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Copy file name to clipboardExpand all lines: CHANGELOG.md
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All notable changes to this project will be documented in this file.
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This project adheres to [Semantic Versioning](http://semver.org/).
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## Dev version
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## [4.7.1] - 2020-05-08
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### Fixed
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- Fix `AttributeError: module 'plotly.graph_objs' has no attribute 'FigureWidget'` exception on `from plotly.graph_objs import *` when `ipywidgets` is not installed. Error also occurred when importing `plotly.figure_factor`. It is now possible to import `plotly.graph_objs.FigureWidget` when `ipywidgets` is not installed, and an informative `ImportError` exception will be raised in the `FigureWidget` constructor ([#2443](https://github.com/plotly/plotly.py/issues/2443), [#1111](https://github.com/plotly/plotly.py/issues/1111)).
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- Fix `TypeError: unhashable type: 'Template'` during `Figure` construction when `plotly.io.templates.default` is set to a `Template` object rather than a string.
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## [4.7.0] - 2020-05-06
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### Updated
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- Updated Plotly.js to version 1.54.1. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/v1.54.1/CHANGELOG.md) for more information. The main new feature of this version of Plotly.js is the possibility to draw layout shapes, using custom dragmodes and corresponding modebar buttons.
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- The sphinx-gallery scraper has been updated to work with different structures of galleries [#2149](https://github.com/plotly/plotly.py/pull/2419)
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### Added
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- The `hover_data` parameter of `px` functions can now be a dictionary. This
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makes it possible to skip hover information for some arguments or to change
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the formatting of hover informatiom [#2377](https://github.com/plotly/plotly.py/pull/2377).
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- The `hover_data` parameter of `px` functions can now be a dictionary. This makes it possible to skip hover information for some arguments or to change the formatting of hover informatiom [#2377](https://github.com/plotly/plotly.py/pull/2377).
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- It's now possible to build a development version of Plotly.py against the build artifacts from a non-`master` branch of Plotly.js, which makes for faster QA and development cycles [#2349](https://github.com/plotly/plotly.py/pull/2349). Thanks [@zouhairm](https://github.com/zouhairm) for this Pull Request!
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### Fixed
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- Plotly Express trendlines now handle missing data correctly [#2357](https://github.com/plotly/plotly.py/pull/2357)
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### Performance
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This version includes several performance improvements ([#2368](https://github.com/plotly/plotly.py/pull/2368), [#2403](https://github.com/plotly/plotly.py/pull/2403)).
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- Child graph objects (e.g. `figure.layout.xaxis`) are no longer created eagerly during graph object construction. Instead, they are created lazily the first time the property is accessed.
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- Property validation is now disabled for select internal operations.
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- When used with Python 3.7 and above, ploty.py now takes advantage of [PEP-562](https://www.python.org/dev/peps/pep-0562/) to perform submodule imports lazily. This dramatically improves import times.
Copy file name to clipboardExpand all lines: README.md
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## Quickstart
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`pip install plotly==4.6.0`
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`pip install plotly==4.7.1`
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Inside [Jupyter notebook](https://jupyter.org/install) (installable with `pip install "notebook>=5.3" "ipywidgets>=7.2"`):
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plotly.py may be installed using pip...
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```
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pip install plotly==4.6.0
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pip install plotly==4.7.1
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```
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or conda.
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```
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conda install -c plotly plotly=4.6.0
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conda install -c plotly plotly=4.7.1
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```
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### Jupyter Notebook Support
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packages using pip...
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```
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pip install jupyterlab==1.2 "ipywidgets==7.5"
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pip install jupyterlab "ipywidgets=7.5"
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```
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or conda.
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```
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conda install jupyterlab=1.2
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conda install "ipywidgets=7.5"
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conda install jupyterlab "ipywidgets=7.5"
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```
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Then run the following commands to install the required JupyterLab extensions (note that this will require [`node`](https://nodejs.org/) to be installed):
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```
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# Avoid "JavaScript heap out of memory" errors during extension installation
Copy file name to clipboardExpand all lines: doc/python/configuration-options.md
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text_representation:
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extension: .md
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format_name: markdown
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format_version: '1.1'
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jupytext_version: 1.1.1
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format_version: '1.2'
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jupytext_version: 1.3.0
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kernelspec:
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display_name: Python 3
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language: python
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name: python
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nbconvert_exporter: python
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pygments_lexer: ipython3
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version: 3.7.0
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version: 3.7.3
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plotly:
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description: How to set the configuration options of figures using the Plotly
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Python graphing library.
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})
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```
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### Add optional shape-drawing buttons to modebar
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Some modebar buttons of Cartesian plots are optional and have to be added explictly, using the `modeBarButtonsToAdd` config attribute. These buttons are used for drawing or erasing shapes. See [the tutorial on shapes and shape drawing](python/shapes#drawing-shapes-on-cartesian-plots) for more details.
Sets the maximum delay between two consecutive clicks to be interpreted as a double-click in milliseconds. This is the time interval between first mousedown and second mouseup. The default timing is 300 ms (less than half a second).
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This setting propagates to all on-subplot double clicks (except for `geo` and `mapbox`).
Copy file name to clipboardExpand all lines: doc/python/datashader.md
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[datashader](https://datashader.org/) creates rasterized representations of large datasets for easier visualization, with a pipeline approach consisting of several steps: projecting the data on a regular grid, creating a color representation of the grid, etc.
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### Passing datashader rasters as a mabox image layer
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### Passing datashader rasters as a mapbox image layer
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We visualize here the spatial distribution of taxi rides in New York City. A higher density
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is observed on major avenues. For more details about mapbox charts, see [the mapbox layers tutorial](/python/mapbox-layers). No mapbox token is needed here.
Then run the following commands to install the required JupyterLab extensions (note that this will require [`node`](https://nodejs.org/) to be installed):
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```
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# Avoid "JavaScript heap out of memory" errors during extension installation
or using `FigureWidget` objects (if the "OPTIONAL" step above was executed).
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```python
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import plotly.graph_objects as go
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fig = go.FigureWidget(data=go.Bar(y=[2, 3, 1]))
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fig
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```
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Please check out our [Troubleshooting guide](https://plotly.com/python/troubleshooting/) if you run into any problems with JupyterLab.
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<!-- #region -->
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See [_Displaying Figures in Python_](/python/renderers/) for more information on the renderers framework, and see [_Plotly FigureWidget Overview_](/python/figurewidget/) for more information on using `FigureWidget`.
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