As you may have discovered, it is legal to make more than one assignment to the same variable. A new assignment makes an existing variable refer to a new value (and stop referring to the old value).
bruce = 5 print bruce, bruce = 7 print bruce
The output of this program is 5 7, because the first time bruce is printed, his value is 5, and the second time, his value is 7. The comma at the end of the first print statement suppresses the newline after the output, which is why both outputs appear on the same line.
Here is what multiple assignment looks like in a state diagram:
With multiple assignment it is especially important to distinguish between an assignment operation and a statement of equality. Because Python uses the equal sign (=) for assignment, it is tempting to interpret a statement like a = b as a statement of equality. It is not!
First, equality is symmetric and assignment is not. For example, in mathematics, if a = 7 then 7 = a. But in Python, the statement a = 7 is legal and 7 = a is not.
Furthermore, in mathematics, a statement of equality is always true. If a = b now, then a will always equal b. In Python, an assignment statement can make two variables equal, but they don’t have to stay that way:
a = 5 b = a # a and b are now equal a = 3 # a and b are no longer equal
The third line changes the value of a but does not change the value of b, so they are no longer equal. (In some programming languages, a different symbol is used for assignment, such as <- or :=, to avoid confusion.)
One of the most common forms of multiple assignment is an update, where the new value of the variable depends on the old.
x = x + 1
This means get the current value of x, add one, and then update x with the new value.
If you try to update a variable that doesn’t exist, you get an error, because Python evaluates the expression on the right side of the assignment operator before it assigns the resulting value to the name on the left:
>>> x = x + 1 Traceback (most recent call last): File "", line 1, in NameError: name 'x' is not defined
Before you can update a variable, you have to initialize it, usually with a simple assignment:
>>> x = 0 >>> x = x + 1 >>>
Updating a variable by adding 1 is called an increment; subtracting 1 is called a decrement.
Computers are often used to automate repetitive tasks. Repeating identical or similar tasks without making errors is something that computers do well and people do poorly.
Repeated execution of a set of statements is called iteration. Because iteration is so common, Python provides several language features to make it easier. The first feature we are going to look at is the while statement.
Here is a function called countdown that demonstrates the use of the while statement:
def countdown(n): while n > 0: print n n = n-1 print "Blastoff!"
You can almost read the while statement as if it were English. It means, While n is greater than 0, continue displaying the value of n and then reducing the value of n by 1. When you get to 0, display the word Blastoff!
More formally, here is the flow of execution for a while statement:
The body consists of all of the statements below the header with the same indentation.
This type of flow is called a loop because the third step loops back around to the top. Notice that if the condition is false the first time through the loop, the statements inside the loop are never executed.
The body of the loop should change the value of one or more variables so that eventually the condition becomes false and the loop terminates. Otherwise the loop will repeat forever, which is called an infinite loop. An endless source of amusement for computer scientists is the observation that the directions on shampoo, Lather, rinse, repeat, are an infinite loop.
In the case of countdown, we can prove that the loop terminates because we know that the value of n is finite, and we can see that the value of n gets smaller each time through the loop, so eventually we have to get to 0. In other cases, it is not so easy to tell. Look at the following function, defined for all positive integers n:
def sequence(n): while n != 1: print n, if n % 2 == 0: # n is even n = n / 2 else: # n is odd n = n * 3 + 1
The condition for this loop is n != 1, so the loop will continue until n is 1, which will make the condition false.
Each time through the loop, the program outputs the value of n and then checks whether it is even or odd. If it is even, the value of n is divided by 2. If it is odd, the value is replaced by n * 3 + 1. For example, if the starting value (the argument passed to sequence) is 3, the resulting sequence is 3, 10, 5, 16, 8, 4, 2, 1.
Since n sometimes increases and sometimes decreases, there is no obvious proof that n will ever reach 1, or that the program terminates. For some particular values of n, we can prove termination. For example, if the starting value is a power of two, then the value of n will be even each time through the loop until it reaches 1. The previous example ends with such a sequence, starting with 16.
Particular values aside, the interesting question is whether we can prove that this program terminates for all values of n. So far, no one has been able to prove it or disprove it!
To write effective computer programs a programmer needs to develop the ability to trace the execution of a computer program. Tracing involves becoming the computer and following the flow of execution through a sample program run, recording the state of all variables and any output the program generates after each instruction is executed.
To understand this process, let’s trace the call to sequence(3) from the previous section. At the start of the trace, we have a local variable, n (the parameter), with an initial value of 3. Since 3 is not equal to 1, the while loop body is executed. 3 is printed and 3 % 2 == 0 is evaluated. Since it evaluates to False, the else branch is executed and 3 * 3 + 1 is evaluated and assigned to n.
To keep track of all this as you hand trace a program, make a column heading on a piece of paper for each variable created as the program runs and another one for output. Our trace so far would look something like this:
n output -- ------ 3 3 10
Since 10 != 1 evaluates to True, the loop body is again executed, and 10 is printed. 10 % 2 == 0 is true, so the if branch is executed and n becomes 5. By the end of the trace we have:
n output -- ------ 3 3 10 10 5 5 16 16 8 8 4 4 2 2 1
Tracing can be a bit tedious and error prone (that’s why we get computers to do this stuff in the first place!), but it is an essential skill for a programmer to have. From this trace we can learn a lot about the way our code works. We can observe that as soon as n becomes a power of 2, for example, the program will require log2(n) executions of the loop body to complete. We can also see that the final 1 will not be printed as output.
The following function counts the number of decimal digits in a positive integer expressed in decimal format:
def num_digits(n): count = 0 while n: count = count + 1 n = n / 10 return count
A call to num_digits(710) will return 3. Trace the execution of this function call to convince yourself that it works.
This function demonstrates another pattern of computation called a counter. The variable count is initialized to 0 and then incremented each time the loop body is executed. When the loop exits, count contains the result – the total number of times the loop body was executed, which is the same as the number of digits.
If we wanted to only count digits that are either 0 or 5, adding a conditional before incrementing the counter will do the trick:
def num_zero_and_five_digits(n): count = 0 while n: digit = n % 10 if digit == 0 or digit == 5: count = count + 1 n = n / 10 return count
Confirm that num_zero_and_five_digits(1055030250) returns 7.
Incrementing a variable is so common that Python provides an abbreviated syntax for it:
>>> count = 0 >>> count += 1 >>> count 1 >>> count += 1 >>> count 2 >>>
count += 1 is an abreviation for count = count + 1 . The increment value does not have to be 1:
>>> n = 2 >>> n += 5 >>> n 7 >>>
There are also abbreviations for -=, *=, /=, and %=:
>>> n = 2 >>> n *= 5 >>> n 10 >>> n -= 4 >>> n 6 >>> n /= 2 >>> n 3 >>> n %= 2 >>> n 1
One of the things loops are good for is generating tabular data. Before computers were readily available, people had to calculate logarithms, sines and cosines, and other mathematical functions by hand. To make that easier, mathematics books contained long tables listing the values of these functions. Creating the tables was slow and boring, and they tended to be full of errors.
When computers appeared on the scene, one of the initial reactions was, This is great! We can use the computers to generate the tables, so there will be no errors. That turned out to be true (mostly) but shortsighted. Soon thereafter, computers and calculators were so pervasive that the tables became obsolete.
Well, almost. For some operations, computers use tables of values to get an approximate answer and then perform computations to improve the approximation. In some cases, there have been errors in the underlying tables, most famously in the table the Intel Pentium used to perform floating-point division.
Although a log table is not as useful as it once was, it still makes a good example of iteration. The following program outputs a sequence of values in the left column and 2 raised to the power of that value in the right column:
x = 1 while x < 13: print x, '\t', 2**x x += 1
The string '\t' represents a tab character. The backslash character in '\t' indicates the beginning of an escape sequence. Escape sequences are used to represent invisible characters like tabs and newlines. The sequence \n represents a newline.
An escape sequence can appear anywhere in a string; in this example, the tab escape sequence is the only thing in the string. How do you think you represent a backslash in a string?
As characters and strings are displayed on the screen, an invisible marker called the cursor keeps track of where the next character will go. After a print statement, the cursor normally goes to the beginning of the next line.
The tab character shifts the cursor to the right until it reaches one of the tab stops. Tabs are useful for making columns of text line up, as in the output of the previous program:
1 2 2 4 3 8 4 16 5 32 6 64 7 128 8 256 9 512 10 1024 11 2048 12 4096
Because of the tab characters between the columns, the position of the second column does not depend on the number of digits in the first column.
A two-dimensional table is a table where you read the value at the intersection of a row and a column. A multiplication table is a good example. Let’s say you want to print a multiplication table for the values from 1 to 6.
A good way to start is to write a loop that prints the multiples of 2, all on one line:
i = 1 while i <= 6: print 2 * i, ' ', i += 1 print
The first line initializes a variable named i, which acts as a counter or loop variable. As the loop executes, the value of i increases from 1 to 6. When i is 7, the loop terminates. Each time through the loop, it displays the value of 2 * i, followed by three spaces.
Again, the comma in the print statement suppresses the newline. After the loop completes, the second print statement starts a new line.
The output of the program is:
2 4 6 8 10 12
So far, so good. The next step is to encapsulate and generalize.
Encapsulation is the process of wrapping a piece of code in a function, allowing you to take advantage of all the things functions are good for. You have already seen two examples of encapsulation: print_parity in chapter 4; and is_divisible in chapter 5.
Generalization means taking something specific, such as printing the multiples of 2, and making it more general, such as printing the multiples of any integer.
This function encapsulates the previous loop and generalizes it to print multiples of n:
def print_multiples(n): i = 1 while i <= 6: print n * i, '\t', i += 1 print
To encapsulate, all we had to do was add the first line, which declares the name of the function and the parameter list. To generalize, all we had to do was replace the value 2 with the parameter n.
If we call this function with the argument 2, we get the same output as before. With the argument 3, the output is:
3 6 9 12 15 18
With the argument 4, the output is:
4 8 12 16 20 24
By now you can probably guess how to print a multiplication table — by calling print_multiples repeatedly with different arguments. In fact, we can use another loop:
i = 1 while i <= 6: print_multiples(i) i += 1
Notice how similar this loop is to the one inside print_multiples. All we did was replace the print statement with a function call.
The output of this program is a multiplication table:
1 2 3 4 5 6 2 4 6 8 10 12 3 6 9 12 15 18 4 8 12 16 20 24 5 10 15 20 25 30 6 12 18 24 30 36
To demonstrate encapsulation again, let’s take the code from the last section and wrap it up in a function:
def print_mult_table(): i = 1 while i <= 6: print_multiples(i) i += 1
This process is a common development plan. We develop code by writing lines of code outside any function, or typing them in to the interpreter. When we get the code working, we extract it and wrap it up in a function.
This development plan is particularly useful if you don’t know how to divide the program into functions when you start writing. This approach lets you design as you go along.
You might be wondering how we can use the same variable, i, in both print_multiples and print_mult_table. Doesn’t it cause problems when one of the functions changes the value of the variable?
The answer is no, because the i in print_multiples and the i in print_mult_table are not the same variable.
Variables created inside a function definition are local; you can’t access a local variable from outside its home function. That means you are free to have multiple variables with the same name as long as they are not in the same function.
The stack diagram for this program shows that the two variables named i are not the same variable. They can refer to different values, and changing one does not affect the other.
The value of i in print_mult_table goes from 1 to 6. In the diagram it happens to be 3. The next time through the loop it will be 4. Each time through the loop, print_mult_table calls print_multiples with the current value of i as an argument. That value gets assigned to the parameter n.
Inside print_multiples, the value of i goes from 1 to 6. In the diagram, it happens to be 2. Changing this variable has no effect on the value of i in print_mult_table.
It is common and perfectly legal to have different local variables with the same name. In particular, names like i and j are used frequently as loop variables. If you avoid using them in one function just because you used them somewhere else, you will probably make the program harder to read.
As another example of generalization, imagine you wanted a program that would print a multiplication table of any size, not just the six-by-six table. You could add a parameter to print_mult_table:
def print_mult_table(high): i = 1 while i <= high: print_multiples(i) i += 1
We replaced the value 6 with the parameter high. If we call print_mult_table with the argument 7, it displays:
1 2 3 4 5 6 2 4 6 8 10 12 3 6 9 12 15 18 4 8 12 16 20 24 5 10 15 20 25 30 6 12 18 24 30 36 7 14 21 28 35 42
This is fine, except that we probably want the table to be square — with the same number of rows and columns. To do that, we add another parameter to print_multiples to specify how many columns the table should have.
Just to be annoying, we call this parameter high, demonstrating that different functions can have parameters with the same name (just like local variables). Here’s the whole program:
def print_multiples(n, high): i = 1 while i <= high: print n*i, '\t', i += 1 print def print_mult_table(high): i = 1 while i <= high: print_multiples(i, high) i += 1
Notice that when we added a new parameter, we had to change the first line of the function (the function heading), and we also had to change the place where the function is called in print_mult_table.
As expected, this program generates a square seven-by-seven table:
1 2 3 4 5 6 7 2 4 6 8 10 12 14 3 6 9 12 15 18 21 4 8 12 16 20 24 28 5 10 15 20 25 30 35 6 12 18 24 30 36 42 7 14 21 28 35 42 49
When you generalize a function appropriately, you often get a program with capabilities you didn’t plan. For example, you might notice that, because ab = ba, all the entries in the table appear twice. You could save ink by printing only half the table. To do that, you only have to change one line of print_mult_table. Change
and you get:
1 2 4 3 6 9 4 8 12 16 5 10 15 20 25 6 12 18 24 30 36 7 14 21 28 35 42 49
A few times now, we have mentioned all the things functions are good for. By now, you might be wondering what exactly those things are. Here are some of them:
Loops are often used in programs that compute numerical results by starting with an approximate answer and iteratively improving it.
For example, one way of computing square roots is Newton’s method. Suppose that you want to know the square root of n. If you start with almost any approximation, you can compute a better approximation with the following formula:
better = (approx + n/approx)/2
By repeatedly applying this formula until the better approximation is equal to the previous one, we can write a function for computing the square root:
def sqrt(n): approx = n/2.0 better = (approx + n/approx)/2.0 while better != approx: approx = better better = (approx + n/approx)/2.0 return approx
Try calling this function with 25 as an argument to confirm that it returns 5.0.
Newton’s method is an example of an algorithm: it is a mechanical process for solving a category of problems (in this case, computing square roots).
It is not easy to define an algorithm. It might help to start with something that is not an algorithm. When you learned to multiply single-digit numbers, you probably memorized the multiplication table. In effect, you memorized 100 specific solutions. That kind of knowledge is not algorithmic.
But if you were lazy, you probably cheated by learning a few tricks. For example, to find the product of n and 9, you can write n - 1 as the first digit and 10 - n as the second digit. This trick is a general solution for multiplying any single-digit number by 9. That’s an algorithm!
Similarly, the techniques you learned for addition with carrying, subtraction with borrowing, and long division are all algorithms. One of the characteristics of algorithms is that they do not require any intelligence to carry out. They are mechanical processes in which each step follows from the last according to a simple set of rules.
In our opinion, it is embarrassing that humans spend so much time in school learning to execute algorithms that, quite literally, require no intelligence.
On the other hand, the process of designing algorithms is interesting, intellectually challenging, and a central part of what we call programming.
Some of the things that people do naturally, without difficulty or conscious thought, are the hardest to express algorithmically. Understanding natural language is a good example. We all do it, but so far no one has been able to explain how we do it, at least not in the form of an algorithm.
Write a single string that:
produces this output.
Add a print statement to the sqrt function defined in section 6.14 that prints out better each time it is calculated. Call your modified function with 25 as an argument and record the results.
Trace the execution of the last version of print_mult_table and figure out how it works.
Write a function print_triangular_numbers(n) that prints out the first n triangular numbers. A call to print_triangular_numbers(5) would produce the following output:
1 1 2 3 3 6 4 10 5 15
(hint: use a web search to find out what a triangular number is.)
Open a file named ch06.py and add the following:
if __name__ == '__main__': import doctest doctest.testmod()
Write a function, is_prime, which takes a single integral argument and returns True when the argument is a prime number and False otherwise. Add doctests to your function as you develop it.
What will num_digits(0) return? Modify it to return 1 for this case. Why does a call to num_digits(-24) result in an infinite loop (hint: -1/10 evaluates to -1)? Modify num_digits so that it works correctly with any integer value. Add the following to the ch06.py file you created in the previous exercise:
def num_digits(n): """ >>> num_digits(12345) 5 >>> num_digits(0) 1 >>> num_digits(-12345) 5 """
Add your function body to num_digits and confirm that it passes the doctests.
Add the following to the ch06.py:
def num_even_digits(n): """ >>> num_even_digits(123456) 3 >>> num_even_digits(2468) 4 >>> num_even_digits(1357) 0 >>> num_even_digits(2) 1 >>> num_even_digits(20) 2 """
Write a body for num_even_digits so that it works as expected.
Add the following to ch06.py:
def print_digits(n): """ >>> print_digits(13789) 9 8 7 3 1 >>> print_digits(39874613) 3 1 6 4 7 8 9 3 >>> print_digits(213141) 1 4 1 3 1 2 """
Write a body for print_digits so that it passes the given doctests.
Write a function sum_of_squares_of_digits that computes the sum of the squares of the digits of an integer passed to it. For example, sum_of_squares_of_digits(987) should return 194, since 9**2 + 8**2 + 7**2 == 81 + 64 + 49 == 194.
def sum_of_squares_of_digits(n): """ >>> sum_of_squares_of_digits(1) 1 >>> sum_of_squares_of_digits(9) 81 >>> sum_of_squares_of_digits(11) 2 >>> sum_of_squares_of_digits(121) 6 >>> sum_of_squares_of_digits(987) 194 """
Check your solution against the doctests above.