XND compared with NumPy

Data conversion from Python values

Sometimes data is naturally in Python object form, for example when using Python's JSON module. This benchmark compares reading a list of int64 values. XND is faster both for type inference and if a dtype is given. The fastest possible way for XND is to read data with a schema-like full type:

$ python data_conversion_int64.py

Type inference
--------------

   xnd:   0.21521806716918945
   numpy: 0.4042797088623047

Dtype provided
--------------

   xnd:   0.17266035079956055
   numpy: 0.4040689468383789

Full type provided
-------------------

   xnd:   0.10901474952697754

This benchmark compares reading a list of tuples, the example is from the NumPy documentation. NumPy is omitted from the type inference section since it infers the "O" python object type:

$ python data_conversion_tuple.py 

Type inference
---------------

xnd:   0.9455676078796387

Dtype provided
--------------

xnd:   1.0472655296325684
numpy: 1.2794170379638672

Full type provided
-------------------

xnd:   0.7372479438781738

Subarray views

This benchmark measures creating subarray views:

$ python subarray.py

Small subarray view
-------------------

   xnd:   0.1433429718017578
   numpy: 0.11738085746765137

Medium sized subarray view
--------------------------

   xnd:   0.24525141716003418
   numpy: 0.24730181694030762

Accessing elements

This benchmark measures accessing elements:

$ python elements.py

Accessing an element in a small array
-------------------------------------

   xnd:   0.23404860496520996
   numpy: 0.1790611743927002

Accessing an element in a medium sized array
--------------------------------------------

   xnd:   0.35030698776245117
   numpy: 0.3094639778137207

Accessing an element in an array of tuples
------------------------------------------

   xnd:   0.24957680702209473
   numpy: 0.7213478088378906