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’)

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’>

При определении кортежа с одним элементом запятая за ним обязательна.

>>> 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’>

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

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

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

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.

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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:

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.

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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