Troubleshooting in Python

Version Problems¶

In order to follow the examples in this documentation site, you should have the latest version of plotly installed (5.x), as detailed in the Getting Started guide. This documentation (under https://plotly.com/python) is compatible with plotly version 4.x but not with version 3.x, for which the documentation is available under https://plotly.com/python/v3. In general you must also have the correct version of the underlying Plotly.js rendering engine installed, and the way to do that depends on the environment in which you are rendering figures: Dash, Jupyter Lab or Classic Notebook, VSCode etc. Read on for details about troubleshooting plotly in these environments.

Import Problems¶

It's very important that you not have a file named plotly.py in the same directory as the Python script you're running, and this includes not naming the script itself plotly.py, otherwise importing plotly can fail with mysterious error messages.

Beyond this, most import problems or AttributeErrors can be traced back to having multiple versions of plotly installed, for example once with conda and once with pip. It's often worthwhile to uninstall with both methods before following the Getting Started instructions from scratch with one or the other. You can run the following commands in a terminal to fully remove plotly before installing again:

$ conda uninstall plotly
$ pip uninstall plotly

Problems can also arise if you have a file named plotly.py in the same directory as the code you are executing.

Dash Problems¶

If you are encountering problems using plotly with Dash please first ensure that you have upgraded dash to the latest version, which will automatically upgrade dash-core-components to the latest version, ensuring that Dash is using an up-to-date version of the Plotly.js rendering engine for plotly. If this does not resolve your issue, please visit our Dash Community Forum and we will be glad to help you out.

This is an example of a plotly graph correctly rendering inside dash:

VSCode Notebook, Nteract and Streamlit Problems¶

Plotly figures render in VSCode using a Plotly.js version bundled with the vscode-python extension, and unfortunately it's often a little out of date compared to the latest version of the plotly module, so the very latest features may not work until the following release of the vscode-python extension. In any case, regularly upgrading your vscode-python extension to the latest version will ensure you have access to the greatest number of recent features.

The situation is similar for environments like Nteract and Streamlit: in these environments you will need a version of these projects that bundles a version Plotly.js that supports the features in the version of plotly that you are running.

Orca Problems¶

Note: as of plotly version 4.9, we recommend using kaleido instead of Orca for static image export

If you get an error message stating that the orca executable that was found is not valid, this may be because another executable with the same name was found on your system. Please specify the complete path to the Plotly-Orca binary that you downloaded (for instance in the Miniconda folder) with the following command:

plotly.io.orca.config.executable = '/home/your_name/miniconda3/bin/orca'

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter