Programming in D for Python Programmers

This section is under development, but in the meantime this link presents an independent perspective on D from the perspective of a python programmer:

• D is like Native Python

AdRoll is known for their use of Python elsewhere, but their data scientists use D. According to Andrew Pascoe, senior data scientist at AdRoll, "[t]he key thing here that separates D from other efficient languages like the oft-suggested C or C++ is that D frees you to program in the style you feel most comfortable with at the given time". He says that they have found that they "can rapidly prototype new infrastructure and analysis tasks, and when efficiency becomes a core concern, we have the ability to refactor that same code base to squeeze as much performance out as possible".

• AdRoll: D is for Data Science

Python's generators and list comprehensions have been thought to be two of the most difficult concepts to replicate in other languages. For the D solution to the problem solved by Python generators, see D Ranges and lazy evaluation. For list comprehensions, see UFCS.

• Walter Bright on UFCS
• Walter Bright youtube talk on component programming in D

• Theoretical article on ranges as a development of the iterator concept by the C++ guru, Dr Andrei Alexandrescu
• Introduction to Ranges by Ali Çehreli - Part I
• Introduction to Ranges by Ali Çehreli - Part II
• Introduction to D slices

• std.range structures and functions in the Phobos standard library
• std.algorithm generic functions in Phobos
• std.array

• Combining co-routines and input ranges for dead-simple iteration

Parallel programming has become increasingly in focus as we approach the beginning of the end of the free lunch from Moore's Law. D makes multiprocessing and threading as simple as possible, but not simpler:

• Free chapter from Dr Andrei Alexandrescu's book on parallelism in D
• Ali Cehreli on parallelism
• Ali Cehreli on message-passing concurrency
• Ali Cehreli on data-sharing concurrency
• std.parallelism