+        "percentage_start": 0,
+        "percentage_end": 0
+      },
+      {
+        "question": "Question 8",
+        "type": "Strongly agree",
+        "value": 2,
+        "percentage": 100,
+        "percentage_start": 0,
+        "percentage_end": 100
+      }
+])
+
+color_scale = alt.Scale(
+    domain=[
+        "Strongly disagree",
+        "Disagree",
+        "Neither agree nor disagree",
+        "Agree",
+        "Strongly agree"
+    ],
+    range=["#c30d24", "#f3a583", "#cccccc", "#94c6da", "#1770ab"]
+)
+
+y_axis = alt.Axis(
+    title='Question',
+    offset=5,
+    ticks=False,
+    minExtent=60,
+    domain=False
+)
+
+alt.Chart(source).mark_bar().encode(
+    x='percentage_start:Q',
+    x2='percentage_end:Q',
+    y=alt.Y('question:N', axis=y_axis),
+    color=alt.Color(
+        'type:N',
+        legend=alt.Legend( title='Response'),
+        scale=color_scale,
+    )
+)

.venv/Lib/site-packages/altair/examples/donut_chart.py

@@ -0,0 +1,18 @@
+"""
+Donut Chart
+-----------
+This example shows how to make a Donut Chart using ``mark_arc``.
+This is adapted from a corresponding Vega-Lite Example:
+`Donut Chart <https://vega.github.io/vega-lite/examples/arc_donut.html>`_.
+"""
+# category: circular plots
+
+import pandas as pd
+import altair as alt
+
+source = pd.DataFrame({"category": [1, 2, 3, 4, 5, 6], "value": [4, 6, 10, 3, 7, 8]})
+
+alt.Chart(source).mark_arc(innerRadius=50).encode(
+    theta=alt.Theta(field="value", type="quantitative"),
+    color=alt.Color(field="category", type="nominal"),
+)

.venv/Lib/site-packages/altair/examples/dot_dash_plot.py

@@ -0,0 +1,40 @@
+"""
+Dot Dash Plot
+=============
+How to make the dot-dash plot presented in Edward Tufte's `Visual Display of Quantitative Information <https://www.edwardtufte.com/tufte/books_vdqi>`_. Based
+on a JavaScript implementation by `g3o2 <https://bl.ocks.org/g3o2/bd4362574137061c243a2994ba648fb8>`_.
+"""
+# category: scatter plots
+import altair as alt
+from vega_datasets import data
+
+source = data.cars()
+
+# Configure the options common to all layers
+brush = alt.selection(type='interval')
+base = alt.Chart(source).add_selection(brush)
+
+# Configure the points
+points = base.mark_point().encode(
+    x=alt.X('Miles_per_Gallon', title=''),
+    y=alt.Y('Horsepower', title=''),
+    color=alt.condition(brush, 'Origin', alt.value('grey'))
+)
+
+# Configure the ticks
+tick_axis = alt.Axis(labels=False, domain=False, ticks=False)
+
+x_ticks = base.mark_tick().encode(
+    alt.X('Miles_per_Gallon', axis=tick_axis),
+    alt.Y('Origin', title='', axis=tick_axis),
+    color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
+)
+
+y_ticks = base.mark_tick().encode(
+    alt.X('Origin', title='', axis=tick_axis),
+    alt.Y('Horsepower', axis=tick_axis),
+    color=alt.condition(brush, 'Origin', alt.value('lightgrey'))
+)
+
+# Build the chart
+y_ticks | (points & x_ticks)

.venv/Lib/site-packages/altair/examples/errorbars_with_ci.py

@@ -0,0 +1,24 @@
+"""
+Error Bars showing Confidence Interval
+======================================
+This example shows how to show error bars using confidence intervals.
+The confidence intervals are computed internally in vega by a non-parametric
+`bootstrap of the mean <https://github.com/vega/vega-statistics/blob/master/src/bootstrapCI.js>`_.
+"""
+# category: other charts
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+error_bars = alt.Chart(source).mark_errorbar(extent='ci').encode(
+  x=alt.X('yield:Q', scale=alt.Scale(zero=False)),
+  y=alt.Y('variety:N')
+)
+
+points = alt.Chart(source).mark_point(filled=True, color='black').encode(
+  x=alt.X('yield:Q', aggregate='mean'),
+  y=alt.Y('variety:N'),
+)
+
+error_bars + points

.venv/Lib/site-packages/altair/examples/errorbars_with_std.py

@@ -0,0 +1,23 @@
+"""
+Error Bar with Standard Deviation
+---------------------------------
+This example shows how to show error bars with standard deviation using crop yields data of different
+in the years of 1930s.
+"""
+# category: other charts
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+error_bars = alt.Chart(source).mark_errorbar(extent='stdev').encode(
+  x=alt.X('yield:Q', scale=alt.Scale(zero=False)),
+  y=alt.Y('variety:N')
+)
+
+points = alt.Chart(source).mark_point(filled=True, color='black').encode(
+  x=alt.X('yield:Q', aggregate='mean'),
+  y=alt.Y('variety:N'),
+)
+
+error_bars + points

.venv/Lib/site-packages/altair/examples/falkensee.py

@@ -0,0 +1,82 @@
+"""
+Falkensee
+-----------------------
+This example is a reproduction of the Falkensee plot found in the vega-lite examples.
+"""
+# category: case studies
+import altair as alt
+
+source = [
+      {"year": "1875", "population": 1309},
+      {"year": "1890", "population": 1558},
+      {"year": "1910", "population": 4512},
+      {"year": "1925", "population": 8180},
+      {"year": "1933", "population": 15915},
+      {"year": "1939", "population": 24824},
+      {"year": "1946", "population": 28275},
+      {"year": "1950", "population": 29189},
+      {"year": "1964", "population": 29881},
+      {"year": "1971", "population": 26007},
+      {"year": "1981", "population": 24029},
+      {"year": "1985", "population": 23340},
+      {"year": "1989", "population": 22307},
+      {"year": "1990", "population": 22087},
+      {"year": "1991", "population": 22139},
+      {"year": "1992", "population": 22105},
+      {"year": "1993", "population": 22242},
+      {"year": "1994", "population": 22801},
+      {"year": "1995", "population": 24273},
+      {"year": "1996", "population": 25640},
+      {"year": "1997", "population": 27393},
+      {"year": "1998", "population": 29505},
+      {"year": "1999", "population": 32124},
+      {"year": "2000", "population": 33791},
+      {"year": "2001", "population": 35297},
+      {"year": "2002", "population": 36179},
+      {"year": "2003", "population": 36829},
+      {"year": "2004", "population": 37493},
+      {"year": "2005", "population": 38376},
+      {"year": "2006", "population": 39008},
+      {"year": "2007", "population": 39366},
+      {"year": "2008", "population": 39821},
+      {"year": "2009", "population": 40179},
+      {"year": "2010", "population": 40511},
+      {"year": "2011", "population": 40465},
+      {"year": "2012", "population": 40905},
+      {"year": "2013", "population": 41258},
+      {"year": "2014", "population": 41777}
+    ]
+
+source2 = [{
+            "start": "1933",
+            "end": "1945",
+            "event": "Nazi Rule"
+          },
+          {
+            "start": "1948",
+            "end": "1989",
+            "event": "GDR (East Germany)"
+          }]
+
+
+source = alt.pd.DataFrame(source)
+source2 = alt.pd.DataFrame(source2)
+
+
+line = alt.Chart(source).mark_line(color='#333').encode(
+    alt.X('year:T', axis=alt.Axis(format='%Y')),
+    y='population'
+).properties(
+    width=500,
+    height=300
+)
+
+point = line.mark_point(color='#333')
+
+rect = alt.Chart(source2).mark_rect().encode(
+    x='start:T',
+    x2='end:T',
+    color='event:N'
+)
+
+rect + line + point

.venv/Lib/site-packages/altair/examples/filled_step_chart.py

@@ -0,0 +1,19 @@
+"""
+Filled Step Chart
+-----------------
+This example shows Google's stock price over time as a step chart with its area filled in and its line emphasized.
+"""
+# category: line charts
+import altair as alt
+from vega_datasets import data
+
+source = data.stocks()
+
+alt.Chart(source).mark_area(
+    color="lightblue",
+    interpolate='step-after',
+    line=True
+).encode(
+    x='date',
+    y='price'
+).transform_filter(alt.datum.symbol == 'GOOG')

.venv/Lib/site-packages/altair/examples/gantt_chart.py

@@ -0,0 +1,20 @@
+"""
+Gantt Chart
+-----------------
+This example shows how to make a simple Gantt chart.
+"""
+# category: other charts
+import altair as alt
+import pandas as pd
+
+source = pd.DataFrame([
+    {"task": "A", "start": 1, "end": 3},
+    {"task": "B", "start": 3, "end": 8},
+    {"task": "C", "start": 8, "end": 10}
+])
+
+alt.Chart(source).mark_bar().encode(
+    x='start',
+    x2='end',
+    y='task'
+)

.venv/Lib/site-packages/altair/examples/gapminder_bubble_plot.py

@@ -0,0 +1,18 @@
+"""
+Gapminder Bubble Plot
+=====================
+This example shows how to make a bubble plot showing the correlation between
+health and income for 187 countries in the world (modified from an example
+in Lisa Charlotte Rost's blog post `'One Chart, Twelve Charting Libraries' <http://lisacharlotterost.github.io/2016/05/17/one-chart-code/>`_.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+source = data.gapminder_health_income.url
+
+alt.Chart(source).mark_circle().encode(
+    alt.X('income:Q', scale=alt.Scale(type='log')),
+    alt.Y('health:Q', scale=alt.Scale(zero=False)),
+    size='population:Q'
+)

.venv/Lib/site-packages/altair/examples/grouped_bar_chart.py

@@ -0,0 +1,17 @@
+"""
+Grouped Bar Chart
+-----------------
+This example shows a grouped bar chart.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+alt.Chart(source).mark_bar().encode(
+    x='year:O',
+    y='sum(yield):Q',
+    color='year:N',
+    column='site:N'
+)

.venv/Lib/site-packages/altair/examples/grouped_bar_chart_horizontal.py

@@ -0,0 +1,17 @@
+"""
+Horizontal Grouped Bar Chart
+----------------------------
+This example shows a horizontal grouped bar chart.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+alt.Chart(source).mark_bar().encode(
+    x='sum(yield):Q',
+    y='year:O',
+    color='year:N',
+    row='site:N'
+)

.venv/Lib/site-packages/altair/examples/grouped_bar_chart_with_error_bars.py

@@ -0,0 +1,25 @@
+"""
+Grouped Bar Chart with Error Bars
+---------------------------------
+This example shows a grouped bar chart with error bars.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+bars = alt.Chart().mark_bar().encode(
+    x='year:O',
+    y=alt.Y('mean(yield):Q', title='Mean Yield'),
+    color='year:N',
+)
+
+error_bars = alt.Chart().mark_errorbar(extent='ci').encode(
+    x='year:O',
+    y='yield:Q'
+)
+
+alt.layer(bars, error_bars, data=source).facet(
+    column='site:N'
+)

.venv/Lib/site-packages/altair/examples/hexbins.py

@@ -0,0 +1,43 @@
+"""
+Hexbin Chart
+-----------------
+This example shows a hexbin chart.
+"""
+import altair as alt
+from vega_datasets import data
+
+source = data.seattle_weather()
+
+# Size of the hexbins
+size = 15
+# Count of distinct x features
+xFeaturesCount = 12
+# Count of distinct y features
+yFeaturesCount = 7
+# Name of the x field
+xField = 'date'
+# Name of the y field
+yField = 'date'
+
+# the shape of a hexagon
+hexagon = "M0,-2.3094010768L2,-1.1547005384 2,1.1547005384 0,2.3094010768 -2,1.1547005384 -2,-1.1547005384Z"
+
+alt.Chart(source).mark_point(size=size**2, shape=hexagon).encode(
+    x=alt.X('xFeaturePos:Q', axis=alt.Axis(title='Month',
+                                           grid=False, tickOpacity=0, domainOpacity=0)),
+    y=alt.Y('day(' + yField + '):O', axis=alt.Axis(title='Weekday',
+                                                   labelPadding=20, tickOpacity=0, domainOpacity=0)),
+    stroke=alt.value('black'),
+    strokeWidth=alt.value(0.2),
+    fill=alt.Color('mean(temp_max):Q', scale=alt.Scale(scheme='darkblue')),
+    tooltip=['month(' + xField + '):O', 'day(' + yField + '):O', 'mean(temp_max):Q']
+).transform_calculate(
+    # This field is required for the hexagonal X-Offset
+    xFeaturePos='(day(datum.' + yField + ') % 2) / 2 + month(datum.' + xField + ')'
+).properties(
+    # Exact scaling factors to make the hexbins fit
+    width=size * xFeaturesCount * 2,
+    height=size * yFeaturesCount * 1.7320508076,  # 1.7320508076 is approx. sin(60°)*2
+).configure_view(
+    strokeWidth=0
+)

.venv/Lib/site-packages/altair/examples/histogram_responsive.py

@@ -0,0 +1,35 @@
+"""
+Histogram with Responsive Bins
+------------------------------
+This shows an example of a histogram with bins that are responsive to a
+selection domain. Click and drag on the bottom panel to see the bins
+change on the top panel.
+"""
+# category: histograms
+import altair as alt
+from vega_datasets import data
+
+source = data.flights_5k.url
+
+brush = alt.selection_interval(encodings=['x'])
+
+base = alt.Chart(source).transform_calculate(
+    time="hours(datum.date) + minutes(datum.date) / 60"
+).mark_bar().encode(
+    y='count():Q'
+).properties(
+    width=600,
+    height=100
+)
+
+alt.vconcat(
+  base.encode(
+    alt.X('time:Q',
+      bin=alt.Bin(maxbins=30, extent=brush),
+      scale=alt.Scale(domain=brush)
+    )
+  ),
+  base.encode(
+    alt.X('time:Q', bin=alt.Bin(maxbins=30)),
+  ).add_selection(brush)
+)

.venv/Lib/site-packages/altair/examples/histogram_with_a_global_mean_overlay.py

@@ -0,0 +1,24 @@
+"""
+Histogram with a Global Mean Overlay
+------------------------------------
+This example shows a histogram with a global mean overlay.
+"""
+# category: histograms
+import altair as alt
+from vega_datasets import data
+
+source = data.movies.url
+
+base = alt.Chart(source)
+
+bar = base.mark_bar().encode(
+    x=alt.X('IMDB_Rating:Q', bin=True, axis=None),
+    y='count()'
+)
+
+rule = base.mark_rule(color='red').encode(
+    x='mean(IMDB_Rating):Q',
+    size=alt.value(5)
+)
+
+bar + rule

.venv/Lib/site-packages/altair/examples/horizon_graph.py

@@ -0,0 +1,41 @@
+"""
+Horizon Graph
+-------------
+This example shows how to make a Horizon Graph with 2 layers. (See https://idl.cs.washington.edu/papers/horizon/ for more details on Horizon Graphs.)
+"""
+# category: area charts
+import altair as alt
+import pandas as pd
+
+source = pd.DataFrame([
+    {"x": 1,  "y": 28}, {"x": 2,  "y": 55},
+    {"x": 3,  "y": 43}, {"x": 4,  "y": 91},
+    {"x": 5,  "y": 81}, {"x": 6,  "y": 53},
+    {"x": 7,  "y": 19}, {"x": 8,  "y": 87},
+    {"x": 9,  "y": 52}, {"x": 10, "y": 48},
+    {"x": 11, "y": 24}, {"x": 12, "y": 49},
+    {"x": 13, "y": 87}, {"x": 14, "y": 66},
+    {"x": 15, "y": 17}, {"x": 16, "y": 27},
+    {"x": 17, "y": 68}, {"x": 18, "y": 16},
+    {"x": 19, "y": 49}, {"x": 20, "y": 15}
+])
+
+area1 = alt.Chart(source).mark_area(
+    clip=True,
+    interpolate='monotone'
+).encode(
+    alt.X('x', scale=alt.Scale(zero=False, nice=False)),
+    alt.Y('y', scale=alt.Scale(domain=[0, 50]), title='y'),
+    opacity=alt.value(0.6)
+).properties(
+    width=500,
+    height=75
+)
+
+area2 = area1.encode(
+    alt.Y('ny:Q', scale=alt.Scale(domain=[0, 50]))
+).transform_calculate(
+    "ny", alt.datum.y - 50
+)
+
+area1 + area2

.venv/Lib/site-packages/altair/examples/horizontal_stacked_bar_chart.py

@@ -0,0 +1,16 @@
+"""
+Horizontal Stacked Bar Chart
+============================
+This is an example of a horizontal stacked bar chart using data which contains crop yields over different regions and different years in the 1930s.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+alt.Chart(source).mark_bar().encode(
+    x='sum(yield)',
+    y='variety',
+    color='site'
+)

.venv/Lib/site-packages/altair/examples/image_tooltip.py

@@ -0,0 +1,21 @@
+"""
+Image tooltip
+-------------
+This example shows how to render images in tooltips.
+Either URLs or local file paths can be used to reference
+the images.
+"""
+# category: other charts
+
+import altair as alt
+import pandas as pd
+
+source = pd.DataFrame.from_records(
+    [{'a': 1, 'b': 1, 'image': 'https://altair-viz.github.io/_static/altair-logo-light.png'},
+     {'a': 2, 'b': 2, 'image': 'https://avatars.githubusercontent.com/u/11796929?s=200&v=4'}]
+)
+alt.Chart(source).mark_circle(size=200).encode(
+    x='a',
+    y='b',
+    tooltip=['image']  # Must be a list for the image to render
+)

.venv/Lib/site-packages/altair/examples/interactive_brush.py

@@ -0,0 +1,19 @@
+"""
+Interactive Rectangular Brush
+=============================
+This example shows how to add a simple rectangular brush to a scatter plot.
+By clicking and dragging on the plot, you can highlight points within the
+range.
+"""
+# category: interactive charts
+import altair as alt
+from vega_datasets import data
+
+source = data.cars()
+brush = alt.selection(type='interval')
+
+alt.Chart(source).mark_point().encode(
+    x='Horsepower:Q',
+    y='Miles_per_Gallon:Q',
+    color=alt.condition(brush, 'Cylinders:O', alt.value('grey')),
+).add_selection(brush)

.venv/Lib/site-packages/altair/examples/interactive_cross_highlight.py

@@ -0,0 +1,49 @@
+"""
+Interactive Chart with Cross-Highlight
+======================================
+This example shows an interactive chart where selections in one portion of
+the chart affect what is shown in other panels. Click on the bar chart to
+see a detail of the distribution in the upper panel.
+"""
+# category: interactive charts
+import altair as alt
+from vega_datasets import data
+
+source = data.movies.url
+
+pts = alt.selection(type="single", encodings=['x'])
+
+rect = alt.Chart(data.movies.url).mark_rect().encode(
+    alt.X('IMDB_Rating:Q', bin=True),
+    alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
+    alt.Color('count()',
+        scale=alt.Scale(scheme='greenblue'),
+        legend=alt.Legend(title='Total Records')
+    )
+)
+
+circ = rect.mark_point().encode(
+    alt.ColorValue('grey'),
+    alt.Size('count()',
+        legend=alt.Legend(title='Records in Selection')
+    )
+).transform_filter(
+    pts
+)
+
+bar = alt.Chart(source).mark_bar().encode(
+    x='Major_Genre:N',
+    y='count()',
+    color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey"))
+).properties(
+    width=550,
+    height=200
+).add_selection(pts)
+
+alt.vconcat(
+    rect + circ,
+    bar
+).resolve_legend(
+    color="independent",
+    size="independent"
+)

.venv/Lib/site-packages/altair/examples/interactive_layered_crossfilter.py

@@ -0,0 +1,45 @@
+"""
+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
+
+source = 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=160,
+    height=130
+)
+
+# gray background with selection
+background = base.encode(
+    color=alt.value('#ddd')
+).add_selection(brush)
+
+# blue highlights on the transformed data
+highlight = base.transform_filter(brush)
+
+# layer the two charts & repeat
+alt.layer(
+    background,
+    highlight,
+    data=source
+).transform_calculate(
+    "time",
+    "hours(datum.date)"
+).repeat(column=["distance", "delay", "time"])

.venv/Lib/site-packages/altair/examples/interactive_legend.py

@@ -0,0 +1,24 @@
+"""
+Interactive Legend
+------------------
+The following shows how to create a chart with an interactive legend, by
+binding the selection to ``"legend"``. Such a binding only works with
+``selection_single`` or ``selection_multi`` when projected over a single
+field or encoding.
+"""
+# category: interactive charts
+import altair as alt
+from vega_datasets import data
+
+source = data.unemployment_across_industries.url
+
+selection = alt.selection_multi(fields=['series'], bind='legend')
+
+alt.Chart(source).mark_area().encode(
+    alt.X('yearmonth(date):T', axis=alt.Axis(domain=False, format='%Y', tickSize=0)),
+    alt.Y('sum(count):Q', stack='center', axis=None),
+    alt.Color('series:N', scale=alt.Scale(scheme='category20b')),
+    opacity=alt.condition(selection, alt.value(1), alt.value(0.2))
+).add_selection(
+    selection
+)

.venv/Lib/site-packages/altair/examples/interactive_scatter_plot.py

@@ -0,0 +1,16 @@
+"""
+Simple Interactive Colored Scatterplot
+--------------------------------------
+This example shows how to make an interactive scatterplot.
+"""
+# category: interactive charts
+import altair as alt
+from vega_datasets import data
+
+source = data.cars()
+
+alt.Chart(source).mark_circle().encode(
+    x='Horsepower',
+    y='Miles_per_Gallon',
+    color='Origin',
+).interactive()

.venv/Lib/site-packages/altair/examples/interval_selection.py

@@ -0,0 +1,32 @@
+"""
+Interval Selection Example
+==========================
+
+This is an example of creating a stacked chart for which the domain of the
+top chart can be selected by interacting with the bottom chart.
+"""
+# category: area charts
+import altair as alt
+from vega_datasets import data
+
+source = data.sp500.url
+
+brush = alt.selection(type='interval', encodings=['x'])
+
+base = alt.Chart(source).mark_area().encode(
+    x = 'date:T',
+    y = 'price:Q'
+).properties(
+    width=600,
+    height=200
+)
+
+upper = base.encode(
+    alt.X('date:T', scale=alt.Scale(domain=brush))
+)
+
+lower = base.properties(
+    height=60
+).add_selection(brush)
+
+upper & lower

.venv/Lib/site-packages/altair/examples/iowa_electricity.py

@@ -0,0 +1,27 @@
+"""
+Iowa's renewable energy boom
+----------------------------
+This example is a fully developed stacked chart using the sample dataset of Iowa's electricity sources.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+source = data.iowa_electricity()
+
+alt.Chart(source, title="Iowa's renewable energy boom").mark_area().encode(
+    x=alt.X(
+        "year:T",
+        title="Year"
+    ),
+    y=alt.Y(
+        "net_generation:Q",
+        stack="normalize",
+        title="Share of net generation",
+        axis=alt.Axis(format=".0%"),
+    ),
+    color=alt.Color(
+        "source:N",
+        legend=alt.Legend(title="Electricity source"),
+    )
+)

.venv/Lib/site-packages/altair/examples/isotype.py

@@ -0,0 +1,78 @@
+'''
+Isotype Visualization
+=====================
+Isotype Visualization shows the distribution of animals across UK and US.
+Inspired by `Only An Ocean Between, 1943 <http://www.thomwhite.co.uk/?p=1303>`_. Population Live Stock, p.13.
+This is adapted from Vega-Lite example https://vega.github.io/editor/#/examples/vega-lite/isotype_bar_chart
+'''
+# category: case studies
+import altair as alt
+import pandas as pd
+
+source = pd.DataFrame([
+      {'country': 'Great Britain', 'animal': 'cattle'},
+      {'country': 'Great Britain', 'animal': 'cattle'},
+      {'country': 'Great Britain', 'animal': 'cattle'},
+      {'country': 'Great Britain', 'animal': 'pigs'},
+      {'country': 'Great Britain', 'animal': 'pigs'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'}
+    ])
+
+domains = ['person', 'cattle', 'pigs', 'sheep']
+
+shape_scale = alt.Scale(
+    domain=domains,
+    range=[
+        'M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 -0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 -0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 -0.6 -0.4 -0.6z',
+        'M4 -2c0 0 0.9 -0.7 1.1 -0.8c0.1 -0.1 -0.1 0.5 -0.3 0.7c-0.2 0.2 1.1 1.1 1.1 1.2c0 0.2 -0.2 0.8 -0.4 0.7c-0.1 0 -0.8 -0.3 -1.3 -0.2c-0.5 0.1 -1.3 1.6 -1.5 2c-0.3 0.4 -0.6 0.4 -0.6 0.4c0 0.1 0.3 1.7 0.4 1.8c0.1 0.1 -0.4 0.1 -0.5 0c0 0 -0.6 -1.9 -0.6 -1.9c-0.1 0 -0.3 -0.1 -0.3 -0.1c0 0.1 -0.5 1.4 -0.4 1.6c0.1 0.2 0.1 0.3 0.1 0.3c0 0 -0.4 0 -0.4 0c0 0 -0.2 -0.1 -0.1 -0.3c0 -0.2 0.3 -1.7 0.3 -1.7c0 0 -2.8 -0.9 -2.9 -0.8c-0.2 0.1 -0.4 0.6 -0.4 1c0 0.4 0.5 1.9 0.5 1.9l-0.5 0l-0.6 -2l0 -0.6c0 0 -1 0.8 -1 1c0 0.2 -0.2 1.3 -0.2 1.3c0 0 0.3 0.3 0.2 0.3c0 0 -0.5 0 -0.5 0c0 0 -0.2 -0.2 -0.1 -0.4c0 -0.1 0.2 -1.6 0.2 -1.6c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 0 -2.7 -0.2 -2.7c-0.1 0 -0.4 2 -0.4 2c0 0 0 0.2 -0.2 0.5c-0.1 0.4 -0.2 1.1 -0.2 1.1c0 0 -0.2 -0.1 -0.2 -0.2c0 -0.1 -0.1 -0.7 0 -0.7c0.1 -0.1 0.3 -0.8 0.4 -1.4c0 -0.6 0.2 -1.3 0.4 -1.5c0.1 -0.2 0.6 -0.4 0.6 -0.4z',
+        'M1.2 -2c0 0 0.7 0 1.2 0.5c0.5 0.5 0.4 0.6 0.5 0.6c0.1 0 0.7 0 0.8 0.1c0.1 0 0.2 0.2 0.2 0.2c0 0 -0.6 0.2 -0.6 0.3c0 0.1 0.4 0.9 0.6 0.9c0.1 0 0.6 0 0.6 0.1c0 0.1 0 0.7 -0.1 0.7c-0.1 0 -1.2 0.4 -1.5 0.5c-0.3 0.1 -1.1 0.5 -1.1 0.7c-0.1 0.2 0.4 1.2 0.4 1.2l-0.4 0c0 0 -0.4 -0.8 -0.4 -0.9c0 -0.1 -0.1 -0.3 -0.1 -0.3l-0.2 0l-0.5 1.3l-0.4 0c0 0 -0.1 -0.4 0 -0.6c0.1 -0.1 0.3 -0.6 0.3 -0.7c0 0 -0.8 0 -1.5 -0.1c-0.7 -0.1 -1.2 -0.3 -1.2 -0.2c0 0.1 -0.4 0.6 -0.5 0.6c0 0 0.3 0.9 0.3 0.9l-0.4 0c0 0 -0.4 -0.5 -0.4 -0.6c0 -0.1 -0.2 -0.6 -0.2 -0.5c0 0 -0.4 0.4 -0.6 0.4c-0.2 0.1 -0.4 0.1 -0.4 0.1c0 0 -0.1 0.6 -0.1 0.6l-0.5 0l0 -1c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 -0.7 -1.2 -0.6 -1.4c0.1 -0.1 0.1 -1.1 0.1 -1.1c0 0 -0.2 0.1 -0.2 0.1c0 0 0 0.9 0 1c0 0.1 -0.2 0.3 -0.3 0.3c-0.1 0 0 -0.5 0 -0.9c0 -0.4 0 -0.4 0.2 -0.6c0.2 -0.2 0.6 -0.3 0.8 -0.8c0.3 -0.5 1 -0.6 1 -0.6z',
+        'M-4.1 -0.5c0.2 0 0.2 0.2 0.5 0.2c0.3 0 0.3 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.4 -0.2c0.1 0 0.2 0.2 0.4 0.1c0.2 0 0.2 -0.2 0.4 -0.3c0.1 0 0.1 -0.1 0.4 0c0.3 0 0.3 -0.4 0.6 -0.4c0.3 0 0.6 -0.3 0.7 -0.2c0.1 0.1 1.4 1 1.3 1.4c-0.1 0.4 -0.3 0.3 -0.4 0.3c-0.1 0 -0.5 -0.4 -0.7 -0.2c-0.3 0.2 -0.1 0.4 -0.2 0.6c-0.1 0.1 -0.2 0.2 -0.3 0.4c0 0.2 0.1 0.3 0 0.5c-0.1 0.2 -0.3 0.2 -0.3 0.5c0 0.3 -0.2 0.3 -0.3 0.6c-0.1 0.2 0 0.3 -0.1 0.5c-0.1 0.2 -0.1 0.2 -0.2 0.3c-0.1 0.1 0.3 1.1 0.3 1.1l-0.3 0c0 0 -0.3 -0.9 -0.3 -1c0 -0.1 -0.1 -0.2 -0.3 -0.2c-0.2 0 -0.3 0.1 -0.4 0.4c0 0.3 -0.2 0.8 -0.2 0.8l-0.3 0l0.3 -1c0 0 0.1 -0.6 -0.2 -0.5c-0.3 0.1 -0.2 -0.1 -0.4 -0.1c-0.2 -0.1 -0.3 0.1 -0.4 0c-0.2 -0.1 -0.3 0.1 -0.5 0c-0.2 -0.1 -0.1 0 -0.3 0.3c-0.2 0.3 -0.4 0.3 -0.4 0.3l0.2 1.1l-0.3 0l-0.2 -1.1c0 0 -0.4 -0.6 -0.5 -0.4c-0.1 0.3 -0.1 0.4 -0.3 0.4c-0.1 -0.1 -0.2 1.1 -0.2 1.1l-0.3 0l0.2 -1.1c0 0 -0.3 -0.1 -0.3 -0.5c0 -0.3 0.1 -0.5 0.1 -0.7c0.1 -0.2 -0.1 -1 -0.2 -1.1c-0.1 -0.2 -0.2 -0.8 -0.2 -0.8c0 0 -0.1 -0.5 0.4 -0.8z'
+    ]
+)
+
+color_scale = alt.Scale(
+    domain=domains,
+    range=['rgb(162,160,152)', 'rgb(194,81,64)', 'rgb(93,93,93)', 'rgb(91,131,149)']
+)
+
+alt.Chart(source).mark_point(filled=True, opacity=1, size=100).encode(
+    alt.X('x:O', axis=None),
+    alt.Y('animal:O', axis=None),
+    alt.Row('country:N', header=alt.Header(title='')),
+    alt.Shape('animal:N', legend=None, scale=shape_scale),
+    alt.Color('animal:N', legend=None, scale=color_scale),
+).transform_window(
+    x='rank()',
+    groupby=['country', 'animal']
+).properties(width=550, height=140)

.venv/Lib/site-packages/altair/examples/isotype_emoji.py

@@ -0,0 +1,63 @@
+'''
+Isotype Visualization with Emoji
+================================
+Isotype Visualization shows the distribution of animals across UK and US, using unicode emoji
+marks rather than custom SVG paths (see https://altair-viz.github.io/gallery/isotype.html).
+This is adapted from Vega-Lite example https://vega.github.io/vega-lite/examples/isotype_bar_chart_emoji.html.
+'''
+# category: case studies
+import altair as alt
+import pandas as pd
+
+source = pd.DataFrame([
+      {'country': 'Great Britain', 'animal': 'cattle'},
+      {'country': 'Great Britain', 'animal': 'cattle'},
+      {'country': 'Great Britain', 'animal': 'cattle'},
+      {'country': 'Great Britain', 'animal': 'pigs'},
+      {'country': 'Great Britain', 'animal': 'pigs'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'Great Britain', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'cattle'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'pigs'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'},
+      {'country': 'United States', 'animal': 'sheep'}
+    ])
+
+
+alt.Chart(source).mark_text(size=45, baseline='middle').encode(
+    alt.X('x:O', axis=None),
+    alt.Y('animal:O', axis=None),
+    alt.Row('country:N', header=alt.Header(title='')),
+    alt.Text('emoji:N')
+).transform_calculate(
+    emoji="{'cattle': '🐄', 'pigs': '🐖', 'sheep': '🐏'}[datum.animal]"
+).transform_window(
+    x='rank()',
+    groupby=['country', 'animal']
+).properties(width=550, height=140)

.venv/Lib/site-packages/altair/examples/isotype_grid.py

@@ -0,0 +1,38 @@
+"""
+Isotype Grid
+------------
+This example is a grid of isotype figures.
+"""
+# category: other charts
+import altair as alt
+import pandas as pd
+
+data = pd.DataFrame([dict(id=i) for i in range(1, 101)])
+
+person = (
+    "M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 "
+    "-0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 "
+    "0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 "
+    "0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 "
+    "0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 "
+    "-0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 "
+    "-0.6 -0.4 -0.6z"
+)
+
+alt.Chart(data).transform_calculate(
+    row="ceil(datum.id/10)"
+).transform_calculate(
+    col="datum.id - datum.row*10"
+).mark_point(
+    filled=True,
+    size=50
+).encode(
+    x=alt.X("col:O", axis=None),
+    y=alt.Y("row:O", axis=None),
+    shape=alt.ShapeValue(person)
+).properties(
+    width=400,
+    height=400
+).configure_view(
+    strokeWidth=0
+)

.venv/Lib/site-packages/altair/examples/layer_line_color_rule.py

@@ -0,0 +1,28 @@
+"""
+Line Chart with Layered Aggregates
+----------------------------------
+This example shows how to make a multi-series line chart of the daily closing
+stock prices for AAPL, AMZN, GOOG, IBM, and MSFT between 2000 and 2010, along
+with a layered rule showing the average values.
+"""
+# category: line charts
+import altair as alt
+from vega_datasets import data
+
+source = data.stocks()
+
+base = alt.Chart(source).properties(width=550)
+
+line = base.mark_line().encode(
+    x='date',
+    y='price',
+    color='symbol'
+)
+
+rule = base.mark_rule().encode(
+    y='average(price)',
+    color='symbol',
+    size=alt.value(2)
+)
+
+line + rule

.venv/Lib/site-packages/altair/examples/layered_area_chart.py

@@ -0,0 +1,16 @@
+"""
+Layered Area Chart
+------------------
+This example shows a layered area chart.
+"""
+# category: area charts
+import altair as alt
+from vega_datasets import data
+
+source = data.iowa_electricity()
+
+alt.Chart(source).mark_area(opacity=0.3).encode(
+    x="year:T",
+    y=alt.Y("net_generation:Q", stack=None),
+    color="source:N"
+)

.venv/Lib/site-packages/altair/examples/layered_bar_chart.py

@@ -0,0 +1,16 @@
+"""
+Layered Bar Chart
+-----------------
+This example shows a segmented bar chart that is layered rather than stacked.  
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.iowa_electricity()
+
+alt.Chart(source).mark_bar(opacity=0.7).encode(
+    x='year:O',
+    y=alt.Y('net_generation:Q', stack=None),
+    color="source",
+)

.venv/Lib/site-packages/altair/examples/layered_chart_bar_mark.py

@@ -0,0 +1,32 @@
+"""
+Bar and Tick Chart
+------------------
+How to layer a tick chart on top of a bar chart.
+"""
+# category: bar charts
+import altair as alt
+import pandas as pd
+
+source = pd.DataFrame({
+    'project': ['a', 'b', 'c', 'd', 'e', 'f', 'g'],
+    'score': [25, 57, 23, 19, 8, 47, 8],
+    'goal': [25, 47, 30, 27, 38, 19, 4]
+})
+
+bar = alt.Chart(source).mark_bar().encode(
+    x='project',
+    y='score'
+).properties(
+    width=alt.Step(40)  # controls width of bar.
+)
+
+tick = alt.Chart(source).mark_tick(
+    color='red',
+    thickness=2,
+    size=40 * 0.9,  # controls width of tick.
+).encode(
+    x='project',
+    y='goal'
+)
+
+bar + tick

.venv/Lib/site-packages/altair/examples/layered_chart_with_dual_axis.py

@@ -0,0 +1,30 @@
+"""
+Layered chart with Dual-Axis
+----------------------------
+This example shows how to create a second independent y axis.
+"""
+# category: other charts
+
+import altair as alt
+from vega_datasets import data
+
+source = data.seattle_weather()
+
+base = alt.Chart(source).encode(
+    alt.X('month(date):T', axis=alt.Axis(title=None))
+)
+
+area = base.mark_area(opacity=0.3, color='#57A44C').encode(
+    alt.Y('average(temp_max)',
+          axis=alt.Axis(title='Avg. Temperature (°C)', titleColor='#57A44C')),
+    alt.Y2('average(temp_min)')
+)
+
+line = base.mark_line(stroke='#5276A7', interpolate='monotone').encode(
+    alt.Y('average(precipitation)',
+          axis=alt.Axis(title='Precipitation (inches)', titleColor='#5276A7'))
+)
+
+alt.layer(area, line).resolve_scale(
+    y = 'independent'
+)

.venv/Lib/site-packages/altair/examples/layered_heatmap_text.py

@@ -0,0 +1,41 @@
+"""
+Text over a Heatmap
+-------------------
+
+An example of a layered chart of text over a heatmap using the cars dataset.
+"""
+# category: other charts
+import altair as alt
+from vega_datasets import data
+
+source = data.cars()
+
+# Configure common options
+base = alt.Chart(source).transform_aggregate(
+    num_cars='count()',
+    groupby=['Origin', 'Cylinders']
+).encode(
+    alt.X('Cylinders:O', scale=alt.Scale(paddingInner=0)),
+    alt.Y('Origin:O', scale=alt.Scale(paddingInner=0)),
+)
+
+# Configure heatmap
+heatmap = base.mark_rect().encode(
+    color=alt.Color('num_cars:Q',
+        scale=alt.Scale(scheme='viridis'),
+        legend=alt.Legend(direction='horizontal')
+    )
+)
+
+# Configure text
+text = base.mark_text(baseline='middle').encode(
+    text='num_cars:Q',
+    color=alt.condition(
+        alt.datum.num_cars > 100,
+        alt.value('black'),
+        alt.value('white')
+    )
+)
+
+# Draw the chart
+heatmap + text

.venv/Lib/site-packages/altair/examples/layered_histogram.py

@@ -0,0 +1,29 @@
+"""
+Layered Histogram
+=================
+This example shows how to use opacity to make a layered histogram in Altair.
+"""
+# category: histograms
+import pandas as pd
+import altair as alt
+import numpy as np
+np.random.seed(42)
+
+# Generating Data
+source = pd.DataFrame({
+    'Trial A': np.random.normal(0, 0.8, 1000),
+    'Trial B': np.random.normal(-2, 1, 1000),
+    'Trial C': np.random.normal(3, 2, 1000)
+})
+
+alt.Chart(source).transform_fold(
+    ['Trial A', 'Trial B', 'Trial C'],
+    as_=['Experiment', 'Measurement']
+).mark_bar(
+    opacity=0.3,
+    binSpacing=0
+).encode(
+    alt.X('Measurement:Q', bin=alt.Bin(maxbins=100)),
+    alt.Y('count()', stack=None),
+    alt.Color('Experiment:N')
+)

.venv/Lib/site-packages/altair/examples/line_chart_with_color_datum.py

@@ -0,0 +1,21 @@
+"""
+Line Chart with datum for color
+-------------------------------
+An example of using ``datum`` and ``repeat`` to color a multi-series line chart.
+This is adapted from this corresponding Vega-Lite Example:
+`Repeat and Layer to Show Different Movie Measures <https://vega.github.io/vega-lite/examples/repeat_layer.html>`_.
+"""
+# category: line charts
+
+import altair as alt
+from vega_datasets import data
+
+source = data.movies()
+
+alt.Chart(source).mark_line().encode(
+    x=alt.X("IMDB_Rating", bin=True),
+    y=alt.Y(
+        alt.repeat("layer"), aggregate="mean", title="Mean of US and Worldwide Gross"
+    ),
+    color=alt.datum(alt.repeat("layer")),
+).repeat(layer=["US_Gross", "Worldwide_Gross"])

.venv/Lib/site-packages/altair/examples/line_chart_with_cumsum.py

@@ -0,0 +1,24 @@
+"""
+Line Chart with Cumulative Sum
+------------------------------
+This chart creates a simple line chart from the cumulative sum of a fields.
+"""
+# category: line charts
+import altair as alt
+from vega_datasets import data
+
+source = data.wheat()
+
+alt.Chart(source).mark_line().transform_window(
+    # Sort the data chronologically
+    sort=[{'field': 'year'}],
+    # Include all previous records before the current record and none after
+    # (This is the default value so you could skip it and it would still work.)
+    frame=[None, 0],
+    # What to add up as you go
+    cumulative_wheat='sum(wheat)'
+).encode(
+    x='year:O',
+    # Plot the calculated field created by the transformation
+    y='cumulative_wheat:Q'
+).properties(width=600)

.venv/Lib/site-packages/altair/examples/line_chart_with_datum.py

@@ -0,0 +1,32 @@
+"""
+Line Chart with datum
+---------------------------------
+An example of using ``datum`` to highlight certain values, including a ``DateTime`` value.
+This is adapted from two corresponding Vega-Lite Examples:
+`Highlight a Specific Value <https://vega.github.io/vega-lite/docs/datum.html#highlight-a-specific-data-value>`_.
+"""
+# category: line charts
+
+import altair as alt
+from vega_datasets import data
+
+source = data.stocks()
+
+lines = (
+    alt.Chart(source)
+    .mark_line()
+    .encode(x="date", y="price", color="symbol")
+)
+
+xrule = (
+    alt.Chart()
+    .mark_rule(color="cyan", strokeWidth=2)
+    .encode(x=alt.datum(alt.DateTime(year=2006, month="November")))
+)
+
+yrule = (
+    alt.Chart().mark_rule(strokeDash=[12, 6], size=2).encode(y=alt.datum(350))
+)
+
+
+lines + yrule + xrule

.venv/Lib/site-packages/altair/examples/line_chart_with_generator.py

@@ -0,0 +1,21 @@
+"""
+Line Chart with Sequence Generator
+----------------------------------
+This examples shows how to create multiple lines using the sequence generator.
+"""
+# category: line charts
+
+import altair as alt
+
+source = alt.sequence(start=0, stop=12.7, step=0.1, as_='x')
+
+alt.Chart(source).mark_line().transform_calculate(
+    sin='sin(datum.x)',
+    cos='cos(datum.x)'
+).transform_fold(
+    ['sin', 'cos']
+).encode(
+    x='x:Q', 
+    y='value:Q', 
+    color='key:N'
+)

.venv/Lib/site-packages/altair/examples/line_chart_with_points.py

@@ -0,0 +1,24 @@
+"""
+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)'
+)

.venv/Lib/site-packages/altair/examples/line_percent.py

@@ -0,0 +1,18 @@
+"""
+Line Chart with Percent axis
+----------------------------
+This example shows how to format the tick labels of the y-axis of a chart as percentages.
+"""
+# category: line charts
+import altair as alt
+from vega_datasets import data
+
+source = data.jobs.url
+
+alt.Chart(source).mark_line().encode(
+    alt.X('year:O'),
+    alt.Y('perc:Q', axis=alt.Axis(format='%')),
+    color='sex:N'
+).transform_filter(
+    alt.datum.job == 'Welder'
+)

.venv/Lib/site-packages/altair/examples/line_with_ci.py

@@ -0,0 +1,22 @@
+"""
+Line Chart with Confidence Interval Band
+----------------------------------------
+How to make a line chart with a bootstrapped 95% confidence interval band.
+"""
+# category: line charts
+import altair as alt
+from vega_datasets import data
+
+source = data.cars()
+
+line = alt.Chart(source).mark_line().encode(
+    x='Year',
+    y='mean(Miles_per_Gallon)'
+)
+
+band = alt.Chart(source).mark_errorband(extent='ci').encode(
+    x='Year',
+    y=alt.Y('Miles_per_Gallon', title='Miles/Gallon'),
+)
+
+band + line

.venv/Lib/site-packages/altair/examples/line_with_log_scale.py

@@ -0,0 +1,18 @@
+"""
+Line Chart with Logarithmic Scale
+---------------------------------
+How to make a line chart on a `Logarithmic scale <https://en.wikipedia.org/wiki/Logarithmic_scale>`_.
+"""
+# category: line charts
+import altair as alt
+from vega_datasets import data
+
+source = data.population()
+
+alt.Chart(source).mark_line().encode(
+    x='year:O',
+    y=alt.Y(
+        'sum(people)',
+        scale=alt.Scale(type="log")  # Here the scale is applied
+    )
+)

.venv/Lib/site-packages/altair/examples/london_tube.py

@@ -0,0 +1,59 @@
+"""
+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: case studies
+import altair as alt
+from vega_datasets import data
+
+boroughs = alt.topo_feature(data.londonBoroughs.url, 'boroughs')
+tubelines = alt.topo_feature(data.londonTubeLines.url, 'line')
+centroids = data.londonCentroids.url
+
+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)
+).transform_calculate(
+    "bLabel", "indexof (datum.name,' ') > 0  ? substring(datum.name,0,indexof(datum.name, ' ')) : datum.name"
+)
+
+line_scale = alt.Scale(domain=["Bakerloo", "Central", "Circle", "District", "DLR",
+                               "Hammersmith & City", "Jubilee", "Metropolitan", "Northern",
+                               "Piccadilly", "Victoria", "Waterloo & City"],
+                       range=["rgb(137,78,36)", "rgb(220,36,30)", "rgb(255,206,0)",
+                              "rgb(1,114,41)", "rgb(0,175,173)", "rgb(215,153,175)",
+                              "rgb(106,114,120)", "rgb(114,17,84)", "rgb(0,0,0)",
+                              "rgb(0,24,168)", "rgb(0,160,226)", "rgb(106,187,170)"])
+
+lines = alt.Chart(tubelines).mark_geoshape(
+    filled=False,
+    strokeWidth=2
+).encode(
+    alt.Color(
+        'id:N',
+        legend=alt.Legend(
+            title=None,
+            orient='bottom-right',
+            offset=0
+        ),
+        scale=line_scale
+    )
+)
+
+background + labels + lines

.venv/Lib/site-packages/altair/examples/multi_series_line.py

@@ -0,0 +1,18 @@
+"""
+Multi Series Line Chart
+-----------------------
+
+This example shows how to make a multi series line chart of the daily closing stock prices for AAPL, AMZN, GOOG, IBM, and MSFT between 2000 and 2010.
+"""
+# category: line charts
+import altair as alt
+from vega_datasets import data
+
+source = data.stocks()
+
+alt.Chart(source).mark_line().encode(
+    x='date',
+    y='price',
+    color='symbol',
+    strokeDash='symbol',
+)

.venv/Lib/site-packages/altair/examples/multifeature_scatter_plot.py

@@ -0,0 +1,17 @@
+"""
+Multifeature Scatter Plot
+=========================
+This example shows how to make a scatter plot with multiple feature encodings.
+"""
+# category: scatter plots
+import altair as alt
+from vega_datasets import data
+
+source = data.iris()
+
+alt.Chart(source).mark_circle().encode(
+    alt.X('sepalLength', scale=alt.Scale(zero=False)),
+    alt.Y('sepalWidth', scale=alt.Scale(zero=False, padding=1)),
+    color='species',
+    size='petalWidth'
+)

.venv/Lib/site-packages/altair/examples/multiline_highlight.py

@@ -0,0 +1,36 @@
+"""
+Multi-Line Highlight
+====================
+This multi-line chart uses an invisible Voronoi tessellation to handle mouseover to
+identify the nearest point and then highlight the line on which the point falls.
+It is adapted from the Vega-Lite example found at
+https://bl.ocks.org/amitkaps/fe4238e716db53930b2f1a70d3401701
+"""
+# category: interactive charts
+import altair as alt
+from vega_datasets import data
+
+source = data.stocks()
+
+highlight = alt.selection(type='single', on='mouseover',
+                          fields=['symbol'], nearest=True)
+
+base = alt.Chart(source).encode(
+    x='date:T',
+    y='price:Q',
+    color='symbol:N'
+)
+
+points = base.mark_circle().encode(
+    opacity=alt.value(0)
+).add_selection(
+    highlight
+).properties(
+    width=600
+)
+
+lines = base.mark_line().encode(
+    size=alt.condition(~highlight, alt.value(1), alt.value(3))
+)
+
+points + lines

.venv/Lib/site-packages/altair/examples/multiline_tooltip.py

@@ -0,0 +1,67 @@
+"""
+Multi-Line Tooltip
+==================
+This example shows how you can use selections and layers to create a
+tooltip-like behavior tied to the x position of the cursor.
+If you are looking for more standard tooltips, it is recommended to use the
+tooltip encoding channel as shown in the
+`Scatter Plot With Tooltips <https://altair-viz.github.io/gallery/scatter_tooltips.html>`_
+example.
+
+The following example employs a little trick to isolate the x-position of the
+cursor: we add some transparent points with only an x encoding (no y encoding)
+and tie a *nearest* selection to these, tied to the "x" field.
+"""
+# category: interactive charts
+import altair as alt
+import pandas as pd
+import numpy as np
+
+np.random.seed(42)
+source = pd.DataFrame(np.cumsum(np.random.randn(100, 3), 0).round(2),
+                    columns=['A', 'B', 'C'], index=pd.RangeIndex(100, name='x'))
+source = source.reset_index().melt('x', var_name='category', value_name='y')
+
+# Create a selection that chooses the nearest point & selects based on x-value
+nearest = alt.selection(type='single', nearest=True, on='mouseover',
+                        fields=['x'], empty='none')
+
+# The basic line
+line = alt.Chart(source).mark_line(interpolate='basis').encode(
+    x='x:Q',
+    y='y:Q',
+    color='category:N'
+)
+
+# Transparent selectors across the chart. This is what tells us
+# the x-value of the cursor
+selectors = alt.Chart(source).mark_point().encode(
+    x='x:Q',
+    opacity=alt.value(0),
+).add_selection(
+    nearest
+)
+
+# Draw points on the line, and highlight based on selection
+points = line.mark_point().encode(
+    opacity=alt.condition(nearest, alt.value(1), alt.value(0))
+)
+
+# Draw text labels near the points, and highlight based on selection
+text = line.mark_text(align='left', dx=5, dy=-5).encode(
+    text=alt.condition(nearest, 'y:Q', alt.value(' '))
+)
+
+# Draw a rule at the location of the selection
+rules = alt.Chart(source).mark_rule(color='gray').encode(
+    x='x:Q',
+).transform_filter(
+    nearest
+)
+
+# Put the five layers into a chart and bind the data
+alt.layer(
+    line, selectors, points, rules, text
+).properties(
+    width=600, height=300
+)

.venv/Lib/site-packages/altair/examples/multiple_interactions.py

@@ -0,0 +1,90 @@
+"""
+Multiple Interactions
+=====================
+This example shows how multiple user inputs can be layered onto a chart. The four inputs have functionality as follows:
+
+* Dropdown: Filters the movies by genre
+* Radio Buttons: Highlights certain films by Worldwide Gross
+* Mouse Drag and Scroll: Zooms the x and y scales to allow for panning.
+
+
+
+"""
+# category: interactive charts
+import altair as alt
+from vega_datasets import data
+
+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, fields=['Release_Year'], name="Release Year_")
+
+
+filter_year = base.add_selection(
+    slider_selection
+).transform_filter(
+    slider_selection
+).properties(title="Slider Filtering")
+
+# A dropdown filter
+genre_dropdown = alt.binding_select(options=genres)
+genre_select = alt.selection_single(fields=['Major_Genre'], bind=genre_dropdown, name="Genre")
+
+filter_genres = base.add_selection(
+    genre_select
+).transform_filter(
+    genre_select
+).properties(title="Dropdown Filtering")
+
+#color changing marks
+rating_radio = alt.binding_radio(options=ratings)
+
+rating_select = alt.selection_single(fields=['MPAA_Rating'], bind=rating_radio, name="Rating")
+rating_color_condition = alt.condition(rating_select,
+                      alt.Color('MPAA_Rating:N', legend=None),
+                      alt.value('lightgray'))
+
+highlight_ratings = base.add_selection(
+    rating_select
+).encode(
+    color=rating_color_condition
+).properties(title="Radio Button Highlighting")
+
+# Boolean selection for format changes
+input_checkbox = alt.binding_checkbox()
+checkbox_selection = alt.selection_single(bind=input_checkbox, name="Big Budget Films")
+
+size_checkbox_condition = alt.condition(checkbox_selection,
+                                        alt.SizeValue(25),
+                                        alt.Size('Hundred_Million_Production:Q')
+                                       )
+
+budget_sizing = base.add_selection(
+    checkbox_selection
+).encode(
+    size=size_checkbox_condition
+).properties(title="Checkbox Formatting")
+
+( filter_year | filter_genres) &  (highlight_ratings | budget_sizing  )

.venv/Lib/site-packages/altair/examples/multiple_marks.py

@@ -0,0 +1,17 @@
+"""
+Multiple Marks
+==============
+This example demonstrates creating a single chart with multiple markers
+representing the same data.
+"""
+# category: other charts
+import altair as alt
+from vega_datasets import data
+
+source = data.stocks()
+
+alt.Chart(source).mark_line(point=True).encode(
+    x='date:T',
+    y='price:Q',
+    color='symbol:N'
+)

.venv/Lib/site-packages/altair/examples/natural_disasters.py

@@ -0,0 +1,29 @@
+"""
+Natural Disasters
+-----------------
+This example shows a visualization of global deaths from natural disasters.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+source = data.disasters.url
+
+alt.Chart(source).mark_circle(
+    opacity=0.8,
+    stroke='black',
+    strokeWidth=1
+).encode(
+    alt.X('Year:O', axis=alt.Axis(labelAngle=0)),
+    alt.Y('Entity:N'),
+    alt.Size('Deaths:Q',
+        scale=alt.Scale(range=[0, 4000]),
+        legend=alt.Legend(title='Annual Global Deaths')
+    ),
+    alt.Color('Entity:N', legend=None)
+).properties(
+    width=450,
+    height=320
+).transform_filter(
+    alt.datum.Entity != 'All natural disasters'
+)

.venv/Lib/site-packages/altair/examples/normalized_stacked_area_chart.py

@@ -0,0 +1,16 @@
+"""
+Normalized Stacked Area Chart
+-----------------------------
+This example shows how to make a normalized stacked area chart.
+"""
+# category: area charts
+import altair as alt
+from vega_datasets import data
+
+source = data.iowa_electricity()
+
+alt.Chart(source).mark_area().encode(
+    x="year:T",
+    y=alt.Y("net_generation:Q", stack="normalize"),
+    color="source:N"
+)

.venv/Lib/site-packages/altair/examples/normalized_stacked_bar_chart.py

@@ -0,0 +1,16 @@
+"""
+Normalized Stacked Bar Chart
+----------------------------
+This is an example of a normalized stacked bar chart using data which contains crop yields over different regions and different years in the 1930s.
+"""
+# category: bar charts
+import altair as alt
+from vega_datasets import data
+
+source = data.barley()
+
+alt.Chart(source).mark_bar().encode(
+    x=alt.X('sum(yield)', stack="normalize"),
+    y='variety',
+    color='site'
+)

.venv/Lib/site-packages/altair/examples/normed_parallel_coordinates.py

@@ -0,0 +1,41 @@
+"""
+Normalized Parallel Coordinates Example
+---------------------------------------
+A `Parallel Coordinates <https://en.wikipedia.org/wiki/Parallel_coordinates>`_
+chart is a chart that lets you visualize the individual data points by drawing
+a single line for each of them.
+
+Such a chart can be created in Altair by first transforming the data into a
+suitable representation.
+
+This example shows a modified parallel coordinates chart with the Iris dataset,
+where the y-axis shows the value after min-max rather than the raw value. It's a
+simplified Altair version of `the VegaLite version <https://vega.github.io/vega-lite/examples/parallel_coordinate.html>`_
+"""
+# category: other charts
+import altair as alt
+from vega_datasets import data
+from altair import datum
+
+source = data.iris()
+
+alt.Chart(source).transform_window(
+    index='count()'
+).transform_fold(
+    ['petalLength', 'petalWidth', 'sepalLength', 'sepalWidth']
+).transform_joinaggregate(
+     min='min(value)', 
+     max='max(value)',
+     groupby=['key']
+).transform_calculate(
+    minmax_value=(datum.value-datum.min)/(datum.max-datum.min), 
+    mid=(datum.min+datum.max)/2
+).mark_line().encode(
+    x='key:N',
+    y='minmax_value:Q',
+    color='species:N',
+    detail='index:N',
+    opacity=alt.value(0.5)
+).properties(width=500)
+
+

.venv/Lib/site-packages/altair/examples/one_dot_per_zipcode.py

@@ -0,0 +1,25 @@
+"""
+One Dot Per Zipcode
+-----------------------
+This example shows a geographical plot with one dot per zipcode.
+"""
+# category: case studies
+import altair as alt
+from vega_datasets import data
+
+# Since the data is more than 5,000 rows we'll import it from a URL
+source = data.zipcodes.url
+
+alt.Chart(source).transform_calculate(
+    "leading digit", alt.expr.substring(alt.datum.zip_code, 0, 1)
+).mark_circle(size=3).encode(
+    longitude='longitude:Q',
+    latitude='latitude:Q',
+    color='leading digit:N',
+    tooltip='zip_code:N'
+).project(
+    type='albersUsa'
+).properties(
+    width=650,
+    height=400
+)

.venv/Lib/site-packages/altair/examples/pacman_chart.py

@@ -0,0 +1,17 @@
+"""
+Pacman Chart
+------------
+Chart made using ``mark_arc`` and constant values.
+This could also be made using 
+``alt.Chart(source).mark_arc(color = "gold", theta = (5/8)*np.pi, theta2 = (19/8)*np.pi,radius=100)``.
+"""
+# category: circular plots
+
+import numpy as np
+import altair as alt
+
+alt.Chart().mark_arc(color="gold").encode(
+    theta=alt.datum((5 / 8) * np.pi, scale=None),
+    theta2=alt.datum((19 / 8) * np.pi),
+    radius=alt.datum(100, scale=None),
+)

.venv/Lib/site-packages/altair/examples/parallel_coordinates.py

@@ -0,0 +1,28 @@
+"""
+Parallel Coordinates Example
+----------------------------
+A `Parallel Coordinates <https://en.wikipedia.org/wiki/Parallel_coordinates>`_
+chart is a chart that lets you visualize the individual data points by drawing
+a single line for each of them.
+Such a chart can be created in Altair by first transforming the data into a
+suitable representation.
+This example shows a parallel coordinates chart with the Iris dataset.
+"""
+# category: other charts
+
+import altair as alt
+from vega_datasets import data
+
+source = data.iris()
+
+alt.Chart(source).transform_window(
+    index='count()'  
+).transform_fold(
+    ['petalLength', 'petalWidth', 'sepalLength', 'sepalWidth']
+).mark_line().encode(
+    x='key:N',
+    y='value:Q',
+    color='species:N',
+    detail='index:N',
+    opacity=alt.value(0.5)
+).properties(width=500)

.venv/Lib/site-packages/altair/examples/percentage_of_total.py

@@ -0,0 +1,21 @@
+"""
+Calculating Percentage of Total
+-------------------------------
+This chart demonstrates how to use a joinaggregate transform to display
+data values as a percentage of total.
+"""
+# category: bar charts
+import altair as alt
+import pandas as pd
+
+source = pd.DataFrame({'Activity': ['Sleeping', 'Eating', 'TV', 'Work', 'Exercise'],
+                           'Time': [8, 2, 4, 8, 2]})
+
+alt.Chart(source).transform_joinaggregate(
+    TotalTime='sum(Time)',
+).transform_calculate(
+    PercentOfTotal="datum.Time / datum.TotalTime"
+).mark_bar().encode(
+    alt.X('PercentOfTotal:Q', axis=alt.Axis(format='.0%')),
+    y='Activity:N'
+)

.venv/Lib/site-packages/altair/examples/pie_chart.py

@@ -0,0 +1,18 @@
+"""
+Pie Chart
+---------
+This example shows how to make a Pie Chart using ``mark_arc``.
+This is adapted from a corresponding Vega-Lite Example:
+`Pie Chart <https://vega.github.io/vega-lite/examples/arc_pie.html>`_.
+"""
+# category: circular plots
+
+import pandas as pd
+import altair as alt
+
+source = pd.DataFrame({"category": [1, 2, 3, 4, 5, 6], "value": [4, 6, 10, 3, 7, 8]})
+
+alt.Chart(source).mark_arc().encode(
+    theta=alt.Theta(field="value", type="quantitative"),
+    color=alt.Color(field="category", type="nominal"),
+)

.venv/Lib/site-packages/altair/examples/pie_chart_with_labels.py

@@ -0,0 +1,24 @@
+"""
+Pie Chart with Labels
+---------------------
+This example shows how to layer text over arc marks (``mark_arc``) to label pie charts.
+This is adapted from a corresponding Vega-Lite Example:
+`Pie Chart with Labels <https://vega.github.io/vega-lite/examples/layer_arc_label.html>`_.
+"""
+# category: circular plots
+
+import pandas as pd
+import altair as alt
+
+source = pd.DataFrame(
+    {"category": ["a", "b", "c", "d", "e", "f"], "value": [4, 6, 10, 3, 7, 8]}
+)
+
+base = alt.Chart(source).encode(
+    theta=alt.Theta("value:Q", stack=True), color=alt.Color("category:N", legend=None)
+)
+
+pie = base.mark_arc(outerRadius=120)
+text = base.mark_text(radius=140, size=20).encode(text="category:N")
+
+pie + text

.venv/Lib/site-packages/altair/examples/poly_fit_regression.py

@@ -0,0 +1,36 @@
+"""
+Polynomial Fit Plot with Regression Transform
+=============================================
+This example shows how to overlay data with multiple fitted polynomials using
+the regression transform.
+"""
+# category: scatter plots
+
+import numpy as np
+import pandas as pd
+import altair as alt
+
+# Generate some random data
+rng = np.random.RandomState(1)
+x = rng.rand(40) ** 2
+y = 10 - 1.0 / (x + 0.1) + rng.randn(40)
+source = pd.DataFrame({"x": x, "y": y})
+
+# Define the degree of the polynomial fits
+degree_list = [1, 3, 5]
+
+base = alt.Chart(source).mark_circle(color="black").encode(
+        alt.X("x"), alt.Y("y")
+)
+
+polynomial_fit = [
+    base.transform_regression(
+        "x", "y", method="poly", order=order, as_=["x", str(order)]
+    )
+    .mark_line()
+    .transform_fold([str(order)], as_=["degree", "y"])
+    .encode(alt.Color("degree:N"))
+    for order in degree_list
+]
+
+alt.layer(base, *polynomial_fit)

.venv/Lib/site-packages/altair/examples/pyramid.py

@@ -0,0 +1,19 @@
+"""
+Pyramid Pie Chart
+-----------------
+Altair reproduction of http://robslink.com/SAS/democd91/pyramid_pie.htm
+"""
+import altair as alt
+import pandas as pd
+
+category = ['Sky', 'Shady side of a pyramid', 'Sunny side of a pyramid']
+color = ["#416D9D", "#674028", "#DEAC58"]
+df = pd.DataFrame({'category': category, 'value': [75, 10, 15]})
+
+alt.Chart(df).mark_arc(outerRadius=80).encode(
+    alt.Theta('value:Q', scale=alt.Scale(range=[2.356, 8.639])),
+    alt.Color('category:N',
+        scale=alt.Scale(domain=category, range=color),
+        legend=alt.Legend(title=None, orient='none', legendX=160, legendY=50)),
+    order='value:Q'
+).properties(width=150, height=150).configure_view(strokeOpacity=0)

.venv/Lib/site-packages/altair/examples/radial_chart.py

@@ -0,0 +1,25 @@
+"""
+Radial Chart
+------------
+This radial plot uses both angular and radial extent to convey multiple dimensions of data.
+This is adapted from a corresponding Vega-Lite Example:
+`Radial Plot <https://vega.github.io/vega-lite/examples/arc_radial.html>`_.
+"""
+# category: circular plots
+
+import pandas as pd
+import altair as alt
+
+source = pd.DataFrame({"values": [12, 23, 47, 6, 52, 19]})
+
+base = alt.Chart(source).encode(
+    theta=alt.Theta("values:Q", stack=True),
+    radius=alt.Radius("values", scale=alt.Scale(type="sqrt", zero=True, rangeMin=20)),
+    color="values:N",
+)
+
+c1 = base.mark_arc(innerRadius=20, stroke="#fff")
+
+c2 = base.mark_text(radiusOffset=10).encode(text="values:Q")
+
+c1 + c2