How to Fix ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject

To fix the ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject error, you need to upgrade numpy using this command: pip install –upgrade numpy.

The ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject error occurs when there is a mismatch between the NumPy version used to compile a Python extension (e.g., a package like SciPy or scikit-learn) and the NumPy version installed in your environment.

If that does not fix the error, you can also uninstall numpy and reinstall numpy using these commands:

pip uninstall numpy

pip install numpy

The most common reason for this error is that your environment has a newer NumPy version than the one used to build the package you are trying to import. This can happen if you installed packages using pip, conda, or another package manager, and there is a mismatch in the NumPy versions.

Alternate solutions

Solution 1: Create a new virtual environment

Using virtual environments to isolate your project dependencies is a good practice.

You can create a new virtual environment using venv (for standard Python) or conda (for Anaconda Python) and then reinstall the required packages.

For standard Python

python -m venv my_new_env
source my_new_env/bin/activate # On Windows, use `my_new_env\Scripts\activate`
pip install numpy
pip install other_package

For Anaconda Python

conda create -n my_new_env
conda activate my_new_env
conda install numpy
conda install other_package

Solution 2: Upgrade or downgrade the problematic package

If you know which package is causing the issue, you can try to upgrade or downgrade the package to a version that is compatible with your installed NumPy version.

pip install other_package==compatible_version

Or, for conda:

conda install other_package=compatible_version

Solution 3: Reinstall NumPy and the problematic package

You can try reinstalling both NumPy and the problematic package to ensure that they are built with compatible versions.

pip uninstall numpy other_package

pip install numpy other_package