Merge branch 'master' into lazyload_plotly

Author: pragyagarg642

.circleci/config.yml

@@ -504,12 +504,8 @@ workflows:
       - python-2.7-optional
       - python-3.5-optional
       - python-3.6-optional
-      # 3.7 optional disabled due to current shapely incompatibility
-      # - python-3.7-optional
-      - python-2.7-plot_ly
-      - python-3.7-plot_ly:
-          requires:
-            - python-2.7-plot_ly
+      - python-3.7-optional
+      - python-3.7-plot_ly
       - python-2-7-orca
       - python-3-5-orca
       - python-3-7-orca

CHANGELOG.md

@@ -2,13 +2,35 @@
 All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
+## [4.6] - 2020-03-31
+
+### Updated
+
+ - Updated Plotly.js to version 1.53.0. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/v1.53.0/CHANGELOG.md) for more information on the numerous new features and bug fixes of this release. The main features of the Plotly.js release are
+    - Introduce range breaks on date axes (for example, to remove week-ends) via `layout.xaxis.rangebreaks`
+    - Introduce a new unified x (or y) hovermode (`layout.hovermode="x unified"`), in which the hover box shows the information for all traces at a given x (or y) position
+    - Add `node.customdata` and `link.customdata` to sankey traces
+- Updated [contributing notes](https://github.com/plotly/plotly.py/blob/master/contributing.md) for more explanations on how to contribute to plotly.py [#2290](https://github.com/plotly/plotly.py/pull/2290). Please give feedback on these notes!
+- Updated documentation examples [#2325](https://github.com/plotly/plotly.py/pull/2325), and to show how to color links in Sankey diagrams [#2291](https://github.com/plotly/plotly.py/pull/2291).
+- Special thanks to [@SylwiaOliwia2](https://github.com/SylwiaOliwia2) and [@dangercrow](https://github.com/dangercrow) for improving our documentation!
+
+
+### Added
+
+- `px.imshow` now accepts [`xarray`](http://xarray.pydata.org/) inputs, with metadata being used for axis labels, hover and colorbar [#2166](https://github.com/plotly/plotly.py/pull/2166)
+
+
+### Fixed
+
+- Fixed handling of `opacity` in `px.pie`, `px.funnel_area`, `px.density_mapbox`, `px.funnel` [#2317](https://github.com/plotly/plotly.py/pull/2317), with thanks to [@tvaucher](https://github.com/tvaucher) for the contribution!
+
 ## [4.5.4] - 2020-03-11
 
 ### Updated
 
 - The documentation of the API https://plot.ly/python-api-reference/ now
   documents the full API [#2243](https://github.com/plotly/plotly.py/pull/2243)
-- New documentation examples for facets [#2235](https://github.com/plotly/plotly.py/pull/2235), legend [#2227](https://github.com/plotly/plotly.py/pull/2227), subplots [#2226](https://github.com/plotly/plotly.py/pull/2226), axes [#2234](https://github.com/plotly/plotly.py/pull/2234) and histograms [#2242](https://github.com/plotly/plotly.py/pull/2242). 
+- New documentation examples for facets [#2235](https://github.com/plotly/plotly.py/pull/2235), legend [#2227](https://github.com/plotly/plotly.py/pull/2227), subplots [#2226](https://github.com/plotly/plotly.py/pull/2226), axes [#2234](https://github.com/plotly/plotly.py/pull/2234) and histograms [#2242](https://github.com/plotly/plotly.py/pull/2242).
   Thanks to [@SylwiaOliwia2](https://github.com/@SylwiaOliwia2) for all these great
   examples!
 

README.md

@@ -15,7 +15,6 @@
             <img src="https://img.shields.io/badge/help_forum-discourse-blue.svg"/>
         </td>
     </tr>
-
     <tr>
         <td>PyPI Downloads</td>
         <td>
@@ -34,7 +33,7 @@
 
 ## Quickstart
 
-`pip install plotly==4.5.4`
+`pip install plotly==4.6.0`
 
 Inside [Jupyter notebook](https://jupyter.org/install) (installable with `pip install "notebook>=5.3" "ipywidgets>=7.2"`):
 
@@ -83,13 +82,13 @@ 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==4.5.4
+pip install plotly==4.6.0
 ```
 
 or conda.
 
 ```
-conda install -c plotly plotly=4.5.4
+conda install -c plotly plotly=4.6.0
 ```
 
 ### Jupyter Notebook Support
@@ -136,10 +135,10 @@ set NODE_OPTIONS=--max-old-space-size=4096
 jupyter labextension install @jupyter-widgets/[email protected] --no-build
 
 # FigureWidget support
-jupyter labextension install plotlywidget@1.5.4 --no-build
+jupyter labextension install plotlywidget@4.6.0 --no-build
 
 # and jupyterlab renderer support
-jupyter labextension install jupyterlab-plotly@1.5.4 --no-build
+jupyter labextension install jupyterlab-plotly@4.6.0 --no-build
 
 # Build extensions (must be done to activate extensions since --no-build is used above)
 jupyter lab build

binder/requirements.txt

@@ -1,5 +1,5 @@
 jupytext
-plotly==4.5.4
+plotly==4.6.0
 jupyter
 notebook
 pandas

contributing.md

@@ -1,15 +1,15 @@
 # Contributing
 
 Thank you for contributing to plotly.py! We are actively looking for
-diverse contributors, with diverse background and skills. 
+diverse contributors, with diverse background and skills.
 
 This guide start by a general description of the different ways to contribute
 to plotly.py, then we explain some technical aspects of preparing your
-contribution. 
+contribution.
 
 ## Code of Conduct
 
-Please check out the [Code of Conduct](CODE_OF_CONDUCT.md). Don't tl:dr; it, 
+Please check out the [Code of Conduct](CODE_OF_CONDUCT.md). Don't tl:dr; it,
 but the general idea is to be nice.
 
 ## What are the different ways to contribute?
@@ -37,7 +37,7 @@ the structure of the code and of the repository.
 - the `plotly.figure_factory` module provides Python "recipes" for building
   advanced visualizations, such as Gantt charts, annotated heatmaps, etc.
   Figure factories are one of the easiest entry points into plotly.py, since
-  they consist of Python-only code, with standalone, well-separated functions. 
+  they consist of Python-only code, with standalone, well-separated functions.
   However, please note that some of the figure factories become less relevant
   as we are introducing more features into `plotly.express`. Some issues in the
   tracker are labeled "figure_factory" and can be good issues to work on. More
@@ -70,14 +70,14 @@ also contribute to the project by
 - reporting bugs (see below).
 
 - submitting feature requests (maybe we'll convince you to contribute it as a
-  pull request!). 
+  pull request!).
 
 - helping other users on the [community forum](https://community.plot.ly/).
   Join the list of [nice people](https://community.plot.ly/u) helping other
   plotly users :-).
 
 We also recommend reading the great
-[how to contribute to open source](https://opensource.guide/how-to-contribute/) 
+[how to contribute to open source](https://opensource.guide/how-to-contribute/)
 guide.
 
 ## Have a Bug Report?
@@ -98,7 +98,7 @@ Below we explain the technical aspects of contributing. It is not strictly neces
 
 Note that if you are modifying a single documentation page, you can do it
 directly on Github by clicking on the "Edit this page on GitHub" link, without
-cloning the repository. 
+cloning the repository.
 
 ## Setup a Development Environment
 
@@ -206,6 +206,7 @@ First update the version of the `plotly.js` dependency in `packages/javascript/p
 Then run the `updateplotlyjs` command with:
 
 ```bash
+$ cd packages/python/plotly
 $ python setup.py updateplotlyjs
 ```
 
@@ -214,6 +215,13 @@ the `plotly/plotly.js` GitHub repository (and place them in
 `plotly/package_data`). It will then regenerate all of the `graph_objs`
 classes based on the new schema.
 
+For dev branches, it is also possible to use `updateplotlyjsdev --devrepo reponame --devbranch branchname` to update to development versions of `plotly.js`. This will fetch the `plotly.js` in the CircleCI artifact of the branch `branchname` of the repo `reponame`. If `--devrepo` or `--devbranch` are omitted, `updateplotlyjsdev` defaults using `plotly/plotly.js` and `master` respectively. For example, to update to a version from a pull request to the `plotly/plotly.js` repo that is numbered 555, run:
+
+```bash
+$ cd packages/python/plotly
+$ python setup.py updateplotlyjsdev --devbranch pull/555
+```
+
 ## Testing
 
 We take advantage of two tools to run tests:

doc/apidoc/_static/plotly-style.css

@@ -36,3 +36,25 @@ code {
 .dropdown-menu>li>a:hover, .dropdown-menu>li>a:focus {
     background: lightgrey;
 }
+
+dt {
+    font-weight: normal;
+}
+
+dd {
+    margin-left: 20px;
+}
+
+blockquote {
+    font-size: 15px;
+    border-left: none;
+}
+
+li {
+    margin-top: 8px;
+    margin-bottom: 8px;
+}
+
+code {
+    background-color: #F5F5F5
+}

doc/apidoc/conf.py

@@ -28,7 +28,7 @@
 # The short X.Y version
 version = ""
 # The full version, including alpha/beta/rc tags
-release = "4.5.4"
+release = "4.6.0"
 
 
 # -- General configuration ---------------------------------------------------

doc/python/2D-Histogram.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.3.0
+      jupytext_version: 1.3.1
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.5
+    version: 3.6.8
   plotly:
     description: How to make 2D Histograms in Python with Plotly.
     display_as: statistical
@@ -30,9 +30,75 @@ jupyter:
     order: 6
     page_type: u-guide
     permalink: python/2D-Histogram/
+    redirect_from:
+      - python/2d-histogram/
+      - python/2d-histograms/
     thumbnail: thumbnail/histogram2d.jpg
 ---
 
+## 2D Histograms or Density Heatmaps
+
+A 2D histogram, also known as a density heatmap, is the 2-dimensional generalization of a [histogram](/python/histograms/) which resembles a [heatmap](/python/heatmaps/) but is computed by grouping a set of points specified by their `x` and `y` coordinates into bins, and applying an aggregation function such as `count` or `sum` (if `z` is provided) to compute the color of the tile representing the bin. This kind of visualization (and the related [2D histogram contour, or density contour](https://plotly.com/python/2d-histogram-contour/)) is often used to manage over-plotting, or situations where showing large data sets as [scatter plots](/python/line-and-scatter/) would result in points overlapping each other and hiding patterns. For data sets of more than a few thousand points, a better approach than the ones listed here would be to [use Plotly with Datashader](/python/datashader/) to precompute the aggregations before displaying the data with Plotly.
+
+## Density Heatmaps with Plotly Express
+
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). The Plotly Express function `density_heatmap()` can be used to produce density heatmaps.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+
+fig = px.density_heatmap(df, x="total_bill", y="tip")
+fig.show()
+```
+
+The number of bins can be controlled with `nbinsx` and `nbinsy` and the [color scale](/python/colorscales/) with `color_continuous_scale`.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+
+fig = px.density_heatmap(df, x="total_bill", y="tip", nbinsx=20, nbinsy=20, color_continuous_scale="Viridis")
+fig.show()
+```
+
+Marginal plots can be added to visualize the 1-dimensional distributions of the two variables. Here we use a marginal [`histogram`](/python/histograms/). Other allowable values are `violin`, `box` and `rug`.
+
+```python
+import plotly.express as px
+df = px.data.tips()
+
+fig = px.density_heatmap(df, x="total_bill", y="tip", marginal_x="histogram", marginal_y="histogram")
+fig.show()
+```
+
+Density heatmaps can also be [faceted](/python/facet-plots/):
+
+```python
+import plotly.express as px
+df = px.data.tips()
+
+fig = px.density_heatmap(df, x="total_bill", y="tip", facet_row="sex", facet_col="smoker")
+fig.show()
+```
+
+### Other aggregation functions than `count`
+
+By passing in a `z` value and a `histfunc`, density heatmaps can perform basic aggregation operations. Here we show average Sepal Length grouped by Petal Length and Petal Width for the Iris dataset.
+
+```python
+import plotly.express as px
+df = px.data.iris()
+
+fig = px.density_heatmap(df, x="petal_length", y="petal_width", z="sepal_length", histfunc="avg")
+fig.show()
+```
+
+### 2D Histograms with Graph Objects
+
+To build this kind of plot without using Plotly Express, we can use the `go.Histogram2d` class.
+
+
 ### 2D Histogram of a Bivariate Normal Distribution ###
 
 ```python