tl;dr

  1. Install Xcode from the App Store, then install the Command Line Tools from Xcode (Preferences | Downloads | Components).
  2. Install Homebrew, gfortran, pip, readline, python, numpy, ipython, and whichever packages you need:
  3.     ruby -e "$(curl -fsSL https://raw.github.com/mxcl/homebrew/go)"
        brew install gfortran
        sudo easy_install pip readline
        sudo pip install --upgrade --force-reinstall numpy 
        sudo pip install ipython scipy pandas matplotlib
    
  4. There is no step 3. No setting up symlinks, monkeying with your PATH, etc. And, except for that first step of getting your compiler toolchain (Xcode) and package manager (brew) set up, none of it's Mac-specific.
This works with, at least, clean installs of 10.8.0, 10.8.1, 10.8.2, and pre-release 10.8.3.

But I'm using an older OS X that doesn't have Python 2.7

Then this guide isn't for you. Sorry.

Why do I have to force-upgrade numpy?

Depending on your OS X version, you may have numpy pre-installed. (Also, a lot of people seem to get numpy installed on their own before running into problems with other packages.) The problem is that if you install numpy with Fortran support disabled, this will cause problems when you install scipy. So, after installing Fortran, upgrade numpy.

(Plus, at the time of this posting, 1.7.0 is pretty new, so if you have it, you probably have 1.6.x.)

Isn't 2.7.2 out of date?

If you need any of the fixes mentioned on the 2.7.3 Release page or Change log, you will have to install 2.7.3 and follow a different guide.

I want to use Python 3, or PyPy, or…

For 3.x, see Installing scipy, etc., for Python 3 on Mac.

For PyPy, as far as I know, numpypy is still a work in progress. If you're willing to assist the development (even just by testing), they'd probably love your help.

For other non-CPython versions like Jython and IronPython, you're out of luck—you'll probably want to find JVM or .NET numeric and scientific libraries and use them instead.

But what about all those 300-line blog posts that say it's easier to start with the python.org installer?

Do they look easier?

All that stuff they make you do monkeying with your PATH or manually setting up symlinks is only necessary because you're setting up another copy of Python 2.7 in parallel with the one Apple already gave you. If you don't do that, you don't have to deal with all that complexity.

If you want to do it anyway, go ahead. But I'd recommend learning what you're actually doing with your PATH—what the difference is between setting it in profile vs. bash_profile, ~/.profile vs. /etc/profile, profile vs. environment.plist, etc.

And, when you go looking for help, the general Python community is not going to be able to help you. The scipy community may actually be able to help you. But nobody ever goes there. Instead, they go to python-list or StackOverflow. Linux and Windows users never have two system-wide Python 2.7 installs. Mac users do, but they all followed one of those same blog posts you did, and all they know is that it works for them.

So, when you inevitably show up on StackOverflow to post the 500th question this year asking "Why is scipy broken?", at least have this information in advance, and put it in your question:

  • Which blog post did you follow (with a link)?
  • What OS X are you on, and does it have an Apple Python 2.7 (/usr/bin/python)?
  • Which additional Python 2.7 did you install (e.g., python.org 64-bit installer, Homebrew package, etc.), and what options did you use?
  • What does "echo $PATH" say from the shell? (And, if your problem is running "import scipy" after an install seemed to work, what does "print(sys.path)" say from Python?)
  • What command failed, and what is the complete error?
If you don't, you will spend hours answering these questions (especially since nobody has ever answered all of them at once—for some reason people seem to feel that answering 1.5 questions out of 5 is sufficient), or you will get irrelevant or even incorrect answers that lead you to waste hours screwing up your system even worse.
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It's been more than a decade since Typical Programmer Greg Jorgensen taught the word about Abject-Oriented Programming.

Much of what he said still applies, but other things have changed. Languages in the Abject-Oriented space have been borrowing ideas from another paradigm entirely—and then everyone realized that languages like Python, Ruby, and JavaScript had been doing it for years and just hadn't noticed (because these languages do not require you to declare what you're doing, or even to know what you're doing). Meanwhile, new hybrid languages borrow freely from both paradigms.

This other paradigm—which is actually older, but was largely constrained to university basements until recent years—is called Functional Addiction.

A Functional Addict is someone who regularly gets higher-order—sometimes they may even exhibit dependent types—but still manages to retain a job.

Retaining a job is of course the goal of all programming. This is why some of these new hybrid languages, like Rust, check all borrowing, from both paradigms, so extensively that you can make regular progress for months without ever successfully compiling your code, and your managers will appreciate that progress. After all, once it does compile, it will definitely work.

Closures

It's long been known that Closures are dual to Encapsulation.

As Abject-Oriented Programming explained, Encapsulation involves making all of your variables public, and ideally global, to let the rest of the code decide what should and shouldn't be private.

Closures, by contrast, are a way of referring to variables from outer scopes. And there is no scope more outer than global.

Immutability

One of the reasons Functional Addiction has become popular in recent years is that to truly take advantage of multi-core systems, you need immutable data, sometimes also called persistent data.

Instead of mutating a function to fix a bug, you should always make a new copy of that function. For example:

function getCustName(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

When you discover that you actually wanted fields 2 and 3 rather than 1 and 2, it might be tempting to mutate the state of this function. But doing so is dangerous. The right answer is to make a copy, and then try to remember to use the copy instead of the original:

function getCustName(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

function getCustName2(custID)
{
    custRec = readFromDB("customer", custID);
    fullname = custRec[2] + ' ' + custRec[3];
    return fullname;
}

This means anyone still using the original function can continue to reference the old code, but as soon as it's no longer needed, it will be automatically garbage collected. (Automatic garbage collection isn't free, but it can be outsourced cheaply.)

Higher-Order Functions

In traditional Abject-Oriented Programming, you are required to give each function a name. But over time, the name of the function may drift away from what it actually does, making it as misleading as comments. Experience has shown that people will only keep once copy of their information up to date, and the CHANGES.TXT file is the right place for that.

Higher-Order Functions can solve this problem:

function []Functions = [
    lambda(custID) {
        custRec = readFromDB("customer", custID);
        fullname = custRec[1] + ' ' + custRec[2];
        return fullname;
    },
    lambda(custID) {
        custRec = readFromDB("customer", custID);
        fullname = custRec[2] + ' ' + custRec[3];
        return fullname;
    },
]

Now you can refer to this functions by order, so there's no need for names.

Parametric Polymorphism

Traditional languages offer Abject-Oriented Polymorphism and Ad-Hoc Polymorphism (also known as Overloading), but better languages also offer Parametric Polymorphism.

The key to Parametric Polymorphism is that the type of the output can be determined from the type of the inputs via Algebra. For example:

function getCustData(custId, x)
{
    if (x == int(x)) {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return int(fullname);
    } else if (x.real == 0) {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return double(fullname);
    } else {
        custRec = readFromDB("customer", custId);
        fullname = custRec[1] + ' ' + custRec[2];
        return complex(fullname);
    }
}

Notice that we've called the variable x. This is how you know you're using Algebraic Data Types. The names y, z, and sometimes w are also Algebraic.

Type Inference

Languages that enable Functional Addiction often feature Type Inference. This means that the compiler can infer your typing without you having to be explicit:


function getCustName(custID)
{
    // WARNING: Make sure the DB is locked here or
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

We didn't specify what will happen if the DB is not locked. And that's fine, because the compiler will figure it out and insert code that corrupts the data, without us needing to tell it to!

By contrast, most Abject-Oriented languages are either nominally typed—meaning that you give names to all of your types instead of meanings—or dynamically typed—meaning that your variables are all unique individuals that can accomplish anything if they try.

Memoization

Memoization means caching the results of a function call:

function getCustName(custID)
{
    if (custID == 3) { return "John Smith"; }
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

Non-Strictness

Non-Strictness is often confused with Laziness, but in fact Laziness is just one kind of Non-Strictness. Here's an example that compares two different forms of Non-Strictness:

/****************************************
*
* TO DO:
*
* get tax rate for the customer state
* eventually from some table
*
****************************************/
// function lazyTaxRate(custId) {}

function callByNameTextRate(custId)
{
    /****************************************
    *
    * TO DO:
    *
    * get tax rate for the customer state
    * eventually from some table
    *
    ****************************************/
}

Both are Non-Strict, but the second one forces the compiler to actually compile the function just so we can Call it By Name. This causes code bloat. The Lazy version will be smaller and faster. Plus, Lazy programming allows us to create infinite recursion without making the program hang:

/****************************************
*
* TO DO:
*
* get tax rate for the customer state
* eventually from some table
*
****************************************/
// function lazyTaxRateRecursive(custId) { lazyTaxRateRecursive(custId); }

Laziness is often combined with Memoization:

function getCustName(custID)
{
    // if (custID == 3) { return "John Smith"; }
    custRec = readFromDB("customer", custID);
    fullname = custRec[1] + ' ' + custRec[2];
    return fullname;
}

Outside the world of Functional Addicts, this same technique is often called Test-Driven Development. If enough tests can be embedded in the code to achieve 100% coverage, or at least a decent amount, your code is guaranteed to be safe. But because the tests are not compiled and executed in the normal run, or indeed ever, they don't affect performance or correctness.

Conclusion

Many people claim that the days of Abject-Oriented Programming are over. But this is pure hype. Functional Addiction and Abject Orientation are not actually at odds with each other, but instead complement each other.
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