sthagen · sthagen · Aug 14, 2021 · Oct 2, 2020 · Jan 2, 2021 · Jun 25, 2021
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -2,10 +2,27 @@
 All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
-## UNRELEASED
+## [5.2.1] - 2021-08-13
+
+### Updated
+
+- Updated Plotly.js to from version 2.2.0 to version 2.3.1. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/master/CHANGELOG.md#231----2021-07-30) for more information. These changes are reflected in the auto-generated `plotly.graph_objects` module. Notable changes include:
+  - new projections in `geo` subplots from a more recent version of `d3-geo`
+  - new number-formatting directives from a more recent version of `d3-format`
+  - Improve rendering of `scattergl`, `splom` and `parcoords` by implementing `plotGlPixelRatio` for those traces
+  - performance improvements and bug fixes
+
+### Added
+  - Extra flags were added to the `gapminder` and `stocks` dataset to facilitate testing, documentation and demos [#3305](https://github.com/plotly/plotly.py/issues/3305)
+  - All line-like Plotly Express functions now accept `markers` argument to display markers, and all but `line_mapbox` accept `symbol` to map a field to the symbol attribute, similar to scatter-like functions [#3326](https://github.com/plotly/plotly.py/issues/3326)
+  - `px.scatter` and `px.density_contours` now support new `trendline` types `'rolling'`, `'expanding'` and `'ewm'` [#2997](https://github.com/plotly/plotly.py/pull/2997)
+  - `px.scatter` and `px.density_contours` now support new `trendline_options` argument to parameterize trendlines, with support for constant control and log-scaling in `'ols'` and specification of the fraction used for `'lowess'`, as well as pass-through to Pandas for `'rolling'`, `'expanding'` and `'ewm'` [#2997](https://github.com/plotly/plotly.py/pull/2997)
+  - `px.scatter` and `px.density_contours` now support new `trendline_scope` argument that accepts the value `'overall'` to request a single trendline for all traces, including across facets and animation frames [#2997](https://github.com/plotly/plotly.py/pull/2997)
+  - A new `px.ecdf()` function for Empirical Cumulative Distribution Functions [#3330](https://github.com/plotly/plotly.py/pull/3330)
 
 ### Fixed
   - Fixed regression introduced in version 5.0.0 where pandas/numpy arrays with `dtype` of Object were being converted to `list` values when added to a Figure ([#3292](https://github.com/plotly/plotly.py/issues/3292), [#3293](https://github.com/plotly/plotly.py/pull/3293))
+  - Better detection of Chrome and Chromium browsers in the Renderers framework, especially on Linux ([#3278](https://github.com/plotly/plotly.py/pull/3278)) with thanks to [@c-chaitanya](https://github.com/c-chaitanya) for the contribution
 
 ## [5.1.0] - 2021-06-28
 

diff --git a/README.md b/README.md
@@ -37,7 +37,7 @@ Our recommended IDE for Plotly’s Python graphing library is Dash Enterprise’
 
 ## Quickstart
 
-`pip install plotly==5.1.0`
+`pip install plotly==5.2.1`
 
 Inside [Jupyter](https://jupyter.org/install) (installable with `pip install "jupyterlab>=3" "ipywidgets>=7.6"`):
 
@@ -86,13 +86,13 @@ Built on top of [plotly.js](https://github.com/plotly/plotly.js), `plotly.py` is
 plotly.py may be installed using pip...
 
 ```
-pip install plotly==5.1.0
+pip install plotly==5.2.1
 ```
 
 or conda.
 
 ```
-conda install -c plotly plotly=5.1.0
+conda install -c plotly plotly=5.2.1
 ```
 
 ### JupyterLab Support
@@ -114,7 +114,7 @@ The instructions above apply to JupyterLab 3.x. **For JupyterLab 2 or earlier**,
 
 ```
 # JupyterLab 2.x renderer support
-jupyter labextension install jupyterlab-plotly@5.1.0 @jupyter-widgets/jupyterlab-manager
+jupyter labextension install jupyterlab-plotly@5.2.1 @jupyter-widgets/jupyterlab-manager
 ```
 
 Please check out our [Troubleshooting guide](https://plotly.com/python/troubleshooting/) if you run into any problems with JupyterLab.

diff --git a/binder/requirements.txt b/binder/requirements.txt
@@ -1,5 +1,5 @@
 jupytext
-plotly==5.1.0
+plotly==5.2.1
 jupyter
 notebook
 pandas==1.0.3

diff --git a/doc/apidoc/conf.py b/doc/apidoc/conf.py
@@ -28,7 +28,7 @@
 # The short X.Y version
 version = ""
 # The full version, including alpha/beta/rc tags
-release = "5.1.0"
+release = "5.2.1"
 
 
 # -- General configuration ---------------------------------------------------

diff --git a/doc/apidoc/plotly.express.rst b/doc/apidoc/plotly.express.rst
@@ -49,6 +49,8 @@ plotly's high-level API for rapid figure generation. ::
     density_heatmap
     density_mapbox
     imshow
+    set_mapbox_access_token
+    get_trendline_results
 
 
 `plotly.express` subpackages
@@ -60,3 +62,4 @@ plotly's high-level API for rapid figure generation. ::
 
    generated/plotly.express.data.rst
    generated/plotly.express.colors.rst
+   generated/plotly.express.trendline_functions.rst
diff --git a/doc/python/3d-line-plots.md b/doc/python/3d-line-plots.md
@@ -37,15 +37,13 @@ jupyter:
 
 ```python
 import plotly.express as px
-import plotly.express as px
 df = px.data.gapminder().query("country=='Brazil'")
 fig = px.line_3d(df, x="gdpPercap", y="pop", z="year")
 fig.show()
 ```
 
 ```python
 import plotly.express as px
-import plotly.express as px
 df = px.data.gapminder().query("continent=='Europe'")
 fig = px.line_3d(df, x="gdpPercap", y="pop", z="year", color='country')
 fig.show()

diff --git a/doc/python/axes.md b/doc/python/axes.md
@@ -51,7 +51,7 @@ Other kinds of subplots and axes are described in other tutorials:
 The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:
 
 - `'linear'` as described in this page
-- `'log'` (see the [log plot tutorial](/python/log-plots/))
+- `'log'` (see the [log plot tutorial](/python/log-plot/))
 - `'date'` (see the [tutorial on timeseries](/python/time-series/))
 - `'category'` (see the [categorical axes tutorial](/python/categorical-axes/))
 - `'multicategory'` (see the [categorical axes tutorial](/python/categorical-axes/))

diff --git a/doc/python/box-plots.md b/doc/python/box-plots.md
@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.6.0
+      jupytext_version: 1.4.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.6
+    version: 3.7.7
   plotly:
     description: How to make Box Plots in Python with Plotly.
     display_as: statistical
@@ -36,13 +36,18 @@ jupyter:
     thumbnail: thumbnail/box.jpg
 ---
 
-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://plotly.com/python/statistical-charts/).
+<!-- #region -->
+A [box plot](https://en.wikipedia.org/wiki/Box_plot) is a statistical representation of the distribution of a variable through its 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://plotly.com/python/statistical-charts/).
+
+
+Alternatives to box plots for visualizing distributions include [histograms](https://plotly.com/python/histograms/), [violin plots](https://plotly.com/python/violin/), [ECDF plots](https://plotly.com/python/ecdf-plots/) and [strip charts](https://plotly.com/python/strip-charts/).
 
 ## Box Plot with `plotly.express`
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 In a box plot created by `px.box`, the distribution of the column given as `y` argument is represented.
+<!-- #endregion -->
 
 ```python
 import plotly.express as px

diff --git a/doc/python/bubble-charts.md b/doc/python/bubble-charts.md
@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.2'
+      jupytext_version: 1.4.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.7
   plotly:
     description: How to make bubble charts in Python with Plotly.
     display_as: basic
@@ -36,7 +36,7 @@ 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://plotly.com/python/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 [scatter plot documentation](https://plotly.com/python/line-and-scatter/).
 
 We first show a bubble chart example using Plotly Express. [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). The size of markers is set from the dataframe column given as the `size` parameter.
 
@@ -222,4 +222,4 @@ fig.show()
 
 ### Reference
 
-See https://plotly.com/python/reference/scatter/ for more information and chart attribute options!
+See https://plotly.com/python/reference/scatter/ for more information and chart attribute options!
diff --git a/doc/python/categorical-axes.md b/doc/python/categorical-axes.md
@@ -41,7 +41,7 @@ This page shows examples of how to configure [2-dimensional Cartesian axes](/pyt
 The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:
 
 - `'linear'` (see the [linear axes tutorial](/python/axes/))
-- `'log'` (see the [log plot tutorial](/python/log-plots/))
+- `'log'` (see the [log plot tutorial](/python/log-plot/))
 - `'date'` (see the [tutorial on timeseries](/python/time-series/))
 - `'category'` see below
 - `'multicategory'` see below
@@ -64,6 +64,39 @@ fig.update_xaxes(type='category')
 fig.show()
 ```
 
+### Categorical Axes and Trace Types
+
+Every cartesian trace type is compatible with categorical axes, not just `bar`.
+
+Scatter plots where one axis is categorical are often known as [dot plots](https://plotly.com/python/dot-plots/).
+
+```python
+import plotly.express as px
+df = px.data.medals_long()
+
+fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
+fig.update_traces(marker_size=10)
+fig.show()
+```
+
+[Box plots]() and [violin plots]() are often shown with one categorical and one continuous axis.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+
+fig = px.box(df, x="sex", y="total_bill", color="smoker")
+fig.show()
+```
+
+```python
+import plotly.express as px
+df = px.data.tips()
+
+fig = px.violin(df, x="sex", y="total_bill", color="smoker")
+fig.show()
+```
+
 ### Controlling the Category Order with Plotly Express
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).

diff --git a/doc/python/dot-plots.md b/doc/python/dot-plots.md
@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.2'
+      jupytext_version: 1.4.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.7
   plotly:
     description: How to make dot plots in Python with Plotly.
     display_as: basic
@@ -35,12 +35,21 @@ jupyter:
 
 #### Basic Dot Plot
 
-Dot plots (also known as [Cleveland dot plots](<https://en.wikipedia.org/wiki/Dot_plot_(statistics)>)) show changes between two (or more) points in time or between two (or more) conditions. Compared to a [bar chart](/python/bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
+Dot plots (also known as [Cleveland dot plots](<https://en.wikipedia.org/wiki/Dot_plot_(statistics)>)) are [scatter plots](https://plotly.com/python/line-and-scatter/) with one categorical axis and one continuous axis. They can be used to show changes between two (or more) points in time or between two (or more) conditions. Compared to a [bar chart](/python/bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
 
 For the same data, we show below how to create a dot plot using either `px.scatter` or `go.Scatter`.
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
+```python
+import plotly.express as px
+df = px.data.medals_long()
+
+fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
+fig.update_traces(marker_size=10)
+fig.show()
+```
+
 ```python
 import plotly.express as px
 import pandas as pd