Fixing "Could not build wheels for scipy" PEP 517 Error

Problem Statement

The "Could not build wheels for scipy which use PEP 517 and cannot be installed directly" error occurs when attempting to install scientific Python packages like SciPy, pandas, or other data science libraries via pip. This error indicates that the build system cannot compile the package from source code into the required wheel format.

This issue is particularly common on:

• ARM-based systems (Raspberry Pi, ARM64 servers)
• macOS with M1/M2 chips
• Systems with outdated build tools
• Python environments missing development dependencies

Root Causes

The error typically stems from these underlying issues:

1. Missing system dependencies - Development libraries and tools required for compilation
2. Outdated pip/setuptools/wheel - Older versions lack support for modern build processes
3. Python version incompatibility - New Python releases may not have compatible pre-built wheels
4. Architecture-specific issues - ARM platforms often lack pre-built binaries
5. Missing development headers - Python development packages not installed

Solutions

1. Update Build Tools

The most common fix is updating your Python packaging tools:

pip install --upgrade pip setuptools wheel

This ensures you have the latest versions that support modern build processes and PEP 517 standards.

2. Install System Dependencies

For SciPy and related scientific packages, install these system dependencies:

Ubuntu/Debian:

sudo apt-get update
sudo apt-get install -y python3-dev python3-pip build-essential libatlas-base-dev gfortran pkg-config libopenblas-dev

CentOS/RHEL:

sudo dnf install -y python3-devel gcc-gfortran openblas-devel
sudo dnf group install -y "Development Tools"

macOS:

# Install Xcode command line tools
xcode-select --install

# Install Homebrew dependencies
brew install openblas
export OPENBLAS=$(brew --prefix openblas)

3. Use Conda/Mamba for Scientific Packages

For scientific computing packages, Conda often provides better dependency resolution:

# Install Miniforge or Miniconda first
conda install -c conda-forge scipy numpy

# Or use mamba for faster resolution
mamba install scipy

4. Python Version Management

New Python versions may lack compatible wheels. Consider:

# Use pyenv to manage multiple Python versions
pyenv install 3.9.7
pyenv global 3.9.7

# Or specify a known compatible version in Dockerfiles
FROM python:3.8  # Instead of python:latest

5. Platform-Specific Solutions

For Apple M1/M2 chips:

# Switch to x86 architecture if needed
arch -x86_64 zsh

# Install with specific flags
export CFLAGS="-falign-functions=8 ${CFLAGS}"
pip install Cython pybind11 pythran
pip install scipy

For ARM devices (Raspberry Pi):

# Pre-install build dependencies
sudo apt-get install -y libatlas3-base libgfortran5

# Use the system package manager when possible
sudo apt-get install -y python3-scipy

6. Alternative Installation Methods

If direct pip installation fails, try these alternatives:

# Use a different package index
pip install scipy -i https://pypi.mirrors.ustc.edu.cn/simple/

# Install without PEP 517 (not recommended long-term)
pip install --no-use-pep517 scipy

# Install from pre-built wheels
pip install --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple scipy

7. Docker-Specific Fixes

In Docker environments, ensure you have all build dependencies:

FROM python:3.8-slim

RUN apt-get update && \
    apt-get install -y \
    build-essential \
    python3-dev \
    libopenblas-dev \
    gfortran \
    pkg-config && \
    rm -rf /var/lib/apt/lists/*

RUN pip install --upgrade pip setuptools wheel
RUN pip install scipy

Common Error Patterns and Fixes

WARNING

If you encounter errors about specific missing packages like h5py, pandas, or xmlsec, install their system dependencies first:

Package	System Dependencies (Ubuntu/Debian)
h5py	libhdf5-dev
pandas	build-essential python3-dev
xmlsec	libxml2-dev libxmlsec1-dev
pycocotools	python3-dev cython

Prevention and Best Practices

1. Use virtual environments to isolate dependencies
2. Prefer Conda for scientific packages on complex platforms
3. Keep build tools updated regularly
4. Check package compatibility with your Python version before installation
5. Use Docker for consistent build environments

TIP

When working with ARM architectures, always check if the package provides pre-built wheels for your specific platform. Many scientific packages now offer ARM-compatible wheels that avoid compilation issues.

When to Seek Alternative Solutions

If you continue experiencing issues:

1. Check the package's official documentation for platform-specific instructions
2. Consider using alternative packages with better platform support
3. File an issue with the package maintainers if you believe it's a bug
4. Use older, stable Python versions (3.8, 3.9) that have better ecosystem support

This comprehensive approach should resolve most "Could not build wheels" errors for SciPy and related scientific Python packages across different platforms and environments.