Cara menggunakan sort zipped list python

The zip() function takes iterables (can be zero or more), aggregates them in a tuple, and returns it.

Example

languages = ['Java', 'Python', 'JavaScript']
versions = [14, 3, 6]

result = zip(languages, versions)
print(list(result))

# Output: [('Java', 14), ('Python', 3), ('JavaScript', 6)]

Syntax of zip()

The syntax of the zip() function is:

zip(*iterables)

zip() Parameters

ParameterDescription

zip(*iterables)

Recommended Reading: Python Iterators, __iter__ and __next__

zip() Return Value

The zip() function returns an iterator of tuples based on the iterable objects.

• If we do not pass any parameter, zip() returns an empty iterator
• If a single iterable is passed, zip() returns an iterator of tuples with each tuple having only one element.
• If multiple iterables are passed, zip() returns an iterator of tuples with each tuple having elements from all the iterables.

  Suppose, two iterables are passed to zip(); one iterable containing three and other containing five elements. Then, the returned iterator will contain three tuples. It's because the iterator stops when the shortest iterable is exhausted.

  The zip() function returns a zip object, which is an iterator of tuples where the first item in each passed iterator is paired together, and then the second item in each passed iterator are paired together etc.

  If the passed iterators have different lengths, the iterator with the least items decides the length of the new iterator.

  Python lists have a built-in method that modifies the list in-place. There is also a built-in function that builds a new sorted list from an iterable.

  In this document, we explore the various techniques for sorting data using Python.

  Sorting Basics

  A simple ascending sort is very easy: just call the function. It returns a new sorted list:

  >>> sorted([5, 2, 3, 1, 4])
  [1, 2, 3, 4, 5]
  

  You can also use the method. It modifies the list in-place (and returns

  >>> sorted({1: 'D', 2: 'B', 3: 'B', 4: 'E', 5: 'A'})
  [1, 2, 3, 4, 5]
  

  1 to avoid confusion). Usually it’s less convenient than - but if you don’t need the original list, it’s slightly more efficient.

  >>> a = [5, 2, 3, 1, 4]
  >>> a.sort()
  >>> a
  [1, 2, 3, 4, 5]
  

  Another difference is that the method is only defined for lists. In contrast, the function accepts any iterable.

  >>> sorted({1: 'D', 2: 'B', 3: 'B', 4: 'E', 5: 'A'})
  [1, 2, 3, 4, 5]
  

  Key Functions

  Both and have a key parameter to specify a function (or other callable) to be called on each list element prior to making comparisons.

  For example, here’s a case-insensitive string comparison:

  >>> sorted("This is a test string from Andrew".split(), key=str.lower)
  ['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']
  

  The value of the key parameter should be a function (or other callable) that takes a single argument and returns a key to use for sorting purposes. This technique is fast because the key function is called exactly once for each input record.

  A common pattern is to sort complex objects using some of the object’s indices as keys. For example:

  >>> student_tuples = [
  ...     ('john', 'A', 15),
  ...     ('jane', 'B', 12),
  ...     ('dave', 'B', 10),
  ... ]
  >>> sorted(student_tuples, key=lambda student: student[2])   # sort by age
  [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
  

  The same technique works for objects with named attributes. For example:

  >>> class Student:
  ...     def __init__(self, name, grade, age):
  ...         self.name = name
  ...         self.grade = grade
  ...         self.age = age
  ...     def __repr__(self):
  ...         return repr((self.name, self.grade, self.age))
  
  >>> student_objects = [
  ...     Student('john', 'A', 15),
  ...     Student('jane', 'B', 12),
  ...     Student('dave', 'B', 10),
  ... ]
  >>> sorted(student_objects, key=lambda student: student.age)   # sort by age
  [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
  

  Operator Module Functions

  The key-function patterns shown above are very common, so Python provides convenience functions to make accessor functions easier and faster. The module has , , and a function.

  Using those functions, the above examples become simpler and faster:

  >>> from operator import itemgetter, attrgetter
  
  >>> sorted(student_tuples, key=itemgetter(2))
  [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
  
  >>> sorted(student_objects, key=attrgetter('age'))
  [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
  

  The operator module functions allow multiple levels of sorting. For example, to sort by grade then by age:

  >>> sorted(student_tuples, key=itemgetter(1,2))
  [('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]
  
  >>> sorted(student_objects, key=attrgetter('grade', 'age'))
  [('john', 'A', 15), ('dave', 'B', 10), ('jane', 'B', 12)]
  

  Ascending and Descending

  Both and accept a reverse parameter with a boolean value. This is used to flag descending sorts. For example, to get the student data in reverse age order:

  >>> sorted(student_tuples, key=itemgetter(2), reverse=True)
  [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]
  
  >>> sorted(student_objects, key=attrgetter('age'), reverse=True)
  [('john', 'A', 15), ('jane', 'B', 12), ('dave', 'B', 10)]
  

  Sort Stability and Complex Sorts

  Sorts are guaranteed to be . That means that when multiple records have the same key, their original order is preserved.

  >>> data = [('red', 1), ('blue', 1), ('red', 2), ('blue', 2)]
  >>> sorted(data, key=itemgetter(0))
  [('blue', 1), ('blue', 2), ('red', 1), ('red', 2)]
  

  Notice how the two records for blue retain their original order so that

  >>> sorted("This is a test string from Andrew".split(), key=str.lower)
  ['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']
  

  3 is guaranteed to precede

  >>> sorted("This is a test string from Andrew".split(), key=str.lower)
  ['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']
  

  4.

  This wonderful property lets you build complex sorts in a series of sorting steps. For example, to sort the student data by descending grade and then ascending age, do the age sort first and then sort again using grade:

  >>> a = [5, 2, 3, 1, 4]
  >>> a.sort()
  >>> a
  [1, 2, 3, 4, 5]
  

  0

  This can be abstracted out into a wrapper function that can take a list and tuples of field and order to sort them on multiple passes.

  >>> a = [5, 2, 3, 1, 4]
  >>> a.sort()
  >>> a
  [1, 2, 3, 4, 5]
  

  1

  The Timsort algorithm used in Python does multiple sorts efficiently because it can take advantage of any ordering already present in a dataset.

  Decorate-Sort-Undecorate

  This idiom is called Decorate-Sort-Undecorate after its three steps:

  • First, the initial list is decorated with new values that control the sort order.

  • Second, the decorated list is sorted.

  • Finally, the decorations are removed, creating a list that contains only the initial values in the new order.

  For example, to sort the student data by grade using the DSU approach:

  >>> a = [5, 2, 3, 1, 4]
  >>> a.sort()
  >>> a
  [1, 2, 3, 4, 5]
  

  2

  This idiom works because tuples are compared lexicographically; the first items are compared; if they are the same then the second items are compared, and so on.

  It is not strictly necessary in all cases to include the index i in the decorated list, but including it gives two benefits:

  • The sort is stable – if two items have the same key, their order will be preserved in the sorted list.

  • The original items do not have to be comparable because the ordering of the decorated tuples will be determined by at most the first two items. So for example the original list could contain complex numbers which cannot be sorted directly.

  Another name for this idiom is Schwartzian transform, after Randal L. Schwartz, who popularized it among Perl programmers.

  Now that Python sorting provides key-functions, this technique is not often needed.

  Comparison Functions

  Unlike key functions that return an absolute value for sorting, a comparison function computes the relative ordering for two inputs.

  For example, a balance scale compares two samples giving a relative ordering: lighter, equal, or heavier. Likewise, a comparison function such as

  >>> sorted("This is a test string from Andrew".split(), key=str.lower)
  ['a', 'Andrew', 'from', 'is', 'string', 'test', 'This']
  

  5 will return a negative value for less-than, zero if the inputs are equal, or a positive value for greater-than.

  It is common to encounter comparison functions when translating algorithms from other languages. Also, some libraries provide comparison functions as part of their API. For example, is a comparison function.

  To accommodate those situations, Python provides to wrap the comparison function to make it usable as a key function:

  >>> a = [5, 2, 3, 1, 4]
  >>> a.sort()
  >>> a
  [1, 2, 3, 4, 5]
  

  3

  Odds and Ends

  • For locale aware sorting, use for a key function or for a comparison function. This is necessary because “alphabetical” sort orderings can vary across cultures even if the underlying alphabet is the same.

  • The reverse parameter still maintains sort stability (so that records with equal keys retain the original order). Interestingly, that effect can be simulated without the parameter by using the builtin function twice:

    >>> a = [5, 2, 3, 1, 4]
    >>> a.sort()
    >>> a
    [1, 2, 3, 4, 5]
    

    4

  • The sort routines use

    >>> student_tuples = [
    ...     ('john', 'A', 15),
    ...     ('jane', 'B', 12),
    ...     ('dave', 'B', 10),
    ... ]
    >>> sorted(student_tuples, key=lambda student: student[2])   # sort by age
    [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
    

    1 when making comparisons between two objects. So, it is easy to add a standard sort order to a class by defining an

    >>> student_tuples = [
    ...     ('john', 'A', 15),
    ...     ('jane', 'B', 12),
    ...     ('dave', 'B', 10),
    ... ]
    >>> sorted(student_tuples, key=lambda student: student[2])   # sort by age
    [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
    

    2 method:

    >>> a = [5, 2, 3, 1, 4]
    >>> a.sort()
    >>> a
    [1, 2, 3, 4, 5]
    

    5

    However, note that

    >>> student_tuples = [
    ...     ('john', 'A', 15),
    ...     ('jane', 'B', 12),
    ...     ('dave', 'B', 10),
    ... ]
    >>> sorted(student_tuples, key=lambda student: student[2])   # sort by age
    [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
    

    1 can fall back to using

    >>> student_tuples = [
    ...     ('john', 'A', 15),
    ...     ('jane', 'B', 12),
    ...     ('dave', 'B', 10),
    ... ]
    >>> sorted(student_tuples, key=lambda student: student[2])   # sort by age
    [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
    

    4 if

    >>> student_tuples = [
    ...     ('john', 'A', 15),
    ...     ('jane', 'B', 12),
    ...     ('dave', 'B', 10),
    ... ]
    >>> sorted(student_tuples, key=lambda student: student[2])   # sort by age
    [('dave', 'B', 10), ('jane', 'B', 12), ('john', 'A', 15)]
    

    2 is not implemented (see ).

  • Key functions need not depend directly on the objects being sorted. A key function can also access external resources. For instance, if the student grades are stored in a dictionary, they can be used to sort a separate list of student names: