Help & Troubleshooting
To start troubleshooting, we need to trace the issue to a bug in the code or to something else (such as your local environment). The first step is to create a new environment with a fresh installation (see Installation for instructions). In many cases, the problem will be resolved by this step.
If the problem can be replicated in the new environment, then it likely is a software bug. Before proceeding, check Common issues to check whether it is a previously identified common issue.
Reporting a bug
To ensure the bug was not already reported by searching on Github under Issues. If you’re unable to find an open issue addressing the problem, open a new one. If possible, use the relevant bug report templates to create the issue.
You should provide the minimal information to reproduce this bug. This guide can help in crafting a minimal bug report. Please include:
The minimal code you are using to generate the report
Version information is essential in reproducing and resolving bugs. Include relevant environment details such as:
operating system (e.g. Windows, Linux, Mac)
Python version (e.g.
Interface: Jupyter notebook (or cloud services like Google Colab, Kaggle Kernels, etc), console or IDE (such as PyCharm,VS Code,etc)
package manager (e.g.
pip freeze > packages.txtor
conda list). Please make sure this is contained in a collapsed section (instructions below)
a sample of the dataset (
df.head()). If the dataset is confidential, for example when it contains company-sensitive information, provide us with a synthetic or open dataset that produces the same error. You can anonymize the column names if necessary.
a description of the dataset and its structure, for example by reporting the DataFrame’s structure through the output of
To craft helpful and easily readable issues, two formatting tricks are recommended:
Code highlighting: wrap all code and error messages in fenced blocks, and in particular add the language identifier. Check the Github docs on highlighting code blocks for details.
Collapsed sections: organize long error messages and requirement listings in collapsed sections. The Github docs on collapsed sections provide detailed information.
Using Stack Overflow
Users with a request for help on how to use
pandas-profiling should consider asking their question on Stack Overflow, under the dedicated
Join the Slack community to connect with both other users and developers that might be able to answer your questions. The #data-profiling and #need-help channels are recommended for questions and issues.