SoFunction
Updated on 2025-05-12

Summary of the difference between conda and pip commands in Anaconda environment

The basic difference between conda and pip

Package source and ecosystem

  • conda: Get packages from Anaconda default repository or conda-forge channels such as

    • Not only manage Python packages, but also manage non-Python dependencies (such as C library, R package, etc.)
    • Especially suitable for complex dependencies in the fields of scientific computing and data science

  • pip: Get packages from Python Package Index (PyPI)

    • Focus on pure Python packages
    • Standard package management tools for Python ecosystem

Dependency parsing mechanism

  • conda: Use SAT solver for dependency resolution

    • Able to handle cross-language dependencies
    • Usually stricter to avoid conflicts

  • pip: Simpler dependency analysis

    • Focus mainly on Python packages
    • Sometimes conflicting dependencies may be allowed to coexist

Actual Differences in Anaconda Environment

Installation package

# Install using condaconda install numpy

# Install using pippip install numpy

Key Difference：

  • The packages installed by conda may contain optimized binary versions (such as MKL optimized NumPy)
  • pip is always installed from source code or wheel

Environmental Management

# Create an environment (conda-specific)conda create -n myenv python=3.8

# Install the package to the current environment (both available)conda install pandas
pip install pandas

Notice: Mixing conda and pip in a conda environment may lead to dependency conflicts

Dependency solution example

# conda can solve the dependencies of complex scientific stacksconda install numpy scipy pandas matplotlib jupyter

# Installing the same combination with pip may encounter more conflictspip install numpy scipy pandas matplotlib jupyter

Best Practice Recommendations

  • Priority to use conda: Especially for scientific computing packages (NumPy, SciPy, etc.)

  • Use with caution: If you must use pip, it is recommended:

    • First install as many packages as possible with conda
    • Use pip to install packages that are not in the conda repository
    • Avoid alternating conda and pip for the same package

  • Environmental isolation: Create independent environments for different projects

conda create -n project_env python=3.8
conda activate project_env
# Conda first install the basic packageconda install numpy pandas
# Use pip to install special packagespip install some_special_package

Check for conflicts:useconda listandpip listCompare installed packages

FAQ

Q: Why do conda installs packages with better performance?A: Many conda packages (such as NumPy, TensorFlow) are precompiled and optimized for specific hardware, while pip installations may require local compilation.

Q: How to know whether to use conda or pip to install a package?A: You can use it firstconda search package_nameSearch, if not, use pip again.

Q: What should I do if the environment is damaged due to mixed use of conda and pip?A: The best solution is to create a new environment and reinstall the package to avoid mixing.

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