From ABC to Python
Python’s first and foremost influence was ABC, a language designed in the early 1980s by Lambert Meertens, Leo Geurts and others at CWI. ABC was meant to be a teaching language, a replacement for BASIC, and a language and environment for personal computing. It was designed by first doing a task analysis of the programming task and then doing several iterations that included serious user testing. My own role in the ABC group was mainly that of implementing the language and its integrated editing environment.
Python’s use of indentation comes directly from ABC, but this idea didn’t originate with ABC--it had already been promoted by Donald Knuth and was a well-known concept of programming style. (The occam programming language also used it.) However, ABC’s authors did invent the use of the colon that separates the lead-in clause from the indented block. After early user testing without the colon, it was discovered that the meaning of the indentation was unclear to beginners being taught the first steps of programming. The addition of the colon clarified it significantly: the colon somehow draws attention to what follows and ties the phrases before and after it together in just the right way.
Python’s major data types also derive from ABC, though with some modifications. ABC’s lists were really bags or multisets, which were always kept sorted using a modified B-tree implementation. Its tables were associative arrays that were similarly kept sorted by key. I found that neither data type was suitable to represent, for example, the sequence of lines read from a file, which I anticipated would be a common use case. (In ABC you'd have to use a table with the line numbers as keys, but that complicates insertions and deletions.) So I changed the list type into a flexible array with insert and delete operations, giving users complete control over the ordering of items in a list. A sort method supported the occasional need for sorted results.
I also replaced the sorted tables with a hash table implementation. I chose a hash table because I believed this to be faster and easier to implement than ABC’s B-tree implementation. The latter was theoretically proven to be asymptotically optimal in space and time consumption for a wide variety of operations, but in practice it had turned out to be difficult to implement correctly due to the complexity of the B-tree algorithms. For the same reason, the performance was also sub-optimal for small table sizes.
I kept ABC’s immutable tuple data type--Python’s tuple packing and unpacking operations are taken directly from ABC. Since tuples are internally represented by arrays, I decided to add array-like indexing and slicing.
One consequence of adding an array-like interface to tuples is that I had to figure out some way to resolve the edge cases of tuples with length 0 or 1. One of the rules I took from ABC was that every data type, when printed or converted to a string, should be represented by an expression that was a valid input to the language’s parser. So, it followed that I needed to have notations for 0- and 1-length tuples. At the same time I didn’t want to lose the distinction between a one-tuple and a bare parenthesized expression, so I settled for an ugly but pragmatic approach where a trailing comma would turn an expression into a one-tuple and "()" would represent a zero-tuple. It's worth nothing that parentheses aren’t normally required by Python’s tuple syntax, except here--I felt representing the empty tuple by “nothing” could too easily mask genuine typos.
Python’s strings started with very similar (immutable) semantics as ABC’s strings, but with a different notation, and 0-based indexing. Since I now had three indexable types, list, tuples, and strings, I decided to generalize these to a common concept, the sequence. This generalization made it so certain core operations such as getting the length (len(s)), indexing (s[i]), slicing (s[i:j]), and iteration (for i in s) worked the same on any sequence type.
Numbers are one of the places where I strayed most from ABC. ABC had two types of numbers at run time; exact numbers which were represented as arbitrary precision rational numbers and approximate numbers which were represented as binary floating point with extended exponent range. The rational numbers didn’t pan out in my view. (Anecdote: I tried to compute my taxes once using ABC. The program, which seemed fairly straightforward, was taking way too long to compute a few simple numbers. Upon investigation it turned out that it was doing arithmetic on numers with thousands of digits of precision, which were to be rounded to guilders and cents for printing.) For Python I therefore chose a more traditional model with machine integers and machine binary floating point. In Python's implementation, these numbers are simply represented by the C datatypes of long and double respectively.
Feeling that there was still an important use case for unbounded exact numbers, I added a bignum data type, which I called long. I already had a bignum implementation that was the result of an unfinished attempt at improving ABC’s implementation a few years earlier (ABC’s original implementation, one of my first contributions to ABC, used a decimal representation internally). Thus, it made sense to me to use this code in Python.
Although I added bignums to Python, it's important to emphasize that I didn’t want to use bignums for all integer operations. From profiling Python programs written by myself and colleagues at CWI, I knew that integer operations represent a significant fraction of the total program running time of most programs. By far, the most common use of integers is to index sequences that fit in memory. Thus, I envisioned machine integers being used for all of the most-common cases and the extra range of bignums only coming into play when doing "serious math" or calculating the national debt of the US in pennies.
The Problem With Numbers
The implementation of numbers, especially integers, is one area where I made several serious design mistakes, but also learned important lessons concerning Python's design.
Since Python had two different integer types, I needed to figure out some way to distinguish between the two types in a program. My solution was to require users to explicitly state when they wanted to use longs by writing numbers with a trailing L (e.g., 1234L). This is one area where Python violated the ABC-inspired philosophy of not requiring users to care about a uninteresting implementation details.
Sadly, this was only a minor detail of much larger problem. A more egregious error was that my implementation of integers and longs had slightly different semantics in certain cases! Since the int type was represented as a machine integer, operations that overflowed would silently clip the result to 32 bits or whatever the precision of the C long type happened to be. In addition, the int type, while normally considered signed, was treated as an unsigned number by bitwise and shift operations and by conversions to/from octal and hexadecimal representations. Longs, on the other hand, were always considered signed. Therefore, some operations would produce a different result depending on whether an argument was represented as an int or a long. For example, given 32-bit arithmetic, 1<<31 (1 shifted left by 31 bits) would produce the largest negative 32-bit integer, and 1<<32 would produce zero, whereas 1L<<31 (1 represented as long shifted left by 31 bits) would produce a long integer equal to 2**31, and 1L<<32 would produce 2**32.
To resolve some of these issues I made a simple fix. Rather than having integer operations silently clip the result, I changed most arithmetic operations to raise an OverflowError exception when the result didn't fit. (The only exception to this checking were the "bit-wise" operations mentioned above, where I assumed that users would expect the behavior of these operations in C.) Had I not added this check, Python users would have undoubtedly started writing code that relied on the semantics of signed binary arithmetic modulo 2**32 (like C users do), and fixing the mistake would have been a much more painful transition to the community.
Although the inclusion of overflow checking might seem like a minor implementation detail, a painful debugging experience made me realize that this was a useful feature. As one of my early programming experiments in Python, I tried to implement a simple mathematical algorithm, the computation of “Meertens numbers”, a bit of recreational mathematics invented by Richard Bird for the occasion of ABC’s primary author’s 25ths anniversary at CWI. The first few Meertens numbers are small, but when translating the algorithm into code I hadn’t realized that the intermediate results of the computation are much larger than 32 bits. It took a long and painful debugging session before I discovered this, and I decided there and then to address the issue by checking all integer operations for overflow, and raising an exception whenever the result could not be represented as a C long. The extra cost of the overflow check would be dwarfed by the overhead I was already incurring due to the implementation choice of allocating a new object for the result.
Sadly, I'm sorry to say that raising an overflow exception was not really the right solution either! At the time, I was stuck on C’s rule “operations on numeric type T return a result of type T”. This rule was also the reason for my other big mistake in integer semantics: truncating the result of integer division, which I will discuss in a later blog post. In hindsight, I should have made integer operations that overflow promote their result to longs. This is the way that Python works today, but it took a long time to make this transition.
Despite the problem with numbers, one very positive thing came out of this experience. I decided that there should be no undefined result values in Python – instead, exceptions should always be raised when no correct return value can be computed. Thus, Python programs would never fail due to undefined values being silently passed around behind the scenes. This is still an important principle of the language, both in the language proper and in the standard library.