Встроенные функции python: какие нужно знать и на какие не стоит тратить время

Кортежи

Кортеж тоже является последовательностью и создается элементами разделёнными запятыми:


>>> companies = «Google», «Microsoft», «Tesla» >>> companies (‘Google’, ‘Microsoft’, ‘Tesla’)

1 2 3

>>>companies=»Google»,»Microsoft»,»Tesla»

>>>companies

(‘Google’,’Microsoft’,’Tesla’)

При определении непустого кортежа скобки не обязательны, но они становятся обязательными когда кортеж является частью большего выражения. Пустой кортеж создаётся пустой парой скобок:

>>> companies = () >>> type(companies) <class ‘tuple’>

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>>>companies=()

>>>type(companies)

<class’tuple’>

При определении кортежа с одним элементом запятая за ним обязательна.

>>> company = «Google», >>> type(company) <class ‘tuple’>

>>> company = («Google»,) >>> type(company) <class ‘tuple’>

1 2 3 4 5 6 7

>>>company=»Google»,

>>>type(company)

<class’tuple’>

>>>company=(«Google»,)

>>>type(company)

<class’tuple’>

Пропуск запятой означает что задано обычное значение, не кортеж.

>>> company = («Google») >>> company ‘Google’

>>> type(company) <class ‘str’>

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>>>company=(«Google»)

>>>company

‘Google’  

>>>type(company)

<class’str’>

Кортежи индексируются как списки, но неизменямы.

>>> companies = («Google», «Microsoft», «Palantir») >>> companies ‘Google’ >>> companies = «Boeing» Traceback (most recent call last): File «<stdin>», line 1, in <module> TypeError: ‘tuple’ object does not support item assignment

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>>>companies=(«Google»,»Microsoft»,»Palantir»)

>>>companies

‘Google’


>>>companies=»Boeing»

Traceback(most recent call last)

File»<stdin>»,line1,in<module>

TypeError’tuple’objectdoes notsupport item assignment

В тоже время, если элементом кортежа является изменяемые объект, такой как список, то он может быть изменен.

>>> companies = (, ) >>> companies (, ) >>> companies.append(«SpaceX») >>> companies (, )

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>>>companies=(«lockheedMartin»,»Boeing»,»Google»,»Microsoft»)

>>>companies

(‘lockheedMartin’,’Boeing’,’Google’,’Microsoft’)

>>>companies.append(«SpaceX»)

>>>companies

(‘lockheedMartin’,’Boeing’,’SpaceX’,’Google’,’Microsoft’)

Python NumPy

NumPy IntroNumPy Getting StartedNumPy Creating ArraysNumPy Array IndexingNumPy Array SlicingNumPy Data TypesNumPy Copy vs ViewNumPy Array ShapeNumPy Array ReshapeNumPy Array IteratingNumPy Array JoinNumPy Array SplitNumPy Array SearchNumPy Array SortNumPy Array FilterNumPy Random Random Intro Data Distribution Random Permutation Seaborn Module Normal Distribution Binomial Distribution Poisson Distribution Uniform Distribution Logistic Distribution Multinomial Distribution Exponential Distribution Chi Square Distribution Rayleigh Distribution Pareto Distribution Zipf Distribution

NumPy ufunc ufunc Intro ufunc Create Function ufunc Simple Arithmetic ufunc Rounding Decimals ufunc Logs ufunc Summations ufunc Products ufunc Differences ufunc Finding LCM ufunc Finding GCD ufunc Trigonometric ufunc Hyperbolic ufunc Set Operations

Other Set Operations

We can test if an item exists in a set or not, using the keyword.

Output

True
False

Built-in Functions with Set

Built-in functions like , , , , , , , etc. are commonly used with sets to perform different tasks.

Function Description
all() Returns if all elements of the set are true (or if the set is empty).
any() Returns if any element of the set is true. If the set is empty, returns .
enumerate() Returns an enumerate object. It contains the index and value for all the items of the set as a pair.
len() Returns the length (the number of items) in the set.
max() Returns the largest item in the set.
min() Returns the smallest item in the set.
sorted() Returns a new sorted list from elements in the set(does not sort the set itself).
sum() Returns the sum of all elements in the set.

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Other Python Set Methods

There are many set methods, some of which we have already used above. Here is a list of all the methods that are available with the set objects:

Method Description
add() Adds an element to the set
clear() Removes all elements from the set
copy() Returns a copy of the set
difference() Returns the difference of two or more sets as a new set
difference_update() Removes all elements of another set from this set
discard() Removes an element from the set if it is a member. (Do nothing if the element is not in set)
intersection() Returns the intersection of two sets as a new set
intersection_update() Updates the set with the intersection of itself and another
isdisjoint() Returns if two sets have a null intersection
issubset() Returns if another set contains this set
issuperset() Returns if this set contains another set
pop() Removes and returns an arbitrary set element. Raises if the set is empty
remove() Removes an element from the set. If the element is not a member, raises a
symmetric_difference() Returns the symmetric difference of two sets as a new set
symmetric_difference_update() Updates a set with the symmetric difference of itself and another
union() Returns the union of sets in a new set
update() Updates the set with the union of itself and others

Python NumPy

NumPy IntroNumPy Getting StartedNumPy Creating ArraysNumPy Array IndexingNumPy Array SlicingNumPy Data TypesNumPy Copy vs ViewNumPy Array ShapeNumPy Array ReshapeNumPy Array IteratingNumPy Array JoinNumPy Array SplitNumPy Array SearchNumPy Array SortNumPy Array FilterNumPy Random Random Intro Data Distribution Random Permutation Seaborn Module Normal Distribution Binomial Distribution Poisson Distribution Uniform Distribution Logistic Distribution Multinomial Distribution Exponential Distribution Chi Square Distribution Rayleigh Distribution Pareto Distribution Zipf Distribution

NumPy ufunc ufunc Intro ufunc Create Function ufunc Simple Arithmetic ufunc Rounding Decimals ufunc Logs ufunc Summations ufunc Products ufunc Differences ufunc Finding LCM ufunc Finding GCD ufunc Trigonometric ufunc Hyperbolic ufunc Set Operations

Списки

Список представляет собой упорядоченную последовательность элементов. Он очень гибкий и является одним из самых используемых типов в Python. Элементы списка не обязательно должны быть одного типа.

Объявить список довольно просто. Внутрь квадратных скобок помещаются элементы списка, разделённые запятой:

Мы можем использовать оператор  для извлечения элемента (такая операция называется “доступ по индексу”) или диапазона элементов (такая операция назвается “извлечение среза”) из списка. В Python индексация начинается с нуля:

Списки являются изменяемым типом, т.е. значения его элементов можно изменить:

Python NumPy

NumPy IntroNumPy Getting StartedNumPy Creating ArraysNumPy Array IndexingNumPy Array SlicingNumPy Data TypesNumPy Copy vs ViewNumPy Array ShapeNumPy Array ReshapeNumPy Array IteratingNumPy Array JoinNumPy Array SplitNumPy Array SearchNumPy Array SortNumPy Array FilterNumPy Random Random Intro Data Distribution Random Permutation Seaborn Module Normal Distribution Binomial Distribution Poisson Distribution Uniform Distribution Logistic Distribution Multinomial Distribution Exponential Distribution Chi Square Distribution Rayleigh Distribution Pareto Distribution Zipf Distribution

NumPy ufunc ufunc Intro ufunc Create Function ufunc Simple Arithmetic ufunc Rounding Decimals ufunc Logs ufunc Summations ufunc Products ufunc Differences ufunc Finding LCM ufunc Finding GCD ufunc Trigonometric ufunc Hyperbolic ufunc Set Operations

Python NumPy

NumPy IntroNumPy Getting StartedNumPy Creating ArraysNumPy Array IndexingNumPy Array SlicingNumPy Data TypesNumPy Copy vs ViewNumPy Array ShapeNumPy Array ReshapeNumPy Array IteratingNumPy Array JoinNumPy Array SplitNumPy Array SearchNumPy Array SortNumPy Array FilterNumPy Random Random Intro Data Distribution Random Permutation Seaborn Module Normal Distribution Binomial Distribution Poisson Distribution Uniform Distribution Logistic Distribution Multinomial Distribution Exponential Distribution Chi Square Distribution Rayleigh Distribution Pareto Distribution Zipf Distribution

NumPy ufunc ufunc Intro ufunc Create Function ufunc Simple Arithmetic ufunc Rounding Decimals ufunc Logs ufunc Summations ufunc Products ufunc Differences ufunc Finding LCM ufunc Finding GCD ufunc Trigonometric ufunc Hyperbolic ufunc Set Operations

Set Methods

Python has a set of built-in methods that you can use on sets.

Method Description
add() Adds an element to the set
clear() Removes all the elements from the set
copy() Returns a copy of the set
difference() Returns a set containing the difference between two or more sets
difference_update() Removes the items in this set that are also included in another, specified set
discard() Remove the specified item
intersection() Returns a set, that is the intersection of two other sets
intersection_update() Removes the items in this set that are not present in other, specified set(s)
isdisjoint() Returns whether two sets have a intersection or not
issubset() Returns whether another set contains this set or not
issuperset() Returns whether this set contains another set or not
pop() Removes an element from the set
remove() Removes the specified element
symmetric_difference() Returns a set with the symmetric differences of two sets
symmetric_difference_update() inserts the symmetric differences from this set and another
union() Return a set containing the union of sets
update() Update the set with the union of this set and others

Methods for Sets

Adding elements

Insertion in set is done through set.add() function, where an appropriate record value is created to store in the hash table. Same as checking for an item, i.e., O(1) on average. However, in worst case it can become O(n).

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Output:

People: {'Idrish', 'Archi', 'Jay'}

Set after adding element: {1, 2, 3, 4, 5, 'Idrish', 'Archi', 'Jay', 'Daxit'}

Union

Two sets can be merged using union() function or | operator. Both Hash Table values are accessed and traversed with merge operation perform on them to combine the elements, at the same time duplicates are removed. Time Complexity of this is O(len(s1) + len(s2)) where s1 and s2 are two sets whose union needs to be done.

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Output:

Union using union() function
{'Karan', 'Idrish', 'Jay', 'Arjun', 'Archil'}

Union using '|' operator
{'Deepanshu', 'Idrish', 'Jay', 'Raju', 'Archil'}

Intersection

This can be done through intersection() or & operator. Common Elements are selected. They are similar to iteration over the Hash lists and combining the same values on both the Table. Time Complexity of this is O(min(len(s1), len(s2)) where s1 and s2 are two sets whose union needs to be done.

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Output:

Intersection using intersection() function
{3, 4}

Intersection using '&' operator
{3, 4}

Difference

To find difference in between sets. Similar to find difference in linked list. This is done through difference() or – operator. Time complexity of finding difference s1 – s2 is O(len(s1))

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Output:

Difference of two sets using difference() function
{0, 1, 2}

Difference of two sets using '-' operator
{0, 1, 2}

Clearing sets

method empties the whole set.

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Output:

Initial set
{1, 2, 3, 4, 5, 6}

Set after using clear() function
set()

However, there are two major pitfalls in Python sets:

  1. The set doesn’t maintain elements in any particular order.
  2. Only instances of immutable types can be added to a Python set.

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