install from source and are running Windows, you will have to ensure that you have a compatible C compiler (MinGW or Visual Studio) installed. How-to install MinGW on Windows 2.1 Python version support suggest installing the MinGW compiler suite following the directions linked to above. Once configured property, run the following on the command line: python setup.py build --compiler=mingw32 python setup.py

install from source and are running Windows, you will have to ensure that you have a compatible C compiler (MinGW or Visual Studio) installed. How-to install MinGW on Windows 2.1 Python version support suggest installing the MinGW compiler suite following the directions linked to above. Once configured property, run the following on the command line: python setup.py build --compiler=mingw32 python setup.py

install from source and are running Windows, you will have to ensure that you have a compatible C compiler (MinGW or Visual Studio) installed. How-to install MinGW on Windows 2.1 Python version support suggest installing the MinGW compiler suite following the directions linked to above. Once configured property, run the following on the command line: python setup.py build --compiler=mingw32 python setup.py

because memory access isn’t checked. For more about boundscheck and wraparound, see the Cython docs on compiler directives. 820 Chapter 3. User Guide pandas: powerful Python data analysis toolkit, Release 1 2 Using Numba A recent alternative to statically compiling Cython code, is to use a dynamic jit-compiler, Numba. Numba gives you the power to speed up your applications with high performance functions languages or Python interpreters. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports

wraparound, see the Cython docs on compiler directives. 2.23.2 Using Numba A recent alternative to statically compiling Cython code, is to use a dynamic jit-compiler, Numba. Numba gives you the power languages or Python interpreters. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports is an error like: Traceback ... ValueError: Big-endian buffer not supported on little-endian compiler To deal with this issue you should convert the underlying NumPy array to the native system byte

see the Cython docs on compiler directives. 2.23.2 Numba (JIT compilation) An alternative to statically compiling Cython code is to use a dynamic just-in-time (JIT) compiler with Numba. Numba allows decorating your function with @jit. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports is an error like: Traceback ... ValueError: Big-endian buffer not supported on little-endian compiler To deal with this issue you should convert the underlying NumPy array to the native system byte

cause memory access isn’t checked. For more about boundscheck and wraparound, see the Cython docs on compiler directives. 2.23. Enhancing performance 939 pandas: powerful Python data analysis toolkit, Release compilation) An alternative to statically compiling Cython code is to use a dynamic just-in-time (JIT) compiler with Numba. Numba allows you to write a pure Python function which can be JIT compiled to native decorating your function with @jit. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports

cause memory access isn’t checked. For more about boundscheck and wraparound, see the Cython docs on compiler directives. 940 Chapter 2. User Guide pandas: powerful Python data analysis toolkit, Release 1 compilation) An alternative to statically compiling Cython code is to use a dynamic just-in-time (JIT) compiler with Numba. Numba allows you to write a pure Python function which can be JIT compiled to native decorating your function with @jit. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports

wraparound, see the Cython docs on compiler directives. 2.23.2 Using Numba A recent alternative to statically compiling Cython code, is to use a dynamic jit-compiler, Numba. Numba gives you the power languages or Python interpreters. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports is an error like: Traceback ... ValueError: Big-endian buffer not supported on little-endian compiler To deal with this issue you should convert the underlying NumPy array to the native system byte

because memory access isn’t checked. For more about boundscheck and wraparound, see the Cython docs on compiler directives. 2.18. Enhancing performance 827 pandas: powerful Python data analysis toolkit, Release 2 Using Numba A recent alternative to statically compiling Cython code, is to use a dynamic jit-compiler, Numba. Numba gives you the power to speed up your applications with high performance functions languages or Python interpreters. Numba works by generating optimized machine code using the LLVM compiler infrastructure at import time, runtime, or statically (using the included pycc tool). Numba supports