Lab 4
Function 1
def anyoneissuma,b,c:
sumcab
sumbac
sumabc
if sumcab:
if sumbca:
if sumabc:
return True
return False
else:
Semester 2, 2019: Lab 4
Note: This lab has more problems than we expect everyone to finish during the lab time. If you do not have time to finish all problems, you can continue working on them later at home, if you have a computer with python set up, in the CSIT lab rooms outside teaching hours, or on one of the computers available across campus, or return to them in a later lab.
Objectives
The purpose of this weeks lab is to:
understand the indexing of 1dimensional sequences;
do some computations over sequences that require iterating through them using loops; andpractice reading and debugging code.
Exercise 0: Reading and debugging code
The following are attempts to define a function that takes three numeric arguments and checks if any one of them is equal to the sum of the other two. For example, anyoneissum1, 3, 2 should return True because 312, while anyoneissum0, 1, 2 should return False.
a All of the functions below are incorrect. For each of them, find examples of arguments that cause it to return the wrong answer.
1
Function 2
def anyoneissuma,b,c:
if bca:
printTrue
if cba:
printTrue
else:
printFalse
return False
Function 3
def anyoneissuma, b, c:
if abc and acb:
return True
else:
return False
b For each of the three functions above, can you work out how they are intended to work? That is, what was the idea of the programmer who wrote them? What comments would be useful to add to explain the thinking? Is it possible to fix them by making only a small change to each function?
Exercise 1: Debugging loops
Below are two attempted solutions to the problems of summing the odd and even digits in a number from Lab 3, respectively. Both of them, however, have some problems: For some arguments, they may return the wrong answer, or not return at all because the loop never ends.
a For each of the two functions, find arguments that cause it to return an incorrect answer, and arguments that cause it to get stuck in an infinite loop either or both may be possible. Arguments to the function must of course! be nonnegative integers.
Hint: Add print calls inside the loop to see what is happening. Print the variables that appear in the loop condition, so you can see if they are changing or not if they are not, then the loop is stuck.
def sumodddigitsnumber:
dsum0
only count odd digits
while number2 ! 0:
add the last digit to the sum
digitnumber10
dsumdsumdigit
divide by 10 rounded down to remove the last digit
numbernumber10
return dsum
def sumevendigitsnumber:
m1the position of the next digit
2
dsum0the sum
while number10m ! 0:
get the m:th digit
digitnumber10m10m1
only add it if even:
if digit20:
dsumdsumdigit mm1
return dsum
b Like in the previous problem, can you work out how the functions are intended to work? That is, what was the idea of the programmer who wrote them? What comments would be useful to add to explain the thinking? Is it possible to fix the errors you uncovered in your testing by making only a small change to each function?
c advanced Here is a more complex function:
def mysterym:
assert typem is int and m0, m must be a nonnegative integer
while m1:
i2
while im:
while mi0:
mmi
ii1 return i
It is called mystery because its not very obvious what it does.
The mystery function is meant to work on any nonnegative integer input, but some arguments will cause
it to get stuck in an infinite loop. Can you find examples of arguments that will cause this?
Learning to read and debug code is a very important skill and it will also show you the value of good naming and commenting!. There are more debugging exercises towards the end of the lab.
Sequence types
We have already seen a number of times that all values in python have a type, such as int, float, str, etc. To determine the type of a value we can use the function typesome expression. python has three builtin sequence types: lists type list, strings type str and tuples type tuple. These sequence types are used to represent different kinds of ordered collections.
To write a list literal, write its elements, separated by commas, in a pair of square brackets:
In 1: mylist1, 2, 3, 4, 5, 6
In 2: typemylist
Out 2: …
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The elements that you write can be expressions. These are evaluated, and the resulting values become the elements of the list:
In 3: mylist2, 21, 22, 23
In 4: mylist
Out 4: …
The NumPy array type
The NumPy library provides a type for represening ndimensional arrays of values usually numbers, but also other types, such as Booleans, and functions for doing calculations with arrays.
NumPy is not part of pythons standard library, but it is installed on the CSIT lab computers, and other computers across campus. If you want to be able to use your own python setup, you have to ensure that you have NumPy installed. The Anaconda distribution includes NumPy, SciPy and matplotlib by default, which is one reason why we recommend it. Read the guide to setting up python for more information.
Like any library module in python, to use NumPy you must first import it:
In 1: import numpy
Remember that the names of all functions in the imported module are prefixed with the module name. That is, you have to write numpy.linspace… instead of just linspace…. When you import a module, you can give it a shorthand name. For example, if your write
In 1: import numpy as np
the functions in the NumPy module will be prefixed with just np instead of numpy. In all the examples
below, we will assume you have imported NumPy with the abbreviation np.
Information about the functions that NumPy provides is available through the builtin help system. However, you can also find documentation of NumPy and SciPy online; the online documentation can be easier to navigate. You can also find some tutorials at numpy.org.
We can create an array from a list:
In 1: mylist1, 2, 3, 4, 5, 6
In 2: myarraynp.arraymylist
In 3: myarray
Out 3: …
In 4: typemyarray
Out 4: …
or simply:
In 5: np.array3, 1.2, 2
Out 5: …
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As shown in the lectures, there are also several functions for creating arrays with specific contents:
In 6: np.zeros10
Out 6: …
In 7: np.ones10
Out 7: …
In 8: np.linspace2, 2, 21
Out 8: …
In 9: np.arange4,9
Out 9: …
linspacefrom, to, num returns an array of num floating point numbers evenly spaced between from and to.
arangefrom,to returns an array of consecutive integers, starting with from and ending at to1. If you provide just one argument, as in arangeto, the starting number defaults to 0.
Indexing sequences
Both list and NumPys ndarray are called sequence data types. Every element in a sequence has an index position. The first element is at index 0. The length of a sequence is the number of elements in the sequence. The index of the last element is the length minus one. The builtin function len returns the length of any sequence.
Indexing a sequence selects a single element from the sequence for example, a character if the sequence is a string. Python also allows indexing sequences from the end, using negative indices. That is, 1 also refers to the last element in the sequence, and lenseq refers to the first.
Exercise 2a
This exercise is to play with the list sequence type. Execute the following in the python shell. For each expression, try to work out what the output will be before you evaluate the expression.
In 1: mylist 1, 2, 3, 4, 5, 6
In 2: mylist1
Out 2: …
In 3: mylist4
Out 3: …
In 4: mylist1
Out 4: …
In 5: Llenmylist
5
In 6: mylistL1
Out 6: …
In 7: mylist1L
Out 7: …
They should all run without error. Is the result of each expression what you expected?
Exercise 2b
As we saw above, you can turn a list into a NumPy array:
In 1: myarraynp.arraymylist
You can now try the indexing expressions that you did with mylist above on myarray instead. Is there
any difference in the result?
Exercise 2c
What does the following statement do?
In 1: myarraynp.arange1,7
Iteration over sequences
Python has two kinds of loop statements: the while loop, which repeatedly executes a suite as long as a condition is true, and the for loop, which executes a suite once for every element of a sequence. To be precise, the for loop works not only on sequences but on any type that is iterable. All sequences are iterable, but later in the course we will see examples of types that are iterable but not sequences.
Both kinds of loop can be used to iterate over a sequence. Which one is most appropriate to implement some function depends on what the function needs to do with the sequence. The for loop is simpler to use, but only allows you to look at one element at a time. The while loop is more complex to use you must initialise and update an index variable, and specify the loop condition correctly but allows you greater flexibility; for example, you can skip elements in the sequence increment the index by more than one or look at elements in more than one position in each iteration.
In the lectures so far, we have only used while loops, and they are sufficient to solve all the problems in this lab. The syntax and execution of the for loop is described in the text books Downey: Section
Traversal with a for loop in Chapter 8; PunchEnbody: Sections 2.1.4 and 2.2.13.
Exercise 3a
The following function takes one argument, a sequence, and counts the number of elements in it that are negative. It is implemented using a while loop.
def countnegativesequence:
count0
index0
while indexlensequence:
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if sequenceindex0:
countcount1
indexindex1
return count
Note that the function will work on any sequence type e.g., both list and array, as long as it contains only numbers.
Rewrite this function so that it uses a for loop instead.
To test your function, you can use the following inputs:
1, 0, 2, 1, 3, 2 3 negative numbers
np.linspace2, 2, 5 2 negative numbers
np.arange53 3 negative numbers
np.sinnp.linspace0, 4np.pi, 50 25 negative numbersnp.zeros10 0 negative numbers
You can create more test cases by making variations of these, or using other arraycreating functions.
Exercise 3b
Write a function called isincreasing that takes a sequence of numbers and returns True iff the elements in the array are in nonstrict increasing order. This means that every element is less than or equal to the next one after it. For example,
for 1, 5, 9 the function should return Truefor 3, 3, 4 the function should return Truefor 3, 4, 2 the function should return False
Is it best to use a for loop or a while loop for this problem? Note: Downey describes different solutions to a very similar problem in Section Looping with Indices in Chapter 9.
Test your function with the examples above, and with the examples you used for exercise 3a.
Also test your function on an empty sequence that is, a list or array with no elements. An empty list can be created with the expressionand an empty array with np.array. Does your function work? Does it work on a sequence with one element?
Exercise 3c
The average or mean of a sequence of numbers is the sum of the numbers divided by the length of the sequence. You can calculate the average of a sequence of numbers using pythons builtin function sum which works on any sequence type, as long as it contains numbers, using the NumPy function np.mean if you convert the sequence to an array first, or writing your own function using a loop over the sequence
as was shown in the lecture.
Write a function mostaveragenumbers which finds and returns the number in the input that is closest to the average of the numbers. You can assume that the argument is a sequence of numbers. By closest, we mean the one that has the smallest absolute difference from the average. You can use the builtin function abs to find the absolute value of a difference. For example, mostaverage1, 2, 3,
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4, 5 should return 3 the average of the numbers in the list is 3.0, and 3 is clearly closest to this. mostaverage3, 4, 3, 1 should also return 3 the average is 2.75, and 3 is closer to 2.75 than is any other number in the list.
More debugging problems
Exercise 4
Here is a function that is meant to return the position index of a given element in a sequence; if the element does not appear in the sequence, it returns the length of the sequence. For example, findelement3,2,1,4, 1 should return 2, since that is the index where we find a 1.
def findelementsequence, element: i0
while sequencei ! element:
if ilensequence:
ii1 ii1
return i
However, the function is not correct. For some inputs it will cause a runtime error. Find an example of arguments that cause an error to occur. Can you correct the error without introducing another?
Programming problems
Note: These are more substantial programming problems. We do not expect that everyone will finish them within the lab time. If you do not have time to finish them during the lab, you should continue working on them later at home, in the CSIT labs after teaching hours, or on one of the computers available in the university libraries or other teaching spaces.
Closest matches
a Write two functions, smallestgreaterseq, value and greatestsmallerseq, value, that take as argument a sequence and a value, and find the smallest element in the sequence that is greater than the given value, and the greatest element in the sequence that is smaller than the given value, respectively.
For example, if the sequence is 13, 3, 22, 14, 2, 18, 17, 6, 9 and the target value is 4, then the smallest greater element is 6 and the greatest smaller element is 2.
You can assume that all elements in the sequence are of the same type as the target value that is, if the sequence is an array of numbers, then the target value is a number.
You can not assume that the elements of the sequence are in any particular order.
You should not assume that the sequence is of any particular type; it could be, for example, a NumPy array, a list, or some other sequence type. Use only operations on the sequence that are valid for all
sequence types.
What happens in your functions if the target value is smaller or greater than all elements in the
sequence?
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b Same as above, but assume the elements in the sequence are sorted in increasing order; can you find an algorithm that is more efficient in this case?
Counting duplicates
If the same value appears more than once in a sequence, we say that all copies of it except the first are duplicates. For example, in array1, 2, 4, 2, 0, 4, the second 2 and second 4 are duplicates; in the string Immaterium, the m is duplicated twice but the i is not a duplicate, because I and i are different characters.
Write a function countduplicatesseq that takes as argument a sequence and returns the number of duplicate elements for example, it should return 2 for both the sequences above. Your function should work on any sequence type for example, both arrays and strings, so use only operations that are common to all sequence types. For the purpose of deciding if an element is a duplicate, use standard equality, that is, theoperator.
Putting stuff in bins
A histogram is way of summarising 1dimensional data that is often used in descriptive statistics. Given a sequence of values, the range of values from smallest to greatest is divided into a number of sections called bins and the number of values that fall into each bin is counted. For example, if the sequence is array2.09, 0.5, 3.48, 1.44, 5.2, 2.86, 2.62, 6.31, and we make three bins by placing the dividing lines at 2 and 4, the resulting counts that is, the histogram will be the sequence 2, 4, 2,
because there are 2 elements less than 2, 4 elements between 2 and 4, and 2 elements4.
a Write a function countinbinvalues, lower, upper that takes as argument a sequence and two values that define the lower and upper sides of a bin, and counts the number of elements in the sequence that fall into this bin. You should treat the bin interval as open on the lower end and closed on the upper end; that is, use a strict comparison lowerelement for the lower end and a nonstrict comparison elementupper for the upper end.
b Write a function histogramvalues, dividers that takes as argument a sequence of values and a sequence of bin dividers, and returns the histogram as a sequence of a suitable type say, an array with the counts in each bin. The number of bins is the number of dividers1; the first bin has no lower limit and the last bin has no upper limit. As in a, elements that are equal to one of the dividers are counted in the bin below.
For example, suppose the sequence of values is the numbers 1,..,10 and the bin dividers are array2, 5, 7; the histogram should be array2, 3, 2, 3.
To test your function, you can create arrays of random values using NumPys random module:
In 1: import numpy.random as rnd
In 2: valuesrnd.normal0, 1, 50
This creates an array of 50 numbers drawn according to the normal distribution with mean 0 and standard deviation 1. The following creates 10 evenly sized bins covering the range of values:
In 1: import numpy as np
In 2: rangenp.maxvaluesnp.minvalues
9
In 3: dividersnp.arange1, 10range10np.minvalues
As you increase the size of the value array, you should find that the histogram becomes more symmetrical and more even.
You can also test your function by comparing it with the histogram function provided by NumPy see helpnumpy.histogram.
Advanced Slicing and array operations
Pythons builtin sequence types and NumPys array provide a mechanism, called slicing, to select parts of a sequence. It is done using the notation sequencestart:end. There is also an extended form of slicing, which takes three arguments, written sequencestart:end:step.
PunchEnbodys book has a detailed description of slicing, including its extended form, in Section 4.1.5 page 183. Downeys book discusses slicing in Section String Slices in Chapter 8; the extended form of slicing is only briefly mentioned in Exercise 83. We will come back to slicing in future labs, so if you do
not have time to cover it this week, thats ok.
To understand what the arguments in a slicing expression mean, you can try the following examples: In 1: myarraynp.arange0,20
In 2: Llenmyarray
In 3: myarray1:L
Out 3: …
In 4: myarray0:L1
Out 4: …
In 5: myarray0:L:2
Out 5: …
In 6: myarrayL:0:1
Out 6: …
In 7: myarray6:66
Out 7: …
In 8: myarray11:116:1
Out 8: …
In 9: myarray2L
Out 9: …
In 10: myarray0:2L
Out 10: …
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All of the slice expressions above should work the same on a list with the same elements. NumPy arrays also support two generalised forms of indexing:
If i is an array of integers, ai returns an array with the elements of a at the indices in i. All values in i must be valid indices for a, that is, between 0 and lena1.
If i is an array of Boolean values ai returns an array with the elements of a at positions where the value in i is True. The length of i must be equal to that of a.
Note that these forms of indexing do not work on other python sequence types such as lists.
Arithmethic operators , , , , , , , comparisons , !, , , ,and bitwise logical operatorsfor and,for or andfor not can all be applied to NumPy arrays. They all perform their operation elementwise. That is, if a and b are two arrays, cab is another array such that ciaibiforiintherange0tolena1. If aisanarrayandbisanonarrayvaluesuch as an int or a float, the operation is done between each element of a and b; again, the result is an array. For the operators that take two arguments all exceptand unaryif they are applied to two arrays, these have to be of the same length. Comparison operators , !, , , ,applied to arrays return arrays of Boolean values.
Exercise 5a advanced
Use the arange function, together with array operations, to write expresssions that construct the following
arrays:
An array with integers 0, .., n, followed by n1, .., 0.
An array of n alternating 1s and 1s, starting with a 1.An array of n alternating 1s and 1s, starting with a 1.
It may be helpful to know that:
When an arithmetic operation such asoris done on an array of Boolean values, these are
converted to integers False0 and True1.
If a and b are two arrays, the call np.concatenatea, b returns an array that contains the elements of a followed by the elements of b and thus its length is lenalenb. Note that there is an extra pair of parentheses around a,b in the function call; this is not a typo.
The function np.repeat can also be useful. Look it up using the help system! Exercise 5b advanced
As was demonstrated in last weeks lecture, with NumPy arrays it is sometimes possible to write complex operations very compactly. For example, counting negative values as in Exercise 3a above can be done with
def countnegativesequence:
return np.sumnp.arraysequence0
Can you use slicing, array operations and other NumPy functions to implement the isincreasing and mostaverage functions on arrays in a similar way?
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