Haskell/Print version

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Table Of Contents

Haskell Basics

Getting set up
Variables and functions
Truth values
Type basics
Lists and tuples
Type basics II
Next steps
Building vocabulary
Simple input and output
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Elementary Haskell

More about lists
Folds, Scans, & Comprehension
Type declarations
Pattern matching
Control structures
More on functions
Higher order functions
Using GHCi effectively
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Intermediate Haskell

Standalone programs
More on datatypes
Other data structures
Classes and types
The Functor class
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Understanding monads
do Notation
Additive monads (MonadPlus)
Monad transformers
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Advanced Haskell

Monoids 50% developed
Applicative Functors 50% developed
Arrow tutorial
Understanding arrows
Continuation passing style (CPS)
Value recursion (MonadFix)
Zippers 75% developed
Mutable objects 0% developed
Concurrency 0% developed
Template Haskell 0% developed
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Fun with Types

Polymorphism basics 25% developed
Existentially quantified types 50% developed
Advanced type classes 50% developed
Phantom types 25% developed
Generalised algebraic data-types (GADT) 75% developed
Datatype algebra
Type constructors & Kinds 0% developed
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Wider Theory

Denotational semantics 75% developed
Equational reasoning
Program derivation
Category theory
The Curry-Howard isomorphism
fix and recursion
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Haskell Performance

Introduction 50% developed
Step by Step Examples 0% developed
Graph reduction 25% developed
Laziness 50% developed
Time and space profiling 0% developed
Strictness 0% developed
Algorithm complexity 25% developed
Data structures
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Libraries Reference



Data structures primer 75% developed


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General Practices

Packaging your software (Cabal)
Using the Foreign Function Interface (FFI)
Generic Programming : Scrap your boilerplate
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Specialised Tasks

Graphical user interfaces (GUI) 50%.svg
Databases 00%.svg
Working with XML 00%.svg
Parsing Mathematical Expressions 100 percents.svg
Writing a Basic Type Checker 00%.svg
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Haskell Basics

Getting set up

Print version


Haskell Basics

Getting set up
Variables and functions
Truth values
Type basics
Lists and tuples
Type basics II
Next steps
Building vocabulary
Simple input and output

This chapter describes how to install the programs you'll need to start coding in Haskell.

Installing Haskell

Haskell is a programming language, i.e. a language in which humans can express how computers should behave. It's like writing a cooking recipe: you write the recipe and the computer executes it.

To use Haskell programs, you need a special program called a Haskell compiler. A compiler takes code written in Haskell and translates it into machine code, a more primitive language that the computer understands. Using the cooking analogy, you write a recipe (your Haskell program) and a cook (a compiler program) does the work of putting together actual ingredients into an edible dish (an executable file). Of course, you can't easily get the recipe from a final dish (and you can't get the Haskell program code from executable after it's compiled).

To start learning Haskell, download and install the Haskell platform. It will contain the "Glasgow Haskell Compiler", or GHC, and everything else you need.

If you're just trying out Haskell, or are averse to downloading and installing the full compiler, you can try Hugs, the lightweight Haskell interpreter (it also happens to be portable). You might also like to play around with TryHaskell, an interpreter hosted online. Note that all instructions here will be for GHC.


UNIX users:

If you are a person who prefers to compile from source: This might be a bad idea with GHC, especially if it's the first time you install it. GHC is itself mostly written in Haskell, so trying to bootstrap it by hand from source is very tricky. Besides, the build takes a very long time and consumes a lot of disk space. If you are sure that you want to build GHC from the source, see Building and Porting GHC at the GHC homepage.

In short, we strongly recommend downloading the Haskell Platform instead of compiling from source.

Very first steps

After you have installed the Haskell Platform, it's now time to write your first Haskell code.

For that, you will use the program called GHCi (the 'i' stands for 'interactive'). Depending on your operating system, perform the following steps:

  • On Windows: Click Start, then Run, then type 'cmd' and hit Enter, then type ghci and hit Enter once more.
  • On MacOS: Open the application "Terminal" found in the "Applications/Utilities" folder, type the letters ghci into the window that appears and hit the Enter key.
  • On Linux: Open a terminal and run the ghci program.

You should get output that looks something like the following:

GHCi, version 7.6.3: http://www.haskell.org/ghc/  :? for help
Loading package ghc-prim ... linking ... done.
Loading package integer-gmp ... linking ... done.
Loading package base ... linking ... done.

The first bit is GHCi's version. It then informs you it's loading the base package, so you'll have access to most of the built-in functions and modules that come with GHC. Finally, the Prelude> bit is known as the prompt. This is where you enter commands, and GHCi will respond with their results.

Now you're ready to write your first Haskell code. In particular, let's try some basic arithmetic:

Prelude> 2 + 2
Prelude> 5 + 4 * 3
Prelude> 2 ^ 5

The operators are similar to what they are in other languages: + is addition, * is multiplication, and ^ is exponentiation (raising to the power of, or a ^ b). Note from the second example that Haskell follows standard order of operations.

Now you know how to use Haskell as a calculator. Actually, Haskell is always basically a calculator - a really powerful one, able to deal not only with numbers but also with other objects like characters, lists, functions, trees, and even other programs (if you aren't familiar with these terms yet, don't worry).

GHCi is a very powerful development environment. As we progress, we will learn how we can load files with source code into GHCi, and evaluate different parts of them.

If you're clear on everything so far (if not, use the talk page and help us improve this Wikibook!), then you are ready for next chapter, in which we will introduce some of the basic concepts of Haskell, along with our first Haskell functions.

Variables and functions

All the examples in this chapter can be typed into a Haskell source file and evaluated by loading that file into GHC or Hugs. Do not include the "Prelude>" prompts at the beginning of input. When the prompt is shown, you can type the code into an environment like GHCi. Otherwise, you should put the code in a file and run it.


We have seen how to use GHCi as a calculator. Of course, this is only practical for short calculations. For longer calculations and for writing Haskell programs, we want to keep track of intermediate results.

Intermediate results can be stored in variables, to which we refer by their name. A variable contains a value, which is substituted for the variable name when the variable is used. For instance, consider the following calculation

Prelude> 3.1416 * 5^2

That is the approximate area of a circle with radius 5, according to the formula A = \pi r^2. It is cumbersome to type in the digits of \pi \approx 3.1416, or even to remember them at all. In fact, an important motivation of programming is delegating mindless repetition and rote memorization to a machine so that our minds are free to deal with more interesting ideas. For the present case, Haskell already includes a variable named pi that stores over a dozen digits of \pi for us. This allows for not just clearer code, but also greater precision.

Prelude> pi
Prelude> pi * 5^2

Note that the variable pi and its value, 3.141592653589793, can be used interchangeably in calculations.

Haskell source files

Whenever we write code that will be used more than momentarily, we save it in a Haskell source file with the extension .hs. Basically, .hs files are plain text. If you need suggestions for text editors appropriate for coding, the Wikipedia article on text editors is a reasonable place to start. Vim and Emacs are popular choices among Haskell programmers. Proper source code editors will provide syntax highlighting, which colors the code in relevant ways to make reading and understanding easier.

To keep things tidy, create a directory (i.e. a folder) in your computer to save the Haskell files you will create while doing the exercises in this book. Call the directory something like HaskellWikibook. Then, create a new file in that directory called Varfun.hs with the following code:

r = 5.0

That code defines the variable r as being 5.0. Setting our own variables can make it easier to do future calculations.

Note: make sure that there are no spaces at the beginning of the line because Haskell is sensitive to whitespace.

Next, navigate to the HaskellWikibook directory in your terminal, start GHCi and load the Varfun.hs file using the :load command:

Prelude> :load Varfun.hs
[1 of 1] Compiling Main             ( Varfun.hs, interpreted )
Ok, modules loaded: Main.

:load can be abbreviated as :l (as in :l Varfun.hs).

If GHCi gives an error like Could not find module 'Varfun.hs', you probably are in the wrong directory. You can use the :cd command to change directories within GHCi (for instance, :cd HaskellWikibook).

With the file loaded, you can use the newly defined variable r in your calculations.

*Main> r
*Main> pi * r^2

So, to calculate the area of a circle of radius 5, we simply define r = 5.0 and then type in the well-known formula \pi r^2 for the area of a circle. There is no need to write the numbers out every time; that's very convenient!

Since this was so much fun, let's add another definition: Change the contents of the source file to

r = 5.0
area = pi * r ^ 2

Save the file and type the :reload (shorter version is :r) command in GHCi to load the new contents (note that this is a continuation of the last session):

*Main> :reload
Compiling Main             ( Varfun.hs, interpreted )
Ok, modules loaded: Main.

Now we have two variables r and area available

*Main> area
*Main> area / r


It is also possible to define variables directly at the GHCi prompt, without a source file. Skipping the details, the syntax for doing so uses the let keyword (a word with a special meaning) and looks like:

Prelude> let area = pi * 5 ^ 2

Although we will occasionally use let this way for expediency, it will become obvious that this practice is not convenient once we move into slightly more complex tasks. So, we are emphasizing the use of source files from the very start.


GHC can also be used as a compiler (that is, you can use GHC to convert your Haskell files into a standalone program that can be run without depending on the interpreter). How that is done will be explained later, in the Standalone programs chapter.


Before we continue, it is good to understand that it is possible to include text in a program without having it treated as code. This is achieved by use of comments. In Haskell, a comment can be started with -- and continues until the end of the line:

x = 5     -- The variable x is 5.
y = 6     -- The variable y is 6.
-- z = 7

In this case, x and y are defined, but z is not. Comments can also go anywhere using the alternative syntax {- ... -}:

x = {- Do this just because you can. -} 5

Comments are generally used for explaining parts of a program that might be, on their own, confusing to readers. Beware of overdoing it, though. Too many comments make programs harder to read. Also, comments must be carefully updated whenever the corresponding code is changed, so that they do not become outdated, incorrect and misleading.

Variables in imperative languages

If you are already familiar with imperative programming languages like C, you will notice that variables in Haskell are quite different from variables as you know them. We now explain why and how.

If you have no programming experience, you might like to skip this section and continue reading with Functions.

Unlike in imperative languages, variables in Haskell do not vary. Once defined, their value never changes; they are immutable. For instance, the following code does not work:

r = 5
r = 2

The variables in functional programming languages are more related to variables in mathematics than to changeable locations in a computer's memory. In a math classroom, you would definitely never see a variable change its value within a single problem. Likewise, in Haskell the compiler will respond to the code above with an error: "multiple declarations of r". Those familiar with imperative programming, which involves explicitly telling the computer what to do, may be used to read this as first setting r = 5 and then changing it to r = 2. In functional programming languages, however, the program is in charge of figuring out what to do with the computer's memory.

Here's another example of a major difference from imperative languages:

r = r + 1

Instead of "incrementing the variable r", this is actually a recursive definition of r in terms of itself (we will explain recursion in detail later on; just remember that there is a radical difference from what would happen in an imperative language). If r had been defined with any value beforehand, then r = r + 1 in Haskell would bring an error message. That is akin to saying, in a mathematical context, that 5 = 5 + 1, which is plainly wrong.

Because their values do not change within a program, variables can be defined in any order. For example, the following fragments of code do exactly the same thing:

 y = x * 2
 x = 3
 x = 3
 y = x * 2

We can write things in any order that we want, there is no notion of "x being declared before y" or the other way around. This is also why you can't declare something more than once; it would be ambiguous otherwise. Of course, using y will still require a value for x, but this is unimportant until you need a specific numeric value.

By now, you might be wondering how you can actually do anything at all in Haskell where variables don't change. But trust us; as we hope to show you in the rest of this book, you can write every program under the sun without ever changing a single variable! In fact, variables that don't change make life much easier because it makes programs much more predictable. That is a key feature of purely functional programming; it requires a very different approach and mindset from those of imperative programming.


Now, suppose that we have multiple circles with different radii whose areas we want to calculate. For instance, let us calculate the area of another circle with radius 3. If we were to build on the program we have just written, r would already have been defined as 5. We could change the entire program of so that r = 3, but then we would lose the ability to calculate the first circle. An alternative is defining a new variable r2, and also another new variable area2 for the area calculated with r2.[1] Our new source file for both circles is:

r  = 5
area  = pi * r ^ 2
r2 = 3
area2 = pi * r2 ^ 2

Clearly, that is unsatisfactory because we are repeating the formula for the area of a circle verbatim. To eliminate this mindless repetition, we would prefer to write it down only once and then apply it to different radii. That's exactly what functions allow us to do.

A function takes an argument value (or parameter) and gives a result value (this is essentially the same as mathematical functions). Defining functions in Haskell is simple: It is like defining a variable, except that we take note of the function argument that we put on the left hand side. For instance, the following is the definition of a function area which depends on an argument which we name r:

area r = pi * r ^ 2

Look closely at the syntax: the function name comes first (in our example, it is area), followed by a space and then the argument (r in the example). Following the = sign, the function definition is a formula that uses the argument in context with other already defined terms.

Now, we can plug in different values for the argument in a call to the function. Save the code in a file, load it into GHCi and try the following:

*Main> area 5
*Main> area 3
*Main> area 17

Thus, we can call this function with different radii to calculate the area of a circle with a radius of any length.

Our function here is defined mathematically as

A(r) = \pi \cdot r^2

In mathematics, the parameter is enclosed between parentheses, as in A(5) = 78.54 or A(3) = 28.27. The Haskell code will also work with parentheses, but they are normally omitted. As Haskell is a functional language, we will use functions all the time, and whenever possible we want to minimize extra symbols.

Parentheses are still used for grouping expressions (any code that gives a value) to be evaluated together. Note how the following expressions are parsed differently:

5 * 3 + 2       -- 15 + 2 = 17 (multiplication is done before addition)
5 * (3 + 2)     -- 5 * 5 = 25 (thanks to the parentheses)
area 5 * 3      -- (area 5) * 3
area (5 * 3)    -- area 15

Notice how Haskell functions take precedence over all other operators such as + and *, in the same way that, for instance, multiplication is done before addition in mathematics.


Let us try to understand what exactly happens when you enter an expression into GHCi. After you press the enter key, GHCi will evaluate the expression you have given. That means it will replace each function with its definition and calculate the results until a single value is left. For example, the evaluation of area 5 proceeds as follows:

   area 5
=>    { replace the left-hand side  area r = ...  by the right-hand side  ... = pi * r^2 }
   pi * 5 ^ 2
=>    { replace  pi  by its numerical value }
   3.141592653589793 * 5 ^ 2
=>    { apply exponentiation (^) }
   3.141592653589793 * 25
=>    { apply multiplication (*) }

As this shows, to apply or call a function means to replace the left-hand side of its definition by its right-hand side. As a last step, GHCi prints the final result on the screen.

Here are some more functions:

double x    = 2 * x
quadruple x = double (double x)
square x    = x * x
half   x    = x / 2
  • Explain how GHCi evaluates quadruple 5.
  • Define a function that subtracts 12 from half its argument.

Multiple parameters

Of course, functions can also have more than one argument. For example, here is a function for calculating the area of a rectangle given its length and its width:

areaRect l w = l * w
*Main> areaRect 5 10

Another example that calculates the area of a triangle \left(A = \frac{bh}{2}\right):

areaTriangle b h = (b * h) / 2
*Main> areaTriangle 3 9

As you can see, multiple arguments are separated by spaces. That's also why you sometimes have to use parentheses to group expressions. For instance, to quadruple a value x, you can't write

quadruple x = double double x

because that would mean to apply a function named double to the two arguments double and x: functions can be arguments to other functions (and you will see why later). Instead, you have to put parentheses around the argument:

quadruple x = double (double x)

Arguments are always passed in the order given. For example:

subtract x y = x - y
*Main> subtract 10 5
*Main> subtract 5 10

Here, subtract 10 5 evaluates to 10 - 5, but subtract 5 10 evaluates to 5 - 10 because the order changes.

  • Write a function to calculate the volume of a box.
  • Approximately how many stones are the famous pyramids at Giza made up of? Hint: you will need estimates for the volume of the pyramids and the volume of each block.

Remark on combining functions

It goes without saying that you can use functions that you have already defined to define new functions, just like you can use the predefined functions like addition (+) or multiplication (*) (operators are defined as functions in Haskell). For example, to calculate the area of a square, we can reuse our function that calculates the area of a rectangle:

areaRect l w = l * w
areaSquare s = areaRect s s
*Main> areaSquare 5

After all, a square is just a rectangle with equal sides.

This principle may seem innocent enough, but it is really powerful, in particular when we start to calculate with other objects instead of numbers.

  • Write a function to calculate the volume of a cylinder. The volume of a cylinder is the area of the base, which is a circle (you already programmed this function in this chapter, so reuse it) multiplied by the height.

Local definitions

where clauses

When defining a function, it is not uncommon to define intermediate results that are local to the function. For instance, consider Heron's formula A = \sqrt{s(s-a)(s-b)(s-c)} for calculating the area of a triangle with sides a, b, and c:

heron a b c = sqrt (s * (s - a) * (s - b) * (s - c))
    s = (a + b + c) / 2

The variable s is half the perimeter of the triangle and it would be tedious to write it out four times in the argument of the square root function sqrt.

Simply write the definitions in sequence will not work...

heron a b c = sqrt (s * (s - a) * (s - b) * (s - c))  -- s is not defined here
s = (a + b + c) / 2                                   -- a, b, and c are not defined here

... because the variables a, b, c are only available in the right-hand side of the function heron, but the definition of s as written here is not part of the right-hand side of heron. To make it part of the right-hand side, we have to use the where keyword.

Note that both the where and the local definitions are indented by 4 spaces, to distinguish them from subsequent definitions. Here is another example that shows a mix of local and top-level definitions:

areaTriangleTrig  a b c = c * height / 2   -- use trigonometry
    cosa   = (b ^ 2 + c ^ 2 - a ^ 2) / (2 * b * c)
    sina   = sqrt (1 - cosa ^ 2)
    height = b * sina
areaTriangleHeron a b c = result           -- use Heron's formula
    result = sqrt (s * (s - a) * (s - b) * (s - c))
    s      = (a + b + c) / 2


If you look closely at the previous example, you'll notice that we have used the variable names a, b, c twice, once for each of the two area functions. How does that work?

Fortunately, the following fragment of code does not contain any unpleasant surprises:

Prelude> let r = 0
Prelude> let area r = pi * r ^ 2
Prelude> area 5

An "unpleasant surprise" here would have been getting 0 for the area because of the let r = 0 definition getting in the way. That does not happen because when you defined r the second time you are talking about a different r. This is something that happens in real life as well. How many people do you know that have the name John? What's interesting about people named John is that most of the time, you can talk about "John" to your friends, and depending on the context, your friends will know which John you are referring to. Programming has a notion similar to context, called scope.

We will not explain the technicalities behind scope (at least not now). Just keep in mind that the value of a parameter is strictly what you pass in when you call the function, regardless of what the variable was called in the function's definition.


  1. Variables store values. In fact, they store any arbitrary Haskell expressions.
  2. Variables do not change.
  3. Functions help you write reusable code.
  4. Functions can accept more than one parameter.

We also learned that comments are non-code text within a source file.


  1. As this example shows, the names of variables may contain numbers as well as letters. Variables must begin with a lowercase letter, but for the rest, any string consisting of letter, numbers, underscore (_) or tick (') is allowed.

Truth values

Equality and other comparisons

In the last chapter, we saw how to use the equals sign to define variables and functions in Haskell. The following code

r = 5

causes occurrences of r to be replaced by 5 in all places where it makes sense to do so according to the scope of the definition. Similarly,

f x = x + 3

causes occurrences of f followed by a number (which is taken as f's argument) to be replaced by that number plus three.

In mathematics, however, the equals sign is also used in a subtly different and equally important way. For instance, consider this simple problem:

Example: Solve the following equation:


When we look at a problem like this one, our immediate concern is not the ability to represent the value 5 as x+3, or vice-versa. Instead, we read the x+3=5 equation as a proposition, which says that some number x gives 5 as result when added to 3. Solving the equation means finding which, if any, values of x make that proposition true. In this example, we can use elementary algebra to determine that x=2 (i.e. 2 is the number we need to substitute to make the equation true, giving 2+3=5.

Comparing values to see if they are equal is also useful in programming. Haskell allows us to write such tests in a natural way that looks just like an equation. Since the equals sign is already used for defining things, we use a double equals sign, == instead. To see that at work, you can start GHCi and enter the proposition we wrote above like this:

Prelude> 2 + 3 == 5

GHCi returns "True" because 2 + 3 is equal to 5. What if we use an equation that is not true?

Prelude> 7 + 3 == 5

Nice and coherent. The next step is to use our own functions in these tests. Let us try it with the function f we mentioned at the start of the module:

Prelude> let f x = x + 3
Prelude> f 2 == 5

Just as expected, since f 2 is just 2 + 3.

In addition to tests for equality, we can just as easily compare two numerical values to see which one is larger. Haskell provides a number of tests including: < (less than), > (greater than), <= (less than or equal to) and >= (greater than or equal to), which work just like == (equal to). For a simple application, we could use < alongside the area function from the previous module to see whether a circle of a certain radius would have an area smaller than some value.

Prelude> let area r = pi * r ^ 2
Prelude> area 5 < 50

Boolean values

Now we know that GHCi can tell us whether some arithmetical propositions are true or false. That's all fine and dandy, but how could that help us to write programs? And what is actually going on when GHCi "answers" such "questions"?

To understand what is happening, consider a different but related issue. If we enter an arithmetical expression in GHCi the expression gets evaluated, and the resulting numerical value is displayed on the screen:

Prelude> 2 + 2

If we replace the arithmetical expression with an equality comparison, something similar seems to happen:

Prelude> 2 == 2

But what is that "True" that gets displayed? It certainly does not look like a number. We can think of it as something that tells us about the veracity of the proposition 2 == 2. From that point of view, it makes sense to regard it as a value – except that instead of representing some kind of count, quantity, etc. it stands for the truth of a proposition. Such values are called truth values, or boolean values[1]. Naturally, there are only two possible boolean values – True and False.

An introduction to types

When we say True and False are values, we are not just making an analogy. Boolean values have the same status as numerical values in Haskell, and indeed you can manipulate them just as well. One trivial example would be equality tests on truth values:

Prelude> True == True
Prelude> True == False

True is indeed equal to True, and True is not equal to False. Now, quickly: can you answer whether 2 is equal to True?

Prelude> 2 == True

    No instance for (Num Bool)
      arising from the literal `2' at <interactive>:1:0
    Possible fix: add an instance declaration for (Num Bool)
    In the first argument of `(==)', namely `2'
    In the expression: 2 == True
    In the definition of `it': it = 2 == True

The correct answer is you can't, because the question just does not make sense. It is impossible to compare a number with something that is not a number, or a boolean with something that is not a boolean. Haskell incorporates that notion, and the ugly error message we got is, in essence, stating exactly that. Ignoring all of the obfuscating clutter (which we will get to understand eventually), that message says that there was a number (Num) on the left side of the ==, and so some kind of number was expected on the right side; however, a boolean value (Bool) is not a number, and so the equality test exploded into flames.

Thus, the general concept is that values have types, and these types define what we can or cannot do with the values. In this case, for instance, True is a value of type Bool, as is False (as for the 2, while there is a well-defined concept of number in Haskell the situation is slightly more complicated, so we will defer the explanation for a little while). Types are a very powerful tool because they provide a way to regulate the behaviour of values with rules which make sense, making it easier to write programs that work correctly. We will come back to the topic of types many times as they are very important to Haskell.

Infix operators

What we have seen so far leads us to the conclusion that an equality test like 2 == 2 is an expression just like 2 + 2, and that it also evaluates to a value in pretty much the same way. That fact is actually given a passing mention on the ugly error message we got on the previous example:

In the expression: 2 == True

Therefore, when we type 2 == 2 in the prompt and GHCi "answers" True it is just evaluating an expression. But there is a deeper truth involved in this process. A hint is provided by the very same error message:

In the first argument of `(==)', namely `2'

GHCi called 2 the first argument of (==). In the previous module, we used argument to describe the values we feed a function with so that it evaluates to a result. It turns out that == is just a function, which takes two arguments, namely the left side and the right side of the equality test. The only special thing about it is the syntax: Haskell allows two-argument functions with names composed only of non-alphanumeric characters to be used as infix operators, that is, placed between their arguments. The only caveat is that if you wish to use such a function in the "standard" way (writing the function name before the arguments, as a prefix operator) the function name must be enclosed in parentheses. So the following expressions are completely equivalent:

Prelude> 4 + 9 == 13
Prelude> (==) (4 + 9) 13

This makes it clear how (==) is a function with two arguments just like areaRect from the previous module. What's more, the same considerations apply to the other relational operators we mentioned (<, >, <=, >=) and to the arithmetical operators (+, *, etc.) – all of them are just functions. This generality is an illustration of one of the strengths of Haskell: it is a language with very few "special cases", which helps to keep things simple. In general, we can say that all tangible things in Haskell are either values or functions.[2]

Boolean operations

To see both truth values and infix operators in action, let's consider the boolean operations which manipulate truth values as in logic propositions. Haskell provides us three basic functions for that purpose:

  • (&&) performs the and operation. Given two boolean values, it evaluates to True if both the first and the second are True, and to False otherwise.
Prelude> (3 < 8) && (False == False)
Prelude> (&&) (6 <= 5) (1 == 1) 
  • (||) performs the or operation. Given two boolean values, it evaluates to True if either the first or the second are True (or if both are true), and to False otherwise.
Prelude> (2 + 2 == 5) || (2 > 0)
Prelude> (||) (18 == 17) (9 >= 11)
  • not performs the negation of a boolean value; that is, it converts True to False and vice-versa.
Prelude> not (5 * 2 == 10)

Another relational operator is not equal to. It is already provided by Haskell as the (/=) function, but if we were to implement it ourselves, a natural way would be:

x /= y = not (x == y)

Note that it is perfectly legal syntax to write operators infix, even when defining them. Completely new operators can also be created out of ASCII symbols (which means mostly the common symbols used on a keyboard).


Now we have explored what is really happening with boolean operators, but we've done little more than testing one-line expressions here. We still don't know how this can be used to make real programs. We will tackle this issue by introducing guards, a feature that relies on boolean values and allows us to write more interesting functions.

Let us implement the absolute value function. The absolute value of a number is the number with its sign discarded[3]; so if the number is negative (that is, smaller than zero) the sign is inverted; otherwise it remains unchanged. We could write the definition as:

|x| = \begin{cases} x, & \mbox{if }  x \ge 0  \\ -x,  & \mbox{if } x < 0. \end{cases}

Here, the actual expression to be used for calculating |x| depends on a set of propositions made about x. If x \ge 0 is true, then we use the first expression, but if x < 0 is the case, then we use the second expression instead. We need a way to express this decision process in Haskell. Using guards, the implementation could look like this:[4]

Example: The abs function.

abs x
    | x < 0     = 0 - x
    | otherwise = x

Remarkably, the above code is about as readable as the corresponding mathematical definition. Let us dissect the components of the definition:

  • We start just like a normal function definition, providing a name for the function, abs, and saying it will take a single parameter, which we will name x.
  • Instead of just following with the = and the right-hand side of the definition, we entered a line break, and, following it, the two alternatives, placed in separate lines.[5] These alternatives are the guards proper. Note that the whitespace is not just for aesthetic reasons; it is necessary for the code to be parsed correctly.
  • Each of the guards begins with a pipe character, |. After the pipe, we put an expression which evaluates to a boolean (also called a boolean condition or a predicate), which is followed by the rest of the definition – the equals sign and the right-hand side which should be used if the predicate evaluates to True.
  • The otherwise case is used when none of the preceding predicates evaluate to True. In this case, if x is not smaller than zero, it must be greater than or equal to zero, so the final predicate could have just as easily been x >= 0; but otherwise works just as well.


There is no syntactical magic behind otherwise. It is defined alongside the default variables and functions of Haskell as simply

otherwise = True

This definition makes otherwise a catch-all guard. As evaluation of the guard predicates is sequential, the otherwise predicate will only be reached if none of the other ones evaluates to True (so make sure you always place otherwise as the last guard!). In general, it is a good idea to always provide an otherwise guard, because a rather ugly runtime error will be produced if none of the predicates is true for some input.


You might be wondering why we wrote 0 - x and not simply -x to denote the sign inversion. Truth is, we could have written the first guard as

    | x < 0    = -x

and it would have worked just as well. The only issue is that this way of expressing sign inversion is actually one of the few "special cases" in Haskell, in that in this case the - is not a function that takes one argument and evaluates to 0 - x, but just a syntactical abbreviation. While very handy, this shortcut occasionally conflicts with the usage of (-) as an actual function (the subtraction operator), which is a potential source of annoyance (for example, try writing three minus negative-four without using any parentheses for grouping). Here, we wrote 0 - x explicitly so that we could take the opportunity to point out this issue.

where and Guards

where clauses are particularly handy when used with guards. For instance, consider a function which computes the number of (real) solutions for a quadratic equation, ax^2 + bx + c = 0:

numOfRealSolutions a b c
    | disc > 0  = 2
    | disc == 0 = 1
    | otherwise = 0
        disc = b^2 - 4*a*c

The where definition is within the scope of all of the guards, sparing us from repeating the expression for disc.


  1. The term is a tribute to the mathematician and philosopher George Boole.
  2. In case you found this statement bold, know that we will go even further in due course.
  3. Technically, that just covers how to get the absolute value of a real number, but let's ignore this detail for now.
  4. abs is also provided by Haskell, so in a real-world situation you don't need to worry about providing an implementation yourself.
  5. We could have joined the lines and written everything in a single line, but in this case it would be a lot less readable.

Type basics

In programming, Types are used to group similar values into categories. In Haskell, the type system is a powerful way of reducing the number of mistakes in your code.


Programming deals with different sorts of entities. For example, consider adding two numbers together:

2 + 3

What are 2 and 3? Well, they are numbers. What about the plus sign in the middle? That's certainly not a number, but it stands for an operation which we can do with two numbers – namely, addition.

Similarly, consider a program that asks you for your name and then greets you with a "Hello" message. Neither your name nor the word Hello are numbers. What are they then? We might refer to all words and sentences and so forth as text. It's normal in programming to use a slightly more esoteric word: String, which is short for "string of characters".

In Haskell, the rule is that all type names have to begin with a capital letter. We shall adhere to this convention henceforth.

Databases illustrate clearly the concept of types. For example, say we had a table in a database to store details about a person's contacts; a kind of personal telephone book. The contents might look like this:

First Name Last Name Telephone number Address
Sherlock Holmes 743756 221B Baker Street London
Bob Jones 655523 99 Long Road Street Villestown

The fields in each entry contain values. Sherlock is a value as is 99 Long Road Street Villestown as well as 655523. As we've said, types are a way of categorizing data, so let's classify the values in this example. The first three fields seem straightforward enough. "First Name" and "Last Name" contain text, so we say that the values are of type String, while "Telephone Number" is clearly a number.

At first glance one may be tempted to classify address as a String. However, the semantics behind an innocent address are quite complex. There are a whole lot of human conventions that dictate how we interpret it. For example, if the beginning of the address text contains a number it is likely the number of the house. If not, then it's probably the name of the house – except if it starts with "PO Box", in which case it's just a postal box address and doesn't indicate where the person lives at all.

Clearly, there's more going on here than just text, as each part of the address has its own meaning. In principle, there is nothing wrong with saying addresses are Strings, but that doesn't capture many important features of addresses. When we describe something as a String, all that we are saying is that it is a sequence of letters, numbers, etc. Recognizing something as a specialized type is far more meaningful. If we know something is an Address, we instantly know much more about the piece of data – for instance, that we can interpret it using the "human conventions" that give meaning to addresses.

In retrospect, we might also apply this rationale to the telephone numbers. It could be a good idea to speak in terms of a TelephoneNumber type. Then, if we were to come across some arbitrary sequence of digits which happened to be of type TelephoneNumber we would have access to a lot more information than if it were just a Number – for instance, we could start looking for things such as area and country codes on the initial digits.

Another reason not to consider the telephone numbers as just Numbers is that doing arithmetics with them makes no sense. What is the meaning and expected effect of, say, multiplying a TelephoneNumber by 100? It would not allow calling anyone by phone. That's a good reason for using a more specialized type than Number. Also, each digit comprising a telephone number is important; it's not acceptable to lose some of them by rounding it or even by omitting leading zeroes.

Why types are useful

So far, it seems that all what we've done was to describe and categorize things, and it may not be obvious why all of this talk would be so important for writing actual programs. Starting with this module, we will explore how Haskell uses types to the programmer's benefit, allowing us to incorporate the semantics behind, say, an address or a telephone number seamlessly in the code.

Using the interactive :type command

The best way to explore how types work in Haskell is from GHCi. The type of any expression can be checked with the immensely useful :type (or shortened to :t) command. Let us test it on the boolean values from the previous module:

Example: Exploring the types of boolean values in GHCi

Prelude> :type True
True :: Bool
Prelude> :type False
False :: Bool
Prelude> :t (3 < 5)
(3 < 5) :: Bool

Usage of :type is straightforward: enter the command into the prompt followed by whatever you want to find the type of. On the third example, we use :t, which we will be using from now on. GHCi will then print the type of the expression. The symbol ::, which will appear in a couple other places, can be read as simply "is of type", and indicates a type signature.

:type reveals that truth values in Haskell are of type Bool, as illustrated above for the two possible values, True and False, as well as for a sample expression that will evaluate to one of them. It is worthy to note at this point that boolean values are not just for value comparisons. Bool captures in a very simple way the semantics of a yes/no answer, and so it can be useful to represent any information of such kind – say, whether a name was found in a spreadsheet, or whether a user has toggled an on/off option.

Characters and strings

Now let's try :t on something new. Literal characters are entered by enclosing them with single quotation marks. For instance, this is the single letter H:

Example: Using the :type command in GHCi on a literal character

Prelude> :t 'H'
'H' :: Char

So, literal character values have type Char (short for "character"). Single quotation marks, however, only work for individual characters. If we need to enter actual text – that is, a string of characters – we use double quotation marks instead:

Example: Using the :t command in GHCi on a literal string

Prelude> :t "Hello World"
"Hello World" :: [Char]

Why did we get Char again? The difference is in the square brackets. [Char] means a number of characters chained together, forming a list. That is what text strings are in Haskell – lists of characters.[1]

  1. Try using :type on the literal value "H" (notice the double quotes). What happens? Why?
  2. Try using :type on the literal value 'Hello World' (notice the single quotes). What happens? Why?

Incidentally, Haskell allows for type synonyms, which work pretty much like synonyms in human languages (words that mean the same thing – say, 'big' and 'large'). In Haskell, type synonyms are alternative names for types. For instance, String is defined as a synonym of [Char], and so we can freely substitute one with the other. Therefore, to say:

"Hello World" :: String

is also perfectly valid, and in many cases a lot more readable. From here on we'll mostly refer to text values as String, rather than [Char].

Functional types

So far, we have seen how values (strings, booleans, characters, etc.) have types and how these types help us to categorize and describe them. Now, the big twist that makes Haskell's type system truly powerful: Functions have types as well.[2] Let's look at some examples to see how that works.

Example: not

We can negate boolean values with not (e.g. not True evaluates to False and vice-versa). To figure out the type of a function, we consider two things: the type of values it takes as its input and the type of value it returns. In this example, things are easy. not takes a Bool (the Bool to be negated), and returns a Bool (the negated Bool). The notation for writing that down is:

Example: Type signature for not

not :: Bool -> Bool

You can read this as "not is a function from things of type Bool to things of type Bool".

Using :t on a function will work just as expected:

Prelude> :t not
not :: Bool -> Bool

The description of a function's type is in terms of the types of argument(s) it takes and gives.

Example: chr and ord

Text presents a problem to computers. Once everything is reduced to its lowest level, all a computer knows how to deal with are 1s and 0s: computers work in binary. As working with binary numbers isn't at all convenient, humans have come up with ways of making computers store text. Every character is first converted to a number, then that number is converted to binary and stored. That's how a piece of text (which is just a sequence of characters) is encoded into binary. Normally, we're only interested in how to encode characters into their numerical representations, because the computer generally takes care of the conversion to binary numbers without our intervention.

The easiest way of converting characters to numbers is simply to write all the possible characters down, then number them. For example, we might decide that 'a' corresponds to 1, then 'b' to 2, and so on. This is exactly what something called the ASCII standard is: take 128 commonly-used characters and number them. Of course, it would be a bore to sit down and look up a character in a big lookup table every time we wanted to encode it, so we've got two functions that do it for us, chr (pronounced 'char') and ord[3]:

Example: Type signatures for chr and ord

chr :: Int  -> Char
ord :: Char -> Int

We already know what Char means. The new type on the signatures above, Int, amounts to integer numbers, and is one of quite a few different types of numbers.[4] The type signature of chr tells us that it takes an argument of type Int, an integer number, and evaluates to a result of type Char. The converse is the case with ord: It takes things of type Char and returns things of type Int. With the info from the type signatures, it becomes immediately clear which of the functions encodes a character into a numeric code (ord) and which does the decoding back to a character (chr).

To make things more concrete, here are a few examples of function calls to chr and ord. Notice that the two functions aren't available by default; so before trying them in GHCi you need to use the :module Data.Char (or :m Data.Char) command to load the Data.Char module, where they are defined.

Example: Function calls to chr and ord

Prelude> :m Data.Char
Prelude Data.Char> chr 97
Prelude Data.Char> chr 98
Prelude Data.Char> ord 'c'

Functions with more than one argument

The style of type signatures we have been using works well enough for functions of one argument, but what would be the type of a function like this one?

Example: A function with more than one argument

xor p q = (p || q) && not (p && q)

(xor is the exclusive-or function, which evaluates to True if either one or the other argument is True, but not both; and False otherwise.)

The general technique for forming the type of a function that accepts more than one argument is simply to write down all the types of the arguments in a row, in order (so in this case p first then q), then link them all with ->. Finally, add the type of the result to the end of the row and stick a final -> in just before it.[5] In this example, we have:

  1. Write down the types of the arguments. In this case, the use of (||) and (&&) gives away that p and q have to be of type Bool:
    Bool                   Bool
    ^^ p is a Bool         ^^ q is a Bool as well
  2. Fill in the gaps with ->:
    Bool -> Bool
  3. Add in the result type and a final ->. In our case, we're just doing some basic boolean operations so the result remains a Bool.
    Bool -> Bool -> Bool
                     ^^ We're returning a Bool
                 ^^ This is the extra -> that got added in 

The final signature, then, is:

Example: The signature of xor

xor :: Bool -> Bool -> Bool

Real world example: openWindow

A library is a collection of common code used by many programs.

As you'll learn in the Haskell in Practice section of the course, one popular group of Haskell libraries are the GUI (Graphical User Interface) ones. These provide functions for dealing with the visual things computer users are familiar with: menus and buttons, application windows, moving the mouse around, etc. One of the functions from one of these libraries is called openWindow, and you can use it to open a new window in your application. For example, say you're writing a word processor, and the user has clicked on the 'Options' button. You need to open a new window which contains all the options that they can change. Let's look at the type signature for this function[6]:

Example: openWindow

openWindow :: WindowTitle -> WindowSize -> Window

Don't panic! Here are a few more types you haven't come across yet. But don't worry, they're quite simple. All three of the types there, WindowTitle, WindowSize and Window are defined by the GUI library that provides openWindow. As we saw when constructing the types above, because there are two arrows, the first two types are the types of the parameters, and the last is the type of the result. WindowTitle holds the title of the window (which typically appears in a title bar at the very top of the window), and WindowSize specifies how big the window should be. The function then returns a value of type Window which represents the actual window.

One key point illustrated by this example, as well as the chr/ord one is that, even if you have never seen the function or don't know how it actually works, a type signature can immediately give you a good general idea of what the function is supposed to do. For that reason, a very useful habit to acquire is testing every new function you meet with :t. If you start doing that now, you'll not only learn about the standard library Haskell functions quite a bit quicker but also develop a useful kind of intuition. Curiosity pays off. :)


Finding types for functions is a basic Haskell skill worth mastering. What are the types of the following functions?

  1. The negate function, which takes an Int and returns that Int with its sign swapped. For example, negate 4 = -4, and negate (-2) = 2
  2. The (||) function, pronounced 'or', that takes two Bools and returns a third Bool which is True if either of the arguments were, and False otherwise.
  3. A monthLength function which takes a Bool which is True if we are considering a leap year and False otherwise, and an Int which is the number of a month; and returns another Int which is the number of days in that month.

For any functions hereafter involving numbers, you can just pretend the numbers are Ints.

  1. f x y = not x && y
  2. g x = (2*x - 1)^2

Type signatures in code

We have explored the basic theory behind types and how they apply to Haskell. Now, we will see how type signatures are used for annotating functions in source files. Let's see what that looks like for xor function from an earlier example:

Example: A function with its signature

xor :: Bool -> Bool -> Bool
xor p q = (p || q) && not (p && q)

That is all we have to do, really. Signatures are placed just before the corresponding functions, for maximum clarity.

The signatures we add in this way serve a dual role. They inform the type of the functions both to human readers of the code and to the compiler/interpreter.

Type inference

We just said that type signatures tell the interpreter (or compiler) what the function type is. Yet we have been writing perfectly good Haskell code without any signatures so far, and it was accepted by GHC/GHCi. Indeed, it is not mandatory to add type signatures. That doesn't mean, however, that when they are missing Haskell simply forgets about types altogether! Instead, when you don't tell Haskell the types of your functions and variables it figures them out through a process called type inference. In essence, the compiler performs inference by starting with the types of things it knows and then working out the types of the rest of the values. Let's see how that works with a general example.

Example: Simple type inference

-- We're deliberately not providing a type signature for this function
isL c = c == 'l'

isL is a function that takes an argument c and returns the result of evaluating c == 'l'. Without a type signature, the type of c and the type of the result are not specified. In the expression c == 'l', however, the compiler knows that 'l' is a Char. Since c and 'l' are being compared with equality with (==) and both arguments of (==) must have the same type,[7] it follows that c must be a Char. Finally, since isL c is the result of (==) it must be a Bool. And thus we have a signature for the function:

Example: isL with a type

isL :: Char -> Bool
isL c = c == 'l'

Indeed, if you leave out the type signature, the Haskell compiler will discover it through this process. You can verify that by using :t on isL with or without a signature.

So, if type signatures are optional in most cases,[8] why should we care so much about them? Here are a few reasons:

  • Documentation: type signatures make your code easier to read. With most functions, the name of the function along with the type of the function is sufficient to guess what the function does. Of course, you should always comment your code properly too, but having the types clearly stated helps a lot, too.
  • Debugging: when you annotate a function with a type signature and then make a typo in the body of the function which changes the type of a variable, the compiler will tell you, at compile-time, that your function is wrong. Leaving off the type signature might allow your erroneous function to compile, and the compiler would assign it the wrong type. You wouldn't know until you ran your program that you made this mistake.

Types and readability

To understand better how signatures can help documentation, here is a somewhat more realistic example. The piece of code quoted below is a tiny module (modules are the typical way of preparing a library), and this way of organizing code is not too different from what you might find when reading source code for the libraries bundled with GHC.


Do not go crazy trying to understand how the functions here actually work; that is beside the point as we still have not covered many of the features being used. Just keep reading and play along.

Example: Module with type signatures

module StringManip where
import Data.Char
uppercase, lowercase :: String -> String
uppercase = map toUpper
lowercase = map toLower
capitalize :: String -> String
capitalize x =
  let capWord []     = []
      capWord (x:xs) = toUpper x : xs
  in unwords (map capWord (words x))

This tiny library provides three string manipulation functions. uppercase converts a string to upper case, lowercase to lower case, and capitalize capitalizes the first letter of every word. Each of these functions takes a String as argument and evaluates to another String. What is relevant to our discussion here is that, even if we do not understand how these functions work, looking at the type signatures allows us to immediately know the types of the arguments and return values. That information, when paired with sensible function names, can make it a lot easier to figure out how we can use the functions.

Note that when functions have the same type we have the option of writing just one signature for all of them, by separating their names with commas, as it was done with uppercase and lowercase.

Types prevent errors

The role of types in preventing errors is central to typed languages. When passing expressions around you have to make sure the types match up like they did here. If they don't, you'll get type errors when you try to compile; your program won't pass the typecheck. That is how types help you to keep your programs bug-free. To take a very trivial example:

Example: A non-typechecking program

"hello" + " world"

Having that line as part of your program will make it fail to compile, because you can't add two strings together! In all likelihood the intention was to use the similar-looking string concatenation operator, which joins two strings together into a single one:

Example: Our erroneous program, fixed

"hello" ++ " world"

An easy typo to make, but because you use Haskell, it was caught when you tried to compile. You didn't have to wait until you ran the program for the bug to become apparent.

That was only a simple example, but the idea of types forming a system to catch mistakes works on a much larger scale too. In general, when you make a change to your program, you'll change the type of one of the elements. If this change isn't something that you intended, or has unforeseen consequences, then it will show up immediately. A lot of Haskell programmers remark that once they have fixed all the type errors in their programs, and their programs compile, that they tend to "just work": run the first time with only minor problems. Run-time errors, where your program goes wrong when you run it rather than when you compile it, are much rarer in Haskell than in other languages. This is a huge advantage of having a strong type system like Haskell does.


  1. Lists, be they of characters or of other things, are very important entities in Haskell, and we will cover them in more detail in a little while.
  2. The deeper truth is that functions are values, just like all the others.
  3. This isn't quite what chr and ord do, but that description fits our purposes well, and it's close enough.
  4. In fact, it is not even the only type for integers! We will meet its relatives in a short while.
  5. This method might seem just a trivial hack by now, but actually there are very deep reasons behind it, which we'll cover in the chapter on Currying.
  6. This has been somewhat simplified to fit our purposes. Don't worry, the essence of the function is there.
  7. As we discussed in "Truth values". That fact is actually stated by the type signature of (==) – if you are curious you can check it, although you will have to wait a little bit more for a full explanation of the notation used in it.
  8. There are a few situations in which the compiler lacks information to infer the type, and so the signature becomes obligatory; and, in some other cases, we can influence to a certain extent the final type of a function or value with a signature. That needn't concern us for the moment, however.

Lists and tuples

Lists and tuples are the two fundamental ways of manipulating several values. They both work by grouping multiple values into a single combined value.


Functions are one of the two major building blocks of any Haskell program. The other is the list. So let's switch over to the interpreter and build lists:

Prelude> let numbers = [1,2,3,4]
Prelude> let truths  = [True, False, False]
Prelude> let strings = ["here", "are", "some", "strings"]

The square brackets delimit the list, and individual elements are separated by commas. The only important restriction is that all elements in a list must be of the same type. Trying to define a list with mixed-type elements results in a typical type error:

Prelude> let mixed = [True, "bonjour"]

    Couldn't match `Bool' against `[Char]'
      Expected type: Bool
      Inferred type: [Char]
    In the list element: "bonjour"
    In the definition of `mixed': mixed = [True, "bonjour"]

Building lists

In addition to specifying the whole list at once using square brackets and commas, you can build them up piece by piece using the (:) operator. This process is often referred to as consing[1].

Example: Consing something on to a list

Prelude> let numbers = [1,2,3,4]
Prelude> numbers
Prelude> 0:numbers

When you cons something on to a list (something:someList), what you get back is another list. Thus, you can keep on consing for as long as you wish. Note that the cons operator evaluates from right to left. Another (more general) way to think of it is that it takes the first value to its left and the whole expression to its right.

Example: Consing lots of things to a list

Prelude> 1:0:numbers
Prelude> 2:1:0:numbers
Prelude> 5:4:3:2:1:0:numbers

In fact, this is how lists are actually built, by consing all elements to the empty list, []. The commas-and-brackets notation is just syntactic sugar (which means a more pleasant and/or readable way to write code). So [1,2,3,4,5] is exactly equivalent to 1:2:3:4:5:[]

You will, however, want to watch out for a potential pitfall in list construction. Whereas something like True:False:[] is perfectly good Haskell, True:False is not:

Example: Whoops!

Prelude> True:False

    Couldn't match `[Bool]' against `Bool'
      Expected type: [Bool]
      Inferred type: Bool
    In the second argument of `(:)', namely `False'
    In the definition of `it': it = True : False

True:False produces a familiar-looking type error message, which tells us that the cons operator (:) (which is really just a function) expected a list as its second argument but we gave it another Bool instead. (:) only knows how to stick things onto lists.[2]

So, when using cons, remember:

  • The elements of the list must have the same type.
  • You can only cons (:) something onto a list, not the other way around (you cannot cons a list onto an element). So, the final item on the right must be a list, and the items on the left must be independent elements, not lists.
  1. Would the following piece of Haskell work: 3:[True,False]? Why or why not?
  2. Write a function cons8 that takes a list and conses 8 (at the beginning) on to it. Test it out on the following lists by doing:
    1. cons8 []
    2. cons8 [1,2,3]
    3. cons8 [True,False]
    4. let foo = cons8 [1,2,3]
    5. cons8 foo
  3. Adapt the above function in a way that 8 is at the end of the list
  4. Write a function that takes two arguments, a list and a thing, and conses the thing onto the list. You should start out with:
     let myCons list thing =

Strings are just lists

As we briefly mentioned in the Type Basics module, strings in Haskell are just lists of characters. That means values of type String can be manipulated just like any other list. For instance, instead of entering strings directly as a sequence of characters enclosed in double quotation marks, they may also be constructed through a sequence of Char values, either linked with (:) and terminated by an empty list or using the commas-and-brackets notation.

Prelude>"hey" == ['h','e','y']
Prelude>"hey" == 'h':'e':'y':[]

Using double-quoted strings is just more syntactic sugar.

Lists within lists

Lists can contain anything, just as long as they are all of the same type. Because lists are things too, lists can contain other lists! Try the following in the interpreter:

Example: Lists can contain lists

Prelude> let listOfLists = [[1,2],[3,4],[5,6]]
Prelude> listOfLists

Lists of lists can be pretty tricky sometimes, because a list of things does not have the same type as a thing all by itself; the type Int, for example, is different from [Int]. Let's sort through these implications with a few exercises:

  1. Which of these are valid Haskell and which are not? Rewrite in cons notation.
    1. [1,2,3,[]]
    2. [1,[2,3],4]
    3. [[1,2,3],[]]
  2. Which of these are valid Haskell, and which are not? Rewrite in comma and bracket notation.
    1. []:[[1,2,3],[4,5,6]]
    2. []:[]
    3. []:[]:[]
    4. [1]:[]:[]
    5. ["hi"]:[1]:[]
  3. Can Haskell have lists of lists of lists? Why or why not?
  4. Why is the following list invalid in Haskell?
    1. [[1,2],3,[4,5]]

Lists of lists can be useful because they allow one to express some kinds of complicated, structured data (two-dimensional matrices, for example). They are also one of the places where the Haskell type system truly shines. Human programmers, or at least this wikibook co-author, get confused all the time when working with lists of lists, and having restrictions of types often helps in wading through the potential mess.


A different notion of many

Tuples are another way of storing multiple values in a single value. There are two key differences between tuples and lists:

  • Tuples have a fixed number of elements (immutable); you can't cons to a tuple. Therefore, it makes sense to use tuples when you know in advance how many values are to be stored. For example, we might want a type for storing 2D coordinates of a point. We know exactly how many values we need for each point (two – the x and y coordinates), so tuples are applicable.
  • The elements of a tuple do not need to be all of the same type. For instance, in a phonebook application we might want to handle the entries by crunching three values into one: the name, phone number, and the address of each person. In such a case, the three values won't have the same type, so lists wouldn't help, but tuples would.

Tuples are created within parentheses with elements delimited by commas. Let's look at some sample tuples:

Example: Some tuples

(True, 1)
("Hello world", False)
(4, 5, "Six", True, 'b')

The first example is a tuple containing two elements: the first one is True, and the second is 1. The next example again has two elements: the first is "Hello world", and the second is False. The third example is a tuple consisting of five elements: the first is 4 (a number), the second is 5 (another number), the third is "Six" (a string), the fourth is True (a boolean value), and the fifth is 'b' (a character).

A quick note on nomenclature: In general you use n-tuple to denote a tuple of size n. 2-tuples (that is, tuples with 2 elements) are normally called pairs and 3-tuples triples. Tuples of greater sizes aren't actually all that common, but if you were to logically extend the naming system, you'd have quadruples, quintuples and so on, hence the general term tuple.

  1. Write down the 3-tuple whose first element is 4, second element is "hello" and third element is True.
  2. Which of the following are valid tuples?
    1. (4, 4)
    2. (4, "hello")
    3. (True, "Blah", "foo")
    4. ()
  3. Lists can be built by consing new elements onto them: you cons a number onto a list of numbers, and get back a list of numbers. It turns out that there is no such way to build up tuples.
    1. Why do you think that is?
    2. Say for the sake of argument, that there was such a function. What would you get if you "consed" something on a tuple?

Tuples are also handy when you want to return more than one value from a function. In many languages, returning two or more things at once often requires wrapping them up in a single-purpose data structure, maybe one that only gets used in that function. In Haskell, you have the very convenient alternative of just returning them as a tuple.

Tuples within tuples (and other combinations)

We can apply the same reasoning to tuples about storing lists within lists. Tuples are things too, so you can store tuples with tuples (within tuples up to any arbitrary level of complexity). Likewise, you could also have lists of tuples, tuples of lists, and all sorts of other combinations along the same lines.

Example: Nesting tuples and lists

((2,3), True)
((2,3), [2,3])
[(1,2), (3,4), (5,6)]

There is one bit of trickiness to watch out for, however. The type of a tuple is defined not only by its size, but by the types of objects it contains. For example, the tuples ("Hello",32) and (47,"World") are fundamentally different. One is of type (String,Int), whereas the other is (Int,String). This has implications for building up lists of tuples. We could very well have lists like [("a",1),("b",9),("c",9)], but having a list like [("a",1),(2,"b"),(9,"c")] is right out. Can you spot the difference?

  1. Which of these are valid Haskell, and why?
    • 1:(2,3)
    • (2,4):(2,3)
    • (2,4):[]
    • [(2,4),(5,5),('a','b')]
    • ([2,4],[2,2])

Retrieving values

So much for putting values into lists and tuples. If they are to be of any use, though, there must be a way of getting back the stored values!

Let's begin with pairs (that is, 2-tuples). A very common use for them is to store the (x, y) coordinates of a point: imagine you have a chess board, and want to specify a specific square. You could do this by labelling all the ranks from 1 to 8, and similarly with the files. Then, a pair (2, 5) could represent the square in rank 2 and file 5. Say we want to define a function for finding all the pieces in a given rank. One way of doing this would be to find the coordinates of all the pieces, then look at the rank part and see whether it's equal to whatever row we're being asked to examine. Once it had the coordinate pair (x, y) of a piece, the function would need to extract the x (the rank coordinate). To do that there are two functions, fst and snd, that retrieve[3] the first and second elements out of a pair, respectively. Let's see some examples:

Example: Using fst and snd

Prelude> fst (2, 5)
Prelude> fst (True, "boo")
Prelude> snd (5, "Hello")

Note that these functions only work on pairs. Why? Yet again, it has to do with types. Pairs and triples (and quadruples, etc.) have necessarily different types, and fst and snd only accept pairs as arguments.[4]

As for lists, the functions head and tail are roughly analogous to fst and snd, in that they disassemble a list by taking apart what (:) joined: head evaluates to the first element of the list, while tail gives the rest of the list.

Example: Using head and tail

Prelude> 2:[7,5,0]
Prelude> head [2,7,5,0]
Prelude> tail [2,7,5,0]

Unfortunately, there is a serious problem with head and tail. If we apply either of them to an empty list...

Prelude> head []
*** Exception: Prelude.head: empty list

... it blows up, as an empty list has no first element, nor any other elements at all. Had this happened in a full program rather than in GHCi, it would crash. We would rather avoid functions that could cause our programs to malfunction and thus leave us with an egg on our faces; therefore, while we will play with head and tail for the moment, we will be on the lookout for better options.


One possible reaction to the warning we just gave would be "What is the problem? Using head and tail is fine if we are careful and never pass them an empty list, or if we somehow test whether a list is empty before calling them." But that way lies madness.

As programs get bigger and more complicated, the number of places in which an empty list that could end up being passed to head and tail grows quickly; and so does the number of places in which we might make a mistake. As a rule of thumb, you should avoid functions that might fail without warning, producing errors. As we advance through the book, we will see that there almost always is a better way to deal with failure.

Pending questions

The four functions introduced here do not appear to be enough to fully solve the problem we started this section with. While fst and snd provide a satisfactory solution for pairs, what about tuples with three or more elements? And with lists, can't we do any better than just breaking them after the first element? head and tail just don't seem to cut it. For the moment, we will have to leave these questions pending, but don't worry - the issues are perfectly solvable. Once we do some necessary groundwork we will return to this subject with a number of chapters on list manipulation. We will explain there how separating head and tail allows us to do anything we want with lists.

  1. Use a combination of fst and snd to extract the 4 from the tuple (("Hello", 4), True).
  2. Normal chess notation is somewhat different to ours: it numbers the rows from 1-8 and the columns a-h; and the column label is customarily given first. Could we label a specific point with a character and a number, like ('a', 4)? What important difference with lists does this illustrate?
  3. Write a function which returns the head and the tail of a list as the first and second elements of a tuple.
  4. Use head and tail to write a function which gives the fifth element of a list. Then, make a critique of it, pointing out any annoyances and pitfalls you notice.

Polymorphic types

As you may have found out already by playing with :t, the type of a list depends on the types of its elements, and is denoted by enclosing it in square brackets:

Prelude>:t [True, False]
[True, False] :: [Bool]
Prelude>:t ["hey", "my"]
["hey", "my"] :: [[Char]]

Therefore, lists of Bool have different types than lists of [Char] (that is, strings), of Int and so on. Since functions only accept arguments of the types specified in the type of the function, that leads to some practical complications. For example, imagine a function that finds the length of a list. But since [Int], [Bool] and [String] are different types it seems we would need separate functions for each case – lengthInts :: [Int] -> Int, as well as a lengthBools :: [Bool] -> Int, as well as a lengthStrings :: [String] -> Int, as well as a...

That would be horribly frustrating because counting how many things there are in a list should be independent of what the things actually are. Fortunately, it does not work like that: there is a single function length, which works on all lists. But how can that possibly work? As usual, checking the type of length provides a good hint that there is something different going on...

Example: Our first polymorphic type

Prelude>:t length
length :: [a] -> Int

The a in the square brackets is not a type – remember that type names always start with uppercase letters. Instead, it is a type variable. When Haskell sees a type variable, it allows any type to take its place. This is exactly what we want. In type theory (a branch of mathematics), this is called polymorphism: functions or values with only a single type are called monomorphic, and things that use type variables to admit more than one type are polymorphic.

Note that within a single type signature, all cases of the same type variable must be of the same type. For example,

f :: a -> a

means that f takes an argument of any type and gives something of the same type as the argument, as opposed to

f :: a -> b

which means that f takes an argument of any type and gives a result of any type. Like a, b is another type variable. In any given function call, the type of the value assigned to the a may or may not match the type of whatever we have for b.

Example: fst and snd

As we saw, you can use the fst and snd functions to extract parts of pairs. By now, you should already be building the habit of wondering "what type is this?" about every function you come across. Let's consider the cases of fst and snd. These two functions take a pair as their argument and return one element of this pair. First of all, the type of a pair depends on the type of its elements, just as with lists, so the functions need to be polymorphic. Also it is important to keep in mind that pairs, and tuples in general, don't have to be homogeneous with respect to types; their different parts can be different types. So if we were to say:

fst :: (a, a) -> a

That would mean fst would only work if the first and second part of the pair given as input had the same type. So what is the correct type? Simply:

Example: The types of fst and snd

fst :: (a, b) -> a
snd :: (a, b) -> b

Note that if you knew nothing about fst and snd other than the type signatures you might guess that they return the first and second parts of a pair, respectively. Although that is correct, the type signature isn't limited to this case. All the signatures say is that they just have to return something with the same type of the first and second parts of the pair.


Give type signatures for the following functions:

  1. The solution to the third exercise of the previous section ("... a function which returns the head and the tail of a list as the first and second elements of a tuple").
  2. The solution to the fourth exercise of the previous section ("... a function which gives the fifth element of a list").
  3. h x y z = chr (x - 2) (remember we discussed chr in the previous chapter).


We have introduced two new notions in this chapter: lists and tuples. Let us sum up the key similarities and differences between them:

  1. Lists are defined by square brackets and commas : [1,2,3].
    • Lists can contain anything as long as all the candidate elements of the list are of the same type.
    • Lists can also be built by the cons operator, (:), but you can only cons things onto lists.
  2. Tuples are defined by parentheses and commas : ("Bob",32)
    • Tuples contain anything, even things of different types.
    • The length of a tuple is encoded in its type. That is, two tuples with different lengths will have different types.
  3. Lists and tuples can be combined in any number of ways: lists within lists, tuples with lists, etc, but their criteria must still be fulfilled for the combinations to be valid.


  1. You might object that "cons" isn't even a proper word. Well, it isn't. LISP programmers invented the verb "to cons" to refer to this specific task of appending an element to the front of a list. "cons" happens to be a mnemonic for "constructor". Later on we will see why that makes sense.
  2. At this point you might be justified in seeing types as an annoying thing. In a way they are, but more often than not they are actually a lifesaver. In any case, when you are programming in Haskell and something blows up, you'll probably want to think "type error".
  3. Or, more technically, "... projections that project the elements..." In math-speak, a function that gets some data out of a structure is called a projection.
  4. Yes, there are ways that a function could be designed to extract the first thing from any size tuple, but it wouldn't be as simple as you might think, and it isn't how the fst and snd functions work.

Type basics II

So far, we have shrewdly avoided number types in our examples. In one exercise, we even went as far as asking you to "pretend" the arguments to (+) had to be of type Int. So, from what are we hiding?

In this chapter, we will show how numerical types are handled in Haskell. While doing so, we will introduce some important features of the type system. Before diving into the text, though, pause for a moment and consider the following question: what should be the type of the function (+)?[1]

The Num class

As far as everyday mathematics is concerned, there are very few restrictions on which kind of numbers we can add together. 2 + 3 (two natural numbers), (-7) + 5.12 (a negative integer and a rational number), \frac{1}{7} + \pi (a rational and an irrational)... all of these are valid – indeed, any two real numbers can be added together. In order to capture such generality in the simplest way possible we would like to have a very general Number type in Haskell, so that the signature of (+) would be simply

(+) :: Number -> Number -> Number

That design, however, does not fit well with the way computers perform arithmetic. While integer numbers in programs can be quite straightforwardly handled as sequences of binary digits in memory, that approach does not work for non-integer real numbers,[2] thus making it necessary for a more involved encoding to support them: floating point numbers. While floating point provides a reasonable way to deal with real numbers in general, it has some inconveniences (most notably, loss of precision) which make using the simpler encoding worthwhile for integer values. We are thus left with at least two different ways of storing numbers, one for integers and another one for general real numbers, which should correspond to different Haskell types. Furthermore, computers are only able to perform operations like (+) on a pair of numbers if they are in the same format. That should put an end to our hopes of using a universal Number type – or even having (+) working with both integers and floating-point numbers...

It is easy, however, to see reality is not that bad. We can use (+) with both integers and floating point numbers:

Prelude>3 + 4
Prelude>4.34 + 3.12

When discussing lists and tuples, we saw that functions can accept arguments of different types if they are made polymorphic. In that spirit, one possible type signature for (+) that would account for the facts above would be:

(+) :: a -> a -> a

(+) would then take two arguments of the same type a (which could be integers or floating-point numbers) and evaluate to a result of type a. There is a problem with that solution, however. As we saw before, the type variable a can stand for any type at all. If (+) had that type signature we would be able to (+) two Bool, or two Char, which would make very little sense. Rather, the actual type signature of (+) takes advantage of a language feature that allows us to express the semantic restriction that a can be any type as long as it is a number type:

(+) :: (Num a) => a -> a -> a

Num is a typeclass - a group of types which includes all types which are regarded as numbers[3]. The (Num a) => part of the signature restricts a to number types – or, more accurately, instances of Num.

Numeric types

But what are the actual number types – the instances of Num that a stands for in the signature? The most important numeric types are Int, Integer and Double:

  • Int corresponds to the vanilla integer type found in most languages. It has fixed maximum and minimum values that depend on a computer's processor. (In 32-bit machines the range goes from -2147483648 to 2147483647).
  • Integer also is used for integer numbers, but unlike Int it supports arbitrarily large values – at the cost of some efficiency.
  • Double is the double-precision floating point type, a good choice for real numbers in the vast majority of cases. (There is also Float, the single-precision counterpart of Double, which is usually less attractive due to further loss of precision.)

These types are available by default in Haskell and are the ones you will generally deal with in everyday tasks.

Polymorphic guesswork

There is one thing we haven't explained yet. If you tried the examples of addition we mentioned at the beginning you know that something like this is perfectly valid:

Prelude> (-7) + 5.12

Here, it seems we are adding two numbers of different types – an integer and a non-integer. Shouldn't the type of (+) make that impossible?

To answer that question we have to see what the types of the numbers we entered actually are:

Prelude> :t (-7)
(-7) :: (Num a) => a

And, lo and behold, (-7) is neither Int nor Integer! Rather, it is a polymorphic constant, which can "morph" into any number type if need be. The reason for that becomes clearer when we look at the other number...

Prelude> :t 5.12
5.12 :: (Fractional t) => t

5.12 is also a polymorphic constant, but one of the Fractional class, which is more restrictive than Num – every Fractional is a Num, but not every Num is a Fractional (for instance, Ints and Integers are not Fractional).

When a Haskell program evaluates (-7) + 5.12, it must settle for an actual type for the numbers. It does so by performing type inference while accounting for the class specifications. (-7) can be any Num, but there are extra restrictions for 5.12, so its type will define what (-7) will become. Since there is no other clues to what the types should be, 5.12 will assume the default Fractional type, which is Double; and, consequently, (-7) will become a Double as well, allowing the addition to proceed normally and return a Double[4].

There is a nice quick test you can do to get a better feel of that process. In a source file, define

x = 2

then load the file in GHCi and check the type of x. Then, change the file to add a y variable,

x = 2
y = x + 3

reload it and check the types of x and y. Finally, modify y to

x = 2
y = x + 3.1

and see what happens with the types of both variables.

Monomorphic trouble

The sophistication of the numerical types and classes occasionally leads to some complications. Consider, for instance, the common division operator (/). It has the following type signature:

(/) :: (Fractional a) => a -> a -> a

Restricting a to fractional types is a must because the division of two integer numbers will often not result in an integer. Nevertheless, we can still write something like

Prelude> 4 / 3

because the literals 4 and 3 are polymorphic constants and therefore assume the type Double at the behest of (/). Suppose, however, we want to divide a number by the length of a list[5]. The obvious thing to do would be using the length function:

Prelude> 4 / length [1,2,3]

Unfortunately, that blows up:

    No instance for (Fractional Int)
      arising from a use of `/' at <interactive>:1:0-17
    Possible fix: add an instance declaration for (Fractional Int)
    In the expression: 4 / length [1, 2, 3]
    In the definition of `it': it = 4 / length [1, 2, 3]

As usual, the problem can be understood by looking at the type signature of length:

length :: [a] -> Int

The result of length is not a polymorphic constant, but an Int; and since an Int is not a Fractional it can't fit the signature of (/).

There is a handy function which provides a way of escaping from this problem. Before following on with the text, try to guess what it does only from the name and signature:

fromIntegral :: (Integral a, Num b) => a -> b

fromIntegral takes an argument of some Integral type (like Int or Integer) and makes it a polymorphic constant. By combining it with length we can make the length of the list fit into the signature of (/):

Prelude> 4 / fromIntegral (length [1,2,3])

While this expression may look overly complex at first, this approach makes it easier to be rigorous when manipulating numbers. If you define a function with an Int argument, it will never be converted to an Integer or Double, unless you explicitly tell the program to do so by using a function like fromIntegral. As a direct consequence of its refined type system, there is a surprising diversity of classes and functions dealing with numbers in Haskell.

Classes beyond numbers

There are many other use cases for typeclasses beyond arithmetic. For example, the type signature of (==) is:

(==) :: (Eq a) => a -> a -> Bool

Like (+) or (/), (==) is a polymorphic function. It compares two values of the same type, which must belong to the class Eq and returns a Bool. Eq is simply the class of types of values which can be compared for equality, and includes all of the basic non-functional types.[6]

Typeclasses are a very general language feature which adds a lot to the power of the type system. Later in the book we will return to this topic to see how to use them in custom ways.


  1. If you followed our recommendations in "Type basics", chances are you have already seen the rather exotic answer by testing with :t... if that is the case, consider the following analysis as a path to understanding the meaning of that signature.
  2. One of the reasons being that between any two real numbers there are infinitely many real numbers – and that can't be directly mapped into a representation in memory no matter what we do.
  3. That is a very loose definition, but will suffice until we are ready to discuss typeclasses in more detail.
  4. For seasoned programmers: This appears to have the same effect that programs in C (and many other languages) manage with an implicit cast – by which an integer literal is silently converted to a double. The difference is that in C, the conversion is done behind your back, while in Haskell it only occurs if the variable/literal is a polymorphic constant. The difference will become clearer shortly, when we show a counter-example.
  5. A reasonable scenario – think of computing an average of the values in a list.
  6. Comparing two functions for equality is considered to be intractable

Building vocabulary

This chapter will be somewhat different from the surrounding ones. Think of it as an interlude, where the main goal is not to introduce new features, but to present advice for studying (and using!) Haskell. Here, we will discuss the importance of acquiring a vocabulary of functions and how this book, along with other resources, can help you with that. First, however, we need to make a few quick points about function composition.

Function composition

Function composition is a really simple concept. It just means applying one function to a value and then applying another function to the result. Consider these two very simple functions:

Example: Simple functions

f x = x + 3
square x = x ^ 2

We can compose them in two different ways, depending on which one we apply first:

Prelude> square (f 1)
Prelude> square (f 2)
Prelude> f (square 1)
Prelude> f (square 2)

The parentheses around the inner function are necessary; otherwise, the interpreter would think that you were trying to get the value of square f, or f square; and both have no meaning.

The composition of two functions results in a function in its own right. If applying f and then square, or vice-versa, to a number were a frequent, meaningful or otherwise important operation in a program, a natural next step would be defining:

Example: Composed functions

squareOfF x = square (f x)
fOfSquare x = f (square x)

There is a second, nifty way of writing composed functions. It uses (.), the function composition operator, and is as simple as putting a period between the two functions:

Example: Composing functions with (.)

squareOfF x = (square . f) x
fOfSquare x = (f . square) x

Note that functions are still applied from right to left, so that g(f(x)) == (g . f) x [1].

The need for a vocabulary

Function composition allows us to define complicated functions using simpler ones as building blocks. One of the key qualities of Haskell is how simple it is to write composed functions, no matter if the base functions are written by ourselves or by someone else,[2] and the extent that helps us in writing simple, elegant and expressive code.

In order to use function composition, we first need to have functions to compose. While naturally the functions we write ourselves will always be available, every installation of GHC comes with a vast assortment of libraries (that is, packaged code), which provide functions for various common tasks. For that reason, it is vital for effective Haskell programming to develop some familiarity with the essential libraries. At the very least, you should know how to find useful functions in the libraries when you need them.

We can look at this issue from a different perspective. We have gone through a substantial portion of the Haskell syntax already; and, by the time we are done with the upcoming Recursion chapter, we could, in principle, write pretty much any list manipulation program we want. However, writing full programs at this point would be terribly inefficient, mainly because we would end up rewriting large parts of the standard libraries. It is far easier to have the libraries deal as much as possible with trivial well-known problems while we dedicate our brain cells to solving the problems we are truly interested in. Furthermore, the functionality provided by libraries helps us in developing our own algorithms.[3]

Prelude and the libraries

Here are a few basic facts about Haskell libraries:

First and foremost, there is Prelude, which is the core library loaded by default in every Haskell program. Alongside with the basic types, it provides a set of ubiquitous and extremely useful functions. We will refer to Prelude and its functions all the time throughout these introductory chapters.

Beyond Prelude, there is a set of core libraries, which provide a much wider range of tools. Although they are provided by default with GHC, they are not loaded automatically like Prelude. Instead, they are made available as modules, which must be imported into your program. Later on, we will explain the minutiae of how modules work. For now, just know that your source file needs lines near the top to import any necessary modules. For example, the function permutations is in the module Data.List, and, to import that, add the line import Data.List to the top of your .hs file. Here's how a full source file looks:

Example: Importing a module in a source file

import Data.List
testPermutations = permutations "Prelude"

For quick GHCi tests, just enter :m +Data.List at the command line to load that module.

Prelude> :m +Data.List
Prelude Data.List> :t permutations
permutations :: [a] -> [[a]]

One exhibit

Before continuing, let us see one (slightly histrionic, we admit) example of what familiarity with a few basic functions from Prelude can bring us.[4] Suppose we need a function which takes a string composed of words separated by spaces and returns that string with the order of the words reversed, so that "Mary had a little lamb" becomes "lamb little a had Mary". Now, we can solve that problem using exclusively what we have seen so far about Haskell, plus a few insights that can be acquired by studying the Recursion chapter. Here is what it might look like. Don't stare at it for too long!

Example: There be dragons

monsterRevWords :: String -> String
monsterRevWords input = rejoinUnreversed (divideReversed input)
    divideReversed s = go1 [] s
        go1 divided [] = divided
        go1 [] (c:cs)
            | testSpace c = go1 [] cs
            | otherwise   = go1 [[]] (c:cs)
        go1 (w:ws) [c]
            | testSpace c = (w:ws)
            | otherwise   = ((c:w):ws)
        go1 (w:ws) (c:c':cs)
            | testSpace c =
                if testSpace c'
                    then go1 (w:ws) (c':cs)
                    else go1 ([c']:w:ws) cs
            | otherwise =
                if testSpace c'
                    then go1 ((c:w):ws) (c':cs)
                    else go1 ((c:w):ws) (c':cs)
    testSpace c = c == ' '
    rejoinUnreversed [] = []
    rejoinUnreversed [w] = reverseList w
    rejoinUnreversed strings = go2 (' ' : reverseList newFirstWord) (otherWords)
        (newFirstWord : otherWords) = reverseList strings
        go2 rejoined ([]:[]) = rejoined
        go2 rejoined ([]:(w':ws')) = go2 (rejoined) ((' ':w'):ws')
        go2 rejoined ((c:cs):ws) = go2 (c:rejoined) (cs:ws)
    reverseList [] = []
    reverseList w = go3 [] w
        go3 rev [] = rev
        go3 rev (c:cs) = go3 (c:rev) cs

There are too many problems with this thing; so let us consider just three of them:

  • If we claimed that monsterRevWords does what is expected, you could either take our word for it, test it exhaustively on all sorts of possible inputs or attempt to understand it and get an awful headache (please don't).
  • Furthermore, if we write a function this ugly and have to fix a bug or slightly modify it later on,[5] we are set for an awful time.
  • Finally, there is at least one easy to spot potential problem: if you have another glance at the definition, about halfway down there is a testSpace helper function which checks if a character is a space or not. The test, however, only includes the common space character (that is, ' '), and not other whitespace characters (tabs, newlines, etc.)[6].

Instead of the junk above, we can do much better by using the following Prelude functions:

  • words, which reliably breaks down a string in whitespace delimited words, returning a list of strings;
  • reverse, which reverses a list (incidentally, that is exactly what the reverseList above does); and
  • unwords, which does the opposite of words;

then function composition means our problem is instantly solved.

Example: revWords done the Haskell way

revWords :: String -> String
revWords input = (unwords . reverse . words) input

That's short, simple, readable and, since Prelude is reliable, bug-free.[7] So, any time some program you are writing begins to look like monsterRevWords, look around and reach for your toolbox - the libraries.

Acquiring vocabulary

After the stern warnings above, you might expect us to continue with diving deep into the standard libraries. That is not the route we will follow, however - at least not in the first part of the book. The Beginner's Track is meant to cover most of the Haskell language functionality in a readable and reasonably compact account, and a linear, systematic study of the libraries would in all likelihood have us sacrificing either one attribute or the other.

In any case, the libraries will remain close at hand as we advance in the course (so there's no need for you to pause your reading just to study the libraries on your own). Here are a few suggestions on resources you can use to learn about them.

With this book

  • Once we enter Elementary Haskell, you will notice several of the exercises - mainly, among those about list processing - involve writing equivalent definitions for Prelude functions. For each of these exercises you do, one more function will be added to your repertoire.
  • Every now and then we will introduce more library functions; maybe within an example, or just with a mention in passing. Whenever we do so, take a minute to test the function and do some experiments. Remember to extend that habitual curiosity about types we mentioned in Type basics to the functions themselves.
  • While the first few chapters are quite tightly-knit, later parts of the book are more independent — especially the third track, Haskell in Practice. There, among other things, you can find chapters on the Hierarchical libraries; and most of their content can be understood soon after having completed Elementary Haskell.
  • As we reach the later parts of the Beginner's track, the concepts we will discuss (monads in particular) will naturally lead to exploration of important parts of the core libraries.

Other resources

  • First and foremost, there is the documentation. While it is probably too dry to be really useful right now, it will prove valuable soon enough. You can read the Prelude specification on-line as well as the documentation of the libraries bundled with GHC, with nice navigation and source code just one click away.
  • Hoogle is a great way to search through the documentation. It is a Haskell search engine which covers the core libraries. You can search for everything from function names to type definitions and more.
  • Beyond the libraries included with GHC, there is a large ecosystem of libraries, made available through Hackage and installable with a tool called cabal. The Hackage site you can find documentation for the libraries available through it. We will not venture outside of the core libraries in the Beginner's Track; however, you will certainly be drawn to Hackage once you begin your own projects. A second Haskell search engine is Hayoo!; it covers all of Hackage.
  • When appropriate, we will give pointers to other useful learning resources, specially when we move towards intermediate and advanced topics.


  1. (.) is modelled after the mathematical operator \circ, which works in the same way: (g \circ f)(x) = g(f(x))
  2. Such ease is not only due to the bits of syntax we mentioned above, but mainly due to features we will explain and discuss in depth later in the book - in particular, higher-order functions.
  3. One simple example is provided by functions like map, filter and the folds, which we will cover in the chapters on list processing just ahead. Another would be the various monad libraries, which will be studied in depth later on.
  4. The example here is inspired by the Simple Unix tools demo in the HaskellWiki.
  5. Co-author's note: "Later on? I wrote that half an hour ago and I'm not totally sure about how it works already..."
  6. A reliable way of checking whether a character is whitespace is with the isSpace function, which is in the module Data.Char.
  7. In case you are wondering, there are lots of other functions, either in Prelude or in Data.List which, in one way or another, would help to make monsterRevWords somewhat saner - just to name a few: (++), concat, groupBy, intersperse. There is no need for them in this case, though, since nothing compares to the one-liner above.

Next steps

This chapter introduces pattern matching, a key feature of Haskell, and two new pieces of syntax: if expressions and let bindings.

if / then / else

Haskell syntax supports garden-variety conditional expressions of the form if... then... (else ...). For instance, consider a function that returns (-1) if its argument is less than 0; 0 if its argument is 0; and 1 if its argument is greater than 0. There is a predefined function which does that job already (it is called signum); for the sake of illustration, though, let's define a version of our own:

Example: The signum function.

mySignum x =
    if x < 0 
        then -1
        else if x > 0
            then 1
            else 0

You can experiment with this as:

*Main> mySignum 5
*Main> mySignum 0
*Main> mySignum (5 - 10)
*Main> mySignum (-1)

The parentheses around "-1" in the last example are required; if missing, the system will think you are trying to subtract 1 from mySignum, which is ill-typed.

In an if/then/else construct, first the condition (in this case x < 0) is evaluated. If it results True, the whole construct evaluates to the then expression; otherwise (if the condition is False), the construct evaluates to the else expression. All of that is pretty intuitive. If you have programmed in an imperative language before, however, it might seem surprising to know that Haskell always requires both a then and an else clause. This is because the construct has to result in a value in both cases - more specifically, a value of the same type in both cases.

if / then / else function definitions like the one above can be easily rewritten with the guards syntax presented in past modules:

Example: From if to guards

mySignum x
    | x < 0     = -1
    | x > 0     = 1
    | otherwise = 0

And conversely, the absolute value function in Truth values can be rendered with if/then/else:

Example: From guards to if

abs x =
    if x < 0 
        then -x
        else x

Why use if/then/else versus guards? As you will see with later examples and in your own programming, each way of handling conditionals may be more readable or convenient depending on the circumstances. In many cases, both options work equally well.

Introducing pattern matching

Suppose we are writing a program which tracks statistics from a racing competition in which racers receive points based on their classification in each race, the scoring rules being:

  • 10 points for the winner;
  • 6 for second-placed;
  • 4 for third-placed;
  • 3 for fourth-placed;
  • 2 for fifth-placed;
  • 1 for sixth-placed;
  • no points for other racers.

We can write a simple function which takes a classification (represented by an integer number: 1 for first place, etc.[1]) and returns how many points were earned. One possible solution uses if/then/else:

Example: Computing points with if/then/else

pts :: Int -> Int
pts x =
    if x == 1
        then 10
        else if x == 2
            then 6
            else if x == 3
                then 4
                else if x == 4
                    then 3
                    else if x == 5
                        then 2
                        else if x == 6
                            then 1
                            else 0

Yuck! Admittedly, it wouldn't look this hideous had we used guards instead of if/then/else, but it still would be tedious to write (and read!) all those equality tests. We can do better, though:

Example: Computing points with a piece-wise definition

pts :: Int -> Int
pts 1 = 10
pts 2 = 6
pts 3 = 4
pts 4 = 3
pts 5 = 2
pts 6 = 1
pts _ = 0

Much better. However, even though defining pts in this style (which we will arbitrarily call piece-wise definition from now on) shows to a reader of the code what the function does in an awesomely clear way, the syntax looks odd given what we have seen of Haskell so far. Why are there seven equations for pts? What are those numbers doing in their left-hand sides? Where has the x gone?

The example above is our first encounter with a key feature of Haskell called pattern matching. When we call the second version of pts, the argument is matched against the numbers on the left side of each of the equations, which in turn are the patterns. This matching is done in the order we wrote the equations; so first of all the argument is matched against the 1 in the first equation. If the argument is indeed 1, we have a match and the first equation is used; and so pts 1 evaluates to 10 as expected. Otherwise, the other equations are tried in order following the same procedure. The final one, though, is rather different: the _ is a special pattern, often called a "wildcard", that might be read as "whatever": it matches with anything; and therefore if the argument doesn't match any of the previous patterns pts will return zero.

As for why there is no x or any other variable standing for the argument, it is simply because we don't need that to write the definitions. All possible return values are constants; and since the point of specifying a variable name on the left side is using it to write the right side, the x is unnecessary in our function.

There is, however, an obvious way to make pts even more concise. The points given to a racer decrease regularly from third place to sixth place, at a rate of one point per position. After noticing that, we can eliminate three of the seven equations as follows:

Example: Mixing styles

pts :: Int -> Int
pts 1 = 10
pts 2 = 6
pts x
    | x <= 6    = 7 - x
    | otherwise = 0

The first thing to point out here is that we can mix both styles of definitions. In fact, when we write pts x in the left side of an equation we are using pattern matching too! As a pattern, the x (or any other variable name) matches anything just like _; the only difference being that it also gives us a name to use on the right side (which, in this case, is necessary to write 7 - x).

We cheated a little when moving from the second version of pts to the third one: they do not do exactly the same thing. Can you spot what the difference is?

Beyond integers, pattern matching works with values of various other types. One handy example are booleans. For instance, the (||) logical-or operator we met in Truth values could be defined as:

Example: (||)

(||) :: Bool -> Bool -> Bool
False || False = False
_     || _     = True


Example: (||), done another way

(||) :: Bool -> Bool -> Bool
True  || _ = True
False || y = y

When matching two or more arguments at once, the equation will only be used if all of them match.

To conclude this section, let us discuss a few things that might go wrong when using pattern matching:

  • If we put a pattern which matches anything (such as the final patterns in each of the pts example) before the more specific ones the latter will be ignored. GHC(i) will typically warn us that "Pattern match(es) are overlapped" in such cases.
  • If no patterns match, an error will be triggered. Generally, it is a good idea to ensure the patterns cover all cases, in the same way that the otherwise guard is not mandatory but highly recommended.
  • Finally, while you can play around with various ways of (re)defining (&&)[2], here is one version that will not work:
(&&) :: Bool -> Bool -> Bool
x && x = x -- oops!
_ && _ = False
The program won't test whether the arguments are equal just because we happened to use the same name for both. As far as the matching goes, we could just as well have written _ && _ in the first case. And even worse: since we gave the same name to both arguments, GHC(i) will refuse the function due to "Conflicting definitions for `x'".

Tuple and list patterns

While the examples above show that pattern matching helps in writing more elegant code, that does not explain why it is so important. So, let's consider the problem of writing a definition for fst, the function which extracts the first element of a pair. At this point, that appears to be an impossible task, as the only way of accessing the first value of the pair is by using fst itself... The following function, however, does the same thing as fst (confirm it in GHCi):

Example: A definition for fst

fst' :: (a, b) -> a
fst' (x, _) = x

It's magic! Instead of using a regular variable in the left side of the equation, we specified the argument with the pattern of the 2-tuple - that is, (,) - filled with a variable and the _ pattern. Then the variable was automatically associated with the first component of the tuple, and we used it to write the right side of the equation. The definition of snd is, of course, analogous.

Furthermore, the trick demonstrated above can be done with lists as well. Here are the actual definitions of head and tail:

Example: head, tail and patterns

head             :: [a] -> a
head (x:_)       =  x
head []          =  error "Prelude.head: empty list"
tail             :: [a] -> [a]
tail (_:xs)      =  xs
tail []          =  error "Prelude.tail: empty list"

The only essential change in relation to the previous example was replacing (,) with the pattern of the cons operator (:). These functions also have an equation using the pattern of the empty list, []; however, since empty lists have no head or tail there is nothing to do other than use error to print a prettier error message.

In summary, the real power of pattern matching comes from how it can be used to access the parts of a complex value. Pattern matching on lists, in particular, will be extensively deployed in Recursion and the chapters that follow it. Later on, we will explore what is happening behind this seemingly magical feature.

let bindings

To conclude this chapter, a brief word about let bindings, which are an alternative to where clauses for making local declarations. For instance, take the problem of finding the roots of a polynomial of the form ax^2+bx+c (or, in other words, the solution to a second degree equation - think back to your middle school math courses). Its solutions are given by:

x = \frac {-b \pm \sqrt{b^2-4ac}} {2a}.

We could write the following function to compute the two values of x:

roots a b c =
    ((-b + sqrt(b * b - 4 * a * c)) / (2 * a),
     (-b - sqrt(b * b - 4 * a * c)) / (2 * a))

Writing the sqrt(b * b - 4 * a * c) term in both cases is annoying, though; we can use a local binding instead, using either where or, as will be demonstrated below, a let declaration:

roots a b c =
    let sdisc = sqrt (b * b - 4 * a * c)
    in  ((-b + sdisc) / (2 * a),
         (-b - sdisc) / (2 * a))

We put the let keyword before the declaration, and then use in to signal we are returning to the "main" body of the function. It is possible to put multiple declarations inside a single let...in block - just make sure they are indented the same amount, otherwise there will be syntax errors:

roots a b c =
    let sdisc = sqrt (b * b - 4 * a * c)
        twice_a = 2 * a
    in  ((-b + sdisc) / twice_a,
         (-b - sdisc) / twice_a)


Still on indentation, the Indentation chapter has a full account of indentation rules; so if doubts about that arise as you code you might want to give it a glance.


  1. Here we will not be much worried about what happens if a nonsensical value (say, (-4)) is passed to the function. In general, however, it is a good idea to give some thought to such "strange" cases, in order to avoid nasty surprises down the road.
  2. If you are going to experiment with it in GHCi, call your version something else to avoid a name clash; say, (&!&).

Simple input and output

Back to the real world

So far, we have discussed many examples of functions that calculate values. Of course, we also want to use our programs for other things. For example, the standard program in the beginning of tutorials about other languages: a program that displays a "hello world" greeting. Here's one Haskell version:

Prelude> putStrLn "Hello, World!"

putStrLn is one of the standard Prelude functions. As the "putStr" part of the name suggests, it takes a String as an argument and prints it to the screen. The "Ln" indicates it also prints a line break, so that whatever else is printed next will appear on a new line.

So now you should be thinking, "what is the type of the putStrLn function?" It takes a String and gives… um… what? What do we call that? The program doesn't get something back that it can use in another function. Instead, the result involves having the computer change the screen. In other words, it does something in the world outside of the program. What type could that have? Let's see what GHCi tell us:

Prelude> :t putStrLn
putStrLn :: String -> IO ()

"IO" stands for "input and output". Wherever there is IO in a type, interaction with the world outside the program is involved. We'll call IO values, such as the result of putStrLn, actions. The other part of the IO type, in this case (), is the type of the return value of the action; that is, the type of what it gives back to the program (as opposed to what it does outside the program). () (read as "unit") is an uninteresting type with just a single value, also called (). Since putStrLn sends output to the world but doesn't return anything to the program, () is used as a placeholder. We might read IO () as "action which returns ()", just like we read [Int] as "list of Int elements".

Here are just a few examples of when we need to use IO:

  • print a string to the screen
  • read a string from a keyboard
  • write data to a file
  • read data from a file

What makes IO actually work? Lots of things happen behind the scenes to take us from a putStrLn to pixels in the screen; however, we don't have to worry about them right now. What we have to know is that a complete Haskell program is actually a big IO action that is run when the program is executed. In a compiled program, this action is called main, and has type IO (). From this point of view, to write a Haskell program is to combine actions and functions to form the overall function main that will be executed when the program is run.

Back in the Type Basics chapter, we mentioned that the type of the openWindow function had been simplified. Can you guess what the simplification was?

Sequencing actions with do

do notation provides a convenient means of putting actions together, which is essential in getting useful things done with Haskell. Let's see what it looks like by considering a more complex program:

Example: What is your name?

main = do
  putStrLn "Please enter your name: "
  name <- getLine
  putStrLn ("Hello, " ++ name ++ ", how are you?")


Even though do notation looks very different from the Haskell code we have seen so far, it is just syntactic sugar for a handful of functions, the most important of them being the (>>=) operator. Ideally, we would explain how those functions work and then introduce do notation. However, there are a number of topics we need to go through before we can give a convincing explanation, notably higher order functions and type classes. Jumping in with do right now is a pragmatic short cut that will allow you to start writing complete programs with IO right now. We will see why do works later in the book, beginning with the Understanding monads chapter.

Before we get into how do works, take a look at getLine. It goes to the outside world, in this case to the terminal, and brings back a String from it. What is its type?

Prelude> :t getLine
getLine :: IO String

That means getLine is an IO action that, when run, will return a String. But what about the input? While functions have types like a -> b which reflect that they take arguments and give back results, getLine doesn't actually take an argument. It takes as input whatever is in the line in the terminal. However, that line in the outside world can't be a value with a type because it isn't even in Haskell yet! The Haskell program can't know in advance what it will be, and indeed the value could be different every time.

As there is no way to predict the exact results of IO actions (since the program doesn't know the state of the outside world until runtime), they have to be executed in a predictable sequence defined in advance in our code. With regular functions that do not perform IO, the exact sequencing of execution is less of a concern as long as the results eventually go to the right places.

In our name program, we're sequencing three actions: a putStrLn with a greeting, a getLine and another putStrLn. With the getLine, we use <- notation, which is a way to get the return value out of an action, so that we can use it elsewhere (in this case, to prepare the final message being printed). The final action defines the type of the whole do block and, in this case, of the program. Here, the final action is the result of a putStrLn, and so it has type IO ().


Write a program which asks the user for the base and height of a right angled triangle, calculates its area and prints it to the screen. The interaction should look something like:

The base?
The height?
The area of that triangle is 8.91
Hint: you can use the function read to convert user strings like "3.3" into numbers like 3.3 and function show to convert a number into string.

Left arrow clarifications

While actions like getLine are almost always used to get values, we are not obliged to actually get them. For example, we could very well have written something like this:

Example: executing getLine directly

main = do
  putStrLn "Please enter your name: "
  putStrLn ("Hello, how are you?")

Clearly, that isn't very useful: the whole point of prompting the user for his or her name was so that we could do something with the result. That being said, it is conceivable that one might wish to read a line and completely ignore the result. In real life, we often get someone's name when meeting them and then promptly forget it even though we keep talking… By omitting the <-, the action will happen, but the data won't be stored anywhere.

It's nicer to actually remember someone's name, of course. So, in order to get the value out of the action, we write name <- getLine, which basically means "run getLine, and put the results in the variable called name."

<- can be used with any action except the last

There are very few restrictions on which actions can have values obtained from them. Consider the following example, where we put the results of each action into a variable (except the last... more on that later):

Example: putting all results into a variable

main = do
  x <- putStrLn "Please enter your name: "
  name <- getLine
  putStrLn ("Hello, " ++ name ++ ", how are you?")

The variable x gets the value out of its action, but that isn't useful in this case because the action returns the unit value (). So while we could technically get the value out of any action, it isn't always worth it.

So, what about that last action? Why can't we get a value out of that? Let's see what happens when we try:

Example: getting the value out of the last action

main = do
  x <- putStrLn "Please enter your name: "
  name <- getLine
  y <- putStrLn ("Hello, " ++ name ++ ", how are you?")

Whoops! Error!

    The last statement in a 'do' construct must be an expression

This is a much more interesting example, but it requires a somewhat deeper understanding of Haskell than we currently have. Suffice it to say, whenever you use <- to get the value of an action, Haskell is always expecting another action to follow it. So the very last action better not have any <-s.

Controlling actions

Normal Haskell constructions like if/then/else can be used within the do notation, but you need to be somewhat careful. For instance, in a simple "guess the number" program, we have:

doGuessing num = do
   putStrLn "Enter your guess:"
   guess <- getLine
   if (read guess) < num
     then do putStrLn "Too low!"
             doGuessing num
     else if (read guess) > num
            then do putStrLn "Too high!"
                    doGuessing num
            else putStrLn "You Win!"

If we think about how the if/then/else construction works, it essentially takes three arguments: the condition, the "then" branch, and the "else" branch. The condition needs to have type Bool, and the two branches can have any type, provided that they have the same type. The type of the entire if/then/else construction is then the type of the two branches.

In the outermost comparison, we have (read guess) < num as the condition. This clearly has the correct type. Let's just consider the "then" branch. The code here is:

          do putStrLn "Too low!"
             doGuessing num

Here, we are sequencing two actions: putStrLn and doGuessing. The first has type IO (), which is fine. The second also has type IO (), which is fine. The type result of the entire computation is precisely the type of the final computation. Thus, the type of the "then" branch is also IO (). A similar argument shows that the type of the "else" branch is also IO (). This means the type of the entire if/then/else construction is IO (), which is just what we want.

Note: be careful if you find yourself thinking, "Well, I already started a do block; I don't need another one." We can't have code like:

    do if (read guess) < num
         then putStrLn "Too low!"
              doGuessing num
         else ...

Here, since we didn't repeat the do, the compiler doesn't know that the putStrLn and doGuessing calls are supposed to be sequenced, and the compiler will think you're trying to call putStrLn with three arguments: the string, the function doGuessing and the integer num, and thus reject the program.


Write a program that asks the user for his or her name. If the name is one of Simon, John or Phil, tell the user that you think Haskell is a great programming language. If the name is Koen, tell them that you think debugging Haskell is fun (Koen Classen is one of the people who works on Haskell debugging); otherwise, tell the user that you don't know who he or she is.

(As far as syntax goes there are a few different ways to do it; write at least a version using if / then / else.)

Actions under the microscope

Actions may look easy up to now, but they are actually a common stumbling block for new Haskellers. If you have run into trouble working with actions, you might consider looking to see if one of your problems or questions matches the cases below. It might be worth skimming this section now, and coming back to it when you actually experience trouble.

Mind your action types

One temptation might be to simplify our program for getting a name and printing it back out. Here is one unsuccessful attempt:

Example: Why doesn't this work?

main =
 do putStrLn "What is your name? "
    putStrLn ("Hello " ++ getLine)

Ouch! Error!

    Couldn't match expected type `[Char]'
           against inferred type `IO String'

Let us boil the example above down to its simplest form. Would you expect this program to compile?

Example: This still does not work

main =
 do putStrLn getLine

For the most part, this is the same (attempted) program, except that we've stripped off the superfluous "What is your name" prompt as well as the polite "Hello". One trick to understanding this is to reason about it in terms of types. Let us compare:

 putStrLn :: String -> IO ()
 getLine  :: IO String

We can use the same mental machinery we learned in Type basics to figure how this went wrong. Simply put, putStrLn is expecting a String as input. We do not have a String, but something tantalisingly close, an IO String. This represents an action that will give us a String when it's run. To obtain the String that putStrLn wants, we need to run the action, and we do that with the ever-handy left arrow, <-.

Example: This time it works

main =
 do name <- getLine
    putStrLn name

Working our way back up to the fancy example:

main =
 do putStrLn "What is your name? "
    name <- getLine
    putStrLn ("Hello " ++ name)

Now the name is the String we are looking for and everything is rolling again.

Mind your expression types too

So, we've made a big deal out of the idea that you can't use actions in situations that don't call for them. The converse of this is that you can't use non-actions in situations that expect actions. Say we want to greet the user, but this time we're so excited to meet them, we just have to SHOUT their name out:

Example: Exciting but incorrect. Why?

import Data.Char (toUpper)
main =
 do name <- getLine
    loudName <- makeLoud name
    putStrLn ("Hello " ++ loudName ++ "!")
    putStrLn ("Oh boy! Am I excited to meet you, " ++ loudName)
-- Don't worry too much about this function; it just capitalises a String
makeLoud :: String -> String
makeLoud s = map toUpper s

This goes wrong...

Couldn't match expected type `IO' against inferred type `[]'
      Expected type: IO t
      Inferred type: String
    In a 'do' expression: loudName <- makeLoud name

This is similar to the problem we ran into above: we've got a mismatch between something that is expecting an IO type, and something which does not produce one. This time, the cause is our use of the left arrow <-; we're trying to left arrow a value of makeLoud name, which really isn't left arrow material. It's basically the same mismatch we saw in the previous section, except now we're trying to use regular old String (the loud name) as an IO String, when those clearly are not the same thing. The latter is an action, something to be run, whereas the former is just an expression minding its own business. We cannot simply use loudName = makeLoud name because a do sequences actions, and loudName = makeLoud name is not an action.

So how do we extricate ourselves from this mess? We have a number of options:

  • We could find a way to turn makeLoud into an action, to make it return IO String. However, we don't want to make actions go out into the world for no reason. Within our program, we can reliably verify how everything is working. When actions engage the outside world, our results are much less predictable. An IO makeLoud could be made somehow, but that would be misguided. Consider another issue though: what if we wanted to use makeLoud from some other, non-IO, function? We really don't want to engage IO actions except when absolutely necessary.
  • We could use a special code called return to promote the loud name into an action, writing something like loudName <- return (makeLoud name). This is slightly better, in that we are at least leaving the makeLoud function itself nice and IO-free, whilst using it in an IO-compatible fashion. That's still moderately clunky because, by virtue of left arrow, we're implying that there's action to be had -- how exciting! -- only to let our reader down with a somewhat anticlimactic return (note: we will learn more about appropriate uses for return in later chapters).
  • Or we could use a let binding...

It turns out that Haskell has a special extra-convenient syntax for let bindings in actions. It looks a little like this:

Example: let bindings in do blocks.

main =
 do name <- getLine
    let loudName = makeLoud name
    putStrLn ("Hello " ++ loudName ++ "!")
    putStrLn ("Oh boy! Am I excited to meet you, " ++ loudName)

If you're paying attention, you might notice that the let binding above is missing an in. This is because let bindings inside do blocks do not require the in keyword. You could very well use it, but then you'd have messy extra do blocks. For what it's worth, the following two blocks of code are equivalent.

sweet unsweet
 do name <- getLine
    let loudName = makeLoud name
    putStrLn ("Hello " ++ loudName ++ "!")
    putStrLn (
        "Oh boy! Am I excited to meet you, "
            ++ loudName)
 do name <- getLine
    let loudName = makeLoud name
    in  do putStrLn ("Hello " ++ loudName ++ "!")
           putStrLn (
               "Oh boy! Am I excited to meet you, "
                   ++ loudName)
  1. Why does the unsweet version of the let binding require an extra do keyword?
  2. Do you always need the extra do?
  3. (extra credit) Curiously, let without in is exactly how we wrote things when we were playing with the interpreter in the beginning of this book. Why is it ok to omit the in keyword in the interpreter, but you'd have to put it in when typing up a source file?

Learn more

At this point, you should have the fundamentals needed to do some fancier input/output. Here are some IO-related topics you may want to check in parallel with the main track of the course.

  • You could continue the sequential track, by learning more about types and eventually monads.
  • Alternately: you could start learning about building graphical user interfaces in the GUI chapter
  • For more IO-related functionality, you could also consider learning more about the System.IO library

Elementary Haskell


Recursion plays a central role in Haskell (and computer science and mathematics in general). Recursion is merely a form of repetition, but sometimes it is taught in a confusing or obscure way. It is easy enough to understand as long as you separate the meaning of a recursive function from its behaviour.

Generally speaking, a recursive function has two parts to its definition. A function is recursive when one part of its definition includes the function itself again. There must also be at least one base case that does not call the function being defined stopping (i.e. termination) condition; otherwise, recursive functions could lead to infinite regress (i.e. an infinite loop).

Numeric recursion

The factorial function

In mathematics, especially combinatorics, there is a function called factorial.[1] It takes a single non-negative integer as an argument, finds all the positive integers less than or equal to "n", and multiplies them all together. For example, the factorial of 6 (denoted as 6!) is 1 \times 2 \times 3 \times 4 \times 5 \times 6 = 720. We can use a recursive style to define this in Haskell:

Let's look at the factorials of two adjacent numbers:

Example: Factorials of consecutive numbers

Factorial of 6 = 6 × 5 × 4 × 3 × 2 × 1
Factorial of 5 =     5 × 4 × 3 × 2 × 1

Notice how we've lined things up. You can see here that the 6! includes the 5!. In fact, 6! is just 6 \times 5!. Let's look at another example:

Example: Factorials of consecutive numbers

Factorial of 4 = 4 × 3 × 2 × 1
Factorial of 3 =     3 × 2 × 1
Factorial of 2 =         2 × 1
Factorial of 1 =             1

The factorial of any number is just that number multiplied by the factorial of the number one less than it. There's one exception: if we ask for the factorial of 0, we don't want to multiply 0 by the factorial of -1 (factorial is only for positive numbers). In fact, we just say the factorial of 0 is 1 (we define it to be so. Just take our word for it that this is right.[2]). So, 0 is the base case for the recursion: when we get to 0 we can immediately say that the answer is 1, no recursion needed. We can summarize the definition of the factorial function as follows:

  • The factorial of 0 is 1.
  • The factorial of any other number is that number multiplied by the factorial of the number one less than it.

We can translate this directly into Haskell:

Example: Factorial function

factorial 0 = 1
factorial n = n * factorial (n - 1)

This defines a new function called factorial. The first line says that the factorial of 0 is 1, and the second line says that the factorial of any other number n is equal to n times the factorial of n - 1. Note the parentheses around the n - 1; without them this would have been parsed as (factorial n) - 1; remember that function application (applying a function to a value) takes precedence over anything else when grouping isn't specified otherwise (we say that function application binds more tightly than anything else).


The factorial function above is best defined in a file, but since it is a small function, it is feasible to write it in GHCi as a one-liner. To do this, we need to add braces (that is, { and } ) and a semicolon:

    > let { factorial 0 = 1; factorial n = n * factorial (n - 1) }

Haskell actually uses line separation and other whitespace as a substitute for these separation and grouping characters. Haskell programmers generally prefer the clean look of separate lines and appropriate indentations, but explicit use of semicolons and other markers is fine whenever preferred. Note that if we left out the braces in this let statement, the function would use only last definition, losing the terminating base case and thus leading to infinite recursion.

The example above demonstrate a very simple relationship between factorial of a number, n, and the factorial of a slightly smaller number, n - 1.

Think of a function call as delegation. The instructions for a recursive function delegate a sub-task. It just so happens that the delegate function uses the same instructions as the delegator; it's only the input data that changes. The only really confusing thing about recursive functions is the fact that each function call uses the same parameter names, so it can be tricky to keep track of the many delegations.

Let's look at what happens when you execute factorial 3:

  • 3 isn't 0, so we calculate the factorial of 2
    • 2 isn't 0, so we calculate the factorial of 1
      • 1 isn't 0, so we calculate the factorial of 0
        • 0 is 0, so we return 1.
      • To complete the calculation for factorial 1, we multiply the current number, 1, by the factorial of 0, which is 1, obtaining 1 (1 × 1).
    • To complete the calculation for factorial 2, we multiply the current number, 2, by the factorial of 1, which is 1, obtaining 2 (2 × 1 × 1).
  • To complete the calculation for factorial 3, we multiply the current number, 3, by the factorial of 2, which is 2, obtaining 6 (3 × 2 × 1 × 1).

(Note that we end up with the one appearing twice, since the base case is 0 rather than 1; but that's okay since multiplying by 1 has no effect. We could have designed factorial to stop at 1 if we had wanted to, but it's conventional, and often useful, to have the factorial of 0 defined.)

We can see how the result of the recursive call is calculated first, then combined using multiplication. Of course, you'll rarely need to "unwind" the recursion like this when reading or composing recursive functions. Compilers have to implement the behaviour, but programmers can work at the abstract level.

One more thing to note about the recursive definition of factorial: the order of the two declarations (one for factorial 0 and one for factorial n) is important. Haskell decides which function definition to use by starting at the top and picking the first one that matches. If we had the general case (factorial n) before the 'base case' (factorial 0), then the general n would match anything passed into it – including 0. The compiler would then conclude that factorial 0 equals 0 * factorial (-1), and so on to negative infinity (which is definitely not what we want). So, always list multiple function definitions starting with the most specific and proceeding to the most general.

  1. Type the factorial function into a Haskell source file and load it into GHCi.
  2. Try examples like factorial 5 and factorial 1000.[3]
    • What about factorial (-1)? Why does this happen?
  3. The double factorial of a number n is the product of every other number from 1 (or 2) up to n. For example, the double factorial of 8 is 8 × 6 × 4 × 2 = 384, and the double factorial of 7 is 7 × 5 × 3 × 1 = 105. Define a doublefactorial function in Haskell.

Loops, recursion and accumulating parameters

Loops are the bread and butter of imperative languages. They are a way to directly specify a sequence of steps to be executed repeatedly by the program. When using an imperative language, loops are often a more natural way of solving problems that would, in Haskell, be dealt with recursion. For example, an idiomatic way of writing a factorial function in C, a typical imperative language, would be using a for loop, like this:

Example: The factorial function in an imperative language

int factorial(int n) {
  int res = 1;
  for ( ; n > 1; n--)
    res *= n;
  return res;

Here, the for loop causes res to be multiplied by n repeatedly. After each repetition, 1 is subtracted from n (that is what n-- does). The repetitions stop when n is no longer greater than 1.

A straightforward translation of such a function to Haskell is not possible, since changing the value of the variables res and n (a destructive update) would not be allowed. However, you can always translate a loop into an equivalent recursive form. The idea is to make each loop variable in need of updating into an argument of a recursive function. For example, here is a recursive "translation" of the above loop into Haskell:

Example: Using recursion to simulate a loop

factorial n = go n 1
    go n res
        | n > 1     = go (n - 1) (res * n)
        | otherwise = res

go is an auxiliary function which actually performs the factorial calculation. It takes an extra argument, res, which is used as an accumulating parameter to build up the final result.


Depending on the languages you are familiar with, you might be concerned about performance problems caused by recursion. As far as Haskell is concerned, you should not be worried. Compilers for Haskell and other functional programming languages include a number of optimisations for recursion, which is not surprising given how often it is needed. Another thing to keep in mind is that Haskell is lazy. That means calculations are only performed once their results are required by other calculations, which helps avoiding some of the performance issues usually associated with recursion. We'll further discuss such issues, and some of the subtleties they involve, in later chapters.

Other recursive functions

As it turns out, there is nothing particularly special about the factorial function; a great many numeric functions can be defined recursively in a natural way. For example, let's think about multiplication. When you were first introduced to multiplication (remember that moment? :)), it may have been through a process of 'repeated addition'. That is, 5 × 4 is the same as summing four copies of the number 5. Of course, summing four copies of 5 is the same as summing three copies, and then adding one more – that is, 5 × 4 = 5 × 3 + 5. This leads us to a natural recursive definition of multiplication:

Example: Multiplication defined recursively

mult _ 0 = 0                      -- anything times 0 is zero
mult n 1 = n                      -- anything times 1 is itself
mult n m = (mult n (m - 1)) + n   -- recurse: multiply by one less, and add an extra copy

Stepping back a bit, we can see how numeric recursion fits into the general recursive pattern. The base case for numeric recursion usually consists of one or more specific numbers (often 0 or 1) for which the answer can be immediately given. The recursive case computes the result by calling the function recursively with a smaller argument and using the result in some manner to produce the final answer. The 'smaller argument' used is often one less than the current argument, leading to recursion which 'walks down the number line' (like the examples of factorial and mult above). However, the prototypical pattern is not the only possibility; the smaller argument could be produced in some other way as well.

  1. Expand out the multiplication 5 × 4 similarly to the expansion we used above for factorial 3.
  2. Define a recursive function power such that power x y raises x to the y power.
  3. You are given a function plusOne x = x + 1. Without using any other (+)s, define a recursive function addition such that addition x y adds x and y together.
  4. (Harder) Implement the function log2, which computes the integer log (base 2) of its argument. That is, log2 computes the exponent of the largest power of 2 which is less than or equal to its argument. For example, log2 16 = 4, log2 11 = 3, and log2 1 = 0. (Small hint: read the last phrase of the paragraph immediately preceding these exercises.)

List-based recursion

A lot of functions in Haskell turn out to be recursive, especially those concerning lists.[4] Let's begin by considering the length function, that finds the length of a list:

Example: The recursive definition of length

length :: [a] -> Int
length []     = 0
length (x:xs) = 1 + length xs

Let's explain the algorithm in English to clarify how it works. The type signature of length tells us that it takes any type of list and produces an Int. The next line says that the length of an empty list is 0; and that, naturally, is the base case. The final line is the recursive case: if a list isn't empty, then it can be broken down into a first element (here called x) and the rest of the list (which will just be the empty list if there are no more elements) which will be called xs (i.e. plural of x). The length of the list is 1 (accounting for the x) plus the length of xs (as in the tail example in Next steps, xs is set when the argument list matches the (:) pattern).

How about the concatenation function (++), which joins two lists together?

Example: The recursive (++)

Prelude> [1,2,3] ++ [4,5,6]
Prelude> "Hello " ++ "world" -- Strings are lists of Chars
"Hello world"
(++) :: [a] -> [a] -> [a]
[] ++ ys     = ys
(x:xs) ++ ys = x : xs ++ ys

This is a little more complicated than length but not too difficult once we break it down. The type says that (++) takes two lists of the same type and produces another list of the same type. The base case says that concatenating the empty list with a list ys is the same as ys itself. Finally, the recursive case breaks the first list into its head (x) and tail (xs) and says that to concatenate the two lists, concatenate the tail of the first list with the second list, and then tack the head x on the front.

There's a pattern here: with list-based functions, the base case usually involves an empty list, and the recursive case involves passing the tail of the list to our function again, so that the list becomes progressively smaller.


Give recursive definitions for the following list-based functions. In each case, think what the base case would be, then think what the general case would look like, in terms of everything smaller than it. (Note that all of these functions are available in Prelude, so you will want to give them different names when testing your definitions in GHCi.)

  1. replicate :: Int -> a -> [a], which takes a count and an element and returns the list which is that element repeated that many times. E.g. replicate 3 'a' = "aaa". (Hint: think about what replicate of anything with a count of 0 should be; a count of 0 is your 'base case'.)
  2. (!!) :: [a] -> Int -> a, which returns the element at the given 'index'. The first element is at index 0, the second at index 1, and so on. Note that with this function, you're recursing both numerically and down a list[5].
  3. (A bit harder.) zip :: [a] -> [b] -> [(a, b)], which takes two lists and 'zips' them together, so that the first pair in the resulting list is the first two elements of the two lists, and so on. E.g. zip [1,2,3] "abc" = [(1, 'a'), (2, 'b'), (3, 'c')]. If either of the lists is shorter than the other, you can stop once either list runs out. E.g. zip [1,2] "abc" = [(1, 'a'), (2, 'b')].

  4. Define length using an auxiliary function and an accumulating parameter, as in the loop-like alternate version of factorial.

Recursion is used to define nearly all functions to do with lists and numbers. The next time you need a list-based algorithm, start with a case for the empty list and a case for the non-empty list and see if your algorithm is recursive.

Don't get TOO excited about recursion...

Although it's very important to have a solid understanding of recursion when programming in Haskell, one rarely has to write functions that are explicitly recursive. Instead, there are all sorts of standard library functions which perform recursion for you in various ways, and one usually ends up using those instead. For example, a much simpler way to implement the factorial function is as follows:

Example: Implementing factorial with a standard library function

factorial n = product [1..n]

Almost seems like cheating, doesn't it? :) This is the version of factorial that most experienced Haskell programmers would write, rather than the explicitly recursive version we started out with. Of course, the product function is using some list recursion behind the scenes,[6] but writing factorial in this way means you, the programmer, don't have to worry about it.


  1. In mathematics, n! normally means the factorial of a non-negative integer n, but that syntax is impossible in Haskell, so we don't use it here.
  2. Actually, defining the factorial of 0 to be 1 is not just arbitrary; it's because the factorial of 0 represents an empty product.
  3. Interestingly, older scientific calculators can't handle things like factorial of 1000 because they run out of memory with that many digits!
  4. This is no coincidence; without mutable variables, recursion is the only way to implement control structures. This might sound like a limitation until you get used to it.
  5. Incidentally, (!!) provides a reasonable solution for the problem of the fourth exercise in Lists and tuples/Retrieving values.
  6. Actually, it's using a function called foldl, which actually does the recursion.

More about lists

We have already met the basic tools for working with lists. We can build lists up from the cons operator (:) and the empty list [], and we can take them apart by using a combination of recursion and pattern matching. In this chapter and the next, we will consider more in-depth techniques for list processing and discover a bit of new notation. Here, we will get our first taste of characteristic Haskell features like infinite lists, list comprehensions, and higher-order functions.


Throughout this chapter, you will read and write functions which sum, subtract, and multiply elements of lists. For simplicity's sake, we will pretend the list elements have to be of type Integer. However, as you will recall from the discussions on Type basics II, there are many different types with the Num typeclass. As an exercise of sorts, you could figure out what the type signatures of such functions would be if we made them polymorphic, allowing for the list elements to have any type in the class Num. To check your signatures, just omit them temporarily, load the functions into GHCi, use :t and let type inference guide you.

Rebuilding lists

We'll start by writing and analysing a function that doubles every element from a list of integers:

doubleList :: [Integer] -> [Integer]
doubleList [] = []
doubleList (n:ns) = (2 * n) : doubleList ns

Here, the base case is the empty list; and it evaluates to an empty list. Otherwise, doubleList builds up a new list by using (:). The first element of this new list is twice the head of the argument, and the rest of the result is obtained by recursively calling doubleList on the tail of the argument. If the tail happens to be an empty list, the base case will be invoked and recursion stops.[1]

By understanding the recursive definition, we can picture what actually happens when we evaluate an expression such as

doubleList [1,2,3,4]

We can work it out longhand by substituting the argument into the function definition, just like schoolbook algebra:

doubleList 1:[2,3,4] = (1*2) : doubleList (2 : [3,4])
                     = (1*2) : (2*2) : doubleList (3 : [4])
                     = (1*2) : (2*2) : (3*2) : doubleList (4 : [])
                     = (1*2) : (2*2) : (3*2) : (4*2) : doubleList []
                     = (1*2) : (2*2) : (3*2) : (4*2) : []
                     = 2 : 4 : 6 : 8 : []
                     = [2, 4, 6, 8]

Thus, we rebuilt the original list replacing every element by its double.

In this longhand evaluation exercise, the moment at which we choose to evaluate the multiplications does not affect the result. We could just as well have done them immediately after each recursive call of doubleList.[2]

This flexibility on evaluation order is reflected in some important properties of Haskell. As a pure functional programming language, it is mostly left to the compiler to decide when to actually evaluate things. As a lazy evaluation language, evaluation is usually deferred until the value is really needed, which may even be never in some cases.[3] From the programmer's point of view, evaluation order rarely matters.[4]


Suppose that we are solving a problem for which we need not only a function to double a list but also one that tripled it. In principle, we could follow the same strategy and define:

tripleList :: [Integer] -> [Integer]
tripleList [] = []
tripleList (n:ns) = (3 * n) : tripleList ns

Both doubleList and tripleList have very limited applicability. Every time we needed multiplying the elements of a list by 4, 8, 17 etc. we would need to write a new list-multiplying function, and all of them would do nearly the same thing. An obvious improvement would be generalizing our function to allow multiplication by any number. Doing so requires a function that takes an Integer multiplicand as well as a list of Integers. Here is a way to define it:

multiplyList :: Integer -> [Integer] -> [Integer]
multiplyList _ [] = []
multiplyList m (n:ns) = (m * n) : multiplyList m ns

This example deploys _ as a "don't care" pattern. The multiplicand is not used for the base case, so instead of giving it a name (like m, n or ns) it is explicitly ignored.

multiplyList solves our current problem for any integer number:

Prelude> multiplyList 17 [1,2,3,4]

Write the following functions and test them out. Don't forget the type signatures.

  1. takeInt returns the first n items in a list. So takeInt 4 [11,21,31,41,51,61] returns [11,21,31,41].
  2. dropInt drops the first n items in a list and returns the rest. so dropInt 3 [11,21,31,41,51] returns [41,51].
  3. sumInt returns the sum of the items in a list.
  4. scanSum adds the items in a list and returns a list of the running totals. So scanSum [2,3,4,5] returns [2,5,9,14].
  5. diffs returns a list of the differences between adjacent items. So diffs [3,5,6,8] returns [2,1,2]. (Hints: one solution involves writing an auxiliary function which takes two lists and calculates the difference between corresponding. Alternatively, you might explore the fact that lists with at least two elements can be matched to a (x:y:ys) pattern.)
The first three functions are in Prelude, under the names take, drop, and sum.

Generalizing even further

We just wrote a function which can multiply the elements of a list by any Integer. But even though multiplyList is much more flexible than doubleList, it's still limited. Initially, we had a function constrained to multiplying the elements by 2, and we had to hard-code a new function to use a different number. With multiplyList, we could still complain about being constrained to apply multiplication to the list elements. What if we wanted to instead add to each element or to compute the square of each element? We would be back rewriting the recursive function for each case.

A key functionality of Haskell will save the day. Since the solution can be quite surprising, we will approach it in a somewhat roundabout way. Consider the type signature of multiplyList:

multiplyList :: Integer -> [Integer] -> [Integer]

The first thing to know is that the -> arrow in type signatures is right associative. That means we can read this signature as:

multiplyList :: Integer -> ([Integer] -> [Integer])

How should we understand that? It tells us that multiplyList is a function that takes one Integer argument and evaluates to another function, which in turn takes a list of Integers and returns another list of Integers.

Consider our earlier doubleList function redefined in terms of multiplyList:

doubleList :: [Integer] -> [Integer]
doubleList xs = multiplyList 2 xs

A practical consequence of what we have just said is that we can, metaphorically speaking, cancel out the `xs`:

doubleList = multiplyList 2

This definition style (with no argument variables) is called "point-free" style. The expressions here are perfectly well-formed and stands on their own, so writing the second argument of multiplyList (which is the same as the only argument of doubleList) is strictly unnecessary. Applying only one argument to multiplyList doesn't fail to evaluate, it just gives us just a more specific function of type [Integer] -> [Integer] instead of finishing with a final [Integer] value.

The trickery above illustrates that functions in Haskell behave much like any other value. Functions that return other functions, and functions can stand alone as objects without mentioning their arguments. It's as though functions were just a normal constants. Could we use functions themselves as arguments even? Well, that's the key to our dilemma with multiplyList. We need a function that takes not only multiplication but any other appropriate function and applies the given function to the elements of a list:

applyToIntegers :: (Integer -> Integer) -> [Integer] -> [Integer]
applyToIntegers _ [] = []
applyToIntegers f (n:ns) = (f n) : applyToIntegers f ns

With applyToIntegers, we can take any Integer -> Integer function and apply it to the elements of a list of Integers. We can, of course, use this generalized function to redefine multiplyList. Specifically, we just use the (*) function as the first argument for applyToIntegers:

multiplyList :: Integer -> [Integer] -> [Integer]
multiplyList = applyToIntegers (*)

If all this abstraction is confusing you, consider a concrete example: When we multiply 5 * 7 in Haskell, the (*) function doesn't just take two arguments at once, it actually first takes the 5 and returns a new 5* function; and that new function then takes one argument and multiplies whatever that is by 5. So, we then give the 7 to the 5* function, and that returns us our final evaluated number (35 in this example).

So, all functions in Haskell really take only one argument. However, as we know how many intermediate functions will have to be generated before our final result is returned, we can treat our functions as if they took many arguments. The number of arguments we generally talk about functions taking is actually the number of functions of one argument between the first argument and the final, non-functional result value.

The process of creating intermediate functions when feeding arguments into a complex function is called currying (named after Haskell Curry, who is also the namesake of the Haskell programming language).

The map function

While applyToIntegers has type (Integer -> Integer) -> [Integer] -> [Integer], there is nothing specific to integers in its algorithm. Therefore, we could define versions such as applyToChars, applyToStrings and applyToLists just by changing the type signature. That would be horribly wasteful, though: we did not climb all the way up to this point just to need a different function for each type! Furthermore, nothing prevents us from changing the signature to, for instance, (Integer -> String) -> [Integer] -> [String]; thus giving a function that takes a function Integer -> String and returns a function [Integer] -> [String] which applies the function originally passed as argument to each element of an Integer list.

The final step of generalization, then, is to make a fully polymorphic version of applyToIntegers, with signature (a -> b) -> [a] -> [b]. Such a function already exists in Prelude: it is called map and defined as:

map :: (a -> b) -> [a] -> [b]
map _ [] = []
map f (x:xs) = (f x) : map f xs

Using it, we can effortlessly implement functions as different as...

multiplyList :: Integer -> [Integer] -> [Integer]
multiplyList = map (*)

... and...

heads :: [[a]] -> [a]
heads = map head
Prelude> heads [[1,2,3,4],[4,3,2,1],[5,10,15]]

map is the ideal general solution for applying a function to each element of a list. Our original doubleList problem was just a very specific version of map. Functions like map which take other functions as arguments are called higher-order functions, and they are extremely useful. In particular, we will meet some other important higher-order functions used for list processing in the next chapter.

  1. Use map to build functions that, given a list xs of Ints, return:
    • A list that is the element-wise negation of xs.
    • A list of lists of Ints xss that, for each element of xs, contains the divisors of xs. You can use the following function to get the divisors:
      divisors p = [ f | f <- [1..p], p `mod` f == 0 ]
    • The element-wise negation of xss.
  2. Implement a Run Length Encoding (RLE) encoder and decoder.
    • The idea of RLE is simple; given some input:
      compress it by taking the length of each run of characters:(4,'a'), (2, 'b'), (3, 'a')
    • The concat and group functions might be helpful. In order to use group, you will need to import the Data.List module. You can access this by typing :m Data.List at the ghci prompt, or by adding import Data.List to your Haskell source code file.
    • What is the type of your encode and decode functions?
    • How would you convert the list of tuples (e.g. [(4,'a'), (6,'b')]) into a string (e.g. "4a6b")?
    • (bonus) Assuming numeric characters are forbidden in the original string, how would you parse that string back into a list of tuples?

Tips and Tricks

Before we carry on with more list processing, let us make a few miscellaneous useful observations about lists in Haskell.

Dot Dot Notation

Haskell has a convenient shorthand for writing ordered lists of regularly-spaced integers. Some examples to illustrate it:

Code             Result
----             ------
[1..10]          [1,2,3,4,5,6,7,8,9,10]
[2,4..10]        [2,4,6,8,10]
[5,4..1]         [5,4,3,2,1]
[1,3..10]        [1,3,5,7,9]

The same notation can be used with characters as well, and even with floating point numbers - though the latter is not necessarily a good idea due to rounding errors. Try this:

[0,0.1 .. 1]


The .. notation only works with sequences with fixed differences between consecutive elements. For instance, you cannot write...


... and expect to magically get back the rest of the Fibonacci series.[5]

Infinite Lists

One of the most mind-bending things about Haskell lists is that they are allowed to be infinite. For example, the following generates the infinite list of integers starting with 1:


(If you try this in GHCi, remember you can stop an evaluation with Ctrl-c).

The same effect could be achieved with a recursive function:

intsFrom n = n : intsFrom (n + 1) -- note there is no base case!
positiveInts = intsFrom 1

This works because Haskell uses lazy evaluation: it never actually evaluates more than it needs at any given moment. In most cases an infinite list can be treated just like an ordinary one. The program will only go into an infinite loop when evaluation would actually require all the values in the list. Examples of this include sorting or printing the entire list. However:

evens = doubleList [1..]

will define "evens" to be the infinite list [2,4,6,8..]. You can pass "evens" into other functions, and this will work as long as you only need to evaluate part of the list for your final result.

Infinite lists are quite useful in Haskell. Often it's more convenient to define an infinite list and then take the first few items than to create a finite list. Functions that process two lists in parallel generally stop with the shortest, so making the second one infinite avoids having to find the length of the first. An infinite list is often a handy alternative to the traditional endless loop at the top level of an interactive program.

A note about head and tail

Given the choice of using either the ( : ) pattern or head/tail to split lists, pattern matching is almost always preferable. While it may be tempting to use head and tail due to simplicity and terseness, it is all too easy to forget that they fail on empty lists - and runtime crashes are never a good thing. While the Prelude function null :: [a] -> Bool provides a sane way of checking for empty lists without pattern matching (it returns True for empty lists and False otherwise), matching an empty list tends to be cleaner and clearer than the corresponding if-then-else expression.

  1. With respect to your solutions to the first set of exercises in this chapter, is there any difference between scanSum (takeInt 10 [1..]) and takeInt 10 (scanSum [1..])?
  2. Write functions that, when applied to lists, give the last element of the list and the list with the last element dropped.
    This functionality is provided by Prelude through the last and init functions. Like head and tail, they blow up when given empty lists.


  1. Had we forgotten the base case, once the recursion got to an empty list the (x:xs) pattern match would fail, and we would get an error.
  2. As long as none of the calculations result in an error or nontermination, which are not problems in this case.
  3. The compiler is still free to evaluate things sooner if it will improve efficiency.
  4. One exception is the case of infinite lists (!) which we will consider in a short while.
  5. http://en.wikipedia.org/wiki/Fibonacci_number

List processing


A fold is a higher order function that, like map, takes a function and a list. However, instead of applying the function element by element, the fold uses it to combine the list elements into a result value.

Let's start looking at a few concrete examples. A function like sum might be implemented as follows:

Example: sum

sum :: [Integer] -> Integer
sum []     = 0
sum (x:xs) = x + sum xs

or product:

Example: product

product :: [Integer] -> Integer
product []     = 1
product (x:xs) = x * product xs

or concat, which takes a list of lists and joins (concatenates) them into one:

Example: concat

concat :: [[a]] -> [a]
concat []     = []
concat (x:xs) = x ++ concat xs

There is a certain pattern of recursion common to all of these examples. This pattern is known as a fold, possibly from the idea that a list is being "folded up" into a single value, or that a function is being "folded between" the elements of the list.

The Standard Prelude defines four fold functions: foldr, foldl, foldr1 and foldl1.


The right-associative foldr folds up a list from the right to left. As it proceeds, foldr uses the given function to combine each of the element with the running value called the accumulator. When calling foldr, the initial value of the accumulator is set as an argument.

foldr            :: (a -> b -> b) -> b -> [a] -> b
foldr f acc []     = acc
foldr f acc (x:xs) = f x (foldr f acc xs)

The first argument is a function with two arguments, the second is a "zero" value for the accumulator, and the third is the list to be folded.

In sum, f is (+), and acc is 0, and in concat, f is (++) and acc is []. In many cases, like all of our examples so far, the function passed to a fold will have both its arguments be of the same type, but this is not necessarily the case in general.

What foldr f acc xs does is to replace each cons (:) in the xs list with the function f, and the empty list at the end with acc.

a : b : c : []


f a (f b (f c acc))

Note how the parentheses nest around the right end of the list.

An elegant visualisation is given by picturing the list data structure as a tree:

  :                         f
 / \                       / \
a   :       foldr f acc   a   f
   / \    ------------->     / \
  b   :                     b   f
     / \                       / \
    c  []                     c   acc

We can see here that foldr (:) [] would return the list completely unchanged. That sort of function that has no effect is called an identity function. You should start building a habit of looking for identity functions in different cases, and we'll discuss them more later when we learn about monoids.


The left-associative foldl processes the list in the opposite direction, starting at the left side with the first element.

foldl            :: (a -> b -> a) -> a -> [b] -> a
foldl f acc []     =  acc
foldl f acc (x:xs) =  foldl f (f acc x) xs

So brackets in the resulting expression accumulate around the left end of the list. Our list above, after being transformed by foldl f acc becomes:

f (f (f acc a) b) c

The corresponding trees look like:

  :                            f
 / \                          / \
a   :       foldl f acc      f   c
   / \    ------------->    / \
  b   :                    f   b 
     / \                  / \
    c  []                acc a

Because all folds include both left and right elements, beginners can get confused by the names. You could think of foldr as short for fold-right-to-left and foldl as fold-left-to-right. The names refer to where the fold starts.


Technical Note: foldl is tail-recursive, that is, it recurses immediately, calling itself. For this reason the compiler will optimise it to a simple loop, which is a good thing performance-wise. However, Haskell is a lazy language, and so the calls to f will be left unevaluated by default, thus building up an unevaluated expression in memory that includes the entire length of the list. To avoid running out of memory, there is a version of foldl called foldl' that is strict in that it forces the evaluation of f immediately at each step.

An apostrophe at the end of a function name is pronounced "tick" as in "fold-L-tick". A tick is a valid character in Haskell identifiers. foldl' can be found in the Data.List library module (which can be imported by adding import Data.List to the beginning of a source file). As a rule of thumb, you should use foldr on lists that might be infinite or where the fold is building up a data structure and use foldl' if the list is known to be finite and comes down to a single value. There is almost never a good reason to use foldl (without the tick), though it might just work if the lists fed to it are not too long.

foldr1 and foldl1

As previously noted, the type declaration for foldr makes it quite possible for the list elements and result to be of different types. For example, "read" is a function that takes a string and converts it into some type (the type system is smart enough to figure out which one). In this case we convert it into a float.

Example: The list elements and results can have different types

addStr :: String -> Float -> Float
addStr str x = read str + x
sumStr :: [String] -> Float
sumStr = foldr addStr 0.0

If you substitute the types Float and String for the type variables a and b in the type of foldr you will see that this is type correct.

There is also a variant called foldr1 ("fold - R - one") which dispenses with an explicit zero by taking the last element of the list instead:

foldr1           :: (a -> a -> a) -> [a] -> a
foldr1 f [x]     =  x
foldr1 f (x:xs)  =  f x (foldr1 f xs)
foldr1 _ []      =  error "Prelude.foldr1: empty list"

And foldl1 as well:

foldl1           :: (a -> a -> a) -> [a] -> a
foldl1 f (x:xs)  =  foldl f x xs
foldl1 _ []      =  error "Prelude.foldl1: empty list"

Note: Just like for foldl, the Data.List library includes foldl1' as a strict version of foldl1.

With foldl1 and foldr1, all the types have to be the same and an empty list is an error. These variants are useful when there is no obvious candidate for the initial accumulator value and we are sure that the list is not going to be empty. When in doubt, stick with foldr or foldl'.

folds and laziness

One reason that right-associative folds are more natural to use in Haskell than left-associative ones is that right folds can operate on infinite lists. A fold that returns an infinite list is perfectly usable in a larger context that doesn't need to access the entire infinite result. In that case, foldr can move along as much as is needed and the compiler will not do any extra. However, a left fold necessarily calls itself recursively until it reaches the end of the input list (because the recursive call is not made in an argument to f). Needless to say, no end will be reached if an input list to foldl is infinite.

As a toy example, consider a function echoes that takes a list of integers and produces a list such that wherever the number n occurs in the input list, it is replicated n times in the output list. To create our echoes function, we will use the prelude function replicate in which replicate n x is a list of length n with x the value of every element.

We can write echoes as a foldr quite handily:

echoes = foldr (\ x xs -> (replicate x x) ++ xs) []

Note: This definition is very compact thanks to the \ x xs -> syntax. Instead of defining a function somewhere else and passing it to foldr we provided the definition in situ. x and xs being the arguments and the right-hand side of the definition being what is after the ->

or, equally handily, as a foldl:

echoes = foldl (\xs x -> xs ++ (replicate x x)) []

but only the foldr version works on an infinite list like [1..]. Try it! (If you try this in GHCi, remember you can stop an evaluation with Ctrl-c, but you have to be quick and keep an eye on the system monitor or your memory will be consumed in no time and your system will hang.)

As a final example, another thing that you might notice is that map itself can be implemented as a fold:

map f = foldr (\x xs -> f x : xs) []

Folding takes a little time to get used to, but it is a fundamental pattern in functional programming and eventually becomes very natural. Any time you want to traverse a list and build up a result from its members, you likely want a fold.

  1. Define the following functions recursively (like the definitions for sum, product and concat above), then turn them into a fold:
    • and :: [Bool] -> Bool, which returns True if a list of Bools are all True, and False otherwise.
    • or :: [Bool] -> Bool, which returns True if any of a list of Bools are True, and False otherwise.
  2. Define the following functions using foldl1 or foldr1:
    • maximum :: Ord a => [a] -> a, which returns the maximum element of a list (hint: max :: Ord a => a -> a -> a returns the maximum of two values).
    • minimum :: Ord a => [a] -> a, which returns the minimum element of a list (hint: min :: Ord a => a -> a -> a returns the minimum of two values).
  3. Use a fold (which one?) to define reverse :: [a] -> [a], which returns a list with the elements in reverse order.
Note that all of these are Prelude functions, so they will be always close at hand when you need them. (Also, that means you must use slightly different names in order to test your answers in GHCi.)


A "scan" is much like a cross between a map and a fold. Folding a list accumulates a single return value, whereas mapping puts each item through a function with no accumulation. A scan does both: it accumulates a value like a fold, but instead of returning a final value it returns a list of all the intermediate values.

The Standard Prelude contains four scan functions:

scanl   :: (a -> b -> a) -> a -> [b] -> [a]

This accumulates the list from the left, and the second argument becomes the first item in the resulting list. So scanl (+) 0 [1,2,3] = [0,1,3,6].

scanl1  :: (a -> a -> a) -> [a] -> [a]

This is the same as scanl, but uses the first item of the list as a zero parameter. It is what you would typically use if the input and output items are the same type. Notice the difference in the type signatures. scanl1 (+) [1,2,3] = [1,3,6].

scanr   :: (a -> b -> b) -> b -> [a] -> [b]
scanr1  :: (a -> a -> a) -> [a] -> [a]

These two functions are the exact counterparts of scanl and scanl1. They accumulate the totals from the right. So:

scanr (+) 0 [1,2,3] = [6,5,3,0]
scanr1 (+) [1,2,3] = [6,5,3]
  1. Write your own definition of scanr, first using recursion, and then using foldr. Do the same for scanl first using recursion then foldl.
  2. Define the following functions:
    • factList :: Integer -> [Integer], which returns a list of factorials from 1 up to its argument. For example, factList 4 = [1,2,6,24].
More to be added


A very common operation performed on lists is filtering, which means generating a new list composed only of elements of the first list that meet a certain condition. One simple example of that would be taking a list of integers and making from it a list which only retains its even numbers.

retainEven :: [Int] -> [Int]
retainEven [] = []
retainEven (n:ns) =
-- mod n 2 computes the remainder for the integer division of n by 2, so if it is zero the number is even
  if (mod n 2) == 0
    then n : (retainEven ns)
    else retainEven ns

That works fine, but it is a slightly verbose solution. It would be nice to have a more concise and general way to write the filter function. This is already provided by Prelude as filter with the following type signature:

filter :: (a -> Bool) -> [a] -> [a]

So, a (a -> Bool) function tests an elements for the condition, there is a list to be filtered, and it returns the filtered list. In order to write retainEven using filter, we need to state the condition as an auxiliary (a -> Bool) function, like this one:

isEven :: Int -> Bool 
isEven n = (mod n 2) == 0

And then retainEven becomes simply:

retainEven ns = filter isEven ns

We used ns instead of xs just because it is sensible to indicate that we know these are numbers and not just anything, but we can ignore that and use a more terse point-free definition:

retainEven = filter isEven

This is just like what we demonstrated before for map and the folds. Like filter, those take another function as argument; and using them point-free emphasizes this "functions-of-functions" aspect.

List comprehensions

A a powerful, concise, and expressive syntactic tool for list processing is the list comprehension. Among other things, list comprehensions can be syntactic sugar for filtering. Instead of using the Prelude filter, we could write retainEven like this:

retainEven es = [n | n <- es, isEven n]

This compact syntax may look a bit intimidating at first, but it is simple to break down. One interpretation is:

  • (Starting from the middle) Take the list es and draw (the "<-") each of its elements as a value n.
  • (After the comma) For each drawn n test the boolean condition isEven n.
  • (Before the vertical bar) If (and only if) the boolean condition is satisfied, append n to the new list being created (note the square brackets around the whole expression).

Thus, if es is equal to [1,2,3,4], then we would get back the list [2,4]. 1 and 3 were not drawn because (isEven n) == False .

The power of list comprehensions comes from being easily extensible. Firstly, we can use as many tests as we wish (even zero!). Multiple conditions are written as a comma-separated list of expressions (which should evaluate to a Boolean, of course). For a simple example, suppose we want to modify retainEven so that only numbers larger than 100 are retained:

retainLargeEvens :: [Int] -> [Int]
retainLargeEvens es = [n | n <- es, isEven n, n > 100]

Furthermore, we are not limited to using n as the element to be appended when generating a new list. Instead, we could place any expression before the vertical bar (if it is compatible with the type of the list, of course). For instance, if we wanted to subtract one from every even number, all it would take is:

evensMinusOne es = [n - 1 | n <- es, isEven n]

In effect, that means the list comprehension syntax incorporates the functionality of map as well as of filter. Now that is concise (and still readable too)!

To further sweeten things, the left arrow notation in list comprehensions can be combined with pattern matching. For example, suppose we had a list of (Int, Int) tuples and we would like to construct a list with the first element of every tuple whose second element is even. Using list comprehensions, we might write it as follows:

firstForEvenSeconds :: [(Int, Int)] -> [Int]
firstForEvenSeconds ps = [fst p | p <- ps, isEven (snd p)] -- here, p is for pairs.

Patterns can make it much more readable:

firstForEvenSeconds ps = [x | (x, y) <- ps, isEven y]

As in other cases, arbitrary expressions may be used before the |. If we wanted a list with the double of those first elements:

doubleOfFirstForEvenSeconds :: [(Int, Int)] -> [Int]
doubleOfFirstForEvenSeconds ps = [2 * x | (x, y) <- ps, isEven y]

Not counting spaces, that function code is shorter than its descriptive name!

There are even more possible tricks:

allPairs :: [(Int, Int)]
allPairs = [(x, y) | x <- [1..4], y <- [5..8]]

This comprehension draws from two lists, and generates all possible (x, y) pairs with the first element in [1..4] and the second in [5..8]. In the final list of pairs, the first elements will be those generated with the first element of the first list (here, 1), then those with the second element of the first list, and so on. In this example, the full list is (linebreaks added for clarity):

Prelude> [(x, y) | x <- [1..4], y <- [5..8]]

Note that we didn't do any filtering here; but we could easily add a condition to restrict the combinations that go into the final list:

somePairs = [(x, y) | x <- [1..4], y <- [5..8], x + y > 8]

This lists only has the pairs with the sum of elements larger than 8; starting with (1,8), then (2,7) and so forth.

  1. Write a returnDivisible :: Int -> [Int] -> [Int] function which filters a list of integers retaining only the numbers divisible by the integer passed as first argument. For integers x and n, x is divisible by n if (mod x n) == 0 (note that the test for evenness is a specific case of that).
    • Write, using list comprehension syntax, a function definition with no case analysis (that is, without multiple equations, if, case, or similar constructs) a [[Int]] -> [[Int]] function which, takes a list of lists of Int and returns a list of the tails of those lists using, as filtering condition, that the head of each [Int] must be larger than 5. Also, your function must not trigger an error when it meets an empty [Int], so you'll need to add an additional test to detect emptiness.
    • Does order matter when listing the conditions for list comprehension? (You can find it out by playing with the function you wrote for the first part of the exercise.)
  2. Over this section we've seen how list comprehensions are essentially syntactic sugar for filter and map. Now work in the opposite direction and define alternative versions of the filter and map using the list comprehension syntax.
  3. Rewrite doubleOfFirstForEvenSeconds using filter and map instead of list comprehension.

Type declarations

You're not restricted to working with just the types provided by default with the language. There are many benefits to defining your own types:

  • Code can be written in terms of the problem being solved, making programs easier to design, write and understand.
  • Related pieces of data can be brought together in ways more convenient and meaningful than simply putting and getting values from lists or tuples.
  • Pattern matching and the type system can be used to their fullest extent by making them work with your custom types.

Haskell has three basic ways to declare a new type:

  • The data declaration, which defines new data types.
  • The type declaration for type synonyms.
  • The newtype declaration, which is a cross between the other two.

In this chapter, we will discuss data. We'll also mention type, which is a convenience feature. We will explain newtype later on, but don't worry too much about it; it exists mainly for optimisation.

data and constructor functions

data is used to define new data types using existing ones as building blocks. Here's a data structure for elements in a simple list of anniversaries:

data Anniversary = Birthday String Int Int Int       -- name, year, month, day
                 | Wedding String String Int Int Int -- spouse name 1, spouse name 2, year, month, day

This declares a new data type Anniversary, which can be either a Birthday or a Wedding. A Birthday contains one string and three integers and a Wedding contains two strings and three integers. The definitions of the two possibilities are separated by the vertical bar. The comments explain to readers of the code about the intended use of these new types. Moreover, with the declaration we also get two constructor functions for Anniversary; appropriately enough, they are called Birthday and Wedding. These functions provide a way to build a new Anniversary.

Types defined by data declarations are often referred to as algebraic data types, which is something we will address further in later chapters.

As usual with Haskell, the case of the first letter is important: type names and constructor functions must start with capital letters. Other than this syntactic detail, constructor functions work pretty much like the "conventional" functions we met so far. In fact, if you use :t in GHCi to query the type of, say, Birthday, you'll get:

*Main> :t Birthday
Birthday :: String -> Int -> Int -> Int -> Anniversary

Meaning it's just a function which takes one String and three Int as arguments and evaluates to an Anniversary. This anniversary will contain the four arguments we passed as specified by the Birthday constructor.

Calling constructors is no different from calling other functions. For example, suppose we have John Smith born on 3rd July 1968:

johnSmith :: Anniversary
johnSmith = Birthday "John Smith" 1968 7 3

He married Jane Smith on 4th March 1987:

smithWedding :: Anniversary
smithWedding = Wedding "John Smith" "Jane Smith" 1987 3 4

These two anniversaries can, for instance, be put in a list:

anniversariesOfJohnSmith :: [Anniversary]
anniversariesOfJohnSmith = [johnSmith, smithWedding]

Or you could just as easily have called the constructors straight away when building the list (although the resulting code looks a bit cluttered).

anniversariesOfJohnSmith = [Birthday "John Smith" 1968 7 3, Wedding "John Smith" "Jane Smith" 1987 3 4]

Deconstructing types

To use our new data types, we must have a way to access their contents. For instance, one very basic operation with the anniversaries defined above would be extracting the names and dates they contain as a String. So we need a showAnniversary function (for the sake of code clarity, we used an auxiliary showDate function but let's ignore it for a moment):

showDate :: Int -> Int -> Int -> String
showDate y m d = show y ++ "-" ++ show m ++ "-" ++ show d
showAnniversary :: Anniversary -> String
showAnniversary (Birthday name year month day) =
   name ++ " born " ++ showDate year month day
showAnniversary (Wedding name1 name2 year month day) =
   name1 ++ " married " ++ name2 ++ " on " ++ showDate year month day

This example shows how we can deconstruct the values built in our data types. showAnniversary takes a single argument of type Anniversary. Instead of just providing a name for the argument on the left side of the definition, however, we specify one of the constructor functions and give names to each argument of the constructor (which correspond to the contents of the Anniversary). A more formal way of describing this "giving names" process is to say we are binding variables. "Binding" is being used in the sense of assigning a variable to each of the values so that we can refer to them on the right side of the function definition.

To handle both "Birthday" and "Wedding" Anniversaries, we needed to provide two function definitions, one for each constructor. When showAnniversary is called, if the argument is a Birthday Anniversary, the first definition is used and the variables name, month, date and year are bound to its contents. If the argument is a Wedding Anniversary, then the second definition is used and the variables are bound in the same way. This process of using a different version of the function depending on the type of constructor is pretty much like what happens when we use a case statement or define a function piece-wise.

Note that the parentheses around the constructor name and the bound variables are mandatory; otherwise the compiler or interpreter would not take them as a single argument. Also, it is important to have it absolutely clear that the expression inside the parentheses is not a call to the constructor function, even though it may look just like one.


Note: The solution of this exercise is given near the end of the chapter, so we recommend that you attempt it before getting there.
Reread the function definitions above. Then look closer at the showDate helper function. We said it was provided "for the sake of code clarity", but there is a certain clumsiness in the way it is used. You have to pass three separate Int arguments to it, but these arguments are always linked to each other as part of a single date. It would make no sense to do things like passing the year, month and day values of the Anniversary in a different order, or to pass the month value twice and omit the day.

  • Could we use what we've seen in this chapter so far to reduce this clumsiness?
  • Declare a Date type which is composed of three Int, corresponding to year, month and date. Then, rewrite showDate so that it uses the new Date data type. What changes will then be needed in showAnniversary and the Anniversary for them to make use of Date?.

type for making type synonyms

As mentioned in the introduction of this module, code clarity is one of the motivations for using custom types. In that spirit, it could be nice to make it clear that the Strings in the Anniversary type are being used as names while still being able to manipulate them like ordinary Strings. This calls for a type declaration:

type Name = String

The code above says that a Name is now a synonym for a String. Any function that takes a String will now take a Name as well (and vice-versa: functions that take Name will accept any String). The right hand side of a type declaration can be a more complex type as well. For example, String itself is defined in the standard libraries as

type String = [Char]

We can do something similar for the list of anniversaries we made use of:

type AnniversaryBook = [Anniversary]

Since type synonyms are mostly just a convenience. They help make the roles of types clearer or provide an alias to such things as complicated list or tuple types. It is largely a matter of personal discretion to decide how type synonyms should be deployed. Abuse of synonyms could make code confusing (for instance, picture a long program using multiple names for common types like Int or String simultaneously).

Incorporating the suggested type synonyms and the Date type we proposed in the exercise(*) of the previous section the code we've written so far looks like this:

((*) last chance to try that exercise without looking at the spoilers.)

type Name = String
data Anniversary = 
   Birthday Name Date
   | Wedding Name Name Date
data Date = Date Int Int Int   -- Year, Month, Day
johnSmith :: Anniversary
johnSmith = Birthday "John Smith" (Date 1968 7 3)
smithWedding :: Anniversary
smithWedding = Wedding "John Smith" "Jane Smith" (Date 1987 3 4)
type AnniversaryBook = [Anniversary]
anniversariesOfJohnSmith :: AnniversaryBook
anniversariesOfJohnSmith = [johnSmith, smithWedding]
showDate :: Date -> String
showDate (Date y m d) = show y ++ "-" ++ show m ++ "-" ++ show d 
showAnniversary :: Anniversary -> String
showAnniversary (Birthday name date) =
   name ++ " born " ++ showDate date
showAnniversary (Wedding name1 name2 date) =
   name1 ++ " married " ++ name2 ++ " on " ++ showDate date

Even in a simple example like this one, there is a noticeable gain in simplicity and clarity compared to the same task using only Ints, Strings, and corresponding lists.

Note that the Date type has a constructor function which is called Date as well. That is perfectly valid and indeed giving the constructor the same name of the type when there is just one constructor is good practice, as a simple way of making the role of the function obvious.


After these initial examples, the mechanics of using constructor functions may look a bit unwieldy, particularly if you're familiar with analogous features in other languages. There are syntactical constructs that make dealing with constructors more convenient. These will be dealt with later on, when we return to the topic of constructors and data types to explore them in detail.

Pattern matching

In the previous modules, we introduced and made occasional reference to pattern matching. Now that we have developed some familiarity with the language, it is time to take a proper, deeper look. We will kick-start the discussion with a condensed description, which we will expanded upon throughout the chapter:

In pattern matching, we attempt to match values against patterns and, if so desired, bind variables to successful matches.


Pattern matching on what?

Some languages like Perl and Python use the term pattern matching for matching regular expressions against strings. The pattern matching we refer to in Haskell is something completely different. In fact, you're probably best off forgetting what you know about pattern matching for now.[1] Haskell pattern matching is used in the same way as in other ML-like languages: to deconstruct values according to their type specification.

Analysing pattern matching

Pattern matching is virtually everywhere. For example, consider this definition of map:

map _ []     = []
map f (x:xs) = f x : map f xs

At surface level, there are four different patterns involved, two per equation.

  • f is a pattern which matches anything at all, and binds the f variable to whatever is matched.
  • (x:xs) is a pattern that matches a non-empty list which is formed by something (which gets bound to the x variable) which was cons'd (by the (:) function) onto something else (which gets bound to xs).
  • [] is a pattern that matches the empty list. It doesn't bind any variables.
  • _ is the pattern which matches anything at all, but doesn't do any binding.

In the (x:xs) pattern, x and xs can be seen as sub-patterns used to match the parts of the list. Just like f, they match anything - though it is evident that if there is a successful match and x has type a, xs will have type [a]. Finally, these considerations imply that xs will also match an empty list, and so a one-element list matches (x:xs).

From the above dissection, we can say pattern matching gives us a way to:

  • recognize values. For instance, when map is called and the second argument matches [] the first equation for map is used instead of the second one.
  • bind variables to the recognized values. In this case, the variables f, x, and xs are assigned to the values passed as arguments to map when the second equation is used, and so we can use these values through the variables in the right-hand side of =. As _ and [] show, binding is not an essential part of pattern matching, but just a side effect of using variable names as patterns.
  • break down values into parts, as the (x:xs) pattern does by binding two variables to parts (head and tail) of a matched argument (the non-empty list).

The connection with constructors

Despite the detailed analysis above, it may seem a little too magical how we break down a list as if we were undoing the effects of the (:) operator. Be careful: this process will not work with any arbitrary operator. For example, one might think of defining a function which uses (++) to chop off the first three elements of a list:

dropThree ([x,y,z] ++ xs) = xs

But that will not work. The function (++) is not allowed in patterns. In fact, most other functions that act on lists are similarly prohibited from pattern matching. Which functions, then, are allowed?

In one word, constructors – the functions used to build values of algebraic data types. Let us consider a random example:

data Foo = Bar | Baz Int

Here Bar and Baz are constructors for the type Foo. You can use them for pattern matching Foo values and bind variables to the Int value contained in a Foo constructed with Baz:

f :: Foo -> Int
f Bar     = 1
f (Baz x) = x - 1

This is exactly like showAnniversary and showDate in the Type declarations module. For instance:

data Date = Date Int Int Int   -- Year, Month, Day
showDate :: Date -> String
showDate (Date y m d) = show y ++ "-" ++ show m ++ "-" ++ show d

The (Date y m d) pattern in the left-hand side of the showDate definition matches a Date (built with the Date constructor) and binds the variables y, m and d to the contents of the Date value.

Why does it work with lists?

As for lists, they are no different from data-defined algebraic data types as far as pattern matching is concerned. It works as if lists were defined with this data declaration (note that the following isn't actually valid syntax: lists are actually too deeply ingrained into Haskell to be defined like this):

data [a] = [] | a : [a]

So the empty list, [] and the (:) function are constructors of the list datatype, and so you can pattern match with them. [] takes no arguments, and therefore no variables can be bound when it is used for pattern matching. (:) takes two arguments, the list head and tail, which may then have variables bound to them when the pattern is recognized.

Prelude> :t []
[] :: [a]
Prelude> :t (:)
(:) :: a -> [a] -> [a]

Furthermore, since [x, y, z] is just syntactic sugar for x:y:z:[], we can achieve something like dropThree using pattern matching alone:

dropThree :: [a] -> [a]
dropThree (_:_:_:xs) = xs
dropThree _          = []

The first pattern will match any list with at least three elements. The catch-all second definition provides a reasonable default[2] when lists fail to match the main pattern, and thus prevents runtime crashes due to pattern match failure.


From the fact that we could write a dropThree function with bare pattern matching it doesn't follow that we should do so! Even though the solution is simple, it is still a waste of effort to code something this specific when we could just use Prelude and settle it with drop 3 xs instead. Mirroring what was said before about baking bare recursive functions, we might say: don't get too excited about pattern matching either...

Tuple constructors

Analogous considerations are valid for tuples. Our access to their components via pattern matching...

fstPlusSnd :: (Num a) => (a, a) -> a
fstPlusSnd (x, y) = x + y
norm3D :: (Floating a) => (a, a, a) -> a
norm3D (x, y, z) = sqrt (x^2 + y^2 + z^2)

... is granted by the existence of tuple constructors. For pairs, the constructor is the comma operator, (,); for larger tuples there are (,,); (,,,) and so on. These operators are slightly unusual in that we can't use them infix in the regular way; so 5 , 3 is not a valid way to write (5, 3). All of them, however, can be used prefix, which is occasionally useful.

Prelude> (,) 5 3
Prelude> (,,,) "George" "John" "Paul" "Ringo"

Matching literal values

As discussed earlier in the book, a simple piece-wise function definition like this one

f :: Int -> Int
f 0 = 1
f 1 = 5
f 2 = 2
f _ = -1

is performing pattern matching as well, matching the argument of f with the Int literals 0, 1 and 2, and finally with _ . In general, numeric and character literals can be used in pattern matching on their own[3] as well as together with constructor patterns. For instance, this function

g :: [Int] -> Bool
g (0:[]) = False
g (0:xs) = True
g _ = False

will evaluate to False for the [0] list, to True if the list has 0 as first element and a non-empty tail and to False in all other cases. Also, lists with literal elements like [1,2,3], or even "abc" (which is equivalent to ['a','b','c']) can be used for pattern matching as well, since these forms are only syntactic sugar for the (:) constructor.

The above considerations are only valid for literal values, so the following will not work:

k = 1
--again, this won't work as expected
h :: Int -> Bool
h k = True
h _ = False
  1. Test the flawed h function above in GHCi, with arguments equal to and different from 1. Then, explain what goes wrong.
  2. In this section about pattern matching with literal values, we made no mention of the boolean values True and False, but we can do pattern matching with them as well, as demonstrated in the Next steps chapter. Can you guess why we omitted them? (Hint: is there anything distinctive about the way we write boolean values?)

Syntax tricks


Sometimes, when matching a pattern with a value, it may be useful to bind a name to the whole value being matched. As-patterns allow exactly this: they are of the form var@pattern and have the additional effect to bind the name var to the whole value being matched by pattern. For instance, here is a toy variation on the map theme:

contrivedMap :: ([a] -> a -> b) -> [a] -> [b]
contrivedMap f [] = []
contrivedMap f list@(x:xs) = f list x : contrivedMap f xs

contrivedMap passes to the parameter function f not only x but also the undivided list used as argument of each recursive call. Writing it without as-patterns would have been a bit clunky because we would have to either use head or needlessly reconstruct the original value of list, i.e. actually evaluate x:xs on the right side:

contrivedMap :: ([a] -> a -> b) -> [a] -> [b]
contrivedMap f [] = []
contrivedMap f (x:xs) = f (x:xs) x : contrivedMap f xs
Implement scanr, as in the exercise in List processing, but this time using an as-pattern.

Introduction to records

For constructors with many elements, records provide a way of naming values in a datatype using the following syntax:

data Foo2 = Bar2 | Baz2 {bazNumber::Int, bazName::String}

Using records allows doing matching and binding only for the variables relevant to the function we're writing, making code much clearer:

h :: Foo2 -> Int
h Baz2 {bazName=name} = length name
h Bar2 {} = 0

Also, the {} pattern can be used for matching a constructor regardless of the datatype elements even if you don't use records in the data declaration:

data Foo = Bar | Baz Int
g :: Foo -> Bool
g Bar {} = True
g Baz {} = False

The function g does not have to be changed if we modify the number or the type of elements of the constructors Bar or Baz.

There are further advantages to using record syntax which we will cover records in more detail in the Named fields section of the More on datatypes chapter.

Where we can use pattern matching

The short answer is that wherever you can bind variables, you can pattern match. Let us have a glance at such places we have seen before; a few more will be introduced in the following chapters.


The most obvious use case is the left-hand side of function definition equations, which were the subject of our examples so far.

map _ []     = []
map f (x:xs) = f x : map f xs

In the map definition we're doing pattern matching on the left hand side of both equations, and also binding variables on the second one.

let expressions and where clauses

Both let and where are ways of doing local variable bindings. As such, you can also use pattern matching in them. A simple example:

y =
    (x:_) = map (*2) [1,2,3]
  in x + 5

Or, equivalently,

y = x + 5
  (x:_) = map (*2) [1,2,3]

Here, x will be bound to the first element of map ((*) 2) [1,2,3]. y, therefore, will evaluate to 2 + 5 = 7.

List comprehensions

After the | in list comprehensions you can pattern match. This is actually extremely useful, and adds a lot to the expressiveness of comprehensions. Let's see how that works with a slightly more sophisticated example. Prelude provides a Maybe type which has the following constructors:

data Maybe a = Nothing | Just a

It is typically used to hold values resulting from an operation which may or may not succeed; if the operation succeeds, the Just constructor is used and the value is passed to it; otherwise Nothing is used.[4] The utility function catMaybes (which is available from Data.Maybe library module) takes a list of Maybes (which may contain both "Just" and "Nothing" Maybes), and retrieves the contained values by filtering out the Nothing values and getting rid of the Just wrappers of the Just x. Writing it with list comprehensions is very straightforward:

catMaybes :: [Maybe a] -> [a]
catMaybes ms = [ x | Just x <- ms ]

Another nice thing about using a list comprehension for this task is that if the pattern match fails (that is, it meets a Nothing) it just moves on to the next element in ms, thus avoiding the need of explicitly handling constructors we are not interested in with alternate function definitions.[5]

do blocks

Within a do block like the ones we used in the Simple input and output chapter, we can pattern match with the left-hand side of the left arrow variable bindings:

putFirstChar = do
    (c:_) <- getLine
    putStrLn [c]

Furthermore, the let bindings in do blocks are, as far as pattern matching is concerned, just the same as the "real" let expressions.


  1. If you came here looking for regex pattern matching, you might be interested in looking at the Haskell Text.Regex library wrapper.
  2. Reasonable for this particular task, and only because it makes sense to expect that dropThree will give [] when applied to a list of, say, two elements. With a different problem, it might not be reasonable to return any list if the first match failed. In a later chapter, we will consider one simple way of dealing with such cases.
  3. As perhaps could be expected, this kind of matching with literals is not constructor-based. Rather, there is an equality comparison behind the scenes
  4. The canonical example of such an operation is looking up values in a dictionary - which might just be a [(a, b)] list with the tuples being key-value pairs, or a more sophisticated implementation. In any case, if we, given an arbitrary key, try to retrieve a value there is no guarantee we will actually find a value associated to the key.
  5. The reason why it works this way instead of crashing out on a pattern matching failure has to do with the real nature of list comprehensions: They are actually wrappers for the list monad. We will eventually explain what that means when we discuss monads.

Control structures

Haskell offers several ways of expressing a choice between different values. We explored some of them in the Haskell Basics chapters. This section will bring together what we have seen thus far, discuss some finer points, and introduce a new control structure.

if and guards revisited

We have already met these constructs. The syntax for if expressions is:

if <condition> then <true-value> else <false-value>

<condition> is an expression which evaluates to a boolean. If the <condition> is True then the <true-value> is returned, otherwise the <false-value> is returned. Note that in Haskell if is an expression (which is converted to a value) and not a statement (which is executed) as in many imperative languages.[1] As a consequence, the else is mandatory in Haskell. Since if is an expression, it must evaluate to a result whether the condition is true or false, and the else ensures this. Furthermore, <true-value> and <false-value> must evaluate to the same type, which will be the type of the whole if expression.

When if expressions are split across multiple lines, they are usually indented by aligning elses with thens, rather than with ifs. A common style looks like this:

describeLetter :: Char -> String
describeLetter c =
    if c >= 'a' && c <= 'z'
        then "Lower case"
        else if c >= 'A' && c <= 'Z'
            then "Upper case"
            else "Not an ASCII letter"

Guards and top-level if expressions are mostly interchangeable. With guards, the example above is a little neater:

describeLetter :: Char -> String
describeLetter c
   | c >= 'a' && c <= 'z' = "Lower case"
   | c >= 'A' && c <= 'Z' = "Upper case"
   | otherwise            = "Not an ASCII letter"

Remember that otherwise it is just an alias to True, and thus the last guard is a catch-all, playing the role of the final else of the if expression.

Guards are evaluated in the order they appear. Consider a set up like the following:

f (pattern1) | predicate1 = w
             | predicate2 = x
f (pattern2) | predicate3 = y
             | predicate4 = z

Here, the argument of f will be pattern-matched against pattern1. If it succeeds, then we proceed to the first set of guards: if predicate1 evaluates to True, then w is returned. If not, then predicate2 is evaluated; and if it is true x is returned. Again, if not, then we proceed to the next case and try to match the argument against pattern2, repeating the guards procedure with predicate3 and predicate4. (Of course, if neither pattern matches or neither predicate is true for the matching pattern there will be a runtime error. Regardless of the chosen control structure, it is important to ensure all cases are covered.)

Embedding if expressions

A handy consequence of if constructs being expressions is that they can be placed anywhere a Haskell expression could be, allowing us to write code like this:

g x y = (if x == 0 then 1 else sin x / x) * y

Note that we wrote the if expression without line breaks for maximum terseness. Unlike if expressions, guard blocks are not expressions; and so a let or a where definition is the closest we can get to this style when using them. Needless to say, more complicated one-line if expressions would be hard to read, making let and where attractive options in such cases.

case expressions

One control structure we haven't talked about yet are case expressions. They are to piece-wise function definitions what if expressions are to guards. Take this simple piece-wise definition:

f 0 = 18
f 1 = 15
f 2 = 12
f x = 12 - x

It is equivalent to - and, indeed, syntactic sugar for:

f x = 
    case x of
        0 -> 18
        1 -> 15
        2 -> 12
        _ -> 12 - x

Whatever definition we pick, the same happens when f is called: The argument x is matched against all of the patterns in order; and on the first match the expression on the right-hand side of the corresponding equal sign (in the piece-wise version) or arrow (in the case version) is evaluated. Note that in this case expression there is no need to write x in the pattern; the wildcard pattern _ gives the same effect.[2]

Indentation is important when using case. The cases must be indented further to the right than the beginning of the line containing the of keyword, and all cases must have the same indentation. For the sake of illustration, here are two other valid layouts for a case expression:

f x = case x of
    0 -> 18
    1 -> 15
    2 -> 12
    _ -> 12 - x

f x = case x of 0 -> 18
                1 -> 15
                2 -> 12
                _ -> 12 - x

Since the left hand side of any case branch is just a pattern, it can also be used for binding, exactly like in piece-wise function definitions:[3]

describeString :: String -> String
describeString str = 
  case str of
    (x:xs) -> "The first character of the string is: " ++ [x] ++ "; and " ++
              "there are " ++ show (length xs) ++ " more characters in it."
    []     -> "This is an empty string."

This function describes some properties of str using a human-readable string. Using case syntax to binds variables to the head and tail of our list is convenient here, but you could also do this with an if-statement (with a condition of null str to pick the empty string case).

Finally, just like if expressions (and unlike piece-wise definitions), case expressions can be embedded anywhere another expression would fit:

data Colour = Black | White | RGB Int Int Int
describeBlackOrWhite :: Colour -> String
describeBlackOrWhite c = 
  "This colour is"
  ++ case c of
       Black           -> " black"
       White           -> " white"
       RGB 0 0 0       -> " black"
       RGB 255 255 255 -> " white"
       _               -> "... uh... something else"
  ++ ", yeah?"

The case block above fits in as any string would. Writing describeBlackOrWhite this way makes let/where unnecessary (although though the resulting definition is not as readable).

Use a case statement to implement a fakeIf function which could be used as a replacement to the familiar if expressions.

Controlling actions, revisited

In the final part of this chapter, we will introduce a few extra points about control structures while revisiting the discussions in the "Simple input and output" chapter. There, in the Controlling actions section, we used the following function to show how to execute actions conditionally within a do block using if expressions:

doGuessing num = do
   putStrLn "Enter your guess:"
   guess <- getLine
   if (read guess) < num
     then do putStrLn "Too low!"
             doGuessing num
     else if (read guess) > num
            then do putStrLn "Too high!"
                    doGuessing num
            else do putStrLn "You Win!"

We can write the same doGuessing function using a case statement. To do this, we first introduce the Prelude function compare which takes two values of the same type (in the Ord class) and returns a value of type Ordering — namely one of GT, LT, EQ, depending on whether the first is greater than, less than, or equal to the second.

doGuessing num = do
  putStrLn "Enter your guess:"
  guess <- getLine
  case compare (read guess) num of
    LT -> do putStrLn "Too low!"
             doGuessing num
    GT -> do putStrLn "Too high!"
             doGuessing num
    EQ -> putStrLn "You Win!"

The dos after the ->s are necessary on the first two options, because we are sequencing actions within each case.

A note about return

Now, we are going to dispel a possible source of confusion. In a typical imperative language (C, for example) an implementation of doGuessing might look like the following (if you don't know C, don't worry with the details, just follow the if-else chain):

void doGuessing(int num) {
  printf("Enter your guess:");
  int guess = atoi(readLine());
  if (guess == num) {
    printf("You win!\n");
    return ();
  // we won't get here if guess == num
  if (guess < num) {
    printf("Too low!\n");
  } else {
    printf("Too high!\n");

This doGuessing first tests the equality case, which does not lead to a new call of doGuessing, and the if has no accompanying else. If the guess was right, a return statement is used to exit the function at once, skipping the other cases. Now, going back to Haskell, action sequencing in do blocks looks a lot like imperative code, and furthermore there actually is a return in Prelude. Then, knowing that case statements (unlike if statements) do not force us to cover all cases, one might be tempted to write a literal translation of the C code above (try running it if you are curious)...

doGuessing num = do
  putStrLn "Enter your guess:"
  guess <- getLine
  case compare (read guess) num of
    EQ -> do putStrLn "You win!"
             return ()
  -- we don't expect to get here if guess == num
  if (read guess < num)
    then do print "Too low!";
            doGuessing num
    else do print "Too high!";
            doGuessing num

... but it won't work! If you guess correctly, the function will first print "You win!," but it will not exit at the return (). Instead, the program will continue to the if expression and check whether guess is less than num. Of course it is not, so the else branch is taken, and it will print "Too high!" and then ask you to guess again. Things aren't any better with an incorrect guess: it will try to evaluate the case statement and get either LT or GT as the result of the compare. In either case, it won't have a pattern that matches, and the program will fail immediately with an exception (as usual, the incomplete case alone should be enough to raise suspicion).

The problem here is that return is not at all equivalent to the C (or Java etc.) statement with the same name. For our immediate purposes, we can say that return is a function.[4] The return () in particular evaluates to an action which does nothing. return does not affect the control flow at all. In the correct guess case, the case statement evaluates to return (), an action of type IO (), and execution just follows along normally.

The bottom line is that while actions and do blocks resemble imperative code, they must be dealt with on their own terms - Haskell terms.

  1. Redo the "Haskell greeting" exercise in Simple input and output/Controlling actions, this time using a case statement.
  2. What does the following program print out? And why?
main =
 do x <- getX
    putStrLn x
getX =
 do return "My Shangri-La"
    return "beneath"
    return "the summer moon"
    return "I will"
    return "return"
    return "again"


  1. If you have programmed in C or Java, you will recognize Haskell's if/then/else as an equivalent to the ternary conditional operator ?: .
  2. To see why this is so, consider our discussion of matching and binding in the Pattern matching section
  3. Thus, case statements are a lot more versatile than most of the superficially similar switch/case statements in imperative languages which are typically restricted to equality tests on integral primitive types.
  4. Superfluous note: somewhat closer to a proper explanation, we might say return is a function which takes a value and makes it into an action which, when evaluated, gives the original value. A return "strawberry" within one of the do blocks we are dealing with would have type IO String - the same type as getLine. Do not worry if that doesn't make sense for now; you will understand what return really does when we actually start discussing monads further ahead on the book.

More on functions

Here are several nice features that make using functions easier.

let and where revisited

As discussed in earlier chapters, let and where are useful in local function definitions. Here, sumStr calls addStr function:

addStr :: Float -> String -> Float
addStr x str = x + read str
sumStr :: [String] -> Float
sumStr = foldl addStr 0.0

But what if we never need addStr anywhere else? Then we could rewrite sumStr using local bindings. We can do that either with a let binding...

sumStr =
   let addStr x str = x + read str
   in foldl addStr 0.0

... or with a where clause...

sumStr = foldl addStr 0.0
   where addStr x str = x + read str

... and the difference appears to be just a question of style: Do we prefer the bindings to come before or after the rest of the definition?

However, there is another important difference between let and where. The let...in construct is an expression just like if/then/else. In contrast, where clauses are like guards and so are not expressions. Thus, let bindings can be used within complex expressions:

f x =
    if x > 0
        then (let lsq = (log x) ^ 2 in tan lsq) * sin x
        else 0

The expression within the outer parentheses is self-contained, and evaluates to the tangent of the square of the logarithm of x. Note that the scope of lsq does not extend beyond the parentheses; so changing the then-branch to

        then (let lsq = (log x) ^ 2 in tan lsq) * (sin x + lsq)

does not work without dropping the parentheses around the let.

Despite not being full expressions, where clauses can be incorporated into case expressions:

describeColour c = 
   "This colour "
   ++ case c of
          Black -> "is black"
          White -> "is white"
          RGB red green blue -> " has an average of the components of " ++ show av
             where av = (red + green + blue) `div` 3
   ++ ", yeah?"

In this example, the indentation of the where clause sets the scope of the av variable so that it only exists as far as the RGB red green blue case is concerned. Placing it at the same indentation of the cases would make it available for all cases. Here is an example with guards:

doStuff :: Int -> String
doStuff x
  | x < 3     = report "less than three"
  | otherwise = report "normal"
    report y = "the input is " ++ y

Note that since there is one equals sign for each guard there is no place we could put a let expression which would be in scope of all guards in the manner of the where clause. So this is a situation in which where is particularly convenient.

Anonymous Functions - lambdas

Why create a formal name for a function like addStr when it only exists within another function's definition, never to be used again? Instead, we can make it an anonymous function also known as a lambda function. Then, sumStr could be defined like this:

sumStr = foldl (\ x str -> x + read str) 0.0

The expression in the parentheses is a lambda function. The backslash is used as the nearest ASCII equivalent to the Greek letter lambda (λ). This lambda function takes two arguments, x and str, and it evaluates to "x + read str". So, the sumStr presented just above is precisely the same as the one that used addStr in a let binding.

Lambdas are handy for writing one-off functions to be used with maps, folds and their siblings, especially where the function in question is simple (beware of cramming complicated expressions in a lambda — it can hurt readability).

Since variables are being bound in a lambda expression (to the arguments, just like in a regular function definition), pattern matching can be used in them as well. A trivial example would be redefining tail with a lambda:

tail' = (\ (_:xs) -> xs)

Note: Since lambdas are a special character in Haskell, the \ on its own will be treated as the function and whatever non-space character is next will be the variable for the first argument. It is still good form to put a space between the lambda and the argument as in normal function syntax (especially to make things clearer when a lambda takes more than one argument).


In Haskell, any function that takes two arguments and has a name consisting entirely of non-alphanumeric characters is considered an operator. The most common examples are the arithmetical ones like addition (+) and subtraction (-). Unlike other functions, operators are normally used infix (written between the two arguments). All operators can also be surrounded with parentheses and then used prefix like other functions:

-- these are the same:
2 + 4
(+) 2 4

We can define new operators in the usual way as other functions — just don't use any alphanumeric characters in their names. For example, here's the set-difference definition from Data.List:

(\\) :: (Eq a) => [a] -> [a] -> [a]
xs \\ ys = foldl (\zs y -> delete y zs) xs ys

As the example above shows, operators can be defined infix as well. The same definition written as prefix also works:

(\\) xs ys = foldl (\zs y -> delete y zs) xs ys

Note that the type declarations for operators have no infix version and must be written with the parentheses.


Sections are a nifty piece of syntactical sugar that can be used with operators. An operator within parentheses and flanked by one of its arguments...

(2+) 4
(+4) 2

... is a new function in its own right. (2+), for instance, has the type (Num a) => a -> a. We can pass sections to other functions, e.g. map (+2) [1..4] == [3..6]. For another example, we can add an extra flourish to the multiplyList function we wrote back in More about lists:

multiplyList :: Integer -> [Integer] -> [Integer]
multiplyList m = map (m*)

If you have a "normal" prefix function and want to use it as an operator, simply surround it with backticks:

1 `elem` [1..4]

This is called making the function infix. It's normally done for readability purposes: 1 `elem` [1..4] reads better than elem 1 [1..4]. You can also define functions infix:

elem :: (Eq a) => a -> [a] -> Bool
x `elem` xs = any (==x) xs

But once again notice that the type signature stays with the prefix style.

Sections even work with infix functions:

(1 `elem`) [1..4]
(`elem` [1..4]) 1

Of course, remember that you can only make binary functions (that is, those that take two arguments) infix..

  • Lambdas are a nice way to avoid defining unnecessary separate functions. Convert the following let- or where-bindings to lambdas:
    • map f xs where f x = x * 2 + 3
    • let f x y = read x + y in foldr f 1 xs
  • Sections are just syntactic sugar for lambda operations. I.e. (+2) is equivalent to \x -> x + 2. What would the following sections 'desugar' to? What would be their types?
    • (4+)
    • (1 `elem`)
    • (`notElem` "abc")

Higher order functions and Currying

At the heart of functional programming is the idea that functions are just like any other value. The power of functional style comes from handling functions themselves as regular values, i.e. by passing functions to other functions and returning them from functions. A function that takes another function (or several functions) as an argument is called a higher-order function. They can be found pretty much anywhere in a Haskell program; and indeed we have already met some of them, such as map and the various folds. We saw commonplace examples of higher-order functions when discussing map in More about lists. Now, we are going to explore some common ways of writing code that manipulates functions.

A sorting algorithm

For a concrete example, we will consider the task of sorting a list. Quicksort is a well-known recursive sorting algorithm. To apply its sorting strategy to a list, we first choose one element and then divide the rest of the list into (A) those elements that should go before the chosen element, (B) those elements equal to the chosen one, and (C) those that should go after. Then, we apply the same algorithm to the unsorted (A) and (C) lists. After enough recursive sorting, we concatenate everything back together and have a final sorted list. That strategy can be translated into a Haskell implementation in a very simple way.

-- Type signature: any list with elements in the Ord class can be sorted.
quickSort :: (Ord a) => [a] -> [a]
-- Base case:
-- If the list is empty, there is nothing to do.
quickSort [] = []
-- The recursive case:
-- We pick the first element as our "pivot", the rest is to be sorted.
-- Note how the pivot itself ends up included the middle part.
quickSort (x : xs) = (quickSort less) ++ (x : equal) ++ (quickSort more)
        less = filter (< x) xs
        equal = filter (== x) xs
        more = filter (> x) xs

It should be pointed out that our quickSort is rather naïve. A more efficient implementation would avoid the three passes through filter at each recursive step and not use (++) to build the sorted list. Furthermore, unlike our implementation, the original quicksort algorithm does the sorting in-place using mutability.[1] We will ignore such concerns for now, as we are more interested in the usage patterns of sorting functions, rather than in exact implementation.

The Ord class

Almost all the basic data types in Haskell are members of the Ord class, which is for ordering tests what Eq is for equality tests. The Ord class defines which ordering is the "natural" one for a given type. It provides a function called compare, with type:

compare :: (Ord a) => a -> a -> Ordering

compare takes two values and compares them, returning an Ordering value, which is LT if the first value is less than the second, EQ if it is equal and GT if it is greater than. For an Ord type, (<), (==) from Eq and (>) can be seen as shortcuts to compare that check for one of the three possibilities and return a Bool to indicate whether the specified ordering is true according to the Ord specification for that type. Note that each of the tests we use with filter in the definition of quickSort corresponds to one of the possible results of compare, and so we might have written, for instance, less as less = filter (\y -> y `compare` x == LT) xs.

Choosing how to compare

With quickSort, sorting any list with elements in the Ord class is easy. Suppose we have a list of String and we want to sort them; we just apply quickSort to the list. For the rest of this chapter, we will use a pseudo-dictionary of just a few words (but dictionaries with thousands of words would work just as well):

dictionary = ["I", "have", "a", "thing", "for", "Linux"]

quickSort dictionary returns:

["I", "Linux", "a", "for", "have", "thing"]

As you can see, capitalization is considered for sorting by default. Haskell Strings are lists of Unicode characters. Unicode (and almost all other encodings of characters) specifies that the character code for capital letters are less than the lower case letters. So "Z" is less than "a".

To get a proper dictionary-like sorting, we need a case insensitive quickSort. To achieve that, we can take a hint from the discussion of compare just above. The recursive case of quickSort can be rewritten as:

quickSort compare (x : xs) = (quickSort compare less) ++ (x : equal) ++ (quickSort compare more)
        less  = filter (\y -> y `compare` x == LT) xs
        equal = filter (\y -> y `compare` x == EQ) xs
        more  = filter (\y -> y `compare` x == GT) xs

While this version is less tidy than the original one, it makes it obvious that the the ordering of the elements hinges entirely on the compare function. That means we only need to replace compare with an (Ord a) => a -> a -> Ordering function of our choice. Therefore, our updated quickSort' is a higher-order function which takes a comparison function along with the list to sort.

quickSort' :: (Ord a) => (a -> a -> Ordering) -> [a] -> [a]
-- No matter how we compare two things the base case doesn't change,
-- so we use the _ "wildcard" to ignore the comparison function.
quickSort' _ [] = []
-- c is our comparison function
quickSort' c (x : xs) = (quickSort' c less) ++ (x : equal) ++ (quickSort' c more)
        less  = filter (\y -> y `c` x == LT) xs
        equal = filter (\y -> y `c` x == EQ) xs
        more  = filter (\y -> y `c` x == GT) xs

We can reuse our quickSort' function to serve many different purposes.

If we wanted a descending order, we could just reverse our original sorted list with reverse (quickSort dictionary). Yet to actually do the initial sort descending, we could supply quickSort' with a comparison function that returns the opposite of the usual Ordering.

-- the usual ordering uses the compare function from the Ord class
usual = compare
-- the descending ordering, note we flip the order of the arguments to compare
descending x y = compare y x
-- the case-insensitive version is left as an exercise!
insensitive = ... 
-- How can we do case-insensitive comparisons without making a big list of all possible cases?


Data.List offers a sort function for sorting lists. It does not use quicksort; rather, it uses an efficient implementation of an algorithm called mergesort. Data.List also includes sortBy, which takes a custom comparison function just like our quickSort'

Write insensitive, such that quickSort' insensitive dictionary gives ["a", "for", "have", "I", "Linux", "thing"].

Higher-Order Functions and Types

The concept of currying (the generating of intermediate functions on the way toward a final result) was first introduced in the earlier chapter "More about lists". This is a good place to revisit how currying works.

Our quickSort' has type (a -> a -> Ordering) -> [a] -> [a].

Most of the time, the type of a higher-order function provides a guideline about how to use it. A straightforward way of reading the type signature would be "quickSort' takes, as its first argument, a function that gives an ordering of two as. Its second argument is a list of as. Finally, it returns a new list of as". This is enough to correctly guess that it uses the given ordering function to sort the list.

Note that the parentheses surrounding a -> a -> Ordering are mandatory. They specify that a -> a -> Ordering forms a single argument that happens to be a function.

Without the parentheses, we would get a -> a -> Ordering -> [a] -> [a] which accepts four arguments (none of which are themselves functions) instead of the desired two, and that wouldn't work as desired.

Remember that the -> operator is right-associative. Thus, our erroneous type signature a -> a -> Ordering -> [a] -> [a] means the same thing as a -> (a -> (Ordering -> ([a] -> [a]))).

Given that -> is right-associative, the explicitly grouped version of the correct quickSort' signature is actually (a -> a -> Ordering) -> ([a] -> [a]). This makes perfect sense. Our original quickSort lacking the adjustable comparison function argument was of type [a] -> [a]. It took a list and sorted it. Our new quickSort' is simply a function that generates quickSort style functions! If we plug in compare for the (a -> a -> Ordering) part, then we just return our original simple quickSort function. If we use a different comparison function for the argument, we generate a different variety of a quickSort function.

Of course, if we not only give a comparison function as an argument but also feed in an actual list to sort, then the final result is not the new quickSort-style function; instead, it continues on and passes the list to the new function and returns the sorted list as our final result.


(Challenging) The following exercise combines what you have learned about higher order functions, recursion and I/O. We are going to recreate what is known in imperative languages as a for loop. Implement a function

for :: a -> (a -> Bool) -> (a -> a) -> (a -> IO ()) -> IO ()
for i p f job = -- ???

An example of how this function would be used might be

for 1 (<10) (+1) print

which prints the numbers 1 to 9 on the screen.

The desired behaviour of for is: starting from an initial value i, for executes job i. It then uses f to modify this value and checks to see if the modified value f i satisfies some condition p. If it doesn't, it stops; otherwise, the for loop continues, using the modified f i in place of i.

  1. Implement the for loop in Haskell.
  2. The paragraph just above gives an imperative description of the for loop. Describe your implementation in more functional terms.

Some more challenging exercises you could try

  1. Consider a task like "print the list of numbers from 1 to 10". Given that print is a function, and we an to apply it to a list of numbers, using map sounds like the natural thing to do. But would it actually work?
  2. Implement a function sequenceIO :: [IO a] -> IO [a]. Given a list of actions, this function runs each of the actions in order and returns all their results as a list.
  3. Implement a function mapIO :: (a -> IO b) -> [a] -> IO [b] which given a function of type a -> IO b and a list of type [a], runs that action on each item in the list, and returns the results.
This exercise was inspired from a blog post by osfameron. No peeking!

Function manipulation

We will close the chapter by discussing a few examples of common and useful general-purpose higher-order functions. Familiarity with these will greatly enhance your skill at both writing and reading Haskell code.

Flipping arguments

flip is a handy little Prelude function. It takes a function of two arguments and returns a version of the same function with the arguments swapped.

flip :: (a -> b -> c) -> b -> a -> c

flip in use:

Prelude> (flip (/)) 3 1
Prelude> (flip map) [1,2,3] (*2)

We could have used flip to write a point-free version of the descending comparing function from the quickSort example:

descending = flip compare

flip is particularly useful when we want to pass a function with two arguments of different types to another function and the arguments are in the wrong order with respect to the signature of the higher-order function.


The (.) composition operator is another higher-order function. It has the signature:

(.) :: (b -> c) -> (a -> b) -> a -> c

(.) takes two functions as arguments and returns a new function which applies both the second argument and then the first.

Composition and higher-order functions provide a range of powerful tricks. For a tiny sample, first consider the inits function, defined in the module Data.List. Quoting the documentation, it "returns all initial segments of the argument, shortest first", so that:

Prelude Data.List> inits [1,2,3]

We can provide a one-line implementation for inits (written point-free for extra dramatic effect) using only the following higher-order functions from Prelude: flip, scanl, (.) and map:

myInits :: [a] -> [[a]]
myInits = map reverse . scanl (flip (:)) []

Swallowing a definition so condensed may look daunting at first, so analyze it slowly, bit by bit, recalling what each function does and using the type signatures as a guide.

The definition of myInits is super concise and clean with use of parentheses kept to a bare minimum. Naturally, if one goes overboard with composition by writing mile-long (.) chains, things will get confusing; but, when deployed reasonably, these point-free styles shine. Furthermore, the implementation is quite "high level": we do not deal explicitly with details like pattern matching or recursion; the functions we deployed — both the higher-order ones and their functional arguments — take care of such plumbing.


($) is a curious higher-order operator. Its type is:

($) :: (a -> b) -> a -> b

It takes a function as its first argument, and all it does is to apply the function to the second argument, so that, for instance, (head $ "abc") == (head "abc").

You might think that ($) is completely useless! However, there are two interesting points about it. First, ($) has very low precedence,[2] unlike regular function application which has the highest precedence. In effect, that means we can avoid confusing nesting of parentheses by breaking precedence with $. We write a non-point-free version of myInits without adding new parentheses:

myInits :: [a] -> [[a]]
myInits xs = map reverse . scanl (flip (:)) [] $ xs

Furthermore, as ($) is just a function which happens to apply functions, and functions are just values, we can write intriguing expressions such as:

map ($ 2) [(2*), (4*), (8*)]

(Yes, that is a list of functions, and it is perfectly legal.)

uncurry and curry

As the name suggests, uncurry is a function that undoes currying; that is, it converts a function of two arguments into a function that takes a pair as its only argument.

uncurry :: (a -> b -> c) -> (a, b) -> c
Prelude> let addPair = uncurry (+)
Prelude> addPair (2, 3)

One interesting use of uncurry occasionally seen in the wild is in combination with ($), so that the first element of a pair is applied to the second.

Prelude> uncurry ($) (reverse, "stressed")

There is also curry, which is the opposite of uncurry.

curry :: ((a, b) -> c) -> a -> b -> c
Prelude> curry addPair 2 3 -- addPair as in the earlier example.

Because most Haskell functions are already curried, curry is nowhere near as common as uncurry.

id and const

Finally, we should mention two functions which, while not higher-order functions themselves, are most often used as arguments to higher-order functions. id, the identity function, is a function with type a -> a that returns its argument unchanged.

Prelude> id "Hello"

Similar in spirit to id, const is an a -> b -> a function that works like this:

Prelude> const "Hello" "world"

const takes two arguments, discards the second and returns the first. Seen as a function of one argument, a -> (b -> a), it returns a constant function, which always returns the same value no matter what argument it is given.

id and const might appear worthless at first. However, when dealing with higher-order functions it is sometimes necessary to pass a dummy function, be it one that does nothing with its argument or one that always returns the same value. id and const give us convenient dummy functions for such cases.

  1. Write implementations for curry, uncurry and const.
  2. Describe what the following functions do without testing them:
    • uncurry const
    • curry fst
    • curry swap, where swap :: (a, b) -> (b, a) swaps the elements of a pair. (swap can be found in Data.Tuple.)
  3. (Very hard) Use foldr to implement foldl. Hint: begin by reviewing the sections about foldr and foldl in List processing. There are two solutions; one is easier but relatively boring and the other is truly interesting. For the interesting one, think carefully about how you would go about composing all functions in a list.


  1. The "true", in-place quicksort can be done in Haskell, but it requires some rather advanced tools that we will not discuss in the Beginners' Track.
  2. As a reminder, precedence here is meant in the same sense that * has higher precedence (i.e. is evaluated first) than + in mathematics.

Using GHCi effectively

GHCi assists in several ways toward more efficient work. Here, we will discuss some of the best practices for using GHCi.

User interface

Tab completion

As in many other terminal programs, you can enter some starting text in GHCi and then hit the Tab key to be presented with a list of all possibilities that start with what you've written so far. When there is only one possibility, using Tab will auto-complete the string. For example fol<Tab> will append letter d (since nothing exists with "fol" other than items that start with "fold"). A second Tab will list the four functions included in Prelude: foldl, foldl1, foldr, and foldr1. More options may show if you have already imported additional modules.

Tab completion works also when you are loading a file with your program into GHCi. For example, after typing :l fi<Tab>, you will be presented with all files that start with "fi" that are present in the current directory (the one you were in when you launched GHCi).

The same also applies when you are importing modules, after typing :m +Da<Tab>, you will be presented with all modules that start with "Da" present in installed packages.

": commands"

On GHCi command line, commands for the interpreter start with the character ":" (colon).

  • :help or :h -- prints a list of all available commands.
  • :load or :l -- loads a given file into GHCi (you must include the filename with the command).
  • :reload or :r -- reloads whatever file had been loaded most recently (useful after changes to the file).
  • :type or :t -- prints the type of a given expression included with the command
  • :module or :m -- loads a given module (include the module name with the command). You can also unload a module by adding a - symbol before the module name.
  • :browse -- gives the type signatures for all functions available from a given module.

Here again, you can use Tab to see the list of commands, type :Tab to see all possible commands.

Timing Functions in GHCi

GHCi provides a basic way to measure how much time a function takes to run, which can be useful for to find out which version of a function runs fastest (such as when there are multiple ways to define something to get the same effective result).

  1. Type :set +s into the ghci command line.
  2. run the function(s) you are testing. The time the function took to run will be displayed after GHCi outputs the results of the function.

Multi-line Input

If you are trying to define a function that takes up multiple lines, or if you want to type a do block into ghci (without writing a file that you then import), there is an easy way to do this:

  1. Begin a new line with :{
  2. Type in your code. Press enter when you need a new line.
  3. Type :} to end the multi-line input.

For example:

   *Main> :{
   *Main| let askname = do
   *Main|               putStrLn "What is your name?"
   *Main|               name <- getLine
   *Main|               putStrLn $ "Hello " ++ name
   *Main| :}

The same can be accomplished by using :set +m command (allow multi-line commands). In this case, an empty line will end the block.

In addition, line breaks in ghci commands can be separated by ;, like this:

   *Main> let askname1 = do ; putStrLn "what is your name?" ; name <- getLine ; putStrLn $ "Hello " ++ name

Intermediate Haskell


Modules are the primary means of organizing Haskell code. We met them in passing when using import statements to put library functions into scope. Beyond allowing us to make better use of libraries, knowledge of modules will help us to shape our own programs and create standalone programs which can be executed independently of GHCi (incidentally, that is the topic of the very next chapter, Standalone programs).


Haskell modules[1] are a useful way to group a set of related functionalities into a single package and manage different functions that may have the same names. The module definition is the first thing that goes in your Haskell file.

A basic module definition looks like:

module YourModule where

Note that

  1. the name of the module begins with a capital letter;
  2. each file contains only one module.

The name of the file is the name of the module plus the .hs file extension. Any dots '.' in the module name are changed for directories.[2] So the module YourModule would be in the file YourModule.hs while a module Foo.Bar would be in the file Foo/Bar.hs or Foo\Bar.hs. Since the module name must begin with a capital letter, the file name must also start with a capital letter.


Modules can themselves import functions from other modules. That is, in between the module declaration and the rest of your code, you may include some import declarations such as

import Data.Char (toLower, toUpper) -- import only the functions toLower and toUpper from Data.Char
import Data.List -- import everything exported from Data.List
import MyModule -- import everything exported from MyModule

Imported datatypes are specified by their name, followed by a list of imported constructors in parenthesis. For example:

import Data.Tree (Tree(Node)) -- import only the Tree data type and its Node constructor from Data.Tree

What if you import some modules that have overlapping definitions? Or if you import a module but want to overwrite a function yourself? There are three ways to handle these cases: Qualified imports, hiding definitions, and renaming imports.

Qualified imports

Say MyModule and MyOtherModule both have a definition for remove_e, which removes all instances of e from a string. However, MyModule only removes lower-case e's, and MyOtherModule removes both upper and lower case. In this case the following code is ambiguous:

import MyModule
import MyOtherModule
-- someFunction puts a c in front of the text, and removes all e's from the rest
someFunction :: String -> String
someFunction text = 'c' : remove_e text

It isn't clear which remove_e is meant! To avoid this, use the qualified keyword:

import qualified MyModule
import qualified MyOtherModule
someFunction text = 'c' : MyModule.remove_e text -- Will work, removes lower case e's
someOtherFunction text = 'c' : MyOtherModule.remove_e text -- Will work, removes all e's
someIllegalFunction text = 'c' : remove_e text -- Won't work as there is no remove_e defined

In the latter code snippet, no function named remove_e is available at all. When we do qualified imports, all the imported values include the module names as a prefix. Incidentally, you can also use the same prefixes even if you did a regular import (in our example, MyModule.remove_e works even if the "qualified" keyword isn't included).


There is an ambiguity between a qualified name like MyModule.remove_e and the function composition operator (.). Writing reverse.MyModule.remove_e is bound to confuse your Haskell compiler. One solution is stylistic: always use spaces for function composition, for example, reverse . remove_e or Just . remove_e or even Just . MyModule.remove_e

Hiding definitions

Now suppose we want to import both MyModule and MyOtherModule, but we know for sure we want to remove all e's, not just the lower cased ones. It will become really tedious to add MyOtherModule before every call to remove_e. Can't we just exclude the remove_e from MyModule?

import MyModule hiding (remove_e)
import MyOtherModule
someFunction text = 'c' : remove_e text

This works because of the word hiding on the import line. Whatever follows the "hiding" keyword will not be imported. Hide multiple items by listing them with parentheses and comma-separation:

import MyModule hiding (remove_e, remove_f)

Note that algebraic datatypes and type synonyms cannot be hidden. These are always imported. If you have a datatype defined in multiple imported modules, you must use qualified names.

Renaming imports

This is not really a technique to allow for overwriting, but it is often used along with the qualified flag. Imagine:

import qualified MyModuleWithAVeryLongModuleName
someFunction text = 'c' : MyModuleWithAVeryLongModuleName.remove_e $ text

Especially when using qualified, this gets irritating. We can improve things by using the as keyword:

import qualified MyModuleWithAVeryLongModuleName as Shorty
someFunction text = 'c' : Shorty.remove_e $ text

This allows us to use Shorty instead of MyModuleWithAVeryLongModuleName as prefix for the imported functions. This renaming works with both qualified and regular importing.

As long as there are no conflicting items, we can import multiple modules and rename them the same:

import MyModule as My
import MyCompletelyDifferentModule as My

In this case, both the functions in MyModule and the functions in MyCompletelyDifferentModule can be prefixed with My.

Combining renaming with limited import

Sometimes it is convenient to use the import directive twice for the same module. A typical scenario is as follows:

import qualified Data.Set as Set
import Data.Set (Set, empty, insert)

This give access to all of the Data.Set module via the alias "Set", and also lets you access a few selected functions (empty, insert, and the constructor) without using the "Set" prefix.


In the examples at the start of this article, the words "import everything exported from MyModule" were used.[3] This raises a question. How can we decide which functions are exported and which stay "internal"? Here's how:

module MyModule (remove_e, add_two) where
add_one blah = blah + 1
remove_e text = filter (/= 'e') text
add_two blah = add_one . add_one $ blah

In this case, only remove_e and add_two are exported. While add_two is allowed to make use of add_one, functions in modules that import MyModule cannot use add_one directly, as it isn't exported.

Datatype export specifications are written similarly to import. You name the type, and follow with the list of constructors in parenthesis:

module MyModule2 (Tree(Branch, Leaf)) where
data Tree a = Branch {left, right :: Tree a} 
            | Leaf a

In this case, the module declaration could be rewritten "MyModule2 (Tree(..))", declaring that all constructors are exported.

Maintaining an export list is good practice not only because it reduces namespace pollution but also because it enables certain compile-time optimizations which are unavailable otherwise.


  1. See the Haskell report for more details on the module system.
  2. In Haskell98, the last standardised version of Haskell before Haskell 2010, the module system was fairly conservative, but recent common practice consists of employing a hierarchical module system, using periods to section off namespaces.
  3. A module may export functions that it imports. Mutually recursive modules are possible but need some special treatment.


Haskell relies on indentation to reduce the verbosity of your code. Despite some complexity in practice, there are really only a couple fundamental layout rules.[1]

The golden rule of indentation

Code which is part of some expression should be indented further in than the beginning of that expression (even if the expression is not the leftmost element of the line).

The easiest example is a 'let' binding group. The equations binding the variables are part of the 'let' expression, and so should be indented further in than the beginning of the binding group: the 'let' keyword. When you start the expression on a separate line, you only need to indent by one space (although more than one space is also acceptable and may be more clear).

 x = a
 y = b

You may also place the first clause alongside the 'let' as long as you indent the rest to line up:

wrong wrong right
 let x = a
  y = b
 let x = a
      y = b
 let x = a
     y = b

This tends to trip up a lot of beginners: All grouped expressions must be exactly aligned. On the first line, Haskell counts everything to the left of the expression as indent, even though it is not whitespace.

Here are some more examples:


do foo

where x = a
      y = b

case x of
  p  -> foo
  p' -> baz

Note that with 'case' it is less common to place the first subsidiary expression on the same line as the 'case' keyword (although it would still be valid code). Hence, the subsidiary expressions in a case expression tend to be indented only one step further than the 'case' line. Also note how we lined up the arrows here: this is purely aesthetic and is not counted as different layout; only indentation (i.e. whitespace beginning on the far-left edge) makes a difference to the interpretation of the layout.

Things get more complicated when the beginning of the expression doesn't start at the left-hand edge. In this case, it's safe to just indent further than the line containing the expression's beginning. So,

myFunction firstArgument secondArgument = do -- the 'do' doesn't start at the left-hand edge
  foo                                        -- so indent these commands more than the beginning of the line containing the 'do'.

Here are some alternative layouts which all work:

myFunction firstArgument secondArgument = 
  do foo

myFunction firstArgument secondArgument = do foo
myFunction firstArgument secondArgument = 

Explicit characters in place of indentation

Indentation is actually optional if you instead use semicolons and curly braces for grouping and separation, as in "one-dimensional" languages like C. Even though the consensus among Haskell programmers is that meaningful indentation leads to better-looking code, understanding how to convert from one style to the other can help understand the indentation rules. The entire layout process can be summed up in three translation rules (plus a fourth one that doesn't come up very often):

  1. If you see one of the layout keywords, (let, where, of, do), insert an open curly brace (right before the stuff that follows it)
  2. If you see something indented to the SAME level, insert a semicolon
  3. If you see something indented LESS, insert a closing curly brace
  4. If you see something unexpected in a list, like where, insert a closing brace before instead of a semicolon.

For instance, this definition...

foo :: Double -> Double
foo x =
    let s = sin x
        c = cos x
    in 2 * s * c

...can be rewritten without caring about the indentation rules as:

foo :: Double -> Double;
foo x = let {
  s = sin x;
  c = cos x;
  } in 2 * s * c

One circumstance in which explicit braces and semicolons can be convenient is when writing one-liners in GHCi:

Prelude> let foo :: Double -> Double; foo x = let { s = sin x; c = cos x } in 2 * s * c

Rewrite this snippet from the Control Structures chapter using explicit braces and semicolons:

doGuessing num = do
  putStrLn "Enter your guess:"
  guess <- getLine
  case compare (read guess) num of
    LT -> do putStrLn "Too low!"
             doGuessing num
    GT -> do putStrLn "Too high!"
             doGuessing num
    EQ -> putStrLn "You Win!"

Layout in action

wrong wrong right right
 do first thing
 second thing
 third thing
 do first thing
  second thing
  third thing
 do first thing
    second thing 
    third thing
   first thing
   second thing 
   third thing

Indent to the first

Due to the "golden rule of indentation" described above, a curly brace within a do block depends not on the do itself but the thing that immediately follows it. For example, this weird-looking block of code is totally acceptable:

first thing
second thing
third thing

As a result, you could also write combined if/do combination like this:

Wrong Right Right
 if foo
    then do first thing
         second thing
         third thing
    else do something else
 if foo
    then do first thing
            second thing
            third thing
    else do something else
 if foo
    then do 
     first thing
     second thing
     third thing
    else do something else

It isn't about the do, it's about lining up all the items that are at the same level within the do.

Thus, all of the following are acceptable:

main = do
 first thing
 second thing


main = 
   first thing
   second thing


main = 
 do first thing
    second thing

if within do

This is a combination which trips up many Haskell programmers. Why does the following block of code not work?

sweet but wrong unsweet and wrong
-- why is this bad?
do first thing
   if condition
   then foo
   else bar
   third thing
-- still bad, just explicitly so
do { first thing
   ; if condition
   ; then foo
   ; else bar
   ; third thing }

Naturally, the Haskell compiler is confused because it thinks that you never finished writing your if expression, before writing a new statement. The compiler sees that you have written something like if condition;, which is bad because it is unfinished. In order to fix this, we need to indent the bottom parts of this if block so that then and else become part of the if statement.

sweet and correct unsweet and correct
-- whew, fixed it!
do first thing
   if condition
     then foo
     else bar
   third thing
-- the fixed version without sugar
do { first thing
   ; if condition
      then foo
      else bar
   ; third thing }

Now, the do block sees the whole if statement as one item. When if-then-else statements are not within do blocks, this specific indentation isn't technically necessary, but it never hurts, so it is a good habit to always indent if-then-else in this way.

The if-within-do issue has tripped up so many Haskellers that one programmer has posted a proposal to the Haskell prime initiative to add optional semicolons between if then else. How would that help?

Issues with indentation are explained further in connection with showing how do is syntactic sugar for the monadic operater (>>=). See Translating the bind operater and the associated footnote about indentation.


  1. See section 2.7 of The Haskell Report (lexemes) on layout.

More on datatypes


One special case of the data declaration is the enumeration — a data type where none of the constructor functions have any arguments:

data Month = January | February | March | April | May | June | July
           | August | September | October | November | December

You can mix constructors that do and do not have arguments, but then the result is not called an enumeration. The following example is not an enumeration because the last constructor takes three arguments:

data Colour = Black | Red | Green | Blue | Cyan
            | Yellow | Magenta | White | RGB Int Int Int

As you will see further on when we discuss classes and derivation, there are practical reasons to distinguish between what is and isn't an enumeration.

Incidentally, the Bool datatype is an enumeration:

data Bool = False | True
    deriving (Bounded, Enum, Eq, Ord, Read, Show)

Named Fields (Record Syntax)

Consider a datatype whose purpose is to hold configuration settings. Usually, when you extract members from this type, you really only care about one or two of the many settings. Moreover, if many of the settings have the same type, you might often find yourself wondering "wait, was this the fourth or fifth element?" One way to clarify is to write accessor functions. Consider the following made-up configuration type for a terminal program:

data Configuration = Configuration
    String   -- User name
    String   -- Local host
    String   -- Remote host
    Bool     -- Is guest?
    Bool     -- Is superuser?
    String   -- Current directory
    String   -- Home directory
    Integer  -- Time connected
  deriving (Eq, Show)

You could then write accessor functions, such as:

getUserName (Configuration un _ _ _ _ _ _ _) = un
getLocalHost (Configuration _ lh _ _ _ _ _ _) = lh
getRemoteHost (Configuration _ _ rh _ _ _ _ _) = rh
getIsGuest (Configuration _ _ _ ig _ _ _ _) = ig
-- And so on...

You could also write update functions to update a single element. Of course, if you add or remove an element in the configuration later, all of these functions now have to take a different number of arguments. This is quite annoying and is an easy place for bugs to slip in. Thankfully, there's a solution: we simply give names to the fields in the datatype declaration, as follows:

data Configuration = Configuration
    { username      :: String
    , localHost     :: String
    , remoteHost    :: String
    , isGuest       :: Bool
    , isSuperuser   :: Bool
    , currentDir    :: String
    , homeDir       :: String
    , timeConnected :: Integer

This will automatically generate the following accessor functions for us:

username :: Configuration -> String
localHost :: Configuration -> String
-- etc.

This also gives us a convenient update method. Here is a short example for a "post working directory" and "change directory" functions that work on Configurations:

changeDir :: Configuration -> String -> Configuration
changeDir cfg newDir =
    if directoryExists newDir -- make sure the directory exists
        then cfg { currentDir = newDir }
        else error "Directory does not exist"
postWorkingDir :: Configuration -> String
postWorkingDir cfg = currentDir cfg

So, in general, to update the field x in a datatype y to z, you write y { x = z }. You can change more than one; each should be separated by commas, for instance, y {x = z, a = b, c = d }.


Those of you familiar with object-oriented languages might be tempted, after all of this talk about "accessor functions" and "update methods", to think of the y{x=z} construct as a setter method, which modifies the value of x in a pre-existing y. It is not like that – remember that in Haskell variables are immutable. Therefore, using the example above, if you do something like conf2 = changeDir conf1 "/opt/foo/bar" conf2 will be defined as a Configuration which is just like conf1 except for having "/opt/foo/bar" as its currentDir, but conf1 will remain unchanged.

It's only sugar

You can, of course, continue to pattern match against Configurations as you did before. The named fields are simply syntactic sugar; you can still write something like:

getUserName (Configuration un _ _ _ _ _ _ _) = un

But there is no need to do this.

Finally, you can pattern match against named fields as in:

getHostData (Configuration { localHost = lh, remoteHost = rh }) = (lh, rh)

This matches the variable lh against the localHost field in the Configuration and the variable rh against the remoteHost field. These matches will succeed, of course. You could also constrain the matches by putting values instead of variable names in these positions, as you would for standard datatypes.

If you are using GHC, then, with the language extension NamedFieldPuns, it is also possible to use this form:

getHostData (Configuration { localHost, remoteHost }) = (localHost, remoteHost)

It can be mixed with the normal form like this:

getHostData (Configuration { localHost, remoteHost = rh }) = (localHost, rh)

(To use this language extension, enter :set -XNamedFieldPuns in the interpreter, or use the {-# LANGUAGE NamedFieldPuns #-} pragma at the beginning of a source file, or pass the -XNamedFieldPuns command-line flag to the compiler.)

You can create values of Configuration in the old way as shown in the first definition below, or in the named field's type, as shown in the second definition:

initCFG = Configuration "nobody" "nowhere" "nowhere" False False "/" "/" 0
initCFG' = Configuration
    { username      = "nobody"
    , localHost     = "nowhere"
    , remoteHost    = "nowhere"
    , isguest       = False
    , issuperuser   = False
    , currentdir    = "/"
    , homedir       = "/"
    , timeConnected = 0

The first way is much shorter, but the second is much clearer.

WARNING: The second style will allow you to write code that omits fields but will still compile, such as:

cfgFoo = Configuration { username = "Foo" }
cfgBar = Configuraton { localHost = "Bar", remoteHost = "Baz" }
cfgUndef = Configuration {}

Trying to evaluate the unspecified fields will then result in a runtime error!

Parameterized Types

Parameterized types are similar to "generic" or "template" types in other languages. A parameterized type takes one or more type parameters. For example, the Standard Prelude type Maybe is defined as follows:

data Maybe a = Nothing | Just a

This says that the type Maybe takes a type parameter a. You can use this to declare, for example:

lookupBirthday :: [Anniversary] -> String -> Maybe Anniversary

The lookupBirthday function takes a list of birthday records and a string and returns a Maybe Anniversary. The usual interpretation of such a type is that if the name given through the string is found in the list of anniversaries the result will be Just the corresponding record; otherwise, it will be Nothing. Maybe is the simplest and most common way of indicating failure in Haskell. It is also sometimes seen in the types of function arguments, as a way to make them optional (the intent being that passing Nothing amounts to omitting the argument).

You can parameterize type and newtype declarations in exactly the same way. Furthermore you can combine parameterized types in arbitrary ways to construct new types.

More than one type parameter

We can also have more than one type parameter. An example of this is the Either type:

data Either a b = Left a | Right b

For example:

pairOff :: Int -> Either String Int
pairOff people
    | people < 0  = Left "Can't pair off negative number of people."
    | people > 30 = Left "Too many people for this activity."
    | even people = Right (people `div` 2)
    | otherwise   = Left "Can't pair off an odd number of people."
groupPeople :: Int -> String
groupPeople people =
    case pairOff people of
        Right groups -> "We have " ++ show groups ++ " group(s)."
        Left problem -> "Problem! " ++ problem

In this example pairOff indicates how many groups you would have if you paired off a certain number of people for your activity. It can also let you know if you have too many people for your activity or if somebody will be left out. So pairOff will return either an Int representing the number of groups you will have, or a String describing the reason why you can't create your groups.

Kind Errors

The flexibility of Haskell parameterized types can lead to errors in type declarations that are somewhat like type errors, except that they occur in the type declarations rather than in the program proper. Errors in these "types of types" are known as "kind" errors. You don't program with kinds: the compiler infers them for itself. But if you get parameterized types wrong then the compiler will report a kind error.

Other data structures

In this chapter, we will work through examples of how use the techniques we have studied thus far can be used to deal with more complex data types. In particular, we will see examples of recursive data structures, which are data types that can contain values of the same type. Recursive data structures play a vital role in many programming techniques, and so even if you are not going to need defining a new one often (as opposed to using the ones available from the libraries) it is important to be aware of what they are and how they can be manipulated. Besides that, following closely the implementations in this chapter is a good exercise for your budding Haskell abilities.


The Haskell library ecosystem provides a wealth of data structures (recursive and otherwise), covering a wide range of practical needs. Beyond lists, there are maps, sets, finite sequences and arrays, among many others. A good place to begin learning about the main ones is the Data structures primer in the Haskell in Practice track. We recommend you to at least skim it once you finish the next few Intermediate Haskell chapters.


One of the most important types of recursive data structures are trees. There are several different kinds of trees, so we will arbitrarily choose a simple one to use as an example. Here is its definition:

data Tree a = Leaf a | Branch (Tree a) (Tree a)

As you can see, it's parameterized; i.e. we can have trees of Ints, trees of Strings, trees of Maybe Ints, trees of (Int, String) pairs and so forth. What makes this data type special is that Tree appears in the definition of itself. A Tree a is either a leaf, containing a value of type a or a branch, from which hang two other trees of type Tree a.

Lists as Trees

As we have seen in More about lists and List Processing, we break lists down into two cases: An empty list (denoted by []), and an element of the specified type plus another list (denoted by (x:xs)). That means the definition of the list data type might look like this:

 -- Pseudo-Haskell, will not actually work (because lists have special syntax).
data [a] = [] | (a:[a])

An equivalent definition you can actually play with is:

data List a = Nil | Cons a (List a)

Like trees, lists are also recursive. For lists, the constructor functions are [] and (:). They correspond to Leaf and Branch in the Tree definition above. That implies we can use Leaf and Branch for pattern matching just as we did with the empty list and the (x:xs).

Maps and Folds

We already know about maps and folds for lists. Now, we can write map and fold functions for our new Tree type. To recap:

data Tree a = Leaf a | Branch (Tree a) (Tree a) deriving (Show)
data [a]    = []     | (:)    a [a]
  -- (:) a [a] is the same as (a:[a]) with prefix instead of infix notation.


Deriving is explained later on in the section Class Declarations. For now, understand it as telling Haskell (and by extension your interpreter) how to display a Tree instance.


Let's take a look at the definition of map for lists:

map :: (a -> b) -> [a] -> [b]
map _ [] = []
map f (x:xs) = f x : map f xs

If we were to write treeMap, what would its type be? Defining the function is easier if you have an idea of what its type should be.

We want treeMap to work on a Tree of some type and return another Tree of some type by applying a function on each element of the tree.

treeMap :: (a -> b) -> Tree a -> Tree b

This is just like the list example.

Now, when talking about a Tree, each Leaf only contains a single value, so all we have to do is apply the given function to that value and then return a Leaf with the altered value:

treeMap :: (a -> b) -> Tree a -> Tree b
treeMap f (Leaf x) = Leaf (f x)

This looks a lot like the empty list case with map. Now, if we have a Branch, it will include two subtrees; what do we do with those? The list-map uses a call to itself on the tail of the list, so we also shall do that with the two subtrees. The complete definition of treeMap is as follows:

treeMap :: (a -> b) -> Tree a -> Tree b
treeMap f (Leaf x) = Leaf (f x)
treeMap f (Branch left right) = Branch (treeMap f left) (treeMap f right)

We can make this a bit more readable by noting that treeMap f is itself a function with type Tree a -> Tree b. This gives us the following revised definition:

treeMap :: (a -> b) -> Tree a -> Tree b
treeMap f = g where
  g (Leaf x) = Leaf (f x)
  g (Branch left right) = Branch (g left) (g right)

If you didn't follow that immediately, try re-reading it. This use of pattern matching may seem weird at first, but it is essential to the use of datatypes. Remember that pattern matching happens on constructor functions.

When you're ready, read on for folds over Trees.


As with map, let's first review the definition of foldr for lists:

foldr :: (a -> b -> b) -> b -> [a] -> b
foldr f acc [] = acc
foldr f acc (x:xs) = f x (foldr f acc xs)

Recall that lists have two constructors:

(:) :: a -> [a] -> [a]  -- takes an element and combines it with the rest of the list
[] :: [a]  -- the empty list takes zero arguments

Thus foldr takes two arguments corresponding to the two constructors:

f :: a -> b -> b  -- a function takes two elements and operates on them to return a single result
acc :: b  -- the accumulator defines what happens with the empty list

Let's take a moment to make this clear. If the initial foldr is given an empty list, then the default accumulator is returned. For functions like (+), the initial accumulator will be 0. With a non-empty list, the value returned by each fold is used in the next fold. When the list runs out, we are back at the empty list, so foldr returns whatever is then the accumulator value from the last fold.

Like foldr for lists, we want treeFold to transform a tree of some type into a value of some other type; so in place of [a] -> b we will have Tree a -> b. How do we specify the transformation? First note that Tree a has two constructors (just like lists have two constructors):

Branch :: Tree a -> Tree a -> Tree a
Leaf :: a -> Tree a

So treeFold will have two arguments corresponding to the two constructors:

fbranch :: b -> b -> b
fleaf :: a -> b

Putting it all together we get the following type definition:

treeFold :: (b -> b -> b) -> (a -> b) -> Tree a -> b

That is, the first argument, of type (b -> b -> b), is a function specifying how to combine subtrees into a single result; the second argument, of type a -> b, is a function specifying what to do with leaves (which are the end of recursion, just like empty-list for lists); and the third argument, of type Tree a, is the whole tree we want to fold.

As with treeMap, we'll avoid repeating the arguments fbranch and fleaf by introducing a local function g:

treeFold :: (b -> b -> b) -> (a -> b)  -> Tree a -> b
treeFold fbranch fleaf = g where
  -- definition of g goes here

The argument fleaf tells us what to do with Leaf subtrees:

g (Leaf x) = fleaf x

The argument fbranch tells us how to combine the results of "folding" two subtrees:

g (Branch left right) = fbranch (g left) (g right)

Our full definition becomes:

treeFold :: (b -> b -> b) -> (a -> b) -> Tree a -> b
treeFold fbranch fleaf = g where
  g (Leaf x) = fleaf x
  g (Branch left right) = fbranch (g left) (g right)

For examples of how these work, copy the Tree data definition and the treeMap and treeFold functions to a Haskell file, along with the following example Tree and example functions to fold over.

tree1 :: Tree Integer
tree1 = 
               (Leaf 1) 
               (Branch (Leaf 2) (Leaf 3))) 
               (Leaf 4) 
               (Branch (Leaf 5) (Leaf 6)))) 
           (Branch (Leaf 7) (Leaf 8)) 
           (Leaf 9))
doubleTree = treeMap (*2)  -- doubles each value in tree
sumTree = treeFold (+) id -- sum of the leaf values in tree
fringeTree = treeFold (++) (: [])  -- list of the leaves of tree

Then load it into GHCi and evaluate:

doubleTree tree1
sumTree tree1
fringeTree tree1

Other datatypes

Map and fold functions can be defined for any kind of data type. In order to generalize the strategy applied for lists and trees, in this final section we will work out a map and a fold for a very strange, intentionally-contrived datatype:

data Weird a b = First a
               | Second b
               | Third [(a,b)]
               | Fourth (Weird a b)

It can be a useful exercise to write the functions as you follow the examples, trying to keep the coding one step ahead of your reading.

General Map

The first important difference in working with this Weird type is that it has two type parameters. For that reason, we will want the map function to take two functions as arguments, one to be applied on the elements of type a and another for the elements of type b. With that accounted for, we can write the type signature of weirdMap:

weirdMap :: (a -> c) -> (b -> d) -> Weird a b -> Weird c d

Next step is defining weirdMap. The key point is that maps preserve the structure of a datatype, so the function must evaluate to a Weird which uses the same constructor as the one used for the original Weird. For that reason, we need one definition to handle each constructor, and these constructors are used as patterns for writing them. As before, to avoid repeating the weirdMap argument list over and over again a where clause comes in handy. A sketch of the function is below:

weirdMap :: (a -> c) -> (b -> d) -> Weird a b -> Weird c d
weirdMap fa fb = g
    g (First x)          = --More to follow
    g (Second y)         = --More to follow
    g (Third z)          = --More to follow
    g (Fourth w)         = --More to follow

The first two cases are fairly straightforward, as there is just a single element of a or b type inside the Weird.

weirdMap :: (a -> c) -> (b -> d) -> Weird a b -> Weird c d
weirdMap fa fb = g
    g (First x)          = First (fa x)
    g (Second y)         = Second (fb y)
    g (Third z)          = --More to follow
    g (Fourth w)         = --More to follow

Third is trickier because it contains a list whose elements are themselves data structures (the tuples). So we need to navigate the nested data structures, apply fa and fb on all elements of type a and b and eventually (as a map must preserve structure) produce a list of tuples – [(c,d)] – to be used with the constructor. The simplest approach might seem to be just breaking down the list inside the Weird and playing with the patterns:

    g (Third []) = Third []
    g (Third ((x,y):zs)) = Third ( (fa x, fb y) : ( (\(Third z) -> z) (g (Third zs)) ) )

This appears to be written as a typical recursive function for lists. We start by applying the functions of interest to the first element in order to obtain the head of the new list, (fa x, fb y). But what we will cons it to? As g requires a Weird argument, we need to make a Weird using the list tail in order to make the recursive call. But then g will give a Weird and not a list, so we have to retrieve the modified list from that – that's the role of the lambda function. Finally, there is also the empty list base case to be defined as well.

After all of that, we are left with a messy function. Every recursive call of g requires wrapping zs into a Weird, while what we really wanted to do was to build a list with (fa x, fb y) and the modified xs. The problem with this solution is that g can (thanks to pattern matching) act directly on the list head but (due to its type signature) can't be called directly on the list tail. For that reason, it would be better to apply fa and fb without breaking down the list with pattern matching (as far as g is directly concerned, at least). But there was a way to directly modify a list element-by-element...

    g (Third z) = Third ( map (\(x, y) -> (fa x, fb y) ) z)

...our good old map function, which modifies all tuples in the list z using a lambda function. In fact, the first attempt at writing the definition looked just like an application of the list map except for the spurious Weird packing and unpacking. We got rid of these by having the pattern splitting of z done by map, which works directly with regular lists. You could find it useful to expand the map definition inside g to see the difference more clearerly. Finally, you may prefer to write this new version in an alternative and clean way using list comprehension syntax:

    g (Third z) = Third [ (fa x, fb y) | (x,y) <- z ]

Adding the Third function, we only have the Fourth left to define:

weirdMap :: (a -> c) -> (b -> d) -> Weird a b -> Weird c d
weirdMap fa fb = g
    g (First x)          = First (fa x)
    g (Second y)         = Second (fb y)
    g (Third z)          = Third ( map (\(x, y) -> (fa x, fb y) ) z)
    g (Fourth w)         = --More to follow

All we need to do is apply g recursively:

weirdMap :: (a -> c) -> (b -> d) -> Weird a b -> Weird c d
weirdMap fa fb = g
    g (First x)          = First (fa x)
    g (Second y)         = Second (fb y)
    g (Third z)          = Third ( map (\(x, y) -> (fa x, fb y) ) z)
    g (Fourth w)         = Fourth (g w)

General Fold

While we were able to define a map by specifying as arguments a function for every separate type, this isn't enough for a fold. For a fold, we'll need a function for every constructor function. With lists, the constructors are [] and (:). The acc argument in the foldr function corresponds to the [] constructor. The f argument in the foldr function corresponds to the (:) constructor. The Weird datatype has four constructors, so we need four functions – one for handling the internal structure of the datatype specified by each constructor. Next, we have an argument of the Weird a b type, and finally we want the whole fold function to evaluate to a value of some other, arbitrary, type. Additionally, each individual function we pass to weirdFold must evaluate to the same type weirdFold does. That allows us to make a mock type signature and sketch the definition:

weirdFold :: (something1 -> c) -> (something2 -> c) -> (something3 -> c) -> (something4 -> c) -> Weird a b -> c
weirdFold f1 f2 f3 f4 = g
    g (First x)          = --Something of type c here
    g (Second y)         = --Something of type c here
    g (Third z)          = --Something of type c here
    g (Fourth w)         = --Something of type c here

Now, we need to figure out to which types something1, something2, something3 and something4 correspond to. That is done by analyzing the constructors, since the functions must take as arguments the elements of the datatype (whose types are specified by the constructor type signature). Again, the types and definitions of the first two functions are easy enough. The third one isn't too difficult either because, for the purposes of folding the list of (a,b), tuples are no different from a simple type (unlike in the map example, the internal structure does not concern us now). The fourth constructor, however, is recursive, and we have to be careful. As with weirdMap, we also need to recursively call the g function. This brings us to the final definition:

weirdFold :: (a -> c) -> (b -> c) -> ([(a,b)] -> c) -> (c -> c) -> Weird a b -> c
weirdFold f1 f2 f3 f4 = g
    g (First x)          = f1 x
    g (Second y)         = f2 y
    g (Third z)          = f3 z
    g (Fourth w)         = f4 (g w)


If you were expecting very complex expressions in the weirdFold above and are surprised by the immediacy of the solution, it might be helpful to have a look on what the common foldr would look like if we wrote it in this style and didn't have the special square-bracket syntax of lists to distract us:

-- List a is [a], Cons is (:) and Nil is []
data List a = Cons a (List a) | Nil
listFoldr :: (a -> b -> b) -> (b) -> List a -> b
listFoldr fCons fNil = g
    g (Cons x xs) = fCons x (g xs)
    g Nil         = fNil

Now it is easier to see the parallels. The extra complications are that Cons (that is, (:)) takes two arguments (and, for that reason, so does fCons) and is recursive, requiring a call to g. Also, fNil is of course not really a function, as it takes no arguments.

Folds on recursive datatypes

As far as folds are concerned, Weird was a fairly nice datatype to deal with. Just one recursive constructor, which isn't even nested inside other structures. What would happen if we added a truly complicated fifth constructor?

    Fifth [Weird a b] a (Weird a a, Maybe (Weird a b))

This is a valid and yet tricky question. In general, the following rules apply:

  • A function to be supplied to a fold has the same number of arguments as the corresponding constructor.
  • The type of the arguments of such a function match the types of the constructor arguments, except if the constructor is recursive (that is, takes an argument of its own type).
  • If a constructor is recursive, any recursive argument of the constructor will correspond to an argument of the type the fold evaluates to.[1]
  • If a constructor is recursive, the complete fold function should be (recursively) applied to the recursive constructor arguments.
  • If a recursive element appears inside another data structure, the appropriate map function for that data structure should be used to apply the fold function to it.

So f5 would have the type:

f5 :: [c] -> a -> (Weird a a, Maybe c) -> c

as the type of Fifth is:

Fifth :: [Weird a b] -> a -> (Weird a a, Maybe (Weird a b)) -> Weird a b

The definition of g for the Fifth constructor will be:

    g (Fifth list x (waa, mc)) = f5 (map g list) x (waa, maybeMap g mc)
        maybeMap f Nothing = Nothing
        maybeMap f (Just w) = Just (f w)

Note that nothing strange happens with the Weird a a part. No g gets called. What's up? This is recursion, right? Well, not really. Weird a a and Weird a b are different types, so it isn't a real recursion. It isn't guaranteed that, for example, f2 will work with something of type 'a', where it expects a type 'b'. It can be true for some cases but is not reliable for every case.

Also look at the definition of maybeMap. Verify that it is indeed a map function as:

  • It preserves structure.
  • Only types are changed.

A nice sounding word

The folds we have defined here are examples of catamorphisms. A catamorphism is a general way to collapse a data structure into a single value. There is deep theory associated with catamorphisms and related recursion schemes; however, we won't go through any of it now, as our main goal here was exercising the mechanics of data structure manipulation in Haskell with believable examples.


  1. This sort of recursiveness, in which the function used for folding can take the result of another fold as an argument, is what confers the folds of data structures such as lists and trees their "accumulating" functionality.

Classes and types

Back in Type basics II we had a brief encounter with type classes as the mechanism used with number types. As we hinted back then, however, classes have many other uses.

Broadly speaking, the point of type classes is to ensure that certain operations will be available for values of chosen types. For example, if we know a type belongs to (or, to use the jargon, instantiates) the class Fractional, then we are guaranteed to, among other things, be able to perform real division with its values.[1]

Classes and instances

Up to now we have seen how existing type classes appear in signatures such as:

(==) :: (Eq a) => a -> a -> Bool

Now it is time to switch perspectives. First, we quote the definition of the Eq class from Prelude:

class  Eq a  where
   (==), (/=) :: a -> a -> Bool
       -- Minimal complete definition:
       --      (==) or (/=)
   x /= y     =  not (x == y)
   x == y     =  not (x /= y)

The definition states that if a type a is to be made an instance of the class Eq it must support the functions (==) and (/=) - the class methods - both of them having type a -> a -> Bool. Additionally, the class provides default definitions for (==) and (/=) in terms of each other. As a consequence, there is no need for a type in Eq to provide both definitions - given one of them, the other will be generated automatically.

With a class defined, we proceed to make existing types instances of it. Here is an arbitrary example of an algebraic data type made into an instance of Eq by an instance declaration:

data Foo = Foo {x :: Integer, str :: String}
instance Eq Foo where
   (Foo x1 str1) == (Foo x2 str2) = (x1 == x2) && (str1 == str2)

And now we can apply (==) and (/=) to Foo values in the usual way:

*Main> Foo 3 "orange" == Foo 6 "apple"
*Main> Foo 3 "orange" /= Foo 6 "apple"

A few important remarks:

  • The class Eq is defined in the Standard Prelude. This code sample defines the type Foo and then declares it to be an instance of Eq. The three definitions (class, data type, and instance) are completely separate and there is no rule about how they are grouped. This works both ways: you could just as easily create a new class Bar and then declare the type Integer to be an instance of it.
  • Classes are not types, but categories of types; and so the instances of a class are types instead of values.[2]
  • The definition of (==) for Foo relies on the fact that the values of its fields (namely Integer and String) are also members of Eq. In fact, almost all types in Haskell are members of Eq (the most notable exception being functions).
  • Type synonyms defined with type keyword cannot be made instances of a class.


Since equality tests between values are commonplace, in all likelihood most of the data types you create in any real program should be members of Eq. A lot of them will also be members of other Prelude classes such as Ord and Show. To avoid large amounts of boilerplate for every new type, Haskell has a convenient way to declare the "obvious" instance definitions using the keyword deriving. So, Foo would be written as:

data Foo = Foo {x :: Integer, str :: String}
    deriving (Eq, Ord, Show)

This makes Foo an instance of Eq with an automatically generated definition of == exactly equivalent to the one we just wrote, and also makes it an instance of Ord and Show for good measure.

You can only use deriving with a limited set of built-in classes, which are described very briefly below:

Equality operators == and /=
Comparison operators < <= > >=; min, max, and compare.
For enumerations only. Allows the use of list syntax such as [Blue .. Green].
Also for enumerations, but can also be used on types that have only one constructor. Provides minBound and maxBound as the lowest and highest values that the type can take.
Defines the function show, which converts a value into a string, and other related functions.
Defines the function read, which parses a string into a value of the type, and other related functions.

The precise rules for deriving the relevant functions are given in the language report. However, they can generally be relied upon to be the "right thing" for most cases. The types of elements inside the data type must also be instances of the class you are deriving.

This provision of special "magic" function synthesis for a limited set of predefined classes goes against the general Haskell philosophy that "built in things are not special", but it does save a lot of typing. Besides that, deriving instances stops us from writing them in the wrong way (an example: an instance of Eq such that x == y would not be equal to y == x would be flat out wrong). [3]

Class inheritance

Classes can inherit from other classes. For example, here is the main part of the definition of Ord in Prelude:

class  (Eq a) => Ord a  where
    compare              :: a -> a -> Ordering
    (<), (<=), (>=), (>) :: a -> a -> Bool
    max, min             :: a -> a -> a

The actual definition is rather longer and includes default implementations for most of the functions. The point here is that Ord inherits from Eq. This is indicated by the => notation in the first line, which mirrors the way classes appear in type signatures. Here, it means that for a type to be an instance of Ord it must also be an instance of Eq, and hence needs to implement the == and /= operations.[4]

A class can inherit from several other classes: just put all the ancestor classes in the parentheses before the =>. Let us illustrate that with yet another Prelude quote:

class  (Num a, Ord a) => Real a  where
    -- | the rational equivalent of its real argument with full precision
    toRational          ::  a -> Rational

Standard classes

This diagram, copied from the Haskell Report, shows the relationships between the classes and types in the Standard Prelude. The names in bold are the classes, while the non-bold text stands for the types that are instances of each class ((->) refers to functions and [], to lists). The arrows linking classes indicate the inheritance relationships, pointing to the inheriting class.


Type constraints

With all pieces in place, we can go full circle by returning to the very first example involving classes in this book:

(+) :: (Num a) => a -> a -> a

(Num a) => is a type constraint, which restricts the type a to instances of the class Num. In fact, (+) is a method of Num, along with quite a few other functions (notably, (*) and (-); but not (/)).

You can put several limits into a type signature like this:

foo :: (Num a, Show a, Show b) => a -> a -> b -> String
foo x y t = 
   show x ++ " plus " ++ show y ++ " is " ++ show (x+y) ++ ".  " ++ show t

Here, the arguments x and y must be of the same type, and that type must be an instance of both Num and Show. Furthermore, the final argument t must be of some (possibly different) type that is also an instance of Show. This example also displays clearly how constraints propagate from the functions used in a definition (in this case, (+) and show) to the function being defined.

Other uses

Beyond simple type signatures, type constraints can be introduced in a number of other places:

  • instance declarations (typical with parametrized types);
  • class declarations (constraints can be introduced in the method signatures in the usual way for any type variable other than the one defining the class[5]);
  • data declarations,[6] where they act as constraints for the constructor signatures.


Type constraints in data declarations are less useful than it might seem at first. Consider:

data (Num a) => Foo a = F1 a | F2 a String

Here, Foo is a type with two constructors, both taking an argument of a type a which must be in Num. However, the (Num a) => constraint is only effective for the F1 and F2 constructors, and not for other functions involving Foo. Therefore, in the following example...

fooSquared :: (Num a) => Foo a -> Foo a
fooSquared (F1 x)   = F1 (x * x)
fooSquared (F2 x s) = F2 (x * x) s

... even though the constructors ensure a will be some type in Num we can't avoid duplicating the constraint in the signature of fooSquared.[7]

A concerted example

To provide a better view of the interplay between types, classes, and constraints, we will present a very simple and somewhat contrived example. We will define a Located class, a Movable class which inherits from it, and a function with a Movable constraint implemented using the methods of the parent class, i.e. Located.

-- Location, in two dimensions.
class Located a where
    getLocation :: a -> (Int, Int)
class (Located a) => Movable a where
    setLocation :: (Int, Int) -> a -> a
-- An example type, with accompanying instances.
data NamedPoint = NamedPoint
    { pointName :: String
    , pointX    :: Int
    , pointY    :: Int
    } deriving (Show)
instance Located NamedPoint where
    getLocation p = (pointX p, pointY p)
instance Movable NamedPoint where
    setLocation (x, y) p = p { pointX = x, pointY = y }
-- Moves a value of a Movable type by the specified displacement.
-- This works for any movable, including NamedPoint.
move :: (Movable a) => (Int, Int) -> a -> a
move (dx, dy) p = setLocation (x + dx, y + dy) p
    (x, y) = getLocation p

A word of advice

Do not read too much into the Movable example just above; it is merely a demonstration of class-related language features. It would be a mistake to think that every single functionality which might be conceivably generalized, such as setLocation, needs a type class of its own. In particular, if all your Located instances should be able to be moved as well then Movable is unnecessary - and if there is just one instance there is no need for type classes at all! Classes are best used when there are several types instantiating it (or if you expect others to write additional instances) and you do not want users to know or care about the differences between the types. An extreme example would be Show: general-purpose functionality implemented by an immense number of types, about which you do not need to know a thing about before calling show. In the following chapters, we will explore a number of important type classes in the libraries; they provide good examples of the sort of functionality which fits comfortably into a class.


  1. To programmers coming from object-oriented languages: A class in Haskell in all likelihood is not what you expect - don't let the terms confuse you. While some of the uses of type classes resemble what is done with abstract classes or Java interfaces, there are fundamental differences which will become clear as we advance.
  2. This is a key difference from most OO languages, where a class is also itself a type.
  3. There are ways to make the magic apply to other classes. GHC extensions allow deriving for a few other common classes for which there is only one correct way of writing the instances, and the GHC generics machinery make it possible to generate instances automatically for custom classes.
  4. If you check the full definition in the Prelude specification, the reason for that becomes clear: the default implementations involve applying (==) to the values being compared.
  5. Constraints for the type defining the class should be set via class inheritance.
  6. And newtype declarations as well, but not type.
  7. Extra note for the curious: This issue is related to some of the problems tackled by the advanced features discussed in the "Fun with types" chapter of the Advanced Track.

The Functor class

In this chapter, we will introduce the important Functor type class.


In Other data structures, we saw operations that apply to all elements of some grouped value. The prime example is map which works on lists. Another example we worked through was the following Tree datatype:

data Tree a = Leaf a | Branch (Tree a) (Tree a) deriving (Show)

The map function we wrote for Tree was:

treeMap :: (a -> b) -> Tree a -> Tree b
treeMap f (Leaf x) = Leaf (f x)
treeMap f (Branch left right) = Branch (treeMap f left) (treeMap f right)

As discussed before, we can conceivably define a map-style function for any arbitrary data structure.

When we first introduced map in More about lists, we went through the process of taking a very specific function for list elements and generalizing to show how map combines any appropriate function with all sorts of lists. Now, we will generalize still further. Instead of map-for-lists and map-for-trees and other distinct maps, how about a general concept of maps for all sorts of mappable types?

Introducing Functor

Functor is a Prelude class for types which can be mapped over. It has a single method, called fmap. The class is defined as follows:

class  Functor f  where
    fmap        :: (a -> b) -> f a -> f b

The usage of the type variable f can look a little strange at first. Here, f is a parametrized data type; in the signature of fmap, f takes a as a type parameter in one of its appearances and b in the other. Let's consider an instance of Functor: <By replacing f with Maybe we get the following signature for fmap...

fmap        :: (a -> b) -> Maybe a -> Maybe b

... which fits the natural definition:

instance  Functor Maybe  where
    fmap f Nothing    =  Nothing
    fmap f (Just x)   =  Just (f x)

(Incidentally, this definition is in Prelude; so, we didn't really need to have implemented maybeMap for that example in the "Other data structures" chapter.)

The Functor instance for lists (also in Prelude) is simple:

instance  Functor []  where
    fmap = map

... and if we replace f with [] in the fmap signature, we get the familiar type of map.

So, fmap is a generalization of map for any parametrized data type.[1]

Naturally, we can provide Functor instances for our own data types. In particular, treeMap can be promptly relocated to an instance:

instance Functor Tree where
    fmap f (Leaf x) = Leaf (f x)
    fmap f (Branch left right) = Branch (fmap f left) (fmap f right)

Here's a quick demo of fmap in action with the instances above (to reproduce it, you only need to load the data and instance declarations for Tree; the others are already in Prelude):

*Main> fmap (2*) [1,2,3,4]
*Main> fmap (2*) (Just 1)
Just 2
*Main> fmap (fmap (2*)) [Just 1, Just 2, Just 3, Nothing]
[Just 2, Just 4, Just 6, Nothing]
*Main> fmap (2*) (Branch (Branch (Leaf 1) (Leaf 2)) (Branch (Leaf 3) (Leaf 4)))
Branch (Branch (Leaf 2) (Leaf 4)) (Branch (Leaf 6) (Leaf 8))


Beyond the [] and Maybe examples mentioned here, Prelude provides a number of other handy Functor instances. The full list can be found in GHC's documentation for the Control.Monad module.

The functor laws

When providing a new instance of Functor, you should ensure it satisfies the two functor laws. There is nothing mysterious about these laws; their role is to guarantee fmap behaves sanely and actually performs a mapping operation (as opposed to some other nonsense).[2] The first law is:

fmap id  ==  id

id is the identity function, which returns its argument unaltered. The first law states that mapping id over a functor must return the functor unchanged.

Next, the second law:

fmap (f . g)  ==  fmap f . fmap g

It states that it should not matter whether we map a composed function or first map one function and then the other (assuming the application order remains the same in both cases).

What did we gain?

At this point, we can ask what benefit we get from the extra layer of generalization brought by the Functor class. There are two significant advantages:

  • The availability of the fmap method relieves us from having to recall, read, and write a plethora of differently named mapping methods (maybeMap, treeMap, weirdMap, ad infinitum). As a consequence, code becomes both cleaner and easier to understand. On spotting a use of fmap, we instantly have a general idea of what is going on.[3]
  • Using the type class system, we can write fmap-based algorithms which work out of the box with any functor - be it [], Maybe, Tree or whichever you need. Indeed, a number of useful classes in the core libraries inherit from Functor.[4].

Type classes make it possible to create general solutions to whole categories of problems. Depending on what you use Haskell for, you may not need to define new classes often, but you will certainly be using type classes all the time. Many of the most powerful features and sophisticated capabilities of Haskell rely on type classes (residing either in the standard libraries or elsewhere). From this point on, classes will be a prominent presence in our studies.


  1. Data structures provide the most intuitive examples; however, there are functors which cannot reasonably be seen as data structures. A commonplace metaphor consists in thinking of functors as containers; like all metaphors, however, it can be stretched only so far.
  2. The functor laws, and indeed the concept of a functor, are grounded on a branch of Mathematics called Category Theory which should not be a concern for you at this point. We will have opportunities to explore related topics in the Advanced Track of this book.
  3. This is analogous to the gain in clarity provided by replacing explicit recursive algorithms on lists with implementations based on higher-order functions.
  4. Note for the curious: For one example, have a peek at Applicative Functors in the Advanced Track (for the moment you can ignore the references to monads there).


Understanding monads

Monads are very useful in Haskell, but the concept is often difficult at first. Since they have so many applications, people often explain them from a particular point of view, and that can confuse your understanding of monads in their full glory.

Historically, monads were introduced into Haskell to perform input/output. A predetermined execution order is crucial for things like reading and writing files, and monadic operations follow an inherent sequence. We discussed sequencing and IO back in Simple input and output using the do notation. Well, do is actually just syntactic sugar over monads.

Monads are by no means limited to input and output. Monads support a whole range of things like exceptions, state, non-determinism, continuations, coroutines, and more. In fact, thanks to the versatility of monads, none of these constructs needed to be built into Haskell as a language; instead, they are defined by the standard libraries.


A monad is defined by three things:

The function and operator are methods of the Monad type class and have types

    return :: a -> M a
    (>>=)  :: M a -> (a -> M b) -> M b

and are required to obey three laws that will be explained later on.

For a concrete example, take the Maybe monad. The type constructor is M = Maybe, while return and (>>=) are defined like this:

    return :: a -> Maybe a
    return x  = Just x
    (>>=)  :: Maybe a -> (a -> Maybe b) -> Maybe b
    m >>= g = case m of
                 Nothing -> Nothing
                 Just x  -> g x

Maybe is the monad, and return brings a value into it by wrapping it with Just. As for (>>=), it takes a m :: Maybe a value and a g :: a -> Maybe b function. If m is Nothing, there is nothing to do and the result is Nothing. Otherwise, in the Just x case, g is applied to x, the underlying value wrapped in Just, to give a Maybe b result, which might be Nothing, depending on what g does to x. To sum it all up, if there is an underlying value in m, we apply g to it, which brings the underlying value back into the Maybe monad.

The key first step to understand how return and (>>=) work is tracking which values and arguments are monadic and which ones aren't. As in so many other cases, type signatures are our guide to the process.

Motivation: Maybe

To see the usefulness of (>>=) and the Maybe monad, consider the following example: Imagine a family database that provides two functions

    father :: Person -> Maybe Person
    mother :: Person -> Maybe Person

These look up the name of someone's father or mother. In case our database is missing some information, Maybe allows us to return a Nothing value instead of crashing the program.

Let's combine our functions to query various grandparents. For instance, the following function looks up the maternal grandfather:

maternalGrandfather :: Person -> Maybe Person
maternalGrandfather p =
    case mother p of
        Nothing -> Nothing
        Just mom -> father mom

Or consider a function that checks whether both grandfathers are in the database:

    bothGrandfathers :: Person -> Maybe (Person, Person)
    bothGrandfathers p =
        case father p of
            Nothing -> Nothing
            Just dad ->
                case father dad of
                    Nothing -> Nothing
                    Just gf1 ->                          -- found first grandfather
                        case mother p of
                            Nothing -> Nothing
                            Just mom ->
                                case father mom of
                                    Nothing -> Nothing
                                    Just gf2 ->          -- found second grandfather
                                        Just (gf1, gf2)

What a mouthful! Every single query might fail by returning Nothing and the whole function must fail with Nothing if that happens.

Clearly there has to be a better way to write that instead of repeating the case of Nothing again and again! Indeed, that's what the Maybe monad is set out to do. For instance, the function retrieving the maternal grandfather has exactly the same structure as the (>>=) operator, so we can rewrite it as:

    maternalGrandfather p = mother p >>= father

With the help of lambda expressions and return, we can rewrite the two grandfathers function as well:

    bothGrandfathers p =
       father p >>=
           (\dad -> father dad >>=
               (\gf1 -> mother p >>=   -- this line works as "\_ -> mother p", but naming gf1 allows later return
                   (\mom -> father mom >>=
                       (\gf2 -> return (gf1,gf2) ))))

While these nested lambda expressions may look confusing to you, the thing to take away here is that (>>=) releases us from listing all the Nothings, shifting the focus back to the interesting part of the code.

To be a little more precise: The result of father p is a monadic value (in this case, either Just dad or Nothing, depending on whether p's dad is in the database). As the father function takes a regular (non-monadic value), the >>= feeds p's dad to it as a non-monadic value. The result of father dad is then monadic again, and the process continues.

So, >>= helps us pass non-monadic values to functions without leaving a monad. In the case of the Maybe monad, the monadic aspect is the qualifier that we don't know with certainty whether the value will be found.

Type class

In Haskell, the Monad type class is used to implement monads. It is provided by the Control.Monad module and included in the Prelude. The class has the following methods:

    class Monad m where
        return :: a -> m a
        (>>=)  :: m a -> (a -> m b) -> m b
        (>>)   :: m a -> m b -> m b
        fail   :: String -> m a

Aside from return and bind, notice the two additional functions (>>) and fail.

The operator (>>) called "then" is a mere convenience and commonly implemented as

    m >> n = m >>= \_ -> n

>> sequences two monadic actions when the second action does not involve the result of the first, which is common for monads like IO.

    printSomethingTwice :: String -> IO ()
    printSomethingTwice str = putStrLn str >> putStrLn str

The function fail handles pattern match failures in do notation. It's an unfortunate technical necessity and doesn't really have anything to do with monads. You are advised to not call fail directly in your code.

Notions of Computation

We've seen how (>>=) and return are very handy for removing boilerplate code that crops up when using Maybe. That, however, is not enough to justify why monads matter so much. We will continue our monad studies by rewriting the two-grandfathers function using do notation with explicit braces and semicolons. Depending on your experience with other programming languages, you may find this very suggestive:

    bothGrandfathers p = do {
        dad <- father p;
        gf1 <- father dad;
        mom <- mother p;
        gf2 <- father mom;
        return (gf1, gf2);

If this looks like a code snippet of an imperative programming language to you, that's because it is. In particular, this imperative language supports exceptions : father and mother are functions that might fail to produce results, i.e. raise an exception, and when that happens, the whole do-block will fail, i.e. terminate with an exception.

In other words, the expression father p, which has type Maybe Person, is interpreted as a statement of an imperative language that returns a Person as result. This is true for all monads: a value of type M a is interpreted as a statement of an imperative language that returns a value of type a as result; and the semantics of this language are determined by the monad M.[2]

Under this interpretation, the bind operator (>>=) is simply a function version of the semicolon. Just like a let expression can be written as a function application,

   let x = foo in x + 3          corresponds to      (\x -> x + 3) foo

an assignment and semicolon can be written as the bind operator:

   x <- foo; return (x + 3)      corresponds to      foo >>= (\x -> return (x + 3))

The return function lifts a value a to M a, a full-fledged statement of the imperative language corresponding to the monad M.

Different semantics of the imperative language correspond to different monads. The following table shows the classic selection that every Haskell programmer should know. If the idea behind monads is still unclear to you, studying each of the examples in the following chapters will not only give you a well-rounded toolbox but also help you understand the common abstraction behind them.

Monad Imperative Semantics Wikibook chapter
Maybe Exception (anonymous) Haskell/Understanding monads/Maybe
Error Exception (with error description) Haskell/Understanding monads/Error
State Global state Haskell/Understanding monads/State
IO Input/Output Haskell/Understanding monads/IO
[] (lists) Nondeterminism Haskell/Understanding monads/List
Reader Environment Haskell/Understanding monads/Reader
Writer Logger Haskell/Understanding monads/Writer

Furthermore, these different semantics need not occur in isolation. As we will see in a few chapters, it is possible to mix and match them by using monad transformers to combine the semantics of multiple monads in a single monad.

Monad Laws

In Haskell, every instance of the Monad type class (and thus all implementations of (>>=) and return) must obey the following three laws:

    m >>= return     =  m                        -- right unit
    return x >>= f   =  f x                      -- left unit
    (m >>= f) >>= g  =  m >>= (\x -> f x >>= g)  -- associativity

Return as neutral element

The behavior of return is specified by the left and right unit laws. They state that return doesn't perform any computation, it just collects values. For instance,

    maternalGrandfather p = do
            mom <- mother p
            gf  <- father mom
            return gf

is exactly the same as

    maternalGrandfather p = do
            mom  <- mother p
            father mom

by virtue of the right unit law.

Associativity of bind

The law of associativity makes sure that (like the semicolon) the bind operator (>>=) only cares about the order of computations, not about their nesting; e.g. we could have written bothGrandfathers like this (compare with our earliest version without do):

    bothGrandfathers p =
       (father p >>= father) >>=
           (\gf1 -> (mother p >>= father) >>=
               (\gf2 -> return (gf1,gf2) ))

The associativity of the then operator (>>) is a special case:

   (m >> n) >> o  =  m >> (n >> o)

Monadic composition

It is easier to picture the associativity of bind by recasting the law as

   (f >=> g) >=> h  =  f >=> (g >=> h)

where (>=>) is the monad composition operator, a close analogue of the function composition operator (.), only with flipped arguments. It is defined as:

   (>=>) :: Monad m => (a -> m b) -> (b -> m c) -> a -> m c
   f >=> g = \x -> f x >>= g

We can also flip monad composition to go the other direction using (<=<). The operation order of (f . g) is the same as (f' <=< g').[3]

Monads and Category Theory

Monads originally come from a branch of mathematics called Category Theory. Fortunately, it is entirely unnecessary to understand category theory in order to understand and use monads in Haskell. The definition of monads in Category Theory actually uses a slightly different presentation. Translated into Haskell, this presentation gives an alternative yet equivalent definition of a monad which can give us some additional insight.[4]

So far, we have defined monads in terms of (>>=) and return. The alternative definition, instead, starts with monads as functors with two additional combinators:

    fmap   :: (a -> b) -> M a -> M b  -- functor
    return :: a -> M a
    join   :: M (M a) -> M a

(Reminder: as discussed in the chapter on the functor class, a functor M can be thought of as container, so that M a "contains" values of type a, with a corresponding mapping function, i.e. fmap, that allows functions to be applied to values inside it.)

Under this interpretation, the functions behave as follows:

  • fmap applies a given function to every element in a container
  • return packages an element into a container,
  • join takes a container of containers and flattens it into a single container.

With these functions, the bind combinator can be defined as follows:

    m >>= g = join (fmap g m)

Likewise, we could give a definition of fmap and join in terms of (>>=) and return:

    fmap f x = x >>= (return . f)
    join x   = x >>= id

Is my Monad a Functor?

At this point we might, with good reason, deduce that all monads are by definition functors as well. While according to category theory that is indeed the case, GHC does it differently because of historic accident in the designing of Haskell. While the Monad and Functor classes aren't connected in current versions, GHC 7.10 will finally fix this issue so that every Monad instance will be connected to a matching Functor instance and the corresponding fmap. Meanwhile, Control.Monad defines liftM, a function with a strangely familiar type signature...

    liftM :: (Monad m) => (a1 -> r) -> m a1 -> m r

As you might suspect, liftM is merely fmap implemented with (>>=) and return, just as we have done above. For a properly implemented monad with a matching Functor (that is, any sensible monad) liftM and fmap are interchangeable.


  1. This return function has nothing to do with the return keyword found in imperative languages like C or Java; don't conflate these two.
  2. By "semantics", we mean what the language allows you to say. In the case of Maybe, the semantics allow us to express failure, as statements may fail to produce a result, leading to the statements that follow it being skipped.
  3. Of course, the functions in regular function composition are non-monadic functions whereas monadic composition takes only monadic functions.
  4. Deep into the Advanced Track, we will cover the theoretical side of the story in the chapter on Category Theory.

The Maybe monad

We introduced monads using Maybe as an example. The Maybe monad represents computations which might "go wrong" by not returning a value. For reference, here are our definitions of return and (>>=) for Maybe as we saw in the last chapter:[1]

    return :: a -> Maybe a
    return x  = Just x
    (>>=)  :: Maybe a -> (a -> Maybe b) -> Maybe b
    (>>=) m g = case m of
                   Nothing -> Nothing
                   Just x  -> g x

Safe functions

The Maybe datatype provides a way to make a safety wrapper around functions which can fail to work for a range of arguments. For example, head and tail only work with non-empty lists. Another typical case, which we will explore in this section, are mathematical functions like sqrt and log; (as far as real numbers are concerned) these are only defined for non-negative arguments.

> log 1000
> log -1000
''ERROR'' -- runtime error

To avoid this crash, a "safe" implementation of log could be:

safeLog :: (Floating a, Ord a) => a -> Maybe a
safeLog x
    | x > 0    = Just (log x)
    | otherwise = Nothing
> safeLog 1000
Just 6.907755278982137
> safeLog -1000

We could write similar "safe functions" for all functions with limited domains such as division, square-root, and inverse trigonometric functions (safeDiv, safeSqrt, safeArcSin, etc. all of which would have the same type as safeLog but definitions specific to their constraints)

If we wanted to combine these monadic functions, the cleanest approach is with monadic composition (which was mentioned briefly near the end of the last chapter) and point-free style:

safeLogSqrt = safeLog <=< safeSqrt

Written in this way, safeLogSqrt resembles a lot its unsafe, non-monadic counterpart:

unsafeLogSqrt = log . sqrt

Lookup tables

A lookup table relates keys to values. You look up a value by knowing its key and using the lookup table. For example, you might have a phone book application with a lookup table where contact names are keys to corresponding phone numbers. An elementary way of implementing lookup tables in Haskell is to use a list of pairs: [(a, b)]. Here a is the type of the keys, and b the type of the values.[2] Here's how the phone book lookup table might look like:

phonebook :: [(String, String)]
phonebook = [ ("Bob",   "01788 665242"),
              ("Fred",  "01624 556442"),
              ("Alice", "01889 985333"),
              ("Jane",  "01732 187565") ]

The most common thing you might do with a lookup table is look up values. Everything is fine if we try to look up "Bob", "Fred", "Alice" or "Jane" in our phone book, but what if we were to look up "Zoe"? Zoe isn't in our phone book, so the lookup would fail. Hence, the Haskell function to look up a value from the table is a Maybe computation (it is available from Prelude):

lookup :: Eq a => a  -- a key
       -> [(a, b)]   -- the lookup table to use
       -> Maybe b    -- the result of the lookup

Let us explore some of the results from lookup:

Prelude> lookup "Bob" phonebook
Just "01788 665242"
Prelude> lookup "Jane" phonebook
Just "01732 187565"
Prelude> lookup "Zoe" phonebook

Now let's expand this into using the full power of the monadic interface. Say, we're now working for the government, and once we have a phone number from our contact, we want to look up this phone number in a big, government-sized lookup table to find out the registration number of their car. This, of course, will be another Maybe-computation. But if the person we're looking for isn't in our phone book; we certainly won't be able to look up their registration number in the governmental database. What we need is a function that will take the results from the first computation and put it into the second lookup only if we get a successful value in the first lookup. Of course, our final result should be Nothing if we get Nothing from the either of the lookups.

getRegistrationNumber :: String       -- their name
                      -> Maybe String -- their registration number
getRegistrationNumber name = 
  lookup name phonebook >>=
    (\number -> lookup number governmentDatabase)

If we then wanted to use the result from the governmental database lookup in a third lookup (say we want to look up their registration number to see if they owe any car tax), then we could extend our getRegistrationNumber function:

getTaxOwed :: String       -- their name
           -> Maybe Double -- the amount of tax they owe
getTaxOwed name = 
  lookup name phonebook >>=
    (\number -> lookup number governmentDatabase) >>=
      (\registration -> lookup registration taxDatabase)

Or, using the do-block style:

getTaxOwed name = do
  number       <- lookup name phonebook
  registration <- lookup number governmentDatabase
  lookup registration taxDatabase

Let's just pause here and think about what would happen if we got a Nothing anywhere. By definition, when the first argument to >>= is Nothing, it just returns Nothing while ignoring whatever function it is given. Thus, a Nothing at any stage in the large computation will result in a Nothing overall, regardless of the other functions. After the first Nothing hits, all >>=s will just pass it to each other, skipping the other function arguments. The technical description says that the structure of the Maybe monad propagates failures.

Open monads

Another trait of the Maybe monad is that it is "open": if we have a Just value, we can see the contents and extract the associated values through pattern matching.

zeroAsDefault :: Maybe Int -> Int
zeroAsDefault mx = case mx of
    Nothing -> 0
    Just x -> x

This usage pattern of replacing Nothing with a default is captured by the fromMaybe function in Data.Maybe.

zeroAsDefault :: Maybe Int -> Int
zeroAsDefault mx = fromMaybe 0 mx

The maybe Prelude function allows us to do it in a more general way, by supplying a function to modify the extracted value.

displayResult :: Maybe Int -> String
displayResult mx = maybe "There was no result" (("The result was " ++) . show) mx
Prelude> :t maybe
maybe :: b -> (a -> b) -> Maybe a -> b
Prelude> displayResult (Just 10)
"The result was 10"
Prelude> displayResult Nothing
"There was no result"

This possibility makes sense for Maybe, as it allows us to recover from failures. Not all monads are open in this way; often, they are designed to hide unnecessary details. return and (>>=) alone do not allow us to extract the underlying value from a monadic computation, and so it is perfectly possible to make a "no-exit" monad, from which it is never possible to extract values. The most obvious example of that is the IO monad.

Maybe and safety

We have seen how Maybe can make code safer by providing a graceful way to deal with failure that does not involve runtime errors. Does that mean we should always use Maybe for everything? Not really.

When you write a function, you are able to tell whether it might fail to produce a result during normal operation of the program,[3] either because the functions you use might fail (as in the examples in this chapter) or because you know some of the argument or intermediate result values do not make sense (for instance, imagine a calculation that is only meaningful if its argument is less than 10). If that is the case, by all means use Maybe to signal failure; it is far better than returning an arbitrary default value or throwing an error.

Now, adding Maybe to a result type without a reason would only make the code more confusing and no safer. The type signature of a function with unnecessary Maybe would tell users of the code that the function could fail when it actually can't. Of course, that is not as bad a lie as the opposite one (that is, claiming that a function will not fail when it actually can), but we really want honest code in all cases. Furthermore, using Maybe forces us to propagate failure (with fmap or monadic code) and eventually handle the failure cases (using pattern matching, the maybe function, or fromMaybe from Data.Maybe). If the function cannot actually fail, coding for failure is an unnecessary complication.


  1. The definitions in the actual instance in Data.Maybe are written a little differently, but are fully equivalent to these.
  2. Check the chapter about maps in Haskell in Practice for a different, and potentially more useful, implementation.
  3. With "normal operation" we mean to exclude failure caused by uncontrollable circumstances in the real world, such as memory exhaustion or a dog chewing the printer cable.

The List monad

Lists are a fundamental part of Haskell, and we've used them extensively before getting to this chapter. The novel insight is that the list type is a monad too!

As monads, lists are used to model nondeterministic computations which may return an arbitrary number of results. There is a certain parallel with how Maybe represented computations which could return zero or one value; but with lists, we can return zero, one, or many values (the number of values being reflected in the length of the list).

List instantiated as monad

The return function for lists simply injects a value into a list:

return x = [x]

In other words, return here makes a list containing one element, namely the single argument it took. The type of the list return is return :: a -> [a], or, equivalently, return :: a -> [] a. The latter style of writing it makes it more obvious that we are replacing the generic type constructor in the signature of return (which we had called M in Understanding monads) by the list type constructor [] (which is distinct from but easy to confuse with the empty list!).

The binding operator is less trivial. We will begin by considering its type, which for the case of lists should be:

[a] -> (a -> [b]) -> [b]

This is just what we'd expect: it pulls out the value from the list to give to a function that returns a new list.

The actual process here involves first mapping a given function over a given list to get back a list of lists, i.e. type [[b]] (of course, many functions which you might use in mapping do not return lists; but, as shown in the type signature above, monadic binding for lists only works with functions that return lists). To get back to a regular list, we then concatenate the elements of our list of lists to get a final result of type [b]. Thus, we can define the list version of (>>=):

xs >>= f = concat (map f xs)

The bind operator is key to understanding how different monads do their jobs, and its definition indicates the chaining strategy for working with the monad.

For the list monad, non-determinism is present because different functions may return any number of different results when mapped over lists.

Bunny invasion

It is easy to incorporate the familiar list processing functions in monadic code. Consider this example: rabbits raise an average of six kits in each litter, half of which will be female. Starting with a single mother, we can model the number of female kits in each successive generation (i.e. the number of new kits after the rabbits grow up and have their own litters):

Prelude> let generation = replicate 3
Prelude> ["bunny"] >>= generation
Prelude> ["bunny"] >>= generation >>= generation

In this silly example all elements are equal, but the same overall logic could be used to model radioactive decay, or chemical reactions, or any phenomena that produces a series of elements starting from a single one.

Board game example

Suppose we are modeling a turn-based board game and want to find all the possible ways the game could progress. We would need a function to calculate the list of options for the next turn, given a current board state:

nextConfigs :: Board -> [Board]
nextConfigs bd = undefined -- details not important

To figure out all the possibilities after two turns, we would again apply our function to each of the elements of our new list of board states. Our function takes a single board state and returns a list of possible new states. Thus, we can use monadic binding to map the function over each element from the list:

nextConfigs bd >>= nextConfigs

In the same fashion, we could to bind the result back to the function yet again (ad infinitum) to generate the next turn's possibilities. Depending on the particular game's rules, we may reach board states that have no possible next-turns; in those cases, our function will return the empty list.[1]

On a side note, we could translate several turns into a do block (like we did for the grandparents example in Understanding monads):

threeTurns :: Board -> [Board]
threeTurns bd = do
  bd1 <- nextConfigs bd  -- bd1 refers to a board configuration after 1 turn
  bd2 <- nextConfigs bd1
  nextConfigs bd2

If the above looks too magical, keep in mind that do notation is syntactic sugar for (>>=) operations. To the right of each left-arrow, there is a function with arguments that evaluate to a list; the variable to the left of the arrow stands for the list elements. After a left-arrow assignment line, there can be later lines that call the assigned variable as an argument for a function. This later function will be performed for each of the elements from within the list that came from the left-arrow line's function. This per-element process corresponds to the `map` in the definition of (>>=). A resulting list of lists (one per element of the original list) will be flattened into a single list (the `concat` in the definition of (>>=)).

List comprehensions

The list monad works in a way that has uncanny similarity to list comprehensions. Let's slightly modify the do block we just wrote for threeTurns so that it ends with a return...

threeTurns bd = do
  bd1 <- nextConfigs bd
  bd2 <- nextConfigs bd1
  bd3 <- nextConfigs bd2
  return bd3

This mirrors exactly the following list comprehension:

threeTurns bd = [ bd3 | bd1 <- nextConfigs bd, bd2 <- nextConfigs bd1, bd3 <- nextConfigs bd2 ]

(In a list comprehension, it is perfectly legal to use the elements drawn from one list to define the following ones, like we did here.)

The resemblance is no coincidence: list comprehensions are, behind the scenes, defined in terms of concatMap and concatMap f xs = concat (map f xs)). That's just the list monad binding definition again! To summarize the nature of the list monad: binding for the list monad is a combination of concatenation and mapping, and so the combined function concatMap is effectively the same as >>= for lists (except for different syntactic order).

For the correspondence between list monad and list comprehension to be complete, we need a way to reproduce the filtering that list comprehensions can do. We will explain how that can be achieved a little later in the Additive monads chapter.

do Notation

Using do blocks as an alternative monad syntax was first introduced way back in the Simple input and output chapter. There, we used do to sequence input/output operations, but we hadn't introduced monads yet. Now, we can see that IO is yet another monad.

Since the following examples all involve IO, we will refer to the computations/monadic values as actions (as we did in the earlier parts of the book). Of course, do works with any monad; there is nothing specific about IO in how it works.

Translating the then operator

The (>>) (then) operator works almost identically in do notation and in unsugared code. For example, suppose we have a chain of actions like the following one:

putStr "Hello" >> 
putStr " " >> 
putStr "world!" >> 
putStr "\n"

We can rewrite that in do notation as follows:

do putStr "Hello"
   putStr " "
   putStr "world!"
   putStr "\n"

This sequence of instructions nearly matches that in any imperative language. In Haskell, we can chain any actions as long as all of them are in the same monad. In the context of the IO monad, the actions include writing to a file, opening a network connection, or asking the user for input.

Here's the step-by-step translation of do notation to unsugared Haskell code:

do action1


action1 >>
do action2

and so on, until the do block is empty.

Translating the bind operator

The (>>=) is a bit more difficult to translate from and to do notation. (>>=) passes a value, namely the result of an action or function, downstream in the binding sequence. do notation assigns a variable name to the passed value using the <-.

do x1 <- action1
   x2 <- action2
   action3 x1 x2

x1 and x2 are the results of action1 and action2. If, for instance, action1 is an IO Integer then x1 will be bound to an Integer). The stored values are passed as arguments to action3, which returns a third action. The do block is broadly equivalent to the following vanilla Haskell snippet:

action1 >>= \ x1 -> action2 >>= \ x2 -> action3 x1 x2

The second argument of (>>=) is a function specifying what to do with the result of the action passed as first argument. Thus, chains of lambdas pass the results downstream. Remember that, without extra parentheses, a lambda extends all the way to the end of the expression. x1 is still in scope at the point we call action3. We can rewrite the chain of lambdas more legibly by using separate lines and indentation:

    \ x1 -> action2
        \ x2 -> action3 x1 x2

That shows the scope of each lambda function clearly. To group things more like the do notation, we could show it like this:

action1 >>= \ x1 ->
  action2 >>= \ x2 ->
    action3 x1 x2

These presentation differences are only a matter of assisting readability.[2]

The fail method

Above, we said the snippet with lambdas was "broadly equivalent" to the do block. The translation is not exact because the do notation adds special handling of pattern match failures. When placed at the left of either <- or ->, x1 and x2 are patterns being matched. Therefore, if action1 returned a Maybe Integer we could write a do block like this...

do Just x1 <- action1
   x2      <- action2
   action3 x1 x2

...and x1 be an Integer. In such a case, what happens if action1 returns Nothing? Ordinarily, the program would crash with an non-exhaustive patterns error, just like the one we get when calling head on an empty list. With do notation, however, failures are handled with the fail method for the relevant monad. The do block above translates to:

action1 >>= f
where f (Just x1) = do x2 <- action2
                       action3 x1 x2
      f _         = fail "..." -- A compiler-generated message.

What fail actually does depends on the monad instance. Though it will often rethrow the pattern matching error, monads that incorporate some sort of error handling may deal with the failure in their own specific ways. For instance, Maybe has fail _ = Nothing; analogously, for the list monad fail _ = [].[3]

The fail method is an artifact of do notation. Rather than calling fail directly, you should rely on automatic handling of pattern match failures whenever you are sure that fail will do something sensible for the monad you are using.

Example: user-interactive program


We are going to interact with the user, so we will use putStr and getLine alternately. To avoid unexpected results in the output, we must disable output buffering when importing System.IO. To do this, put hSetBuffering stdout NoBuffering at the top of your code. To handle this otherwise, you would explicitly flush the output buffer before each interaction with the user (namely a getLine) using hFlush stdout. If you are testing this code with ghci, you don't have such problems.

Consider this simple program that asks the user for their first and last names:

nameDo :: IO ()
nameDo = do putStr "What is your first name? "
            first <- getLine
            putStr "And your last name? "
            last <- getLine
            let full = first ++ " " ++ last
            putStrLn ("Pleased to meet you, " ++ full ++ "!")

A possible translation into vanilla monadic code:

nameLambda :: IO ()
nameLambda = putStr "What is your first name? " >>
             getLine >>= \ first ->
             putStr "And your last name? " >>
             getLine >>= \ last ->
             let full = first ++ " " ++ last
             in putStrLn ("Pleased to meet you, " ++ full ++ "!")

In cases like this, where we just want to chain several actions, the imperative style of do notation feels natural and convenient. In comparison, monadic code with explicit binds and lambdas is something of an acquired taste.

Notice that example above includes a let statement in the do block. The de-sugared version is simply a regular let expression where the in part is whatever follows from the do syntax.

Returning values

The last statement in do notation is the overall result of the do block. In the previous example, the result was of the type IO (), i.e. an empty value in the IO monad.

Suppose that we want to rewrite the example but return an IO String with the acquired name. All we need to do is add a return:

nameReturn :: IO String
nameReturn = do putStr "What is your first name? "
                first <- getLine
                putStr "And your last name? "
                last <- getLine
                let full = first ++ " " ++ last
                putStrLn ("Pleased to meet you, " ++ full ++ "!")
                return full

This example will "return" the full name as a string inside the IO monad, which can then be utilized downstream elsewhere:

greetAndSeeYou :: IO ()
greetAndSeeYou = do name <- nameReturn
                    putStrLn ("See you, " ++ name ++ "!")

Here, nameReturn will be run and the returned result (called "full" in the nameReturn function) will be assigned to the variable "name" in our new function. The greeting part of nameReturn will be printed to the screen because that is part of the calculation process. Then, the additional "see you" message will print as well, and the final returned value is back to being IO ().

If you know imperative languages like C, you might think return in Haskell matches return elsewhere. A small variation on the example will dispel that impression:

nameReturnAndCarryOn = do putStr "What is your first name? "
                          first <- getLine
                          putStr "And your last name? "
                          last <- getLine
                          let full = first++" "++last
                          putStrLn ("Pleased to meet you, "++full++"!")
                          return full
                          putStrLn "I am not finished yet!"

The string in the extra line will be printed out because return is not a final statement interrupting the flow (as it would be in C and other languages). Indeed, the type of nameReturnAndCarryOn is IO (), — the type of the final putStrLn action. After the function is called, the IO String created by the return full will disappear without a trace.

Just sugar

As a syntactical convenience, do notation does not add anything essential, but it is often preferable for clarity and style. However, do is never used for a single action. The Haskell "Hello world" is simply:

main = putStrLn "Hello world!"

Snippets like this one are totally redundant:

fooRedundant = do x <- bar
                  return x

Thanks to the monad laws, we can and should write simply:

foo = bar

A subtle but crucial point relates to function composition: As we already know, the greetAndSeeYou action in the section just above could be rewritten as:

greetAndSeeYou :: IO ()
greetAndSeeYou = nameReturn >>= \ name -> putStrLn ("See you, " ++ name ++ "!")

While you might find the lambda a little unsightly, suppose we had a printSeeYou function defined elsewhere:

printSeeYou :: String -> IO ()
printSeeYou name = putStrLn ("See you, " ++ name ++ "!")

Now, we can have a clean function definition with neither lambdas or do:

greetAndSeeYou :: IO ()
greetAndSeeYou = nameReturn >>= printSeeYou

Or, if we had a non-monadic seeYou function:

seeYou :: String -> String
seeYou name = "See you, " ++ name ++ "!"

Then we can write:

-- Reminder: liftM f m == m >>= return . f == fmap f m
greetAndSeeYou :: IO ()
greetAndSeeYou = liftM seeYou nameReturn >>= putStrLn

Keep this last example with liftM in mind; we will soon return to using non-monadic functions in monadic code, and liftM will be useful there.


  1. As an optional advanced exercise: research how we could do recursive binding to find all possible results for games that have a finite number of possibilities. Furthermore, consider how we might handle the empty list results when they are reached and still retain the list of possible final actual board states.
  2. Actually, the indentation isn't needed in this case. This is equally valid:
    action1 >>= \ x1 ->
    action2 >>= \ x2 ->
    action3 x1 x2

    Of course, we could use even more indentation if we wanted. Here's an extreme example:


    While that indention is certainly overkill, it could be worse:

      >>= \
          -> action2 >>=
              x2 ->
                action3 x1

    That is valid Haskell but is baffling to read; so please don't ever write like that. Write your code with consistent and meaningful groupings.

  3. This explains why, as we pointed out in the "Pattern matching" chapter, pattern matching failures in list comprehensions are silently ignored.

The IO monad

Haskell is a functional and lazy language. However, the real world effects of input/output operations can't be expressed through pure functions. Furthermore, in most cases I/O can't be done lazily. Since lazy computations are only performed when their values become necessary, unfettered lazy I/O would make the order of execution of the real world effects unpredictable. Haskell addresses these issues through the IO monad.

Input/output and purity

Haskell functions are pure: when given the same arguments, they return the same results. Pure functions are reliable and predictable; they ease debugging and validation. Test cases can also be set up easily since we can be sure that nothing other than the arguments will influence a function's result. Being entirely contained within the program, the Haskell compiler can evaluate functions thoroughly in order to optimize the compiled code.

So, how do we manage actions like opening a network connection, writing a file, reading input from the outside world, or anything else that does something more than returning a calculated result? Well, the key is: these actions are not functions. The IO monad is a means to represent actions as Haskell values, so that we can manipulate them with pure functions.

Combining functions and I/O actions

Let's combine functions with I/O to create a full program that will:

  1. Ask the user to insert a string
  2. Read their string
  3. Use liftM to apply a function shout that capitalizes all the letters from the string
  4. Write the resulting string

module Main where
import Data.Char (toUpper)
import Control.Monad
main = putStrLn "Write your string: " >> liftM shout getLine >>= putStrLn
shout = map toUpper

We have a full-blown program, but we didn't include any type definitions. Which parts are functions and which are IO actions or other values? We can load our program in ghci and check the types:

main :: IO ()
putStrLn :: String -> IO ()
"Write your string: " :: [Char]
(>>) :: Monad m => m a -> m b -> m b
liftM :: Monad m => (a1 -> r) -> m a1 -> m r
shout :: [Char] -> [Char]
getLine :: IO String
(>>=) :: Monad m => m a -> (a -> m b) -> m b

Whew, that a lot of information there. We've seen all of this before, but let's review.

main is IO (). That's not a function. Functions are of types a -> b. Our entire program is an IO action.

putStrLn is a function, but it results in an IO action. The "Write your string: " text is a String (remember, that's just a synonym for [Char]). It is used as an argument for putStrLn and is incorporated into the IO action that results. So, putStrLn is a function, but putStrLn x evaluates to an IO action. The () part of the IO type indicates that nothing is available to be passed on to any later functions or actions.

That last part is key. We sometimes say informally that an IO action "returns" something; however, taking that too literally leads to confusion. It is clear what we mean when we talk about functions returning results, but IO actions are not functions. Let's skip down to getLine — an IO action that does provide a value. getLine is not a function that returns a String because getLine isn't a function. Rather, getLine is an IO action which, when evaluated, will materialize a String, which can then be passed to later functions through, for instance, fmap/liftM and (>>=).

When we use getLine to get a String, the value is monadic because it is wrapped in IO functor. We cannot pass the value directly to a function that takes plain (non-monadic, or non-functorial) values. liftM does the work of taking a non-monadic function while passing in and returning monadic values.

As we've seen already, (>>=) does the work of passing a monadic value into a function that takes a non-monadic value and returns a monadic value. It may seem inefficient for liftM to take the non-monadic result of its given function and return a monadic value only for (>>=) to then pass the underlying non-monadic value to the next function. It is precisely this sort of chaining, however, that creates the reliable sequencing that make monads so effective at integrating pure functions with IO actions.

do notation review

Given the emphasis on sequencing, the do notation can be especially appealing with the IO monad. Our program

putStrLn "Write your string: " >> liftM shout getLine >>= putStrLn

could be written as:

do putStrLn "Write your string: "
   string <- getLine
   putStrLn (shout string)

The universe as part of our program

One way of viewing the IO monad is to consider IO a as a computation which provides a value of type a while changing the state of the world by doing input and output. Obviously, you cannot literally set the state of the world; it is hidden from you, as the IO functor is abstract (that is, you cannot dig into it to see the underlying values; it is closed in a way opposite to that in which Maybe can be said to be open). Seen this way, IO is roughly analogous to the State monad, which we will meet shortly. With State, however, the state being changed is made of normal Haskell values, and so we can manipulate it directly with pure functions.

Understand that this idea of the universe as an object affected and affecting Haskell values through IO is only a metaphor; a loose interpretation at best. The more mundane fact is that IO simply brings some very base-level operations into the Haskell language.[1] Remember that Haskell is an abstraction, and that Haskell programs must be compiled to machine code in order to actually run. The actual workings of IO happen at a lower level of abstraction, and are wired into the very definition of the Haskell language.[2]

Pure and impure

Consider the following snippet:

speakTo :: (String -> String) -> IO String
speakTo fSentence = liftM fSentence getLine
-- Usage example.
sayHello :: IO String
sayHello = speakTo (\name -> "Hello, " ++ name ++ "!")

In most other programming languages, which do not have separate types for I/O actions, speakTo would have a type akin to:

speakTo :: (String -> String) -> String

With such a type, however, speakTo would not be a function at all! Functions produce the same results when given the same arguments; the String delivered by speakTo, however, also depends on whatever is typed at the terminal prompt. In Haskell, we avoid that pitfall by returning an IO String, which is not a String but a promise that some String will be delivered by carrying out certain instructions involving I/O (in this case, the I/O consists of getting a line of input from the terminal). Though the String can be different each time speakTo is evaluated, the I/O instructions are always the same.

When we say Haskell is a purely functional language, we mean that all of its functions are really functions, which is not the case in most other languages. To be precise, Haskell expressions are always referentially transparent; that is, you can always replace an expression (such as speakTo) with its value (in this case, \fSentence -> liftM fSentence getLine) without changing the behaviour of the program. The String delivered by getLine, in contrast, is opaque; its value is not specified and can't be discovered in advance by the program. If speakTo had the problematic type we mentioned above, sayHello would be a String; however, replacing it by any specific string would break the program.

In spite of Haskell being purely functional, IO actions can be said to be impure because their impact on the outside world are side effects (as opposed to the regular effects that are entirely contained within Haskell). Programming languages that lack purity may have side-effects in many other places connected with various calculations. Purely functional languages, however, assure that even expressions with impure values are referentially transparent. That means we can talk about, reason about and handle impurity in a purely functional way, using purely functional machinery such as functors and monads. While IO actions are impure, all of the Haskell functions that manipulate them remain pure.

Functional purity, coupled to the fact that I/O shows up in types, benefit Haskell programmers in various ways. The guarantees about referential transparency increase a lot the potential for compiler optimizations. IO values being distinguishable through types alone make it possible to immediately tell where we are engaging with side effects or opaque values. As IO itself is just another functor, we maintain to the fullest extent the predictability and ease of reasoning associated with pure functions.

Functional and imperative

When we introduced monads, we said that a monadic expression can be interpreted as a statement of an imperative language. That interpretation is immediately compelling for IO, as the language around IO actions looks a lot like a conventional imperative language. It must be clear, however, that we are talking about an interpretation. We are not saying that monads or do notation turn Haskell into an imperative language. The point is merely that you can view and understand monadic code in terms of imperative statements. The semantics may be imperative, but the implementation of monads and (>>=) is still purely functional. To make this distinction clear, let's look at a little illustration:

int x;
scanf("%d", &x);
printf("%d\n", x);

This is a snippet of C, a typical imperative language. In it, we declare a variable x, read its value from user input with scanf and then print it with printf. We can, within an IO do block, write a Haskell snippet that performs the same function and looks quite similar:

x <- readLn
print x

Semantically, the snippets are nearly equivalent.[3] In the C code, however, the statements directly correspond to instructions to be carried out by the program. The Haskell snippet, on the other hand, is desugared to:

readLn >>= \x -> print x

The desugared version has no statements, only functions being applied. We tell the program the order of the operations indirectly as a simple consequence of data dependencies: when we chain monadic computations with (>>=), we get the later results by applying functions to the results of the earlier ones. It just happens that, for instance, evaluating print x leads to a string to be printed in the terminal.

When using monads, Haskell allows us to write code with imperative semantics while keeping the advantages of functional programming.

I/O in the libraries

So far the only I/O primitives we have used were putStrLn and getLine and small variations thereof. The standard libraries, however, offer many other useful functions and actions involving IO. We present some of the most important ones in the IO chapter in Haskell in Practice, including the basic functionality needed for reading from and writing to files.

Monadic control structures

Given that monads allow us to express sequential execution of actions in a wholly general way, could we use them to implement common iterative patterns, such as loops? In this section, we will present a few of the functions from the standard libraries which allow us to do precisely that. While the examples are presented here applied to IO, keep in mind that the following ideas apply to every monad.

Remember, there is nothing magical about monadic values; we can manipulate them just like any other values in Haskell. Knowing that, we might think to try the following function to get five lines of user input:

fiveGetLines = replicate 5 getLine

That won't do, however (try it in GHCi!). The problem is that replicate produces, in this case, a list of actions, while we want an action which returns a list (that is, IO [String] rather than [IO String]). What we need is a fold to run down the list of actions, executing them and combining the results into a single list. As it happens, there is a Prelude function which does that: sequence.

sequence :: (Monad m) => [m a] -> m [a]

And so, we get the desired action with:

fiveGetLines = sequence $ replicate 5 getLine

replicate and sequence form an appealing combination; so Control.Monad offers a replicateM function for repeating an action an arbitrary number of times. Control.Monad provides a number of other convenience functions in the same spirit - monadic zips, folds, and so forth.

fiveGetLinesAlt = replicateM 5 getLine

A particularly important combination is map and sequence. Together, they allow us to make actions from a list of values, run them sequentially, and collect the results. mapM, a Prelude function, captures this pattern:

mapM :: (Monad m) => (a -> m b) -> [a] -> m [b]

We also have variants of the above functions with a trailing underscore in the name, such as sequence_, mapM_ and replicateM_. These discard any final values and so are appropriate when you are only interested in performing actions. Compared with their underscore-less counterparts, these functions are like the distinction between (>>) and (>>=)). mapM_ for instance has the following type:

mapM_ :: (Monad m) => (a -> m b) -> [a] -> m ()

Finally, it is worth mentioning that Control.Monad also provides forM and forM_, which are flipped versions of mapM and mapM_. forM_ happens to be the idiomatic Haskell counterpart to the imperative for-each loop; and the type signature suggests that neatly:

forM_ :: (Monad m) => [a] -> (a -> m b) -> m ()

  1. Using the monadic functions we have just introduced, write a function which prints an arbitrary list of values.
  2. Generalize the bunny invasion example in the list monad chapter for an arbitrary number of generations.
  3. What is the expected behavior of sequence for the Maybe monad?


  1. The technical term is "primitive", as in primitive operations.
  2. The same can be said about all higher-level programming languages, of course. Incidentally, Haskell's IO operations can actually be extended via the Foreign Function Interface (FFI) which can make calls to C libraries. As C can use inline assembly code, Haskell has can indirectly engage with anything a computer can do. Still, Haskell functions manipulate such outside operations only indirectly as values in IO functors.
  3. One difference is that x is a mutable variable in C, and so it is possible to declare it in one statement and set its value in the next; Haskell never allows such mutability. If we wanted to imitate the C code even more closely, we could have used an IORef, which is a cell that contains a value which can be destructively updated. For obvious reasons, IORefs can only be used within the IO monad.

The State monad

If you have programmed in any other language before, you likely wrote some functions that "kept state". For those new to the concept, a state is one or more variables that are required to perform some computation but are not among the arguments of the relevant function. Object-oriented languages, like C++, make extensive usage of state variables within objects in the form of member variables. Procedural languages, like C, use variables declared outside the current scope to keep track of state.

In Haskell, however, such techniques cannot be applied in a straightforward way; they require mutable variables, and that clashes with Haskell's functional purity. We can usually keep track of state by passing parameters from function to function or by pattern matching of various sorts, but in some cases it is appropriate to find a more general or convenient solution. We will consider the common example of how the State monad can assist us in generating pseudo-random numbers.

Pseudo-Random Numbers

Generating actual random numbers is a very complicated subject. Computer programming almost always sticks to pseudo-random numbers. They are called "pseudo" because they are not truly random. Starting from an initial state (commonly called the seed), pseudo-random number generators produce a sequence of numbers that have the appearance of being random.

Every time a pseudo-random number is requested, a global state is updated.[1] Sequences of pseudo-random numbers can be replicated exactly if the initial seed and the algorithm is known.

Implementation in Haskell

Producing a pseudo-random number in most programming languages is very simple: there is usually a function, such as C or C++'s rand(), that provides a pseudo-random value (or a truly random one, depending on the implementation). Haskell has a similar one in the System.Random module:

> :m System.Random
> :t randomIO
randomIO :: Random a => IO a
> randomIO

This function references a mutable state that is held outside Haskell and interacted with via IO, so values obtained using randomIO will be different every time.

Example: Rolling Dice

randomRIO (1,6)

Suppose we are coding a game in which at some point we need an element of chance. In real-life games that is often obtained by means of dice. So, let's create a dice-throwing function in Haskell.

We'll use the function randomR to specify an interval from which the pseudo-random values will be taken; in the case of a die, it is randomR (1,6). To make sure we get new values each time we roll, we'll use the IO version of randomR:

import Control.Monad
import System.Random
rollDiceIO :: IO (Int, Int)
rollDiceIO = liftM2 (,) (randomRIO (1,6)) (randomRIO (1,6))

That function rolls two dice. Here, liftM2 is used to make the non-monadic two-argument function (,) work within a monad. The (,) is the non-infix version of the tuple constructor. Thus, the two die rolls will be returned (in IO) as a tuple.

  1. Implement a function rollNDiceIO :: Int -> IO [Int] that, given an integer (a number of die rolls), returns a list of that number of pseudo-random integers between 1 and 6.

Getting Rid of the IO Monad

A disadvantage of randomIO is that it requires us to utilize the IO monad and store our state outside the program where we can't control what happens to it. We would prefer to only use IO when we really have a good reason to interact with the outside world.

To avoid the IO Monad, we can build a local generator. From the System.Random module, we can use the random and mkStdGen functions to generate tuples containing a pseudo-random number together with a new generator to use the next time the function is called.

> :m System.Random
> let generator = mkStdGen 0           -- "0" is our seed
> generator
1 1
> random generator :: (Int, StdGen)
(2092838931,1601120196 1655838864)

Now, we've avoided the IO Monad, but there are new problems. First and foremost, if we want to use generator to get random numbers, the obvious definition...

> let randInt = fst . random $ generator :: Int
> randInt

...is useless; it will always give back the same value, 2092838931, every time (because the same generator is always used). To solve this, we can take the second member of the tuple (i.e. the new generator) and feed it to a new call to random:

> let (randInt, generator') = random generator :: (Int, StdGen)
> randInt                            -- Same value
> random generator' :: (Int, StdGen) -- Using new generator' returned from “random generator”
(-2143208520,439883729 1872071452)

Of course, this is clumsy and tedious. We need to keep creating new functions for new calls, and we're stuck with the fuss of having to carefully pass the generator around.

Dice without IO

We can re-do our dice throw with our new approach:

> randomR (1,6) (mkStdGen 0)
(6, 40014 40692)

This tuple combines the result of throwing a single die with a new generator number. A simple implementation for throwing two dice is then:

clumsyRollDice :: (Int, Int)
clumsyRollDice = (n, m)
        (n, g) = randomR (1,6) (mkStdGen 0)
        (m, _) = randomR (1,6) g
  1. Implement a function rollDice :: StdGen -> ((Int, Int), StdGen) that, given a generator, return a tuple with our random numbers as first element and the last generator as the second.

The implementation of clumsyRollDice works as a one-off, but we have to manually write the passing of generator g from one where clause to the other. This approach will become increasingly cumbersome if we want to produce larger sets of pseudo-random numbers. It is also error-prone: what if we pass one of the middle generators to the wrong line in the where clause?

What we really need is a way to automate the extraction of the second member of the tuple (i.e. the new generator) and feed it to a new call to random. This is where the State monad comes into the picture.

Introducing State


In this chapter we will use the state monad provided by the module Control.Monad.Trans.State of the transformers package. By reading Haskell code in the wild, you will soon meet Control.Monad.State, a module of the closely related mtl package. The differences between these two modules need not concern us at the moment; everything we discuss here also applies to the mtl variant.

The Haskell type State describes functions that consume a state and produce a tuple that contains a result along with the new state after the result has been extracted.

The state function is wrapped by a data type definition which comes along with a runState accessor so that pattern matching becomes unnecessary. For our current purposes, consider the definition equivalent to:[2]

newtype State s a = State { runState :: s -> (a, s) }

Here, s is the type of the state, and a the type of the produced result. Calling our type State is arguably a bit of a misnomer because the wrapped value is not the state itself but a state processor.


Notice that we defined the data type with the newtype keyword, rather than the usual data. newtype can be used only for types with just one constructor and just one field. It ensures that the trivial wrapping and unwrapping of the single field is eliminated by the compiler. For that reason, simple wrapper types such as State are usually defined with newtype. Would defining a synonym with type be enough in such cases? Not really, because type does not allow us to define instances for the new data type, which is what we are about to do...

Instantiating the Monad

In contrast to the monads we have met thus far, State has two type parameters. To define a Monad, we need to combine State with a second parameter.

instance Monad (State s) where

So, there are many different State monads including State String, State Int, State SomeLargeDataStructure, and so on…

The return function is implemented as:

return :: a -> State s a
return x = State ( \ st -> (x, st) )

In words, giving a value to return produces a function wrapped in the State constructor. The function takes a state value, and returns it unchanged as the second member of a tuple, together with the specified result value.

Binding is a bit intricate:

(>>=) :: State s a -> (a -> State s b) -> State s b
processor >>= processorGenerator = State $ \ st -> 
                                   let (x, st') = runState processor st
                                   in runState (processorGenerator x) st'

(>>=) is given a state processor and a function that can generate another processor using the result of the first one. The two processors are combined to obtain a function that takes the initial state, and returns the second result and state (i.e. after the second function has processed them).

Loose schematic representation of how bind creates a new state processor (pAB) from the given state processor (pA) and the given generator (f). s1, s2 and s3 are actual states. v2 and v3 are values. pA, pB and pAB are state processors. The diagram ignores wrapping and unwrapping of the functions in the State wrapper.

The diagram shows this schematically, for a slightly different, but equivalent form of the ">>=" (bind) function, given below (where wpA and wpAB are wrapped versions of pA and pAB).

-- pAB = s1 --> pA --> (v2,s2) --> pB --> (v3,s3)        
wpA >>= f = wpAB          
    where wpAB = State $ \s1 -> let pA = runState wpA
                                    (v2, s2) = pA s1
                                    pB = runState $ f v2
                                    (v3,s3) = pB s2
                                in  (v3,s3)

Setting and Accessing the State

The monad instantiation allows us to manipulate various state processors, but you may at this point wonder where exactly the original state comes from in the first place. State s is also an instance of the MonadState class, which provides two additional functions:

put newState = State $ \_ -> ((), newState)

Given a state, this function will generate a state processor. The processor's input will be disregarded, and the output will be a tuple carrying the state we provided. Since we do not care about the result (we are discarding the input, after all), the first element of the tuple will be "null".[3]

The specular operation reads the state. This is accomplished by get:

get = State $ \st -> (st, st)

The resulting state processor produces the input st in both positions of the output tuple (i.e. both as a result and as a state) so that it may be bound to other processors.

Getting Values and State

From the definition of State, we know that runState is an accessor to apply to a State a b value to get the state-processing function. That function, given an initial state, will return the extracted value and the new state.

Other similar functions are evalState and execState. Given a State a b and an initial state, the function evalState will return the extracted value only, whereas execState will return only the new state.

evalState :: State s a -> s -> a
evalState processor st = fst ( runState processor st )
execState :: State s a -> s -> s
execState processor st = snd ( runState processor st )

Dice and state

Let's use the State monad for our dice throw examples.

To avoid the confusion with "State" and "state processor", we'll use a type synonym:

import Control.Monad.Trans.State
import System.Random
type GeneratorState = State StdGen

So, GeneratorState Int is in essence a StdGen -> (Int, StdGen) function and is a processor of the generator state. The generator state itself is produced by the mkStdGen function. Note that GeneratorState does not specify what type of values we are going to extract, only the type of the state.

We can now produce a function that, given a StdGen generator, outputs a number between 1 and 6.

rollDie :: GeneratorState Int
rollDie = do generator <- get
             let (value, newGenerator) = randomR (1,6) generator
             put newGenerator
             return value

Let's go through each of the steps:

  1. First, we take out the pseudo-random generator with <- in conjunction with get. get overwrites the monadic value (The 'a' in 'm a') with the state, binding the generator to the state. (If in doubt, recall the definition of get and >>= above).
  2. Then, we use the randomR function to produce an integer between 1 and 6 using the generator we took; we also store the new generator graciously returned by randomR.
  3. We then set the state to be the newGenerator using the put function, so that the next call will use a different pseudo-random generator;
  4. Finally, we inject the result into the GeneratorState monad using return.

We can finally use our monadic die:

> evalState rollDie (mkStdGen 0)

Why have we involved monads and built such an intricate framework only to do exactly what fst $ randomR (1,6) already does? Well, consider the following function:

rollDice :: GeneratorState (Int, Int)
rollDice = liftM2 (,) rollDie rollDie

We obtain a function producing two pseudo-random numbers in a tuple. Note that these are in general different:

> evalState rollDice (mkStdGen 666)

Under the hood, the monads are passing state to each other. It was previously very clunky using randomR (1,6) because we had to pass state manually. Now, the monad is taking care of that for us. Assuming we know how to use the lifting functions, constructing intricate combinations of pseudo-random numbers (tuples, lists, whatever) has suddenly become much easier.

  1. Similarly to what was done for rollNDiceIO, implement a function rollNDice :: Int -> GeneratorState [Int] that, given an integer, returns a list with that number of pseudo-random integers between 1 and 6.

Pseudo-random values of different types

Until now, we considered only Int as the type of the produced pseudo-random number. However, already when we defined the GeneratorState monad, we saw that it did not specify anything about the type of the returned value. In fact, there is one implicit assumption: that we can produce values of such a type with a call to random.

The Random class (capitalized) includes default implementations for functions generating Int, Char, Integer, Bool, Double and Float, so you can immediately generate any of those.

Because GeneratorState is "agnostic" in regard to the type of the pseudo-random value it produces, we can write a similarly "agnostic" function (analogous to rollDie) that provides a pseudo-random value of unspecified type (as long as it is an instance of Random):

getRandom :: Random a => GeneratorState a
getRandom = do generator <- get
               let (value, newGenerator) = random generator
               put newGenerator
               return value

Compared to rollDie, this function does not specify the Int type in its signature and uses random instead of randomR; otherwise, it is just the same. getRandom can be used for any instance of Random:

> evalState getRandom (mkStdGen 0) :: Bool
> evalState getRandom (mkStdGen 0) :: Char
> evalState getRandom (mkStdGen 0) :: Double
> evalState getRandom (mkStdGen 0) :: Integer

Indeed, it becomes quite easy to conjure all these at once:

allTypes :: GeneratorState (Int, Float, Char, Integer, Double, Bool, Int)
allTypes = liftM (,,,,,,) getRandom
                     `ap` getRandom
                     `ap` getRandom
                     `ap` getRandom
                     `ap` getRandom
                     `ap` getRandom
                     `ap` getRandom

Here we are forced to used the ap function, defined in Control.Monad, since there exists no liftM7 (the standard libraries only go to liftM5). As you can see, ap fits multiple computations into an application of the (lifted) n-element-tuple constructor (in this case the 7-item (,,,,,,)). To understand ap further, look at its signature:

ap :: (Monad m) => m (a -> b) -> m a -> m b

Remember then that type a in Haskell can be a function as well as a value, and compare to:

>:type liftM (,,,,,,) getRandom
liftM (,,,,,) getRandom :: (Random a1) =>
                          State StdGen (b -> c -> d -> e -> f -> (a1, b, c, d, e, f))

The monad m is obviously State StdGen (which we "nicknamed" GeneratorState), while ap's first argument is function b -> c -> d -> e -> f -> (a1, b, c, d, e, f). Applying ap over and over (in this case 6 times), we finally get to the point where b is an actual value (in our case, a 7-element tuple), not another function. To sum it up, ap applies a function-in-a-monad to a monadic value (compare with liftM, which applies a function not in a monad to a monadic value).

So much for understanding the implementation. Function allTypes provides pseudo-random values for all default instances of Random; an additional Int is inserted at the end to prove that the generator is not the same, as the two Ints will be different.

> evalState allTypes (mkStdGen 0)
  1. If you are not convinced that State is worth using, try to implement a function equivalent to evalState allTypes without making use of monads, i.e. with an approach similar to clumsyRollDice above.


  1. There are also other ways to seed a pseudo-random number generator without using a global state for the program. For example, a program could have an algorithm that starts with a seed from checking the current date and time (assuming the computer's clock is functioning, this will never be a repeated value).
  2. The subtle issue with our approach is that the transformers package implements the State type in a somewhat different way. The differences do not affect how we use or understand State; as a consequence of them, however, Control.Monad.Trans.State does not export a State constructor. Rather, there is a state function,
    state :: (s -> (a, s)) -> State s a

    which does the same job. As for why the implementation is not the obvious one we presented above, we will get back to that a few chapters down the road.

  3. The technical term for the type of () is unit.

Additive monads (MonadPlus)

In our studies so far, we saw that the Maybe and list monads both represent the number of results a computation can have. That is, you use Maybe when you want to indicate that a computation can fail somehow (i.e. it can have 0 results or 1 result), and you use the list monad when you want to indicate a computation could have many valid answers ranging from 0 results to many results.

Given two computations in one of these monads, it might be interesting to amalgamate all valid solutions into a single result. For example, within the list Monad, we can concatenate two lists of valid solutions.


MonadPlus defines two methods. mzero is the monadic value standing for zero results; while mplus is a binary function which combines two computations.

class Monad m => MonadPlus m where
  mzero :: m a
  mplus :: m a -> m a -> m a

Here are the two instance declarations for Maybe and the list monad:

instance MonadPlus [] where
  mzero = []
  mplus = (++)
instance MonadPlus Maybe where
  mzero                   = Nothing
  Nothing `mplus` Nothing = Nothing -- 0 solutions + 0 solutions = 0 solutions
  Just x  `mplus` Nothing = Just x  -- 1 solution  + 0 solutions = 1 solution
  Nothing `mplus` Just x  = Just x  -- 0 solutions + 1 solution  = 1 solution
  Just x  `mplus` Just y  = Just x  -- 1 solution  + 1 solution  = 2 solutions,
                                    -- but Maybe can only have up to one solution,
                                    -- so we disregard the second one.

Also, if you import Control.Monad.Error, then (Either e) becomes an instance:

instance (Error e) => MonadPlus (Either e) where
  mzero            = Left noMsg
  Left _  `mplus` n = n
  Right x `mplus` _ = Right x

Like Maybe, (Either e) represents computations that can fail. Unlike Maybe, (Either e) allows the failing computations to include an error "message" (which is usually a String). Typically, Left s means a failed computation carrying an error message s, and Right x means a successful computation with result x.

Example: parallel parsing

Traditional input parsing involves functions which consume an input one character at a time. That is, a parsing function takes an input string and chops off (i.e. 'consumes') characters from the front if they satisfy certain criteria. For example, you could write a function which consumes one uppercase character. If the characters on the front of the string don't satisfy the given criteria, the parser has failed; so such functions are candidates for Maybe.

Let's use mplus to run two parsers in parallel. That is, we use the result of the first one if it succeeds, and otherwise, we use the result of the second. If both fail, then our whole parser returns Nothing.

In the example below, we consume a digit in the input and return the digit that was parsed.

digit :: Int -> String -> Maybe Int
digit i s | i > 9 || i < 0 = Nothing
          | otherwise      = do
  let (c:_) = s
  if [c] == show i then Just i else Nothing

Our guards assure that the Int we are checking for is a single digit. Otherwise, we are just checking that the the first character of our String matches the digit we are checking for. If it passes, we return the digit wrapped in a Just. The do-block assures that any failed pattern match will result in returning Nothing.

We can use our digit function with <mplus> to parse Strings of binary digits:

binChar :: String -> Maybe Int
binChar s = digit 0 s `mplus` digit 1 s

Parser libraries often make use of MonadPlus in this way. If you are curious, check the (+++) operator in Text.ParserCombinators.ReadP, or (<|>) in Text.ParserCombinators.Parsec.Prim.

The MonadPlus laws

Instances of MonadPlus are required to fulfill several rules, just as instances of Monad are required to fulfill the three monad laws. Unfortunately, the MonadPlus laws aren't fully agreed on. The most common approach says that mzero and mplus form a monoid. By that, we mean:

-- mzero is a neutral element
mzero `mplus` m  =  m
m `mplus` mzero  =  m
-- mplus is associative
-- (but not all instances obey this law because it makes some infinite structures impossible)
m `mplus` (n `mplus` o)  =  (m `mplus` n) `mplus` o

There is nothing fancy about "forming a monoid": in the above, "neutral element" and "associative" here is just like how addition of integer numbers is said to be associative and to have zero as neutral element. In fact, this analogy is the source of the names mzero and mplus.

The Haddock documentation for Control.Monad quotes additional laws:

mzero >>= f  =  mzero
m >> mzero   =  mzero

And the HaskellWiki page cites another (with controversy):

(m `mplus` n) >>= k  =  (m >>= k) `mplus` (n >>= k)

There are even more sets of laws available. Sometimes monads like IO are used as a MonadPlus. Consult All About Monads and the Haskell Wiki page on MonadPlus for extra more information about such issues.

Useful functions

Beyond the basic mplus and mzero, there are two other general-purpose functions involving MonadPlus:


A common task when working with MonadPlus: take a list of monadic values, e.g. [Maybe a] or [[a]], and fold it down with mplus. msum fulfills this role:

msum :: MonadPlus m => [m a] -> m a
msum = foldr mplus mzero

In a sense, msum generalizes the list-specific concat operation. Indeed, the two are equivalent when working on lists. For Maybe, msum finds the first Just x in the list and returns Nothing if there aren't any.


When discussing the list monad we noted how similar it was to list comprehensions, but we didn't discuss how to mirror list comprehension filtering. The guard function allows us to do exactly that.

Consider the following comprehension which retrieves all pythagorean triples (i.e. trios of integer numbers which work as the lengths of the sides for a right triangle). First we'll examine the brute-force approach. We'll use a boolean condition for filtering; namely, Pythagoras' theorem:

pythags = [ (x, y, z) | z <- [1..], x <- [1..z], y <- [x..z], x^2 + y^2 == z^2 ]

The translation of the comprehension above to the list monad is:

pythags = do
  z <- [1..]
  x <- [1..z]
  y <- [x..z]
  guard (x^2 + y^2 == z^2)
  return (x, y, z)

The guard function works like this:

guard :: MonadPlus m => Bool -> m ()
guard True  = return ()
guard _ = mzero

Concretely, guard will reduce a do-block to mzero if its predicate is False. Given the first law stated in the 'MonadPlus laws' section above, an mzero on the left-hand side of an >>= operation will produce mzero again. As do-blocks are decomposed to lots of expressions joined up by (>>=), an mzero at any point will cause the entire do-block to become mzero.

To further illustrate, we will examine guard in the special case of the list monad, extending on the pythags function above. First, here is guard defined for the list monad:

guard :: Bool -> [()]
guard True  = [()]
guard _ = []

Basically, guard blocks off a route. In pythags, we want to block off all the routes (or combinations of x, y and z) where x^2 + y^2 == z^2 is False. Let's look at the expansion of the above do-block to see how it works:

pythags =
  [1..] >>= \z ->
  [1..z] >>= \x ->
  [x..z] >>= \y ->
  guard (x^2 + y^2 == z^2) >>= \_ ->
  return (x, y, z)

Replacing >>= and return with their definitions for the list monad (and using some let-bindings to keep it readable), we obtain:

pythags =
 let ret x y z = [(x, y, z)]
     gd  z x y = concatMap (\_ -> ret x y z) (guard $ x^2 + y^2 == z^2)
     doY z x   = concatMap (gd  z x) [x..z]
     doX z     = concatMap (doY z  ) [1..z]
     doZ       = concatMap (doX    ) [1..]
 in doZ

Remember that guard returns the empty list in the case of its argument being False. Mapping across the empty list produces the empty list, no matter what function you pass in. So the empty list produced by the call to guard in the binding of gd will cause gd to be the empty list, and therefore ret to be the empty list.

To understand why this matters, think about list-computations as a tree. With our Pythagorean triple algorithm, we need a branch starting from the top for every choice of z, then a branch from each of these branches for every value of x, then from each of these, a branch for every value of y. So the tree looks like this:

   |____________________________________________ ...
   |                     |                    |
x  1                     2                    3
   |_______________ ...  |_______________ ... |_______________ ...
   |      |      |       |      |      |      |      |      |
y  1      2      3       2      3      4      3      4      5
   |___...|___...|___... |___...|___...|___...|___...|___...|___...
   | | |  | | |  | | |   | | |  | | |  | | |  | | |  | | |  | | |
z  1 2 3  2 3 4  3 4 5   2 3 4  3 4 5  4 5 6  3 4 5  4 5 6  5 6 7

Each combination of x, y and z represents a route through the tree. Once all the functions have been applied, each branch is concatenated together, starting from the bottom. Any route where our predicate doesn't hold evaluates to an empty list, and so has no impact on this concat operation.

  1. Prove the MonadPlus laws for Maybe and the list monad.
  2. We could augment our above parser to involve a parser for any character:
     -- | Consume a given character in the input, and return the character we 
     --   just consumed, paired with rest of the string. We use a do-block  so that
     --   if the pattern match fails at any point, fail of the Maybe monad (i.e.
     --   Nothing) is returned.
     char :: Char -> String -> Maybe (Char, String)
     char c s = do
       let (c':s') = s
       if c == c' then Just (c, s') else Nothing
    It would then be possible to write a hexChar function which parses any valid hexidecimal character (0-9 or a-f). Try writing this function (hint: map digit [0..9] :: [String -> Maybe Int]).

Relationship with monoids

When discussing the MonadPlus laws, we alluded to the mathematical concept of monoids. It turns out that there is a Monoid class in Haskell, defined in Data.Monoid. A fuller presentation of will be given in a later chapter. For now, a minimal definition of Monoid implements two methods; namely, a neutral element (or 'zero') and an associative binary operation (or 'plus').

class Monoid m where 
  mempty  :: m
  mappend :: m -> m -> m

For example, lists form a simple monoid:

instance Monoid [a] where
  mempty  = []
  mappend = (++)

Sounds familiar, doesn't it? In spite of the uncanny resemblance to MonadPlus, there is a subtle yet key difference. Note the usage of [a] instead of [] in the instance declaration. Monoids are not necessarily "containers" of anything or parametrically polymorphic. For instance, the integer numbers on form a monoid under addition with 0 as neutral element.

In any case, MonadPlus instances look very similar to monoids, as both feature concepts of zero and plus. Indeed, we could even make MonadPlus a subclass of Monoid if it were worth the trouble:

 instance MonadPlus m => Monoid (m a) where
   mempty  = mzero
   mappend = mplus


Due to the "free" type variable a in the instance definition, the snippet above is not valid Haskell 98. If you want to test it, you will have to enable the GHC language extension FlexibleInstances:

  • If you are testing with GHCi, start it with the command line option -XFlexibleInstances.
  • Alternatively, if you are running a compiled program, add {-# LANGUAGE FlexibleInstances #-} to the top of your source file.

Again, Monoids and MonadPlus work at different levels. As noted before, there is no requirement for monoids to be parameterized in relation to "contained" or related type. More formally, monoids have kind *, but instances of MonadPlus (which are monads) have kind * -> *.


Monadic parser combinators

Monads provide a clean means of embedding a domain specific parsing language directly into Haskell without the need for external tools or code generators.


To do:
write the page! In the meantime, here is a paper to read and a practical chapter on Parsing Monads in Haskell from this wikibook to get you started.

Monad transformers

Print version (Solutions)

Understanding monads