What are the technical reasons why languages like Python and Ruby are interpreted (out of the box) instead of compiled? It seems to me like it should not be too hard for people knowledgeable in this domain to make these languages not be interpreted like they are today, and we would see significant performance gains. So certainly I am missing something.
Several reasons:
faster development loop, write-test vs write-compile-link-test
easier to arrange for dynamic behavior (reflection, metaprogramming)
makes the whole system portable (just recompile the underlying C code and you are good to go on a new platform)
Think of what would happen if the system was not interpreted. Say you used translation-to-C as the mechanism. The compiled code would periodically have to check if it had been superseded by metaprogramming. A similar situation arises with eval()-type functions. In those cases, it would have to run the compiler again, an outrageously slow process, or it would have to also have the interpreter around at run-time anyway.
The only alternative here is a JIT compiler. These systems are highly complex and sophisticated and have even bigger run-time footprints than all the other alternatives. They start up very slowly, making them impractical for scripting. Ever seen a Java script? I haven't.
So, you have two choices:
all the disadvantages of both a compiler and an interpreter
just the disadvantages of an interpreter
It's not surprising that generally the primary implementation just goes with the second choice. It's quite possible that some day we may see secondary implementations like compilers appearing. Ruby 1.9 and Python have bytecode VM's; those are ½-way there. A compiler might target just non-dynamic code, or it might have various levels of language support declarable as options. But since such a thing can't be the primary implementation, it represents a lot of work for a very marginal benefit. Ruby already has 200,000 lines of C in it...
I suppose I should add that one can always add a compiled C (or, with some effort, any other language) extension. So, say you have a slow numerical operation. If you add, say Array#newOp with a C implementation then you get the speedup, the program stays in Ruby (or whatever) and your environment gets a new instance method. Everybody wins! So this reduces the need for a problematic secondary implementation.
Exactly like (in the typical implementation of) Java or C#, Python gets first compiled into some form of bytecode, depending on the implementation (CPython uses a specialized form of its own, Jython uses JVM just like a typical Java, IronPython uses CLR just like a typical C#, and so forth) -- that bytecode then gets further processed for execution by a virtual machine (AKA interpreter), which may also generate machine code "just in time" -- known as JIT -- if and when warranted (CLR and JVM implementations often do, CPython's own virtual machine typically doesn't but can be made to do so e.g. with psyco or Unladen Swallow).
JIT may pay for itself for sufficiently long-running programs (if memory's way cheaper than CPU cycles), but it may not (due to slower startup times and larger memory footprint), especially when the types also have to be inferred or specialized as part of the code generation. Generating machine code without type inference or specialization is easy if that's what you want, e.g. freeze does it for you, but it really doesn't present the advantages that "machine code fetishists" attribute to it. E.g., you get an executable binary of 1.5 to 2 MB in lieu of a tiny "hello world" .pyc -- not much point!-). That executable is stand-alone and distributable as such, but it will only work on a very specific narrow range of operating systems and CPU architectures, so the tradeoffs are quite iffy in most cases. And, the time it takes to prepare the executable is quite long indeed, so it would be a crazy choice to make that mode of operation the default one.
Merely replacing an interpreter with a compiler won't give you as big a performance boost as you might think for a language like Python. When most time is actually spend doing symbolic lookups of object members in dictionaries, it doesn't really matter if the call to the function performing such lookup is interpreted, or is native machine code - the difference, while not quite negligible, will be dwarfed by lookup overhead.
To really improve performance, you need optimizing compilers. And optimization techniques here are very different from what you have with C++, or even Java JIT - an optimizing compiler for a dynamically typed / duck typed language such as Python needs to do some very creative type inference (including probabilistic - i.e. "90% chance of it being T" and then generating efficient machine code for that case with a check/branch before it) and escape analysis. This is hard.
I think the biggest reason for the languages being interpreted is portability. As a programmer you can write code that will run in an interpreter not a specific OS. So your programs behave more uniformly across platforms (more so than compiled languages). Another advantage I can think of is it's easier to have a dynamic type system in an interpreted language. I think the creators of the language were thinking having a language where programmers can be more productive due to automatic memory management, dynamic type system and meta programming wins over any performance loss due to the language being interpreted. If you are concerned about performance you can always compile the language to native machine code employing a technique like JIT compilation.
Today, there is no longer a strong distinction between "compiled" and "interpreted" languages. Python is in fact compiled just as much as Java is, the only differences are:
The Python compiler is much faster than the Java compiler
Python automatically compiles source code as it is executed, there is no separate "compile" step required
Python bytecode is different from JVM bytecode
Python even has a function called compile() which is an interface to the compiler.
It sounds like the distinction you are making is between "dynamically typed" and "statically typed" languages. In dynamic languages such as Python, you can write code like:
def fn(x, y):
return x.foo(y)
Notice that the types of x and y are not specified. At runtime, this function will look at x to see whether it has a member function named foo, and if so will call it with y. If not, it will throw a runtime error that indicates no such function was found. This sort of runtime lookup is much easier to represent using an intermediate representation like bytecode, where a runtime VM does the lookup instead of having to generate machine code to do the lookup itself (or, call a function to do the lookup which is what the bytecode will do anyway).
Python has projects such as Psyco, PyPy, and Unladen Swallow that take various approaches to compiling Python object code into something closer to native code. There is active research in this area but there is not (as yet) a simple answer.
The effort required to create a good compiler to generate native code for a new language is staggering. Small research groups typically take 5 to 10 years (examples: SML/NJ, Haskell, Clean, Cecil, lcc, Objective Caml, MLton, and many others). And when the language in question requires type checking and other decisions to be made at run time, a compiler writer has to work much harder to get good native-code performance (for an excellent example, see work by Craig Chambers and later Urs Hoelzle on Self). The performance gains you might hope for are harder to realize than you might think. This phenomenon partly explains why so many dynamically typed languages are interpreted.
As noted, a decent interpreter is also instantly portable, while porting compilers to new machine architectures takes substantial effort (and is a problem I personally have been working on for over 20 years, with some time off for good behavior). So an interpreter is a way to reach a wide audience quickly.
Finally, although fast compilers and slow interpreters exist, it's usually easer to make the edit-translate-go cycle faster by using an interpreter. (For some nice examples of fast compilers see the aforementioned lcc as well as Ken Thompson's go compiler. For an example of a relatively slow interpreter see GHCi.
Well, isn't one of the strengths of these languages that they are so easily scriptable? They wouldn't be if they were compiled. And on the other hand, dynamic languages are easier to intereprete than to compile.
In a compiled language, the loop you get into when making software is
Make a change
Compile changes
Test changes
goto 1
Interpreted languages tend to be faster to make stuff in because you get to cut out step two of that process (and when you're dealing with a large system where compile times can be upwards of two minutes, step two can add a significant amount of time).
This isn't necessarily the reason python|ruby designers thought of, but keep in mind that "How efficiently does the machine run this?" is only half the software development problem.
It also seems like it would be easier to compile code in a language that's interpreted naturally than it would be to add an interpreter to a language that's compiled by default.
REPL. Don't knock it 'till you've tried it. :)
By design.
The authors wanted something where they can write scripts into.
Python gets compiled the first time it is executed though
Compiling Ruby at least is notoriously hard. I'm working on one, and as part of that I wrote a blog post enumerating some of the issues here.
Specifically, Ruby is suffering from a very unclear (i.e. non-existent) boundary between the "read" and "execute" phase of the program that makes it hard to compile efficiently. You could just emulate what the interpreter does, but then you're not going to see much speed up, so it wouldn't be worth the effort. If you want to compile it efficiently you then face a lot of additional complications to handle the extreme level of dynamism in Ruby.
The good news is that there are techniques for overcoming this. Self, Smalltalk and Lisp/Scheme's have dealt quite successfully with most of the same issues. But it takes time to sift through it and figure out how to make it work with Ruby. It also doesn't help that Ruby has a very convoluted grammar.
Raw compute performance is probably not a goal of most interpreted languages. Interpreted languages are typically more concerned about programmer productivity than raw speed. In most cases these languages are plenty fast enough for the tasks the languages were designed to tackle.
Given that, and that just about the only advantages of a compiler are type checking (difficult to do in a dynamic language) and speed, there's not much incentive to write compilers for most interpreted languages.
Related
I am an (old) engineer not a programmer so forgive me for asking a naïve question.
My understanding is that to get really fast execution times for a program, it needs to be compiled to native machine code. And there are a relatively small number of languages still in use that do this (e.g. C and C++).
But I much prefer the syntax of Python over that of the C-derived compiled languages. However my understanding is that interpreted Python (and pseudo-compiled Python run on a virtual machine) cannot match the execution speed of a truly compiled language.
Is there some reason that a true native-code Python compiler cannot be developed?
[I am interested specifically in Python but I am not aware of any language that can be interpreted and also compiled to native machine code.]
The key distinction is a clean separation between compile time and run time. In Python, for instance, import happens at runtime, and can happen conditionally. And per the halting problem, that means a compiler cannot determine up front if a given import will happen. Yet, this affects the code that would need to be generated.
As Bill the Lizard notes, if the language does have a clean distinction, an interpreter can still choose to ignore it. C's #include can happen before main, but that does not mean an interpreter must do so.
Outside the syntax, Python is also virtually uncompilable due to weak typing. In C, + has a very limited set of meanings - integer, floating point or pointer, and the compiler will know the static type of the arguments. C++ has far more extensive overloading, but the same basic principle applies. virtual functions allow some run-time flexibility, but from an finite set of options, all compiled before main starts.
Also not syntax is the memory model - both C and C++ have a memory model that's an improved derivative of Java's memory model, which makes threading quite efficient. (Unlike Java, not every object can be synchronized, you need special members). As CPU's gain more and more cores, the advantages only continue to grow. Compilers can see pretty well where memory and CPU registers need to be brought in sync.
Hi I'm currently learning Python since the syntax feels so succinct and the idioms match well with my mental model.
However I'm also interested in learning about OS internals and reverse engineering software, which ultimately means knowing C in a rather thorough capacity.
When originally picking a language I did lots of reading and comparisons, and it seems that a number thrown out a lot is that to write short idiomatic statements in Python would require the equivalent of a few hundred lines of C (I'd guess code for memory management, writing the code for dictionaries,lists etc) that we take for granted as built into the Python language.
1) With an average C programmer, is that 100-200 lines of code per Python idiom anywhere near accurate?
Because C doesn't come built-in with Python-like constructs such as dictionaries/lists(with all their nice methods etc):
2) Do C programmers tend to build these constructs from scratch and then re-use them between projects to greatly reduce the actual amount of hand coding for their projects?
I assume re-using libraries like boost:: stuff also again, reduces some of the boilerplate hand coding also...
3) But does using popular libraries and re-using common code one has written before in C for basic constructs/etc, how much does that revise the lines of code written in C compared to the code in Python of a enthusiast sized code base?
I know specific numbers aren't possible, but is it possible with libraries, code reuse etc, to have a development time in C close to that of Python without being a Linus Torvalds style coding machine?
Thanks!
but is it possible with libraries, code reuse etc, to have a development time in C close to that of Python
No.
You've missed the most important point.
Python's interactive. It's not edit-compile-link-execute-break-debug. It's edit-debug.
Boost is C++, not C (emphatically not C -- virtually all of it makes heavy use of templates and such that aren't part of C).
Yes, C programmers tend to build up personal libraries of code for all sorts of "stuff" -- data structures, algorithms, user interfaces, and so on. There are also a fair number of other libraries for everything from basic string manipulation to database connectivity, user interfaces, basic algorithms and data structures, etc.
Comparing productivity between the two can be difficult though -- even if something can be done in one line of code either way, there's a greater chance that the C programmer will end up doing extra work to find and learn to use that particular library. OTOH, if he has used it before, the two might be directly competitive of (in a few cases) C might be more productive.
I'd guess Python ends up more productive more often, but trying to guess how much so is difficult (and lines of code usually won't be a good indication either).
As I did serious c programming I read a book that claimed libraries are worth to write. (Especially in C which considered a low level language)
Libraries are build for reuse.
If you use libraries you write one line like detectFace( faceDesriptor ) or renderPDF( document) is doesn't matter whether an idiom in another language is more concise or not.
Lines of code isn't a proper metric if it is about what would more efficient.
It depends.
Try to write an interrupt handler in python. Someone could probably make it work but it's going to be a dancing bear, the dancing is not good but it is surprising that a bear can do it. Want to write an OS or do some embedded programming you're not going to be able to use python. It's telling that the main python implementation is written in C.
That being said I'm amazed at some of the low-level stuff that you can do with python. The high-level stuff is almost a given if you're measuring lines of code. Python is just a higher-level language.
They are both very useful tools, just for different types of projects. Knowing both would be very useful, particularly when you need to interface to some new functionality in python that doesn't yet have a python binding.
For the types of projects most developers work on python is going to be more consice and quicker to write and debug. You may be able to make a library of reusable C code, but a good python programmer will be doing the same thing with their python code, at a higher level.
I think Python is more productive for small projects (up to a few thousand lines of code).
On the other hand, C is better suited for large projects (even though IMHO there are better languages for that, such as Ada): static type-checking allows to find many errors at compile time that are much more difficult to detect at run-time, especially in a large program.
In a larger C project, the lack of lists and other powerful data structures that are found in Python can be compensated by implementing or using custom libraries. I agree with user stacker that by using well-designed libraries your C code can be pretty concise.
Depends greatly on the task and the size of the project. For many small interesting tasks, I would not be surprised by 100:1 smaller Python code simply because the standard libraries are extremely good. If you find, buy, or build C/C++ libraries that do what you want, I imagine the ratio would be much more like 3:1 on big projects.
However, finding, buying, and building C/C++ libraries does take time and effort, so I believe in the vast majority of cases, Python is going to be much faster to develop in.
First, I am aware that dynamic languages is a term used mainly by a vendor; I am using it just to have a container word to include languages like Perl (a favorite of mine), Python, Tcl, Ruby, PHP and so on. They are interpreted but I am interested here to refer to languages featuring strong capability to support the programmer efficiency and the support for typical constructs of modern interpreted languages
My question is: there are dynamic languages can be compiled efficiently in native executable code - typically for Windows platforms? Which ones? Maybe using some third part ad-hoc tools? I am not talking about huge executables carrying with them a full interpreter or some similar tricks nor some smart module able to include its own dependances or some required modules, but a honest, straight, standard, solid executable code.
If not, there is some technical reason inhibiting the availability of such a best-of-both-world feature?
Thanks!
Daniel
I think you're operating under a misunderstanding: These executables aren't huge because they just lump the interpreter in there, they're huge because the whole runtime is in there.
On Windows, most of your runtime is already installed, so you don't have to ship it. You think your program is small, but a quick look at the virtual memory mappings will tell you that even a small "hello-world" type program written in C is a couple megabytes big.
That's just how big useful runtimes are.
If you really want to keep your ship-size small, your only choice is to use the runtimes that are already there, and that means C/C++ and (recently) dot-net.
If you really can't swallow the runtime, Forth is as small as it gets.
The best, most aggressive dynamic languages with the best compilers for Windows are the commercial Lisps. They do a lot of inlining and pruning when producing executables, so you end up shipping only what you use. They are still 1.5x to 5x larger than C/C++ programs.
As far as languages that you know: Perl is as fat as they get. ActiveState has perlapp which I'm sure you're already aware of, but you dismissed because of it's size. Revisit it if you can.
Now, to answer your question (is) there is some technical reason inhibiting the availability of such a best-of-both-world feature?: Yes.
Perl cannot be statically analyzed (proof), which means there's no way for a perl compiler to tell what can be discarded. That means every part of Perl's runtime needs to be available to your program becuase there's no way for your program to indicate what parts can be discarded.
That means that getting a smaller executable is equivalent to getting a smaller runtime, and you should be comfortable accepting that if the perl developers knew how to make the perl runtime smaller without discarding any features, they'd probably do it.
If you are willing to write in a strict subset of Python or PHP, these languages can be analyzed. Shed Skin and HipHop-php are pretty good, but they're still quite large, and they don't support all of Pythons and PHP's features which means that some modules will simply not work. To my knowledge, nobody has implemented pruning for either of these languages (most of the focus in these compilers is in improving their lackluster performance) and it may be another decade or more before anyone bothers, however these still will be the restrictions you have to accept when doing this sort of thing.
The PyPy project does what you describe for a fairly complete subset of Python.
In the general case, this is a very hard problem to solve, largely due to the very attributes that make these languages "dynamic": late binding, weakly-typed variables, data structures and containers, eval facilities, a fuzzy divide between programming and meta-programming, etc. But a lot of effort is being poured into it, such as the JavaScript JIT-compiler projects listed here.
Shed Skin is an experimental (and restricted) Python-to-C++ compiler that can do what you describe. As Marcelo indicates above with PyPy, there are limitations on what you can compile with Shed Skin, but if you are willing to accept the restrictions, you can achieve large speedups.
I'm looking at implementing a fuzzy logic controller based on either PyFuzzy (Python) or FFLL (C++) libraries.
I'd prefer to work with python but am unsure if the performance will be acceptable in the embedded environment it will work in (either ARM or embedded x86 proc both ~64Mbs of RAM).
The main concern is that response times are as fast as possible (an update rate of 5hz+ would be ideal >2Hz is required). The system would be reading from multiple (probably 5) sensors from an RS232 port and provide 2/3 outputs based on the results of the fuzzy evaluation.
Should I be concerned that Python will be too slow for this task?
In general, you shouldn't obsess over performance until you've actually seen it become a problem. Since we don't know the details of your app, we can't say how it'd perform if implemented in Python. And since you haven't implemented it yet, neither can you.
Implement the version you're most comfortable with, and can implement fastest, first. Then benchmark it. And if it is too slow, you have three options which should be done in order:
First, optimize your Python code
If that's not enough, write the most performance-critical functions in C/C++, and call that from your Python code
And finally, if you really need top performance, you might have to rewrite the whole thing in C++. But then at least you'll have a working prototype in Python, and you'll have a much clearer idea of how it should be implemented. You'll know what pitfalls to avoid, and you'll have an already correct implementation to test against and compare results to.
Python is very slow at handling large amounts of non-string data. For some operations, you may see that it is 1000 times slower than C/C++, so yes, you should investigate into this and do necessary benchmarks before you make time-critical algorithms in Python.
However, you can extend python with modules in C/C++ code, so that time-critical things are fast, while still being able to use python for the main code.
Make it work, then make it work fast.
If most of your runtime is spent in C libraries, the language you use to call these libraries isn't important. What language are your time-eating libraries written in ?
From your description, speed should not be much of a concern (and you can use C, cython, whatever you want to make it faster), but memory would be. For environments with 64 Mb max (where the OS and all should fit as well, right ?), I think there is a good chance that python may not be the right tool for target deployment.
If you have non trivial logic to handle, I would still prototype in python, though.
I never really measured the performance of pyfuzzy's examples, but as the new version 0.1.0 can read FCL files as FFLL does. Just describe your fuzzy system in this format, write some wrappers, and check the performance of both variants.
For reading FCL with pyfuzzy you need the antlr python runtime, but after reading you should be able to pickle the read object, so you don't need the antlr overhead on the target.
I would be interested to learn about large scale development in Python and especially in how do you maintain a large code base?
When you make incompatibility changes to the signature of a method, how do you find all the places where that method is being called. In C++/Java the compiler will find it for you, how do you do it in Python?
When you make changes deep inside the code, how do you find out what operations an instance provides, since you don't have a static type to lookup?
How do you handle/prevent typing errors (typos)?
Are UnitTest's used as a substitute for static type checking?
As you can guess I almost only worked with statically typed languages (C++/Java), but I would like to try my hands on Python for larger programs. But I had a very bad experience, a long time ago, with the clipper (dBase) language, which was also dynamically typed.
Don't use a screw driver as a hammer
Python is not a statically typed language, so don't try to use it that way.
When you use a specific tool, you use it for what it has been built. For Python, it means:
Duck typing : no type checking. Only behavior matters. Therefore your code must be designed to use this feature. A good design means generic signatures, no dependences between components, high abstraction levels.. So if you change anything, you won't have to change the rest of the code. Python will not complain either, that what it has been built for. Types are not an issue.
Huge standard library. You do not need to change all your calls in the program if you use standard features you haven't coded yourself. And Python come with batteries included. I keep discovering them everyday. I had no idea of the number of modules I could use when I started and tried to rewrite existing stuff like everybody. It's OK, you can't get it all right from the beginning.
You don't write Java, C++, Python, PHP, Erlang, whatever, the same way. They are good reasons why there is room for each of so many different languages, they do not do the same things.
Unit tests are not a substitute
Unit tests must be performed with any language. The most famous unit test library (JUnit) is from the Java world!
This has nothing to do with types. You check behaviors, again. You avoid trouble with regression. You ensure your customer you are on tracks.
Python for large scale projects
Languages, libraries and frameworks
don't scale. Architectures do.
If you design a solid architecture, if you are able to make it evolves quickly, then it will scale. Unit tests help, automatic code check as well. But they are just safety nets. And small ones.
Python is especially suitable for large projects because it enforces some good practices and has a lot of usual design patterns built-in. But again, do not use it for what it is not designed. E.g : Python is not a technology for CPU intensive tasks.
In a huge project, you will most likely use several different technologies anyway. As a SGBD (French for DBMS) and a templating language, or else. Python is no exception.
You will probably want to use C/C++ for the part of your code you need to be fast. Or Java to fit in a Tomcat environment. Don't know, don't care. Python can play well with these.
As a conclusion
My answer may feel a bit rude, but don't get me wrong: this is a very good question.
A lot of people come to Python with old habits. I screwed myself trying to code Java like Python. You can, but will never get the best of it.
If you have played / want to play with Python, it's great! It's a wonderful tool. But just a tool, really.
I had some experience with modifying "Frets On Fire", an open source python "Guitar Hero" clone.
as I see it, python is not really suitable for a really large scale project.
I found myself spending a large part of the development time debugging issues related to assignment of incompatible types, things that static typed laguages will reveal effortlessly at compile-time.
also, since types are determined on run-time, trying to understand existing code becomes harder, because you have no idea what's the type of that parameter you are currently looking at.
in addition to that, calling functions using their name string with the __getattr__ built in function is generally more common in Python than in other programming languages, thus getting the call graph to a certain function somewhat hard (although you can call functions with their name in some statically typed languages as well).
I think that Python really shines in small scale software, rapid prototype development, and gluing existing programs together, but I would not use it for large scale software projects, since in those types of programs maintainability becomes the real issue, and in my opinion python is relatively weak there.
Since nobody pointed out pychecker, pylint and similar tools, I will: pychecker and pylint are tools that can help you find incorrect assumptions (about function signatures, object attributes, etc.) They won't find everything that a compiler might find in a statically typed language -- but they can find problems that such compilers for such languages can't find, too.
Python (and any dynamically typed language) is fundamentally different in terms of the errors you're likely to cause and how you would detect and fix them. It has definite downsides as well as upsides, but many (including me) would argue that in Python's case, the ease of writing code (and the ease of making it structurally sound) and of modifying code without breaking API compatibility (adding new optional arguments, providing different objects that have the same set of methods and attributes) make it suitable just fine for large codebases.
my 0.10 EUR:
i have several python application in 'production'-state. our company use java, c++ and python. we develop with the eclipse ide (pydev for python)
unittests are the key-solution for the problem. (also for c++ and java)
the less secure world of "dynamic-typing" will make you less careless about your code quality
BY THE WAY:
large scale development doesn't mean, that you use one single language!
large scale development often uses a handful of languages specific to the problem.
so i agree to the-hammer-problem :-)
PS: static-typing & python
Here are some items that have helped me maintain a fairly large system in python.
Structure your code in layers. i.e separate biz logic, presentation logic and your persistence layers. Invest a bit of time in defining these layers and make sure everyone on the project is brought in. For large systems creating a framework that forces you into a certain way of development can be key as well.
Tests are key, without unit tests you will likely end up with an unmanagable code base several times quicker than with other languages. Keep in mind that unit tests are often not sufficient, make sure to have several integration/acceptance tests you can run quickly after any major change.
Use Fail Fast principle. Add assertions for cases you feel your code maybe vulnerable.
Have standard logging/error handling that will help you quickly navigate to the issue
Use an IDE( pyDev works for me) that provides type ahead, pyLint/Checker integration to help you detect common typos right away and promote some coding standards
Carefull about your imports, never do from x import * or do relative imports without use of .
Do refactor, a search/replace tool with regular expressions is often all you need to do move methods/class type refactoring.
Incompatible changes to the signature of a method. This doesn't happen as much in Python as it does in Java and C++.
Python has optional arguments, default values, and far more flexibility in defining method signatures. Also, duck typing means that -- for example -- you don't have to switch from some class to an interface as part of a significant software change. Things just aren't as complex.
How do you find all the places where that method is being called? grep works for dynamic languages. If you need to know every place a method is used, grep (or equivalent IDE-supported search) works great.
How do you find out what operations an instance provides, since you don't have a static type to lookup?
a. Look at the source. You don't have the Java/C++ problem of object libraries and jar files to contend with. You don't need all the elaborate aids and tools that those languages require.
b. An IDE can provide signature information under many common circumstances. You can, easily, defeat your IDE's reasoning powers. When that happens, you should probably review what you're doing to be sure it makes sense. If your IDE can't reason out your type information, perhaps it's too dynamic.
c. In Python, you often work through the interactive interpreter. Unlike Java and C++, you can explore your instances directly and interactively. You don't need a sophisticated IDE.
Example:
>>> x= SomeClass()
>>> dir(x)
How do you handle/prevent typing errors? Same as static languages: you don't prevent them. You find and correct them. Java can only find a certain class of typos. If you have two similar class or variable names, you can wind up in deep trouble, even with static type checking.
Example:
class MyClass { }
class MyClassx extends MyClass { }
A typo with these two class names can cause havoc. ["But I wouldn't put myself in that position with Java," folks say. Agreed. I wouldn't put myself in that position with Python, either; you make classes that are profoundly different, and will fail early if they're misused.]
Are UnitTest's used as a substitute for static type checking? Here's the other Point of view: static type checking is a substitute for clear, simple design.
I've worked with programmers who weren't sure why an application worked. They couldn't figure out why things didn't compile; the didn't know the difference between abstract superclass and interface, and the couldn't figure out why a change in place makes a bunch of other modules in a separate JAR file crash. The static type checking gave them false confidence in a flawed design.
Dynamic languages allow programs to be simple. Simplicity is a substitute for static type checking. Clarity is a substitute for static type checking.
My general rule of thumb is to use dynamic languages for small non-mission-critical projects and statically-typed languages for big projects. I find that code written in a dynamic language such as python gets "tangled" more quickly. Partly that is because it is much quicker to write code in a dynamic language and that leads to shortcuts and worse design, at least in my case. Partly it's because I have IntelliJ for quick and easy refactoring when I use Java, which I don't have for python.
The usual answer to that is testing testing testing. You're supposed to have an extensive unit test suite and run it often, particularly before a new version goes online.
Proponents of dynamically typed languages make the case that you have to test anyway because even in a statically typed language conformance to the crude rules of the type system covers only a small part of what can potentially go wrong.