Introduction to Arrays

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Mandatory Lesson Feedback Survey

Overview

Questions

  • What are different types of arrays?
  • How is data stored and retrieved from an array
  • Why nested arrays?
  • What are tuples?

Objectives

  • Understanding difference between lists and tuples.
  • Building concepts of operations on arrays.
  • knowing storing multidimensional data.
  • Understanding mutability and immutability.




So far, we have been using variables to store individual values. In some circumstances, we may need to access multiple values to perform operations. In such occasions, defining a variable for every single value can become very tedious. To address this, we use arrays.

Arrays are variables that hold any number of values. Python provides 3 types of built-in arrays: list, tuple, and set. There are a several common features amongst all arrays in Python; however, each type of array enjoys its own range of unique features that facilitate specific operations.

Remember

Each item inside an array may be referred to as a member or item of that array.

Lists

Resource for Lists

Lists are the most frequently used type of arrays in Python. It is therefore important to understand how they work, and that how can we use them and features they offer to our advantage.

The easiest way to imagine how a list works is to think of it as a table that can have any number of rows. This is akin to a spreadsheet with one column. For instance, suppose we have a table with 4 rows in a spreadsheet application as follows:

The number of rows in an array determine the length. The above table has 4 rows; therefore it is said to have a length of 4.

Implementation

Remember

To implement a list in Python, we place the values separated by commas inside square brackets [1, 2, 3, …].

PYTHON 

table = [5, 21, 5, -1]

print(table)

OUTPUT 

[5, 21, 5, -1]

PYTHON 

print(type(table))

OUTPUT 

<class 'list'>

Do it Yourself

Implement a list array called fibonacci, whose members represent the first 8 numbers of the Fibonacci sequence as follows:

FIBONACCI NUMBERS (FIRST 8)
1 1 2 3 5 8 13 21

PYTHON 

fibonacci = [1, 1, 2, 3, 5, 8, 13, 21]

Indexing

In arrays, an index is an integer number that corresponds to a specific item.

You can think of an index as a unique reference or a key that corresponds to a specific row in a table. We don’t always write the row number when we create a table. However, we always know that the 3rd row of a table means that we start from the first row (row #1), count 3 rows down and there we find the 3rd row.

The only difference in Python is that we don’t take the first row as row #1; instead, we consider it to be row #0. As a consequence of starting from #0, we count rows in our table down to row #2 instead of #3 to find the 3rd row. So our table may in essence be visualised as follows:

Remember

Python uses a zero-based indexing system. This means that the first row of an array, regardless of its type, is always #0.

With that in mind, we can use the index for each value to retrieve it from a list.

Given a list of 4 members stored in a variable called table:

table = [5, 21, 5, -1]
we can visualise the referencing protocol in Python as follows:

As demonstrated in the diagram; to retrieve a member of an array through its index, we write the name of the variable immediately followed by the index value inside a pair of square brackets — e.g. table[2].

PYTHON 

print(table[2])

OUTPUT 

5

PYTHON 

print(table[0])

OUTPUT 

5

PYTHON 

item = table[3]

print(item)

OUTPUT 

-1

Do it Yourself

Retrieve and display the 5th Fibonacci number from the list you created in previous DIY.

PYTHON 

print(fibonacci[4])

OUTPUT 

5

It is sometimes more convenient to index an array backwards — that is, to reference the members from the bottom of the array. This is called negative indexing and is particularly useful when we are dealing with very lengthy arrays. The indexing system in Python support both positive and negative indexing systems.

The table above therefore may also be represented as follows:

Remember

Unlike the normal indexing system, which starts from #0, negative indexes start from #-1 so that it will always be clear which indexing system is being used.

If the index is a negative number, the indices are counted from the end of the list. We can implement negative indices the same way we do positive ones:

PYTHON 

print(table[-1])

OUTPUT 

-1

PYTHON 

print(table[-2])

OUTPUT 

5

PYTHON 

print(table[-3])

OUTPUT 

21

We know that in table, index #-3 refers the same value as index #1. So let us go ahead and test this:

PYTHON 

equivalence = table[-3] == table[1]

print(equivalence)

OUTPUT 

True

If the index requested is larger than the length of the list minus one, an IndexError will be raised:

PYTHON 

print(table[4])

OUTPUT 

IndexError: list index out of range

Remember

The values stored in a list may be referred to as the members of that list.

Do it Yourself

Retrieve and display the last Fibonacci number from the list you created in DIY.

PYTHON 

print(fibonacci[-1])

OUTPUT 

21

Slicing

We may retrieve more than one value from a list at a time, as long as the values are in consecutive rows. This process is known as , and may be visualised as follows:

Remember

Python is a non-inclusive language. This means that in table[a:b], a slice includes all the values from, and including index a right down to, but excluding, index b.

Given a list representing the above table:

table = [5, 21, 5, -1]

we may retrieve a slice of table as follows:

PYTHON 

my_slice = table[1:3]

print(my_slice)

OUTPUT 

[21, 5]
print(table[0:2])

If the first index of a slice is #0, the slice may also be written as:

PYTHON 

print(table[:2])

OUTPUT 

[5, 21]

Negative slicing is also possible:

PYTHON 

# Retrieves every item from the first member down
# to, but excluding the last one:
print(table[:-1])

OUTPUT 

[5, 21, 5]

PYTHON 

print(table[1:-2])

OUTPUT 

[21]

If the second index of a slice represents the last index of a list, it be written as:

PYTHON 

print(table[2:])

OUTPUT 

[5, -1]

PYTHON 

print(table[-3:])

OUTPUT 

[21, 5, -1]

We may store indices and slices in variables:

PYTHON 

start, end = 1, 3
new_table = table[start:end]

print(new_table)

OUTPUT 

[21, 5]

The slice() function may also be used to create a slice variable:

PYTHON 

my_slice = slice(1, 3)

print(table[my_slice])

OUTPUT 

[21, 5]

Do it Yourself

Retrieve and display a slice of Fibonacci numbers from the list you created in DIY that includes all the members from the 2nd number onwards — i.e. the slice must not include the first value in the list.

PYTHON 

print(fibonacci[1:])

OUTPUT 

[1, 2, 3, 5, 8, 13, 21]

Note

Methods are features of Object-Oriented Programming (OOP), a programming paradigm that we do not discuss in the context of this course. You can think of a method as a function that is associated with a specific type. The job of a method is to provide a certain functionality unique to the type it is associated with. In this case, .index() is a method of type list that given a value, finds and produces its index from the list.

From value to index

Given a list entitled table as:

PYTHON 

table = [5, 21, 5, -1]

we can also find out the index of a specific value. To do so, we use the .index() method:

PYTHON 

print(table.index(21))

OUTPUT 

1

PYTHON 

last_item = table.index(-1)

print(last_item)

OUTPUT 

3

If a value is repeated more than once in the list, the index corresponding to the first instance of that value is returned:

PYTHON 

print(table.index(5))

OUTPUT 

0

If a value does not exist in the list, using .index() will raise a ValueError:

PYTHON 

print(table.index(9))

OUTPUT 

ValueError: 9 is not in list

Do it Yourself

Find and display the index of these values from the list of Fibonacci numbers that you created in DIY:

  • 1
  • 5
  • 21

PYTHON 

print(fibonacci.index(1))

print(fibonacci.index(5))

print(fibonacci.index(21))

OUTPUT 

0
4
7

Mutability

Arrays of type list are modifiable. That is, we can add new values, change the existing ones, or remove them from the array all together. Variable types that allow their contents to be modified are referred to as mutable types in programming.

Addition of new members

Given a list called table as:

We can add new values to table using .append():

PYTHON 

table.append(29)

print(table)

OUTPUT 

[5, 21, 5, -1, 29]

PYTHON 

table.append('a text')

print(table)

OUTPUT 

[5, 21, 5, -1, 29, 'a text']

Sometimes, it may be necessary to insert a value at a specific index in a list. To do so, we may use .insert(), which takes two input arguments; the first representing the index, and the second the value to be inserted:

PYTHON 

table.insert(3, 56)

print(table)

OUTPUT 

[5, 21, 5, 56, -1, 29, 'a text']

Do it Yourself

Given fibonacci the list representing the first 8 numbers in the Fibonacci sequence that you created in DIY:

  1. The 10th number in the Fibonacci sequence is 55. Add this value to fibonacci.

  2. Now that you have added 55 to the list, it no longer provides a correct representation of the Fibonacci sequence. Alter fibonacci and insert the missing number such that your it correctly represents the first 10 numbers in the Fibonacci sequence, as follows:

FIBONACCI NUMBERS (FIRST 8)
1 1 2 3 5 8 13 21 34 55

PYTHON 

fibonacci.append(55)

PYTHON 

fibonacci.insert(8, 34)

Modification of members

Given a list as:

PYTHON 

table = [5, 21, 5, 56, -1, 29, 'a text']

We can also modify the exiting value or values inside a list. This process is sometimes referred to as item assignment:

PYTHON 

# Changing the value of the 2nd member.

table[1] = 174

print(table)

OUTPUT 

[5, 174, 5, 56, -1, 29, 'a text']

PYTHON 

table[-4] = 19

print(table)

OUTPUT 

[5, 174, 5, 19, -1, 29, 'a text']

It is also possible to perform item assignment over a slice containing any number of values. Note that when modifying a slice, the replacement values must be the same length as the slice we are trying to replace:

PYTHON 

print('Before:', table)

replacement = [-38, 0]

print('Replacement length:', len(replacement))
print('Replacement length:', len(table[2:4]))

# The replacement process:
table[2:4] = replacement

print('After:', table)

OUTPUT 

Before: [5, 174, 5, 19, -1, 29, 'a text']
Replacement length: 2
Replacement length: 2
After: [5, 174, -38, 0, -1, 29, 'a text']

PYTHON 

# Using the existing value to determine the new value:
table[2] = table[2] + 50

print(table)

OUTPUT 

[5, 174, 12, 0, -1, 29, 'a text']

Do it Yourself

Given a list containing the first 10 prime numbers as:

primes = [2, 3, 5, 11, 7, 13, 17, 19, 23, 29]

However, values 11 and 7 have been misplaced in the sequence. Correct the order by replacing the slice of primes that represents [11, 7] with [7, 11].

PYTHON 

primes = [2, 3, 5, 11, 7, 13, 17, 19, 23, 29]

primes[3:5] = [7, 11]

Removal of members

When removing a value from a list array, we have two options depending on our needs: we either remove the member and retain the value in another variable, or we remove it and dispose of the value.

To remove a value from a list without retaining it, we use .remove(). The method takes one input argument, which is the value we would like to remove from our list:

PYTHON 

table.remove(174)

print(table)

OUTPUT 

[5, 12, 0, -1, 29, 'a text']

Alternatively, we can use del; a Python syntax that we can use in this context to delete a specific member using its index:

PYTHON 

del table[-1]

print(table)

OUTPUT 

[5, 12, 0, -1, 29]

As established above, we can also delete a member and retain its value. Of course we can do so by holding the value inside another variable before deleting it.

Whilst that is a valid approach, Python’s list provide us with .pop() to simplify the process even further. The method takes one input argument for the index of the member to be removed. It removes the member from the list and returns its value, so that we can retain it in a variable:

PYTHON 

removed_value = table.pop(2)

print('Removed value:', removed_value)
print(table)

OUTPUT 

Removed value: 0
[5, 12, -1, 29]

Do it Yourself

We know that the nucleotides of DNA include A, C, T, and G.

Given a list representing the nucleotides of a DNA strand as:

strand = ['A', 'C', 'G', 'G', 'C', 'M', 'T', 'A']

  1. Find the index of the invalid nucleotide in strand.

  2. Use the index you found to remove the invalid nucleotide from strand and retain the value in another variable. Display the result as:

Removed from the strand: X
New strand: [X, X, X, ...]
  1. What do you think happens once we run the following code, and why? What would be the final result displayed on the screen?
strand.remove('G')

print(strand)

PYTHON 

strand = ['A', 'C', 'G', 'G', 'C', 'M', 'T', 'A']

outlier_index = strand.index('M')

PYTHON 

outlier_value = strand.pop(outlier_index)

print('Removed from the strand:', outlier_value)
print('New strand:', strand)

OUTPUT 

Removed from the strand: M
New strand: ['A', 'C', 'G', 'G', 'C', 'T', 'A']

One of the two G nucleotides, the one at index 2 of the original array, is removed. This means that the .remove() method removes only first instance of a member in an array. The output would therefore be:

['A', 'C', 'G', 'C', 'M', 'T', 'A']

Method–mediated operations

We already know that methods are akin to functions that are associated with a specific type. In this subsection, we will be looking into how operations are performed using methods. To that end, we will not be introducing anything new, but recapitulate what we already know from different perspectives.

So far in this chapter, we have learned how to perform different operations on list arrays in Python. You may have noticed that some operations return a result that we can store in a variable, whilst others change the original value.

With that in mind, we can divide operations performed using methods into two general categories:

  1. Operations that return a result without changing the original array:

PYTHON 

table = [1, 2, 3, 4]

index = table.index(3)

print(index)
print(table)

OUTPUT 

2
[1, 2, 3, 4]
  1. Operations that use specific methods to change the original array, but do not necessarily return anything (in-place operations):

PYTHON 

table = [1, 2, 3, 4]

table.append(5)

print(table)

OUTPUT 

[1, 2, 3, 4, 5]

If we attempt to store the output of an operation that does not a return result inside a variable, the variable will be created, but its value will be set to None:

PYTHON 

result = table.append(6)

print(result)
print(table)

OUTPUT 

None
[1, 2, 3, 4, 5, 6]

It is important to know the difference between these types of operations. So as a rule of thumb, when we use methods to perform an operation, we can only change the original value if it is an instance of a mutable type. See Table to find out which built-in types are mutable in Python.

The methods that are associated with immutable objects always return the results and do not provide the ability to alter the original value:

  • In-place operation on a mutable object of type list:

PYTHON 

table = [5, 6, 7]

table.remove(6)

print(table)

OUTPUT 

[5, 7]
  • In-place operation on an immutable object of type str:

PYTHON 

string = '567'

string.remove(20)

OUTPUT 

AttributeError: 'str' object has no attribute 'remove'

PYTHON 

print(string)

OUTPUT 

567
  • Normal operation on a mutable object of type list:

PYTHON 

table = [5, 6, 7]

ind = table.index(6)

print(ind)

OUTPUT 

1
  • Normal operation on a mutable object of type list:

PYTHON 

string = '567'

ind = string.index('6')

print(ind)

OUTPUT 

1

List members

A list is a collection of members that are independent of each other. Each member has its own type, and is therefore subject to the properties and limitation of that type:

PYTHON 

table = [1, 2.1, 'abc']

print(type(table[0]))
print(type(table[1]))
print(type(table[2]))

OUTPUT 

<class 'int'>
<class 'float'>
<class 'str'>

For instance, mathematical operations may be considered a feature of all numeric types demonstrated in Table. However, unless in specific circumstance described in subsection Non-numeric values, such operations do not apply to instance of type str.

PYTHON 

table = [1, 2.1, 'abc']

table[0] += 1
table[-1] += 'def'

print(table)

OUTPUT 

[2, 2.1, 'abcdef']

Likewise, the list plays the role of a container that may incorporate any number of values. Thus far, we have learned how to handle individual members of a list. In this subsection, we will be looking at several techniques that help us address different circumstances where we look at a list from a ‘wholist’ perspective; that is, a container whose members are unknown to us.

Membership test

Membership test operations [advanced]

We can check to see whether or not a specific value is a member of a list using the operator syntax in:

PYTHON 

items = [1, 2.4, 'John', 5, 4]

print(2.4 in items)

OUTPUT 

True

PYTHON 

print(3 in items)

OUTPUT 

False

The results may be stored in a variable:

PYTHON 

has_five = 5 in items

print(has_five)

OUTPUT 

True

Similar to any other logical expression, we can negate membership tests by using :

PYTHON 

expr = 10 not in items

print(expr)

OUTPUT 

True

PYTHON 

expr = 5 not in items

print(expr)

OUTPUT 

False

Remember

When testing against str values — i.e. text; don’t forget that in programming, operations involving texts are always case-sensitive.

PYTHON 

items = [1, 2.4, 'John', 5, 4]

john_capital = 'John'
john_small = 'john'

print(john_capital in items)
print(john_small in items)

OUTPUT 

True
False

For numeric values, int and float may be used interchangeably:

PYTHON 

print(4 in items)

OUTPUT 

True

PYTHON 

print(4.0 in items)

OUTPUT 

True

Similar to other logical expression, membership tests may be incorporated into conditional statements:

PYTHON 

if 'John' in items:
    print('Hello John')
else:
    print('Hello')

OUTPUT 

Hello John

Do it Yourself

Given a list of randomly generated peptide sequences as:

PYTHON 

peptides = [
  'FAEKE', 'DMSGG', 'CMGFT', 'HVEFW', 'DCYFH', 'RDFDM', 'RTYRA',
  'PVTEQ', 'WITFR', 'SWANQ', 'PFELC', 'KSANR', 'EQKVL', 'SYALD',
  'FPNCF', 'SCDYK', 'MFRST', 'KFMII', 'NFYQC', 'LVKVR', 'PQKTF',
  'LTWFQ', 'EFAYE', 'GPCCQ', 'VFDYF', 'RYSAY', 'CCTCG', 'ECFMY',
  'CPNLY', 'CSMFW', 'NNVSR', 'SLNKF', 'CGRHC', 'LCQCS', 'AVERE',
  'MDKHQ', 'YHKTQ', 'HVRWD', 'YNFQW', 'MGCLY', 'CQCCL', 'ACQCL'
  ]

Determine whether or not each of the following sequences exist in peptides; and if so, what is their corresponding index:

  • IVADH
  • CMGFT
  • DKAKL
  • THGYP
  • NNVSR

Display the results in the following format:

Sequence XXXXX was found at index XX

PYTHON 

sequence = "IVADH"
if sequence in peptides:
    index = peptides.index(sequence)
    print('Sequence', sequence, 'was found at index',  index)

PYTHON 

sequence = "CMGFT"
if sequence in peptides:
    index = peptides.index(sequence)
    print('Sequence', sequence, 'was found at index',  index)

OUTPUT 

Sequence CMGFT was found at index 2

PYTHON 

sequence = "DKAKL"
if sequence in peptides:
    index = peptides.index(sequence)
    print('Sequence', sequence, 'was found at index',  index)

PYTHON 

sequence = "THGYP"
if sequence in peptides:
    index = peptides.index(sequence)
    print('Sequence', sequence, 'was found at index',  index)

PYTHON 

sequence = "NNVSR"
if sequence in peptides:
    index = peptides.index(sequence)
    print('Sequence', sequence, 'was found at index',  index)

OUTPUT 

Sequence NNVSR was found at index 30

Length

Built-in functions: len

The number of members contained within a list defines its length. Similar to the length of str values as discussed in mathematical operations DIY-I and DIY-IV, we use the built-in function len() also to determine the length of a list:

PYTHON 

items = [1, 2.4, 'John', 5, 4]

print(len(items))

OUTPUT 

5

PYTHON 

print(len([1, 5, 9]))

OUTPUT 

3

The len() function always returns an integer value (int) equal to or greater than zero. We can store the length in a variable and use it in different mathematical or logical operations:

PYTHON 

table = [1, 2, 3, 4]
items_length = len(items)
table_length = len(table)

print(items_length + table_length)

OUTPUT 

9

PYTHON 

print(len(table) > 2)

OUTPUT 

True

We can also use the length of an array in conditional statements:

PYTHON 

students = ['Julia', 'John', 'Jane', 'Jack']
present = ['Julia', 'John', 'Jane', 'Jack', 'Janet']

if len(present) == len(students):
    print('All the students are here.')
else:
    print('One or more students are not here yet.')

OUTPUT 

One or more students are not here yet.

Remember

Both in and len() may be used in reference to any type of array or sequence in Python.

See Table to find out which built-in types in Python are regarded as a sequence.

Do it Yourself

Given the list of random peptides defined in DIY:

  1. Define a list called overlaps containing the sequences whose presence in peptides you confirmed in DIY.
  2. Determine the length of peptides.
  3. Determine the length of overlaps.

Display yours results as follows:

overlaps = ['XXXXX', 'XXXXX', ...]
Length of peptides: XX
Length of overlaps: XX

PYTHON 

overlaps = list()

sequence = "IVADH"
if sequence in peptides:
    overlaps.append(sequence)

sequence = "CMGFT"
if sequence in peptides:
    overlaps.append(sequence)

sequence = "DKAKL"
if sequence in peptides:
    overlaps.append(sequence)

sequence = "THGYP"
if sequence in peptides:
    overlaps.append(sequence)

sequence = "NNVSR"
if sequence in peptides:
    overlaps.append(sequence)

print('overlaps:', overlaps)

OUTPUT 

overlaps: ['CMGFT', 'NNVSR']

PYTHON 

print('Length of peptides:', len(peptides))

OUTPUT 

Length of peptides: 42

PYTHON 

print('Length of overlaps:', len(overlaps))

OUTPUT 

Length of overlaps: 2

Weak References and Copies

In our discussion on mutability, we also discussed some of the in-place operations such as .remove() and .append() that we use to modify an existing list. The use of these operations gives rise the following question: What if we need to perform an in-place operation, but also want to preserve the original array?

In such cases, we create a deep copy of the original array before we call the method and perform the operation.

Suppose we have:

PYTHON 

table_a = [1, 2, 3, 4]

A weak reference for table_a, also referred to as an alias or a symbolic link, may be defined as follows:

PYTHON 

table_b = table_a

print(table_a, table_b)

OUTPUT 

[1, 2, 3, 4] [1, 2, 3, 4]

Now if we perform an in-place operation on only one of the two variables (the original or the alias) as follows:

PYTHON 

table_a.append(5)

we will in effect change both of them:

PYTHON 

print(table_a, table_b)

OUTPUT 

[1, 2, 3, 4, 5] [1, 2, 3, 4, 5]

This is useful if we need to change the name of a variable under certain conditions to make our code more explicit and readable; however, it does nothing to preserve an actual copy of the original data.

To retain a copy of the original array, however, we must perform a deep copy as follows:

PYTHON 

table_c = table_b.copy()

print(table_b, table_c)

OUTPUT 

[1, 2, 3, 4, 5] [1, 2, 3, 4, 5]

where table_c represents a deep copy of table_b.

In this instance, performing an in-place operation on one variable would not have any impacts on the other one:

PYTHON 

table_b.append(6)

print(table_a, table_b, table_c)

OUTPUT 

[1, 2, 3, 4, 5, 6] [1, 2, 3, 4, 5, 6] [1, 2, 3, 4, 5]

where both the original array and its weak reference (table_a and table_b) changed without influencing the deep copy (table_c).

There is also a shorthand for the .copy() method to create a deep copy. As far as arrays of type list are concerned, writing:

new_table = original_table[:]

is exactly the same as writing:

new_table = original_table.copy()

Here is an example:

PYTHON 

table_a = ['a', 3, 'b']
table_b = table_a
table_c = table_a.copy()
table_d = table_a[:]

table_a[1] = 5

print(table_a, table_b, table_c, table_d)

OUTPUT 

['a', 5, 'b'] ['a', 5, 'b'] ['a', 3, 'b'] ['a', 3, 'b']

Whilst both the original array and its weak reference (table_a and table_b) changed in this example; the deep copies (table_c and table_d) have remained unchanged.

Do it Yourself

When defining a consensus sequence, it is common to include annotations to represent ambiguous amino acids. Four such annotations are as follows:

Given a list of amino acids as:

PYTHON 

amino_acids = [
    'A', 'R', 'N', 'D', 'C', 'E', 'Q', 'G', 'H', 'I',
    'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V'
  ]

  1. Use amino_acids to create an independent list called amino_acids_annotations that contains all the standard amino acids.

  1. Add to amino_acids_annotations the 1-letter annotations for the ambiguous amino acids as outlined in the table.

  1. Evaluate the lengths for amino_acids and amino_acids_annotations and retain the result in a new list called lengths.

  1. Using logical operations, test the two values stored in lengths for equivalence and display the result as a boolean (i.e. True or False) output.

PYTHON 

amino_acid_annotations = amino_acids.copy()

PYTHON 

ambiguous_annotations = ['X', 'B', 'Z', 'J']

amino_acid_annotations.extend(ambiguous_annotations)

PYTHON 

lengths = [len(amino_acids), len(amino_acid_annotations)]

PYTHON 

equivalence = lengths[0] == lengths[1]

print(equivalence)

OUTPUT 

False

Conversion to list

As highlighted earlier in the section, arrays in Python can contain any value regardless of type. We can exploit this feature to extract some interesting information about the data we store in an array.

To that end, we can convert any sequence to a list. See Table to find out which of the built-in types in Python are considered to be a sequence.

Suppose we have the sequence for Protein Kinase A Gamma (catalytic) subunit for humans as follows:

PYTHON 

# Multiple lines of text may be split into
# several lines inside parenthesis:

human_pka_gamma = (
  'MAAPAAATAMGNAPAKKDTEQEESVNEFLAKARGDFLYRWGNPAQNTASSDQFERLRTLGMGSFGRVMLV'
  'RHQETGGHYAMKILNKQKVVKMKQVEHILNEKRILQAIDFPFLVKLQFSFKDNSYLYLVMEYVPGGEMFS'
  'RLQRVGRFSEPHACFYAAQVVLAVQYLHSLDLIHRDLKPENLLIDQQGYLQVTDFGFAKRVKGRTWTLCG'
  'TPEYLAPEIILSKGYNKAVDWWALGVLIYEMAVGFPPFYADQPIQIYEKIVSGRVRFPSKLSSDLKDLLR'
  'SLLQVDLTKRFGNLRNGVGDIKNHKWFATTSWIAIYEKKVEAPFIPKYTGPGDASNFDDYEEEELRISIN'
  'EKCAKEFSEF'
	)

print(type(human_pka_gamma))

OUTPUT 

<class 'str'>

We can now convert our sequence from its original type of str to list by using list() as a function. Doing so will automatically decompose the text down to individual characters:

PYTHON 

# The function "list" may be used to convert string
# variables into a list of characters:
pka_list = list(human_pka_gamma)

print(pka_list)

OUTPUT 

['M', 'A', 'A', 'P', 'A', 'A', 'A', 'T', 'A', 'M', 'G', 'N', 'A', 'P', 'A', 'K', 'K', 'D', 'T', 'E', 'Q', 'E', 'E', 'S', 'V', 'N', 'E', 'F', 'L', 'A', 'K', 'A', 'R', 'G', 'D', 'F', 'L', 'Y', 'R', 'W', 'G', 'N', 'P', 'A', 'Q', 'N', 'T', 'A', 'S', 'S', 'D', 'Q', 'F', 'E', 'R', 'L', 'R', 'T', 'L', 'G', 'M', 'G', 'S', 'F', 'G', 'R', 'V', 'M', 'L', 'V', 'R', 'H', 'Q', 'E', 'T', 'G', 'G', 'H', 'Y', 'A', 'M', 'K', 'I', 'L', 'N', 'K', 'Q', 'K', 'V', 'V', 'K', 'M', 'K', 'Q', 'V', 'E', 'H', 'I', 'L', 'N', 'E', 'K', 'R', 'I', 'L', 'Q', 'A', 'I', 'D', 'F', 'P', 'F', 'L', 'V', 'K', 'L', 'Q', 'F', 'S', 'F', 'K', 'D', 'N', 'S', 'Y', 'L', 'Y', 'L', 'V', 'M', 'E', 'Y', 'V', 'P', 'G', 'G', 'E', 'M', 'F', 'S', 'R', 'L', 'Q', 'R', 'V', 'G', 'R', 'F', 'S', 'E', 'P', 'H', 'A', 'C', 'F', 'Y', 'A', 'A', 'Q', 'V', 'V', 'L', 'A', 'V', 'Q', 'Y', 'L', 'H', 'S', 'L', 'D', 'L', 'I', 'H', 'R', 'D', 'L', 'K', 'P', 'E', 'N', 'L', 'L', 'I', 'D', 'Q', 'Q', 'G', 'Y', 'L', 'Q', 'V', 'T', 'D', 'F', 'G', 'F', 'A', 'K', 'R', 'V', 'K', 'G', 'R', 'T', 'W', 'T', 'L', 'C', 'G', 'T', 'P', 'E', 'Y', 'L', 'A', 'P', 'E', 'I', 'I', 'L', 'S', 'K', 'G', 'Y', 'N', 'K', 'A', 'V', 'D', 'W', 'W', 'A', 'L', 'G', 'V', 'L', 'I', 'Y', 'E', 'M', 'A', 'V', 'G', 'F', 'P', 'P', 'F', 'Y', 'A', 'D', 'Q', 'P', 'I', 'Q', 'I', 'Y', 'E', 'K', 'I', 'V', 'S', 'G', 'R', 'V', 'R', 'F', 'P', 'S', 'K', 'L', 'S', 'S', 'D', 'L', 'K', 'D', 'L', 'L', 'R', 'S', 'L', 'L', 'Q', 'V', 'D', 'L', 'T', 'K', 'R', 'F', 'G', 'N', 'L', 'R', 'N', 'G', 'V', 'G', 'D', 'I', 'K', 'N', 'H', 'K', 'W', 'F', 'A', 'T', 'T', 'S', 'W', 'I', 'A', 'I', 'Y', 'E', 'K', 'K', 'V', 'E', 'A', 'P', 'F', 'I', 'P', 'K', 'Y', 'T', 'G', 'P', 'G', 'D', 'A', 'S', 'N', 'F', 'D', 'D', 'Y', 'E', 'E', 'E', 'E', 'L', 'R', 'I', 'S', 'I', 'N', 'E', 'K', 'C', 'A', 'K', 'E', 'F', 'S', 'E', 'F']

Do it Yourself

Ask the user to enter a sequence of single-letter amino acids in lower case. Convert the sequence to list and:

  1. Count the number of serine and threonine residues and display the result in the following format:
Total number of serine residues: XX
Total number of threonine residues: XX
  1. Check whether or not the sequence contains both serine and threonine residues:
  • If it does, display:
The sequence does contain both serine and threonine residues.
  • if it does not, display:
The sequence does not contain both serine and threonine residues.
sequence_str = input('Please enter a sequence of signle-letter amino acids in lower-case: ')

sequence = list(sequence_str)

ser_count = sequence.count('s')
thr_count = sequence.count('t')

print('Total number of serine residues:', ser_count)

print('Total number of threonine residues:', thr_count)
if ser_count > 0 and thr_count > 0:
    response_state = ''
else:
    response_state = 'not'

print(
    'The sequence does',
    'response_state',
    'contain both serine and threonine residues.'
		)

Advanced Topic

Generators represent a specific type in Python whose results are not immediately evaluated. This is a technique referred to as lazy evaluation in functional programming, and is often used in the context of a for-loop. This is because they postpone the evaluation of their results for as long as possible. We do not discuss generators in the course, but you can find out more about them in the official documentations.

Useful methods

Data Structures: More on Lists

In this subsection, we will be reviewing some of the useful and important methods that are associated with object of type list. To that end, we shall use snippets of code that exemplify such methods in practice. A cheatsheet of the methods associated with the built-in arrays in Python can be helpful.

Commons operations for list, tuple, and set arrays in Python.
Commons operations for list, tuple, and set arrays in Python.

The methods outline here are not individually described; however, at this point, you should be able to work out what they do by looking at their names and respective examples.

Count a specific value within a list:

PYTHON 

table_a = [1, 2, 2, 2]
table_b = [15, 16]

print(table_a.count(2))

OUTPUT 

3

Extend a list:

PYTHON 

table_a = [1, 2, 2, 2]
table_b = [15, 16]

table_c = table_a.copy()  # deep copy.
table_c.extend(table_b)

print(table_a, table_b, table_c)

OUTPUT 

[1, 2, 2, 2] [15, 16] [1, 2, 2, 2, 15, 16]

Extend a list by adding two lists to each other. Note that adding two lists is not an in-place operation:

PYTHON 

table_a = [1, 2, 2, 2]
table_b = [15, 16]

table_c = table_a + table_b

print(table_a, table_b, table_c)

OUTPUT 

[1, 2, 2, 2] [15, 16] [1, 2, 2, 2, 15, 16]

PYTHON 

table_a = [1, 2, 2, 2]
table_b = [15, 16]

table_c = table_a.copy()  # deep copy.
table_d = table_a + table_b

print(table_c == table_d)

OUTPUT 

False

We can also reverse the values in a list. There are two methods for doing so:Being a generator means that the output of the function is not evaluated immediately; and instead, we get a generic output:

  1. Through an in-place operation using .reverse()

PYTHON 

table = [1, 2, 2, 2, 15, 16]
table.reverse()

print("Reversed:", table)

OUTPUT 

Reversed: [16, 15, 2, 2, 2, 1]
  1. Through reversed(), which is a build-in generator function.

PYTHON 

table = [1, 2, 2, 2, 15, 16]
table_rev = reversed(table)

print("Result:", table_rev)
print("Type:", type(table_rev))

OUTPUT 

Result: <list_reverseiterator object at 0x7f858532f790>
Type: <class 'list_reverseiterator'>

We can, however, force the evaluation process by converting the generator results onto a list:

PYTHON 

table_rev_evaluated = list(table_rev)

print('Evaluated:', table_rev_evaluated)

OUTPUT 

Evaluated: [16, 15, 2, 2, 2, 1]

Members of a list may be sorted in-place as follows:

PYTHON 

table = [16, 2, 15, 1, 2, 2]
table.sort()

print("Sorted (ascending):", table)

OUTPUT 

Sorted (ascending): [1, 2, 2, 2, 15, 16]

Advanced Topic

There is also the built-in function sorted() that works in a similar way to reversed(). Also a generator function, it offers more advanced features that are beyond the scope of this course. You can find out more about it from the official documentations and examples.

The .sort() method takes an optional keyword argument entitled reverse (default: False). If set to True, the method will perform a descending sort:

PYTHON 

table = [16, 2, 15, 1, 2, 2]
table.sort(reverse=True)

print("Sorted (descending):", table)

OUTPUT 

Sorted (descending): [16, 15, 2, 2, 2, 1]

We can also create an empty list, so that we can add members to it later in our code using .append() or .extend() amongst other tools:

PYTHON 

table = list()

print(table)

OUTPUT 

[]

PYTHON 

table.append(5)

print(table)

OUTPUT 

[5]

PYTHON 

another_table = ['Jane', 'Janette']
table.extend(another_table)

print(another_table)

OUTPUT 

['Jane', 'Janette']

Do it Yourself

Create a list, and experiment with each of the methods provided in the above example. Try including members of different types in your list and see how each of these methods behave.

This DIY was intended to encourage you to experiment with the methods outlined.

Nested arrays

At this point, you should be comfortable with creating, handling, and manipulating arrays of type list in Python. It is important to have a relatively good understanding of the principles outlined in this section so far before you start learning about nested arrays.

We have already established that arrays can contain any value regardless of type. This means that they also contain other arrays. An array that includes at least one member that is itself an array is referred to as a nested arrays. This can be thought of as a table with more than one column:

Remember

Arrays can contain values of any type. This rule applies to nested arrays too. We have exclusively included int numbers in our table to trivialise that example.

Implementation

The table can be written in Python as a nested array:

PYTHON 

# The list has 3 members, 2 of which
# are arrays of type list:
table = [[1, 2, 3], 4, [7, 8]]

print(table)

OUTPUT 

[[1, 2, 3], 4, [7, 8]]

Indexing

The indexing principles for nested arrays is slightly different. To retrieve an individual member in a nested list, we always reference the row index, followed by the column index.

We may visualise the process as follows:

To retrieve an entire row, we only need to include the reference for that row:

PYTHON 

print(table[0])

OUTPUT 

[1, 2, 3]

and to retrieve a specific member, we include the reference for both the row and column:

PYTHON 

print(table[0][1])

OUTPUT 

2

We may also extract slices from a nested array. The protocol is identical to normal arrays described in subsection slicing. In nested arrays, however, we may take slices from the columns as well as the rows:

PYTHON 

print(table[:2])

OUTPUT 

[[1, 2, 3], 4]

PYTHON 

print(table[0][:2])

OUTPUT 

[1, 2]

Note that only 2 of the 3 members in table are arrays of type list:

PYTHON 

print(table[0], type(table[0]))

OUTPUT 

[1, 2, 3] <class 'list'>

PYTHON 

print(table[2], type(table[2]))

OUTPUT 

[7, 8] <class 'list'>

However, there is another member that is not an array:

PYTHON 

print(table[1], type(table[1]))

OUTPUT 

4 <class 'int'>

In most circumstances, we would want all the members in an array to be homogeneous in type — i.e. we want them all to have the same type. In such cases, we can implement the table as:

PYTHON 

table = [[1, 2, 3], [4], [7, 8]]

print(table[1], type(table[1]))

OUTPUT 

[4] <class 'list'>

An array with only one member — e.g. [4], is sometimes referred to as a singleton array.

Do it Yourself

Give then following of pathogens and their corresponding diseases:

  1. Substituting N/A for None, create an array to represent the table in the original order. Retain the array in a variable and display the result.

  1. Modify the array you created so that the members are sorted descendingly and display the result.

PYTHON 

disease_pathogen = [
  ["Bacterium", "Negative", "Shigella flexneri" , "Bacillary dysentery"],
  ["Prion", None, "PrP(sc)", "Transmissible spongiform encephalopathies"],
  ["Bacterium", "Negative", "Vibrio cholerae", "Cholera"],
  ["Bacterium", "Negative", "Listeria monocytogenes", "Listeriosis"],
  ["Virus", None, "Hepatitis C", "Hepatitis"],
  ["Bacterium", "Negative", "Helicobacter pylori", "Peptic ulcers"],
  ["Bacterium", "Negative", "Mycobacterium tuberculosis", "Tuberculosis"],
  ["Bacterium", "Negative", "Chlamydia trachomatis", "Chlamydial diseases"],
  ["Virus", None, "Human Immunodeficiency Virus", "Human Immunodeficiency"]
  ]

print(disease_pathogen)

OUTPUT 

[['Bacterium', 'Negative', 'Shigella flexneri', 'Bacillary dysentery'], ['Prion', None, 'PrP(sc)', 'Transmissible spongiform encephalopathies'], ['Bacterium', 'Negative', 'Vibrio cholerae', 'Cholera'], ['Bacterium', 'Negative', 'Listeria monocytogenes', 'Listeriosis'], ['Virus', None, 'Hepatitis C', 'Hepatitis'], ['Bacterium', 'Negative', 'Helicobacter pylori', 'Peptic ulcers'], ['Bacterium', 'Negative', 'Mycobacterium tuberculosis', 'Tuberculosis'], ['Bacterium', 'Negative', 'Chlamydia trachomatis', 'Chlamydial diseases'], ['Virus', None, 'Human Immunodeficiency Virus', 'Human Immunodeficiency']]

PYTHON 

disease_pathogen.sort(reverse=True)

print(disease_pathogen)

OUTPUT 

[['Virus', None, 'Human Immunodeficiency Virus', 'Human Immunodeficiency'], ['Virus', None, 'Hepatitis C', 'Hepatitis'], ['Prion', None, 'PrP(sc)', 'Transmissible spongiform encephalopathies'], ['Bacterium', 'Negative', 'Vibrio cholerae', 'Cholera'], ['Bacterium', 'Negative', 'Shigella flexneri', 'Bacillary dysentery'], ['Bacterium', 'Negative', 'Mycobacterium tuberculosis', 'Tuberculosis'], ['Bacterium', 'Negative', 'Listeria monocytogenes', 'Listeriosis'], ['Bacterium', 'Negative', 'Helicobacter pylori', 'Peptic ulcers'], ['Bacterium', 'Negative', 'Chlamydia trachomatis', 'Chlamydial diseases']]

Dimensions

A nested array is considered two dimensional or 2D when:

  • all of the members in a nested array are arrays themselves;

  • all of the sub-arrays have the same length — i.e. all the columns in the table are filled and have the same number of rows; and,

  • all of the members of the sub-arrays are homogeneous in type — i.e. they all have the same type (e.g. int).

A two dimensional arrays may be visualised as follows:

Advanced Topic

Nested arrays may themselves be nested. This means that, if needed, we can have 3, 4 or n dimensional arrays, too. Analysis and organisation of such arrays is an important part of a field known as optimisation in computer science and mathematics. Optimisation is itself the cornerstone of machine learning, and addresses the problem known as curse of dimensionality.

Such arrays are referred to in mathematics as a matrix. We can therefore represent a two-dimensional array as a mathematical matrix. To that end, the above array would translate to the annotation displayed in equation below.

\[table=\begin{bmatrix} 1&2&3\\ 4&5&6\\ 7&8&9\\ \end{bmatrix}\]

The implementation of these arrays is identical to the implementation of other nested arrays. We can therefore code our table in Python as:

PYTHON 

table = [
  [1, 2, 3],
  [4, 5, 6],
  [7, 8, 9]
  ]

print(table)

OUTPUT 

[[1, 2, 3], [4, 5, 6], [7, 8, 9]]

PYTHON 

print(table[2])

OUTPUT 

[7, 8, 9]

PYTHON 

print(table[1][0])

OUTPUT 

4

PYTHON 

print(table[:2])

OUTPUT 

[[1, 2, 3], [4, 5, 6]]

Do it Yourself

Computers see images as multidimensional arrays (matrices). In its simplest form, an image is a two-dimensional array containing only 2 colours.

Given the following black and white image:

  1. Considering that black and white squares represent zeros and ones respectively, create a two-dimensional array to represent the above image. Display the results.

  1. Create a new array, but this time use False and True to represent black and white respectively.

Display the results.

PYTHON 

cross = [
		    [0, 0, 0, 0, 0, 0, 0],
		    [0, 1, 0, 0, 0, 1, 0],
		    [0, 0, 1, 0, 1, 0, 0],
		    [0, 0, 0, 1, 0, 0, 0],
		    [0, 0, 1, 0, 1, 0, 0],
		    [0, 1, 0, 0, 0, 1, 0],
		    [0, 0, 0, 0, 0, 0, 0]
		]

print(cross)

OUTPUT 

[[0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0]]

PYTHON 

cross_bool = [
  [False, False, False, False, False, False, False],
  [False, True, False, False, False, True, False],
  [False, False, True, False, True, False, False],
  [False, False, False, True, False, False, False],
  [False, False, True, False, True, False, False],
  [False, True, False, False, False, True, False],
  [False, False, False, False, False, False, False]
  ]

print(cross_bool)

OUTPUT 

[[False, False, False, False, False, False, False], [False, True, False, False, False, True, False], [False, False, True, False, True, False, False], [False, False, False, True, False, False, False], [False, False, True, False, True, False, False], [False, True, False, False, False, True, False], [False, False, False, False, False, False, False]]

Summary

At this point, you should be familiar with arrays and how they work in general. Throughout this section, we talked about list, which is one the most popular types of built-in arrays in Python. To that end, we learned:

  • how to list from the scratch;

  • how to manipulate list using different methods;

  • how to use indexing and slicing techniques to our advantage;

  • mutability — a concept we revisit in the forthcoming lessons;

  • in-place operations, and the difference between weak references and deep copies;

  • nested and multi-dimensional arrays; and,

  • how to convert other sequences (e.g. str) to list.

Tuple

Data Structures: Tuples and Sequences

Another type of built-in arrays, tuple is an immutable alternative to list. That is, once created, the contents may not be modified in any way. One reason we use tuples is to ensure that the contents of our array does not change accidentally.

For instance, we know that in the Wnt signaling pathway, there are two co-receptors. This is final, and would not change at any point in our programme.

Remember

To implement a tuple in Python, we place our values separated by commas inside parenthesis or (1, 2, 3, …).

PYTHON 

pathway = 'Wnt Signaling'
coreceptors = ('Frizzled', 'LRP')

print(type(coreceptors))

OUTPUT 

<class 'tuple'>

PYTHON 

print(coreceptors)

OUTPUT 

('Frizzled', 'LRP')

PYTHON 

wnt = (pathway, coreceptors)

print(type(wnt))

OUTPUT 

<class 'tuple'>

PYTHON 

print(wnt)

OUTPUT 

('Wnt Signaling', ('Frizzled', 'LRP'))

PYTHON 

print(wnt[0])

OUTPUT 

Wnt Signaling

Indexing and slicing principles for tuple is identical to list, which we discussed in subsection indexing and slicing respectively.

Conversion to tuple

Similar to list, we can convert other sequences to tuple:

PYTHON 

numbers_list = [1, 2, 3, 4, 5]

print(type(numbers_list))

OUTPUT 

<class 'list'>

PYTHON 

numbers = tuple(numbers_list)

print(numbers)

OUTPUT 

(1, 2, 3, 4, 5)

PYTHON 

print(type(numbers))

OUTPUT 

<class 'tuple'>

PYTHON 

text = 'This is a string.'

print(type(text))

OUTPUT 

<class 'str'>

PYTHON 

characters = tuple(text)

print(characters)

OUTPUT 

('T', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 's', 't', 'r', 'i', 'n', 'g', '.')

PYTHON 

print(type(characters))

OUTPUT 

<class 'tuple'>

Immutability

In contrast with list, however, if we attempt to change the contents of a tuple, a TypeError is raised:

PYTHON 

coreceptors[1] = 'LRP5/6'

OUTPUT 

TypeError: 'tuple' object does not support item assignment

Even though tuple is an immutable type, it can contain both mutable and immutable objects:

PYTHON 

# (immutable, immutable, immutable, mutable)
mixed_tuple = (1, 2.5, 'abc', (3, 4), [5, 6])

print(mixed_tuple)

OUTPUT 

(1, 2.5, 'abc', (3, 4), [5, 6])

and mutable objects inside a tuple may still be changed:

PYTHON 

print(mixed_tuple, type(mixed_tuple))

OUTPUT 

(1, 2.5, 'abc', (3, 4), [5, 6]) <class 'tuple'>

PYTHON 

print(mixed_tuple[4], type(mixed_tuple[4]))

OUTPUT 

[5, 6] <class 'list'>

Advanced Topic

Why / how can we change mutable objects inside a tuple when it is immutable? Members of a tuple or not directly stored in the memory. An immutable value (e.g. an int) has an existing, predefined reference in the memory. When used in a tuple, it is that reference that is associated with the tuple, and not the value itself. On the other hand, a mutable object does not have a predefined reference in the memory and is instead created on request somewhere in the memory (wherever there is enough free space). Whilst we can never change or redefine predefined references, we can always manipulate something we have defined ourselves. When we make such an alteration, the location of our mutable object in the memory may well change, but its reference — which is what is stored in a tuple, remains identical. You can find out what is the reference an object in Python using the function id(). If you experiment with it, you will notice that the reference to an immutable object (e.g. an int value) would never change, no matter how many time you define it in a different context or variable. In contrast, the reference number to a mutable object (e.g. a list) changes every time it is defined, even if it contains exactly the same values.

PYTHON 

# Lists are mutable, so we can alter their values:
mixed_tuple[4][1] = 15

print(mixed_tuple)

OUTPUT 

(1, 2.5, 'abc', (3, 4), [5, 15])

PYTHON 

mixed_tuple[4].append(25)

print(mixed_tuple)

OUTPUT 

(1, 2.5, 'abc', (3, 4), [5, 15, 25])

PYTHON 

# We cannot remove the list from the tuple,
# but we can empty it by clearing its members:
mixed_tuple[4].clear()

print(mixed_tuple)

OUTPUT 

(1, 2.5, 'abc', (3, 4), [])

Tuples may be empty or have a single value (singleton):

PYTHON 

member_a = tuple()

print(member_a, type(member_a), len(member_a))

OUTPUT 

() <class 'tuple'> 0

PYTHON 

# Empty parentheses also generate an empty tuple.
# Remember: we cannot add values to an empty tuple later.
member_b = ()

print(member_b, type(member_b), len(member_b))

OUTPUT 

() <class 'tuple'> 0

PYTHON 

# Singleton - Note that it is essential to include
# a comma after the value in a single-member tuple:
member_c = ('John Doe',)

print(member_c, type(member_c), len(member_c))

OUTPUT 

('John Doe',) <class 'tuple'> 1

PYTHON 

# If the comma is not included, a singleton tuple
# is not constructed:
member_d = ('John Doe')

print(member_d, type(member_d), len(member_d))

OUTPUT 

John Doe <class 'str'> 8

Packing and unpacking

A tuple may be constructed without parenthesis. This is an implicit operation and is known as packing.

Remember

Implicit processes must be used sparingly. As always, the more coherent the code, the better it is.

PYTHON 

numbers = 1, 2, 3, 5, 7, 11

print(numbers, type(numbers), len(numbers))

OUTPUT 

(1, 2, 3, 5, 7, 11) <class 'tuple'> 6

PYTHON 

# Note that for a singleton, we still need to
# include the comma.
member = 'John Doe',

print(member, type(member), len(member))

OUTPUT 

('John Doe',) <class 'tuple'> 1

The reverse of this process is known as unpacking. Unpacking is no longer considered an implicit process because it replaces unnamed values inside an array, with named variables:

PYTHON 

dimensions = 14, 17, 12

x, y, z = dimensions

print(x)

OUTPUT 

14

PYTHON 

print(x, y)

OUTPUT 

14 17

PYTHON 

member = ('Jane Doe', 28, 'London', 'Student', 'Female')
name, age, city, status, gender = member

print('Name:', name, '- Age:', age)

OUTPUT 

Name: Jane Doe - Age: 28

Do it Yourself

Given:

PYTHON 

protein_info = ('GFP', 238)

Unpack protein_info into two distinct variables protein_name and protein_length.

PYTHON 

protein_name, protein_length = protein_info

Note

There is another type of tuple in Python entitled namedtuple. It allows for the members of a tuple to be named independently (e.g. member.name or member.age), and thereby eliminates the need for unpacking. It was originally implemented by Raymond Hettinger, one of Python’s core developers, for Python 2.4 (in 2004) but was much neglected at the time. It has since gained popularity as a very useful tool. namedtuple is not a built-in tool, so it is not discussed here. However, it is included in the default library and is installed as a part of Python. If you are particularly adventurous, or want to learn more, feel free to have a look at the official documentations and examples. Raymond is also a regular speaker at PyCon (International Python Conferences), recordings of which are available on YouTube. He also uses his Twitter to talk about small, but important features in Python (yes, tweets!).

Summary

In this section, we learned about tuple, another type of built-in arrays in Python that is immutable. This means that once created, the array can no longer be altered. We saw that trying to change the value of a tuple raises a TypeError. We also established that list and tuple follow an identical indexing protocol, and that they have 2 methods in common: .index()() and .count(). Finally, we talked about packing and unpacking techniques, and how they improve the quality and readability of our code.

If you are interested in learning about list and tuple in more depth, have a look at the official documentations of Sequence Types – list, tuple, range.

Interesting Fact

Graph theory was initially developed by the renowned Swiss mathematician and logician Leonhard Euler (1707 – 1783). However, graphs in the sense discussed here were introduced by the English mathematician James Joseph Sylvester (1814 – 1897).

Exercises

End of chapter Exercises

  1. We have
table = [[1, 2, 3], ['a', 'b'], [1.5, 'b', 4], [2]]

what is the length of table and why?

Store your answer in a variable and display it using print().

  1. Given the sequence for the Gamma (catalytic) subunit of the Protein Kinase A as:
human_pka_gamma = (
  'MAAPAAATAMGNAPAKKDTEQEESVNEFLAKARGDFLYRWGNPAQNTASSDQFERLRTLGMGSFGRVML'
  'VRHQETGGHYAMKILNKQKVVKMKQVEHILNEKRILQAIDFPFLVKLQFSFKDNSYLYLVMEYVPGGEM'
  'FSRLQRVGRFSEPHACFYAAQVVLAVQYLHSLDLIHRDLKPENLLIDQQGYLQVTDFGFAKRVKGRTWT'
  'LCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAVGFPPFYADQPIQIYEKIVSGRVRFPSKLSSDLK'
  'DLLRSLLQVDLTKRFGNLRNGVGDIKNHKWFATTSWIAIYEKKVEAPFIPKYTGPGDASNFDDYEEEEL'
  'RISINEKCAKEFSEF'
  )

Using the sequence;

  • work out and display the number of Serine (S) residues.

  • work out and display the number of Threonine (T) residues.

  • calculate and display the total number of Serine and Threonine residues in the following format:

Serine: X
Threonine: X
  • create a nested array to represent the following table, and call it :

  1. Explain why in the previous question, we used the term nested instead of two-dimensional in reference to the array? Store your answer in a variable and display it using print().

  2. Graph theory is a prime object of discrete mathematics and is utilised for the non-linear analyses of data. The theory is extensively used in systems biology, and is gaining momentum in bioinformatics too. In essence, a graph is a structure that represents a set of object (nodes) and the connections between them (edges).

The aforementioned connections are described using a special binary (zero and one) matrix known as the adjacency matrix. The elements of this matrix indicate whether or not a pair of nodes in the graph are adjacent to one another.

where each row in the matrix represents a node of origin in the graph, and each column a node of destination:

If the graph maintains a connection (edge) between 2 nodes (e.g. between nodes A and B in the graph above), the corresponding value between those nodes would be #1 in the matrix, and if there are no connections, the corresponding value would #0.

Given the following graph:

Determine the adjacency matrix and implement it as a two-dimensional array in Python. Display the final array.

Q1

PYTHON 

table = [[1, 2, 3], ['a', 'b'], [1.5, 'b', 4], [2]]

table_length = len(table)
print('length of Table:', table_length)

reason = (
    "The length of a `list` is a function of its "
    "distinct members, regardless of their types."
)

print('')
print(reason)

OUTPUT 

length of Table: 4

The length of a `list` is a function of its distinct members, regardless of their types.

Q2

PYTHON 

human_pka_gamma = (
  'MAAPAAATAMGNAPAKKDTEQEESVNEFLAKARGDFLYRWGNPAQNTASSDQFERLRTLGMGSFGRVML'
  'VRHQETGGHYAMKILNKQKVVKMKQVEHILNEKRILQAIDFPFLVKLQFSFKDNSYLYLVMEYVPGGEM'
  'FSRLQRVGRFSEPHACFYAAQVVLAVQYLHSLDLIHRDLKPENLLIDQQGYLQVTDFGFAKRVKGRTWT'
  'LCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAVGFPPFYADQPIQIYEKIVSGRVRFPSKLSSDLK'
  'DLLRSLLQVDLTKRFGNLRNGVGDIKNHKWFATTSWIAIYEKKVEAPFIPKYTGPGDASNFDDYEEEEL'
  'RISINEKCAKEFSEF'
  )

total_serine = human_pka_gamma.count("S")
total_threonine = human_pka_gamma.count("T")

print('Serine:', total_serine)
print('Threonine:', total_threonine)

residues = [
    ['S', total_serine],
    ['T', total_threonine]
    ]
print(residues)

OUTPUT 

Serine: 19
Threonine: 13
[['S', 19], ['T', 13]]

Q3

PYTHON 

answer = (
  "Members of a two-dimensional array must themselves be arrays of "
  "equal lengths containing identically typed members."
)
print(answer)

OUTPUT 

Members of a two-dimensional array must themselves be arrays of equal lengths containing identically typed members.

Q4

PYTHON 

# Column initials:
#    S, H, A, A, G
adjacency_matrix = [
    [0, 1, 0, 0, 0],  # Stress
    [0, 0, 1, 0, 0],  # Hypothalamus
    [0, 0, 0, 1, 0],  # Anterior Pituitary Gland
    [0, 0, 0, 0, 1],  # Adrenal Cortex
    [0, 1, 1, 0, 0],  # Glucocorticoids
]

print(adjacency_matrix)

OUTPUT 

[[0, 1, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 1], [0, 1, 1, 0, 0]]

Key Points

  • lists and tuples are 2 types of arrays.
  • An index is a unique reference to a specific value and Python uses a zero-based indexing system.
  • lists are mutable because their contents to be modified.
  • slice(), .pop(), .index(), .remove() and .insert() are some of the key functions used on mutable arrays.
  • tuples are immutable which means its contents cannot be modified.