Fluent Python

My notes when reading Luciano Ramalho's Fluent Python.
The Python Data Model
By implementing special methods (A.K.A. magic methods, like __len__, __getitem__, __repr__ etc.. Often pronounced dunder xx), your objects can utilize built-in functions and syntax like len(), [], for ... in ... and thus be considered Pythonic.
Understanding the Pythonic len(xx) over xx.len(): Think of these functions as unary operators.
An Array of Sequences
List Comprehension vs Generator Expression
List Comprehension: list_a = [i + j for i in ... for j in ...]
This is a cartesian products example. for i in ... part is the outer loop.
Generator Expression: xx(i + j for i in ... for j in ...)
The syntactic difference is () vs []. But under the hood it saves space by yielding item one by one so a full list is never constructed. Also it can be used to build many other containers.
Tuples
Tuples (Iterable) Unpacking
Use case: parallel assignment (can be nested), swap, % string formatting print('%s %s' % tup), passing function parameter f(*tup).
Works for any iterable as long as the iterable yields exactly one item per variable in the receiving end. The only exception is using * dicussed below.
a, *b, c = range(5) and b is [1, 2, 3]. Only one * prefix variable is allowed.
Named Tuples collections.namedtuple
Construction: Passing construct parameters by name or position. NamedTup._make(iterable). NamedTup(*iterable).
Accessing field by name or position.
._asdict() return a collections.OrderedDict
Methods and attributes as an "immutable list": No appending/poping/inserting nor any inplace ops.
Slicing
[:3] exclude the last item.
Slice object.
s = slice(begin, end, stride)
line[s]
Under the hood:
# v[a]
v.__getitem__(a)
# v[a, b]. Multidimensional. Used in Numpy.
v.__getitem__((a, b))
Ellipsis object: function parameters f(a, ..., z) and slice a[i:...]. If a is four dimentional, this is a shortcut for a[i, :, :, :]. It is mostly used in Numpy.
Assignment using slices. Some interesting example from the book:
>>> l = list(range(10))
>>> l
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> l[2:5] = [20, 30]
>>> l
[0, 1, 20, 30, 5, 6, 7, 8, 9]
>>> del l[5:7]
>>> l
[0, 1, 20, 30, 5, 8, 9]
>>> l[3::2] = [11, 22]
>>> l
[0, 1, 20, 11, 5, 22, 9]
>>> l[2:5] = 100  
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: can only assign an iterable
>>> l[2:5] = [100]
>>> l
[0, 1, 100, 22, 9]
+ and * and augmented assignment on sequences
my_list = [[]] * 3 will result in a list with three references to the same inner list. List comprehension avoids this problem. [[] for i in range(3)].
+= and *= will first try to use __iadd__ and fall back to __add__ and create a new object.
list.sort and sorted
list.sort sorts inplace and returns None.
sorted library function accepts any iterable object.
Both accept two parameters: reverse bool and key for the name of a function that produces sorting keys.
Misc
​Online Python Tutor​
Last modified 2yr ago