Important Topics Continue:
4. Nesting :
>>> M = [[1, 2, 3], # A 3 × 3 matrix, as nested lists
[4, 5, 6], # Code can span lines if bracketed
[7, 8, 9]]
>>> M
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
Here, we’ve coded a list that contains three other lists. The effect is to represent a
3 × 3 matrix of numbers. Such a structure can be accessed in a variety of ways:
>>> M[1] # Get row 2
[4, 5, 6]
>>> M[1][2] # Get row 2, then get item 3 within the row
6
5. Comprehensions :
>>> col2 = [row[1] for row in M] # Collect the items in column 2
>>> col2
[2, 5, 8]
>>> M # The matrix is unchanged
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> [row[1] + 1 for row in M] # Add 1 to each item in column 2
[3, 6, 9]
>>> [row[1] for row in M if row[1] % 2 == 0] # Filter out odd items
[2, 8]
>>> diag = [M[i][i] for i in [0, 1, 2]] # Collect a diagonal from matrix
>>> diag
[1, 5, 9]
>>> doubles = [c * 2 for c in 'spam'] # Repeat characters in a string
>>> doubles
['ss', 'pp', 'aa', 'mm']
>>> list(range(4)) # 0..3 (list() required in 3.X)
[0, 1, 2, 3]
>>> list(range(−6, 7, 2)) # −6 to +6 by 2 (need list() in 3.X)
[−6, −4, −2, 0, 2, 4, 6]
>>> [[x ** 2, x ** 3] for x in range(4)] # Multiple values, "if" filters
[[0, 0], [1, 1], [4, 8], [9, 27]]
>>> [[x, x / 2, x * 2] for x in range(−6, 7, 2) if x > 0]
[[2, 1, 4], [4, 2, 8], [6, 3, 12]]
>>> G = (sum(row) for row in M) # Create a generator of row sums
>>> next(G) # iter(G) not required here
6
>>> next(G) # Run the iteration protocol next()
15
>>> next(G)
24
>>> list(map(sum, M)) # Map sum over items in M
[6, 15, 24]
In Python 2.7 and 3.X, comprehension syntax can also be used to create sets and
dictionaries:
>>> {sum(row) for row in M} # Create a set of row sums
{24, 6, 15}
>>> {i : sum(M[i]) for i in range(3)} # Creates key/value table of row sums
{0: 6, 1: 15, 2: 24}
In fact, lists, sets, dictionaries, and generators can all be built with comprehensions in
3.X and 2.7:
>>> [ord(x) for x in 'spaam'] # List of character ordinals
[115, 112, 97, 97, 109]
>>> {ord(x) for x in 'spaam'} # Sets remove duplicates
{112, 97, 115, 109}
>>> {x: ord(x) for x in 'spaam'} # Dictionary keys are unique
{'p': 112, 'a': 97, 's': 115, 'm': 109}
>>> (ord(x) for x in 'spaam') # Generator of values
<generator object <genexpr> at 0x000000000254DAB0>
Important Topics Continue:
6. Dictionaries :
Python dictionaries are something completely different —they are not sequences at all, but are instead known as mappings.
Mappings are also collections of other objects, but they store objects by key instead of by relative position.
Dictionaries are coded in curly braces and consist of a series of “key: value” pairs.
Dictionaries are useful anytime we need to associate a set of values with keys—to describe the properties of something,
As an example, consider the following three-item dictionary (with keys “food,” “quantity,” and “color,” perhaps the details of a hypothetical menu item?):
>>> D = {'food': 'Spam', 'quantity': 4, 'color': 'pink'}
>>> D['food'] # Fetch value of key 'food'
'Spam'
>>> D['quantity'] += 1 # Add 1 to 'quantity' value
>>> D
{'color': 'pink', 'food': 'Spam', 'quantity': 5}
>>> D = {}
>>> D['name'] = 'Bob' # Create keys by assignment
>>> D['job'] = 'dev'
>>> D['age'] = 40
>>> D
{'age': 40, 'job': 'dev', 'name': 'Bob'}
>>> print(D['name'])
Bob
We can also make dictionaries by passing to the dict type name either keyword arguments (a special name=value syntax in function calls)
>>> bob1 = dict(name='Bob', job='dev', age=40)
>>> bob1
{'age': 40, 'name': 'Bob', 'job': 'dev'}
>>> bob2 = dict(zip(['name', 'job', 'age'], ['Bob', 'dev', 40])) # Zipping
>>> bob2
{'job': 'dev', 'name': 'Bob', 'age': 40}
