np = numpy++: numpy with added convenience functionality
np – create numpy arrays as np[1,3,5], and more
np = numpy + handy tools
For the numerical Python package numpy itself, see http://www.numpy.org/.
The idea of np is to provide a way of creating numpy arrays with a compact syntax and without an explicit function call. Making the module name np subscriptable, while still keeping it essentially an alias for numpy, does this in a clean way.
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- Python 3+ (Probably works with older versions too)
np can be installed with pip:
$ pip install np
or directly from the source code:
$ git clone https://github.com/k7hoven/np.git $ cd np $ python setup.py install
A popular style of using numpy has been to import it as np:
>>> import numpy as np >>> my_array = np.array([[1, 2], [3, 4]]) >>> column_vector = np.array([[1, 2, 3]]).T
The most important feature of np is to make the creation of arrays less verbose, while everything else works as before. The above code becomes:
>>> import np >>> my_array = np[[1, 2], [3, 4]] >>> column_vector = np[[1, 2, 3]].T
As you can see from the above example, you can create numpy arrays by subscripting the np module. Since most people would have numpy imported as np anyway, this requires no additional names to clutter the namespace. Also, the syntax np[1,2,3] resembles the syntax for bytes literals, b"asd".
The np package also provides a convenient way of ensuring something is a numpy array, that is, a shortcut to numpy.asanyarray():
>>> import np >>> mylist = [1, 3, 5] >>> mylist + [7, 9, 11] [1, 3, 5, 7, 9, 11] >>> np(mylist) + [7, 9, 11] array([8, 12, 16])
- Bug fix
- Improved experimental dtype shortcuts: np.f[1,2], np.i32[1,2], etc.
- PyPI-friendly readme
- First distributable version
- Easy arrays such as np[[1,2],[3,4]]
- Shortcut for np.asanyarray(obj): np(obj)
- Experimental dtype shortcuts: np.f64[[1,2],[3,4]]
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