Meet python: little notes 4 - high-level characteristics

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♥ Slice

Obtaining elements within required range from list or tuple (The results remain the same type as the original one.).

>>> L = list(range(100))
>>> L
[0, 1, 2, ..., 99]

>>> L[:3]   # Access first three indexed elements, i.e. [0 1 2], 0 could be ...
[0, 1, 2]   # omitted being the first index

>>> L[-10:]
[90, 91, 92, 93, 94, 95, 96, 97, 98, 99]

>>> L[:10:2]   # The third number denote the slice interval
[0, 2, 4, 6, 8]

>>> (0, 1, 2, 3, 4, 5)[:3]   # An example for tuple
(0, 1, 2)

>>> 'ABCDEF'[1:2]   # An example for string
'B'

♥ Interation

• Using for...in to tranverse a list, tuple or other kinds of iterable structure.

# dictionary: note that keys in a dict are not scored in the list order
>>> d = {'a': 1, 'b': 2, 'c': 3}
>>> for key in d
        print(key)
a
b
c

# string
>>> for ch in 'ABC'
        print(ch)
A
B
C

# Decide if an object is an iterable object
>>> from collections import Iterable
>>> isinstance(123, Iterable)
False                           # Integer is not iterable

• Realise ordered iteration: enumerate

>>> for i, value in enumerate(['A', 'B', 'C'])   # change a list to key-value
                                                 # pair
        print(i, value)
0 A
1 B
2 C

♥ List comprehensions

Construct a list.

# Normal way
>>> L = []
>>> for x in range(1, 11)
        l.append(x * x)
>>> L
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]  

# List comprehension
>>> [x * x for x in range(1, 11)]
[1, 4, 9, 16, 25, 36, 49, 64, 81, 100]  

# Double deck
>>> [m + n for m in 'ABC' for n in 'XYZ']
['AX', 'AY', 'AZ', 'BX', 'BY', 'BZ', 'CX', 'CY', 'CZ']

# Construct a list using two variables with list comprehension
>>> d = {'x': 'A', 'y': 'B', 'z': 'C'}
>>> [k + '=' + v for k, v in d.items()]
['y=B', 'x=A', 'z=C']

♥ Generator

• A special iterable function.
• One characteristic of generator is that it breaks every time when coming across yield, restarts at exactly where it breaks last time next calling, and will not return a value unless meeting stopIteration (return value is included there). Examples of constructing a generator and calling:

# First way to produce a generator
>>> L = [x * x for x in range(10)]
>>> L
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
# Change [] to (), a generator is obtained instead of a list
>>> g = (x * x for x in range(10))
>>> g
<generator object <genexpr> at 0x1022ef630>

# Second way: yield.
def fib(max):   # Fibonacci sequence
    n, a, b = 0, 0, 1
    while n < max:
        yield b
        a, b = b, a + b
        n = n + 1
    return 'done'
>>> g = fib(6)
>>> while True:
...     try:
...         x = next(g)   # obtain next elements in generator
...         print('g:', x)
...     except StopIteration as e:
...         print('Generator return value:', e.value)
...         break

♥ Iterator

• Object can be called using next() and return next value, actually a data flow.
• Note, iterator is different from iterable.

>>> from collections import Iterable
>>> isinstance([], Iterable)
True
>>> isinstance([], Iterable)
False
# Invert an iterable to iterator.
>>> isinstance(iter([]), Iterator)
True

• Advantage: iterator is an ordered sequence, but will not calculate next value unless required, thus is of efficiency.
• Summary
  
  objects can be used in for are iterables;
  
  all generators are iterators;
  
  iterebles can be converted to iterators via iter().