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fix internal links and add troubleshooting guide
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‎python/3d-volume.md

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thumbnail: thumbnail/3d-volume-plots.jpg
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---
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A volume plot with `go.Volume` shows several partially transparent isosurfaces for volume rendering. The API of `go.Volume` is close to the one of `go.Isosurface`. However, whereas [isosurface plots](./3d-isosurface-plots/) show all surfaces with the same opacity, tweaking the `opacityscale` parameter of `go.Volume` results in a depth effect and better volume rendering.
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A volume plot with `go.Volume` shows several partially transparent isosurfaces for volume rendering. The API of `go.Volume` is close to the one of `go.Isosurface`. However, whereas [isosurface plots](/python/3d-isosurface-plots/) show all surfaces with the same opacity, tweaking the `opacityscale` parameter of `go.Volume` results in a depth effect and better volume rendering.
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## Simple volume plot with go.Volume
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vol /= vol.max()
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fig = go.Figure(data=go.Volume(
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x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
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x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
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isomin=0.2,
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isomax=0.7,
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isomax=0.5,
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opacity=0.1, # max opacity
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opacityscale=[[-0.5, 1], [-0.2, 0], [0.2, 0], [0.5, 1]],
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surface_count=21,
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surface_count=21,
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colorscale='RdBu'
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))
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fig.show()
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fig = go.Figure(data=go.Volume(
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x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
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x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
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value=vol.flatten(),
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isomin=0.2,
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isomax=0.7,
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fig = go.Figure(data=go.Volume(
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x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
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x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
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fig = go.Figure(data=go.Volume(
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x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
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x=X.flatten(), y=Y.flatten(), z=Z.flatten(),
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value=vol.flatten(),
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isomin=0.2,
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See https://plot.ly/python/reference/#volume for more information and chart attribute options!
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#### See also
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[3D isosurface documentation](./3d-isosurface-plots/)
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[3D isosurface documentation](/python/3d-isosurface-plots/)

‎python/annotated_heatmap.md

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---
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#### Simple Annotated Heatmap
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For more examples with Heatmaps, see [this page](../heatmaps/).
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For more examples with Heatmaps, see [this page](/python/heatmaps/).
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```python
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import plotly.figure_factory as ff

‎python/choropleth-maps.md

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A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a heatmap using geographical boundaries. It is used to represent spatial variations of a quantity. See also the [index of other geographical charts](../maps/).
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A [Choropleth Map](https://en.wikipedia.org/wiki/Choropleth_map) is a heatmap using geographical boundaries. It is used to represent spatial variations of a quantity. See also the [index of other geographical charts](/python/maps/).
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Below we show how to create Choropleth Maps using either Plotly Express' `px.choropleth` or the lower-level `go.Choropleth`.
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‎python/cone-plot.md

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thumbnail: thumbnail/3dcone.png
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---
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A cone plot is the 3D equivalent of a 2D [quiver plot](./quiver-plots/), i.e., it represents a 3D vector field using cones to represent the direction and norm of the vectors. 3-D coordinates are given by `x`, `y` and `z`, and the coordinates of the vector field by `u`, `v` and `w`.
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A cone plot is the 3D equivalent of a 2D [quiver plot](/python/quiver-plots/), i.e., it represents a 3D vector field using cones to represent the direction and norm of the vectors. 3-D coordinates are given by `x`, `y` and `z`, and the coordinates of the vector field by `u`, `v` and `w`.
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### Basic 3D Cone
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fig.update_layout(scene_camera_eye=dict(x=-0.76, y=1.8, z=0.92))
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fig.show()
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```
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fig.update_traces(y=[1, 2, 3], z=[1, 1, 1],
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fig.update_traces(y=[1, 2, 3], z=[1, 1, 1],
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‎python/dot-plots.md

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#### Basic Dot Plot
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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](../bar-charts/), dot plots can be less cluttered and allow for an easier comparison between conditions.
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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.
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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`.
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‎python/getting-started.md

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<!-- #region -->
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See [*Displaying Figures in Python*](../renderers/) for more information on the renderers framework, and see [*Plotly FigureWidget Overview*](../figurewidget/) for more information on using `FigureWidget`.
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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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#### JupyterLab Support (Python 3.5+)
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See [*Displaying Figures in Python*](../renderers/) for more information on the renderers framework, and see [*Plotly FigureWidget Overview*](../figurewidget/) for more information on using `FigureWidget`.
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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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See [*Static Image Export in Python*](/python/static-image-export/) for more information on static image export.
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### Where to next?
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Now that you have everything installed, you are ready to start reading and running examples of [basic charts](../basic-charts/), [statistical charts](../statistical-charts/), [scientific charts](../scientific-charts/), [financial charts](../#financial-charts), [geographic charts](../maps/), and [3-dimensional charts](../3d-charts/).
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Now that you have everything installed, you are ready to start reading and running examples of [basic charts](/python/basic-charts/), [statistical charts](/python/statistical-charts/), [scientific charts](/python/scientific-charts/), [financial charts](/python/#financial-charts), [geographic charts](/python/maps/), and [3-dimensional charts](/python/3d-charts/).
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For a complete overview of all of the ways that figures can be created and updated, see the [*Plotly User Guide for Python*](../user-guide/).
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For a complete overview of all of the ways that figures can be created and updated, see the [*Plotly User Guide for Python*](/python/user-guide/).
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For information on configuring figure layout options (e.g. axes, titles, legends, etc) and styling figures (e.g. colors, fonts, annotations, images, shapes, etc.), see [*Plotly Fundamentals*](../plotly-fundamentals).
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For information on configuring figure layout options (e.g. axes, titles, legends, etc) and styling figures (e.g. colors, fonts, annotations, images, shapes, etc.), see [*Plotly Fundamentals*](/python/plotly-fundamentals).
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For information on theming plotly figures, see [*Theming and templates with plotly for Python*](../templates/).
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For information on theming plotly figures, see [*Theming and templates with plotly for Python*](/python/templates/).
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For information on all of the ways that plotly figures can be displayed, see [*Displaying plotly figures with plotly for Python*](../renderers/).
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For information on all of the ways that plotly figures can be displayed, see [*Displaying plotly figures with plotly for Python*](/python/renderers/).
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‎python/lines-on-maps.md

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For other geographical and map charts see [the maps index page](/python/maps/).
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‎python/multiple-axes.md

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#### Reference
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All of the y-axis properties are found here: https://plot.ly/python/reference/#YAxis. For more information on creating subplots see the [Subplots in Python](../subplots/) section.
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All of the y-axis properties are found here: https://plot.ly/python/reference/#YAxis. For more information on creating subplots see the [Subplots in Python](/python/subplots/) section.

‎python/orca-management.md

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### Overview
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This section covers the lower-level details of how plotly.py uses orca to perform static image generation. Please refer to the [Static Image Export](../static-image-export/) section for general information on creating static images from plotly.py figures.
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This section covers the lower-level details of how plotly.py uses orca to perform static image generation. Please refer to the [Static Image Export](/python/static-image-export/) section for general information on creating static images from plotly.py figures.
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### What is Orca?
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Orca is an [Electron](https://electronjs.org/) application that inputs plotly figure specifications and converts them into static images. Orca can run as a command-line utility or as a long-running server process. In order to provide the fastest possible image export experience, plotly.py launches orca in server mode, and communicates with it over a local port. See https://github.com/plotly/orca for more information.

‎python/parallel-coordinates-plot.md

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[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). In a parallel coordinates plot with `px.parallel_coordinates`, each row of the DataFrame is represented by a polyline mark which traverses a set of parallel axes, one for each of the dimensions. For other representations of multivariate data, also see [radar charts](../radar-chart/) and [scatterplot matrix (SPLOM)](../splom/).
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[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). In a parallel coordinates plot with `px.parallel_coordinates`, each row of the DataFrame is represented by a polyline mark which traverses a set of parallel axes, one for each of the dimensions. For other representations of multivariate data, also see [radar charts](/python/radar-chart/) and [scatterplot matrix (SPLOM)](/python/splom/).
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‎python/plotly-express.md

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**Note**: Plotly Express was previously its own separately-installed `plotly_express` package but is now part of `plotly`!
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This notebook demonstrates various `plotly.express` features. [Reference documentation](https://plotly.github.io/plotly_express/plotly_express/) is also available, as well as a [tutorial on input argument types](./px-arguments).
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This notebook demonstrates various `plotly.express` features. [Reference documentation](https://plotly.github.io/plotly_express/plotly_express/) is also available, as well as a [tutorial on input argument types](/python/px-arguments).
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You can also read our original [Medium announcement article](https://medium.com/@plotlygraphs/introducing-plotly-express-808df010143d) for more information on this library.
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‎python/quiver-plots.md

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[Cone plot](/python/cone-plot) for the 3D equivalent of quiver plots.
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‎python/radar-chart.md

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A [Radar Chart](https://en.wikipedia.org/wiki/Radar_chart) (also known as a spider plot or star plot) displays multivariate data in the form of a two-dimensional chart of quantitative variables represented on axes originating from the center. The relative position and angle of the axes is typically uninformative. It is equivalent to a [parallel coordinates plot](../parallel-coordinates-plot/) with the axes arranged radially.
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A [Radar Chart](https://en.wikipedia.org/wiki/Radar_chart) (also known as a spider plot or star plot) displays multivariate data in the form of a two-dimensional chart of quantitative variables represented on axes originating from the center. The relative position and angle of the axes is typically uninformative. It is equivalent to a [parallel coordinates plot](/python/parallel-coordinates-plot/) with the axes arranged radially.
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For a Radar Chart, use a [polar chart](../polar-chart/) with categorical angular variables, with `px.line_polar` for data available as a tidy pandas DataFrame, or with `go.Scatterpolar` in the general case. See more examples of [polar charts here](../polar-chart/).
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For a Radar Chart, use a [polar chart](/python/polar-chart/) with categorical angular variables, with `px.line_polar` for data available as a tidy pandas DataFrame, or with `go.Scatterpolar` in the general case. See more examples of [polar charts here](/python/polar-chart/).
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#### Radar Chart with Plotly Express
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‎python/static-image-export.md

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If you want to know more about how the orca integration works, or if you need to troubleshoot an issue, please check out the [Orca Management](../orca-management/) section.
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If you want to know more about how the orca integration works, or if you need to troubleshoot an issue, please check out the [Orca Management](/python/orca-management/) section.

‎python/streamline-plots.md

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A Streamline plot is a representation based on a 2-D vector field interpreted as a velocity field, consisting of closed curves tangent to the velocity field. In the case of a stationary velocity field, streamlines coincide with trajectories (see also the [Wikipedia page on streamlines, streaklines and pathlines](https://en.wikipedia.org/wiki/Streamlines,_streaklines,_and_pathlines)).
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#### Reference

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