Resolving numpy.dtype Binary Incompatibility Error

Problem Statement

When installing pandas 2.1.1 with numpy 2.0.0 in Python 3.10 environments (like Google Colab), attempting to import pandas results in this critical error:

ValueError: numpy.dtype size changed, may indicate binary incompatibility.  
Expected 96 from C header, got 88 from PyObject

This occurs because pandas 2.1.1 relies on C extensions built against numpy 1.x, which are incompatible with the new memory layout in numpy 2.0.0. The binary incompatibility manifests as a size mismatch when numpy's dtype objects are interpreted by pandas' compiled extensions.

Solution: Downgrade Numpy to 1.x Series

Pandas 2.1.1 isn't compatible with numpy 2.0.0. The immediate solution is to install a numpy version below 2.0.0. This maintains pandas functionality while avoiding the binary incompatibility.

Recommended Fix

Install NumPy 1.26.4

pip uninstall numpy -y
pip install numpy==1.26.4

Alternative: Install Latest NumPy 1.x

pip uninstall numpy -y
pip install "numpy<2"

REQUIRED STEP

After downgrading numpy, restart your Python session/kernel:

• In Jupyter: Click "Restart Kernel"
• In scripts: Relaunch your Python interpreter
• In Google Colab: Use "Runtime → Restart runtime"

Verification

After downgrading and restarting, verify a successful pandas import:

import numpy as np
import pandas as pd

print(f"NumPy version: {np.__version__}")
print(f"Pandas version: {pd.__version__}")

Key Reasons for Compatibility Issue

1. ABI Changes in NumPy 2.0: NumPy 2.0 introduced breaking changes to its internal data structures to improve performance and future capabilities
2. Pandas Pre-Compiled Extensions: Pandas wheels (pre-built binaries) are compiled against NumPy 1.x headers
3. Version Mismatch: The C-extensions expect 96-byte dtype objects (NumPy 1.x), but receive 88-byte objects from NumPy 2.0

Component	Expected Size (Bytes)	Received Size (Bytes)
numpy.dtype	96	88

Related Compatibility Information

• Official NumPy issue tracking this: numpy#26710
• Pandas compatibility with NumPy 2.0 is expected in future releases (check release notes)
• Google Colab's default environment includes compatible versions unless manually overridden

Production Workflow Recommendation

Instead of manual version specification, use a requirements.txt file with compatible versions:

# requirements.txt
pandas==2.1.1
numpy>=1.26.4,<2

Temporary Workarounds Beyond Downgrading

If downgrading isn't feasible, these alternatives exist:

# Use latest pandas pre-release with numpy 2.0 support
pip install --pre pandas

# Install pandas from source (requires C compiler)
pip install --no-binary pandas pandas

:::caution Workaround Limitations

• Pre-release pandas versions may be unstable
• Source compilation requires development tools and environment configuration :::

By following the numpy 1.x installation instructions and restarting your environment, you'll resolve this binary incompatibility while maintaining access to the latest stable pandas functionality.