python/next -> python

Author: nicolaskruchten

python/3d-axes.md

@@ -43,7 +43,7 @@ jupyter:
 attributes such as `xaxis`, `yaxis` and `zaxis` parameters, in order to
 set the range, title, ticks, color etc. of the axes.
 
-For creating 3D charts, see [this page](https://plot.ly/python/next/3d-charts/).
+For creating 3D charts, see [this page](https://plot.ly/python/3d-charts/).
 
 ```python
 import plotly.graph_objects as go

python/3d-scatter-plots.md

@@ -39,7 +39,7 @@ jupyter:
 
 ## 3D scatter plot with plotly express
 
-Like the [2D scatter plot](https://plot.ly/python/next/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space. Note that [Plotly Express](../plotly-express/) functions take as a first argument a [tidy `pandas.DataFrame`](https://www.jeannicholashould.com/tidy-data-in-python.html) such as the ones defined in ``px.data``.
+Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space. Note that [Plotly Express](../plotly-express/) functions take as a first argument a [tidy `pandas.DataFrame`](https://www.jeannicholashould.com/tidy-data-in-python.html) such as the ones defined in ``px.data``.
 
 ```python
 import plotly.express as px
@@ -79,7 +79,7 @@ fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
 #### Basic 3D Scatter Plot
 
 When data are not available as tidy dataframes, it is also possible to use the more generic `go.Scatter3D` from `plotly.graph_objs`.
-Like the [2D scatter plot](https://plot.ly/python/next/line-and-scatter/) `go.Scatter`, `go.Scatter3d` plots individual data in three-dimensional space.
+Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `go.Scatter`, `go.Scatter3d` plots individual data in three-dimensional space.
 
 ```python
 import plotly.graph_objects as go

python/bar-charts.md

@@ -327,7 +327,7 @@ fig.show()
 ```
 
 ### Horizontal Bar Charts
-See examples of horizontal bar charts [here](https://plot.ly/python/next/horizontal-bar-charts/).
+See examples of horizontal bar charts [here](https://plot.ly/python/horizontal-bar-charts/).
 
 
 ### Dash Example

python/box-plots.md

@@ -37,14 +37,14 @@ jupyter:
     v4upgrade: true
 ---
 
-A [box plot](https://en.wikipedia.org/wiki/Box_plot) is a statistical representation of numerical data through their quartiles. The ends of the box represent the lower and upper quartiles, while the median (second quartile) is marked by a line inside the box. For other statistical representations of numerical data, see [other statistical charts](https://plot.ly/python/next/statistical-charts/).
+A [box plot](https://en.wikipedia.org/wiki/Box_plot) is a statistical representation of numerical data through their quartiles. The ends of the box represent the lower and upper quartiles, while the median (second quartile) is marked by a line inside the box. For other statistical representations of numerical data, see [other statistical charts](https://plot.ly/python/statistical-charts/).
 
 
 ## Box Plot with plotly express
 
 [Plotly Express](../plotly-express/) functions take as a first argument a [tidy `pandas.DataFrame`](https://www.jeannicholashould.com/tidy-data-in-python.html). In a box plot created by `px.box`, the distribution of the column given as `y` argument is represented.
 
-If your data are not available as a tidy dataframe, you can use ``go.Box`` as [described below](https://plot.ly/python/next/box-plots/#box-plot-with-go.Box).
+If your data are not available as a tidy dataframe, you can use ``go.Box`` as [described below](https://plot.ly/python/box-plots/#box-plot-with-go.Box).
 
 ```python
 import plotly.express as px

python/bubble-charts.md

@@ -40,7 +40,7 @@ jupyter:
 
 ## Bubble chart with plotly.express
 
-A [bubble chart](https://en.wikipedia.org/wiki/Bubble_chart) is a scatter plot in which a third dimension of the data is shown through the size of markers. For other types of scatter plot, see the [line and scatter page](https://plot.ly/python/next/line-and-scatter/).
+A [bubble chart](https://en.wikipedia.org/wiki/Bubble_chart) is a scatter plot in which a third dimension of the data is shown through the size of markers. For other types of scatter plot, see the [line and scatter page](https://plot.ly/python/line-and-scatter/).
 
 We first show a bubble chart example using plotly express. [Plotly Express](../plotly-express/) functions take as a first argument a [tidy `pandas.DataFrame`](https://www.jeannicholashould.com/tidy-data-in-python.html). The size of markers is set from the dataframe column given as the `size` parameter.
 

python/county-choropleth.md

@@ -272,7 +272,7 @@ fig.layout.template = None
 fig.show()
 ```
 
-Also see Mapbox county choropleths made in Python: [https://plot.ly/python/mapbox-county-choropleth/](https://plot.ly/python/next/mapbox-county-choropleth/)
+Also see Mapbox county choropleths made in Python: [https://plot.ly/python/mapbox-county-choropleth/](https://plot.ly/python/mapbox-county-choropleth/)
 
 
 #### Reference

python/custom-buttons.md

@@ -41,7 +41,7 @@ The [updatemenu method](https://plot.ly/python/reference/#layout-updatemenus-but
 - `"restyle"`: modify data or data attributes
 - `"relayout"`: modify layout attributes
 - `"update"`: modify data **and** layout attributes
-- `"animate"`: start or pause an [animation](https://plot.ly/python/next/#animations))
+- `"animate"`: start or pause an [animation](https://plot.ly/python/#animations))
 
 
 #### Restyle Button

python/distplot.md

@@ -38,7 +38,7 @@ jupyter:
 
 ## Combined statistical representations with px.histogram
 
-Several representations of statistical distributions are available in plotly, such as [histograms](https://plot.ly/python/next/histograms/), [violin plots](https://plot.ly/python/next/violin/), [box plots](https://plot.ly/python/next/box-plots/) (see [the complete list here](https://plot.ly/python/next/statistical-charts/)). It is also possible to combine several representations in the same plot.
+Several representations of statistical distributions are available in plotly, such as [histograms](https://plot.ly/python/histograms/), [violin plots](https://plot.ly/python/violin/), [box plots](https://plot.ly/python/box-plots/) (see [the complete list here](https://plot.ly/python/statistical-charts/)). It is also possible to combine several representations in the same plot.
 
 For example, the `plotly.express` function `px.histogram` can add a subplot with a different statistical representation than the histogram, given by the parameter `marginal`. [Plotly Express](../plotly-express/) functions take as a first argument a [tidy `pandas.DataFrame`](https://www.jeannicholashould.com/tidy-data-in-python.html).
 

python/dropdowns.md

@@ -42,7 +42,7 @@ The [updatemenu method](https://plot.ly/python/reference/#layout-updatemenus-but
 - `"restyle"`: modify data or data attributes
 - `"relayout"`: modify layout attributes
 - `"update"`: modify data **and** layout attributes
-- `"animate"`: start or pause an [animation](https://plot.ly/python/next/#animations)
+- `"animate"`: start or pause an [animation](https://plot.ly/python/#animations)
 
 
 ## Restyle Dropdown

python/error-bars.md

@@ -39,7 +39,7 @@ jupyter:
 
 ### Error Bars with plotly express
 
-[Plotly Express](../plotly-express/) functions take as a first argument a [tidy `pandas.DataFrame`](https://www.jeannicholashould.com/tidy-data-in-python.html). For functions representing 2D data points such as [`px.scatter`](https://plot.ly/python/next/line-and-scatter/), [`px.line`](https://plot.ly/python/next/line-charts/), [`px.bar`](https://plot.ly/python/next/bar-charts/) etc., error bars are given as a column name which is the value of the `error_x` (for the error on x position) and `error_y` (for the error on y position).
+[Plotly Express](../plotly-express/) functions take as a first argument a [tidy `pandas.DataFrame`](https://www.jeannicholashould.com/tidy-data-in-python.html). For functions representing 2D data points such as [`px.scatter`](https://plot.ly/python/line-and-scatter/), [`px.line`](https://plot.ly/python/line-charts/), [`px.bar`](https://plot.ly/python/bar-charts/) etc., error bars are given as a column name which is the value of the `error_x` (for the error on x position) and `error_y` (for the error on y position).
 
 ```python
 import plotly.express as px

python/facet-plots.md

@@ -40,7 +40,7 @@ jupyter:
 ### Facet and Trellis Plots
 
 Facet plots, also known as trellis plots or small multiples, are figures made up of multiple subplots which have the same set of axes, where each subplot shows a subset of the data. While it is straightforward to use `plotly`'s [subplot capabilities]
-(/python/subplots/) to make such figures, it's far easier to use the built-in `facet_row` and `facet_col` arguments in the various [Plotly Express](/python/next/plotly-express/) functions.
+(/python/subplots/) to make such figures, it's far easier to use the built-in `facet_row` and `facet_col` arguments in the various [Plotly Express](/python/plotly-express/) functions.
 
 ### Scatter Plot Column Facets
 

python/gantt.md

@@ -40,7 +40,7 @@ jupyter:
 
 A [Gantt chart](https://en.wikipedia.org/wiki/Gantt_chart) is a type of bar chart that illustrates a project schedule. The chart lists the tasks to be performed on the vertical axis, and time intervals on the horizontal axis. The width of the horizontal bars in the graph shows the duration of each activity.
 
-See also the [bar charts examples](https://plot.ly/python/next/bar-charts/).
+See also the [bar charts examples](https://plot.ly/python/bar-charts/).
 
 
 #### Simple Gantt Chart

python/getting-started.md

@@ -36,7 +36,7 @@ jupyter:
 
 <!-- #region -->
 ### Overview
-The plotly Python library ([plotly.py](https://plot.ly/python/next/)) is an interactive, [open-source](https://github.com/plotly/plotly.py) plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.
+The plotly Python library ([plotly.py](https://plot.ly/python/)) is an interactive, [open-source](https://github.com/plotly/plotly.py) plotting library that supports over 40 unique chart types covering a wide range of statistical, financial, geographic, scientific, and 3-dimensional use-cases.
 
 Built on top of the Plotly JavaScript library ([plotly.js](https://plot.ly/javascript/)), plotly.py enables Python users to create beautiful interactive web-based visualizations that can be displayed in Jupyter notebooks, saved to standalone HTML files, or served as part of pure Python-built web applications using Dash.
 
@@ -221,7 +221,7 @@ or conda.
 $ conda install -c plotly/label/test plotly-geo=1.0.0rc1
 ```
 
-See [*USA County Choropleth Maps in Python*](https://plot.ly/python/next/county-choropleth/) for more information on the county choropleth figure factory.
+See [*USA County Choropleth Maps in Python*](https://plot.ly/python/county-choropleth/) for more information on the county choropleth figure factory.
 
 #### Chart Studio Support
 The `chart-studio` package can be used to upload plotly figures to Plotly's Chart

python/histograms.md

@@ -118,7 +118,7 @@ fig.show()
 
 #### Visualizing the distribution
 
-With the `marginal` keyword, a subplot is drawn alongside the histogram, visualizing the distribution. See [the distplot page](https://plot.ly/python/next/distplot/)for more examples of combined statistical representations.
+With the `marginal` keyword, a subplot is drawn alongside the histogram, visualizing the distribution. See [the distplot page](https://plot.ly/python/distplot/)for more examples of combined statistical representations.
 
 ```python
 import plotly.express as px

python/horizontal-bar-charts.md

@@ -37,7 +37,7 @@ jupyter:
     v4upgrade: true
 ---
 
-See more examples of bar charts (including vertical bar charts) and styling options [here](https://plot.ly/python/next/bar-charts/).
+See more examples of bar charts (including vertical bar charts) and styling options [here](https://plot.ly/python/bar-charts/).
 
 
 ### Horizontal Bar Chart with plotly express
@@ -342,4 +342,4 @@ fig.show()
 ```
 
 ### Reference
-See more examples of bar charts and styling options [here](https://plot.ly/python/next/bar-charts/).<br> See https://plot.ly/python/reference/#bar for more information and chart attribute options!
+See more examples of bar charts and styling options [here](https://plot.ly/python/bar-charts/).<br> See https://plot.ly/python/reference/#bar for more information and chart attribute options!

python/images.md

@@ -73,7 +73,7 @@ fig.show()
 ```
 
 #### Add a Logo
-See more examples of [adding logos to charts](https://plot.ly/python/next/logos/)!
+See more examples of [adding logos to charts](https://plot.ly/python/logos/)!
 
 ```python
 import plotly.graph_objects as go

python/line-and-scatter.md

@@ -92,7 +92,7 @@ fig.show()
 
 #### Line and Scatter Plots
 
-Use `mode` argument to choose between markers, lines, or a combination of both. For more options about line plots, see also the [line charts notebook](https://plot.ly/python/next/line-charts/) and the [filled area plots notebook](https://plot.ly/python/next/filled-area-plots/).
+Use `mode` argument to choose between markers, lines, or a combination of both. For more options about line plots, see also the [line charts notebook](https://plot.ly/python/line-charts/) and the [filled area plots notebook](https://plot.ly/python/filled-area-plots/).
 
 ```python
 import plotly.graph_objects as go
@@ -124,7 +124,7 @@ fig.show()
 
 #### Bubble Scatter Plots
 
-In [bubble charts](https://en.wikipedia.org/wiki/Bubble_chart), a third dimension of the data is shown through the size of markers. For more examples, see the [bubble chart notebook](https://plot.ly/python/next/bubble-charts/)
+In [bubble charts](https://en.wikipedia.org/wiki/Bubble_chart), a third dimension of the data is shown through the size of markers. For more examples, see the [bubble chart notebook](https://plot.ly/python/bubble-charts/)
 
 ```python
 import plotly.graph_objects as go

python/line-charts.md

@@ -42,7 +42,7 @@ jupyter:
 
 [Plotly Express](../plotly-express/) functions take as a first argument a [tidy `pandas.DataFrame`](https://www.jeannicholashould.com/tidy-data-in-python.html). With ``px.line``, each data point is represented as a vertex (which location is given by the `x` and `y` columns) of a **polyline mark** in 2D space.
 
-For more examples of line plots, see the [line and scatter notebook](https://plot.ly/python/next/line-and-scatter/).
+For more examples of line plots, see the [line and scatter notebook](https://plot.ly/python/line-and-scatter/).
 
 #### Simple Line Plot with plotly.express
 

python/ohlc-charts.md

@@ -29,7 +29,7 @@ jupyter:
 
 The [OHLC](https://en.wikipedia.org/wiki/Open-high-low-close_chart) chart (for open, high, low and close) is a style of financial chart describing open, high, low and close values for a given `x` coordinate (most likely time). The tip of the lines represent the `low` and `high` values and the horizontal segments represent the `open` and `close` values. Sample points where the close value is higher (lower) then the open value are called increasing (decreasing). By default, increasing items are drawn in green whereas decreasing are drawn in red.
 
-See also [Candlestick Charts](https://plot.ly/python/next/candlestick-charts/) and [other financial charts](https://plot.ly/python/next/#financial-charts).
+See also [Candlestick Charts](https://plot.ly/python/candlestick-charts/) and [other financial charts](https://plot.ly/python/#financial-charts).
 
 #### Simple OHLC Chart with Pandas
 

python/polar-chart.md

@@ -72,7 +72,7 @@ fig = px.line_polar(wind, r="frequency", theta="direction", color="strength", li
 fig.show()
 ```
 
-See also the [wind rose page](https://plot.ly/python/next/wind-rose-charts/) for more wind rose visualizations in polar coordinates.
+See also the [wind rose page](https://plot.ly/python/wind-rose-charts/) for more wind rose visualizations in polar coordinates.
 
 
 ## Polar Scatter Plot with go.Scatterpolar

python/renderers.md

@@ -158,7 +158,7 @@ The `plotly_mimetype` renderer creates a specification of the plotly figure (cal
 These are aliases for `plotly_mimetype` since this renderer is a good choice when working in JupyterLab, nteract, and the Visual Studio Code notebook interface.
 
 ##### Static image renderers
-A set of renderers is provided for displaying figures as static images.  These renderers all rely on the orca static image export utility. See the [Static Image Export](https://plot.ly/python/next/static-image-export/) page for more information on getting set up with orca.
+A set of renderers is provided for displaying figures as static images.  These renderers all rely on the orca static image export utility. See the [Static Image Export](https://plot.ly/python/static-image-export/) page for more information on getting set up with orca.
 
 ###### `png`, `jpeg`, and `svg`
 These renderers display figures as static PNG, JPEG, and SVG images respectively.  These renderers are useful for user interfaces that do not support inline HTML output, but do support inline static images.  Examples include the [QtConsole](https://qtconsole.readthedocs.io/en/stable/), [Spyder](https://www.spyder-ide.org/), and the PyCharm [notebook interface](https://www.jetbrains.com/help/pycharm/jupyter-notebook-support.html).
@@ -231,6 +231,6 @@ Dash is a Python framework for building web applications, and it provides built-
 It is important to note that Dash does not use the renderers framework discussed above, so you should not use the `.show` figure method or the `plotly.io.show` function inside Dash applications.
 
 ## Displaying figures using ipywidgets
-Plotly figures can be displayed in [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/) contexts using `plotly.graph_objects.FigureWidget` objects.  `FigureWidget` is a figure graph object (Just like `plotly.graph_objects.Figure`) so you can add traces to it and update it just like a regular `Figure`.  But `FigureWidget` is also an ipywidgets object, which means that you can display it alongside other ipywidgets to build user interfaces right in the notebook.  See the [Plotly FigureWidget Overview](https://plot.ly/python/next/figurewidget/) for more information on integrating plotly figures with ipywidgets.
+Plotly figures can be displayed in [ipywidgets](https://ipywidgets.readthedocs.io/en/stable/) contexts using `plotly.graph_objects.FigureWidget` objects.  `FigureWidget` is a figure graph object (Just like `plotly.graph_objects.Figure`) so you can add traces to it and update it just like a regular `Figure`.  But `FigureWidget` is also an ipywidgets object, which means that you can display it alongside other ipywidgets to build user interfaces right in the notebook.  See the [Plotly FigureWidget Overview](https://plot.ly/python/figurewidget/) for more information on integrating plotly figures with ipywidgets.
 
 It is important to note that `FigureWidget` does not use the renderers framework discussed above, so you should not use the `.show` figure method or the `plotly.io.show` function on `FigureWidget` objects.

python/scatter-plots-on-maps.md

@@ -40,7 +40,7 @@ jupyter:
 
 ### Geographical Scatter Plot with px.scatter_geo
 
-For data available as a tidy pandas DataFrame, use the [Plotly Express](/python/next/plotly-express/) function `px.scatter_geo` for a geographical scatter plot. The `size` argument is used to set the size of markers from a given column of the DataFrame.
+For data available as a tidy pandas DataFrame, use the [Plotly Express](/python/plotly-express/) function `px.scatter_geo` for a geographical scatter plot. The `size` argument is used to set the size of markers from a given column of the DataFrame.
 
 ```python
 import plotly.express as px

python/scattermapbox.md

@@ -45,7 +45,7 @@ To plot on Mapbox maps with Plotly you'll need a Mapbox account and a public [Ma
 
 ### Basic example with Plotly Express
 
-For data available as a tidy pandas DataFrame, use the [Plotly Express](/python/next/plotly-express/) function `px.scatter_mapbox` for a scatter plot on a tile map.
+For data available as a tidy pandas DataFrame, use the [Plotly Express](/python/plotly-express/) function `px.scatter_mapbox` for a scatter plot on a tile map.
 
 ```python
 import plotly.express as px

python/templates.md

@@ -28,7 +28,7 @@ jupyter:
     display_as: file_settings
     has_thumbnail: true
     name: Theming and templates
-    page_type: example_index
+    page_type: u-guide
     layout: user-guide
     permalink: python/templates/
     thumbnail: thumbnail/theming-and-templates.png