Scapy runs natively on Linux, and on most Unixes with libpcap and its python wrappers (see scapy's installation page). The same code base now runs natively on both Python 2 and Python 3. Scapy ≥ 2.4.x needs Python2 ≥ 2.7. What makes scapy different from most other networking tools. First, with most other tools, you. You should setup python on your Mac this way. I’m tired of having to look this up, or worse, describe it ineffectively to coworkers. So here it is, all in one place.
Windows Good solutions for Windows are,, (which both provide binary installers for Windows, OS X and Linux). Both of these packages include Python, NumPy and many additional packages. A lightweight alternative is to download the Python installer from and the NumPy installer for your Python version from the Sourceforge. The NumPy installer includes binaries for different CPU’s (without SSE instructions, with SSE2 or with SSE3) and installs the correct one automatically. If needed, this can be bypassed from the command line with. Prerequisites Building NumPy requires the following software installed: • Python 2.6.x, 2.7.x, 3.2.x or newer On Debian and derivatives (Ubuntu): python, python-dev (or python3-dev) On Windows: the official python installer at is enough Make sure that the Python package distutils is installed before continuing. For example, in Debian GNU/Linux, installing python-dev also installs distutils.
Python must also be compiled with the zlib module enabled. This is practically always the case with pre-packaged Pythons. • Compilers To build any extension modules for Python, you’ll need a C compiler.
Various NumPy modules use FORTRAN 77 libraries, so you’ll also need a FORTRAN 77 compiler installed. Note that NumPy is developed mainly using GNU compilers. Compilers from other vendors such as Intel, Absoft, Sun, NAG, Compaq, Vast, Porland, Lahey, HP, IBM, Microsoft are only supported in the form of community feedback, and may not work out of the box. GCC 4.x (and later) compilers are recommended. • Linear Algebra libraries NumPy does not require any external linear algebra libraries to be installed. However, if these are available, NumPy’s setup script can detect them and use them for building. A number of different LAPACK library setups can be used, including optimized LAPACK libraries such as ATLAS, MKL or the Accelerate/vecLib framework on OS X.
FORTRAN ABI mismatch The two most popular open source fortran compilers are g77 and gfortran. Unfortunately, they are not ABI compatible, which means that concretely you should avoid mixing libraries built with one with another. In particular, if your blas/lapack/atlas is built with g77, you must use g77 when building numpy and scipy; on the contrary, if your atlas is built with gfortran, you must build numpy/scipy with gfortran.
This applies for most other cases where different FORTRAN compilers might have been used.