NumPy 1.15.0 is out and is said to include a lot of changes including several cleanups, deprecations of old functions. It also includes improvements to many existing functions. The Python versions supported by NumPy 1.15.0 are 2.7, 3.4 to 3.7.
Some of the highlights in this release include
- NumPy has switched to pytest for testing as this version no longer contains the maintained nose framework. However, the old nose based interface is still available for downstream projects.
- A new numpy.printoptions context manager can now set print options temporarily for the scope of the with block::
... print(np.array([2.0]) / 3)
- Improvements to the histogram functions. This version includes numpy.histogram_bin_edges, a function to get the edges of the bins used by a histogram without needing to calculate the histogram.
- Support for unicode field names in python 2.7.
- Improved support for PyPy.
- Fixes and improvements to numpy.einsum, which evaluates Einstein summation convention on the operands.
New features in the NumPy 1.15.0
Added np.gcd and np.lcm ufuncs for integer and objects types
Both np.gcd and np.lcm used for computing the greatest common divisor, and the lowest common multiple respectively. These work on all the numpy integer types, as well as the
built in arbitrary-precision Decimal and long types.
Support for cross-platform builds for iOS
The build system in this version has been modified to add support for the _PYTHON_HOST_PLATFORM environment variable, used by distutils when
compiling on one platform for another platform. This makes it possible to compile NumPy for iOS targets.
Addition of return_indices keyword for np.intersect1d
New keyword return_indices returns the indices of the two input arrays that correspond to the common elements.
This version has an added experimental support for the 64-bit RISC-V architecture.
Future Changes expected in the further versions
Both NumPy 1.16 and NumPy 1.17 will be dropping support for Python 3.4 and Python 2.7 respectively.
Read more about this release in detail on its GitHub Page
- Implementing matrix operations using SciPy and NumPy
- NumPy: Commonly Used Functions
- Installing NumPy, SciPy, matplotlib, and IPython