Merge pull request #2336 from plotly/wide_form2

PX Input upgrades - wide form, constant, identity, auto-orientation etc

Author: nicolaskruchten

Diff for: CHANGELOG.md

@@ -2,6 +2,28 @@
 All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
+## [4.8.0] - not yet released
+
+### Added
+
+- `plotly` now provides a Plotly Express-backed Pandas-compatible plotting backend, which can be activated via `pandas.options.plotting.backend = "plotly"`. Note that it is not intended to implement every Pandas plotting function, nor is it intended to replicate the behaviour of every argument, although per the changes below, `x` and `y` should behave similarly. ([#2336](https://github.com/plotly/plotly.py/pull/2336))
+- New datasets have been added to `plotly.express.data`: `stocks`, `experiment`, `medals_wide` and `medals_long`. ([#2336](https://github.com/plotly/plotly.py/pull/2336))
+- plotly `go.Figure` and `go.FigureWidget` now have a `_repr_html_` and a `_repr_mimebundle_` method, which are [standard hooks for integration in systems based on IPython](https://ipython.readthedocs.io/en/stable/config/integrating.html). In particular, with `_repr_html_` plotly figures can now be used within [sphinx-gallery](https://sphinx-gallery.github.io/stable/index.html) without any scraper. These additions should not change anything to the way plotly figures are displayed in notebook environments, since the `_ipython_display_` method (already present in earlier versions) takes precedence over the new methods.
+
+### Updated
+
+- The behaviour of the `x`, `y`, `orientation`, `histfunc`, `violinmode`, `boxmode` and `stripmode` arguments for 2d-cartesian functions in Plotly Express (i.e. `scatter`, `line`, `area`, `bar`, `histogram`, `violin`, `box`, `strip`, `funnel`, `density_heatmap` and `density_contour`) has been refined ([#2336](https://github.com/plotly/plotly.py/pull/2336)):
+    - if `x` or `y` is missing, it is inferred to be the index of `data_frame` if `data_frame` provided, otherwise a stable index of integers starting at 0. In the case of `px.bar`, if the provided value is not continuous, the missing value is treated as a column of 1s named "count", so as to behave more like `px.histogram` and to avoid sizing the resulting bars differently based on their position in the column. Previously, missing values defaulted to integers starting at 0 *per trace* which made it potentially inconsistent or misleading.
+    - if `x` (`y`) is missing, `orientation` now defaults to `v` (`h`). Previously it always defaulted to `v` but this is not considered a breaking change, as the cases in which it now defaults to `h` caused unreadable output if set to `v`.
+    - if both `x` and `y` are provided and one of them does not contain continuous values, `orientation` defaults to the value perpendicular to that axis. Previously it always defaulted to `v` but this is not considered a breaking change, as the cases in which it now defaults to `h` caused unreadable output if set to `v`.
+    - if either `x` or `y` (but not both) may now be provided as a list of column references into `data_frame` or columns of data, in which case the imputed data frame will be treated as "wide" data and `melt()`ed internally before applying the usual mapping rules, with function-specific defaults.
+    - if neither `x` nor `y` is provided but `data_frame` is, the data frame will be treated as "wide" with defaults depending on the value of `orientation` (and `orientation` has accordingly been added to `scatter`, `line`, `density_heatmap`, and `density_contour` for this purpose). Previously this would have resulted in an empty figure.
+    - if both `x` and `y` are provided to `histogram`, and if `x`, `y` and `z` are provided to `density_heatmap` or `density_contour`, then `histfunc` now defaults to `sum` so as to avoid ignoring the provided data, and to cause `histogram` and `bar` to behave more similarly.
+    - `violinmode`, `boxmode` and `stripmode` now default to `overlay` if `x` (`y`) in in `v` (`h`) orientation is also mapped to `color`, to avoid strange spacing issues with the previous default of `group` in all cases.
+- The Plotly Express arguments `color_discrete_map`, `symbol_map` and `line_dash_map` now accept the string `"identity"` which causes the corresponding input data to be used as-is rather than mapped into `color_discrete_sequence`, `symbol_sequence` or `line_dash_sequence`, respectively. ([#2336](https://github.com/plotly/plotly.py/pull/2336))
+- Plotly Express now accepts `px.Constant` or `px.Range` objects in the place of column references so as to express constant or increasing integer values. ([#2336](https://github.com/plotly/plotly.py/pull/2336))
+
+
 ## [4.7.1] - 2020-05-08
 
 ### Fixed

Diff for: binder/requirements.txt

@@ -2,7 +2,7 @@ jupytext
 plotly==4.7.0
 jupyter
 notebook
-pandas
+pandas==1.0.3
 statsmodels==0.10.1
 scipy
 patsy==0.5.1

Diff for: doc/python/2D-Histogram.md

@@ -42,7 +42,7 @@ A 2D histogram, also known as a density heatmap, is the 2-dimensional generaliza
 
 ## 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.
+[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 Plotly Express function `density_heatmap()` can be used to produce density heatmaps.
 
 ```python
 import plotly.express as px

Diff for: doc/python/2d-histogram-contour.md

@@ -40,7 +40,7 @@ A 2D histogram contour plot, also known as a density contour plot, is a 2-dimens
 
 ## Density Contours 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_contour()` can be used to produce density contours.
+[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 Plotly Express function `density_contour()` can be used to produce density contours.
 
 ```python
 import plotly.express as px

Diff for: doc/python/3d-scatter-plots.md

@@ -35,7 +35,7 @@ jupyter:
 
 ## 3D scatter plot 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/).
+[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/).
 
 Like the [2D scatter plot](https://plotly.com/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space.
 

Diff for: doc/python/bar-charts.md

@@ -35,7 +35,7 @@ jupyter:
 
 ### Bar chart 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/).
+[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/).
 
 With `px.bar`, each row of the DataFrame is represented as a rectangular mark.
 

Diff for: doc/python/box-plots.md

@@ -40,7 +40,7 @@ A [box plot](https://en.wikipedia.org/wiki/Box_plot) is a statistical representa
 
 ## Box Plot 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/).
+[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.
 

Diff for: doc/python/bubble-charts.md

@@ -38,7 +38,7 @@ jupyter:
 
 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/).
 
-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 "tidy" 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.
+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.
 
 ```python
 import plotly.express as px

Diff for: doc/python/bubble-maps.md

@@ -39,7 +39,7 @@ Plotly figures made with `px.scatter_geo`, `px.line_geo` or `px.choropleth` func
 
 ### Bubble map 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/). With `px.scatter_geo`, each line of the dataframe is represented as a marker point. The column set as the `size` argument gives the size of markers.
+[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/). With `px.scatter_geo`, each line of the dataframe is represented as a marker point. The column set as the `size` argument gives the size of markers.
 
 ```python
 import plotly.express as px

Diff for: doc/python/choropleth-maps.md

@@ -56,7 +56,7 @@ The GeoJSON data is passed to the `geojson` argument, and the data is passed int
 
 ### Choropleth Map 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/).
+[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/).
 
 #### GeoJSON with `feature.id`
 
@@ -208,7 +208,6 @@ fig.show()
 ```python
 import plotly.graph_objects as go
 
-# Load data frame and tidy it.
 import pandas as pd
 df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')
 

Diff for: doc/python/cufflinks.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.3.1
+      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: 3.6.8
+    version: 3.7.7
   plotly:
     description: How to use and configure discrete color sequences, also known as
       categorical or qualitative color scales.
@@ -178,6 +178,15 @@ fig = px.bar(df, y="continent", x="pop", color="continent", orientation="h", hov
 fig.show()
 ```
 
+If your data set already contains valid CSS colors which you wish to use directly, you can pass the special value `"identity"` to `color_discrete_map`, in which case the legend is hidden by default, and the color does not appear in the hover label:
+
+```python
+import plotly.express as px
+
+fig = px.bar(x=["a","b","c"], y=[1,3,2], color=["red", "goldenrod", "#00D"], color_discrete_map="identity")
+fig.show()
+```
+
 ### Controlling Discrete Color Order
 
 Plotly Express lets you specify an ordering over categorical variables with `category_orders`, which will apply to colors and legends as well as symbols, [axes](/python/axes/) and [facets](/python/facet-plots/). This can be used with either `color_discrete_sequence` or `color_discrete_map`.

Diff for: doc/python/discrete-color.md

@@ -37,7 +37,7 @@ jupyter:
 
 Several representations of statistical distributions are available in plotly, such as [histograms](https://plotly.com/python/histograms/), [violin plots](https://plotly.com/python/violin/), [box plots](https://plotly.com/python/box-plots/) (see [the complete list here](https://plotly.com/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](/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/).
+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](/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

Diff for: doc/python/distplot.md

@@ -37,9 +37,9 @@ jupyter:
 
 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.
 
-For the same data, we show below how to create a dot plot using either `px.scatter` (for a tidy pandas DataFrame) or `go.Scatter`.
+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 "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-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/).
 
 ```python
 import plotly.express as px