From 8a66cbec80119812d2ef09b6ffb802d7ea302dbc Mon Sep 17 00:00:00 2001 From: Michael Charlton Date: Fri, 21 Apr 2023 10:37:24 +0100 Subject: [PATCH] Categories transposed in data documentation (#3026) In an example column-x is specified as 'quantitative', when it should be 'nominal'. And column-y is 'nominal', when it should be 'quantitative'. Co-authored-by: Mattijn van Hoek --- doc/user_guide/data.rst | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/doc/user_guide/data.rst b/doc/user_guide/data.rst index 382375434..d48008dbb 100644 --- a/doc/user_guide/data.rst +++ b/doc/user_guide/data.rst @@ -25,8 +25,8 @@ There are many different ways of specifying a dataset: When data is specified as a pandas DataFrame, Altair uses the data type information provided by pandas to automatically determine the data types required in the encoding. For example, here we specify data via a pandas DataFrame -and Altair automatically detects that the x-column should be visualized on a quantitative scale -and that the y-column should be visualized on a categorical (nominal) scale: +and Altair automatically detects that the x-column should be visualized on a categorical (nominal) scale +and that the y-column should be visualized on a quantitative scale: .. altair-plot:: @@ -349,7 +349,7 @@ GeoDataFrame and visualize these using the ``mark_geoshape``. gdf_geoms -Since the spatial data in our example is not geographic, +Since the spatial data in our example is not geographic, we use ``project`` configuration ``type="identity", reflectY=True`` to draw the geometries without applying a geographic projection. By using ``alt.Color(...).scale(None)`` we disable the automatic color assignment in Altair @@ -486,7 +486,7 @@ TopoJSON File by URL ^^^^^^^^^^^^^^^^^^^^ Altair can load TopoJSON resources directly from a web URL. As -explained in :ref:`spatial-data-inline-topojson`, we have to use the +explained in :ref:`spatial-data-inline-topojson`, we have to use the ``feature`` parameter to specify the object name (here ``boroughs``) and define the type of data as ``topjoson`` in the ``alt.DataFormat()`` object. @@ -496,11 +496,11 @@ define the type of data as ``topjoson`` in the ``alt.DataFormat()`` object. from vega_datasets import data url_topojson = data.londonBoroughs.url - + data_url_topojson = alt.Data( url=url_topojson, format=alt.DataFormat(feature="boroughs", type="topojson") ) - + data_url_topojson Note: There also exist a shorthand to extract the objects from a @@ -532,7 +532,7 @@ specific regions such as individual countries, states or counties. Here below we draw the same Boroughs of London, but now as mesh only. -Note: you have to explicitly define ``filled=False`` to draw multi(lines) +Note: you have to explicitly define ``filled=False`` to draw multi(lines) without fill color. .. altair-plot:: @@ -540,14 +540,14 @@ without fill color. from vega_datasets import data url_topojson = data.londonBoroughs.url - + data_url_topojson_mesh = alt.Data( url=url_topojson, format=alt.DataFormat(mesh="boroughs", type="topojson") ) alt.Chart(data_url_topojson_mesh, title="Border London-Boroughs").mark_geoshape( filled=False - ) + ) .. _spatial-data-nested-geojson: @@ -569,9 +569,9 @@ in the list of dictionaries: {"color": "#A9CDE0", "geo": {"type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[3.2, 0], [3.2, 1.25], [4.32, 1.25], [4.32, 0], [3.47, 0], [3.2, 0]]]}}}, ] data_nested_features = alt.Data(values=nested_features) - + alt.Chart(data_nested_features, title="Vega-Altair").mark_geoshape().encode( - shape="geo:G", + shape="geo:G", color=alt.Color("color:N").scale(None) ).project(type="identity", reflectY=True)