docs: new dataset source, from vega_dataset to altair.dataset

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -16,7 +16,7 @@ body:
       label: What happened?
       description: |
         Describe what happened and how to reproduce the bug.
-        Include the full code and data to reproduce it. Use a simple toy data set, e.g. from `vega_datasets`.
+        Include the full code and data to reproduce it. Use a simple toy data set, e.g. from `altair.datasets`.
     validations:
       required: true
   - type: textarea

CONTRIBUTING.md

@@ -198,7 +198,7 @@ Every example should:
 - not make any external calls to download data within the script (i.e. don't
   use urllib). You can define your data directly within the example file,
   generate your data using pandas and numpy, or you can use data
-  available in the `vega_datasets` package.
+  available in the `altair.datasets` module.
 
 The easiest way to get started would be to adapt examples from the [Vega-Lite
 example gallery](https://vega.github.io/vega-lite/examples/) which are missing
@@ -225,12 +225,9 @@ Some additional notes:
   If the example requires multiple dataframes then this does not apply. See
   other examples for guidance. 
 - Example code should not require downloading external datasets. We suggest
-  using the `vega_datasets` package if possible.
-  If you are using the `vega_datasets` package there are multiple ways to refer
-  to a data source. If the dataset you would like to use is included in local
-  installation (`vega_datasets.local_data.list_datasets()`) then the data can
-  be referenced directly, such as `source = data.iris()`. If the data is not
-  included then it should be referenced by URL, such as `source =
+  using the `altair.datasets` module if possible.
+  If you are using the `altair.datasets` module there are multiple ways to refer
+  to a data source. The data can be referenced directly, such as `source = data.penguins()`, or it can be referenced by URL, such as `source =
   data.movies.url`. This is to ensure that Altair's automated test suite does
   not depend on availability of external HTTP resources.
 - If VlConvert does not support PNG export of the chart (e.g. in the case of emoji),

README.md

@@ -31,7 +31,7 @@ Here is an example using Vega-Altair to quickly visualize and display a dataset
 import altair as alt
 
 # load a simple dataset as a pandas DataFrame
-from vega_datasets import data
+from altair.datasets import data
 cars = data.cars()
 
 alt.Chart(cars).mark_point().encode(
@@ -48,7 +48,7 @@ With a few modifications to the example above we can create a linked histogram t
 
 ```python 
 import altair as alt
-from vega_datasets import data
+from altair.datasets import data
 
 source = data.cars()
 

altair/theme.py

@@ -221,7 +221,7 @@ def custom_theme() -> theme.ThemeConfig:
 
     Until another theme has been enabled, all charts will use defaults set in ``custom_theme()``::
 
-        from vega_datasets import data
+        from altair.datasets import data
 
         source = data.stocks()
         lines = (

altair/vegalite/v6/api.py

@@ -996,14 +996,16 @@ def then(self, statement: _StatementType, /, **kwds: Any) -> Then[Any]:
         Simple conditions may be expressed without defining a default::
 
             import altair as alt
-            from vega_datasets import data
+            from altair.datasets import data
 
             source = data.movies()
-            predicate = (alt.datum.IMDB_Rating == None) | (alt.datum.Rotten_Tomatoes_Rating == None)
+            predicate = (alt.datum["IMDB Rating"] == None) | (
+                alt.datum["Rotten Tomatoes Rating"] == None
+            )
 
             alt.Chart(source).mark_point(invalid=None).encode(
-                x="IMDB_Rating:Q",
-                y="Rotten_Tomatoes_Rating:Q",
+                x="IMDB Rating:Q",
+                y="Rotten Tomatoes Rating:Q",
                 color=alt.when(predicate).then(alt.value("grey")),
             )
         """
@@ -1072,7 +1074,7 @@ def otherwise(
         Points outside of ``brush`` will not appear highlighted::
 
             import altair as alt
-            from vega_datasets import data
+            from altair.datasets import data
 
             source = data.cars()
             brush = alt.selection_interval()
@@ -1159,7 +1161,7 @@ def when(
         Chain calls to express precise queries::
 
             import altair as alt
-            from vega_datasets import data
+            from altair.datasets import data
 
             source = data.cars()
             color = (
@@ -1264,19 +1266,21 @@ def then(self, statement: _StatementType, /, **kwds: Any) -> Then[_Conditions]:
         Multiple conditions with an implicit default::
 
             import altair as alt
-            from vega_datasets import data
+            from altair.datasets import data
 
             source = data.movies()
-            predicate = (alt.datum.IMDB_Rating == None) | (alt.datum.Rotten_Tomatoes_Rating == None)
+            predicate = (alt.datum["IMDB Rating"] == None) | (
+                alt.datum["Rotten Tomatoes Rating"] == None
+            )
             color = (
                 alt.when(predicate)
                 .then(alt.value("grey"))
-                .when(alt.datum.IMDB_Votes < 5000)
+                .when(alt.datum["IMDB Votes"] < 5000)
                 .then(alt.value("lightblue"))
             )
 
             alt.Chart(source).mark_point(invalid=None).encode(
-                x="IMDB_Rating:Q", y="Rotten_Tomatoes_Rating:Q", color=color
+                x="IMDB Rating:Q", y="Rotten Tomatoes Rating:Q", color=color
             )
         """
         condition = self._when_then(statement, kwds)
@@ -1344,7 +1348,7 @@ def when(
     Setting up a common chart::
 
         import altair as alt
-        from vega_datasets import data
+        from altair.datasets import data
 
         source = data.cars()
         brush = alt.selection_interval()
@@ -3166,7 +3170,7 @@ def transform_filter(
 
             import altair as alt
             from altair import datum
-            from vega_datasets import data
+            from altair.datasets import data
 
             source = data.population.url
             chart = (

doc/case_studies/exploring-weather.rst

@@ -23,7 +23,7 @@ dataframes, and contains a loader for this and other built-in datasets:
 .. altair-plot::
     :output: repr
 
-    from vega_datasets import data
+    from altair.datasets import data
 
     df = data.seattle_weather()
     df.head()

doc/conf.py

@@ -158,7 +158,6 @@
         },
     ],
     "header_links_before_dropdown": 4,
-    "announcement": """This website is for version 5. You can find the documentation for version 4 <a href='https://altair-viz.github.io/altair-viz-v4/'>here</a>.""",
     "analytics": {
         "plausible_analytics_domain": "altair-viz.github.io",
         "plausible_analytics_url": ("https://views.scientific-python.org/js/script.js"),

doc/getting_started/installation.rst

@@ -38,6 +38,5 @@ for details on how to contribute to the Altair project.
 
 .. _conda: https://docs.conda.io/
 .. _Vega-Lite: http://vega.github.io/vega-lite
-.. _vega_datasets: https://github.com/altair-viz/vega_datasets
 .. _JupyterLab: http://jupyterlab.readthedocs.io/
 .. _Jupyter Notebook: https://jupyter-notebook.readthedocs.io/

doc/getting_started/overview.rst

@@ -17,7 +17,7 @@ an interactive scatter plot:
     import altair as alt
 
     # load a sample dataset as a pandas DataFrame
-    from vega_datasets import data
+    from altair.datasets import data
     cars = data.cars()
 
     # make the chart 

doc/getting_started/resources.rst

@@ -104,14 +104,6 @@ Altair backend for the pandas plotting API.
 .. List of links.
 .. _altair_pandas: https://github.com/altair-viz/altair_pandas
 
-vega_datasets_
-~~~~~~~~~~~~~~
-
-Offline access to the Vega datasets used in the Altair documentation.
-
-.. List of links.
-.. _vega_datasets: https://github.com/altair-viz/vega_datasets
-
 altair_recipes_
 ~~~~~~~~~~~~~~~
 

doc/user_guide/compound_charts.rst

@@ -34,7 +34,7 @@ same data; for example:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     stocks = data.stocks.url
 
@@ -76,22 +76,22 @@ heat-map:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     source = data.movies.url
 
     heatmap = alt.Chart(source).mark_rect().encode(
-        alt.X('IMDB_Rating:Q').bin(),
-        alt.Y('Rotten_Tomatoes_Rating:Q').bin(),
+        alt.X('IMDB Rating:Q').bin(),
+        alt.Y('Rotten Tomatoes Rating:Q').bin(),
         alt.Color('count()').scale(scheme='greenblue')
     )
 
     points = alt.Chart(source).mark_circle(
         color='black',
         size=5,
     ).encode(
-        x='IMDB_Rating:Q',
-        y='Rotten_Tomatoes_Rating:Q',
+        x='IMDB Rating:Q',
+        y='Rotten Tomatoes Rating:Q',
     )
 
     heatmap + points
@@ -121,7 +121,7 @@ distribution of its points:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     penguins = data.penguins.url
 
@@ -174,7 +174,7 @@ with a ``brush`` selection to add interaction:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     source = data.sp500.url
 
@@ -219,7 +219,7 @@ showing how ``repeat`` can be used to build the chart more efficiently:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     penguins = data.penguins.url
 
@@ -251,7 +251,7 @@ method, makes this type of chart a bit easier to produce:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
     penguins = data.penguins.url
 
     alt.Chart(penguins).mark_point().encode(
@@ -271,21 +271,21 @@ encodings for the row and/or column which can be referred to in the chart's
 encoding specification using ``alt.repeat('row')`` or ``alt.repeat('column')``.
 
 Another option to use the ``repeat`` method is for layering. Here below the
-columns ``US_Gross`` and ``Worldwide_Gross`` are layered on the ``y``-axis
+columns ``US Gross`` and ``Worldwide Gross`` are layered on the ``y``-axis
 using ``alt.repeat('layer')``:
 
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     source = data.movies()
 
     alt.Chart(source).mark_line().encode(
-        x=alt.X("IMDB_Rating").bin(),
+        x=alt.X("IMDB Rating").bin(),
         y=alt.Y(alt.repeat('layer')).aggregate('mean').title("Mean of US and Worldwide Gross"),
         color=alt.ColorDatum(alt.repeat('layer'))
-    ).repeat(layer=["US_Gross", "Worldwide_Gross"])
+    ).repeat(layer=["US Gross", "Worldwide Gross"])
 
 Currently ``repeat`` can only be encodings (not, e.g., data transforms)
 but there is discussion within the Vega-Lite community about making this pattern

doc/user_guide/configuration.rst

@@ -98,7 +98,7 @@ the :meth:`Chart.configure_header` method. Here is an example:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     source = data.cars.url
 
@@ -142,7 +142,7 @@ Here is an example:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     source = data.cars.url
 
@@ -270,7 +270,7 @@ Here is an example:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     source = data.cars.url
 
@@ -305,7 +305,7 @@ be controlled:
 .. altair-plot::
 
     import altair as alt
-    from vega_datasets import data
+    from altair.datasets import data
 
     source = data.cars.url