Study Guide
Chapter 2: Learning to Program in Python
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University:
Massachusetts Institute of Technology -
Course:
6.01SC | Introduction to Electrical Engineering and Computer Science I Academic year:
2019
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Author:
mason mc44
Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
21
Chapter 2
Learning to Program in Python
Depending on your previous programming background, we recommend different paths through
the available readings:
• If you have never programmed before: you should start with a general introduction to pro
gramming and Python. We recommend Python for Software Design: How to Think Like a
Computer Scientist, by Allen Downey. This is a good introductory text that uses Python to
present basic ideas of computer science and programming. It is available for purchase in
hardcopy, or as a free download from:
http://www.greenteapress.com/thinkpython
After that, you can go straight to the next chapter.
• If you have programmed before, but not in Python: you should read the rest of this chapter
for a quick overview of Python, and how it may differ from other programming languages
with which you are familiar.
• If you have programmed in Python: you should skip to the next chapter.
Everyone should have a bookmark in their browser for Python Tutorial, by Guido Van Rossum.
This is the standard tutorial reference by the inventor of Python. It is accessible at:
http://docs.python.org/tut/tut.html
In the rest of this chapter, we will assume you know how to program in some language, but are
new to Python. We will use Java as an informal running comparative example. In this section we
will cover what we think are the most important differences between Python and what you may
already know about programming; but these notes are by no means complete.
2.1 Using Python
Python is designed for easy interaction between a user and the computer. It comes with an inter
active mode called a listener or shell. The shell gives a prompt (usually something like »>) and
waits for you to type in a Python expression or program. Then it will evaluate the expression you
entered, and print out the value of the result. So, for example, an interaction with the Python shell
might look like this: Chapter 2 Learning to Program in Python
>>> 5 +
10
>>> x =
>>> x
6
>>> x +
12
>>> y =
>>> y +
’hihi’
>>>
6.01— Spring 2011— April 25, 2011
22
5
6
x
’hi’
y
So, you can use Python as a fancy calculator. And as you define your own procedures in Python,
you can use the shell to test them or use them to compute useful results.
2.1.1 Indentation and line breaks
Every programming language has to have some method for indicating grouping of instructions.
Here is how you write an if-then-else structure in Java:
if (s == 1){
s = s + 1;
a = a - 10;
} else {
s = s + 10;
a = a + 10;
}
The braces specify what statements are executed in the if case. It is considered good style to
indent your code to agree with the brace structure, but it is not required. In addition, the semi
colons are used to indicate the end of a statement, independent of the locations of the line breaks
in the file. So, the following code fragment has the same meaning as the previous one, although
it is much harder to read and understand.
if (s == 1){
s = s
+ 1;
a = a - 10;
} else {
s = s + 10;
a = a + 10;
}
In Python, on the other hand, there are no braces for grouping or semicolons for termination.
Indentation indicates grouping and line breaks indicate statement termination. So, in Python, we
would write the previous example as
if s ==
s =
a =
else:
s =
a =
1:
s + 1
a - 10
s + 10
a + 10 Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
23
There is no way to put more than one statement on a single line. 3 If you have a statement that is
too long for a line, you can signal it with a backslash:
aReallyLongVariableNameThatMakesMyLinesLong = \
aReallyLongVariableNameThatMakesMyLinesLong + 1
It is easy for Java programmers to get confused about colons and semi-colons in Python. Here is
the deal: (1) Python does not use semi-colons; (2) Colons are used to start an indented block, so
they appear on the first line of a procedure definition, when starting a while or for loop, and
after the condition in an if, elif, or else.
Is one method better than the other? No. It is entirely a matter of taste. The Python method is
pretty unusual. But if you are going to use Python, you need to remember that indentation and
line breaks are significant.
2.1.2 Types and declarations
Java programs are what is known as statically and strongly typed. Thus, the types of all the
variables must be known at the time that the program is written. This means that variables have
to be declared to have a particular type before they are used. It also means that the variables
cannot be used in a way that is inconsistent with their type. So, for instance, you would declare x
to be an integer by saying
int x;
x = 6 * 7;
But you would get into trouble if you left out the declaration, or did
int x;
x = "thing";
because a type checker is run on your program to make sure that you don’t try to use a variable
in a way that is inconsistent with its declaration.
In Python, however, things are a lot more flexible. There are no variable declarations, and the
same variable can be used at different points in your program to hold data objects of different
types. So, the following is fine, in Python:
if x == 1:
x = 89.3
else:
x = "thing"
The advantage of having type declarations and compile-time type checking, as in Java, is that a
compiler can generate an executable version of your program that runs very quickly, because it
can be certain about what kind of data is stored in each variable, and it does not have to check it at
runtime. An additional advantage is that many programming mistakes can be caught at compile
3
Actually, you can write something like if a > b:
if or a for is only one line long.
a = a + 1 all on one line, if the work you need to do inside an Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
24
time, rather than waiting until the program is being run. Java would complain even before your
program started to run that it could not evaluate
3 + "hi"
Python would not complain until it was running the program and got to that point.
The advantage of the Python approach is that programs are shorter and cleaner looking, and pos
sibly easier to write. The flexibility is often useful: In Python, it is easy to make a list or array with
objects of different types stored in it. In Java, it can be done, but it is trickier. The disadvantage
of the Python approach is that programs tend to be slower. Also, the rigor of compile-time type
checking may reduce bugs, especially in large programs.
2.1.3 Modules
As you start to write bigger programs, you will want to keep the procedure definitions in multiple
files, grouped together according to what they do. So, for example, we might package a set of
utility functions together into a single file, called utility.py. This file is called a module in
Python.
Now, if we want to use those procedures in another file, or from the the Python shell, we will
need to say
import utility
so that all those procedures become available to us and to the Python interpereter. Now, if we
have a procedure in utility.py called foo, we can use it with the name utility.foo. You can
read more about modules, and how to reference procedures defined in modules, in the Python
documentation.
2.1.4 Interaction and Debugging
We encourage you to adopt an interactive style of programming and debugging. Use the Python
shell a lot. Write small pieces of code and test them. It is much easier to test the individual pieces
as you go, rather than to spend hours writing a big program, and then find it does not work, and
have to sift through all your code, trying to find the bugs.
But, if you find yourself in the (inevitable) position of having a big program with a bug in it, do
not despair. Debugging a program does not require brilliance or creativity or much in the way of
insight. What it requires is persistence and a systematic approach.
First of all, have a test case (a set of inputs to the procedure you are trying to debug) and know
what the answer is supposed to be. To test a program, you might start with some special cases:
what if the argument is 0 or the empty list? Those cases might be easier to sort through first (and
are also cases that can be easy to get wrong). Then try more general cases.
Now, if your program gets your test case wrong, what should you do? Resist the temptation to
start changing your program around, just to see if that will fix the problem. Do not change any
code until you know what is wrong with what you are doing now, and therefore believe that the
change you make is going to correct the problem. Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
25
Ultimately, for debugging big programs, it is most useful to use a software development environ
ment with a serious debugger. But these tools can sometimes have a steep learning curve, so in
this class we will learn to debug systematically using “print” statements.
One good way to use “print” statements to help in debugging is to use a variation on binary
search. Find a spot roughly halfway through your code at which you can predict the values of
variables, or intermediate results your computation. Put a print statement there that lists expected
as well as actual values of the variables. Run your test case, and check. If the predicted values
match the actual ones, it is likely that the bug occurs after this point in the code; if they do not,
then you have a bug prior to this point (of course, you might have a second bug after this point,
but you can find that later). Now repeat the process by finding a location halfway between the
beginning of the procedure and this point, placing a print statement with expected and actual
values, and continuing. In this way you can narrow down the location of the bug. Study that part
of the code and see if you can see what is wrong. If not, add some more print statements near the
problematic part, and run it again. Don’t try to be smart....be systematic and indefatigable!
You should learn enough of Python to be comfortable writing basic programs, and to be able to
efficiently look up details of the language that you don’t know or have forgotten.
2.2 Procedures
In Python, the fundamental abstraction of a computation is as a procedure (other books call them
“functions” instead; we will end up using both terms). A procedure that takes a number as an
argument and returns the argument value plus 1 is defined as:
def f(x):
return x + 1
The indentation is important here, too. All of the statements of the procedure have to be indented
one level below the def. It is crucial to remember the return statement at the end, if you want
your procedure to return a value. So, if you defined f as above, then played with it in the shell, 4
you might get something like this:
>>> f
>>> f(4)
5
>>> f(f(f(4)))
7
If we just evaluate f, Python tells us it is a function. Then we can apply it to 4 and get 5, or apply
it multiple times, as shown.
What if we define
4
Although you can type procedure definitions directly into the shell, you will not want to work that way, because if
there is a mistake in your definition, you will have to type the whole thing in again. Instead, you should type your
procedure definitions into a file, and then get Python to evaluate them. Look at the documentation for Idle or the 6.01
FAQ for an explanation of how to do that. Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
26
def g(x):
x + 1
Now, when we play with it, we might get something like this:
>>> g(4)
>>> g(g(4))
Traceback (most recent
File "", line
File "", line
TypeError: unsupported
call last):
1, in ?
2, in g
operand type(s) for +: ’NoneType’ and ’int’
What happened!! First, when we evaluated g(4), we got nothing at all, because our definition of
g did not return anything. Well...strictly speaking, it returned a special value called None, which
the shell does not bother printing out. The value None has a special type, called NoneType. So,
then, when we tried to apply g to the result of g(4), it ended up trying to evaluate g(None),
which made it try to evaluate None + 1, which made it complain that it did not know how to
add something of type NoneType and something of type int.
Whenever you ask Python to do something it cannot do, it will complain. You should learn to
read the error messages, because they will give you valuable information about what is wrong
with what you were asking.
Print vs Return
Here are two different function definitions:
def f1(x):
print x + 1
def f2(x):
return x + 1
What happens when we call them?
>>> f1(3)
4
>>> f2(3)
4
It looks like they behave in exactly the same way. But they don’t, really. Look at this example:
>>> print(f1(3))
4
None
>>> print(f2(3))
4
In the case of f1, the function, when evaluated, prints 4; then it returns the value None, which is
printed by the Python shell. In the case of f2, it does not print anything, but it returns 4, which is
printed by the Python shell. Finally, we can see the difference here: Chapter 2 Learning to Program in Python
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>>> f1(3) + 1
4
Traceback (most recent call last):
File "", line 1, in ?
TypeError: unsupported operand type(s) for +: ’NoneType’ and ’int’
>>> f2(3) + 1
5
In the first case, the function does not return a value, so there is nothing to add to 1, and an error
is generated. In the second case, the function returns the value 4, which is added to 1, and the
result, 5, is printed by the Python read-eval-print loop.
The book Think Python, which we recommend reading, was translated from a version for Java, and
it has a lot of print statements in it, to illustrate programming concepts. But for just about every
thing we do, it will be returned values that matter, and printing will be used only for debugging,
or to give information to the user.
Print is very useful for debugging. It is important to know that you can print out as many items
as you want in one line:
>>> x = 100
>>> print ’x’, x, ’x squared’, x*x, ’xiv’, 14
x 100 x squared 10000 xiv 14
We have also snuck in another data type on you: strings. A string is a sequence of characters. You
can create a string using single or double quotes; and access individual elements of strings using
indexing.
>>> s1 = ’hello world’
>>> s2 = "hello world"
>>> s1 == s2
True
>>> s1[3]
’l’
As you can see, indexing refers to the extraction of a particular element of a string, by using
square brackets [i] where i is a number that identifies the location of the character that you
wish to extract (note that the indexing starts with 0 ).
Look in the Python documentation for more about strings.
2.3 Control structures
Python has control structures that are slightly different from those in other languages.
2.3.1 Conditionals
Booleans
Before we talk about conditionals, we need to clarify the Boolean data type. It has values True
and False. Typical expressions that have Boolean values are numerical comparisons: Chapter 2 Learning to Program in Python
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28
>>> 7 > 8
False
>>> -6 <= 9
True
We can also test whether data items are equal to one another. Generally we use == to test for
equality. It returns True if the two objects have equal values. Sometimes, however, we will be
interested in knowing whether the two items are the exact same object (in the sense discussed in
section 3.3). In that case we use is:
>>> [1, 2] == [1, 2]
True
>>> [1, 2] is [1, 2]
False
>>> a = [1, 2]
>>> b = [1, 2]
>>> c = a
>>> a == b
True
>>> a is b
False
>>> a == c
True
>>> a is c
True
Thus, in the examples above, we see that == testing can be applied to nested structures, and basi
cally returns true if each of the individual elements is the same. However, is testing, especially
when applied to nested structures, is more refined, and only returns True if the two objects point
to exactly the same instance in memory.
In addition, we can combine Boolean values conveniently using and, or, and not:
>>> 7 > 8 or 8 > 7
True
>>> not 7 > 8
True
>>> 7 == 7 and 8 > 7
True
If
Basic conditional statements have the form: 5
if :
...
else:
5
See the Python documentation for more variations. Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
29
...
When the interpreter encounters a conditional statement, it starts by evaluating , getting either True or False as a result. 6 If the result is True, then it will eval
uate ,...,; if it is False, then it will evaluate ,...,. Crucially, it always evaluates only one set of the statements.
Now, for example, we can implement a procedure that returns the absolute value of its argument.
def abs(x):
if x >= 0:
return x
else:
return -x
We could also have written
def abs(x):
if x >= 0:
result = x
else:
result = -x
return result
Python uses the level of indentation of the statements to decide which ones go in the groups of
statements governed by the conditionals; so, in the example above, the return result statement
is evaluated once the conditional is done, no matter which branch of the conditional is evaluated.
For and While
If we want to do some operation or set of operations several times, we can manage the process in
several different ways. The most straightforward are for and while statements (often called for
and while loops).
A for loop has the following form:
for in :
...
The interpreter starts by evaluating listExpr. If it does not yield a list, tuple, or string 7, an error
occurs. If it does yield a list or list-like structure, then the block of statements will, under normal
circumstances, be executed one time for every value in that list. At the end, the variable
will remain bound to the last element of the list (and if it had a useful value before the for was
evaluated, that value will have been permanently overwritten).
Here is a basic for loop:
6
7
In fact, Python will let you put any expression in the place of , and it will treat the values 0, 0.0, [],
’’, and None as if they were False and everything else as True.
or, more esoterically, another object that can be iterated over. Chapter 2 Learning to Program in Python
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30
result = 0
for x in [1, 3, 4]:
result = result + x * x
At the end of this execution, result will have the value 26, and x will have the value 4.
One situation in which the body is not executed once for each value in the list is when a return
statement is encountered. No matter whether return is nested in a loop or not, if it is evaluated it
immediately causes a value to be returned from a procedure call. So, for example, we might write
a procedure that tests to see if an item is a member of a list, and returns True if it is and False if
it is not, as follows:
def member(x, items):
for i in items:
if x == i:
return True
return False
The procedure loops through all of the elements in items, and compares them to x. As soon as
it finds an item i that is equal to x, it can quit and return the value True from the procedure. If
it gets all the way through the loop without returning, then we know that x is not in the list, and
we can return False.
Exercise 2.1.
Write a procedure that takes a list of numbers, nums, and a limit, limit,
and returns a list which is the shortest prefix of nums the sum of whose
values is greater than limit. Use for. Try to avoid using explicit indexing
into the list. (Hint: consider the strategy we used in member.)
Range
Very frequently, we will want to iterate through a list of integers, often as indices. Python provides
a useful procedure, range, which returns lists of integers. It can be used in complex ways, but the
basic usage is range(n), which returns a list of integers going from 0 up to, but not including, its
argument. So range(3) returns [0, 1, 2].
Exercise 2.2.
Write a procedure that takes n as an argument and returns the sum of the
squares of the integers from 1 to n-1. It should use for and range. Chapter 2 Learning to Program in Python
Exercise 2.3.
6.01— Spring 2011— April 25, 2011
31
What is wrong with this procedure, which is supposed to return True if
the element x occurs in the list items, and False otherwise?
def member (x, items):
for i in items:
if x == i:
return True
else:
return False
While
You should use for whenever you can, because it makes the structure of your loops clear. Some
times, however, you need to do an operation several times, but you do not know in advance how
many times it needs to be done. In such situations, you can use a while statement, of the form:
while :
...
In order to evaluate a while statement, the interpreter evaluates , getting a
Boolean value. If the value is False, it skips all the statements and evaluation moves on to the
next statement in the program. If the value is True, then the statements are executed, and the
is evaluated again. If it is False, execution of the loop is terminated, and if it is
True, it goes around again.
It will generally be the case that you initialize a variable before the while statement, change that
variable in the course of executing the loop, and test some property of that variable in the Boolean
expression. Imagine that you wanted to write a procedure that takes an argument n and returns
the largest power of 2 that is smaller than n. You might do it like this:
def pow2Smaller(n):
p = 1
while p*2 < n:
p = p*2
return p
Lists
Python has a built-in list data structure that is easy to use and incredibly convenient. So, for
instance, you can say
>>>
>>>
1
>>>
3
>>>
y = [1, 2, 3]
y[0]
y[2]
y[-1] Chapter 2 Learning to Program in Python
3
>>>
2
>>>
3
>>>
[1,
>>>
[4,
>>>
[4,
>>>
[1,
6.01— Spring 2011— April 25, 2011
32
y[-2]
len(y)
y + [4]
2, 3, 4]
[4] + y
1, 2, 3]
[4,5,6] + y
5, 6, 1, 2, 3]
y
2, 3]
A list is written using square brackets, with entries separated by commas. You can get elements
out by specifying the index of the element you want in square brackets, but note that, like for
strings, the indexing starts with 0. Note that you can index elements of a list starting from the
initial (or zeroth) one (by using integers), or starting from the last one (by using negative integers).
You can add elements to a list using ’+’, taking advantage of Python operator overloading. Note
that this operation does not change the original list, but makes a new one.
Another useful thing to know about lists is that you can make slices of them. A slice of a list is
sublist; you can get the basic idea from examples.
>>>
>>>
[0,
>>>
[1,
>>>
[3,
>>>
[0,
>>>
[0,
>>>
[0,
b = range(10)
b
1, 2, 3, 4, 5,
b[1:]
2, 3, 4, 5, 6,
b[3:]
4, 5, 6, 7, 8,
b[:7]
1, 2, 3, 4, 5,
b[:-1]
1, 2, 3, 4, 5,
b[:-2]
1, 2, 3, 4, 5,
6, 7, 8, 9]
7, 8, 9]
9]
6]
6, 7, 8]
6, 7]
Iteration over lists
What if you had a list of integers, and you wanted to add them up and return the sum? Here are
a number of different ways of doing it. 8
8
For any program you will ever need to write, there will be a huge number of different ways of doing it. How should
you choose among them? The most important thing is that the program you write be correct, and so you should choose
the approach that will get you to a correct program in the shortest amount of time. That argues for writing it in the way
that is cleanest, clearest, shortest. Another benefit of writing code that is clean, clear and short is that you will be better
able to understand it when you come back to it in a week or a month or a year, and that other people will also be better
able to understand it. Sometimes, you will have to worry about writing a version of a program that runs very quickly,
and it might be that in order to make that happen, you will have to write it less cleanly or clearly or briefly. But it is
important to have a version that is correct before you worry about getting one that is fast. Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
33
First, here is a version in a style you might have learned to write in a Java class (actually, you
would have used for, but Python does not have a for that works like the one in C and Java).
def addList1(l):
sum = 0
listLength = len(l)
i = 0
while (i < listLength):
sum = sum + l[i]
i = i + 1
return sum
It increments the index i from 0 through the length of the list - 1, and adds the appropriate element
of the list into the sum. This is perfectly correct, but pretty verbose and easy to get wrong.
Here is a version using Python’s for loop.
def addList2(l):
sum = 0
for i in range(len(l)):
sum = sum + l[i]
return sum
A loop of the form
for x in l:
something
will be executed once for each element in the list l, with the variable x containing each successive
element in l on each iteration. So,
for x in range(3):
print x
will print 0 1 2. Back to addList2, we see that i will take on values from 0 to the length of the
list minus 1, and on each iteration, it will add the appropriate element from l into the sum. This
is more compact and easier to get right than the first version, but still not the best we can do!
This one is even more direct.
def addList3(l):
sum = 0
for v in l:
sum = sum + v
return sum
We do not ever really need to work with the indices. Here, the variable v takes on each successive
value in l, and those values are accumulated into sum.
For the truly lazy, it turns out that the function we need is already built into Python. It is called
sum:
def addList4(l):
return sum(l) Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
34
In section ??, we will see another way to do addList, which many people find more beautiful
than the methods shown here.
List Comprehensions
Python has a very nice built-in facility for doing many iterative operations, called list comprehen
sions. The basic template is
[ for in if ]
where is a single variable (or a tuple of variables), is an expression that evalu
ates to a list, tuple, or string, and is an expression that may use the variable .
The if is optional; if it is present, then only those values of for which
that expression is True are included in the resulting computation.
You can view a list comprehension as a special notation for a particular, very common, class of
for loops. It is equivalent to the following:
*resultVar* = []
for in :
if :
*resultVar*.append()
*resultVar*
We used a kind of funny notation *resultVar* to indicate that there is some anonymous list that
is getting built up during the evaluation of the list comprehension, but we have no real way of
accessing it. The result is a list, which is obtained by successively binding to elements of
the result of evaluating , testing to see whether they meet a condition, and if they
meet the condition, evaluating and collecting the results into a list.
Whew. It is probably easier to understand it by example.
>>> [x/2.0 for x in [4, 5, 6]]
[2.0, 2.5, 3.0]
>>> [y**2 + 3 for y in [1, 10, 1000]]
[4, 103, 1000003]
>>> [a[0] for a in [[’Hal’, ’Abelson’],[’Jacob’,’White’],
[’Leslie’,’Kaelbling’]]]
[’Hal’, ’Jacob’, ’Leslie’]
>>> [a[0]+’!’ for a in [[’Hal’, ’Abelson’],[’Jacob’,’White’],
[’Leslie’,’Kaelbling’]]]
[’Hal!’, ’Jacob!’, ’Leslie!’]
Imagine that you have a list of numbers and you want to construct a list containing just the ones
that are odd. You might write
>>> nums = [1, 2, 5, 6, 88, 99, 101, 10000, 100, 37, 101]
>>> [x for x in nums if x%2==1]
[1, 5, 99, 101, 37, 101]
Note the use of the if conditional here to include only particular values of x. Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
35
And, of course, you can combine this with the other abilities of list comprehensions, to, for exam
ple, return the squares of the odd numbers:
>>> [x*x for x in nums if x%2==1]
[1, 25, 9801, 10201, 1369, 10201]
You can also use structured assignments in list comprehensions
>>> [first for (first, last) in [[’Hal’, ’Abelson’],[’Jacob’,’White’],
[’Leslie’,’Kaelbling’]]]
[’Hal’, ’Jacob’, ’Leslie’]
>>> [first+last for (first, last) in [[’Hal’, ’Abelson’],[’Jacob’,’White’],
[’Leslie’,’Kaelbling’]]]
[’HalAbelson’, ’JacobWhite’, ’LeslieKaelbling’]
Another built-in function that is useful with list comprehensions is zip. Here are some examples
of how it works:
> zip([1, 2, 3],[4, 5, 6])
[(1, 4), (2, 5), (3, 6)]
> zip([1,2], [3, 4], [5, 6])
[(1, 3, 5), (2, 4, 6)]
Here is an example of using zip with a list comprehension:
>>> [first+last for (first, last) in zip([’Hal’, ’Jacob’, ’Leslie’],
[’Abelson’,’White’,’Kaelbling’])]
[’HalAbelson’, ’JacobWhite’, ’LeslieKaelbling’]
Note that this last example is very different from this one:
>>> [first+last for first in [’Hal’, ’Jacob’, ’Leslie’] \
for last in [’Abelson’,’White’,’Kaelbling’]]
[’HalAbelson’, ’HalWhite’, ’HalKaelbling’, ’JacobAbelson’, ’JacobWhite’,
’JacobKaelbling’, ’LeslieAbelson’, ’LeslieWhite’, ’LeslieKaelbling’]
Nested list comprehensions behave like nested for loops, the expression in the list comprehen
sion is evaluated for every combination of the values of the variables.
2.4 Common Errors and Messages
Here are some common Python errors and error messages to watch out for. Please let us know if
you have any favorite additions for this list.
• A complaint about NoneType often means you forgot a return.
def plus1 (x):
x + 1
>>> y = plus1(x)
>>> plus1(x) + 2
Traceback (most recent call last):
File "", line 1, in
TypeError: unsupported operand type(s) for +: ’NoneType’ and ’int’ Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
36
• Weird results from math can result from integer division
>>> 3/ 9
0
• “Unsubscriptable object” means you are trying to get an element out of something that isn’t a
dictionary, list, or tuple.
>>> x = 1
>>> x[3]
Traceback (most recent call last):
File "", line 1, in
TypeError: ’int’ object is unsubscriptable
• “Object is not callable” means you are trying to use something that isn’t a procedure or method
as if it were.
>>> x = 1
>>> x(3)
Traceback (most recent call last):
File "", line 1, in
TypeError: ’int’ object is not callable
• “List index out of range” means you are trying to read or write an element of a list that is not
present.
>>> a = range(5)
>>> a
[0, 1, 2, 3, 4]
>>> a[5]
Traceback (most recent call last):
File "", line 1, in
IndexError: list index out of range
• “Maximum recursion depth exceeded” means you have a recursive procedure that is nested
very deeply or your base case is not being reached due to a bug.
def fizz(x):
return fizz(x - 1)
>>> fizz(10)
Traceback (most recent call last):
File "", line 1, in
File "", line 2, in fizz
File "", line 2, in fizz
...
File "", line 2, in fizz
RuntimeError: maximum recursion depth exceeded
• “Key Error” means that you are trying to look up an element in a dictionary that is not present.
>>> d = {’a’:7, ’b’:8}
>>> d[’c’]
Traceback (most recent call last):
File "", line 1, in
KeyError: ’c’ Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
37
• Another common error is forgetting the self before calling a method. This generates the same
error that you would get if you tried to call a function that wasn’t defined at all.
Traceback (most recent call last):
File "", line 1, in
File "V2.py", line 22, in __add__
return add(v)
NameError: global name ’add’ is not defined
2.5 Python Style
Software engineering courses often provide very rigid guidelines on the style of programming,
specifying the appropriate amount of indentation, or what to capitalize, or whether to use un
derscores in variable names. Those things can be useful for uniformity and readability of code,
especially when a lot of people are working on a project. But they are mostly arbitrary: a style is
chosen for consistency and according to some person’s aesthetic preferences.
There are other matters of style that seem, to us, to be more fundamental, because they directly
affect the readability or efficiency of the code.
• Avoid recalculation of the same value.
You should compute it once and assign it to a variable instead; otherwise, if you have a bug
in the calculation (or you want to change the program), you will have to change it multiple
times. It is also inefficient.
• Avoid repetition of a pattern of computation.
You should use a function instead, again to avoid having to change or debug the same basic
code multiple times.
• Avoid numeric indexing.
You should use destructuring if possible, since it is much easier to read the code and therefore
easier to get right and to modify later.
• Avoid excessive numeric constants.
You should name the constants, since it is much easier to read the code and therefore easier to
get right and to modify later.
Here are some examples of simple procedures that exhibit various flaws. We’ll talk about what
makes them problematic.
2.5.1 Normalize a vector
Let’s imagine we want to normalize a vector of three values; that is to compute a new vector of
three values, such that its length is 1. Here is our first attempt; it is a procedure that takes as input
a list of three numbers, and returns a list of three numbers:
def normalize3(v):
return [v[0]/math.sqrt(v[0]**2+v[1]**2+v[2]**2),
v[1]/math.sqrt(v[0]**2+v[1]**2+v[2]**2),
v[2]/math.sqrt(v[0]**2+v[1]**2+v[2]**2)] Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
38
This is correct, but it looks pretty complicated. Let’s start by noticing that we’re recalculating the
denominator three times, and instead save the value in a variable.
def normalize3(v):
magv = math.sqrt(v[0]**2+v[1]**2+v[2]**2)
return [v[0]/magv,v[1]/magv,v[2]/magv]
Now, we can see a repeated pattern, of going through and dividing each element by magv. Also,
we observe that the computation of the magnitude of a vector is a useful and understandable
operation in its own right, and should probably be put in its own procedure. That leads to this
procedure:
def mag(v):
return math.sqrt(sum([vi**2 for vi in v]))
def normalize3(v):
return [vi/mag(v) for vi in v]
This is especially nice, because now, in fact, it applies not just to vectors of length three. So, it’s
both shorter and more general than what we started with. But, one of our original problems has
snuck back in: we’re recomputing mag(v) once for each element of the vector. So, finally, here’s a
version that we’re very happy with: 9
def mag(v):
return math.sqrt(sum([vi**2 for vi in v]))
def normalize3(v):
magv = mag(v)
return [vi/magv for vi in v]
2.5.2 Perimeter of a polygon
Now, let’s consider the problem of computing the length of the perimeter of a polygon. The input
is a list of vertices, encoded as a list of lists of two numbers (such as [[1, 2], [3.4, 7.6],
[-4.4, 3]]). Here is our first attempt:
def perim(vertices):
result = 0
for i in range(len(vertices)-1):
result = result + math.sqrt((vertices[i][0]-vertices[i+1][0])**2 + \
(vertices[i][1]-vertices[i+1][1])**2)
return result + math.sqrt((vertices[-1][0]-vertices[0][0])**2 + \
(vertices[-1][1]-vertices[0][1])**2)
9
Note that there is still something for someone to be unhappy with here: the use of a list comprehension means that
we’re creating a new list, which we are just going to sum up; that’s somewhat less efficient than adding the values
up in a loop. However, as we said at the outset, for almost every program, clarity matters more than efficiency. And
once you have something that’s clear and correct, you can selectively make the parts that are executed frequently more
efficient. Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
39
Again, this works, but it ain’t pretty. The main problem is that someone reading the code doesn’t
immediately see what all that subtraction and squaring is about. We can fix this by defining
another procedure:
def perim(vertices):
result = 0
for i in range(len(vertices)-1):
result = result + pointDist(vertices[i],vertices[i+1])
return result + pointDist(vertices[-1],vertices[0])
def pointDist(p1,p2):
return math.sqrt(sum([(p1[i] - p2[i])**2 for i in range(len(p1))]))
Now, we’ve defined a new procedure pointDist, which computes the Euclidean distance be
tween two points. And, in fact, we’ve written it generally enough to work on points of any
dimension (not just two). Just for fun, here’s another way to compute the distance, which some
people would prefer and others would not.
def pointDist(p1,p2):
return math.sqrt(sum([(c1 - c2)**2 for (c1, c2) in zip(p1, p2)]))
For this to make sense, you have to understand zip. Here’s an example of how it works:
> zip([1, 2, 3],[4, 5, 6])
[(1, 4), (2, 5), (3, 6)]
2.5.3 Bank transfer
What if we have two values, representing bank accounts, and want to transfer an amount of
money amt between them? Assume that a bank account is represented as a list of values, such as
[’Alyssa’, 8300343.03, 0.05], meaning that ’Alyssa’ has a bank balance of $8,300,343.03,
and has to pay a 5-cent fee for every bank transaction. We might write this procedure as follows.
It moves the amount from one balance to the other, and subtracts the transaction fee from each
account.
def transfer(a1, a2, amt):
a1[1] = a1[1] - amt - a1[2]
a2[1] = a2[1] + amt - a2[2]
To understand what it’s doing, you really have to read the code at a detailed level. Furthermore,
it’s easy to get the variable names and subscripts wrong.
Here’s another version that abstracts away the common idea of a deposit (which can be positive
or negative) into a procedure, and uses it twice:
def transfer(a1, a2, amt):
deposit(a1, -amt)
deposit(a2, amt)
def deposit(a, amt):
a[1] = a[1] + amt - a[2] Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
40
Now, transfer looks pretty clear, but deposit could still use some work. In particular, the use
of numeric indices to get the components out of the bank account definition is a bit cryptic (and
easy to get wrong). 10
def deposit(a, amt):
(name, balance, fee) = a
a[1] = balance + amt - fee
Here, we’ve used a destructuring assignment statement to give names to the components of the
account. Unfortunately, when we want to change an element of the list representing the account,
we still have to index it explicitly. Given that we have to use explicit indices, this approach in
which we name them might be better.
acctName = 0
acctBalance = 1
acctFee = 2
def deposit(a, amt):
a[acctBalance] = a[acctBalance] + amt - a[acctFee]
Strive, in your programming, to make your code as simple, clear, and direct as possible. Occa
sionally, the simple and clear approach will be too inefficient, and you’ll have to do something
more complicated. In such cases, you should still start with something clear and simple, and in
the end, you can use it as documentation.
2.5.4 Coding examples
Following are some attempts at defining a procedure isSubset, which takes two arguments, a
and b, and returns True if a is a subset of b, assuming that a and b are represented as lists of
elements.
Here is one solution to the problem which is in the Pythonic functional style.
def isSubset(a, b):
return reduce(operator.and_, [x in b for x in a])
This is short and direct. The expression x in b tests to see if the item x is in the list b. So,
[x in b for x in a] is a list of Booleans, one for each element in a, indicating whether that
element is in b. Finally, we reduce that list using the and operator 11 , which will have the value
True if all of the elements of the list are True, and False otherwise.
An alternative is to do it recursively:
def isSubset(a, b):
if a == []:
return True
else:
return a[0] in b and isSubset(a[1:], b)
10
11
We’ll see other approaches to this when we start to look at object-oriented programming. But it’s important to apply
basic principles of naming and clarity no matter whether you’re using assembly language or Java.
To get versions of basic Python operations in the form of procedures, you need to do import operator. Now, you can
do addition with operator.add(3, 4). Because and already has special syntactic significance in Python, they had to
name the operator version of it something different, and so it is operator.and_. Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
41
The base case of the recursion is that a is the empty list; in that case, it’s clear that a is a subset of
b. Otherwise, we can define our answer in terms of isSubset, but asking a simpler question (in
this case, on a list for a which is one element smaller). So, we say that a is a subset of b if the first
element of a is a member of b and the set of rest of the elements of a is a subset of b.
We could go even farther in compressing the recursive solution:
def isSubset(a, b):
return a == None or a[0] in b and isSubset(a[1:], b)
Here, we are taking advantage of the fact that in Python (and most other languages), the or oper
ator has the “early out” property. That is, if we are evaluating e1 or e2, we start by evaluating
e1, and if it is True, then we know the result of the expression has to be True, and therefore
we return without evaluating e2. So, or can act as a kind of conditional operator. Some people
would find this example too abstruse (shorter isn’t always better), and some would find it very
beautiful.
Here is another good solution, this time in the imperative style:
def isSubset(a, b):
for x in a:
if not x in b:
return False
return True
It works by going through the elements in a, in order. If any of those elements is not in b, then we
can immediately quit and return False. Otherwise, if we have gotten through all of the elements
in a, and each of them has been in b, then a really is a subset of b and we can return True.
Here is another good imperative example:
def isSubset(a, b):
result = True
for x in a:
result = result and x in b
return result
This procedure starts by initializing a result to True, which is the identity element for and (it
plays the same role as 0 for add). Then it goes all the way through the list a, and ands the old
result with x in b. This computation is very similar to the computation done in the functional
version, but rather than creating the whole list of Booleans and then reducing, we interleave the
individual membership tests with combining them into a result.
All of these examples are short and clear, and we’d be happy to see you write any of them. How
ever, there are lots of versions of this procedure that students have written that we are not happy
with. Here are some examples of procedures that are correct, in the sense that they return the
right answer, but incorrect, in the sense that they are long or confused or hard to read. Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
Bad Example 1.
def isSubset(a,b):
list1=[]
for i in a:
for j in b:
if i==j:
list1.append(i)
break
if len(list1)==len(a):
return True
else:
return False
This procedure works by going through both lists and making a list of the
items that are in common (basically computing the intersection). Then, it
checks to see whether the intersection is of the same length as a. There are
several problems here:
• Using the idea of computing the intersection and then seeing whether
it is equal to a is a nice idea. But it’s hard to see that’s what this code
is doing. Much better would be to make a procedure to compute the
intersection, and then call it explicitly and compare the result to a.
• Comparing the length of the intersection and the length of a is danger
ous. Because we allow repetitions of elements in the lists representing
sets, two sets that are equal, in the set sense, may not have the same
length.
• The break statement exits one level of the loop you are currently exe
cuting. It is often hard to understand and best avoided.
• If you ever find yourself writing:
if condition:
return True
else:
return False
You could always replace that with
return condition
which is shorter and clearer.
42 Chapter 2 Learning to Program in Python
6.01— Spring 2011— April 25, 2011
Bad Example 2.
def isSubset(a,b):
itera = 0
okay = True
while itera < len(a):
iterb = 0
tempokay = False
while iterb < len(b):
if a[itera] == b[iterb]:
tempokay = True
iterb = iterb + 1
if tempokay == False:
okay = False
itera = itera + 1
return okay
This procedure is basically iterating over both lists, which is fine, but it is
using while rather than for to do so, which makes it hard to follow. We
can rewrite it with for:
def isSubset(a,b):
okay = True
for itera in range(len(a)):
tempokay = False
for iterb in range(len(b)):
if a[itera] == b[iterb]:
tempokay = True
if tempokay == False:
okay = False
return okay
continued
43 Chapter 2 Learning to Program in Python
Bad Example 3.
6.01— Spring 2011— April 25, 2011
Previous bad example, being made over.
Now, the remaining lack of clarity is in the handling of the Booleans. We
can replace
if a[itera] == b[iterb]:
tempokay = True
with
tempokay = tempokay or (a[itera] == b[iterb])
which will set tempokay to True if a[itera] == b[iterb], and other
wise leave it the same. And we can replace
if tempokay == False:
okay = False
with
okay = okay and tempokay
It still has the effect that if tempokay is false, then okay will be false, and
otherwise it will be as it was before. So, now we have:
def isSubset(a,b):
okay = True
for itera in range(len(a)):
tempokay = False
for iterb in range(len(b)):
tempokay = tempokay or a[itera] == b[iterb]
okay = okay and tempokay
return okay
The logical structure is becoming clearer. It might be even better if we were
to write:
def isSubset(a,b):
foundAllAsSoFar = True
for itera in range(len(a)):
foundThisA = False
for iterb in range(len(b)):
foundThisA = foundThisA or a[itera] == b[iterb]
foundAllAsSoFar = foundAllAsSoFar and foundThisA
return okay
44 Chapter 2 Learning to Program in Python
Exercise 2.4.
6.01— Spring 2011— April 25, 2011
Now, see if you can, first, explain, and then improve this example!
def issubset(a,b):
i = 0
j = 0
while i < len(a):
c = False
while j < len(b):
if a[i] == b[j]:
c = True
j = j+1
if c:
c = False
else:
return False
j = 0
i = i+1
return True
45
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