File “”, line 1 [x, x2 for x in range(6)]
^^^^^^^
SyntaxError: did you forget parentheses around the comprehension target?

flatten a list using a listcomp with two ‘for’ #

vec = [[1,2,3], [4,5,6], [7,8,9]]
[num for elem in vec for num in elem]
[1, 2, 3, 4, 5, 6, 7, 8, 9]

List comprehensions can contain complex expressions and nested
functions:

from math import pi
[str(round(pi, i)) for i in range(1, 6)]
[‘3.1’, ‘3.14’, ‘3.142’, ‘3.1416’, ‘3.14159’]

5.1.4. Nested List Comprehensions #

The initial expression in a list comprehension can be any arbitrary
expression, including another list comprehension.

Consider the following example of a 3×4 matrix implemented as a list
of 3 lists of length 4:

matrix = [
… [1, 2, 3, 4],
… [5, 6, 7, 8],
… [9, 10, 11, 12],
… ]

The following list comprehension will transpose rows and columns:

[[row[i] for row in matrix] for i in range(4)]
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

As we saw in the previous section, the inner list comprehension is
evaluated in the context of the “for” that follows it, so this example
is equivalent to:

transposed = []
for i in range(4):
… transposed.append([row[i] for row in matrix])
…
transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

which, in turn, is the same as:

transposed = []
for i in range(4):
… # the following 3 lines implement the nested listcomp
… transposed_row = []
… for row in matrix:
… transposed_row.append(row[i])
… transposed.append(transposed_row)
…
transposed
[[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]

In the real world, you should prefer built-in functions to complex
flow statements. The “zip()” function would do a great job for this
use case:

list(zip(*matrix))
[(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]

See Unpacking Argument Lists for details on the asterisk in this line.

5.2. The “del” statement #

There is a way to remove an item from a list given its index instead
of its value: the “del” statement. This differs from the “pop()”
method which returns a value. The “del” statement can also be used to
remove slices from a list or clear the entire list (which we did
earlier by assignment of an empty list to the slice). For example:

a = [-1, 1, 66.25, 333, 333, 1234.5]
del a[0]
a
[1, 66.25, 333, 333, 1234.5]
del a[2:4]
a
[1, 66.25, 1234.5]
del a[:]
a
[]

“del” can also be used to delete entire variables:

del a

Referencing the name “a” hereafter is an error (at least until another
value is assigned to it). We’ll find other uses for “del” later.

5.3. Tuples and Sequences #

We saw that lists and strings have many common properties, such as
indexing and slicing operations. They are two examples of sequence
data types (see Sequence Types — list, tuple, range). Since Python
is an evolving language, other sequence data types may be added.
There is also another standard sequence data type: the tuple.

A tuple consists of a number of values separated by commas, for
instance:

t = 12345, 54321, ‘hello!’
t[0]
12345
t
(12345, 54321, ‘hello!’)

Tuples may be nested: #

u = t, (1, 2, 3, 4, 5)
u
((12345, 54321, ‘hello!’), (1, 2, 3, 4, 5))

Tuples are immutable: #

t[0] = 88888
Traceback (most recent call last):
File “”, line 1, in
TypeError: ‘tuple’ object does not support item assignment

but they can contain mutable objects: #

v = ([1, 2, 3], [3, 2, 1])
v
([1, 2, 3], [3, 2, 1])

As you see, on output tuples are always enclosed in parentheses, so
that nested tuples are interpreted correctly; they may be input with
or without surrounding parentheses, although often parentheses are
necessary anyway (if the tuple is part of a larger expression). It is
not possible to assign to the individual items of a tuple, however it
is possible to create tuples which contain mutable objects, such as
lists.

Though tuples may seem similar to lists, they are often used in
different situations and for different purposes. Tuples are
immutable, and usually contain a heterogeneous sequence of elements
that are accessed via unpacking (see later in this section) or
indexing (or even by attribute in the case of “namedtuples”). Lists
are mutable, and their elements are usually homogeneous and are
accessed by iterating over the list.

A special problem is the construction of tuples containing 0 or 1
items: the syntax has some extra quirks to accommodate these. Empty
tuples are constructed by an empty pair of parentheses; a tuple with
one item is constructed by following a value with a comma (it is not
sufficient to enclose a single value in parentheses). Ugly, but
effective. For example:

empty = ()
singleton = ‘hello’, # <– note trailing comma
len(empty)
0
len(singleton)
1
singleton
(‘hello’,)

The statement “t = 12345, 54321, ‘hello!’” is an example of tuple packing: the values “12345”, “54321” and “‘hello!’” are packed
together in a tuple. The reverse operation is also possible:

x, y, z = t

This is called, appropriately enough, sequence unpacking and works
for any sequence on the right-hand side. Sequence unpacking requires
that there are as many variables on the left side of the equals sign
as there are elements in the sequence. Note that multiple assignment
is really just a combination of tuple packing and sequence unpacking.

5.4. Sets #

Python also includes a data type for sets. A set is an unordered
collection with no duplicate elements. Basic uses include membership
testing and eliminating duplicate entries. Set objects also support
mathematical operations like union, intersection, difference, and
symmetric difference.

Curly braces or the “set()” function can be used to create sets.
Note: to create an empty set you have to use “set()”, not “{}”; the
latter creates an empty dictionary, a data structure that we discuss
in the next section.

Here is a brief demonstration:

basket = {‘apple’, ‘orange’, ‘apple’, ‘pear’, ‘orange’, ‘banana’}
print(basket) # show that duplicates have been removed
{‘orange’, ‘banana’, ‘pear’, ‘apple’}
‘orange’ in basket # fast membership testing
True
‘crabgrass’ in basket
False

Demonstrate set operations on unique letters from two words #

a = set(‘abracadabra’)
b = set(‘alacazam’)
a # unique letters in a
{‘a’, ‘r’, ‘b’, ‘c’, ‘d’}
a – b # letters in a but not in b
{‘r’, ‘d’, ‘b’}
a | b # letters in a or b or both
{‘a’, ‘c’, ‘r’, ‘d’, ‘b’, ‘m’, ‘z’, ‘l’}
a & b # letters in both a and b
{‘a’, ‘c’}
a ^ b # letters in a or b but not both
{‘r’, ‘d’, ‘b’, ‘m’, ‘z’, ‘l’}

Similarly to list comprehensions, set comprehensions are also
supported:

a = {x for x in ‘abracadabra’ if x not in ‘abc’}
a
{‘r’, ‘d’}

5.5. Dictionaries #

Another useful data type built into Python is the dictionary (see
Mapping Types — dict). Dictionaries are sometimes found in other
languages as “associative memories” or “associative arrays”. Unlike
sequences, which are indexed by a range of numbers, dictionaries are
indexed by keys, which can be any immutable type; strings and
numbers can always be keys. Tuples can be used as keys if they
contain only strings, numbers, or tuples; if a tuple contains any
mutable object either directly or indirectly, it cannot be used as a
key. You can’t use lists as keys, since lists can be modified in place
using index assignments, slice assignments, or methods like “append()”
and “extend()”.

It is best to think of a dictionary as a set of key: value pairs,
with the requirement that the keys are unique (within one dictionary).
A pair of braces creates an empty dictionary: “{}”. Placing a comma-
separated list of key:value pairs within the braces adds initial
key:value pairs to the dictionary; this is also the way dictionaries
are written on output.

The main operations on a dictionary are storing a value with some key
and extracting the value given the key. It is also possible to delete
a key:value pair with “del”. If you store using a key that is already
in use, the old value associated with that key is forgotten. It is an
error to extract a value using a non-existent key.

Performing “list(d)” on a dictionary returns a list of all the keys
used in the dictionary, in insertion order (if you want it sorted,
just use “sorted(d)” instead). To check whether a single key is in the
dictionary, use the “in” keyword.

Here is a small example using a dictionary:

tel = {‘jack’: 4098, ‘sape’: 4139}
tel[‘guido’] = 4127
tel
{‘jack’: 4098, ‘sape’: 4139, ‘guido’: 4127}
tel[‘jack’]
4098
del tel[‘sape’]
tel[‘irv’] = 4127
tel
{‘jack’: 4098, ‘guido’: 4127, ‘irv’: 4127}
list(tel)
[‘jack’, ‘guido’, ‘irv’]
sorted(tel)
[‘guido’, ‘irv’, ‘jack’]
‘guido’ in tel
True
‘jack’ not in tel
False

The “dict()” constructor builds dictionaries directly from sequences
of key-value pairs:

dict([(‘sape’, 4139), (‘guido’, 4127), (‘jack’, 4098)])
{‘sape’: 4139, ‘guido’: 4127, ‘jack’: 4098}

In addition, dict comprehensions can be used to create dictionaries
from arbitrary key and value expressions:

{x: x**2 for x in (2, 4, 6)}
{2: 4, 4: 16, 6: 36}

When the keys are simple strings, it is sometimes easier to specify
pairs using keyword arguments:

dict(sape=4139, guido=4127, jack=4098)
{‘sape’: 4139, ‘guido’: 4127, ‘jack’: 4098}

5.6. Looping Techniques #

When looping through dictionaries, the key and corresponding value can
be retrieved at the same time using the “items()” method.

knights = {‘gallahad’: ‘the pure’, ‘robin’: ‘the brave’}
for k, v in knights.items():
… print(k, v)
…
gallahad the pure
robin the brave

When looping through a sequence, the position index and corresponding
value can be retrieved at the same time using the “enumerate()”
function.

for i, v in enumerate([‘tic’, ‘tac’, ‘toe’]):
… print(i, v)
…
0 tic
1 tac
2 toe

To loop over two or more sequences at the same time, the entries can
be paired with the “zip()” function.

questions = [‘name’, ‘quest’, ‘favorite color’]
answers = [‘lancelot’, ‘the holy grail’, ‘blue’]
for q, a in zip(questions, answers):
… print(‘What is your {0}? It is {1}.’.format(q, a))
…
What is your name? It is lancelot.
What is your quest? It is the holy grail.
What is your favorite color? It is blue.

To loop over a sequence in reverse, first specify the sequence in a
forward direction and then call the “reversed()” function.

for i in reversed(range(1, 10, 2)):
… print(i)
…
9
7
5
3
1

To loop over a sequence in sorted order, use the “sorted()” function
which returns a new sorted list while leaving the source unaltered.

basket = [‘apple’, ‘orange’, ‘apple’, ‘pear’, ‘orange’, ‘banana’]
for i in sorted(basket):
… print(i)
…
apple
apple
banana
orange
orange
pear

Using “set()” on a sequence eliminates duplicate elements. The use of
“sorted()” in combination with “set()” over a sequence is an idiomatic
way to loop over unique elements of the sequence in sorted order.

basket = [‘apple’, ‘orange’, ‘apple’, ‘pear’, ‘orange’, ‘banana’]
for f in sorted(set(basket)):
… print(f)
…
apple
banana
orange
pear

It is sometimes tempting to change a list while you are looping over
it; however, it is often simpler and safer to create a new list
instead.

import math
raw_data = [56.2, float(‘NaN’), 51.7, 55.3, 52.5, float(‘NaN’), 47.8]
filtered_data = []
for value in raw_data:
… if not math.isnan(value):
… filtered_data.append(value)
…
filtered_data
[56.2, 51.7, 55.3, 52.5, 47.8]

5.7. More on Conditions #

The conditions used in “while” and “if” statements can contain any
operators, not just comparisons.

The comparison operators “in” and “not in” are membership tests that
determine whether a value is in (or not in) a container. The
operators “is” and “is not” compare whether two objects are really the
same object. All comparison operators have the same priority, which
is lower than that of all numerical operators.

Comparisons can be chained. For example, “a < b == c” tests whether
“a” is less than “b” and moreover “b” equals “c”.

Comparisons may be combined using the Boolean operators “and” and
“or”, and the outcome of a comparison (or of any other Boolean
expression) may be negated with “not”. These have lower priorities
than comparison operators; between them, “not” has the highest
priority and “or” the lowest, so that “A and not B or C” is equivalent
to “(A and (not B)) or C”. As always, parentheses can be used to
express the desired composition.

The Boolean operators “and” and “or” are so-called short-circuit
operators: their arguments are evaluated from left to right, and
evaluation stops as soon as the outcome is determined. For example,
if “A” and “C” are true but “B” is false, “A and B and C” does not
evaluate the expression “C”. When used as a general value and not as
a Boolean, the return value of a short-circuit operator is the last
evaluated argument.

It is possible to assign the result of a comparison or other Boolean
expression to a variable. For example,

string1, string2, string3 = ”, ‘Trondheim’, ‘Hammer Dance’
non_null = string1 or string2 or string3
non_null
‘Trondheim’

Note that in Python, unlike C, assignment inside expressions must be
done explicitly with the walrus operator “:=”. This avoids a common
class of problems encountered in C programs: typing “=” in an
expression when “==” was intended.

5.8. Comparing Sequences and Other Types #

Sequence objects typically may be compared to other objects with the
same sequence type. The comparison uses lexicographical ordering:
first the first two items are compared, and if they differ this
determines the outcome of the comparison; if they are equal, the next
two items are compared, and so on, until either sequence is exhausted.
If two items to be compared are themselves sequences of the same type,
the lexicographical comparison is carried out recursively. If all
items of two sequences compare equal, the sequences are considered
equal. If one sequence is an initial sub-sequence of the other, the
shorter sequence is the smaller (lesser) one. Lexicographical
ordering for strings uses the Unicode code point number to order
individual characters. Some examples of comparisons between sequences
of the same type:

(1, 2, 3) < (1, 2, 4)
[1, 2, 3] < [1, 2, 4]
‘ABC’ < ‘C’ < ‘Pascal’ < ‘Python’
(1, 2, 3, 4) < (1, 2, 4)
(1, 2) < (1, 2, -1)
(1, 2, 3) == (1.0, 2.0, 3.0)
(1, 2, (‘aa’, ‘ab’)) < (1, 2, (‘abc’, ‘a’), 4)

Note that comparing objects of different types with “<” or “>” is
legal provided that the objects have appropriate comparison methods.
For example, mixed numeric types are compared according to their
numeric value, so 0 equals 0.0, etc. Otherwise, rather than providing
an arbitrary ordering, the interpreter will raise a “TypeError”
exception.

-[ Footnotes ]-

[1] Other languages may return the mutated object, which allows method
chaining, such as “d->insert(“a”)->remove(“b”)->sort();”.