Boxed values are data structures that are minimal wrappers around primitive types*. Boxed values are typically stored as pointers to objects on the heap.
Thus, boxed values use more memory and take at minimum two memory lookups to access: once to get the pointer, and another to follow that pointer to the primitive. Obviously this isn't the kind of thing you want in your inner loops. On the other hand, boxed values typically play better with other types in the system. Since they are first-class data structures in the language, they have the expected metadata and structure that other data structures have.
In Java and Haskell generic collections can't contain unboxed values. Generic collections in .NET can hold unboxed values with no penalties. Where Java's generics are only used for compile-time type checking, .NET will generate specific classes for each generic type instantiated at run time.
Java and Haskell have unboxed arrays, but they're distinctly less convenient than the other collections. However, when peak performance is needed it's worth a little inconvenience to avoid the overhead of boxing and unboxing.
* For this discussion, a primitive value is any that can be stored on the call stack, rather than stored as a pointer to a value on the heap. Frequently that's just the machine types (ints, floats, etc), structs, and sometimes static sized arrays. .NET-land calls them value types (as opposed to reference types). Java folks call them primitive types. Haskellions just call them unboxed.
** I'm also focusing on Java, Haskell, and C# in this answer, because that's what I know. For what it's worth, Python, Ruby, and Javascript all have exclusively boxed values. This is also known as the "Everything is an object" approach***.
*** Caveat: A sufficiently advanced compiler / JIT can in some cases actually detect that a value which is semantically boxed when looking at the source, can safely be an unboxed value at runtime. In essence, thanks to brilliant language implementors your boxes are sometimes free.
The stack is the memory set aside as scratch space for a thread of execution. When a function is called, a block is reserved on the top of the stack for local variables and some bookkeeping data. When that function returns, the block becomes unused and can be used the next time a function is called. The stack is always reserved in a LIFO (last in first out) order; the most recently reserved block is always the next block to be freed. This makes it really simple to keep track of the stack; freeing a block from the stack is nothing more than adjusting one pointer.
The heap is memory set aside for dynamic allocation. Unlike the stack, there's no enforced pattern to the allocation and deallocation of blocks from the heap; you can allocate a block at any time and free it at any time. This makes it much more complex to keep track of which parts of the heap are allocated or freed at any given time; there are many custom heap allocators available to tune heap performance for different usage patterns.
Each thread gets a stack, while there's typically only one heap for the application (although it isn't uncommon to have multiple heaps for different types of allocation).
To answer your questions directly:
To what extent are they controlled by the OS or language runtime?
The OS allocates the stack for each system-level thread when the thread is created. Typically the OS is called by the language runtime to allocate the heap for the application.
What is their scope?
The stack is attached to a thread, so when the thread exits the stack is reclaimed. The heap is typically allocated at application startup by the runtime, and is reclaimed when the application (technically process) exits.
What determines the size of each of them?
The size of the stack is set when a thread is created. The size of the heap is set on application startup, but can grow as space is needed (the allocator requests more memory from the operating system).
What makes one faster?
The stack is faster because the access pattern makes it trivial to allocate and deallocate memory from it (a pointer/integer is simply incremented or decremented), while the heap has much more complex bookkeeping involved in an allocation or deallocation. Also, each byte in the stack tends to be reused very frequently which means it tends to be mapped to the processor's cache, making it very fast. Another performance hit for the heap is that the heap, being mostly a global resource, typically has to be multi-threading safe, i.e. each allocation and deallocation needs to be - typically - synchronized with "all" other heap accesses in the program.
A clear demonstration:
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Best Answer
Python doesn't have a library built in, but the excellent dateutil library provides a parse() method that's pretty accommodating.
From simple (assuming today is 9/25):
To ambigous:
To all over the board:
Take a look at the documentation for it here:
http://labix.org/python-dateutil#head-c0e81a473b647dfa787dc11e8c69557ec2c3ecd2